{children}
+
+
+
+
+
+
+
+ );
+}
diff --git a/components/Pre.tsx b/components/Pre.tsx
new file mode 100644
index 000000000..6514b2367
--- /dev/null
+++ b/components/Pre.tsx
@@ -0,0 +1,91 @@
+import { useEffect, useRef, useState, FC } from "react";
+import classNames from "classnames";
+import { copyToClipboard } from "lib/copyToClipboard";
+
+type Props = {
+ className: string;
+};
+
+const Pre: FC
+
+ -
+ {menu.map((submenu: any) => (
+
-
+
+ {submenu.subMenuTitle} +
+-
+ {submenu.items.map((item: any) => (
+
- + + + {item.title} + + + + ))} +
+ ))}
+
+
+ );
+};
+
+export default Pre;
diff --git a/components/Tabs.tsx b/components/Tabs.tsx
new file mode 100644
index 000000000..873ed996d
--- /dev/null
+++ b/components/Tabs.tsx
@@ -0,0 +1,46 @@
+import { Tab } from "@headlessui/react";
+
+function classNames(...classes: string[]) {
+ return classes.filter(Boolean).join(" ");
+}
+
+export default function Tabs({
+ options,
+}: {
+ options: { title: string; content: string | JSX.Element }[];
+}) {
+ return (
+
+
+ Copied!
+
+
+
+
+
+ {children}
+
+
+
+
+ {options?.map((option) => (
+
+ classNames(
+ "w-full py-2.5 text-sm leading-5 font-medium text-blue-700 rounded-lg",
+ "focus:outline-none focus:ring-2 ring-offset-2 ring-offset-blue-400 ring-white ring-opacity-60",
+ selected
+ ? "bg-white shadow"
+ : "text-blue-100 hover:bg-white/[0.12] hover:text-white"
+ )
+ }
+ >
+ {option.title}
+
+ ))}
+
+
+ {options?.map((option, idx) => (
+
+ {option.content}
+
+ ))}
+
+
+
+ );
+}
diff --git a/components/Toc.tsx b/components/Toc.tsx
new file mode 100644
index 000000000..4acb3b7ca
--- /dev/null
+++ b/components/Toc.tsx
@@ -0,0 +1,69 @@
+import Link from "next/link";
+import { PencilAltIcon } from "@heroicons/react/solid";
+import { StaticPageProps } from "lib/utils";
+
+type TocProps = Pick
+
+
+
+ );
+}
diff --git a/components/VersionSelect.tsx b/components/VersionSelect.tsx
new file mode 100644
index 000000000..12be636dd
--- /dev/null
+++ b/components/VersionSelect.tsx
@@ -0,0 +1,116 @@
+import { Fragment } from "react";
+import { useRouter } from "next/router";
+import { CheckIcon, SelectorIcon } from "@heroicons/react/solid";
+import { Listbox, Transition } from "@headlessui/react";
+
+const versionRoutesHaystack: { [key: string]: string } = {
+ "v0.9.0": "/overview/intro",
+ "v0.8.0": "/overview/v0.8.0/intro",
+ "v0.7.0": "/overview/v0.7.0/intro",
+ "v0.6.0": "/overview/v0.6.0/intro",
+ "v0.5.0": "/usage/v0.5.0/intro",
+ "v0.4.0": "/usage/v0.4.0/intro",
+};
+
+const versionRoutesBenchmarks: { [key: string]: string } = {
+ "v0.9.0": "/benchmarks/v0.9.0",
+ "v0.8.0": "/benchmarks/v0.8.0",
+ "v0.7.0": "/benchmarks/v0.7.0",
+ "v0.6.0": "/benchmarks/v0.6.0",
+ "v0.5.0": "/benchmarks/v0.5.0",
+ "v0.4.0": "/benchmarks/v0.4.0",
+};
+
+type Props = {
+ docsType: string;
+};
+
+export default function VersionSelect({ docsType = "haystack" }: Props) {
+ const router = useRouter();
+ let versions: any = [];
+ if (docsType === "haystack") {
+ versions = Object.keys(versionRoutesHaystack);
+ } else if (docsType == "benchmarks") {
+ versions = Object.keys(versionRoutesBenchmarks);
+ }
+ const versionInUrl = versions?.find((v: string) => router.asPath.includes(v));
+
+ const handleVersionChange = (version: string) => {
+ if (docsType === "haystack") {
+ return router.push(versionRoutesHaystack[`${version}`]);
+ } else if (docsType == "benchmarks") {
+ return router.push(versionRoutesBenchmarks[`${version}`]);
+ }
+ };
+
+ return (
+
+
+
+
+
+ {editOnGitHubLink && (
+
+
+ Stars
+ | +{stars}
+
+
+
+
+ )}
+ -
+ {toc?.map((c) => (
+
- + {c.text} + + ))} +
+
+
+
+ );
+}
diff --git a/components/markdown.module.css b/components/markdown.module.css
new file mode 100644
index 000000000..771260169
--- /dev/null
+++ b/components/markdown.module.css
@@ -0,0 +1,103 @@
+.markdown {
+ @apply col-span-12 lg:col-span-8 xl:col-span-9;
+}
+
+.markdown h1 {
+ @apply text-4xl sm:text-5xl mb-4 font-semibold pr-28;
+}
+
+.markdown h2 {
+ @apply text-2xl sm:text-3xl mb-4 font-semibold;
+}
+
+.markdown h2::before {
+ content: "";
+ display: block;
+ height: 75px;
+ margin-top: -75px;
+ visibility: hidden;
+}
+
+.markdown h3 {
+ @apply text-xl sm:text-2xl mb-4 font-semibold;
+}
+
+.markdown h3::before {
+ content: "";
+ display: block;
+ height: 75px;
+ margin-top: -75px;
+ visibility: hidden;
+}
+
+.markdown h4 {
+ @apply text-lg mb-2 font-bold;
+}
+
+.markdown h4::before {
+ content: "";
+ display: block;
+ height: 75px;
+ margin-top: -75px;
+ visibility: hidden;
+}
+
+.markdown p {
+ @apply mb-3;
+}
+
+.markdown ul,
+.markdown ol,
+.markdown blockquote,
+.markdown img {
+ @apply mb-6;
+}
+
+.markdown ul {
+ @apply list-disc list-inside;
+}
+
+.markdown ol {
+ @apply list-decimal list-inside;
+}
+
+.markdown a {
+ @apply text-blue-600 underline;
+}
+
+.markdown li {
+ @apply mb-1;
+}
+
+.markdown li p {
+ @apply inline;
+}
+
+.markdown li div {
+ @apply my-6;
+}
+
+.markdown code {
+ @apply bg-light-grey-BG text-dark-blue py-1 px-2 mr-1 text-sm rounded break-words;
+}
+
+.markdown pre code {
+ @apply bg-transparent text-off-white p-0 m-0 block mb-2;
+}
+
+.markdown table {
+ @apply table-auto w-full mb-4;
+}
+
+.markdown thead {
+ @apply w-full;
+}
+
+.markdown tr {
+ @apply flex w-full justify-evenly bg-light-grey-BG border dark:bg-gray-800;
+}
+
+.markdown td,
+.markdown th {
+ @apply w-full text-center p-2 border-l border-r;
+}
diff --git a/components/non-mdx.module.css b/components/non-mdx.module.css
new file mode 100644
index 000000000..5cdbbe910
--- /dev/null
+++ b/components/non-mdx.module.css
@@ -0,0 +1,28 @@
+.nonMdx h1 {
+ @apply mt-12;
+}
+
+.nonMdx h1:first-of-type {
+ @apply mt-0;
+}
+
+.nonMdx h2,
+.nonMdx h3 {
+ @apply pt-6;
+}
+
+.nonMdx img {
+ @apply mb-4;
+}
+
+.nonMdx code {
+ @apply bg-light-grey-BG text-dark-blue py-1 px-2 mr-1 text-sm rounded break-words dark:bg-black;
+}
+
+.nonMdx pre code {
+ @apply bg-transparent text-off-white p-0 m-0 dark:bg-black;
+}
+
+.nonMdx pre {
+ @apply bg-monokai-dark-grey p-4 mb-4 overflow-x-auto rounded dark:bg-black;
+}
diff --git a/docs/v0.4.0/menu.json b/docs/v0.4.0/menu.json
new file mode 100644
index 000000000..ccfec48b3
--- /dev/null
+++ b/docs/v0.4.0/menu.json
@@ -0,0 +1,75 @@
+[
+ {
+ "subMenuTitle": "Usage",
+ "pathPrefix": "/usage/",
+ "items": [
+ { "slug": "intro", "title": "What is Haystack?" },
+ {
+ "slug": "get-started",
+ "title": "Get Started"
+ },
+ {
+ "slug": "document-store",
+ "title": "DocumentStore"
+ },
+ { "slug": "retriever", "title": "Retriever" },
+ { "slug": "reader", "title": "Reader" },
+ {
+ "slug": "domain-adaptation",
+ "title": "Domain Adaptation"
+ },
+ { "slug": "glossary", "title": "Glossary" },
+ { "slug": "roadmap", "title": "Roadmap" }
+ ]
+ },
+ {
+ "subMenuTitle": "Tutorials",
+ "pathPrefix": "/tutorials/",
+ "items": [
+ {
+ "slug": "first-qa-system",
+ "title": "Build Your First QA System"
+ },
+ {
+ "slug": "fine-tuning-a-model",
+ "title": "Fine-tuning a Model on Your Own Data"
+ },
+ {
+ "slug": "without-elasticsearch",
+ "title": "Build a QA System Without Elasticsearch"
+ },
+ {
+ "slug": "existing-faqs",
+ "title": "Utilizing existing FAQs for Question Answering"
+ },
+ {
+ "slug": "evaluation",
+ "title": "Evaluation of a QA System"
+ },
+ {
+ "slug": "dense-passage-retrieval",
+ "title": "Better Retrieval via \"Dense Passage Retrieval\""
+ }
+ ]
+ },
+ {
+ "subMenuTitle": "API Reference",
+ "pathPrefix": "/reference/",
+ "items": [
+ {
+ "slug": "document-store",
+ "title": "Document Store"
+ },
+ { "slug": "retriever", "title": "Retriever" },
+ { "slug": "reader", "title": "Reader" },
+ {
+ "slug": "preprocessor",
+ "title": "Preprocessor"
+ },
+ {
+ "slug": "file-converters",
+ "title": "File Converters"
+ }
+ ]
+ }
+]
diff --git a/src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/database.md b/docs/v0.4.0/usage/document_store.mdx
similarity index 70%
rename from src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/database.md
rename to docs/v0.4.0/usage/document_store.mdx
index f55d14606..f8accda6d 100644
--- a/src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/database.md
+++ b/docs/v0.4.0/usage/document_store.mdx
@@ -1,80 +1,53 @@
----
-title: "Database"
-metaTitle: "Document Store"
-metaDescription: ""
-slug: "/docs/database"
-date: "2020-09-03"
-id: "databasemd"
----
-
-
# Document Stores
You can think of the Document Store as a "database" that:
-- stores your texts and meta data
-- provides them to the retriever at query time
-There are different DocumentStores in Haystack to fit different use cases and tech stacks.
+- stores your texts and meta data
+- provides them to the retriever at query time
+
+There are different DocumentStores in Haystack to fit different use cases and tech stacks.
## Initialisation
Initialising a new Document Store is straight forward.
-
+
+
+ {versionInUrl || versions[0]}
+
+
+
+
+
+
+
+ {versions.map((version: string, versionIdx: string) => (
+
+ `${
+ active
+ ? "text-dark-blue bg-light-grey-BG"
+ : "text-gray-900"
+ }
+ cursor-pointer select-none relative py-2 pl-10 pr-4`
+ }
+ value={version}
+ >
+ {({ selected, active }) => (
+ <>
+
+ {version}
+
+ {selected ? (
+
+
+
+ ) : null}
+
+ )}
+
+ ))}
+
+
+
+
+
-
-
Each DocumentStore constructor allows for arguments specifying how to connect to existing databases and the names of indexes.
See API documentation for more info.
+
+
## Preparing Documents
DocumentStores expect Documents in dictionary form, like that below.
@@ -91,6 +64,8 @@ dicts = [
document_store.write_documents(dicts)
```
+
+
## File Conversion
There are a range of different file converters in Haystack that can help get your data into the right format.
@@ -98,6 +73,7 @@ Haystack features support for txt, pdf and docx formats and there is even a conv
See the File Converters section in the API docs for more information.
+
Haystack also has a `convert_files_to_dicts()` utility function that will convert
all txt or pdf files in a given folder into this dictionary format.
@@ -107,10 +83,14 @@ dicts = convert_files_to_dicts(dir_path=doc_dir)
document_store.write_documents(dicts)
```
+
+
## Writing Documents
Haystack allows for you to write store documents in an optimised fashion so that query times can be kept low.
+
+
### For Sparse Retrievers
For **sparse**, keyword based retrievers such as BM25 and TF-IDF,
@@ -121,6 +101,8 @@ The creation of the inverted index which optimises querying speed is handled aut
document_store.write_documents(dicts)
```
+
+
### For Dense Retrievers
For **dense** neural network based retrievers like Dense Passage Retrieval, or Embedding Retrieval,
@@ -139,83 +121,74 @@ Having GPU acceleration will significantly speed this up.
+
+
+
## Choosing the right document store
-The Document stores have different characteristics. You should choose one depending on the maturity of your project, the use case and technical environment:
+The Document stores have different characteristics. You should choose one depending on the maturity of your project, the use case and technical environment:
-
-
-
-
+
-
+### Elasticsearch
```python
document_store = ElasticsearchDocumentStore()
```
-
-
-
-
-
+
-
+### FAISS
```python
document_store = FAISSDocumentStore()
```
-
-
-
-
-
+
-
+### In Memory
```python
document_store = InMemoryDocumentStore()
```
-
-
-
-
-
-
-
+### SQL
```python
document_store = SQLDocumentStore()
```
-
-
+### Elasticsearch
-
+
#### Our recommendations
-**Restricted environment:** Use the `InMemoryDocumentStore`, if you are just giving Haystack a quick try on a small sample and are working in a restricted environment that complicates running Elasticsearch or other databases
+**Restricted environment:** Use the `InMemoryDocumentStore`, if you are just giving Haystack a quick try on a small sample and are working in a restricted environment that complicates running Elasticsearch or other databases
**Allrounder:** Use the `ElasticSearchDocumentStore`, if you want to evaluate the performance of different retrieval options (dense vs. sparse) and are aiming for a smooth transition from PoC to production
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/domain_adaptation.md b/docs/v0.4.0/usage/domain_adaptation.mdx
similarity index 80%
rename from src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/domain_adaptation.md
rename to docs/v0.4.0/usage/domain_adaptation.mdx
index 607b0d27b..84661ba36 100644
--- a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/domain_adaptation.md
+++ b/docs/v0.4.0/usage/domain_adaptation.mdx
@@ -1,12 +1,3 @@
----
-title: "Domain Adaptation"
-metaTitle: "Domain Adaptation"
-metaDescription: ""
-slug: "/docs/domain_adaptation"
-date: "2020-09-03"
-id: "domain_adaptationmd"
----
-
# Domain Adaptation
## Generalisation
@@ -22,6 +13,8 @@ But you don’t always need to know what a HP Valve is to answer “What is conn
The answer might be there in plain language.
In the same way, many QA models have a good enough grasp of language to answer questions about concepts in an unseen domain.
+
+
## Finetuning
Any model that can be loaded into Haystack can also be finetuned within Haystack.
@@ -39,33 +32,30 @@ See Tutorial 2 for a runnable example of this process.
If you’re interested in measuring how much your model has improved,
please also check out Tutorial 5 which walks through the steps needed to perform evaluation.
+
+
## Generating Labels
Using our [Haystack Annotate tool](https://annotate.deepset.ai/login) (Beta),
you can easily create a labelled dataset using your own documents featuring your own question/ answer pairs.
-
-
-
+
Features include:
+- Structured workspaces via organisations, projects and users
-* Structured workspaces via organisations, projects and users
+- Easy upload of your own documents and labels in a variety of formats (txt, pdf, SQuAD style)
-
-* Easy upload of your own documents and labels in a variety of formats (txt, pdf, SQuAD style)
-
-
-* Export of labels to be used directly in Haystack
+- Export of labels to be used directly in Haystack
Annotate also supports two different workflows:
+- Think up questions and answers while reading passages (SQuAD style)
-* Think up questions and answers while reading passages (SQuAD style)
-
+- Have a set of predefined questions and look for answers in the document (~ Natural Questions style)
-* Have a set of predefined questions and look for answers in the document (~ Natural Questions style)
+
## User Feedback
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/get_started.md b/docs/v0.4.0/usage/get_started.mdx
similarity index 68%
rename from src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/get_started.md
rename to docs/v0.4.0/usage/get_started.mdx
index d4652eb24..fd1e29d31 100644
--- a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/get_started.md
+++ b/docs/v0.4.0/usage/get_started.mdx
@@ -1,61 +1,62 @@
----
-title: "Get Started"
-metaTitle: "Get Started"
-metaDescription: ""
-slug: "/docs/get_started"
-date: "2020-09-03"
-id: "get_startedmd"
----
-
# Get Started
## Installation
-
-
-
-
+
-
+**Pros:**
-**Pros:**
- Fast & accurate sparse retrieval
- Basic support for dense retrieval
-- Production-ready
+- Production-ready
- Many options to tune sparse retrieval
-**Cons:**
+**Cons:**
+
- Slow for dense retrieval with more than ~ 1 Mio documents
-
-
-
-
-
+
-
+### FAISS
+
+**Pros:**
-**Pros:**
- Fast & accurate dense retrieval
- Highly scalable due to approximate nearest neighbour algorithms (ANN)
-- Many options to tune dense retrieval via different index types
+- Many options to tune dense retrieval via different index types
**Cons:**
+
- No efficient sparse retrieval
-
-
-
-
-
+### SQL
-
+### In Memory
**Pros:**
+
- Simple
- Exists already in many environments
**Cons:**
+
- Only compatible with minimal TF-IDF Retriever
- Bad retrieval performance
- Not recommended for production
-
-
-
-
-
-
-
+
**Pros:**
+
- Simple & fast to test
- No database requirements
-**Cons:**
+**Cons:**
+
- Not scalable
- Not persisting your data on disk
-
-
-
-
+
-
-
-
-
-
-The most straightforward way to install Haystack is through pip.
- -```python -$ pip install farm-haystack -``` - -
-- -```python -$ pip install farm-haystack -``` - -
-
-
-
-
-
-If you’d like to run a specific, unreleased version of Haystack, or make edits to the way Haystack runs,
-you’ll want to install it using `git` and `pip --editable`.
-This clones a copy of the repo to a local directory and runs Haystack from there.
- -```python -$ git clone https://github.com/deepset-ai/haystack.git -$ cd haystack -$ pip install --editable . -``` - -By default, this will give you the latest version of the master branch. Use regular git commands to switch between different branches and commits. -
-- -```python -$ git clone https://github.com/deepset-ai/haystack.git -$ cd haystack -$ pip install --editable . -``` - -By default, this will give you the latest version of the master branch. Use regular git commands to switch between different branches and commits. -
+ The most straightforward way to install Haystack is through pip. +
+
+ pip install farm-haystack
+
+
+ ),
+ },
+ {
+ title: "Editable",
+ content: (
+
+
+ ),
+ },
+ ]}
+/>
Note: On Windows add the arg `-f https://download.pytorch.org/whl/torch_stable.html` to install PyTorch correctly
+
+
+
## The Building Blocks of Haystack
Here’s a sample of some Haystack code showing the most important components.
For a working code example, check out our starter tutorial.
+
```python
# DocumentStore: holds all your data
document_store = ElasticsearchDocumentStore()
@@ -80,6 +81,8 @@ prediction = finder.get_answers(question)
print_answers(prediction)
```
+
+
## Loading Documents into the DocumentStore
In Haystack, DocumentStores expect Documents in a dictionary format. They are loaded as follows:
@@ -102,19 +105,18 @@ This splitting can have a big impact on speed and performance.
**General Guide**: If Haystack is running very slowly, you might want to try splitting your text into smaller Documents.
If you want an improvement to performance, you might want to try concatenating text to make larger Documents.
+
+
## Running Queries
**Querying** involves searching for an answer to a given question within the full document store.
This process will:
+- make the Retriever filter for a small set of relevant candidate documents
-* make the Retriever filter for a small set of relevant candidate documents
-
-
-* get the Reader to process this set of candidate documents
-
+- get the Reader to process this set of candidate documents
-* return potential answers to the given question
+- return potential answers to the given question
Usually, there are tight time constraints on querying and so it needs to be a lightweight operation.
When documents are loaded, Haystack will precompute any of the results that might be useful at query time.
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/terms.md b/docs/v0.4.0/usage/glossary.mdx
similarity index 96%
rename from src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/terms.md
rename to docs/v0.4.0/usage/glossary.mdx
index e851cce0c..cd83fdc0e 100644
--- a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/terms.md
+++ b/docs/v0.4.0/usage/glossary.mdx
@@ -1,12 +1,3 @@
----
-title: "Glossary"
-metaTitle: "Glossary"
-metaDescription: ""
-slug: "/docs/terms"
-date: "2020-09-03"
-id: "termsmd"
----
-
# Glossary
**BERT** - A popular, transformer based language model which has been improved upon but is still considered a common benchmark.
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/intro.md b/docs/v0.4.0/usage/intro.mdx
similarity index 66%
rename from src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/intro.md
rename to docs/v0.4.0/usage/intro.mdx
index ffd17502b..fd802609a 100644
--- a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/intro.md
+++ b/docs/v0.4.0/usage/intro.mdx
@@ -1,28 +1,20 @@
----
-title: "What is Haystack"
-metaTitle: "What is Haystack"
-metaDescription: ""
-slug: "/docs/intro"
-date: "2020-09-03"
-id: "intromd"
----
-
# What is Haystack?
Haystack is an **open-source framework** for building **end-to-end question answering systems** for large document collections.
Recent advances in NLP have enabled the application of QA to real world settings
and Haystack is designed to be the bridge between research and industry.
+- **Latest NLP models**: Utilize all transformer based models (BERT, RoBERTa, MiniLM, DPR ...) and smoothly switch when new ones get published
-* **Latest NLP models**: Utilize all transformer based models (BERT, RoBERTa, MiniLM, DPR ...) and smoothly switch when new ones get published
+- **Flexible databases**: Load data into and query from a range of databases such as Elasticsearch, FAISS, SQL and more
-* **Flexible databases**: Load data into and query from a range of databases such as Elasticsearch, FAISS, SQL and more
+- **Scalability**: Production-ready deployments that scale to millions of documents
-* **Scalability**: Production-ready deployments that scale to millions of documents
+- **End-to-End**: All tooling you need to implement, evaluate, improve and run a QA system
-* **End-to-End**: All tooling you need to implement, evaluate, improve and run a QA system
+- **Domain adaptation**: Fine-tune models to your own domain & improve them continuously via user feedback
-* **Domain adaptation**: Fine-tune models to your own domain & improve them continuously via user feedback
+
## Use cases
@@ -30,19 +22,17 @@ and Haystack is designed to be the bridge between research and industry.
Take the leap from using keyword search on your own documents to semantic search with Haystack.
+- Store your documents in the database of your choice (Elasticsearch, SQL, in memory, FAISS)
-* Store your documents in the database of your choice (Elasticsearch, SQL, in memory, FAISS)
-
-
-* Perform question driven queries.
+- Perform question driven queries.
Expect to see results that highlight the very sentence that contains the answer to your question.
Thanks to the power of Transformer based language models, results are chosen based on compatibility in meaning
rather than lexical overlap.
+
-
-
+
### Information Extractor
@@ -50,48 +40,46 @@ Automate the extraction of relevant information from a set of documents that per
Haystack can:
+- Apply a set of standard questions to each document in a store
-* Apply a set of standard questions to each document in a store
-
-
-* Return a NO_ANSWER if a given document does not contain the answer to a question
+- Return a NO_ANSWER if a given document does not contain the answer to a question
Say you have the financial reports for different companies over different years.
You can gather a set of standard questions which are applicable to each financial report,
-like *what is the revenue forecast for 2020?* or *what are the main sources of income?*.
+like _what is the revenue forecast for 2020?_ or _what are the main sources of income?_.
Haystack will try to find an answer for each question within each document!
We’ve seen this style of application be particularly effective in the sphere of finance and patent law
but we see a lot of potential in using this to gain a better overview of academic papers and internal business documents.
+
+
+
### FAQ Style Question Answering
Leverage existing FAQ documents and semantic similarity search to answer new incoming questions.
The workflow is as follows:
+- Store a set of FAQ documents in Haystack
-* Store a set of FAQ documents in Haystack
-
-
-* The user presents a new question
-
+- The user presents a new question
-* Haystack will find the closest match to the new question in the FAQ documents
+- Haystack will find the closest match to the new question in the FAQ documents
-
-* The user will be presented with the most similar Question Answer pair
+- The user will be presented with the most similar Question Answer pair
Haystack’s flexibility allows you to give new users more dynamic access to your existing documentation.
-## Technology
-Haystack is powered by a Retriever-Reader pipeline in order to optimise for both speed and accuracy.
+
+## Technology
+Haystack is powered by a Retriever-Reader pipeline in order to optimise for both speed and accuracy.
-
+
**Readers**, also known as Open-Domain QA systems in Machine Learning speak,
are powerful models that do close analysis of documents and perform the core task of question answering.
@@ -100,28 +88,25 @@ However, it is not currently feasible to use the Reader directly on large collec
+
The **Retriever** assists the Reader by acting as a lightweight filter that reduces the number of documents that the Reader has to process.
It does this by:
+- Scanning through all documents in the database
-* Scanning through all documents in the database
-
-
-* Quickly identifying the relevant and dismissing the irrelevant
+- Quickly identifying the relevant and dismissing the irrelevant
-
-* Passing on only a small candidate set of documents to the Reader
+- Passing on only a small candidate set of documents to the Reader
Current methods fall into one of the two categories:
+- sparse
-* sparse
- * keyword based
- * fast indexing and querying
- * e.g. BM25
-
+ - keyword based
+ - fast indexing and querying
+ - e.g. BM25
-* dense
- * neural network based
- * computationally heavy indexing but fast querying
- * e.g. Dense Passage Retrieval
+- dense
+ - neural network based
+ - computationally heavy indexing but fast querying
+ - e.g. Dense Passage Retrieval
diff --git a/docs/v0.4.0/usage/reader.mdx b/docs/v0.4.0/usage/reader.mdx
new file mode 100644
index 000000000..43959236c
--- /dev/null
+++ b/docs/v0.4.0/usage/reader.mdx
@@ -0,0 +1,423 @@
+# Reader
+
+The Reader, also known as Open-Domain QA systems in Machine Learning speak,
+is the core component that enables Haystack to find the answers that you need.
+Haystack’s Readers are:
+
+- built on the latest transformer based language models
+
+- strong in their grasp of semantics
+
+- sensitive to syntactic structure
+
+- state-of-the-art in QA tasks like SQuAD and Natural Questions
+
++ If you’d like to run a specific, unreleased version of Haystack, or + make edits to the way Haystack runs, you’ll want to install it using + `git` and `pip --editable`. This clones a copy of the repo to a + local directory and runs Haystack from there. +
+++git clone https://github.com/deepset-ai/haystack.git+cd haystack+pip install --editable .+
+ By default, this will give you the latest version of the master + branch. Use regular git commands to switch between different + branches and commits. +
+++ + ), + }, + { + title: "Transformers", + content: ( +model = "deepset/roberta-base-squad2"+reader = FARMReader(model, use_gpu=True)+finder = Finder(reader, retriever)+
+
+ ),
+ },
+ ]}
+/>
+
+While these models can work on CPU, it is recommended that they are run using GPUs to keep query times low.
+
+
+
+## Choosing the Right Model
+
+In Haystack, you can start using pretrained QA models simply by providing its HuggingFace Model Hub name to the Reader.
+The loading of model weights is handled by Haystack,
+and you have the option of using the QA pipeline from deepset FARM or HuggingFace Transformers (see FARM vs Transformers for details).
+
+Currently, there are a lot of different models out there and it can be rather overwhelming trying to pick the one that fits your use case.
+To get you started, we have a few recommendations for you to try out.
+
+++model = "deepset/roberta-base-squad2"+reader = TransformersReader(model, use_gpu=1)+finder = Finder(reader, retriever)+
+
+ RoBERTa (base)
+An optimised variant of BERT and a great starting point.
+++from haystack.reader import FARMReader+reader = FARMReader("deepset/roberta-base-squad2")+
-
+
- Pro: Strong all round model +
- + Con: There are other models that are either faster or more + accurate + +
+
+ MiniLM
++ A cleverly distilled model that sacrifices a little accuracy for + speed. +
+++from haystack.reader import FARMReader+reader = FARMReader("deepset/minilm-uncased-squad2")+
-
+
- Pro: Inference speed up to 50% faster than BERT base +
- + Con: Still doesn’t match the best base sized models in accuracy + +
+
+
+ ),
+ },
+ {
+ title: "Transformers",
+ content: (
+ ALBERT (XXL)
+Large, powerful, SotA model.
+++from haystack.reader import FARMReader++ reader = FARMReader("ahotrod/albert_xxlargev1_squad2_512") ++
-
+
- + Pro: Better accuracy than any other open source model in QA + +
- + Con: The computational power needed make it impractical for most + use cases + +
+
+ ),
+ },
+ ]}
+/>
+
+
+
+**All-rounder**: In the class of base sized models trained on SQuAD, **RoBERTa** has shown better performance than BERT
+and can be capably handled by any machine equipped with a single NVidia V100 GPU.
+We recommend this as the starting point for anyone wanting to create a performant and computationally reasonable instance of Haystack.
+
+**Built for Speed**: If speed and GPU memory are more of a priority to you than accuracy,
+you should try the MiniLM model.
+It is a smaller model that is trained to mimic larger models through the distillation process,
+and it outperforms the BERT base on SQuAD even though it is about 40% smaller.
+
+
+
+**State of the Art Accuracy**: For most, **ALBERT XXL** will be too large to feasibly work with.
+But if performance is your sole concern, and you have the computational resources,
+you might like to try ALBERT XXL which has set SoTA performance on SQuAD 2.0.
+
+
+
+
+
+## Languages other than English
+
+Haystack is also very well suited to open-domain QA on languages other than English.
+While models are comparatively more performant on English,
+thanks to a wealth of available English training data,
+there are a couple QA models that are directly usable in Haystack.
+
+
+
+ RoBERTa (base)
+An optimised variant of BERT and a great starting point.
+++from haystack.reader import TransformersReader++ reader = TransformersReader("deepset/roberta-base-squad2") ++
-
+
- Pro: Strong all round model +
- + Con: There are other models that are either faster or more + accurate + +
+
+ MiniLM
++ A cleverly distilled model that sacrifices a little accuracy for + speed. +
+++from haystack.reader import TransformersReader++ reader = TransformersReader("deepset/minilm-uncased-squad2") ++
-
+
- Pro: Inference speed up to 50% faster than BERT base +
- + Con: Still doesn’t match the best base sized models in accuracy + +
+
+ ALBERT (XXL)
+Large, powerful, SotA model.
+++from haystack.reader import TransformersReader++ reader = + TransformersReader("ahotrod/albert_xxlargev1_squad2_512") ++
-
+
- + Pro: Better accuracy than any other open source model in QA + +
- + Con: The computational power needed make it impractical for most + use cases + +
from haystack.reader import FARMReader
+
+ reader = FARMReader("illuin/camembert-base-fquad")
+
+
+ ),
+ },
+ {
+ title: "Italian",
+ content: (
+ ++ ), + }, + { + title: "Zero-shot", + content: ( +from haystack.reader import FARMReader++ reader = + FARMReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa") ++
++ ), + }, + ]} + /> + ), + }, + { + title: "Transformers", + content: ( +from haystack.reader import FARMReader++ reader = FARMReader("deepset/xlm-roberta-large-squad2") ++
from haystack.reader import TransformersReader
+
+ reader = TransformersReader("illuin/camembert-base-fquad")
+
+
+ ),
+ },
+ {
+ title: "Italian",
+ content: (
+ ++ ), + }, + { + title: "Zero-shot", + content: ( +from haystack.reader import TransformersReader++ reader = + TransformersReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa") ++
++ ), + }, + ]} + /> + ), + }, + ]} +/> + + + +The **French** and **Italian models** are both monolingual language models trained on French and Italian versions of the SQuAD dataset +and their authors report decent results in their model cards +[here](https://huggingface.co/illuin/camembert-base-fquad) and [here](https://huggingface.co/mrm8488/bert-italian-finedtuned-squadv1-it-alfa) . +Note that there is also a [large variant](https://huggingface.co/illuin/camembert-large-fquad) of the French model available on the model hub. +There also exist Korean QA models on the model hub but their performance is not reported. + +The **zero-shot model** that is shown above is a **multilingual XLM-RoBERTa Large** that is trained on English SQuAD. +It is clear, from our [evaluations](https://huggingface.co/deepset/xlm-roberta-large-squad2#model_card), +that the model has been able to transfer some of its English QA capabilities to other languages, +but still its performance lags behind that of the monolingual models. +Nonetheless, if there is not yet a monolingual model for your language and it is one of the 100 supported by XLM-RoBERTa, +this zero-shot model may serve as a decent first baseline. + +When using a Reader of any language, it’s important to ensure that the Retriever is also compatible. +While sparse methods like BM25 and TF-IDF are language agnostic, +dense method like Dense Passage Retrieval are trained for a particular language. + + + + + +## Deeper Dive: FARM vs Transformers + +Apart from the **model weights**, Haystack Readers contain all the components found in end-to-end open domain QA systems. +This includes **tokenization**, **embedding computation**, **span prediction** and **candidate aggregation**. +While the handling of model weights is the same between the FARM and Transformers libraries, their QA pipelines differ in some ways. +The major points are: + +- The **TransformersReader** will sometimes predict the same span twice while duplicates are removed in the **FARMReader** + +- The **FARMReader** currently uses the tokenizers from the HuggingFace Transformers library while the **TransformersReader** uses the tokenizers from the HuggingFace Tokenizers library + +- Start and end logits are normalized per passage and multiplied in the **TransformersReader** while they are summed and not normalised in the **FARMReader** + +If you’re interested in the finer details of these points, have a look at [this](https://github.com/deepset-ai/haystack/issues/248#issuecomment-661977237) GitHub comment. + +We see value in maintaining both kinds of Readers since Transformers is a very familiar library to many of Haystack’s users +but we at deepset can more easily update and optimise the FARM pipeline for speed and performance. + + + +Haystack also has a close integration with FARM which means that you can further fine-tune your Readers on labelled data using a FARMReader. +See our tutorials for an end-to-end example or below for a shortened example. + +```python +# Initialise Reader +model = "deepset/roberta-base-squad2" +reader = FARMReader(model) + +# Perform finetuning +train_data = "PATH/TO_YOUR/TRAIN_DATA" +train_filename = "train.json" +save_dir = "finetuned_model" +reader.train(train_data, train_filename, save_dir=save_dir) + +# Load +finetuned_reader = FARMReader(save_dir) +``` + + + +## Deeper Dive: From Language Model to Haystack Reader + +Language models form the core of most modern NLP systems and that includes the Readers in Haystack. +They build a general understanding of language when performing training tasks such as Masked Language Modeling or Replaced Token Detection +on large amounts of text. +Well trained language models capture the word distribution in one or more languages +but more importantly, convert input text into a set of word vectors that capture elements of syntax and semantics. + +In order to convert a language model into a Reader model, it needs first to be trained on a Question Answering dataset. +To do so requires the addition of a question answering prediction head on top of the language model. +The task can be thought of as a token classification task where every input token is assigned a probability of being +either the start or end token of the correct answer. +In cases where the answer is not contained within the passage, the prediction head is also expected to return a `no_answer` prediction. + + + +Since language models are limited in the number of tokens which they can process in a single forward pass, +a sliding window mechanism is implemented to handle variable length documents. +This functions by slicing the document into overlapping passages of (approximately) `max_seq_length` +that are each offset by `doc_stride` number of tokens. +These can be set when the Reader is initialized. + +from haystack.reader import TransformersReader++ reader = + TransformersReader("deepset/xlm-roberta-large-squad2") ++
++ + ), + }, + { + title: "Transformers", + content: ( +from haystack.reader import FARMReader++ reader = FARMReader(... max_seq_len=384, doc_stride=128 ...) ++
+
+ ),
+ },
+ ]}
+/>
+
+Predictions are made on each individual passage and the process of aggregation picks the best candidates across all passages.
+If you’d like to learn more about what is happening behind the scenes, have a look at [this](https://medium.com/deepset-ai/modern-question-answering-systems-explained-4d0913744097) article.
+
+
diff --git a/src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/retriever.md b/docs/v0.4.0/usage/retriever.mdx
similarity index 81%
rename from src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/retriever.md
rename to docs/v0.4.0/usage/retriever.mdx
index 2e14fe36c..d18888269 100644
--- a/src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/retriever.md
+++ b/docs/v0.4.0/usage/retriever.mdx
@@ -1,12 +1,3 @@
----
-title: "Retriever"
-metaTitle: "Retriever"
-metaDescription: ""
-slug: "/docs/retriever"
-date: "2020-09-03"
-id: "retrievermd"
----
-
# Retriever
The Retriever is a lightweight filter that can quickly go through the full document store and pass a set of candidate documents to the Reader.
@@ -14,23 +5,24 @@ It is an tool for sifting out the obvious negative cases, saving the Reader from
Recommendations:
+- BM25 (sparse)
-* BM25 (sparse)
-
-
-* Dense Passage Retrieval (dense)
+- Dense Passage Retrieval (dense)
+
Note that not all Retrievers can be paired with every DocumentStore.
Here are the combinations which are supported:
-| | Memory | Elasticsearch | SQL | FAISS |
-| --- | --- | --- | ---- | ---- |
-| BM25 | N | Y | N | N |
-| TF-IDF | Y | Y | Y | N |
-| Embedding | Y | Y | N | Y |
-| DPR | Y | Y | N | Y |
+| | Memory | Elasticsearch | SQL | FAISS |
+| --------- | ------ | ------------- | --- | ----- |
+| BM25 | N | Y | N | N |
+| TF-IDF | Y | Y | Y | N |
+| Embedding | Y | Y | N | Y |
+| DPR | Y | Y | N | Y |
+
+
## TF-IDF
@@ -38,11 +30,9 @@ Here are the combinations which are supported:
TF-IDF is a commonly used baseline for information retrieval that exploits two key intuitions:
+- documents that have more lexical overlap with the query are more likely to be relevant
-* documents that have more lexical overlap with the query are more likely to be relevant
-
-
-* words that occur in fewer documents are more significant than words that occur in many documents
+- words that occur in fewer documents are more significant than words that occur in many documents
Given a query, a tf-idf score is computed for each document as follows:
@@ -52,15 +42,15 @@ score = tf * idf
Where:
+- `tf` is how many times words in the query occur in that document.
-* `tf` is how many times words in the query occur in that document.
-
-
-* `idf` is the inverse of the fraction of documents containing the word.
+- `idf` is the inverse of the fraction of documents containing the word.
In practice, both terms are usually log normalised. If you’d like to learn more about the exact details of the algorithm,
have a look at [this video](https://www.youtube.com/watch?v=hNXwhF0OZ_o).
+
+
### Initialisation
```python
@@ -71,6 +61,8 @@ retriever = TfidfRetriever(document_store)
finder = Finder(reader, retriever)
```
+
+
## BM25 (Recommended)
### Description
@@ -78,11 +70,11 @@ finder = Finder(reader, retriever)
BM25 is a variant of TF-IDF that we recommend you use if you are looking for a retrieval method that does not need a neural network for indexing.
It improves upon its predecessor in two main aspects:
+- It saturates `tf` after a set number of occurrences of the given term in the document
-* It saturates `tf` after a set number of occurrences of the given term in the document
-
+- It normalises by document length so that short documents are favoured over long documents if they have the same amount of word overlap with the query
-* It normalises by document length so that short documents are favoured over long documents if they have the same amount of word overlap with the query
+
### Initialisation
@@ -97,6 +89,9 @@ finder = Finder(reader, retriever)
See [this](https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-and-its-variables) blog post for more details about the algorithm.
+
+
+
## Dense Passage Retrieval (Recommended)
### Description
@@ -104,31 +99,29 @@ See [this](https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-
[Dense Passage Retrieval](https://arxiv.org/abs/2004.04906) is a highly performant retrieval method that calculates relevance using dense representations.
Key features:
+- One BERT base model to encode documents
-* One BERT base model to encode documents
+- One Bert base model to encode queries
-
-* One Bert base model to encode queries
-
-
-* Ranking of documents done by dot product similarity between query and document embeddings
+- Ranking of documents done by dot product similarity between query and document embeddings
+
Indexing using DPR is comparatively expensive in terms of required computation since all documents in the database need to be processed through the transformer.
The embeddings that are created in this step can be stored in FAISS, a database optimized for vector similarity.
DPR can also work with the ElasticsearchDocumentStore or the InMemoryDocumentStore.
There are two design decisions that have made DPR particularly performant.
+- Separate encoders for document and query helps since queries are much shorter than documents
-* Separate encoders for document and query helps since queries are much shorter than documents
-
-
-* Training with ‘In-batch negatives’ (gold labels are treated as negative examples for other samples in same batch) is highly efficient
+- Training with ‘In-batch negatives’ (gold labels are treated as negative examples for other samples in same batch) is highly efficient
In Haystack, you can simply download the pretrained encoders needed to start using DPR.
If you’d like to learn how to set up a DPR based system, have a look at our tutorials.
+
+
### Initialisation
```python
@@ -145,6 +138,9 @@ finder = Finder(reader, retriever)
+
+
+
## Embedding Retrieval
### Description
@@ -157,6 +153,8 @@ They are particular suited to cases where your query input is similar in style t
i.e. when you are searching for most similar documents.
This is not inherently suited to query based search where the length, language and format of the query usually significantly differs from the searched for text.
+
+
### Initialisation
```python
@@ -168,6 +166,8 @@ retriever = EmbeddingRetriever(document_store=document_store,
finder = Finder(reader, retriever)
```
+
+
## Deeper Dive: Dense vs Sparse
Broadly speaking, retrieval methods can be split into two categories: **dense** and **sparse**.
@@ -175,28 +175,24 @@ Broadly speaking, retrieval methods can be split into two categories: **dense**
**Sparse** methods, like TF-IDF and BM25, operate by looking for shared keywords between the document and query.
They are:
+- simple but effective
-* simple but effective
-
-
-* don’t need to be trained
+- don’t need to be trained
-
-* work on any language
+- work on any language
More recently, **dense** approaches such as Dense Passage Retrieval (DPR) have shown even better performance than their sparse counter parts.
These methods embed both document and query into a shared embedding space using deep neural networks
and the top candidates are the nearest neighbour documents to the query.
They are:
+- powerful but computationally more expensive especially during indexing
-* powerful but computationally more expensive especially during indexing
-
+- trained using labelled datasets
-* trained using labelled datasets
+- language specific
-
-* language specific
+
### Qualitative Differences
@@ -212,6 +208,9 @@ they only care about whether they are present or absent in the text.
As such, **they handle out-of-vocabulary words with no problem**.
+
+
+
### Indexing
Dense methods perform indexing by processing all the documents through a neural network and storing the resulting vectors.
@@ -219,10 +218,14 @@ This is a much more expensive operation than the creation of the inverted-index
and will require significant computational power and time.
+
+
+
### Terminology
+
The terms **dense** and **sparse** refer to the representations that the algorithms build for each document and query.
**Sparse** methods characterise texts using vectors with one dimension corresponding to each word in the vocabulary.
Dimensions will be zero if the word is absent and non-zero if it is present.
diff --git a/src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/roadmap.md b/docs/v0.4.0/usage/roadmap.mdx
similarity index 63%
rename from src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/roadmap.md
rename to docs/v0.4.0/usage/roadmap.mdx
index bf28c14e5..2fa9c0fd9 100644
--- a/src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/roadmap.md
+++ b/docs/v0.4.0/usage/roadmap.mdx
@@ -1,45 +1,49 @@
----
-title: "Roadmap"
-metaTitle: "Roadmap and Zenhub Access"
-metaDescription: "How to access our open roadmap"
-slug: "/docs/roadmap"
-date: "2020-09-24"
-id: "roadmapmd"
----
-
# Open roadmap
-We believe open-source is more than open source code. It's a lot about people, collaboration, transparency and trust.
-Therefore, we decided to be as open as possible with our roadmap and sprint planning.
-In fact, you can see all of it in real-time on GitHub.
-We hope this helps to clarify the direction of the open-source project and inspires discussions in the community.
+We believe open-source is more than open source code. It's a lot about people, collaboration, transparency and trust.
+Therefore, we decided to be as open as possible with our roadmap and sprint planning.
+In fact, you can see all of it in real-time on GitHub.
+We hope this helps to clarify the direction of the open-source project and inspires discussions in the community.
+
+
## How to access it
### Add Zenhub Plugin
+
We decided for Zenhub, as it allows a close integration with GitHub and real-time sharing of roadmaps and sprints.
Once you have installed the browser plugin below, you will see additional tabs and infos on the Haystack GitHub page.
-Zenhub Plugin: https://www.zenhub.com/extension
+Zenhub Plugin: https://www.zenhub.com/extension
+
+
### Roadmap
+
_Zenhub Tab -> Roadmap (left menu)_
Here you can find our most recent roadmap with the **high-level projects** that are planned for the upcoming quarters.
-We update it regularly and refine the projects as they come closer in time.
-
-
+We update it regularly and refine the projects as they come closer in time.
+
+
+
+
### Board
+
_Zenhub Tab -> Board (left menu)_
-If you are interested in the **operational tasks** and their status, you can find our agile board here.
+If you are interested in the **operational tasks** and their status, you can find our agile board here.
+
+
+
+
-
### Additional issue details & Releases
+
_Right panel in regular Github issues_
-With Zenhub you can also see some additional tags in every GitHub issue.
-For those of you who wonder about the next release date: We aim for releases every ~ 4 weeks and will tag the issues that will need to be finished before a bit in advance.
+With Zenhub you can also see some additional tags in every GitHub issue.
+For those of you who wonder about the next release date: We aim for releases every ~ 4 weeks and will tag the issues that will need to be finished before a bit in advance.
-
+
diff --git a/docs/v0.5.0/menu.json b/docs/v0.5.0/menu.json
new file mode 100644
index 000000000..247fd1fb4
--- /dev/null
+++ b/docs/v0.5.0/menu.json
@@ -0,0 +1,87 @@
+[
+ {
+ "subMenuTitle": "Usage",
+ "pathPrefix": "/usage/",
+ "items": [
+ { "slug": "intro", "title": "What is Haystack?" },
+ {
+ "slug": "get-started",
+ "title": "Get Started"
+ },
+ {
+ "slug": "document-store",
+ "title": "DocumentStore"
+ },
+ { "slug": "retriever", "title": "Retriever" },
+ { "slug": "reader", "title": "Reader" },
+ {
+ "slug": "preprocessing",
+ "title": "Preprocessing"
+ },
+ {
+ "slug": "languages",
+ "title": "Languages Other Than English"
+ },
+ {
+ "slug": "domain-adaptation",
+ "title": "Domain Adaptation"
+ },
+ { "slug": "glossary", "title": "Glossary" },
+ { "slug": "roadmap", "title": "Roadmap" },
+ {
+ "slug": "annotation",
+ "title": "Annotation Tool"
+ }
+ ]
+ },
+ {
+ "subMenuTitle": "Tutorials",
+ "pathPrefix": "/tutorials/",
+ "items": [
+ {
+ "slug": "first-qa-system",
+ "title": "Build Your First QA System"
+ },
+ {
+ "slug": "fine-tuning-a-model",
+ "title": "Fine-tuning a Model on Your Own Data"
+ },
+ {
+ "slug": "without-elasticsearch",
+ "title": "Build a QA System Without Elasticsearch"
+ },
+ {
+ "slug": "existing-faqs",
+ "title": "Utilizing existing FAQs for Question Answering"
+ },
+ {
+ "slug": "evaluation",
+ "title": "Evaluation of a QA System"
+ },
+ {
+ "slug": "dense-passage-retrieval",
+ "title": "Better Retrieval via \"Dense Passage Retrieval\""
+ }
+ ]
+ },
+ {
+ "subMenuTitle": "API Reference",
+ "pathPrefix": "/reference/",
+ "items": [
+ {
+ "slug": "document-store",
+ "title": "Document Store"
+ },
+ { "slug": "retriever", "title": "Retriever" },
+ { "slug": "reader", "title": "Reader" },
+ {
+ "slug": "preprocessor",
+ "title": "Preprocessor"
+ },
+ {
+ "slug": "file-converters",
+ "title": "File Converters"
+ }
+ ]
+ }
+]
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/annotation.md b/docs/v0.5.0/usage/annotation.mdx
similarity index 80%
rename from src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/annotation.md
rename to docs/v0.5.0/usage/annotation.mdx
index ed29d18c8..d22e35fae 100644
--- a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/annotation.md
+++ b/docs/v0.5.0/usage/annotation.mdx
@@ -1,12 +1,3 @@
----
-title: "Annotation Tool"
-metaTitle: "Annotation Tool"
-metaDescription: ""
-slug: "/docs/annotation"
-date: "2020-09-03"
-id: "annotationmd"
----
-
# Annotation Tool
- Create labels with different techniques: Come up with questions (+ answers) while reading passages (SQuAD style) or have a set of predefined questions and look for answers in the document (~ Natural Questions).
@@ -14,12 +5,17 @@ id: "annotationmd"
- Upload your documents or import labels from an existing SQuAD-style dataset
- Export your labels in SQuAD Format
-
+
+
+
+
+## Hosted version
+
+Signup here: [Haystack Annotation Tool](https://annotate.deepset.ai/login)
-# Hosted version
- Signup here: [Haystack Annotation Tool](https://annotate.deepset.ai/login)
+
-# Local version (Docker)
+## Local version (Docker)
1. Configure credentials & database in the [`docker-compose.yml`](https://github.com/deepset-ai/haystack/blob/master/annotation_tool/docker-compose.yml):
@@ -37,11 +33,12 @@ The credentials should match in database image and application configuration.
POSTGRES_PASSWORD: "somesafepassword"
POSTGRES_DB: "databasename"
-
2. Run docker-compose by executing `docker-compose up`.
-
3. The UI should be available at `localhost:7001`.
-# Usage
+
+
+## Usage
+
The manual (of a slightly earlier version) can be found [here](https://drive.google.com/file/d/1Wv3OIC0Z7ibHIzOm9Xw_r0gjTFmpl-33/view). While it doesn't include all latest features, the basic workflow and tips for label quality are still the same.
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/document_store.md b/docs/v0.5.0/usage/document_store.mdx
similarity index 66%
rename from src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/document_store.md
rename to docs/v0.5.0/usage/document_store.mdx
index 2dcbe4093..e8caaa879 100644
--- a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/document_store.md
+++ b/docs/v0.5.0/usage/document_store.mdx
@@ -1,87 +1,60 @@
----
-title: "Document Store"
-metaTitle: "Document Store"
-metaDescription: ""
-slug: "/docs/documentstore"
-date: "2020-09-03"
-id: "documentstoremd"
----
-
-
# DocumentStores
You can think of the DocumentStore as a "database" that:
-- stores your texts and meta data
-- provides them to the retriever at query time
-There are different DocumentStores in Haystack to fit different use cases and tech stacks.
+- stores your texts and meta data
+- provides them to the retriever at query time
+
+There are different DocumentStores in Haystack to fit different use cases and tech stacks.
## Initialisation
Initialising a new DocumentStore is straight forward.
-++from haystack.reader import TransformersReader++ reader = TransformersReader(... max_seq_len=384, doc_stride=128 + ...) ++
+
-
-
Each DocumentStore constructor allows for arguments specifying how to connect to existing databases and the names of indexes.
See API documentation for more info.
+
+
## Input Format
DocumentStores expect Documents in dictionary form, like that below.
They are loaded using the `DocumentStore.write_documents()` method.
See [Preprocessing](/docs/v0.5.0/preprocessingmd) for more information on how to best prepare your data.
-[//]: # (Add link to preprocessing section)
+[//]: # "Add link to preprocessing section"
```python
document_store = ElasticsearchDocumentStore()
@@ -94,6 +67,8 @@ dicts = [
document_store.write_documents(dicts)
```
+
+
## Writing Documents (Sparse Retrievers)
Haystack allows for you to write store documents in an optimised fashion so that query times can be kept low.
@@ -105,6 +80,8 @@ The creation of the inverted index which optimises querying speed is handled aut
document_store.write_documents(dicts)
```
+
+
## Writing Documents (Dense Retrievers)
For **dense** neural network based retrievers like Dense Passage Retrieval, or Embedding Retrieval,
@@ -123,83 +100,74 @@ Having GPU acceleration will significantly speed this up.
+
+
+
## Choosing the Right Document Store
-The Document Stores have different characteristics. You should choose one depending on the maturity of your project, the use case and technical environment:
+The Document Stores have different characteristics. You should choose one depending on the maturity of your project, the use case and technical environment:
-
-
-
-
+
-
+### Elasticsearch
```python
document_store = ElasticsearchDocumentStore()
```
-
-
-
-
-
+
-
+### FAISS
```python
document_store = FAISSDocumentStore()
```
-
-
-
-
-
+
-
+### In Memory
```python
document_store = InMemoryDocumentStore()
```
-
-
-
-
-
-
-
+### SQL
```python
document_store = SQLDocumentStore()
```
-
-
+### Elasticsearch
-
+
#### Our Recommendations
-**Restricted environment:** Use the `InMemoryDocumentStore`, if you are just giving Haystack a quick try on a small sample and are working in a restricted environment that complicates running Elasticsearch or other databases
+**Restricted environment:** Use the `InMemoryDocumentStore`, if you are just giving Haystack a quick try on a small sample and are working in a restricted environment that complicates running Elasticsearch or other databases
**Allrounder:** Use the `ElasticSearchDocumentStore`, if you want to evaluate the performance of different retrieval options (dense vs. sparse) and are aiming for a smooth transition from PoC to production
diff --git a/src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/domain_adaptation.md b/docs/v0.5.0/usage/domain_adaptation.mdx
similarity index 80%
rename from src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/domain_adaptation.md
rename to docs/v0.5.0/usage/domain_adaptation.mdx
index 607b0d27b..84661ba36 100644
--- a/src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/domain_adaptation.md
+++ b/docs/v0.5.0/usage/domain_adaptation.mdx
@@ -1,12 +1,3 @@
----
-title: "Domain Adaptation"
-metaTitle: "Domain Adaptation"
-metaDescription: ""
-slug: "/docs/domain_adaptation"
-date: "2020-09-03"
-id: "domain_adaptationmd"
----
-
# Domain Adaptation
## Generalisation
@@ -22,6 +13,8 @@ But you don’t always need to know what a HP Valve is to answer “What is conn
The answer might be there in plain language.
In the same way, many QA models have a good enough grasp of language to answer questions about concepts in an unseen domain.
+
+
## Finetuning
Any model that can be loaded into Haystack can also be finetuned within Haystack.
@@ -39,33 +32,30 @@ See Tutorial 2 for a runnable example of this process.
If you’re interested in measuring how much your model has improved,
please also check out Tutorial 5 which walks through the steps needed to perform evaluation.
+
+
## Generating Labels
Using our [Haystack Annotate tool](https://annotate.deepset.ai/login) (Beta),
you can easily create a labelled dataset using your own documents featuring your own question/ answer pairs.
-
-
-
+
Features include:
+- Structured workspaces via organisations, projects and users
-* Structured workspaces via organisations, projects and users
+- Easy upload of your own documents and labels in a variety of formats (txt, pdf, SQuAD style)
-
-* Easy upload of your own documents and labels in a variety of formats (txt, pdf, SQuAD style)
-
-
-* Export of labels to be used directly in Haystack
+- Export of labels to be used directly in Haystack
Annotate also supports two different workflows:
+- Think up questions and answers while reading passages (SQuAD style)
-* Think up questions and answers while reading passages (SQuAD style)
-
+- Have a set of predefined questions and look for answers in the document (~ Natural Questions style)
-* Have a set of predefined questions and look for answers in the document (~ Natural Questions style)
+
## User Feedback
diff --git a/src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/get_started.md b/docs/v0.5.0/usage/get_started.mdx
similarity index 68%
rename from src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/get_started.md
rename to docs/v0.5.0/usage/get_started.mdx
index d4652eb24..fd1e29d31 100644
--- a/src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/get_started.md
+++ b/docs/v0.5.0/usage/get_started.mdx
@@ -1,61 +1,62 @@
----
-title: "Get Started"
-metaTitle: "Get Started"
-metaDescription: ""
-slug: "/docs/get_started"
-date: "2020-09-03"
-id: "get_startedmd"
----
-
# Get Started
## Installation
-
-
-
-
+
+
+### FAISS
-
+**Pros:**
-**Pros:**
- Fast & accurate sparse retrieval
- Basic support for dense retrieval
-- Production-ready
+- Production-ready
- Many options to tune sparse retrieval
-**Cons:**
+**Cons:**
+
- Slow for dense retrieval with more than ~ 1 Mio documents
-
-
-
-
-
+
-
+**Pros:**
-**Pros:**
- Fast & accurate dense retrieval
- Highly scalable due to approximate nearest neighbour algorithms (ANN)
-- Many options to tune dense retrieval via different index types
+- Many options to tune dense retrieval via different index types
**Cons:**
+
- No efficient sparse retrieval
-
-
-
-
-
+### SQL
-
+### In Memory
**Pros:**
+
- Simple
- Exists already in many environments
**Cons:**
+
- Only compatible with minimal TF-IDF Retriever
- Bad retrieval performance
- Not recommended for production
-
-
-
-
-
-
-
+
**Pros:**
+
- Simple & fast to test
- No database requirements
-**Cons:**
+**Cons:**
+
- Not scalable
- Not persisting your data on disk
-
-
-
-
+
-
-
-
-
-
-The most straightforward way to install Haystack is through pip.
- -```python -$ pip install farm-haystack -``` - -
-- -```python -$ pip install farm-haystack -``` - -
-
-
-
-
-
-If you’d like to run a specific, unreleased version of Haystack, or make edits to the way Haystack runs,
-you’ll want to install it using `git` and `pip --editable`.
-This clones a copy of the repo to a local directory and runs Haystack from there.
- -```python -$ git clone https://github.com/deepset-ai/haystack.git -$ cd haystack -$ pip install --editable . -``` - -By default, this will give you the latest version of the master branch. Use regular git commands to switch between different branches and commits. -
-- -```python -$ git clone https://github.com/deepset-ai/haystack.git -$ cd haystack -$ pip install --editable . -``` - -By default, this will give you the latest version of the master branch. Use regular git commands to switch between different branches and commits. -
+ The most straightforward way to install Haystack is through pip. +
+
+ pip install farm-haystack
+
+
+ ),
+ },
+ {
+ title: "Editable",
+ content: (
+
+
+ ),
+ },
+ ]}
+/>
Note: On Windows add the arg `-f https://download.pytorch.org/whl/torch_stable.html` to install PyTorch correctly
+
+
+
## The Building Blocks of Haystack
Here’s a sample of some Haystack code showing the most important components.
For a working code example, check out our starter tutorial.
+
```python
# DocumentStore: holds all your data
document_store = ElasticsearchDocumentStore()
@@ -80,6 +81,8 @@ prediction = finder.get_answers(question)
print_answers(prediction)
```
+
+
## Loading Documents into the DocumentStore
In Haystack, DocumentStores expect Documents in a dictionary format. They are loaded as follows:
@@ -102,19 +105,18 @@ This splitting can have a big impact on speed and performance.
**General Guide**: If Haystack is running very slowly, you might want to try splitting your text into smaller Documents.
If you want an improvement to performance, you might want to try concatenating text to make larger Documents.
+
+
## Running Queries
**Querying** involves searching for an answer to a given question within the full document store.
This process will:
+- make the Retriever filter for a small set of relevant candidate documents
-* make the Retriever filter for a small set of relevant candidate documents
-
-
-* get the Reader to process this set of candidate documents
-
+- get the Reader to process this set of candidate documents
-* return potential answers to the given question
+- return potential answers to the given question
Usually, there are tight time constraints on querying and so it needs to be a lightweight operation.
When documents are loaded, Haystack will precompute any of the results that might be useful at query time.
diff --git a/src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/terms.md b/docs/v0.5.0/usage/glossary.mdx
similarity index 99%
rename from src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/terms.md
rename to docs/v0.5.0/usage/glossary.mdx
index e851cce0c..cb98a53c4 100644
--- a/src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/terms.md
+++ b/docs/v0.5.0/usage/glossary.mdx
@@ -1,11 +1,11 @@
----
+
# Glossary
diff --git a/src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/intro.md b/docs/v0.5.0/usage/intro.mdx
similarity index 66%
rename from src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/intro.md
rename to docs/v0.5.0/usage/intro.mdx
index ffd17502b..fd802609a 100644
--- a/src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/intro.md
+++ b/docs/v0.5.0/usage/intro.mdx
@@ -1,28 +1,20 @@
----
-title: "What is Haystack"
-metaTitle: "What is Haystack"
-metaDescription: ""
-slug: "/docs/intro"
-date: "2020-09-03"
-id: "intromd"
----
-
# What is Haystack?
Haystack is an **open-source framework** for building **end-to-end question answering systems** for large document collections.
Recent advances in NLP have enabled the application of QA to real world settings
and Haystack is designed to be the bridge between research and industry.
+- **Latest NLP models**: Utilize all transformer based models (BERT, RoBERTa, MiniLM, DPR ...) and smoothly switch when new ones get published
-* **Latest NLP models**: Utilize all transformer based models (BERT, RoBERTa, MiniLM, DPR ...) and smoothly switch when new ones get published
+- **Flexible databases**: Load data into and query from a range of databases such as Elasticsearch, FAISS, SQL and more
-* **Flexible databases**: Load data into and query from a range of databases such as Elasticsearch, FAISS, SQL and more
+- **Scalability**: Production-ready deployments that scale to millions of documents
-* **Scalability**: Production-ready deployments that scale to millions of documents
+- **End-to-End**: All tooling you need to implement, evaluate, improve and run a QA system
-* **End-to-End**: All tooling you need to implement, evaluate, improve and run a QA system
+- **Domain adaptation**: Fine-tune models to your own domain & improve them continuously via user feedback
-* **Domain adaptation**: Fine-tune models to your own domain & improve them continuously via user feedback
+
## Use cases
@@ -30,19 +22,17 @@ and Haystack is designed to be the bridge between research and industry.
Take the leap from using keyword search on your own documents to semantic search with Haystack.
+- Store your documents in the database of your choice (Elasticsearch, SQL, in memory, FAISS)
-* Store your documents in the database of your choice (Elasticsearch, SQL, in memory, FAISS)
-
-
-* Perform question driven queries.
+- Perform question driven queries.
Expect to see results that highlight the very sentence that contains the answer to your question.
Thanks to the power of Transformer based language models, results are chosen based on compatibility in meaning
rather than lexical overlap.
+
-
-
+
### Information Extractor
@@ -50,48 +40,46 @@ Automate the extraction of relevant information from a set of documents that per
Haystack can:
+- Apply a set of standard questions to each document in a store
-* Apply a set of standard questions to each document in a store
-
-
-* Return a NO_ANSWER if a given document does not contain the answer to a question
+- Return a NO_ANSWER if a given document does not contain the answer to a question
Say you have the financial reports for different companies over different years.
You can gather a set of standard questions which are applicable to each financial report,
-like *what is the revenue forecast for 2020?* or *what are the main sources of income?*.
+like _what is the revenue forecast for 2020?_ or _what are the main sources of income?_.
Haystack will try to find an answer for each question within each document!
We’ve seen this style of application be particularly effective in the sphere of finance and patent law
but we see a lot of potential in using this to gain a better overview of academic papers and internal business documents.
+
+
+
### FAQ Style Question Answering
Leverage existing FAQ documents and semantic similarity search to answer new incoming questions.
The workflow is as follows:
+- Store a set of FAQ documents in Haystack
-* Store a set of FAQ documents in Haystack
-
-
-* The user presents a new question
-
+- The user presents a new question
-* Haystack will find the closest match to the new question in the FAQ documents
+- Haystack will find the closest match to the new question in the FAQ documents
-
-* The user will be presented with the most similar Question Answer pair
+- The user will be presented with the most similar Question Answer pair
Haystack’s flexibility allows you to give new users more dynamic access to your existing documentation.
-## Technology
-Haystack is powered by a Retriever-Reader pipeline in order to optimise for both speed and accuracy.
+
+## Technology
+Haystack is powered by a Retriever-Reader pipeline in order to optimise for both speed and accuracy.
-
+
**Readers**, also known as Open-Domain QA systems in Machine Learning speak,
are powerful models that do close analysis of documents and perform the core task of question answering.
@@ -100,28 +88,25 @@ However, it is not currently feasible to use the Reader directly on large collec
+
The **Retriever** assists the Reader by acting as a lightweight filter that reduces the number of documents that the Reader has to process.
It does this by:
+- Scanning through all documents in the database
-* Scanning through all documents in the database
-
-
-* Quickly identifying the relevant and dismissing the irrelevant
+- Quickly identifying the relevant and dismissing the irrelevant
-
-* Passing on only a small candidate set of documents to the Reader
+- Passing on only a small candidate set of documents to the Reader
Current methods fall into one of the two categories:
+- sparse
-* sparse
- * keyword based
- * fast indexing and querying
- * e.g. BM25
-
+ - keyword based
+ - fast indexing and querying
+ - e.g. BM25
-* dense
- * neural network based
- * computationally heavy indexing but fast querying
- * e.g. Dense Passage Retrieval
+- dense
+ - neural network based
+ - computationally heavy indexing but fast querying
+ - e.g. Dense Passage Retrieval
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/languages.md b/docs/v0.5.0/usage/languages.mdx
similarity index 52%
rename from src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/languages.md
rename to docs/v0.5.0/usage/languages.mdx
index 17ea592d3..3e9809b1a 100644
--- a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/languages.md
+++ b/docs/v0.5.0/usage/languages.mdx
@@ -1,38 +1,31 @@
----
-title: "Languages Other Than English"
-metaTitle: "Languages Other Than English"
-metaDescription: ""
-slug: "/docs/languages"
-date: "2020-11-05"
-id: "languagesmd"
----
-
# Languages Other Than English
Haystack is well suited to open-domain QA on languages other than English.
While our defaults are tuned for English,
-you will find some tips and tricks here for using Haystack in your language.
+you will find some tips and tricks here for using Haystack in your language.
+
+
-##Retrievers
+## Retrievers
The sparse retriever methods themselves(BM25, TF-IDF) are language agnostic.
Their only requirement is that the text be split into words.
The ElasticsearchDocumentStore relies on an analyzer to impose word boundaries,
but also to handle punctuation, casing and stop words.
-The default analyzer is an English analyzer.
+The default analyzer is an English analyzer.
While it can still work decently for a large range of languages,
you will want to set it to your language's analyzer for optimal performance.
In some cases, such as with Thai, the default analyzer is completely incompatible.
-See [this page](https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-lang-analyzer.html)
+See [this page](https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-lang-analyzer.html)
for the full list of language specific analyzers.
```python
document_store = ElasticsearchDocumentStore(analyzer="thai")
```
-The models used in dense retrievers are language specific.
-Be sure to check language of the model used in your EmbeddingRetriever.
+The models used in dense retrievers are language specific.
+Be sure to check language of the model used in your EmbeddingRetriever.
The default model that is loaded in the DensePassageRetriever is for English.
We are currently working on training a German DensePassageRetriever model and know other teams who work on further languages.
If you have a language model and a question answering dataset in your own language, you can also train a DPR model using Haystack!
@@ -51,111 +44,107 @@ dense_passage_retriever.train(self,
n_epochs: int = 3)
```
-##Readers
+
+
+## Readers
While models are comparatively more performant on English,
thanks to a wealth of available English training data,
there are a couple QA models that are directly usable in Haystack.
-+ If you’d like to run a specific, unreleased version of Haystack, or + make edits to the way Haystack runs, you’ll want to install it using + `git` and `pip --editable`. This clones a copy of the repo to a + local directory and runs Haystack from there. +
+++git clone https://github.com/deepset-ai/haystack.git+cd haystack+pip install --editable .+
+ By default, this will give you the latest version of the master + branch. Use regular git commands to switch between different + branches and commits. +
+
-
-
+
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-```python
-reader = FARMReader("illuin/camembert-base-fquad")
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = FARMReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa")
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = FARMReader("deepset/xlm-roberta-large-squad2")
-```
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-```python
-reader = TransformersReader("illuin/camembert-base-fquad")
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = TransformersReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa")
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = TransformersReader("deepset/xlm-roberta-large-squad2")
-```
-
-
-from haystack.reader import FARMReader
+
+ reader = FARMReader("illuin/camembert-base-fquad")
+
+
+ ),
+ },
+ {
+ title: "Italian",
+ content: (
+ ++ ), + }, + { + title: "Zero-shot", + content: ( +from haystack.reader import FARMReader++ reader = + FARMReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa") ++
++ ), + }, + ]} + /> + ), + }, + { + title: "Transformers", + content: ( +from haystack.reader import FARMReader++ reader = FARMReader("deepset/xlm-roberta-large-squad2") ++
from haystack.reader import TransformersReader
+
+ reader = TransformersReader("illuin/camembert-base-fquad")
+
+
+ ),
+ },
+ {
+ title: "Italian",
+ content: (
+ ++ ), + }, + { + title: "Zero-shot", + content: ( +from haystack.reader import TransformersReader++ reader = + TransformersReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa") ++
++ ), + }, + ]} + /> + ), + }, + ]} +/> + + The **French** and **Italian models** are both monolingual language models trained on French and Italian versions of the SQuAD dataset and their authors report decent results in their model cards @@ -170,5 +159,4 @@ but still its performance lags behind that of the monolingual models. Nonetheless, if there is not yet a monolingual model for your language and it is one of the 100 supported by XLM-RoBERTa, this zero-shot model may serve as a decent first baseline. -[//]: # (Add link to Reader training, create section in reader.md on training Reader) - +[//]: # "Add link to Reader training, create section in reader.md on training Reader" diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/preprocessing.md b/docs/v0.5.0/usage/preprocessing.mdx similarity index 59% rename from src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/preprocessing.md rename to docs/v0.5.0/usage/preprocessing.mdx index 544c72838..4fbd39550 100644 --- a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/preprocessing.md +++ b/docs/v0.5.0/usage/preprocessing.mdx @@ -1,26 +1,17 @@ ---- -title: "Preprocessing" -metaTitle: "Preprocessing" -metaDescription: "" -slug: "/docs/preprocessing" -date: "2020-09-03" -id: "preprocessingmd" ---- - # Preprocessing Haystack includes a suite of tools to: - -* extract text from different file types, -* normalize white space -* split text into smaller pieces to optimize retrieval + +- extract text from different file types, +- normalize white space +- split text into smaller pieces to optimize retrieval These data preprocessing steps can have a big impact on the systems performance and effective handling of data is key to getting the most out of Haystack. -The Document Store expects its inputs to come in the following format. +The Document Store expects its inputs to come in the following format. The sections below will show you all the tools you'll need to ready your data for storing. - + ```python docs = [ { @@ -30,58 +21,65 @@ docs = [ ] ``` + + ## File Conversion -There are a range of different file converters in Haystack that +There are a range of different file converters in Haystack that can extract text from files and cast them into the unified dictionary format shown above. Haystack features support for txt, pdf and docx files and there is even a converter that leverages Apache Tika. Please refer to [the API docs](/docs/v0.5.0/file_convertersmd) to see which converter best suits you. -from haystack.reader import TransformersReader++ reader = + TransformersReader("deepset/xlm-roberta-large-squad2") ++
-
-
+
-
-
-
-
-
-
-```python
-converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=["de","en"])
-doc = converter.convert(file_path=file, meta=None)
-```
-
-
-
-
-
-
-
-
-
-```python
-converter = DocxToTextConverter(remove_numeric_tables=True, valid_languages=["de","en"])
-doc = converter.convert(file_path=file, meta=None)
-```
-
-
-
-
-
-
-
-
-
-
-Haystack also has a `convert_files_to_dicts()` utility function that will convert
-all txt or pdf files in a given folder into this dictionary format.
-
-```python
-docs = convert_files_to_dicts(dir_path=doc_dir)
-```
-
-
-from haystack.file_converter import PDFToTextConverter
+
+ converter = PDFToTextConverter(remove_numeric_tables=True,
+ valid_languages=["de","en"])
+
+ doc = converter.convert(file_path=file, meta=None)
+
+ ),
+ },
+ {
+ title: "DOCX",
+ content: (
+ ++ ), + }, + { + title: "From a Directory", + content: ( +from haystack.file_converter import DocxToTextConverter++ converter = DocxToTextConverter(remove_numeric_tables=True, + valid_languages=["de","en"]) ++doc = converter.convert(file_path=file, meta=None)+
+
+ ),
+ },
+ ]}
+/>
+
+
## PreProcessor
@@ -101,12 +99,14 @@ processor = PreProcessor(clean_empty_lines=True,
docs = processor.process(d)
```
-* `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
-* `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
-* `clean_header_footer` will remove any long header or footer texts that are repeated on each page
-* `split_by` determines what unit the document is split by: `'word'`, `'sentence'` or `'passage'`
-* `split_length` sets a maximum number of `'word'`, `'sentence'` or `'passage'` units per output document
-* `split_respect_sentence_boundary` ensures that document boundaries do not fall in the middle of sentences
+- `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
+- `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
+- `clean_header_footer` will remove any long header or footer texts that are repeated on each page
+- `split_by` determines what unit the document is split by: `'word'`, `'sentence'` or `'passage'`
+- `split_length` sets a maximum number of `'word'`, `'sentence'` or `'passage'` units per output document
+- `split_respect_sentence_boundary` ensures that document boundaries do not fall in the middle of sentences
+
+
## Impact of Document Splitting
@@ -116,8 +116,8 @@ This is not always ideal as long documents can have negative impacts on both spe
Document length has a very direct impact on the speed of the Reader.
**If you halve the length of your documents, you can expect that the Reader will double in speed.**
-It is generally not a good idea to let document boundaries fall in the middle of sentences.
-Doing so means that each document will contain incomplete sentence fragments
+It is generally not a good idea to let document boundaries fall in the middle of sentences.
+Doing so means that each document will contain incomplete sentence fragments
which maybe be hard for both retriever and reader to interpret.
For **sparse retrievers**, very long documents pose a challenge since the signal of the relevant section of text
@@ -129,7 +129,3 @@ As such, it is important that documents are not longer than the dense retriever'
By default, Haystack's DensePassageRetriever model has a maximum length of 256 tokens.
As such, we recommend that documents contain significantly less words.
We have found decent performance with **documents around 100 words long**.
-
-
-
-
diff --git a/docs/v0.5.0/usage/reader.mdx b/docs/v0.5.0/usage/reader.mdx
new file mode 100644
index 000000000..ec1aa546d
--- /dev/null
+++ b/docs/v0.5.0/usage/reader.mdx
@@ -0,0 +1,303 @@
+# Reader
+
+The Reader, also known as Open-Domain QA systems in Machine Learning speak,
+is the core component that enables Haystack to find the answers that you need.
+Haystack’s Readers are:
+
+- built on the latest transformer based language models
+
+- strong in their grasp of semantics
+
+- sensitive to syntactic structure
+
+- state-of-the-art in QA tasks like SQuAD and Natural Questions
+
++ Haystack also has a`convert_files_to_dicts()` utility function that + will convert all txt or pdf files in a given folder into this + dictionary format. +
++++ from haystack.preprocessor.utils import convert_files_to_dicts ++docs = convert_files_to_dicts(dir_path=doc_dir)+
++ + ), + }, + { + title: "Transformers", + content: ( +model = "deepset/roberta-base-squad2"+reader = FARMReader(model, use_gpu=True)+finder = Finder(reader, retriever)+
+
+ ),
+ },
+ ]}
+/>
+
+While these models can work on CPU, it is recommended that they are run using GPUs to keep query times low.
+
+
+
+## Choosing the Right Model
+
+In Haystack, you can start using pretrained QA models simply by providing its HuggingFace Model Hub name to the Reader.
+The loading of model weights is handled by Haystack,
+and you have the option of using the QA pipeline from deepset FARM or HuggingFace Transformers (see FARM vs Transformers for details).
+
+Currently, there are a lot of different models out there and it can be rather overwhelming trying to pick the one that fits your use case.
+To get you started, we have a few recommendations for you to try out.
+
+++model = "deepset/roberta-base-squad2"+reader = TransformersReader(model, use_gpu=1)+finder = Finder(reader, retriever)+
+
+ RoBERTa (base)
+An optimised variant of BERT and a great starting point.
+++from haystack.reader import FARMReader+reader = FARMReader("deepset/roberta-base-squad2")+
-
+
- Pro: Strong all round model +
- + Con: There are other models that are either faster or more + accurate + +
+
+ MiniLM
++ A cleverly distilled model that sacrifices a little accuracy for + speed. +
+++from haystack.reader import FARMReader+reader = FARMReader("deepset/minilm-uncased-squad2")+
-
+
- Pro: Inference speed up to 50% faster than BERT base +
- + Con: Still doesn’t match the best base sized models in accuracy + +
+
+
+ ),
+ },
+ {
+ title: "Transformers",
+ content: (
+ ALBERT (XXL)
+Large, powerful, SotA model.
+++from haystack.reader import FARMReader++ reader = FARMReader("ahotrod/albert_xxlargev1_squad2_512") ++
-
+
- + Pro: Better accuracy than any other open source model in QA + +
- + Con: The computational power needed make it impractical for most + use cases + +
+
+ ),
+ },
+ ]}
+/>
+
+
+
+**All-rounder**: In the class of base sized models trained on SQuAD, **RoBERTa** has shown better performance than BERT
+and can be capably handled by any machine equipped with a single NVidia V100 GPU.
+We recommend this as the starting point for anyone wanting to create a performant and computationally reasonable instance of Haystack.
+
+**Built for Speed**: If speed and GPU memory are more of a priority to you than accuracy,
+you should try the MiniLM model.
+It is a smaller model that is trained to mimic larger models through the distillation process,
+and it outperforms the BERT base on SQuAD even though it is about 40% smaller.
+
+
+
+**State of the Art Accuracy**: For most, **ALBERT XXL** will be too large to feasibly work with.
+But if performance is your sole concern, and you have the computational resources,
+you might like to try ALBERT XXL which has set SoTA performance on SQuAD 2.0.
+
+
+
+
+
+
+
+## Deeper Dive: FARM vs Transformers
+
+Apart from the **model weights**, Haystack Readers contain all the components found in end-to-end open domain QA systems.
+This includes **tokenization**, **embedding computation**, **span prediction** and **candidate aggregation**.
+While the handling of model weights is the same between the FARM and Transformers libraries, their QA pipelines differ in some ways.
+The major points are:
+
+- The **TransformersReader** will sometimes predict the same span twice while duplicates are removed in the **FARMReader**
+
+- The **FARMReader** currently uses the tokenizers from the HuggingFace Transformers library while the **TransformersReader** uses the tokenizers from the HuggingFace Tokenizers library
+
+- Start and end logits are normalized per passage and multiplied in the **TransformersReader** while they are summed and not normalised in the **FARMReader**
+
+If you’re interested in the finer details of these points, have a look at [this](https://github.com/deepset-ai/haystack/issues/248#issuecomment-661977237) GitHub comment.
+
+We see value in maintaining both kinds of Readers since Transformers is a very familiar library to many of Haystack’s users
+but we at deepset can more easily update and optimise the FARM pipeline for speed and performance.
+
+
+
+Haystack also has a close integration with FARM which means that you can further fine-tune your Readers on labelled data using a FARMReader.
+See our tutorials for an end-to-end example or below for a shortened example.
+
+```python
+# Initialise Reader
+model = "deepset/roberta-base-squad2"
+reader = FARMReader(model)
+
+# Perform finetuning
+train_data = "PATH/TO_YOUR/TRAIN_DATA"
+train_filename = "train.json"
+save_dir = "finetuned_model"
+reader.train(train_data, train_filename, save_dir=save_dir)
+
+# Load
+finetuned_reader = FARMReader(save_dir)
+```
+
+
+
+## Deeper Dive: From Language Model to Haystack Reader
+
+Language models form the core of most modern NLP systems and that includes the Readers in Haystack.
+They build a general understanding of language when performing training tasks such as Masked Language Modeling or Replaced Token Detection
+on large amounts of text.
+Well trained language models capture the word distribution in one or more languages
+but more importantly, convert input text into a set of word vectors that capture elements of syntax and semantics.
+
+In order to convert a language model into a Reader model, it needs first to be trained on a Question Answering dataset.
+To do so requires the addition of a question answering prediction head on top of the language model.
+The task can be thought of as a token classification task where every input token is assigned a probability of being
+either the start or end token of the correct answer.
+In cases where the answer is not contained within the passage, the prediction head is also expected to return a `no_answer` prediction.
+
+
+
+Since language models are limited in the number of tokens which they can process in a single forward pass,
+a sliding window mechanism is implemented to handle variable length documents.
+This functions by slicing the document into overlapping passages of (approximately) `max_seq_length`
+that are each offset by `doc_stride` number of tokens.
+These can be set when the Reader is initialized.
+
+
+
+ RoBERTa (base)
+An optimised variant of BERT and a great starting point.
+++from haystack.reader import TransformersReader++ reader = TransformersReader("deepset/roberta-base-squad2") ++
-
+
- Pro: Strong all round model +
- + Con: There are other models that are either faster or more + accurate + +
+
+ MiniLM
++ A cleverly distilled model that sacrifices a little accuracy for + speed. +
+++from haystack.reader import TransformersReader++ reader = TransformersReader("deepset/minilm-uncased-squad2") ++
-
+
- Pro: Inference speed up to 50% faster than BERT base +
- + Con: Still doesn’t match the best base sized models in accuracy + +
+
+ ALBERT (XXL)
+Large, powerful, SotA model.
+++from haystack.reader import TransformersReader++ reader = + TransformersReader("ahotrod/albert_xxlargev1_squad2_512") ++
-
+
- + Pro: Better accuracy than any other open source model in QA + +
- + Con: The computational power needed make it impractical for most + use cases + +
++ + ), + }, + { + title: "Transformers", + content: ( +from haystack.reader import FARMReader++ reader = FARMReader(... max_seq_len=384, doc_stride=128 ...) ++
+
+ ),
+ },
+ ]}
+/>
+
+Predictions are made on each individual passage and the process of aggregation picks the best candidates across all passages.
+If you’d like to learn more about what is happening behind the scenes, have a look at [this](https://medium.com/deepset-ai/modern-question-answering-systems-explained-4d0913744097) article.
+
+
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/retriever.md b/docs/v0.5.0/usage/retriever.mdx
similarity index 81%
rename from src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/retriever.md
rename to docs/v0.5.0/usage/retriever.mdx
index 2e14fe36c..d18888269 100644
--- a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/retriever.md
+++ b/docs/v0.5.0/usage/retriever.mdx
@@ -1,12 +1,3 @@
----
-title: "Retriever"
-metaTitle: "Retriever"
-metaDescription: ""
-slug: "/docs/retriever"
-date: "2020-09-03"
-id: "retrievermd"
----
-
# Retriever
The Retriever is a lightweight filter that can quickly go through the full document store and pass a set of candidate documents to the Reader.
@@ -14,23 +5,24 @@ It is an tool for sifting out the obvious negative cases, saving the Reader from
Recommendations:
+- BM25 (sparse)
-* BM25 (sparse)
-
-
-* Dense Passage Retrieval (dense)
+- Dense Passage Retrieval (dense)
+
Note that not all Retrievers can be paired with every DocumentStore.
Here are the combinations which are supported:
-| | Memory | Elasticsearch | SQL | FAISS |
-| --- | --- | --- | ---- | ---- |
-| BM25 | N | Y | N | N |
-| TF-IDF | Y | Y | Y | N |
-| Embedding | Y | Y | N | Y |
-| DPR | Y | Y | N | Y |
+| | Memory | Elasticsearch | SQL | FAISS |
+| --------- | ------ | ------------- | --- | ----- |
+| BM25 | N | Y | N | N |
+| TF-IDF | Y | Y | Y | N |
+| Embedding | Y | Y | N | Y |
+| DPR | Y | Y | N | Y |
+
+
## TF-IDF
@@ -38,11 +30,9 @@ Here are the combinations which are supported:
TF-IDF is a commonly used baseline for information retrieval that exploits two key intuitions:
+- documents that have more lexical overlap with the query are more likely to be relevant
-* documents that have more lexical overlap with the query are more likely to be relevant
-
-
-* words that occur in fewer documents are more significant than words that occur in many documents
+- words that occur in fewer documents are more significant than words that occur in many documents
Given a query, a tf-idf score is computed for each document as follows:
@@ -52,15 +42,15 @@ score = tf * idf
Where:
+- `tf` is how many times words in the query occur in that document.
-* `tf` is how many times words in the query occur in that document.
-
-
-* `idf` is the inverse of the fraction of documents containing the word.
+- `idf` is the inverse of the fraction of documents containing the word.
In practice, both terms are usually log normalised. If you’d like to learn more about the exact details of the algorithm,
have a look at [this video](https://www.youtube.com/watch?v=hNXwhF0OZ_o).
+
+
### Initialisation
```python
@@ -71,6 +61,8 @@ retriever = TfidfRetriever(document_store)
finder = Finder(reader, retriever)
```
+
+
## BM25 (Recommended)
### Description
@@ -78,11 +70,11 @@ finder = Finder(reader, retriever)
BM25 is a variant of TF-IDF that we recommend you use if you are looking for a retrieval method that does not need a neural network for indexing.
It improves upon its predecessor in two main aspects:
+- It saturates `tf` after a set number of occurrences of the given term in the document
-* It saturates `tf` after a set number of occurrences of the given term in the document
-
+- It normalises by document length so that short documents are favoured over long documents if they have the same amount of word overlap with the query
-* It normalises by document length so that short documents are favoured over long documents if they have the same amount of word overlap with the query
+
### Initialisation
@@ -97,6 +89,9 @@ finder = Finder(reader, retriever)
See [this](https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-and-its-variables) blog post for more details about the algorithm.
+
+
+
## Dense Passage Retrieval (Recommended)
### Description
@@ -104,31 +99,29 @@ See [this](https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-
[Dense Passage Retrieval](https://arxiv.org/abs/2004.04906) is a highly performant retrieval method that calculates relevance using dense representations.
Key features:
+- One BERT base model to encode documents
-* One BERT base model to encode documents
+- One Bert base model to encode queries
-
-* One Bert base model to encode queries
-
-
-* Ranking of documents done by dot product similarity between query and document embeddings
+- Ranking of documents done by dot product similarity between query and document embeddings
+
Indexing using DPR is comparatively expensive in terms of required computation since all documents in the database need to be processed through the transformer.
The embeddings that are created in this step can be stored in FAISS, a database optimized for vector similarity.
DPR can also work with the ElasticsearchDocumentStore or the InMemoryDocumentStore.
There are two design decisions that have made DPR particularly performant.
+- Separate encoders for document and query helps since queries are much shorter than documents
-* Separate encoders for document and query helps since queries are much shorter than documents
-
-
-* Training with ‘In-batch negatives’ (gold labels are treated as negative examples for other samples in same batch) is highly efficient
+- Training with ‘In-batch negatives’ (gold labels are treated as negative examples for other samples in same batch) is highly efficient
In Haystack, you can simply download the pretrained encoders needed to start using DPR.
If you’d like to learn how to set up a DPR based system, have a look at our tutorials.
+
+
### Initialisation
```python
@@ -145,6 +138,9 @@ finder = Finder(reader, retriever)
+
+
+
## Embedding Retrieval
### Description
@@ -157,6 +153,8 @@ They are particular suited to cases where your query input is similar in style t
i.e. when you are searching for most similar documents.
This is not inherently suited to query based search where the length, language and format of the query usually significantly differs from the searched for text.
+
+
### Initialisation
```python
@@ -168,6 +166,8 @@ retriever = EmbeddingRetriever(document_store=document_store,
finder = Finder(reader, retriever)
```
+
+
## Deeper Dive: Dense vs Sparse
Broadly speaking, retrieval methods can be split into two categories: **dense** and **sparse**.
@@ -175,28 +175,24 @@ Broadly speaking, retrieval methods can be split into two categories: **dense**
**Sparse** methods, like TF-IDF and BM25, operate by looking for shared keywords between the document and query.
They are:
+- simple but effective
-* simple but effective
-
-
-* don’t need to be trained
+- don’t need to be trained
-
-* work on any language
+- work on any language
More recently, **dense** approaches such as Dense Passage Retrieval (DPR) have shown even better performance than their sparse counter parts.
These methods embed both document and query into a shared embedding space using deep neural networks
and the top candidates are the nearest neighbour documents to the query.
They are:
+- powerful but computationally more expensive especially during indexing
-* powerful but computationally more expensive especially during indexing
-
+- trained using labelled datasets
-* trained using labelled datasets
+- language specific
-
-* language specific
+
### Qualitative Differences
@@ -212,6 +208,9 @@ they only care about whether they are present or absent in the text.
As such, **they handle out-of-vocabulary words with no problem**.
+
+
+
### Indexing
Dense methods perform indexing by processing all the documents through a neural network and storing the resulting vectors.
@@ -219,10 +218,14 @@ This is a much more expensive operation than the creation of the inverted-index
and will require significant computational power and time.
+
+
+
### Terminology
+
The terms **dense** and **sparse** refer to the representations that the algorithms build for each document and query.
**Sparse** methods characterise texts using vectors with one dimension corresponding to each word in the vocabulary.
Dimensions will be zero if the word is absent and non-zero if it is present.
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/roadmap.md b/docs/v0.5.0/usage/roadmap.mdx
similarity index 63%
rename from src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/roadmap.md
rename to docs/v0.5.0/usage/roadmap.mdx
index bf28c14e5..2fa9c0fd9 100644
--- a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/roadmap.md
+++ b/docs/v0.5.0/usage/roadmap.mdx
@@ -1,45 +1,49 @@
----
-title: "Roadmap"
-metaTitle: "Roadmap and Zenhub Access"
-metaDescription: "How to access our open roadmap"
-slug: "/docs/roadmap"
-date: "2020-09-24"
-id: "roadmapmd"
----
-
# Open roadmap
-We believe open-source is more than open source code. It's a lot about people, collaboration, transparency and trust.
-Therefore, we decided to be as open as possible with our roadmap and sprint planning.
-In fact, you can see all of it in real-time on GitHub.
-We hope this helps to clarify the direction of the open-source project and inspires discussions in the community.
+We believe open-source is more than open source code. It's a lot about people, collaboration, transparency and trust.
+Therefore, we decided to be as open as possible with our roadmap and sprint planning.
+In fact, you can see all of it in real-time on GitHub.
+We hope this helps to clarify the direction of the open-source project and inspires discussions in the community.
+
+
## How to access it
### Add Zenhub Plugin
+
We decided for Zenhub, as it allows a close integration with GitHub and real-time sharing of roadmaps and sprints.
Once you have installed the browser plugin below, you will see additional tabs and infos on the Haystack GitHub page.
-Zenhub Plugin: https://www.zenhub.com/extension
+Zenhub Plugin: https://www.zenhub.com/extension
+
+
### Roadmap
+
_Zenhub Tab -> Roadmap (left menu)_
Here you can find our most recent roadmap with the **high-level projects** that are planned for the upcoming quarters.
-We update it regularly and refine the projects as they come closer in time.
-
-
+We update it regularly and refine the projects as they come closer in time.
+
+
+
+
### Board
+
_Zenhub Tab -> Board (left menu)_
-If you are interested in the **operational tasks** and their status, you can find our agile board here.
+If you are interested in the **operational tasks** and their status, you can find our agile board here.
+
+
+
+
-
### Additional issue details & Releases
+
_Right panel in regular Github issues_
-With Zenhub you can also see some additional tags in every GitHub issue.
-For those of you who wonder about the next release date: We aim for releases every ~ 4 weeks and will tag the issues that will need to be finished before a bit in advance.
+With Zenhub you can also see some additional tags in every GitHub issue.
+For those of you who wonder about the next release date: We aim for releases every ~ 4 weeks and will tag the issues that will need to be finished before a bit in advance.
-
+
diff --git a/docs/v0.6.0/menu.json b/docs/v0.6.0/menu.json
new file mode 100644
index 000000000..21ae394a4
--- /dev/null
+++ b/docs/v0.6.0/menu.json
@@ -0,0 +1,106 @@
+[
+ {
+ "subMenuTitle": "Overview",
+ "pathPrefix": "/overview/",
+ "items": [
+ {
+ "slug": "get-started",
+ "title": "Get Started"
+ },
+ { "slug": "intro", "title": "What is Haystack?" },
+ { "slug": "use-cases", "title": "Use Cases" },
+ { "slug": "roadmap", "title": "Roadmap" },
+ { "slug": "glossary", "title": "Glossary" }
+ ]
+ },
+ {
+ "subMenuTitle": "Usage",
+ "pathPrefix": "/usage/",
+ "items": [
+ {
+ "slug": "preprocessing",
+ "title": "Preprocessing"
+ },
+ {
+ "slug": "document-store",
+ "title": "DocumentStore"
+ },
+ { "slug": "retriever", "title": "Retriever" },
+ { "slug": "reader", "title": "Reader" },
+ { "slug": "generator", "title": "Generator" },
+ { "slug": "pipelines", "title": "Pipelines" },
+ {
+ "slug": "languages",
+ "title": "Languages Other Than English"
+ },
+ {
+ "slug": "domain-adaptation",
+ "title": "Domain Adaptation"
+ },
+ {
+ "slug": "optimization",
+ "title": "Optimization"
+ },
+ {
+ "slug": "annotation",
+ "title": "Annotation Tool"
+ }
+ ]
+ },
+ {
+ "subMenuTitle": "Tutorials",
+ "pathPrefix": "/tutorials/",
+ "items": [
+ {
+ "slug": "first-qa-system",
+ "title": "Build Your First QA System"
+ },
+ {
+ "slug": "fine-tuning-a-model",
+ "title": "Fine-tuning a Model on Your Own Data"
+ },
+ {
+ "slug": "without-elasticsearch",
+ "title": "Build a QA System Without Elasticsearch"
+ },
+ {
+ "slug": "existing-faqs",
+ "title": "Utilizing existing FAQs for Question Answering"
+ },
+ {
+ "slug": "evaluation",
+ "title": "Evaluation of a QA System"
+ },
+ {
+ "slug": "dense-passage-retrieval",
+ "title": "Better Retrieval via \"Dense Passage Retrieval\""
+ },
+ {
+ "slug": "retrieval-augmented-generation",
+ "title": "Generative QA with \"Retrieval-Augmented Generation\""
+ }
+ ]
+ },
+ {
+ "subMenuTitle": "API Reference",
+ "pathPrefix": "/reference/",
+ "items": [
+ {
+ "slug": "document-store",
+ "title": "Document Store"
+ },
+ { "slug": "retriever", "title": "Retriever" },
+ { "slug": "reader", "title": "Reader" },
+ { "slug": "generator", "title": "Generator" },
+ {
+ "slug": "preprocessor",
+ "title": "Preprocessor"
+ },
+ {
+ "slug": "file-converters",
+ "title": "File Converters"
+ },
+ { "slug": "pipelines", "title": "Pipelines" }
+ ]
+ }
+]
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/get_started.md b/docs/v0.6.0/overview/get_started.mdx
similarity index 66%
rename from src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/get_started.md
rename to docs/v0.6.0/overview/get_started.mdx
index c2fc003f7..f279f3df0 100644
--- a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/get_started.md
+++ b/docs/v0.6.0/overview/get_started.mdx
@@ -1,61 +1,61 @@
----
-title: "Get Started"
-metaTitle: "Get Started"
-metaDescription: ""
-slug: "/docs/get_started"
-date: "2020-09-03"
-id: "get_startedmd"
----
-
# Get Started
## Installation
-++from haystack.reader import TransformersReader++ reader = TransformersReader(... max_seq_len=384, doc_stride=128 + ...) ++
-
-
+
-
-
-
-
-
-The most straightforward way to install Haystack is through pip.
- -```python -$ pip install farm-haystack -``` - -
-- -```python -$ pip install farm-haystack -``` - -
-
-
-
-
-
-If you’d like to run a specific, unreleased version of Haystack, or make edits to the way Haystack runs,
-you’ll want to install it using `git` and `pip --editable`.
-This clones a copy of the repo to a local directory and runs Haystack from there.
- -```python -$ git clone https://github.com/deepset-ai/haystack.git -$ cd haystack -$ pip install --editable . -``` - -By default, this will give you the latest version of the master branch. Use regular git commands to switch between different branches and commits. -
-- -```python -$ git clone https://github.com/deepset-ai/haystack.git -$ cd haystack -$ pip install --editable . -``` - -By default, this will give you the latest version of the master branch. Use regular git commands to switch between different branches and commits. -
+ The most straightforward way to install Haystack is through pip. +
+
+ pip install farm-haystack
+
+
+ ),
+ },
+ {
+ title: "Editable",
+ content: (
+
+
+ ),
+ },
+ ]}
+/>
Note: On Windows add the arg `-f https://download.pytorch.org/whl/torch_stable.html` to install PyTorch correctly
+
+
## The Building Blocks of Haystack
Here’s a sample of some Haystack code showing the most important components.
For a working code example, check out our starter tutorial.
+
```python
# DocumentStore: holds all your data
document_store = ElasticsearchDocumentStore()
@@ -80,6 +80,8 @@ prediction = finder.get_answers(question)
print_answers(prediction)
```
+
+
## Loading Documents into the DocumentStore
In Haystack, DocumentStores expect Documents in a dictionary format. They are loaded as follows:
@@ -99,26 +101,25 @@ When we talk about Documents in Haystack, we are referring specifically to the i
You might want to use all the text in one file as a Document, or split it into multiple Documents.
This splitting can have a big impact on speed and performance.
-+ If you’d like to run a specific, unreleased version of Haystack, or + make edits to the way Haystack runs, you’ll want to install it using + `git` and `pip --editable`. This clones a copy of the repo to a + local directory and runs Haystack from there. +
+++git clone https://github.com/deepset-ai/haystack.git+cd haystack+pip install --editable .+
+ By default, this will give you the latest version of the master + branch. Use regular git commands to switch between different + branches and commits. +
+
+
**Tip:** If Haystack is running very slowly, you might want to try splitting your text into smaller Documents.
If you want an improvement to performance, you might want to try concatenating text to make larger Documents.
+
+
## Running Queries
**Querying** involves searching for an answer to a given question within the full document store.
This process will:
+- make the Retriever filter for a small set of relevant candidate documents
-* make the Retriever filter for a small set of relevant candidate documents
-
-
-* get the Reader to process this set of candidate documents
-
+- get the Reader to process this set of candidate documents
-* return potential answers to the given question
+- return potential answers to the given question
Usually, there are tight time constraints on querying and so it needs to be a lightweight operation.
When documents are loaded, Haystack will precompute any of the results that might be useful at query time.
@@ -146,7 +147,7 @@ When the query is complete, you can expect to see results that look something li
]
```
-
+
**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/terms.md b/docs/v0.6.0/overview/glossary.mdx
similarity index 96%
rename from src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/terms.md
rename to docs/v0.6.0/overview/glossary.mdx
index 6254848f7..5e7b0c371 100644
--- a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/terms.md
+++ b/docs/v0.6.0/overview/glossary.mdx
@@ -1,11 +1,11 @@
----
+
# Glossary
@@ -26,7 +26,7 @@ FARM’s question answering models are used in Haystack’s Readers.
**Finder** - The component in Haystack that connects the Retriever to the Reader.
-
+
**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/intro.md b/docs/v0.6.0/overview/intro.mdx
similarity index 67%
rename from src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/intro.md
rename to docs/v0.6.0/overview/intro.mdx
index 858a02ffd..79be721a6 100644
--- a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/intro.md
+++ b/docs/v0.6.0/overview/intro.mdx
@@ -1,11 +1,11 @@
----
+
# What is Haystack?
@@ -13,25 +13,23 @@ Haystack is an **open-source framework** for building **end-to-end question answ
Recent advances in NLP have enabled the application of QA to real world settings
and Haystack is designed to be the bridge between research and industry.
+- **Latest NLP models**: Utilize all transformer based models (BERT, RoBERTa, MiniLM, DPR ...) and smoothly switch when new ones get published
-* **Latest NLP models**: Utilize all transformer based models (BERT, RoBERTa, MiniLM, DPR ...) and smoothly switch when new ones get published
+- **Flexible databases**: Load data into and query from a range of databases such as Elasticsearch, FAISS, SQL and more
-* **Flexible databases**: Load data into and query from a range of databases such as Elasticsearch, FAISS, SQL and more
+- **Scalability**: Production-ready deployments that scale to millions of documents
-* **Scalability**: Production-ready deployments that scale to millions of documents
+- **End-to-End**: All tooling you need to implement, evaluate, improve and run a QA system
-* **End-to-End**: All tooling you need to implement, evaluate, improve and run a QA system
-
-* **Domain adaptation**: Fine-tune models to your own domain & improve them continuously via user feedback
+- **Domain adaptation**: Fine-tune models to your own domain & improve them continuously via user feedback
+
## Technology
Haystack is powered by a Retriever-Reader pipeline in order to optimise for both speed and accuracy.
-
-
-
+
**Readers**, also known as Open-Domain QA systems in Machine Learning speak,
are powerful models that do close analysis of documents and perform the core task of question answering.
@@ -40,28 +38,25 @@ However, it is not currently feasible to use the Reader directly on large collec
+
The **Retriever** assists the Reader by acting as a lightweight filter that reduces the number of documents that the Reader has to process.
It does this by:
+- Scanning through all documents in the database
-* Scanning through all documents in the database
+- Quickly identifying the relevant and dismissing the irrelevant
-
-* Quickly identifying the relevant and dismissing the irrelevant
-
-
-* Passing on only a small candidate set of documents to the Reader
+- Passing on only a small candidate set of documents to the Reader
Current methods fall into one of the two categories:
+- sparse
-* sparse
- * keyword based
- * fast indexing and querying
- * e.g. BM25
-
+ - keyword based
+ - fast indexing and querying
+ - e.g. BM25
-* dense
- * neural network based
- * computationally heavy indexing but fast querying
- * e.g. Dense Passage Retrieval
+- dense
+ - neural network based
+ - computationally heavy indexing but fast querying
+ - e.g. Dense Passage Retrieval
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/roadmap.md b/docs/v0.6.0/overview/roadmap.mdx
similarity index 73%
rename from src/pages/docs/versions/master/site/en/usage/usage/roadmap.md
rename to docs/v0.6.0/overview/roadmap.mdx
index bf28c14e5..d4737869e 100644
--- a/src/pages/docs/versions/master/site/en/usage/usage/roadmap.md
+++ b/docs/v0.6.0/overview/roadmap.mdx
@@ -1,45 +1,50 @@
----
+
# Open roadmap
-We believe open-source is more than open source code. It's a lot about people, collaboration, transparency and trust.
-Therefore, we decided to be as open as possible with our roadmap and sprint planning.
-In fact, you can see all of it in real-time on GitHub.
-We hope this helps to clarify the direction of the open-source project and inspires discussions in the community.
+We believe open-source is more than open source code. It's a lot about people, collaboration, transparency and trust.
+Therefore, we decided to be as open as possible with our roadmap and sprint planning.
+In fact, you can see all of it in real-time on GitHub.
+We hope this helps to clarify the direction of the open-source project and inspires discussions in the community.
## How to access it
### Add Zenhub Plugin
+
We decided for Zenhub, as it allows a close integration with GitHub and real-time sharing of roadmaps and sprints.
Once you have installed the browser plugin below, you will see additional tabs and infos on the Haystack GitHub page.
-Zenhub Plugin: https://www.zenhub.com/extension
+Zenhub Plugin: https://www.zenhub.com/extension
### Roadmap
+
_Zenhub Tab -> Roadmap (left menu)_
Here you can find our most recent roadmap with the **high-level projects** that are planned for the upcoming quarters.
-We update it regularly and refine the projects as they come closer in time.
-
-
+We update it regularly and refine the projects as they come closer in time.
+
+
### Board
+
_Zenhub Tab -> Board (left menu)_
-If you are interested in the **operational tasks** and their status, you can find our agile board here.
+If you are interested in the **operational tasks** and their status, you can find our agile board here.
+
+
-
### Additional issue details & Releases
+
_Right panel in regular Github issues_
-With Zenhub you can also see some additional tags in every GitHub issue.
-For those of you who wonder about the next release date: We aim for releases every ~ 4 weeks and will tag the issues that will need to be finished before a bit in advance.
+With Zenhub you can also see some additional tags in every GitHub issue.
+For those of you who wonder about the next release date: We aim for releases every ~ 4 weeks and will tag the issues that will need to be finished before a bit in advance.
-
+
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/use_cases.md b/docs/v0.6.0/overview/use_cases.mdx
similarity index 72%
rename from src/pages/docs/versions/master/latest/site/en/usage/usage/use_cases.md
rename to docs/v0.6.0/overview/use_cases.mdx
index 76b420410..643dc5086 100644
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/use_cases.md
+++ b/docs/v0.6.0/overview/use_cases.mdx
@@ -1,12 +1,11 @@
----
+
# Use cases
@@ -14,19 +13,15 @@ id: "use_casesmd"
Take the leap from using keyword search on your own documents to semantic search with Haystack.
+- Store your documents in the database of your choice (Elasticsearch, SQL, in memory, FAISS)
-* Store your documents in the database of your choice (Elasticsearch, SQL, in memory, FAISS)
-
-
-* Perform question driven queries.
+- Perform question driven queries.
Expect to see results that highlight the very sentence that contains the answer to your question.
Thanks to the power of Transformer based language models, results are chosen based on compatibility in meaning
rather than lexical overlap.
-
-
-
+
## Information Extractor
@@ -34,36 +29,31 @@ Automate the extraction of relevant information from a set of documents that per
Haystack can:
+- Apply a set of standard questions to each document in a store
-* Apply a set of standard questions to each document in a store
-
-
-* Return a NO_ANSWER if a given document does not contain the answer to a question
+- Return a NO_ANSWER if a given document does not contain the answer to a question
Say you have the financial reports for different companies over different years.
You can gather a set of standard questions which are applicable to each financial report,
-like *what is the revenue forecast for 2020?* or *what are the main sources of income?*.
+like _what is the revenue forecast for 2020?_ or _what are the main sources of income?_.
Haystack will try to find an answer for each question within each document!
We’ve seen this style of application be particularly effective in the sphere of finance and patent law
but we see a lot of potential in using this to gain a better overview of academic papers and internal business documents.
+
## FAQ Style Question Answering
Leverage existing FAQ documents and semantic similarity search to answer new incoming questions.
The workflow is as follows:
+- Store a set of FAQ documents in Haystack
-* Store a set of FAQ documents in Haystack
-
-
-* The user presents a new question
-
-
-* Haystack will find the closest match to the new question in the FAQ documents
+- The user presents a new question
+- Haystack will find the closest match to the new question in the FAQ documents
-* The user will be presented with the most similar Question Answer pair
+- The user will be presented with the most similar Question Answer pair
Haystack’s flexibility allows you to give new users more dynamic access to your existing documentation.
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/annotation.md b/docs/v0.6.0/usage/annotation.mdx
similarity index 80%
rename from src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/annotation.md
rename to docs/v0.6.0/usage/annotation.mdx
index ed29d18c8..d22e35fae 100644
--- a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/annotation.md
+++ b/docs/v0.6.0/usage/annotation.mdx
@@ -1,12 +1,3 @@
----
-title: "Annotation Tool"
-metaTitle: "Annotation Tool"
-metaDescription: ""
-slug: "/docs/annotation"
-date: "2020-09-03"
-id: "annotationmd"
----
-
# Annotation Tool
- Create labels with different techniques: Come up with questions (+ answers) while reading passages (SQuAD style) or have a set of predefined questions and look for answers in the document (~ Natural Questions).
@@ -14,12 +5,17 @@ id: "annotationmd"
- Upload your documents or import labels from an existing SQuAD-style dataset
- Export your labels in SQuAD Format
-
+
+
+
+
+## Hosted version
+
+Signup here: [Haystack Annotation Tool](https://annotate.deepset.ai/login)
-# Hosted version
- Signup here: [Haystack Annotation Tool](https://annotate.deepset.ai/login)
+
-# Local version (Docker)
+## Local version (Docker)
1. Configure credentials & database in the [`docker-compose.yml`](https://github.com/deepset-ai/haystack/blob/master/annotation_tool/docker-compose.yml):
@@ -37,11 +33,12 @@ The credentials should match in database image and application configuration.
POSTGRES_PASSWORD: "somesafepassword"
POSTGRES_DB: "databasename"
-
2. Run docker-compose by executing `docker-compose up`.
-
3. The UI should be available at `localhost:7001`.
-# Usage
+
+
+## Usage
+
The manual (of a slightly earlier version) can be found [here](https://drive.google.com/file/d/1Wv3OIC0Z7ibHIzOm9Xw_r0gjTFmpl-33/view). While it doesn't include all latest features, the basic workflow and tips for label quality are still the same.
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/document_store.md b/docs/v0.6.0/usage/document_store.mdx
similarity index 68%
rename from src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/document_store.md
rename to docs/v0.6.0/usage/document_store.mdx
index 8adcccb07..36ad2163c 100644
--- a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/document_store.md
+++ b/docs/v0.6.0/usage/document_store.mdx
@@ -1,31 +1,19 @@
----
-title: "Document Store"
-metaTitle: "Document Store"
-metaDescription: ""
-slug: "/docs/documentstore"
-date: "2020-09-03"
-id: "documentstoremd"
----
-
-
# DocumentStores
You can think of the DocumentStore as a "database" that:
-- stores your texts and meta data
-- provides them to the retriever at query time
-There are different DocumentStores in Haystack to fit different use cases and tech stacks.
+- stores your texts and meta data
+- provides them to the retriever at query time
+
+There are different DocumentStores in Haystack to fit different use cases and tech stacks.
## Initialisation
Initialising a new DocumentStore is straight forward.
-
+
-
-
Each DocumentStore constructor allows for arguments specifying how to connect to existing databases and the names of indexes.
See API documentation for more info.
+
+
## Input Format
DocumentStores expect Documents in dictionary form, like that below.
They are loaded using the `DocumentStore.write_documents()` method.
See [Preprocessing](/docs/latest/preprocessingmd) for more information on the cleaning and splitting steps that will help you maximize Haystack's performance.
-[//]: # (Add link to preprocessing section)
+[//]: # "Add link to preprocessing section"
```python
document_store = ElasticsearchDocumentStore()
@@ -97,6 +70,8 @@ dicts = [
document_store.write_documents(dicts)
```
+
+
## Writing Documents (Sparse Retrievers)
Haystack allows for you to write store documents in an optimised fashion so that query times can be kept low.
@@ -108,6 +83,8 @@ The creation of the inverted index which optimises querying speed is handled aut
document_store.write_documents(dicts)
```
+
+
## Writing Documents (Dense Retrievers)
For **dense** neural network based retrievers like Dense Passage Retrieval, or Embedding Retrieval,
@@ -126,86 +103,77 @@ Having GPU acceleration will significantly speed this up.
+
+
+
## Choosing the Right Document Store
-The Document Stores have different characteristics. You should choose one depending on the maturity of your project, the use case and technical environment:
+The Document Stores have different characteristics. You should choose one depending on the maturity of your project, the use case and technical environment:
-
-
-
-
+
-
+### Elasticsearch
```python
document_store = ElasticsearchDocumentStore()
@@ -34,57 +22,42 @@ document_store = OpenDistroElasticsearchDocumentStore()
```
-
-
-
-
-
+
-
+### FAISS
```python
document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
```
-
-
-
-
-
+
-
+### In Memory
```python
document_store = InMemoryDocumentStore()
```
-
-
-
-
-
-
-
+### SQL
```python
document_store = SQLDocumentStore()
```
-
-
+### Elasticsearch
-
+
-
-
-
-
+
+
+### FAISS
-
+**Pros:**
-**Pros:**
- Fast & accurate sparse retrieval with many tuning options
- Basic support for dense retrieval
- Production-ready
- Support also for Open Distro
-**Cons:**
+**Cons:**
+
- Slow for dense retrieval with more than ~ 1 Mio documents
-
-
-
-
-
+
-
+**Pros:**
-**Pros:**
- Fast & accurate dense retrieval
- Highly scalable due to approximate nearest neighbour algorithms (ANN)
- Many options to tune dense retrieval via different index types (more info [here](https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index))
**Cons:**
+
- No efficient sparse retrieval
-
-
-
-
-
+### SQL
-
+### In Memory
**Pros:**
+
- Simple
- Exists already in many environments
**Cons:**
+
- Only compatible with minimal TF-IDF Retriever
- Bad retrieval performance
- Not recommended for production
-
-
-
-
-
-
-
+
**Pros:**
+
- Simple & fast to test
- No database requirements
- Supports MySQL, PostgreSQL and SQLite
-**Cons:**
+**Cons:**
+
- Not scalable
- Not persisting your data on disk
-
-
+
#### Our Recommendations
-**Restricted environment:** Use the `InMemoryDocumentStore`, if you are just giving Haystack a quick try on a small sample and are working in a restricted environment that complicates running Elasticsearch or other databases
+**Restricted environment:** Use the `InMemoryDocumentStore`, if you are just giving Haystack a quick try on a small sample and are working in a restricted environment that complicates running Elasticsearch or other databases
**Allrounder:** Use the `ElasticSearchDocumentStore`, if you want to evaluate the performance of different retrieval options (dense vs. sparse) and are aiming for a smooth transition from PoC to production
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/domain_adaptation.md b/docs/v0.6.0/usage/domain_adaptation.mdx
similarity index 70%
rename from src/pages/docs/versions/master/latest/site/en/usage/usage/domain_adaptation.md
rename to docs/v0.6.0/usage/domain_adaptation.mdx
index 9bd415e61..c1abcbee4 100644
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/domain_adaptation.md
+++ b/docs/v0.6.0/usage/domain_adaptation.mdx
@@ -1,12 +1,3 @@
----
-title: "Domain Adaptation"
-metaTitle: "Domain Adaptation"
-metaDescription: ""
-slug: "/docs/domain_adaptation"
-date: "2020-09-03"
-id: "domain_adaptationmd"
----
-
# Domain Adaptation
## Generalisation
@@ -17,7 +8,7 @@ Though SQuAD is composed entirely of Wikipedia articles, these models are flexib
Before trying to adapt these models to your domain, we’d recommend trying one of the off the shelf models.
We’ve found that these models are often flexible enough for a wide range of use cases.
-
+
**Intuition**
@@ -28,6 +19,7 @@ In the same way, many QA models have a good enough grasp of language to answer q
+
## Finetuning
@@ -43,7 +35,7 @@ reader.train(data_dir=train_data,
At the end of training, the finetuned model will be saved in the specified `save_dir` and can be loaded as a `Reader`.
-
+
**Recommendation**
@@ -53,57 +45,55 @@ please also check out Tutorial 5 which walks through the steps needed to perform
+
+
## Generating Labels
Using our [Haystack Annotate tool](https://annotate.deepset.ai/login) (Beta),
you can easily create a labelled dataset using your own documents featuring your own question/ answer pairs.
-
-
-
+
Features include:
+- Structured workspaces via organisations, projects and users
-* Structured workspaces via organisations, projects and users
-
-
-* Easy upload of your own documents and labels in a variety of formats (txt, pdf, SQuAD style)
+- Easy upload of your own documents and labels in a variety of formats (txt, pdf, SQuAD style)
-
-* Export of labels to be used directly in Haystack
+- Export of labels to be used directly in Haystack
Annotate also supports two different workflows:
+- Think up questions and answers while reading passages (SQuAD style)
-* Think up questions and answers while reading passages (SQuAD style)
-
+- Have a set of predefined questions and look for answers in the document (~ Natural Questions style)
-* Have a set of predefined questions and look for answers in the document (~ Natural Questions style)
+
## User Feedback
-A simpler and faster process to finetune models to your domain is to utilise user feedback.
-Dedicated annotation work can be costly and time consuming
-but user feedback is an efficient alternative since it allows for labels to be generated by users, for users,
+A simpler and faster process to finetune models to your domain is to utilise user feedback.
+Dedicated annotation work can be costly and time consuming
+but user feedback is an efficient alternative since it allows for labels to be generated by users, for users,
all while the system is already in production.
We, for example, have used a simple thumbs up / down system in our demos to allow
users to give feedback.
-
+
Through the Rest API, users can annotate each Haystack result as being either:
-* Correct
-* Correct document but wrong text
-* Wrong document and wrong text
+- Correct
+- Correct document but wrong text
+- Wrong document and wrong text
To get started, follow these steps:
-* Start up the REST API
- * The simplest way to do this is to call `docker-compose up` from the root directory of the Haystack repository
- * Alternatively, run `gunicorn rest_api.application:app -b 0.0.0.0:8000 -k uvicorn.workers.UvicornWorker -t 300`
-* Make a POST request to the `doc-qa-feedback` endpoint with the following payload:
+- Start up the REST API
+ - The simplest way to do this is to call `docker-compose up` from the root directory of the Haystack repository
+ - Alternatively, run `gunicorn rest_api.application:app -b 0.0.0.0:8000 -k uvicorn.workers.UvicornWorker -t 300`
+- Make a POST request to the `doc-qa-feedback` endpoint with the following payload:
+
```
{
"question": "string",
@@ -115,10 +105,12 @@ To get started, follow these steps:
"offset_start_in_doc": 0
}
```
+
To fill out all these values, you can use the response from an executed search request on the `doc-qa` endpoint.
-* Export your labels in SQuAD format by making a GET request to the `export-doc-qa-feedback` endpoint
-``` python
+- Export your labels in SQuAD format by making a GET request to the `export-doc-qa-feedback` endpoint
+
+```python
# SQUAD format
{
"data": [
@@ -146,12 +138,11 @@ To fill out all these values, you can use the response from an executed search r
}
```
-User feedback labelling also works with FAQ style QA.
+User feedback labelling also works with FAQ style QA.
Refer to the Swagger API documentation (http://127.0.0.1:8000/docs) for more details.
-This labelled data can then be used to
-augment the training data and enable `Reader` finetuning.
-Alternatively, they can also be used to form an evaluation set to
-measure the performance of trained models,
+This labelled data can then be used to
+augment the training data and enable `Reader` finetuning.
+Alternatively, they can also be used to form an evaluation set to
+measure the performance of trained models,
or monitor how well the model is doing in a live environment.
-
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/generator.md b/docs/v0.6.0/usage/generator.mdx
similarity index 68%
rename from src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/generator.md
rename to docs/v0.6.0/usage/generator.mdx
index 354f93f40..4ebf13388 100644
--- a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/generator.md
+++ b/docs/v0.6.0/usage/generator.mdx
@@ -1,15 +1,6 @@
----
-title: "Generator"
-metaTitle: "Generator"
-metaDescription: ""
-slug: "/docs/generator"
-date: "2020-11-05"
-id: "generatormd"
----
-
# Generator
-
+
**Example**
@@ -26,10 +17,11 @@ With the advent of Transformer based retrieval methods such as [Dense Passage Re
retriever and generator can be trained concurrently from the one loss signal.
Pros
-* More appropriately phrased answers
-* Able to syntehsize information from different texts
-* Can draw on latent knowledge stored in language model
+
+- More appropriately phrased answers
+- Able to syntehsize information from different texts
+- Can draw on latent knowledge stored in language model
Cons
-* Not easy to track what piece of information the generator is basing its response off of
+- Not easy to track what piece of information the generator is basing its response off of
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/languages.md b/docs/v0.6.0/usage/languages.mdx
similarity index 52%
rename from src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/languages.md
rename to docs/v0.6.0/usage/languages.mdx
index edf53419a..6b378f703 100644
--- a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/languages.md
+++ b/docs/v0.6.0/usage/languages.mdx
@@ -1,38 +1,31 @@
----
-title: "Languages Other Than English"
-metaTitle: "Languages Other Than English"
-metaDescription: ""
-slug: "/docs/languages"
-date: "2020-11-05"
-id: "languagesmd"
----
-
# Languages Other Than English
Haystack is well suited to open-domain QA on languages other than English.
While our defaults are tuned for English,
-you will find some tips and tricks here for using Haystack in your language.
+you will find some tips and tricks here for using Haystack in your language.
+
+
-##Retrievers
+## Retrievers
The sparse retriever methods themselves(BM25, TF-IDF) are language agnostic.
Their only requirement is that the text be split into words.
The ElasticsearchDocumentStore relies on an analyzer to impose word boundaries,
but also to handle punctuation, casing and stop words.
-The default analyzer is an English analyzer.
+The default analyzer is an English analyzer.
While it can still work decently for a large range of languages,
you will want to set it to your language's analyzer for optimal performance.
In some cases, such as with Thai, the default analyzer is completely incompatible.
-See [this page](https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-lang-analyzer.html)
+See [this page](https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-lang-analyzer.html)
for the full list of language specific analyzers.
```python
document_store = ElasticsearchDocumentStore(analyzer="thai")
```
-The models used in dense retrievers are language specific.
-Be sure to check language of the model used in your EmbeddingRetriever.
+The models used in dense retrievers are language specific.
+Be sure to check language of the model used in your EmbeddingRetriever.
The default model that is loaded in the DensePassageRetriever is for English.
We are currently working on training a German DensePassageRetriever model and know other teams who work on further languages.
If you have a language model and a question answering dataset in your own language, you can also train a DPR model using Haystack!
@@ -51,111 +44,107 @@ dense_passage_retriever.train(self,
n_epochs: int = 3)
```
-##Readers
+
+
+## Readers
While models are comparatively more performant on English,
thanks to a wealth of available English training data,
there are a couple QA models that are directly usable in Haystack.
-
+
+
-
-
+
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-```python
-reader = FARMReader("illuin/camembert-base-fquad")
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = FARMReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa")
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = FARMReader("deepset/xlm-roberta-large-squad2")
-```
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-```python
-reader = TransformersReader("illuin/camembert-base-fquad")
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = TransformersReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa")
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = TransformersReader("deepset/xlm-roberta-large-squad2")
-```
-
-
-from haystack.reader import FARMReader
+
+ reader = FARMReader("illuin/camembert-base-fquad")
+
+
+ ),
+ },
+ {
+ title: "Italian",
+ content: (
+ ++ ), + }, + { + title: "Zero-shot", + content: ( +from haystack.reader import FARMReader++ reader = + FARMReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa") ++
++ ), + }, + ]} + /> + ), + }, + { + title: "Transformers", + content: ( +from haystack.reader import FARMReader++ reader = FARMReader("deepset/xlm-roberta-large-squad2") ++
from haystack.reader import TransformersReader
+
+ reader = TransformersReader("illuin/camembert-base-fquad")
+
+
+ ),
+ },
+ {
+ title: "Italian",
+ content: (
+ ++ ), + }, + { + title: "Zero-shot", + content: ( +from haystack.reader import TransformersReader++ reader = + TransformersReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa") ++
++ ), + }, + ]} + /> + ), + }, + ]} +/> + + The **French** and **Italian models** are both monolingual language models trained on French and Italian versions of the SQuAD dataset and their authors report decent results in their model cards @@ -170,5 +159,4 @@ but still its performance lags behind that of the monolingual models. Nonetheless, if there is not yet a monolingual model for your language and it is one of the 100 supported by XLM-RoBERTa, this zero-shot model may serve as a decent first baseline. -[//]: # (Add link to Reader training, create section in reader.md on training Reader) - +[//]: # "Add link to Reader training, create section in reader.md on training Reader" diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/optimization.md b/docs/v0.6.0/usage/optimization.mdx similarity index 86% rename from src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/optimization.md rename to docs/v0.6.0/usage/optimization.mdx index 8a46a54a2..019ca80c3 100644 --- a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/optimization.md +++ b/docs/v0.6.0/usage/optimization.mdx @@ -1,17 +1,8 @@ ---- -title: "Optimization" -metaTitle: "Optimization" -metaDescription: "" -slug: "/docs/optimization" -date: "2020-11-05" -id: "optimizationmd" ---- - # Optimization ## Document Length -Document length has a very direct impact on the speed of the Reader +Document length has a very direct impact on the speed of the Reader which is why we recommend using the `PreProcessor` class to clean and split your documents. **If you halve the length of your documents, you will halve the workload placed onto your Reader.** @@ -25,13 +16,17 @@ By default, Haystack's DensePassageRetriever model has a maximum length of 256 t As such, we recommend that documents contain significantly less words. We have found decent performance with **documents around 100 words long**. + + ## Respecting Sentence Boundaries -When splitting documents, it is generally not a good idea to let document boundaries fall in the middle of sentences. -Doing so means that each document will contain incomplete sentence fragments +When splitting documents, it is generally not a good idea to let document boundaries fall in the middle of sentences. +Doing so means that each document will contain incomplete sentence fragments which maybe be hard for both retriever and reader to interpret. It is therefore recommended to set `split_respect_sentence_boundary=True` when initializing your `PreProcessor`. + + ## Choosing the Right top-k Values The `top-k` parameter in both the `Retriever` and `Reader` determine how many results they return. @@ -41,23 +36,26 @@ while `Reader` `top-k` determines how many answer candidates to show. In our experiments, we have found that **`Retriever` `top_k=10` gives decent overall performance** and so we have set this as the default in Haystack. -The choice of `Retriever` `top-k` is a trade-off between speed and accuracy, +The choice of `Retriever` `top-k` is a trade-off between speed and accuracy, especially when there is a `Reader` in the pipeline. -Setting it higher means passing more documents to the `Reader`, +Setting it higher means passing more documents to the `Reader`, thus reducing the chance that the answer-containing passage is missed. However, passing more documents to the `Reader` will create a larger workload for the component. These parameters can easily be tweaked as follows if using a `Finder`: -``` python + +```python answers = finder.get_answers(retriever_top_k=10, reader_top_k=5) ``` + or like this if directly calling the `Retriever`: -``` python + +```python retrieved_docs = retriever.retrieve(top_k=10) ``` -from haystack.reader import TransformersReader++ reader = + TransformersReader("deepset/xlm-roberta-large-squad2") ++
+
+
+ ),
+ },
+ {
+ title: "Transformers",
+ content: (
+
**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/pipelines.md b/docs/v0.6.0/usage/pipelines.mdx
similarity index 89%
rename from src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/pipelines.md
rename to docs/v0.6.0/usage/pipelines.mdx
index 5633a7c70..0bd483a42 100644
--- a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/pipelines.md
+++ b/docs/v0.6.0/usage/pipelines.mdx
@@ -1,26 +1,18 @@
----
-title: "Pipelines"
-metaTitle: "Pipelines"
-metaDescription: ""
-slug: "/docs/pipelines"
-date: "2020-11-05"
-id: "pipelinesmd"
----
-
# Pipelines
+
+
+ ),
+ },
+ {
+ title: "Transformers",
+ content: (
+
-
+
+
+ ),
+ },
+ {
+ title: "Transformers",
+ content: (
+
-
-The new Pipelines class was added in Haystack 0.6.0 to give a more flexible way of defining your processing steps.
+The new Pipelines class was added in Haystack 0.6.0 to give a more flexible way of defining your processing steps.
It replaces the Finder class which will be deprecated in the next version.
+
### Flexible Pipelines powered by DAGs
+
In order to build modern search pipelines, you need two things: powerful building blocks and a flexible way to stick them together.
-The `Pipeline` class is exactly build for this purpose and enables many search scenarios beyond QA. The core idea: you can build a Directed Acyclic Graph (DAG) where each node is one "building block" (Reader, Retriever, Generator ...). Here's a simple example for a "standard" Open-Domain QA Pipeline:
+The `Pipeline` class is exactly build for this purpose and enables many search scenarios beyond QA. The core idea: you can build a Directed Acyclic Graph (DAG) where each node is one "building block" (Reader, Retriever, Generator ...). Here's a simple example for a "standard" Open-Domain QA Pipeline:
```python
p = Pipeline()
@@ -31,13 +23,19 @@ res = p.run(query="What did Einstein work on?", top_k_retriever=1)
```
You can **draw the DAG** to better inspect what you are building:
+
```python
p.draw(path="custom_pipe.png")
```
+
+
+
### Multiple retrievers
-You can now also use multiple Retrievers and join their results:
+
+You can now also use multiple Retrievers and join their results:
+
```python
p = Pipeline()
p.add_node(component=es_retriever, name="ESRetriever", inputs=["Query"])
@@ -46,20 +44,28 @@ p.add_node(component=JoinDocuments(join_mode="concatenate"), name="JoinResults",
p.add_node(component=reader, name="QAReader", inputs=["JoinResults"])
res = p.run(query="What did Einstein work on?", top_k_retriever=1)
```
+
+
+
### Custom nodes
-You can easily build your own custom nodes. Just respect the following requirements:
+
+You can easily build your own custom nodes. Just respect the following requirements:
1. Add a method `run(self, **kwargs)` to your class. `**kwargs` will contain the output from the previous node in your graph.
2. Do whatever you want within `run()` (e.g. reformatting the query)
3. Return a tuple that contains your output data (for the next node) and the name of the outgoing edge `output_dict, "output_1`
4. Add a class attribute `outgoing_edges = 1` that defines the number of output options from your node. You only need a higher number here if you have a decision node (see below).
+
+
### Decision nodes
-Or you can add decision nodes where only one "branch" is executed afterwards. This allows, for example, to classify an incoming query and depending on the result routing it to different modules:
+
+Or you can add decision nodes where only one "branch" is executed afterwards. This allows, for example, to classify an incoming query and depending on the result routing it to different modules:
-```python
+
+```python
class QueryClassifier():
outgoing_edges = 2
@@ -80,11 +86,14 @@ Or you can add decision nodes where only one "branch" is executed afterwards. Th
res = p.run(query="What did Einstein work on?", top_k_retriever=1)
```
+
+
### Default Pipelines (replacing the "Finder")
+
Last but not least, we added some "Default Pipelines" that allow you to run standard patterns with very few lines of code.
This is replacing the `Finder` class which is now deprecated.
-```
+```python
from haystack.pipeline import DocumentSearchPipeline, ExtractiveQAPipeline, Pipeline, JoinDocuments
# Extractive QA
@@ -103,8 +112,8 @@ res = doc_pipe.run(query="Physics Einstein", top_k_retriever=1)
doc_pipe = FAQPipeline(retriever=retriever)
res = doc_pipe.run(query="How can I change my address?", top_k_retriever=3)
-```
-See also the [Pipelines API documentation](/docs/latest/apipipelinesmd) for more details.
+```
-We plan many more features around the new pipelines incl. parallelized execution, distributed execution, definition via YAML files, dry runs - so stay tuned ...
+See also the [Pipelines API documentation](/docs/latest/apipipelinesmd) for more details.
+We plan many more features around the new pipelines incl. parallelized execution, distributed execution, definition via YAML files, dry runs - so stay tuned ...
diff --git a/docs/v0.6.0/usage/preprocessing.mdx b/docs/v0.6.0/usage/preprocessing.mdx
new file mode 100644
index 000000000..12e8025e8
--- /dev/null
+++ b/docs/v0.6.0/usage/preprocessing.mdx
@@ -0,0 +1,109 @@
+# Preprocessing
+
+Haystack includes a suite of tools to:
+
+- extract text from different file types,
+- normalize white space
+- split text into smaller pieces to optimize retrieval
+
+These data preprocessing steps can have a big impact on the systems performance
+and effective handling of data is key to getting the most out of Haystack.
+
+The Document Store expects its inputs to come in the following format.
+The sections below will show you all the tools you'll need to ready your data for storing.
+
+```python
+docs = [
+ {
+ 'text': DOCUMENT_TEXT_HERE,
+ 'meta': {'name': DOCUMENT_NAME, ...}
+ }, ...
+]
+```
+
+
+
+## File Conversion
+
+There are a range of different file converters in Haystack that
+can extract text from files and cast them into the unified dictionary format shown above.
+Haystack features support for txt, pdf and docx files and there is even a converter that leverages Apache Tika.
+Please refer to [the API docs](/docs/latest/file_convertersmd) to see which converter best suits you.
+
+from haystack.file_converter import PDFToTextConverter
+
+ converter = PDFToTextConverter(remove_numeric_tables=True,
+ valid_languages=["de","en"])
+
+ doc = converter.convert(file_path=file, meta=None)
+
+ ),
+ },
+ {
+ title: "DOCX",
+ content: (
+ ++ ), + }, + { + title: "From a Directory", + content: ( +from haystack.file_converter import DocxToTextConverter++ converter = DocxToTextConverter(remove_numeric_tables=True, + valid_languages=["de","en"]) ++doc = converter.convert(file_path=file, meta=None)+
+
+ ),
+ },
+ ]}
+/>
+
+
+
+## PreProcessor
+
+While each of the above conversion methods produce documents that are already in the format expected by the Document Store,
+it is recommended that they are further processed in order to ensure optimal Retriever and Reader performance.
+The `PreProcessor` takes one of the documents created by the converter as input,
+performs various cleaning steps and splits them into multiple smaller documents.
+
+For suggestions on how best to split your documents, see [Optimization](/docs/latest/optimizationmd)
+
+```python
+doc = converter.convert(file_path=file, meta=None)
+processor = PreProcessor(clean_empty_lines=True,
+ clean_whitespace=True,
+ clean_header_footer=True,
+ split_by="word",
+ split_length=200,
+ split_respect_sentence_boundary=True)
+docs = processor.process(d)
+```
+
+- `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
+- `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
+- `clean_header_footer` will remove any long header or footer texts that are repeated on each page
+- `split_by` determines what unit the document is split by: `'word'`, `'sentence'` or `'passage'`
+- `split_length` sets a maximum number of `'word'`, `'sentence'` or `'passage'` units per output document
+- `split_respect_sentence_boundary` ensures that document boundaries do not fall in the middle of sentences
diff --git a/docs/v0.6.0/usage/reader.mdx b/docs/v0.6.0/usage/reader.mdx
new file mode 100644
index 000000000..8502666f2
--- /dev/null
+++ b/docs/v0.6.0/usage/reader.mdx
@@ -0,0 +1,315 @@
+# Reader
+
+The Reader, also known as Open-Domain QA systems in Machine Learning speak,
+is the core component that enables Haystack to find the answers that you need.
+Haystack’s Readers are:
+
+- built on the latest transformer based language models
+
+- strong in their grasp of semantics
+
+- sensitive to syntactic structure
+
+- state-of-the-art in QA tasks like SQuAD and Natural Questions
+
++ Haystack also has a`convert_files_to_dicts()` utility function that + will convert all txt or pdf files in a given folder into this + dictionary format. +
++++ from haystack.preprocessor.utils import convert_files_to_dicts ++docs = convert_files_to_dicts(dir_path=doc_dir)+
++model = "deepset/roberta-base-squad2"+reader = FARMReader(model, use_gpu=True)+finder = Finder(reader, retriever)+
+
+ ),
+ },
+ ]}
+/>
+
+++model = "deepset/roberta-base-squad2"+reader = TransformersReader(model, use_gpu=1)+finder = Finder(reader, retriever)+
+
+**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
+
+
+
+While these models can work on CPU, it is recommended that they are run using GPUs to keep query times low.
+
+
+
+## Choosing the Right Model
+
+In Haystack, you can start using pretrained QA models simply by providing its HuggingFace Model Hub name to the Reader.
+The loading of model weights is handled by Haystack,
+and you have the option of using the QA pipeline from deepset FARM or HuggingFace Transformers (see FARM vs Transformers for details).
+
+Currently, there are a lot of different models out there and it can be rather overwhelming trying to pick the one that fits your use case.
+To get you started, we have a few recommendations for you to try out.
+
+
+
+ RoBERTa (base)
+An optimised variant of BERT and a great starting point.
+++from haystack.reader import FARMReader+reader = FARMReader("deepset/roberta-base-squad2")+
-
+
- Pro: Strong all round model +
- + Con: There are other models that are either faster or more + accurate + +
+
+ MiniLM
++ A cleverly distilled model that sacrifices a little accuracy for + speed. +
+++from haystack.reader import FARMReader+reader = FARMReader("deepset/minilm-uncased-squad2")+
-
+
- Pro: Inference speed up to 50% faster than BERT base +
- + Con: Still doesn’t match the best base sized models in accuracy + +
+
+ ALBERT (XXL)
+Large, powerful, SotA model.
+++from haystack.reader import FARMReader++ reader = FARMReader("ahotrod/albert_xxlargev1_squad2_512") ++
-
+
- + Pro: Better accuracy than any other open source model in QA + +
- + Con: The computational power needed make it impractical for most + use cases + +
+
+ ),
+ },
+ ]}
+/>
+
+
+
+
+
+ RoBERTa (base)
+An optimised variant of BERT and a great starting point.
+++from haystack.reader import TransformersReader++ reader = TransformersReader("deepset/roberta-base-squad2") ++
-
+
- Pro: Strong all round model +
- + Con: There are other models that are either faster or more + accurate + +
+
+ MiniLM
++ A cleverly distilled model that sacrifices a little accuracy for + speed. +
+++from haystack.reader import TransformersReader++ reader = TransformersReader("deepset/minilm-uncased-squad2") ++
-
+
- Pro: Inference speed up to 50% faster than BERT base +
- + Con: Still doesn’t match the best base sized models in accuracy + +
+
+ ALBERT (XXL)
+Large, powerful, SotA model.
+++from haystack.reader import TransformersReader++ reader = + TransformersReader("ahotrod/albert_xxlargev1_squad2_512") ++
-
+
- + Pro: Better accuracy than any other open source model in QA + +
- + Con: The computational power needed make it impractical for most + use cases + +
+
+**Recommendations:**
+
+**All-rounder**: In the class of base sized models trained on SQuAD, **RoBERTa** has shown better performance than BERT
+and can be capably handled by any machine equipped with a single NVidia V100 GPU.
+We recommend this as the starting point for anyone wanting to create a performant and computationally reasonable instance of Haystack.
+
+**Built for Speed**: If speed and GPU memory are more of a priority to you than accuracy,
+you should try the MiniLM model.
+It is a smaller model that is trained to mimic larger models through the distillation process,
+and it outperforms the BERT base on SQuAD even though it is about 40% smaller.
+
+
+
+**State of the Art Accuracy**: For most, **ALBERT XXL** will be too large to feasibly work with.
+But if performance is your sole concern, and you have the computational resources,
+you might like to try ALBERT XXL which has set SoTA performance on SQuAD 2.0.
+
+
+
+
+
+
+
+
+
+## Deeper Dive: FARM vs Transformers
+
+Apart from the **model weights**, Haystack Readers contain all the components found in end-to-end open domain QA systems.
+This includes **tokenization**, **embedding computation**, **span prediction** and **candidate aggregation**.
+While the handling of model weights is the same between the FARM and Transformers libraries, their QA pipelines differ in some ways.
+The major points are:
+
+- The **TransformersReader** will sometimes predict the same span twice while duplicates are removed in the **FARMReader**
+
+- The **FARMReader** currently uses the tokenizers from the HuggingFace Transformers library while the **TransformersReader** uses the tokenizers from the HuggingFace Tokenizers library
+
+- Start and end logits are normalized per passage and multiplied in the **TransformersReader** while they are summed and not normalised in the **FARMReader**
+
+If you’re interested in the finer details of these points, have a look at [this](https://github.com/deepset-ai/haystack/issues/248#issuecomment-661977237) GitHub comment.
+
+We see value in maintaining both kinds of Readers since Transformers is a very familiar library to many of Haystack’s users
+but we at deepset can more easily update and optimise the FARM pipeline for speed and performance.
+
+
+
+Haystack also has a close integration with FARM which means that you can further fine-tune your Readers on labelled data using a FARMReader.
+See our tutorials for an end-to-end example or below for a shortened example.
+
+```python
+# Initialise Reader
+model = "deepset/roberta-base-squad2"
+reader = FARMReader(model)
+
+# Perform finetuning
+train_data = "PATH/TO_YOUR/TRAIN_DATA"
+train_filename = "train.json"
+save_dir = "finetuned_model"
+reader.train(train_data, train_filename, save_dir=save_dir)
+
+# Load
+finetuned_reader = FARMReader(save_dir)
+```
+
+
+
+## Deeper Dive: From Language Model to Haystack Reader
+
+Language models form the core of most modern NLP systems and that includes the Readers in Haystack.
+They build a general understanding of language when performing training tasks such as Masked Language Modeling or Replaced Token Detection
+on large amounts of text.
+Well trained language models capture the word distribution in one or more languages
+but more importantly, convert input text into a set of word vectors that capture elements of syntax and semantics.
+
+In order to convert a language model into a Reader model, it needs first to be trained on a Question Answering dataset.
+To do so requires the addition of a question answering prediction head on top of the language model.
+The task can be thought of as a token classification task where every input token is assigned a probability of being
+either the start or end token of the correct answer.
+In cases where the answer is not contained within the passage, the prediction head is also expected to return a `no_answer` prediction.
+
+
+
+Since language models are limited in the number of tokens which they can process in a single forward pass,
+a sliding window mechanism is implemented to handle variable length documents.
+This functions by slicing the document into overlapping passages of (approximately) `max_seq_length`
+that are each offset by `doc_stride` number of tokens.
+These can be set when the Reader is initialized.
+
+++from haystack.reader import FARMReader++ reader = FARMReader(... max_seq_len=384, doc_stride=128 ...) ++
+
+ ),
+ },
+ ]}
+/>
+
+Predictions are made on each individual passage and the process of aggregation picks the best candidates across all passages.
+If you’d like to learn more about what is happening behind the scenes, have a look at [this](https://medium.com/deepset-ai/modern-question-answering-systems-explained-4d0913744097) article.
+
+
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/retriever.md b/docs/v0.6.0/usage/retriever.mdx
similarity index 74%
rename from src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/retriever.md
rename to docs/v0.6.0/usage/retriever.mdx
index 96f36288f..cc3a70df3 100644
--- a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/retriever.md
+++ b/docs/v0.6.0/usage/retriever.mdx
@@ -1,41 +1,34 @@
----
-title: "Retriever"
-metaTitle: "Retriever"
-metaDescription: ""
-slug: "/docs/retriever"
-date: "2020-09-03"
-id: "retrievermd"
----
-
# Retriever
The Retriever is a lightweight filter that can quickly go through the full document store and pass a set of candidate documents to the Reader.
It is an tool for sifting out the obvious negative cases, saving the Reader from doing more work than it needs to and speeding up the querying process.
-++from haystack.reader import TransformersReader++ reader = TransformersReader(... max_seq_len=384, doc_stride=128 + ...) ++
+
**Recommendations**
-* BM25 (sparse)
+- BM25 (sparse)
-* Dense Passage Retrieval (dense)
+- Dense Passage Retrieval (dense)
+
Note that not all Retrievers can be paired with every DocumentStore.
Here are the combinations which are supported:
-| | Memory | Elasticsearch | SQL | FAISS |
-| --- | --- | --- | ---- | ---- |
-| BM25 | N | Y | N | N |
-| TF-IDF | Y | Y | Y | N |
-| Embedding | Y | Y | N | Y |
-| DPR | Y | Y | N | Y |
+| | Memory | Elasticsearch | SQL | FAISS |
+| --------- | ------ | ------------- | --- | ----- |
+| BM25 | N | Y | N | N |
+| TF-IDF | Y | Y | Y | N |
+| Embedding | Y | Y | N | Y |
+| DPR | Y | Y | N | Y |
See [Optimization](/docs/latest/optimizationmd) for suggestions on how to choose top-k values.
+
## TF-IDF
@@ -43,11 +36,9 @@ See [Optimization](/docs/latest/optimizationmd) for suggestions on how to choose
TF-IDF is a commonly used baseline for information retrieval that exploits two key intuitions:
+- documents that have more lexical overlap with the query are more likely to be relevant
-* documents that have more lexical overlap with the query are more likely to be relevant
-
-
-* words that occur in fewer documents are more significant than words that occur in many documents
+- words that occur in fewer documents are more significant than words that occur in many documents
Given a query, a tf-idf score is computed for each document as follows:
@@ -57,15 +48,15 @@ score = tf * idf
Where:
+- `tf` is how many times words in the query occur in that document.
-* `tf` is how many times words in the query occur in that document.
-
-
-* `idf` is the inverse of the fraction of documents containing the word.
+- `idf` is the inverse of the fraction of documents containing the word.
In practice, both terms are usually log normalised. If you’d like to learn more about the exact details of the algorithm,
have a look at [this video](https://www.youtube.com/watch?v=hNXwhF0OZ_o).
+
+
### Initialisation
```python
@@ -76,12 +67,14 @@ retriever = TfidfRetriever(document_store)
finder = Finder(reader, retriever)
```
-
+
**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
+
+
## BM25 (Recommended)
### Description
@@ -89,11 +82,11 @@ finder = Finder(reader, retriever)
BM25 is a variant of TF-IDF that we recommend you use if you are looking for a retrieval method that does not need a neural network for indexing.
It improves upon its predecessor in two main aspects:
+- It saturates `tf` after a set number of occurrences of the given term in the document
-* It saturates `tf` after a set number of occurrences of the given term in the document
+- It normalises by document length so that short documents are favoured over long documents if they have the same amount of word overlap with the query
-
-* It normalises by document length so that short documents are favoured over long documents if they have the same amount of word overlap with the query
+
### Initialisation
@@ -105,7 +98,7 @@ retriever = ElasticsearchRetriever(document_store)
finder = Finder(reader, retriever)
```
-
+
**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
@@ -114,6 +107,9 @@ finder = Finder(reader, retriever)
See [this](https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-and-its-variables) blog post for more details about the algorithm.
+
+
+
## Dense Passage Retrieval (Recommended)
### Description
@@ -121,41 +117,39 @@ See [this](https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-
[Dense Passage Retrieval](https://arxiv.org/abs/2004.04906) is a highly performant retrieval method that calculates relevance using dense representations.
Key features:
+- One BERT base model to encode documents
-* One BERT base model to encode documents
-
+- One Bert base model to encode queries
-* One Bert base model to encode queries
-
-
-* Ranking of documents done by dot product similarity between query and document embeddings
+- Ranking of documents done by dot product similarity between query and document embeddings
+
Indexing using DPR is comparatively expensive in terms of required computation since all documents in the database need to be processed through the transformer.
The embeddings that are created in this step can be stored in FAISS, a database optimized for vector similarity.
DPR can also work with the ElasticsearchDocumentStore or the InMemoryDocumentStore.
-
+
**Tip**
When using DPR, it is recommended that you use the dot product similarity function since that is how it is trained.
-To do so, simply provide `similarity="dot_product"` when initializing the DocumentStore
+To do so, simply provide `similarity="dot_product"` when initializing the DocumentStore
as is done in the code example below.
There are two design decisions that have made DPR particularly performant.
+- Separate encoders for document and query helps since queries are much shorter than documents
-* Separate encoders for document and query helps since queries are much shorter than documents
-
-
-* Training with ‘In-batch negatives’ (gold labels are treated as negative examples for other samples in same batch) is highly efficient
+- Training with ‘In-batch negatives’ (gold labels are treated as negative examples for other samples in same batch) is highly efficient
In Haystack, you can simply download the pretrained encoders needed to start using DPR.
If you’d like to learn how to set up a DPR based system, have a look at our tutorials.
+
+
### Initialisation
```python
@@ -170,13 +164,13 @@ retriever = DensePassageRetriever(
finder = Finder(reader, retriever)
```
-
+
**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
-
+
**Tip:** Haystack supports training of your own DPR model! Tutorial coming soon!
@@ -184,6 +178,9 @@ finder = Finder(reader, retriever)
+
+
+
## Embedding Retrieval
### Description
@@ -196,16 +193,18 @@ They are particular suited to cases where your query input is similar in style t
i.e. when you are searching for most similar documents.
This is not inherently suited to query based search where the length, language and format of the query usually significantly differs from the searched for text.
-
+
**Tip**
-When using Sentence Transformer models, we recommend that you use a cosine similarity function.
-To do so, simply provide `similarity="cosine"` when initializing the DocumentStore
+When using Sentence Transformer models, we recommend that you use a cosine similarity function.
+To do so, simply provide `similarity="cosine"` when initializing the DocumentStore
as is done in the code example below.
+
+
### Initialisation
```python
@@ -217,12 +216,14 @@ retriever = EmbeddingRetriever(document_store=document_store,
finder = Finder(reader, retriever)
```
-
+
+
+ ),
+ },
+ {
+ title: "Editable",
+ content: (
+
**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
+
+
## Deeper Dive: Dense vs Sparse
Broadly speaking, retrieval methods can be split into two categories: **dense** and **sparse**.
@@ -230,28 +231,24 @@ Broadly speaking, retrieval methods can be split into two categories: **dense**
**Sparse** methods, like TF-IDF and BM25, operate by looking for shared keywords between the document and query.
They are:
+- simple but effective
-* simple but effective
-
-
-* don’t need to be trained
+- don’t need to be trained
-
-* work on any language
+- work on any language
More recently, **dense** approaches such as Dense Passage Retrieval (DPR) have shown even better performance than their sparse counter parts.
These methods embed both document and query into a shared embedding space using deep neural networks
and the top candidates are the nearest neighbour documents to the query.
They are:
+- powerful but computationally more expensive especially during indexing
-* powerful but computationally more expensive especially during indexing
-
+- trained using labelled datasets
-* trained using labelled datasets
+- language specific
-
-* language specific
+
### Qualitative Differences
@@ -267,6 +264,9 @@ they only care about whether they are present or absent in the text.
As such, **they handle out-of-vocabulary words with no problem**.
+
+
+
### Indexing
Dense methods perform indexing by processing all the documents through a neural network and storing the resulting vectors.
@@ -274,10 +274,14 @@ This is a much more expensive operation than the creation of the inverted-index
and will require significant computational power and time.
+
+
+
### Terminology
+
The terms **dense** and **sparse** refer to the representations that the algorithms build for each document and query.
**Sparse** methods characterise texts using vectors with one dimension corresponding to each word in the vocabulary.
Dimensions will be zero if the word is absent and non-zero if it is present.
diff --git a/docs/v0.7.0/menu.json b/docs/v0.7.0/menu.json
new file mode 100644
index 000000000..1ead4df11
--- /dev/null
+++ b/docs/v0.7.0/menu.json
@@ -0,0 +1,111 @@
+[
+ {
+ "subMenuTitle": "Overview",
+ "pathPrefix": "/overview/",
+ "items": [
+ {
+ "slug": "get-started",
+ "title": "Get Started"
+ },
+ { "slug": "intro", "title": "What is Haystack?" },
+ { "slug": "use-cases", "title": "Use Cases" },
+ { "slug": "roadmap", "title": "Roadmap" },
+ { "slug": "glossary", "title": "Glossary" }
+ ]
+ },
+ {
+ "subMenuTitle": "Usage",
+ "pathPrefix": "/usage/",
+ "items": [
+ {
+ "slug": "preprocessing",
+ "title": "Preprocessing"
+ },
+ {
+ "slug": "document-store",
+ "title": "DocumentStore"
+ },
+ { "slug": "retriever", "title": "Retriever" },
+ { "slug": "reader", "title": "Reader" },
+ { "slug": "generator", "title": "Generator" },
+ { "slug": "pipelines", "title": "Pipelines" },
+ {
+ "slug": "languages",
+ "title": "Languages Other Than English"
+ },
+ {
+ "slug": "domain-adaptation",
+ "title": "Domain Adaptation"
+ },
+ {
+ "slug": "optimization",
+ "title": "Optimization"
+ },
+ {
+ "slug": "annotation",
+ "title": "Annotation Tool"
+ }
+ ]
+ },
+ {
+ "subMenuTitle": "Tutorials",
+ "pathPrefix": "/tutorials/",
+ "items": [
+ {
+ "slug": "first-qa-system",
+ "title": "Build Your First QA System"
+ },
+ {
+ "slug": "fine-tuning-a-model",
+ "title": "Fine-tuning a Model on Your Own Data"
+ },
+ {
+ "slug": "without-elasticsearch",
+ "title": "Build a QA System Without Elasticsearch"
+ },
+ {
+ "slug": "existing-faqs",
+ "title": "Utilizing existing FAQs for Question Answering"
+ },
+ {
+ "slug": "evaluation",
+ "title": "Evaluation of a QA System"
+ },
+ {
+ "slug": "dense-passage-retrieval",
+ "title": "Better Retrieval via \"Dense Passage Retrieval\""
+ },
+ {
+ "slug": "retrieval-augmented-generation",
+ "title": "Generative QA with \"Retrieval-Augmented Generation\""
+ },
+ {
+ "slug": "preprocessing",
+ "title": "Preprocessing your Documents"
+ },
+ { "slug": "train-dpr", "title": "How to Train DPR" }
+ ]
+ },
+ {
+ "subMenuTitle": "API Reference",
+ "pathPrefix": "/reference/",
+ "items": [
+ {
+ "slug": "document-store",
+ "title": "Document Store"
+ },
+ { "slug": "retriever", "title": "Retriever" },
+ { "slug": "reader", "title": "Reader" },
+ { "slug": "generator", "title": "Generator" },
+ {
+ "slug": "preprocessor",
+ "title": "Preprocessor"
+ },
+ {
+ "slug": "file-converters",
+ "title": "File Converters"
+ },
+ { "slug": "pipelines", "title": "Pipelines" }
+ ]
+ }
+]
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/get_started.md b/docs/v0.7.0/overview/get_started.mdx
similarity index 66%
rename from src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/get_started.md
rename to docs/v0.7.0/overview/get_started.mdx
index c2fc003f7..144bc0a5a 100644
--- a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/get_started.md
+++ b/docs/v0.7.0/overview/get_started.mdx
@@ -1,61 +1,61 @@
----
-title: "Get Started"
-metaTitle: "Get Started"
-metaDescription: ""
-slug: "/docs/get_started"
-date: "2020-09-03"
-id: "get_startedmd"
----
-
# Get Started
## Installation
-
-
-
+
-
-
-
-
-
-The most straightforward way to install Haystack is through pip.
- -```python -$ pip install farm-haystack -``` - -
-- -```python -$ pip install farm-haystack -``` - -
-
-
-
-
-
-If you’d like to run a specific, unreleased version of Haystack, or make edits to the way Haystack runs,
-you’ll want to install it using `git` and `pip --editable`.
-This clones a copy of the repo to a local directory and runs Haystack from there.
- -```python -$ git clone https://github.com/deepset-ai/haystack.git -$ cd haystack -$ pip install --editable . -``` - -By default, this will give you the latest version of the master branch. Use regular git commands to switch between different branches and commits. -
-- -```python -$ git clone https://github.com/deepset-ai/haystack.git -$ cd haystack -$ pip install --editable . -``` - -By default, this will give you the latest version of the master branch. Use regular git commands to switch between different branches and commits. -
+ The most straightforward way to install Haystack is through pip. +
+
+ pip install farm-haystack
+
+
+
+ ),
+ },
+ ]}
+/>
Note: On Windows add the arg `-f https://download.pytorch.org/whl/torch_stable.html` to install PyTorch correctly
+
+
## The Building Blocks of Haystack
Here’s a sample of some Haystack code showing the most important components.
For a working code example, check out our starter tutorial.
+
```python
# DocumentStore: holds all your data
document_store = ElasticsearchDocumentStore()
@@ -80,6 +80,8 @@ prediction = finder.get_answers(question)
print_answers(prediction)
```
+
+
## Loading Documents into the DocumentStore
In Haystack, DocumentStores expect Documents in a dictionary format. They are loaded as follows:
@@ -99,26 +101,25 @@ When we talk about Documents in Haystack, we are referring specifically to the i
You might want to use all the text in one file as a Document, or split it into multiple Documents.
This splitting can have a big impact on speed and performance.
-+ If you’d like to run a specific, unreleased version of Haystack, or + make edits to the way Haystack runs, you’ll want to install it using + `git` and `pip --editable`. This clones a copy of the repo to a + local directory and runs Haystack from there. +
+++git clone https://github.com/deepset-ai/haystack.git+cd haystack+pip install --editable .+
+ By default, this will give you the latest version of the master + branch. Use regular git commands to switch between different + branches and commits. +
+
+
**Tip:** If Haystack is running very slowly, you might want to try splitting your text into smaller Documents.
If you want an improvement to performance, you might want to try concatenating text to make larger Documents.
+
+
## Running Queries
**Querying** involves searching for an answer to a given question within the full document store.
This process will:
+- make the Retriever filter for a small set of relevant candidate documents
-* make the Retriever filter for a small set of relevant candidate documents
-
-
-* get the Reader to process this set of candidate documents
-
+- get the Reader to process this set of candidate documents
-* return potential answers to the given question
+- return potential answers to the given question
Usually, there are tight time constraints on querying and so it needs to be a lightweight operation.
When documents are loaded, Haystack will precompute any of the results that might be useful at query time.
@@ -146,7 +147,7 @@ When the query is complete, you can expect to see results that look something li
]
```
-
+
**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/terms.md b/docs/v0.7.0/overview/glossary.mdx
similarity index 96%
rename from src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/terms.md
rename to docs/v0.7.0/overview/glossary.mdx
index 6254848f7..e8b4f4a90 100644
--- a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/terms.md
+++ b/docs/v0.7.0/overview/glossary.mdx
@@ -1,12 +1,3 @@
----
-title: "Glossary"
-metaTitle: "Glossary"
-metaDescription: ""
-slug: "/docs/terms"
-date: "2020-09-03"
-id: "termsmd"
----
-
# Glossary
**BERT** - A popular, transformer based language model which has been improved upon but is still considered a common benchmark.
@@ -26,7 +17,7 @@ FARM’s question answering models are used in Haystack’s Readers.
**Finder** - The component in Haystack that connects the Retriever to the Reader.
-
+
**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/intro.md b/docs/v0.7.0/overview/intro.mdx
similarity index 61%
rename from src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/intro.md
rename to docs/v0.7.0/overview/intro.mdx
index 858a02ffd..3c5bac046 100644
--- a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/intro.md
+++ b/docs/v0.7.0/overview/intro.mdx
@@ -1,37 +1,26 @@
----
-title: "What is Haystack"
-metaTitle: "What is Haystack"
-metaDescription: ""
-slug: "/docs/intro"
-date: "2020-09-03"
-id: "intromd"
----
-
# What is Haystack?
Haystack is an **open-source framework** for building **end-to-end question answering systems** for large document collections.
Recent advances in NLP have enabled the application of QA to real world settings
and Haystack is designed to be the bridge between research and industry.
+- **Latest NLP models**: Utilize all transformer based models (BERT, RoBERTa, MiniLM, DPR ...) and smoothly switch when new ones get published
-* **Latest NLP models**: Utilize all transformer based models (BERT, RoBERTa, MiniLM, DPR ...) and smoothly switch when new ones get published
-
-* **Flexible databases**: Load data into and query from a range of databases such as Elasticsearch, FAISS, SQL and more
+- **Flexible databases**: Load data into and query from a range of databases such as Elasticsearch, FAISS, SQL and more
-* **Scalability**: Production-ready deployments that scale to millions of documents
+- **Scalability**: Production-ready deployments that scale to millions of documents
-* **End-to-End**: All tooling you need to implement, evaluate, improve and run a QA system
+- **End-to-End**: All tooling you need to implement, evaluate, improve and run a QA system
-* **Domain adaptation**: Fine-tune models to your own domain & improve them continuously via user feedback
+- **Domain adaptation**: Fine-tune models to your own domain & improve them continuously via user feedback
+
## Technology
Haystack is powered by a Retriever-Reader pipeline in order to optimise for both speed and accuracy.
-
-
-
+
**Readers**, also known as Open-Domain QA systems in Machine Learning speak,
are powerful models that do close analysis of documents and perform the core task of question answering.
@@ -40,28 +29,25 @@ However, it is not currently feasible to use the Reader directly on large collec
+
The **Retriever** assists the Reader by acting as a lightweight filter that reduces the number of documents that the Reader has to process.
It does this by:
+- Scanning through all documents in the database
-* Scanning through all documents in the database
-
-
-* Quickly identifying the relevant and dismissing the irrelevant
+- Quickly identifying the relevant and dismissing the irrelevant
-
-* Passing on only a small candidate set of documents to the Reader
+- Passing on only a small candidate set of documents to the Reader
Current methods fall into one of the two categories:
+- sparse
-* sparse
- * keyword based
- * fast indexing and querying
- * e.g. BM25
-
+ - keyword based
+ - fast indexing and querying
+ - e.g. BM25
-* dense
- * neural network based
- * computationally heavy indexing but fast querying
- * e.g. Dense Passage Retrieval
+- dense
+ - neural network based
+ - computationally heavy indexing but fast querying
+ - e.g. Dense Passage Retrieval
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/roadmap.md b/docs/v0.7.0/overview/roadmap.mdx
similarity index 63%
rename from src/pages/docs/versions/master/latest/site/en/usage/usage/roadmap.md
rename to docs/v0.7.0/overview/roadmap.mdx
index bf28c14e5..40eba526a 100644
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/roadmap.md
+++ b/docs/v0.7.0/overview/roadmap.mdx
@@ -1,45 +1,41 @@
----
-title: "Roadmap"
-metaTitle: "Roadmap and Zenhub Access"
-metaDescription: "How to access our open roadmap"
-slug: "/docs/roadmap"
-date: "2020-09-24"
-id: "roadmapmd"
----
-
# Open roadmap
-We believe open-source is more than open source code. It's a lot about people, collaboration, transparency and trust.
-Therefore, we decided to be as open as possible with our roadmap and sprint planning.
-In fact, you can see all of it in real-time on GitHub.
-We hope this helps to clarify the direction of the open-source project and inspires discussions in the community.
+We believe open-source is more than open source code. It's a lot about people, collaboration, transparency and trust.
+Therefore, we decided to be as open as possible with our roadmap and sprint planning.
+In fact, you can see all of it in real-time on GitHub.
+We hope this helps to clarify the direction of the open-source project and inspires discussions in the community.
## How to access it
### Add Zenhub Plugin
+
We decided for Zenhub, as it allows a close integration with GitHub and real-time sharing of roadmaps and sprints.
Once you have installed the browser plugin below, you will see additional tabs and infos on the Haystack GitHub page.
-Zenhub Plugin: https://www.zenhub.com/extension
+Zenhub Plugin: https://www.zenhub.com/extension
### Roadmap
+
_Zenhub Tab -> Roadmap (left menu)_
Here you can find our most recent roadmap with the **high-level projects** that are planned for the upcoming quarters.
-We update it regularly and refine the projects as they come closer in time.
-
-
+We update it regularly and refine the projects as they come closer in time.
+
+
### Board
+
_Zenhub Tab -> Board (left menu)_
-If you are interested in the **operational tasks** and their status, you can find our agile board here.
+If you are interested in the **operational tasks** and their status, you can find our agile board here.
+
+
-
### Additional issue details & Releases
+
_Right panel in regular Github issues_
-With Zenhub you can also see some additional tags in every GitHub issue.
-For those of you who wonder about the next release date: We aim for releases every ~ 4 weeks and will tag the issues that will need to be finished before a bit in advance.
+With Zenhub you can also see some additional tags in every GitHub issue.
+For those of you who wonder about the next release date: We aim for releases every ~ 4 weeks and will tag the issues that will need to be finished before a bit in advance.
-
+
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/use_cases.md b/docs/v0.7.0/overview/use_cases.mdx
similarity index 66%
rename from src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/use_cases.md
rename to docs/v0.7.0/overview/use_cases.mdx
index 76b420410..b712e58ca 100644
--- a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/use_cases.md
+++ b/docs/v0.7.0/overview/use_cases.mdx
@@ -1,32 +1,18 @@
----
-title: "Use Cases"
-metaTitle: "Use Cases"
-metaDescription: ""
-slug: "/docs/use_cases"
-date: "2020-11-16"
-id: "use_casesmd"
----
-
-
# Use cases
## Semantic Search System
Take the leap from using keyword search on your own documents to semantic search with Haystack.
+- Store your documents in the database of your choice (Elasticsearch, SQL, in memory, FAISS)
-* Store your documents in the database of your choice (Elasticsearch, SQL, in memory, FAISS)
-
-
-* Perform question driven queries.
+- Perform question driven queries.
Expect to see results that highlight the very sentence that contains the answer to your question.
Thanks to the power of Transformer based language models, results are chosen based on compatibility in meaning
rather than lexical overlap.
-
-
-
+
## Information Extractor
@@ -34,36 +20,31 @@ Automate the extraction of relevant information from a set of documents that per
Haystack can:
+- Apply a set of standard questions to each document in a store
-* Apply a set of standard questions to each document in a store
-
-
-* Return a NO_ANSWER if a given document does not contain the answer to a question
+- Return a NO_ANSWER if a given document does not contain the answer to a question
Say you have the financial reports for different companies over different years.
You can gather a set of standard questions which are applicable to each financial report,
-like *what is the revenue forecast for 2020?* or *what are the main sources of income?*.
+like _what is the revenue forecast for 2020?_ or _what are the main sources of income?_.
Haystack will try to find an answer for each question within each document!
We’ve seen this style of application be particularly effective in the sphere of finance and patent law
but we see a lot of potential in using this to gain a better overview of academic papers and internal business documents.
+
## FAQ Style Question Answering
Leverage existing FAQ documents and semantic similarity search to answer new incoming questions.
The workflow is as follows:
+- Store a set of FAQ documents in Haystack
-* Store a set of FAQ documents in Haystack
-
-
-* The user presents a new question
-
-
-* Haystack will find the closest match to the new question in the FAQ documents
+- The user presents a new question
+- Haystack will find the closest match to the new question in the FAQ documents
-* The user will be presented with the most similar Question Answer pair
+- The user will be presented with the most similar Question Answer pair
Haystack’s flexibility allows you to give new users more dynamic access to your existing documentation.
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/annotation.md b/docs/v0.7.0/usage/annotation.mdx
similarity index 80%
rename from src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/annotation.md
rename to docs/v0.7.0/usage/annotation.mdx
index ed29d18c8..d22e35fae 100644
--- a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/annotation.md
+++ b/docs/v0.7.0/usage/annotation.mdx
@@ -1,12 +1,3 @@
----
-title: "Annotation Tool"
-metaTitle: "Annotation Tool"
-metaDescription: ""
-slug: "/docs/annotation"
-date: "2020-09-03"
-id: "annotationmd"
----
-
# Annotation Tool
- Create labels with different techniques: Come up with questions (+ answers) while reading passages (SQuAD style) or have a set of predefined questions and look for answers in the document (~ Natural Questions).
@@ -14,12 +5,17 @@ id: "annotationmd"
- Upload your documents or import labels from an existing SQuAD-style dataset
- Export your labels in SQuAD Format
-
+
+
+
+
+## Hosted version
+
+Signup here: [Haystack Annotation Tool](https://annotate.deepset.ai/login)
-# Hosted version
- Signup here: [Haystack Annotation Tool](https://annotate.deepset.ai/login)
+
-# Local version (Docker)
+## Local version (Docker)
1. Configure credentials & database in the [`docker-compose.yml`](https://github.com/deepset-ai/haystack/blob/master/annotation_tool/docker-compose.yml):
@@ -37,11 +33,12 @@ The credentials should match in database image and application configuration.
POSTGRES_PASSWORD: "somesafepassword"
POSTGRES_DB: "databasename"
-
2. Run docker-compose by executing `docker-compose up`.
-
3. The UI should be available at `localhost:7001`.
-# Usage
+
+
+## Usage
+
The manual (of a slightly earlier version) can be found [here](https://drive.google.com/file/d/1Wv3OIC0Z7ibHIzOm9Xw_r0gjTFmpl-33/view). While it doesn't include all latest features, the basic workflow and tips for label quality are still the same.
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/document_store.md b/docs/v0.7.0/usage/document_store.mdx
similarity index 68%
rename from src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/document_store.md
rename to docs/v0.7.0/usage/document_store.mdx
index 8adcccb07..36ad2163c 100644
--- a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/document_store.md
+++ b/docs/v0.7.0/usage/document_store.mdx
@@ -1,31 +1,19 @@
----
-title: "Document Store"
-metaTitle: "Document Store"
-metaDescription: ""
-slug: "/docs/documentstore"
-date: "2020-09-03"
-id: "documentstoremd"
----
-
-
# DocumentStores
You can think of the DocumentStore as a "database" that:
-- stores your texts and meta data
-- provides them to the retriever at query time
-There are different DocumentStores in Haystack to fit different use cases and tech stacks.
+- stores your texts and meta data
+- provides them to the retriever at query time
+
+There are different DocumentStores in Haystack to fit different use cases and tech stacks.
## Initialisation
Initialising a new DocumentStore is straight forward.
-
+
-
-
Each DocumentStore constructor allows for arguments specifying how to connect to existing databases and the names of indexes.
See API documentation for more info.
+
+
## Input Format
DocumentStores expect Documents in dictionary form, like that below.
They are loaded using the `DocumentStore.write_documents()` method.
See [Preprocessing](/docs/latest/preprocessingmd) for more information on the cleaning and splitting steps that will help you maximize Haystack's performance.
-[//]: # (Add link to preprocessing section)
+[//]: # "Add link to preprocessing section"
```python
document_store = ElasticsearchDocumentStore()
@@ -97,6 +70,8 @@ dicts = [
document_store.write_documents(dicts)
```
+
+
## Writing Documents (Sparse Retrievers)
Haystack allows for you to write store documents in an optimised fashion so that query times can be kept low.
@@ -108,6 +83,8 @@ The creation of the inverted index which optimises querying speed is handled aut
document_store.write_documents(dicts)
```
+
+
## Writing Documents (Dense Retrievers)
For **dense** neural network based retrievers like Dense Passage Retrieval, or Embedding Retrieval,
@@ -126,86 +103,77 @@ Having GPU acceleration will significantly speed this up.
+
+
+
## Choosing the Right Document Store
-The Document Stores have different characteristics. You should choose one depending on the maturity of your project, the use case and technical environment:
+The Document Stores have different characteristics. You should choose one depending on the maturity of your project, the use case and technical environment:
-
-
-
-
+
-
+### Elasticsearch
```python
document_store = ElasticsearchDocumentStore()
@@ -34,57 +22,42 @@ document_store = OpenDistroElasticsearchDocumentStore()
```
-
-
-
-
-
+
-
+### FAISS
```python
document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
```
-
-
-
-
-
+
-
+### In Memory
```python
document_store = InMemoryDocumentStore()
```
-
-
-
-
-
-
-
+### SQL
```python
document_store = SQLDocumentStore()
```
-
-
+### Elasticsearch
-
+
-
-
-
-
+
+
+### FAISS
-
+**Pros:**
-**Pros:**
- Fast & accurate sparse retrieval with many tuning options
- Basic support for dense retrieval
- Production-ready
- Support also for Open Distro
-**Cons:**
+**Cons:**
+
- Slow for dense retrieval with more than ~ 1 Mio documents
-
-
-
-
-
+
-
+**Pros:**
-**Pros:**
- Fast & accurate dense retrieval
- Highly scalable due to approximate nearest neighbour algorithms (ANN)
- Many options to tune dense retrieval via different index types (more info [here](https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index))
**Cons:**
+
- No efficient sparse retrieval
-
-
-
-
-
+### SQL
-
+### In Memory
**Pros:**
+
- Simple
- Exists already in many environments
**Cons:**
+
- Only compatible with minimal TF-IDF Retriever
- Bad retrieval performance
- Not recommended for production
-
-
-
-
-
-
-
+
**Pros:**
+
- Simple & fast to test
- No database requirements
- Supports MySQL, PostgreSQL and SQLite
-**Cons:**
+**Cons:**
+
- Not scalable
- Not persisting your data on disk
-
-
+
#### Our Recommendations
-**Restricted environment:** Use the `InMemoryDocumentStore`, if you are just giving Haystack a quick try on a small sample and are working in a restricted environment that complicates running Elasticsearch or other databases
+**Restricted environment:** Use the `InMemoryDocumentStore`, if you are just giving Haystack a quick try on a small sample and are working in a restricted environment that complicates running Elasticsearch or other databases
**Allrounder:** Use the `ElasticSearchDocumentStore`, if you want to evaluate the performance of different retrieval options (dense vs. sparse) and are aiming for a smooth transition from PoC to production
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/domain_adaptation.md b/docs/v0.7.0/usage/domain_adaptation.mdx
similarity index 70%
rename from src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/domain_adaptation.md
rename to docs/v0.7.0/usage/domain_adaptation.mdx
index 9bd415e61..614be50d7 100644
--- a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/domain_adaptation.md
+++ b/docs/v0.7.0/usage/domain_adaptation.mdx
@@ -1,12 +1,3 @@
----
-title: "Domain Adaptation"
-metaTitle: "Domain Adaptation"
-metaDescription: ""
-slug: "/docs/domain_adaptation"
-date: "2020-09-03"
-id: "domain_adaptationmd"
----
-
# Domain Adaptation
## Generalisation
@@ -17,7 +8,7 @@ Though SQuAD is composed entirely of Wikipedia articles, these models are flexib
Before trying to adapt these models to your domain, we’d recommend trying one of the off the shelf models.
We’ve found that these models are often flexible enough for a wide range of use cases.
-
+
**Intuition**
@@ -28,6 +19,7 @@ In the same way, many QA models have a good enough grasp of language to answer q
+
## Finetuning
@@ -43,7 +35,7 @@ reader.train(data_dir=train_data,
At the end of training, the finetuned model will be saved in the specified `save_dir` and can be loaded as a `Reader`.
-
+
**Recommendation**
@@ -53,57 +45,55 @@ please also check out Tutorial 5 which walks through the steps needed to perform
+
+
## Generating Labels
Using our [Haystack Annotate tool](https://annotate.deepset.ai/login) (Beta),
you can easily create a labelled dataset using your own documents featuring your own question/ answer pairs.
-
-
-
+
Features include:
+- Structured workspaces via organisations, projects and users
-* Structured workspaces via organisations, projects and users
-
-
-* Easy upload of your own documents and labels in a variety of formats (txt, pdf, SQuAD style)
+- Easy upload of your own documents and labels in a variety of formats (txt, pdf, SQuAD style)
-
-* Export of labels to be used directly in Haystack
+- Export of labels to be used directly in Haystack
Annotate also supports two different workflows:
+- Think up questions and answers while reading passages (SQuAD style)
-* Think up questions and answers while reading passages (SQuAD style)
-
+- Have a set of predefined questions and look for answers in the document (~ Natural Questions style)
-* Have a set of predefined questions and look for answers in the document (~ Natural Questions style)
+
## User Feedback
-A simpler and faster process to finetune models to your domain is to utilise user feedback.
-Dedicated annotation work can be costly and time consuming
-but user feedback is an efficient alternative since it allows for labels to be generated by users, for users,
+A simpler and faster process to finetune models to your domain is to utilise user feedback.
+Dedicated annotation work can be costly and time consuming
+but user feedback is an efficient alternative since it allows for labels to be generated by users, for users,
all while the system is already in production.
We, for example, have used a simple thumbs up / down system in our demos to allow
users to give feedback.
-
+
Through the Rest API, users can annotate each Haystack result as being either:
-* Correct
-* Correct document but wrong text
-* Wrong document and wrong text
+- Correct
+- Correct document but wrong text
+- Wrong document and wrong text
To get started, follow these steps:
-* Start up the REST API
- * The simplest way to do this is to call `docker-compose up` from the root directory of the Haystack repository
- * Alternatively, run `gunicorn rest_api.application:app -b 0.0.0.0:8000 -k uvicorn.workers.UvicornWorker -t 300`
-* Make a POST request to the `doc-qa-feedback` endpoint with the following payload:
+- Start up the REST API
+ - The simplest way to do this is to call `docker-compose up` from the root directory of the Haystack repository
+ - Alternatively, run `gunicorn rest_api.application:app -b 0.0.0.0:8000 -k uvicorn.workers.UvicornWorker -t 300`
+- Make a POST request to the `doc-qa-feedback` endpoint with the following payload:
+
```
{
"question": "string",
@@ -115,10 +105,12 @@ To get started, follow these steps:
"offset_start_in_doc": 0
}
```
+
To fill out all these values, you can use the response from an executed search request on the `doc-qa` endpoint.
-* Export your labels in SQuAD format by making a GET request to the `export-doc-qa-feedback` endpoint
-``` python
+- Export your labels in SQuAD format by making a GET request to the `export-doc-qa-feedback` endpoint
+
+```python
# SQUAD format
{
"data": [
@@ -146,12 +138,11 @@ To fill out all these values, you can use the response from an executed search r
}
```
-User feedback labelling also works with FAQ style QA.
+User feedback labelling also works with FAQ style QA.
Refer to the Swagger API documentation (http://127.0.0.1:8000/docs) for more details.
-This labelled data can then be used to
-augment the training data and enable `Reader` finetuning.
-Alternatively, they can also be used to form an evaluation set to
-measure the performance of trained models,
+This labelled data can then be used to
+augment the training data and enable `Reader` finetuning.
+Alternatively, they can also be used to form an evaluation set to
+measure the performance of trained models,
or monitor how well the model is doing in a live environment.
-
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/generator.md b/docs/v0.7.0/usage/generator.mdx
similarity index 68%
rename from src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/generator.md
rename to docs/v0.7.0/usage/generator.mdx
index 354f93f40..056a9bc47 100644
--- a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/generator.md
+++ b/docs/v0.7.0/usage/generator.mdx
@@ -1,15 +1,6 @@
----
-title: "Generator"
-metaTitle: "Generator"
-metaDescription: ""
-slug: "/docs/generator"
-date: "2020-11-05"
-id: "generatormd"
----
-
# Generator
-
+
**Example**
@@ -26,10 +17,11 @@ With the advent of Transformer based retrieval methods such as [Dense Passage Re
retriever and generator can be trained concurrently from the one loss signal.
Pros
-* More appropriately phrased answers
-* Able to syntehsize information from different texts
-* Can draw on latent knowledge stored in language model
+
+- More appropriately phrased answers
+- Able to syntehsize information from different texts
+- Can draw on latent knowledge stored in language model
Cons
-* Not easy to track what piece of information the generator is basing its response off of
+- Not easy to track what piece of information the generator is basing its response off of
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/languages.md b/docs/v0.7.0/usage/languages.mdx
similarity index 52%
rename from src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/languages.md
rename to docs/v0.7.0/usage/languages.mdx
index 4b15b0716..f6a9b62c8 100644
--- a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/languages.md
+++ b/docs/v0.7.0/usage/languages.mdx
@@ -1,17 +1,8 @@
----
-title: "Languages Other Than English"
-metaTitle: "Languages Other Than English"
-metaDescription: ""
-slug: "/docs/languages"
-date: "2020-11-05"
-id: "languagesmd"
----
-
# Languages Other Than English
Haystack is well suited to open-domain QA on languages other than English.
While our defaults are tuned for English,
-you will find some tips and tricks here for using Haystack in your language.
+you will find some tips and tricks here for using Haystack in your language.
##Retrievers
@@ -20,19 +11,19 @@ Their only requirement is that the text be split into words.
The ElasticsearchDocumentStore relies on an analyzer to impose word boundaries,
but also to handle punctuation, casing and stop words.
-The default analyzer is an English analyzer.
+The default analyzer is an English analyzer.
While it can still work decently for a large range of languages,
you will want to set it to your language's analyzer for optimal performance.
In some cases, such as with Thai, the default analyzer is completely incompatible.
-See [this page](https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-lang-analyzer.html)
+See [this page](https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-lang-analyzer.html)
for the full list of language specific analyzers.
```python
document_store = ElasticsearchDocumentStore(analyzer="thai")
```
-The models used in dense retrievers are language specific.
-Be sure to check language of the model used in your EmbeddingRetriever.
+The models used in dense retrievers are language specific.
+Be sure to check language of the model used in your EmbeddingRetriever.
The default model that is loaded in the DensePassageRetriever is for English.
We are currently working on training a German DensePassageRetriever model and know other teams who work on further languages.
If you have a language model and a question answering dataset in your own language, you can also train a DPR model using Haystack!
@@ -51,111 +42,107 @@ dense_passage_retriever.train(self,
n_epochs: int = 3)
```
-##Readers
+
+
+## Readers
While models are comparatively more performant on English,
thanks to a wealth of available English training data,
there are a couple QA models that are directly usable in Haystack.
-
+
+
-
-
+
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-```python
-reader = FARMReader("illuin/camembert-base-fquad")
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = FARMReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa")
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = FARMReader("deepset/xlm-roberta-large-squad2")
-```
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-```python
-reader = TransformersReader("illuin/camembert-base-fquad")
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = TransformersReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa")
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = TransformersReader("deepset/xlm-roberta-large-squad2")
-```
-
-
-from haystack.reader import FARMReader
+
+ reader = FARMReader("illuin/camembert-base-fquad")
+
+
+ ),
+ },
+ {
+ title: "Italian",
+ content: (
+ ++ ), + }, + { + title: "Zero-shot", + content: ( +from haystack.reader import FARMReader++ reader = + FARMReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa") ++
++ ), + }, + ]} + /> + ), + }, + { + title: "Transformers", + content: ( +from haystack.reader import FARMReader++ reader = FARMReader("deepset/xlm-roberta-large-squad2") ++
from haystack.reader import TransformersReader
+
+ reader = TransformersReader("illuin/camembert-base-fquad")
+
+
+ ),
+ },
+ {
+ title: "Italian",
+ content: (
+ ++ ), + }, + { + title: "Zero-shot", + content: ( +from haystack.reader import TransformersReader++ reader = + TransformersReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa") ++
++ ), + }, + ]} + /> + ), + }, + ]} +/> + + The **French** and **Italian models** are both monolingual language models trained on French and Italian versions of the SQuAD dataset and their authors report decent results in their model cards @@ -169,5 +156,4 @@ but still its performance lags behind that of the monolingual models. Nonetheless, if there is not yet a monolingual model for your language and it is one of the 100 supported by XLM-RoBERTa, this zero-shot model may serve as a decent first baseline. -[//]: # (Add link to Reader training, create section in reader.md on training Reader) - +[//]: # "Add link to Reader training, create section in reader.md on training Reader" diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/optimization.md b/docs/v0.7.0/usage/optimization.mdx similarity index 86% rename from src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/optimization.md rename to docs/v0.7.0/usage/optimization.mdx index 8a46a54a2..019ca80c3 100644 --- a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/optimization.md +++ b/docs/v0.7.0/usage/optimization.mdx @@ -1,17 +1,8 @@ ---- -title: "Optimization" -metaTitle: "Optimization" -metaDescription: "" -slug: "/docs/optimization" -date: "2020-11-05" -id: "optimizationmd" ---- - # Optimization ## Document Length -Document length has a very direct impact on the speed of the Reader +Document length has a very direct impact on the speed of the Reader which is why we recommend using the `PreProcessor` class to clean and split your documents. **If you halve the length of your documents, you will halve the workload placed onto your Reader.** @@ -25,13 +16,17 @@ By default, Haystack's DensePassageRetriever model has a maximum length of 256 t As such, we recommend that documents contain significantly less words. We have found decent performance with **documents around 100 words long**. + + ## Respecting Sentence Boundaries -When splitting documents, it is generally not a good idea to let document boundaries fall in the middle of sentences. -Doing so means that each document will contain incomplete sentence fragments +When splitting documents, it is generally not a good idea to let document boundaries fall in the middle of sentences. +Doing so means that each document will contain incomplete sentence fragments which maybe be hard for both retriever and reader to interpret. It is therefore recommended to set `split_respect_sentence_boundary=True` when initializing your `PreProcessor`. + + ## Choosing the Right top-k Values The `top-k` parameter in both the `Retriever` and `Reader` determine how many results they return. @@ -41,23 +36,26 @@ while `Reader` `top-k` determines how many answer candidates to show. In our experiments, we have found that **`Retriever` `top_k=10` gives decent overall performance** and so we have set this as the default in Haystack. -The choice of `Retriever` `top-k` is a trade-off between speed and accuracy, +The choice of `Retriever` `top-k` is a trade-off between speed and accuracy, especially when there is a `Reader` in the pipeline. -Setting it higher means passing more documents to the `Reader`, +Setting it higher means passing more documents to the `Reader`, thus reducing the chance that the answer-containing passage is missed. However, passing more documents to the `Reader` will create a larger workload for the component. These parameters can easily be tweaked as follows if using a `Finder`: -``` python + +```python answers = finder.get_answers(retriever_top_k=10, reader_top_k=5) ``` + or like this if directly calling the `Retriever`: -``` python + +```python retrieved_docs = retriever.retrieve(top_k=10) ``` -from haystack.reader import TransformersReader++ reader = + TransformersReader("deepset/xlm-roberta-large-squad2") ++
+
+
+ ),
+ },
+ {
+ title: "Transformers",
+ content: (
+
**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/pipelines.md b/docs/v0.7.0/usage/pipelines.mdx
similarity index 89%
rename from src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/pipelines.md
rename to docs/v0.7.0/usage/pipelines.mdx
index 23ff1015e..1c36bced7 100644
--- a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/pipelines.md
+++ b/docs/v0.7.0/usage/pipelines.mdx
@@ -1,26 +1,18 @@
----
-title: "Pipelines"
-metaTitle: "Pipelines"
-metaDescription: ""
-slug: "/docs/pipelines"
-date: "2020-11-05"
-id: "pipelinesmd"
----
-
# Pipelines
+
+
+ ),
+ },
+ {
+ title: "Transformers",
+ content: (
+
-
+
+
+ ),
+ },
+ {
+ title: "Transformers",
+ content: (
+
-
-The new Pipelines class was added in Haystack 0.6.0 to give a more flexible way of defining your processing steps.
+The new Pipelines class was added in Haystack 0.6.0 to give a more flexible way of defining your processing steps.
It replaces the Finder class which will be deprecated in the next version.
+
### Flexible Pipelines powered by DAGs
+
In order to build modern search pipelines, you need two things: powerful building blocks and a flexible way to stick them together.
-The `Pipeline` class is exactly build for this purpose and enables many search scenarios beyond QA. The core idea: you can build a Directed Acyclic Graph (DAG) where each node is one "building block" (Reader, Retriever, Generator ...). Here's a simple example for a "standard" Open-Domain QA Pipeline:
+The `Pipeline` class is exactly build for this purpose and enables many search scenarios beyond QA. The core idea: you can build a Directed Acyclic Graph (DAG) where each node is one "building block" (Reader, Retriever, Generator ...). Here's a simple example for a "standard" Open-Domain QA Pipeline:
```python
p = Pipeline()
@@ -31,13 +23,19 @@ res = p.run(query="What did Einstein work on?", top_k_retriever=1)
```
You can **draw the DAG** to better inspect what you are building:
+
```python
p.draw(path="custom_pipe.png")
```
+
+
+
### Multiple retrievers
-You can now also use multiple Retrievers and join their results:
+
+You can now also use multiple Retrievers and join their results:
+
```python
p = Pipeline()
p.add_node(component=es_retriever, name="ESRetriever", inputs=["Query"])
@@ -46,20 +44,28 @@ p.add_node(component=JoinDocuments(join_mode="concatenate"), name="JoinResults",
p.add_node(component=reader, name="QAReader", inputs=["JoinResults"])
res = p.run(query="What did Einstein work on?", top_k_retriever=1)
```
+
+
+
### Custom nodes
-You can easily build your own custom nodes. Just respect the following requirements:
+
+You can easily build your own custom nodes. Just respect the following requirements:
1. Add a method `run(self, **kwargs)` to your class. `**kwargs` will contain the output from the previous node in your graph.
2. Do whatever you want within `run()` (e.g. reformatting the query)
3. Return a tuple that contains your output data (for the next node) and the name of the outgoing edge `output_dict, "output_1`
4. Add a class attribute `outgoing_edges = 1` that defines the number of output options from your node. You only need a higher number here if you have a decision node (see below).
+
+
### Decision nodes
-Or you can add decision nodes where only one "branch" is executed afterwards. This allows, for example, to classify an incoming query and depending on the result routing it to different modules:
+
+Or you can add decision nodes where only one "branch" is executed afterwards. This allows, for example, to classify an incoming query and depending on the result routing it to different modules:
-```python
+
+```python
class QueryClassifier():
outgoing_edges = 2
@@ -80,11 +86,14 @@ Or you can add decision nodes where only one "branch" is executed afterwards. Th
res = p.run(query="What did Einstein work on?", top_k_retriever=1)
```
+
+
### Default Pipelines (replacing the "Finder")
+
Last but not least, we added some "Default Pipelines" that allow you to run standard patterns with very few lines of code.
This is replacing the `Finder` class which is now deprecated.
-```
+```python
from haystack.pipeline import DocumentSearchPipeline, ExtractiveQAPipeline, Pipeline, JoinDocuments
# Extractive QA
@@ -103,8 +112,8 @@ res = doc_pipe.run(query="Physics Einstein", top_k_retriever=1)
doc_pipe = FAQPipeline(retriever=retriever)
res = doc_pipe.run(query="How can I change my address?", top_k_retriever=3)
-```
-See also the [Pipelines API documentation](/docs/latest/apipipelinesmd) for more details.
+```
-We plan many more features around the new pipelines incl. parallelized execution, distributed execution, definition via YAML files, dry runs - so stay tuned ...
+See also the [Pipelines API documentation](/docs/latest/apipipelinesmd) for more details.
+We plan many more features around the new pipelines incl. parallelized execution, distributed execution, definition via YAML files, dry runs - so stay tuned ...
diff --git a/docs/v0.7.0/usage/preprocessing.mdx b/docs/v0.7.0/usage/preprocessing.mdx
new file mode 100644
index 000000000..4ba723af1
--- /dev/null
+++ b/docs/v0.7.0/usage/preprocessing.mdx
@@ -0,0 +1,113 @@
+# Preprocessing
+
+Haystack includes a suite of tools to:
+
+- extract text from different file types,
+- normalize white space
+- split text into smaller pieces to optimize retrieval
+
+These data preprocessing steps can have a big impact on the systems performance
+and effective handling of data is key to getting the most out of Haystack.
+
+The Document Store expects its inputs to come in the following format.
+The sections below will show you all the tools you'll need to ready your data for storing.
+
+```python
+docs = [
+ {
+ 'text': DOCUMENT_TEXT_HERE,
+ 'meta': {'name': DOCUMENT_NAME, ...}
+ }, ...
+]
+```
+
+
+
+## File Conversion
+
+There are a range of different file converters in Haystack that
+can extract text from files and cast them into the unified dictionary format shown above.
+Haystack features support for txt, pdf and docx files and there is even a converter that leverages Apache Tika.
+Please refer to [the API docs](/docs/latest/file_convertersmd) to see which converter best suits you.
+
+from haystack.file_converter import PDFToTextConverter
+
+ converter = PDFToTextConverter(remove_numeric_tables=True,
+ valid_languages=["de","en"])
+
+ doc = converter.convert(file_path=file, meta=None)
+
+ ),
+ },
+ {
+ title: "DOCX",
+ content: (
+ ++ ), + }, + { + title: "From a Directory", + content: ( +from haystack.file_converter import DocxToTextConverter++ converter = DocxToTextConverter(remove_numeric_tables=True, + valid_languages=["de","en"]) ++doc = converter.convert(file_path=file, meta=None)+
+
+ ),
+ },
+ ]}
+/>
+
+
+
+## PreProcessor
+
+While each of the above conversion methods produce documents that are already in the format expected by the Document Store,
+it is recommended that they are further processed in order to ensure optimal Retriever and Reader performance.
+The `PreProcessor` takes one of the documents created by the converter as input,
+performs various cleaning steps and splits them into multiple smaller documents.
+
+For suggestions on how best to split your documents, see [Optimization](/docs/latest/optimizationmd)
+
+```python
+doc = converter.convert(file_path=file, meta=None)
+processor = PreProcessor(
+ clean_empty_lines=True,
+ clean_whitespace=True,
+ clean_header_footer=True,
+ split_by="word",
+ split_length=200,
+ split_respect_sentence_boundary=True,
+ split_overlap=0
+)
+docs = processor.process(d)
+```
+
+- `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
+- `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
+- `clean_header_footer` will remove any long header or footer texts that are repeated on each page
+- `split_by` determines what unit the document is split by: `'word'`, `'sentence'` or `'passage'`
+- `split_length` sets a maximum number of `'word'`, `'sentence'` or `'passage'` units per output document
+- `split_respect_sentence_boundary` ensures that document boundaries do not fall in the middle of sentences
+- `split_overlap` sets the amount of overlap between two adjacent documents after a split. Setting this to a positive number essentially enables the sliding window approach.
diff --git a/docs/v0.7.0/usage/reader.mdx b/docs/v0.7.0/usage/reader.mdx
new file mode 100644
index 000000000..8502666f2
--- /dev/null
+++ b/docs/v0.7.0/usage/reader.mdx
@@ -0,0 +1,315 @@
+# Reader
+
+The Reader, also known as Open-Domain QA systems in Machine Learning speak,
+is the core component that enables Haystack to find the answers that you need.
+Haystack’s Readers are:
+
+- built on the latest transformer based language models
+
+- strong in their grasp of semantics
+
+- sensitive to syntactic structure
+
+- state-of-the-art in QA tasks like SQuAD and Natural Questions
+
++ Haystack also has a`convert_files_to_dicts()` utility function that + will convert all txt or pdf files in a given folder into this + dictionary format. +
++++ from haystack.preprocessor.utils import convert_files_to_dicts ++docs = convert_files_to_dicts(dir_path=doc_dir)+
++model = "deepset/roberta-base-squad2"+reader = FARMReader(model, use_gpu=True)+finder = Finder(reader, retriever)+
+
+ ),
+ },
+ ]}
+/>
+
+++model = "deepset/roberta-base-squad2"+reader = TransformersReader(model, use_gpu=1)+finder = Finder(reader, retriever)+
+
+**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
+
+
+
+While these models can work on CPU, it is recommended that they are run using GPUs to keep query times low.
+
+
+
+## Choosing the Right Model
+
+In Haystack, you can start using pretrained QA models simply by providing its HuggingFace Model Hub name to the Reader.
+The loading of model weights is handled by Haystack,
+and you have the option of using the QA pipeline from deepset FARM or HuggingFace Transformers (see FARM vs Transformers for details).
+
+Currently, there are a lot of different models out there and it can be rather overwhelming trying to pick the one that fits your use case.
+To get you started, we have a few recommendations for you to try out.
+
+
+
+ RoBERTa (base)
+An optimised variant of BERT and a great starting point.
+++from haystack.reader import FARMReader+reader = FARMReader("deepset/roberta-base-squad2")+
-
+
- Pro: Strong all round model +
- + Con: There are other models that are either faster or more + accurate + +
+
+ MiniLM
++ A cleverly distilled model that sacrifices a little accuracy for + speed. +
+++from haystack.reader import FARMReader+reader = FARMReader("deepset/minilm-uncased-squad2")+
-
+
- Pro: Inference speed up to 50% faster than BERT base +
- + Con: Still doesn’t match the best base sized models in accuracy + +
+
+ ALBERT (XXL)
+Large, powerful, SotA model.
+++from haystack.reader import FARMReader++ reader = FARMReader("ahotrod/albert_xxlargev1_squad2_512") ++
-
+
- + Pro: Better accuracy than any other open source model in QA + +
- + Con: The computational power needed make it impractical for most + use cases + +
+
+ ),
+ },
+ ]}
+/>
+
+
+
+
+
+ RoBERTa (base)
+An optimised variant of BERT and a great starting point.
+++from haystack.reader import TransformersReader++ reader = TransformersReader("deepset/roberta-base-squad2") ++
-
+
- Pro: Strong all round model +
- + Con: There are other models that are either faster or more + accurate + +
+
+ MiniLM
++ A cleverly distilled model that sacrifices a little accuracy for + speed. +
+++from haystack.reader import TransformersReader++ reader = TransformersReader("deepset/minilm-uncased-squad2") ++
-
+
- Pro: Inference speed up to 50% faster than BERT base +
- + Con: Still doesn’t match the best base sized models in accuracy + +
+
+ ALBERT (XXL)
+Large, powerful, SotA model.
+++from haystack.reader import TransformersReader++ reader = + TransformersReader("ahotrod/albert_xxlargev1_squad2_512") ++
-
+
- + Pro: Better accuracy than any other open source model in QA + +
- + Con: The computational power needed make it impractical for most + use cases + +
+
+**Recommendations:**
+
+**All-rounder**: In the class of base sized models trained on SQuAD, **RoBERTa** has shown better performance than BERT
+and can be capably handled by any machine equipped with a single NVidia V100 GPU.
+We recommend this as the starting point for anyone wanting to create a performant and computationally reasonable instance of Haystack.
+
+**Built for Speed**: If speed and GPU memory are more of a priority to you than accuracy,
+you should try the MiniLM model.
+It is a smaller model that is trained to mimic larger models through the distillation process,
+and it outperforms the BERT base on SQuAD even though it is about 40% smaller.
+
+
+
+**State of the Art Accuracy**: For most, **ALBERT XXL** will be too large to feasibly work with.
+But if performance is your sole concern, and you have the computational resources,
+you might like to try ALBERT XXL which has set SoTA performance on SQuAD 2.0.
+
+
+
+
+
+
+
+
+
+## Deeper Dive: FARM vs Transformers
+
+Apart from the **model weights**, Haystack Readers contain all the components found in end-to-end open domain QA systems.
+This includes **tokenization**, **embedding computation**, **span prediction** and **candidate aggregation**.
+While the handling of model weights is the same between the FARM and Transformers libraries, their QA pipelines differ in some ways.
+The major points are:
+
+- The **TransformersReader** will sometimes predict the same span twice while duplicates are removed in the **FARMReader**
+
+- The **FARMReader** currently uses the tokenizers from the HuggingFace Transformers library while the **TransformersReader** uses the tokenizers from the HuggingFace Tokenizers library
+
+- Start and end logits are normalized per passage and multiplied in the **TransformersReader** while they are summed and not normalised in the **FARMReader**
+
+If you’re interested in the finer details of these points, have a look at [this](https://github.com/deepset-ai/haystack/issues/248#issuecomment-661977237) GitHub comment.
+
+We see value in maintaining both kinds of Readers since Transformers is a very familiar library to many of Haystack’s users
+but we at deepset can more easily update and optimise the FARM pipeline for speed and performance.
+
+
+
+Haystack also has a close integration with FARM which means that you can further fine-tune your Readers on labelled data using a FARMReader.
+See our tutorials for an end-to-end example or below for a shortened example.
+
+```python
+# Initialise Reader
+model = "deepset/roberta-base-squad2"
+reader = FARMReader(model)
+
+# Perform finetuning
+train_data = "PATH/TO_YOUR/TRAIN_DATA"
+train_filename = "train.json"
+save_dir = "finetuned_model"
+reader.train(train_data, train_filename, save_dir=save_dir)
+
+# Load
+finetuned_reader = FARMReader(save_dir)
+```
+
+
+
+## Deeper Dive: From Language Model to Haystack Reader
+
+Language models form the core of most modern NLP systems and that includes the Readers in Haystack.
+They build a general understanding of language when performing training tasks such as Masked Language Modeling or Replaced Token Detection
+on large amounts of text.
+Well trained language models capture the word distribution in one or more languages
+but more importantly, convert input text into a set of word vectors that capture elements of syntax and semantics.
+
+In order to convert a language model into a Reader model, it needs first to be trained on a Question Answering dataset.
+To do so requires the addition of a question answering prediction head on top of the language model.
+The task can be thought of as a token classification task where every input token is assigned a probability of being
+either the start or end token of the correct answer.
+In cases where the answer is not contained within the passage, the prediction head is also expected to return a `no_answer` prediction.
+
+
+
+Since language models are limited in the number of tokens which they can process in a single forward pass,
+a sliding window mechanism is implemented to handle variable length documents.
+This functions by slicing the document into overlapping passages of (approximately) `max_seq_length`
+that are each offset by `doc_stride` number of tokens.
+These can be set when the Reader is initialized.
+
+++from haystack.reader import FARMReader++ reader = FARMReader(... max_seq_len=384, doc_stride=128 ...) ++
+
+ ),
+ },
+ ]}
+/>
+
+Predictions are made on each individual passage and the process of aggregation picks the best candidates across all passages.
+If you’d like to learn more about what is happening behind the scenes, have a look at [this](https://medium.com/deepset-ai/modern-question-answering-systems-explained-4d0913744097) article.
+
+
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/retriever.md b/docs/v0.7.0/usage/retriever.mdx
similarity index 75%
rename from src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/retriever.md
rename to docs/v0.7.0/usage/retriever.mdx
index bc56ebc11..55c898085 100644
--- a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/retriever.md
+++ b/docs/v0.7.0/usage/retriever.mdx
@@ -1,41 +1,34 @@
----
-title: "Retriever"
-metaTitle: "Retriever"
-metaDescription: ""
-slug: "/docs/retriever"
-date: "2020-09-03"
-id: "retrievermd"
----
-
# Retriever
The Retriever is a lightweight filter that can quickly go through the full document store and pass a set of candidate documents to the Reader.
It is an tool for sifting out the obvious negative cases, saving the Reader from doing more work than it needs to and speeding up the querying process.
-++from haystack.reader import TransformersReader++ reader = TransformersReader(... max_seq_len=384, doc_stride=128 + ...) ++
+
**Recommendations**
-* BM25 (sparse)
+- BM25 (sparse)
-* Dense Passage Retrieval (dense)
+- Dense Passage Retrieval (dense)
+
Note that not all Retrievers can be paired with every DocumentStore.
Here are the combinations which are supported:
-| | Memory | Elasticsearch | SQL | FAISS |
-| --- | --- | --- | ---- | ---- |
-| BM25 | N | Y | N | N |
-| TF-IDF | Y | Y | Y | N |
-| Embedding | Y | Y | N | Y |
-| DPR | Y | Y | N | Y |
+| | Memory | Elasticsearch | SQL | FAISS |
+| --------- | ------ | ------------- | --- | ----- |
+| BM25 | N | Y | N | N |
+| TF-IDF | Y | Y | Y | N |
+| Embedding | Y | Y | N | Y |
+| DPR | Y | Y | N | Y |
See [Optimization](/docs/latest/optimizationmd) for suggestions on how to choose top-k values.
+
## TF-IDF
@@ -43,11 +36,9 @@ See [Optimization](/docs/latest/optimizationmd) for suggestions on how to choose
TF-IDF is a commonly used baseline for information retrieval that exploits two key intuitions:
+- documents that have more lexical overlap with the query are more likely to be relevant
-* documents that have more lexical overlap with the query are more likely to be relevant
-
-
-* words that occur in fewer documents are more significant than words that occur in many documents
+- words that occur in fewer documents are more significant than words that occur in many documents
Given a query, a tf-idf score is computed for each document as follows:
@@ -57,15 +48,15 @@ score = tf * idf
Where:
+- `tf` is how many times words in the query occur in that document.
-* `tf` is how many times words in the query occur in that document.
-
-
-* `idf` is the inverse of the fraction of documents containing the word.
+- `idf` is the inverse of the fraction of documents containing the word.
In practice, both terms are usually log normalised. If you’d like to learn more about the exact details of the algorithm,
have a look at [this video](https://www.youtube.com/watch?v=hNXwhF0OZ_o).
+
+
### Initialisation
```python
@@ -76,12 +67,14 @@ retriever = TfidfRetriever(document_store)
finder = Finder(reader, retriever)
```
-
+
**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
+
+
## BM25 (Recommended)
### Description
@@ -89,11 +82,11 @@ finder = Finder(reader, retriever)
BM25 is a variant of TF-IDF that we recommend you use if you are looking for a retrieval method that does not need a neural network for indexing.
It improves upon its predecessor in two main aspects:
+- It saturates `tf` after a set number of occurrences of the given term in the document
-* It saturates `tf` after a set number of occurrences of the given term in the document
-
+- It normalises by document length so that short documents are favoured over long documents if they have the same amount of word overlap with the query
-* It normalises by document length so that short documents are favoured over long documents if they have the same amount of word overlap with the query
+
### Initialisation
@@ -105,7 +98,7 @@ retriever = ElasticsearchRetriever(document_store)
finder = Finder(reader, retriever)
```
-
+
**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
@@ -114,6 +107,9 @@ finder = Finder(reader, retriever)
See [this](https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-and-its-variables) blog post for more details about the algorithm.
+
+
+
## Dense Passage Retrieval (Recommended)
### Description
@@ -121,39 +117,37 @@ See [this](https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-
[Dense Passage Retrieval](https://arxiv.org/abs/2004.04906) is a highly performant retrieval method that calculates relevance using dense representations.
Key features:
+- One BERT base model to encode documents
-* One BERT base model to encode documents
-
-
-* One BERT base model to encode queries
+- One BERT base model to encode queries
-
-* Ranking of documents done by dot product similarity between query and document embeddings
+- Ranking of documents done by dot product similarity between query and document embeddings
+
Indexing using DPR is comparatively expensive in terms of required computation since all documents in the database need to be processed through the transformer.
The embeddings that are created in this step can be stored in FAISS, a database optimized for vector similarity.
DPR can also work with the ElasticsearchDocumentStore or the InMemoryDocumentStore.
There are two design decisions that have made DPR particularly performant.
+- Separate encoders for document and query helps since queries are much shorter than documents
-* Separate encoders for document and query helps since queries are much shorter than documents
-
-
-* Training with ‘In-batch negatives’ (gold labels are treated as negative examples for other samples in same batch) is highly efficient
+- Training with ‘In-batch negatives’ (gold labels are treated as negative examples for other samples in same batch) is highly efficient
In Haystack, you can simply download the pretrained encoders needed to start using DPR.
If you’d like to learn how to set up a DPR based system, have a look at the [tutorial](/docs/latest/tutorial6md)!
+
+
### Initialisation
-
+
**Tip**
When using DPR, it is recommended that you use the dot product similarity function since that is how it is trained.
-To do so, simply provide `similarity="dot_product"` when initializing the DocumentStore
+To do so, simply provide `similarity="dot_product"` when initializing the DocumentStore
as is done in the code example below.
@@ -170,22 +164,23 @@ retriever = DensePassageRetriever(
finder = Finder(reader, retriever)
```
-
+
**Training DPR:** Haystack supports training of your own DPR model! Check out the [tutorial](/docs/latest/tutorial9md) to see how this is done!
-
+
**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
-
-
+
+
+
## Embedding Retrieval
### Description
@@ -198,16 +193,18 @@ They are particular suited to cases where your query input is similar in style t
i.e. when you are searching for most similar documents.
This is not inherently suited to query based search where the length, language and format of the query usually significantly differs from the searched for text.
-
+
**Tip**
-When using Sentence Transformer models, we recommend that you use a cosine similarity function.
-To do so, simply provide `similarity="cosine"` when initializing the DocumentStore
+When using Sentence Transformer models, we recommend that you use a cosine similarity function.
+To do so, simply provide `similarity="cosine"` when initializing the DocumentStore
as is done in the code example below.
+
+
### Initialisation
```python
@@ -219,12 +216,14 @@ retriever = EmbeddingRetriever(document_store=document_store,
finder = Finder(reader, retriever)
```
-
+
+
+ ),
+ },
+ {
+ title: "Editable",
+ content: (
+
**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
+
+
## Deeper Dive: Dense vs Sparse
Broadly speaking, retrieval methods can be split into two categories: **dense** and **sparse**.
@@ -232,28 +231,24 @@ Broadly speaking, retrieval methods can be split into two categories: **dense**
**Sparse** methods, like TF-IDF and BM25, operate by looking for shared keywords between the document and query.
They are:
+- simple but effective
-* simple but effective
-
-
-* don’t need to be trained
+- don’t need to be trained
-
-* work on any language
+- work on any language
More recently, **dense** approaches such as Dense Passage Retrieval (DPR) have shown even better performance than their sparse counter parts.
These methods embed both document and query into a shared embedding space using deep neural networks
and the top candidates are the nearest neighbour documents to the query.
They are:
+- powerful but computationally more expensive especially during indexing
-* powerful but computationally more expensive especially during indexing
-
+- trained using labelled datasets
-* trained using labelled datasets
+- language specific
-
-* language specific
+
### Qualitative Differences
@@ -269,6 +264,9 @@ they only care about whether they are present or absent in the text.
As such, **they handle out-of-vocabulary words with no problem**.
+
+
+
### Indexing
Dense methods perform indexing by processing all the documents through a neural network and storing the resulting vectors.
@@ -276,10 +274,14 @@ This is a much more expensive operation than the creation of the inverted-index
and will require significant computational power and time.
+
+
+
### Terminology
+
The terms **dense** and **sparse** refer to the representations that the algorithms build for each document and query.
**Sparse** methods characterise texts using vectors with one dimension corresponding to each word in the vocabulary.
Dimensions will be zero if the word is absent and non-zero if it is present.
diff --git a/docs/v0.8.0/menu.json b/docs/v0.8.0/menu.json
new file mode 100644
index 000000000..2abcb9de4
--- /dev/null
+++ b/docs/v0.8.0/menu.json
@@ -0,0 +1,115 @@
+[
+ {
+ "subMenuTitle": "Overview",
+ "pathPrefix": "/overview/",
+ "items": [
+ {
+ "slug": "get-started",
+ "title": "Get Started"
+ },
+ { "slug": "intro", "title": "What is Haystack?" },
+ { "slug": "use-cases", "title": "Use Cases" },
+ { "slug": "roadmap", "title": "Roadmap" },
+ { "slug": "glossary", "title": "Glossary" }
+ ]
+ },
+ {
+ "subMenuTitle": "Usage",
+ "pathPrefix": "/usage/",
+ "items": [
+ {
+ "slug": "preprocessing",
+ "title": "Preprocessing"
+ },
+ {
+ "slug": "document-store",
+ "title": "DocumentStore"
+ },
+ { "slug": "retriever", "title": "Retriever" },
+ { "slug": "reader", "title": "Reader" },
+ { "slug": "generator", "title": "Generator" },
+ { "slug": "pipelines", "title": "Pipelines" },
+ {
+ "slug": "languages",
+ "title": "Languages Other Than English"
+ },
+ {
+ "slug": "domain-adaptation",
+ "title": "Domain Adaptation"
+ },
+ {
+ "slug": "optimization",
+ "title": "Optimization"
+ },
+ {
+ "slug": "annotation",
+ "title": "Annotation Tool"
+ }
+ ]
+ },
+ {
+ "subMenuTitle": "Tutorials",
+ "pathPrefix": "/tutorials/",
+ "items": [
+ {
+ "slug": "first-qa-system",
+ "title": "Build Your First QA System"
+ },
+ {
+ "slug": "fine-tuning-a-model",
+ "title": "Fine-tuning a Model on Your Own Data"
+ },
+ {
+ "slug": "without-elasticsearch",
+ "title": "Build a QA System Without Elasticsearch"
+ },
+ {
+ "slug": "existing-faqs",
+ "title": "Utilizing existing FAQs for Question Answering"
+ },
+ {
+ "slug": "evaluation",
+ "title": "Evaluation of a QA System"
+ },
+ {
+ "slug": "dense-passage-retrieval",
+ "title": "Better Retrieval via \"Dense Passage Retrieval\""
+ },
+ {
+ "slug": "retrieval-augmented-generation",
+ "title": "Generative QA with \"Retrieval-Augmented Generation\""
+ },
+ {
+ "slug": "preprocessing",
+ "title": "Preprocessing your Documents"
+ },
+ { "slug": "train-dpr", "title": "How to Train DPR" },
+ {
+ "slug": "knowledge-graph",
+ "title": "Question Answering on a Knowledge Graph"
+ }
+ ]
+ },
+ {
+ "subMenuTitle": "API Reference",
+ "pathPrefix": "/reference/",
+ "items": [
+ {
+ "slug": "document-store",
+ "title": "Document Store"
+ },
+ { "slug": "retriever", "title": "Retriever" },
+ { "slug": "reader", "title": "Reader" },
+ { "slug": "generator", "title": "Generator" },
+ {
+ "slug": "preprocessor",
+ "title": "Preprocessor"
+ },
+ {
+ "slug": "file-converters",
+ "title": "File Converters"
+ },
+ { "slug": "pipelines", "title": "Pipelines" }
+ ]
+ }
+]
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/get_started.md b/docs/v0.8.0/overview/get_started.mdx
similarity index 66%
rename from src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/get_started.md
rename to docs/v0.8.0/overview/get_started.mdx
index c2fc003f7..ddd545e11 100644
--- a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/get_started.md
+++ b/docs/v0.8.0/overview/get_started.mdx
@@ -1,61 +1,62 @@
----
-title: "Get Started"
-metaTitle: "Get Started"
-metaDescription: ""
-slug: "/docs/get_started"
-date: "2020-09-03"
-id: "get_startedmd"
----
-
# Get Started
## Installation
-
-
-
+
-
-
-
-
-
-The most straightforward way to install Haystack is through pip.
- -```python -$ pip install farm-haystack -``` - -
-- -```python -$ pip install farm-haystack -``` - -
-
-
-
-
-
-If you’d like to run a specific, unreleased version of Haystack, or make edits to the way Haystack runs,
-you’ll want to install it using `git` and `pip --editable`.
-This clones a copy of the repo to a local directory and runs Haystack from there.
- -```python -$ git clone https://github.com/deepset-ai/haystack.git -$ cd haystack -$ pip install --editable . -``` - -By default, this will give you the latest version of the master branch. Use regular git commands to switch between different branches and commits. -
-- -```python -$ git clone https://github.com/deepset-ai/haystack.git -$ cd haystack -$ pip install --editable . -``` - -By default, this will give you the latest version of the master branch. Use regular git commands to switch between different branches and commits. -
+ The most straightforward way to install Haystack is through pip. +
+
+ pip install farm-haystack
+
+
+
+ ),
+ },
+ ]}
+/>
Note: On Windows add the arg `-f https://download.pytorch.org/whl/torch_stable.html` to install PyTorch correctly
+
+
+
## The Building Blocks of Haystack
Here’s a sample of some Haystack code showing the most important components.
For a working code example, check out our starter tutorial.
+
```python
# DocumentStore: holds all your data
document_store = ElasticsearchDocumentStore()
@@ -80,6 +81,8 @@ prediction = finder.get_answers(question)
print_answers(prediction)
```
+
+
## Loading Documents into the DocumentStore
In Haystack, DocumentStores expect Documents in a dictionary format. They are loaded as follows:
@@ -99,26 +102,25 @@ When we talk about Documents in Haystack, we are referring specifically to the i
You might want to use all the text in one file as a Document, or split it into multiple Documents.
This splitting can have a big impact on speed and performance.
-+ If you’d like to run a specific, unreleased version of Haystack, or + make edits to the way Haystack runs, you’ll want to install it using + `git` and `pip --editable`. This clones a copy of the repo to a + local directory and runs Haystack from there. +
+++git clone https://github.com/deepset-ai/haystack.git+cd haystack+pip install --editable .+
+ By default, this will give you the latest version of the master + branch. Use regular git commands to switch between different + branches and commits. +
+
+
**Tip:** If Haystack is running very slowly, you might want to try splitting your text into smaller Documents.
If you want an improvement to performance, you might want to try concatenating text to make larger Documents.
+
+
## Running Queries
**Querying** involves searching for an answer to a given question within the full document store.
This process will:
+- make the Retriever filter for a small set of relevant candidate documents
-* make the Retriever filter for a small set of relevant candidate documents
-
-
-* get the Reader to process this set of candidate documents
-
+- get the Reader to process this set of candidate documents
-* return potential answers to the given question
+- return potential answers to the given question
Usually, there are tight time constraints on querying and so it needs to be a lightweight operation.
When documents are loaded, Haystack will precompute any of the results that might be useful at query time.
@@ -146,7 +148,7 @@ When the query is complete, you can expect to see results that look something li
]
```
-
+
**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/terms.md b/docs/v0.8.0/overview/glossary.mdx
similarity index 96%
rename from src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/terms.md
rename to docs/v0.8.0/overview/glossary.mdx
index 6254848f7..e8b4f4a90 100644
--- a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/terms.md
+++ b/docs/v0.8.0/overview/glossary.mdx
@@ -1,12 +1,3 @@
----
-title: "Glossary"
-metaTitle: "Glossary"
-metaDescription: ""
-slug: "/docs/terms"
-date: "2020-09-03"
-id: "termsmd"
----
-
# Glossary
**BERT** - A popular, transformer based language model which has been improved upon but is still considered a common benchmark.
@@ -26,7 +17,7 @@ FARM’s question answering models are used in Haystack’s Readers.
**Finder** - The component in Haystack that connects the Retriever to the Reader.
-
+
**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/intro.md b/docs/v0.8.0/overview/intro.mdx
similarity index 61%
rename from src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/intro.md
rename to docs/v0.8.0/overview/intro.mdx
index 858a02ffd..3c5bac046 100644
--- a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/intro.md
+++ b/docs/v0.8.0/overview/intro.mdx
@@ -1,37 +1,26 @@
----
-title: "What is Haystack"
-metaTitle: "What is Haystack"
-metaDescription: ""
-slug: "/docs/intro"
-date: "2020-09-03"
-id: "intromd"
----
-
# What is Haystack?
Haystack is an **open-source framework** for building **end-to-end question answering systems** for large document collections.
Recent advances in NLP have enabled the application of QA to real world settings
and Haystack is designed to be the bridge between research and industry.
+- **Latest NLP models**: Utilize all transformer based models (BERT, RoBERTa, MiniLM, DPR ...) and smoothly switch when new ones get published
-* **Latest NLP models**: Utilize all transformer based models (BERT, RoBERTa, MiniLM, DPR ...) and smoothly switch when new ones get published
-
-* **Flexible databases**: Load data into and query from a range of databases such as Elasticsearch, FAISS, SQL and more
+- **Flexible databases**: Load data into and query from a range of databases such as Elasticsearch, FAISS, SQL and more
-* **Scalability**: Production-ready deployments that scale to millions of documents
+- **Scalability**: Production-ready deployments that scale to millions of documents
-* **End-to-End**: All tooling you need to implement, evaluate, improve and run a QA system
+- **End-to-End**: All tooling you need to implement, evaluate, improve and run a QA system
-* **Domain adaptation**: Fine-tune models to your own domain & improve them continuously via user feedback
+- **Domain adaptation**: Fine-tune models to your own domain & improve them continuously via user feedback
+
## Technology
Haystack is powered by a Retriever-Reader pipeline in order to optimise for both speed and accuracy.
-
-
-
+
**Readers**, also known as Open-Domain QA systems in Machine Learning speak,
are powerful models that do close analysis of documents and perform the core task of question answering.
@@ -40,28 +29,25 @@ However, it is not currently feasible to use the Reader directly on large collec
+
The **Retriever** assists the Reader by acting as a lightweight filter that reduces the number of documents that the Reader has to process.
It does this by:
+- Scanning through all documents in the database
-* Scanning through all documents in the database
-
-
-* Quickly identifying the relevant and dismissing the irrelevant
+- Quickly identifying the relevant and dismissing the irrelevant
-
-* Passing on only a small candidate set of documents to the Reader
+- Passing on only a small candidate set of documents to the Reader
Current methods fall into one of the two categories:
+- sparse
-* sparse
- * keyword based
- * fast indexing and querying
- * e.g. BM25
-
+ - keyword based
+ - fast indexing and querying
+ - e.g. BM25
-* dense
- * neural network based
- * computationally heavy indexing but fast querying
- * e.g. Dense Passage Retrieval
+- dense
+ - neural network based
+ - computationally heavy indexing but fast querying
+ - e.g. Dense Passage Retrieval
diff --git a/docs/v0.8.0/overview/roadmap.mdx b/docs/v0.8.0/overview/roadmap.mdx
new file mode 100644
index 000000000..40eba526a
--- /dev/null
+++ b/docs/v0.8.0/overview/roadmap.mdx
@@ -0,0 +1,41 @@
+# Open roadmap
+
+We believe open-source is more than open source code. It's a lot about people, collaboration, transparency and trust.
+Therefore, we decided to be as open as possible with our roadmap and sprint planning.
+In fact, you can see all of it in real-time on GitHub.
+We hope this helps to clarify the direction of the open-source project and inspires discussions in the community.
+
+## How to access it
+
+### Add Zenhub Plugin
+
+We decided for Zenhub, as it allows a close integration with GitHub and real-time sharing of roadmaps and sprints.
+Once you have installed the browser plugin below, you will see additional tabs and infos on the Haystack GitHub page.
+
+Zenhub Plugin: https://www.zenhub.com/extension
+
+### Roadmap
+
+_Zenhub Tab -> Roadmap (left menu)_
+
+Here you can find our most recent roadmap with the **high-level projects** that are planned for the upcoming quarters.
+We update it regularly and refine the projects as they come closer in time.
+
+
+
+### Board
+
+_Zenhub Tab -> Board (left menu)_
+
+If you are interested in the **operational tasks** and their status, you can find our agile board here.
+
+
+
+### Additional issue details & Releases
+
+_Right panel in regular Github issues_
+
+With Zenhub you can also see some additional tags in every GitHub issue.
+For those of you who wonder about the next release date: We aim for releases every ~ 4 weeks and will tag the issues that will need to be finished before a bit in advance.
+
+
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/use_cases.md b/docs/v0.8.0/overview/use_cases.mdx
similarity index 66%
rename from src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/use_cases.md
rename to docs/v0.8.0/overview/use_cases.mdx
index 76b420410..b712e58ca 100644
--- a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/use_cases.md
+++ b/docs/v0.8.0/overview/use_cases.mdx
@@ -1,32 +1,18 @@
----
-title: "Use Cases"
-metaTitle: "Use Cases"
-metaDescription: ""
-slug: "/docs/use_cases"
-date: "2020-11-16"
-id: "use_casesmd"
----
-
-
# Use cases
## Semantic Search System
Take the leap from using keyword search on your own documents to semantic search with Haystack.
+- Store your documents in the database of your choice (Elasticsearch, SQL, in memory, FAISS)
-* Store your documents in the database of your choice (Elasticsearch, SQL, in memory, FAISS)
-
-
-* Perform question driven queries.
+- Perform question driven queries.
Expect to see results that highlight the very sentence that contains the answer to your question.
Thanks to the power of Transformer based language models, results are chosen based on compatibility in meaning
rather than lexical overlap.
-
-
-
+
## Information Extractor
@@ -34,36 +20,31 @@ Automate the extraction of relevant information from a set of documents that per
Haystack can:
+- Apply a set of standard questions to each document in a store
-* Apply a set of standard questions to each document in a store
-
-
-* Return a NO_ANSWER if a given document does not contain the answer to a question
+- Return a NO_ANSWER if a given document does not contain the answer to a question
Say you have the financial reports for different companies over different years.
You can gather a set of standard questions which are applicable to each financial report,
-like *what is the revenue forecast for 2020?* or *what are the main sources of income?*.
+like _what is the revenue forecast for 2020?_ or _what are the main sources of income?_.
Haystack will try to find an answer for each question within each document!
We’ve seen this style of application be particularly effective in the sphere of finance and patent law
but we see a lot of potential in using this to gain a better overview of academic papers and internal business documents.
+
## FAQ Style Question Answering
Leverage existing FAQ documents and semantic similarity search to answer new incoming questions.
The workflow is as follows:
+- Store a set of FAQ documents in Haystack
-* Store a set of FAQ documents in Haystack
-
-
-* The user presents a new question
-
-
-* Haystack will find the closest match to the new question in the FAQ documents
+- The user presents a new question
+- Haystack will find the closest match to the new question in the FAQ documents
-* The user will be presented with the most similar Question Answer pair
+- The user will be presented with the most similar Question Answer pair
Haystack’s flexibility allows you to give new users more dynamic access to your existing documentation.
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/annotation.md b/docs/v0.8.0/usage/annotation.mdx
similarity index 80%
rename from src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/annotation.md
rename to docs/v0.8.0/usage/annotation.mdx
index ed29d18c8..d22e35fae 100644
--- a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/annotation.md
+++ b/docs/v0.8.0/usage/annotation.mdx
@@ -1,12 +1,3 @@
----
-title: "Annotation Tool"
-metaTitle: "Annotation Tool"
-metaDescription: ""
-slug: "/docs/annotation"
-date: "2020-09-03"
-id: "annotationmd"
----
-
# Annotation Tool
- Create labels with different techniques: Come up with questions (+ answers) while reading passages (SQuAD style) or have a set of predefined questions and look for answers in the document (~ Natural Questions).
@@ -14,12 +5,17 @@ id: "annotationmd"
- Upload your documents or import labels from an existing SQuAD-style dataset
- Export your labels in SQuAD Format
-
+
+
+
+
+## Hosted version
+
+Signup here: [Haystack Annotation Tool](https://annotate.deepset.ai/login)
-# Hosted version
- Signup here: [Haystack Annotation Tool](https://annotate.deepset.ai/login)
+
-# Local version (Docker)
+## Local version (Docker)
1. Configure credentials & database in the [`docker-compose.yml`](https://github.com/deepset-ai/haystack/blob/master/annotation_tool/docker-compose.yml):
@@ -37,11 +33,12 @@ The credentials should match in database image and application configuration.
POSTGRES_PASSWORD: "somesafepassword"
POSTGRES_DB: "databasename"
-
2. Run docker-compose by executing `docker-compose up`.
-
3. The UI should be available at `localhost:7001`.
-# Usage
+
+
+## Usage
+
The manual (of a slightly earlier version) can be found [here](https://drive.google.com/file/d/1Wv3OIC0Z7ibHIzOm9Xw_r0gjTFmpl-33/view). While it doesn't include all latest features, the basic workflow and tips for label quality are still the same.
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/document_store.md b/docs/v0.8.0/usage/document_store.mdx
similarity index 71%
rename from src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/document_store.md
rename to docs/v0.8.0/usage/document_store.mdx
index 57c26b48f..6f14e75f0 100644
--- a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/document_store.md
+++ b/docs/v0.8.0/usage/document_store.mdx
@@ -1,37 +1,26 @@
----
-title: "Document Store"
-metaTitle: "Document Store"
-metaDescription: ""
-slug: "/docs/documentstore"
-date: "2020-09-03"
-id: "documentstoremd"
----
-
-
# DocumentStores
You can think of the DocumentStore as a "database" that:
-- stores your texts and meta data
-- provides them to the retriever at query time
-There are different DocumentStores in Haystack to fit different use cases and tech stacks.
+- stores your texts and meta data
+- provides them to the retriever at query time
+
+There are different DocumentStores in Haystack to fit different use cases and tech stacks.
## Initialisation
Initialising a new DocumentStore within Haystack is straight forward.
-
+
-
-
Each DocumentStore constructor allows for arguments specifying how to connect to existing databases and the names of indexes.
See API documentation for more info.
+
+
## Input Format
DocumentStores expect Documents in dictionary form, like that below.
They are loaded using the `DocumentStore.write_documents()` method.
See [Preprocessing](/docs/latest/preprocessingmd) for more information on the cleaning and splitting steps that will help you maximize Haystack's performance.
-[//]: # (Add link to preprocessing section)
+[//]: # "Add link to preprocessing section"
```python
document_store = ElasticsearchDocumentStore()
@@ -131,6 +112,8 @@ dicts = [
document_store.write_documents(dicts)
```
+
+
## Writing Documents (Sparse Retrievers)
Haystack allows for you to write store documents in an optimised fashion so that query times can be kept low.
@@ -142,6 +125,8 @@ The creation of the inverted index which optimises querying speed is handled aut
document_store.write_documents(dicts)
```
+
+
## Writing Documents (Dense Retrievers)
For **dense** neural network based retrievers like Dense Passage Retrieval, or Embedding Retrieval,
@@ -160,106 +145,95 @@ Having GPU acceleration will significantly speed this up.
+
+
+
## Choosing the Right Document Store
-The Document Stores have different characteristics. You should choose one depending on the maturity of your project, the use case and technical environment:
+The Document Stores have different characteristics. You should choose one depending on the maturity of your project, the use case and technical environment:
-
-
-
-
+### Milvus
-
+### Elasticsearch
[Install](https://www.elastic.co/guide/en/elasticsearch/reference/current/install-elasticsearch.html)
Elasticsearch and then [start](https://www.elastic.co/guide/en/elasticsearch/reference/current/starting-elasticsearch.html)
-an instance.
+an instance.
If you have Docker set up, we recommend pulling the Docker image and running it.
+
```bash
docker pull docker.elastic.co/elasticsearch/elasticsearch:7.9.2
docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.9.2
@@ -47,78 +36,70 @@ Note that we also support [Open Distro for Elasticsearch](https://opendistro.git
Follow [their documentation](https://opendistro.github.io/for-elasticsearch-docs/docs/install/)
to run it and connect to it using Haystack's `OpenDistroElasticsearchDocumentStore` class.
+
-
-
-
-
-
+
+
+### FAISS
-
+Follow the [official documentation](https://www.milvus.io/docs/v1.0.0/milvus_docker-cpu.md) to start a Milvus instance via Docker.
+Note that we also have a utility function `haystack.utils.launch_milvus` that can start up a Milvus instance.
-Follow the [official documentation](https://www.milvus.io/docs/v1.0.0/milvus_docker-cpu.md) to start a Milvus instance via Docker
-
You can initialize the Haystack object that will connect to this instance as follows:
+
```python
+from haystack.document_store import MilvusDocumentStore
+
document_store = MilvusDocumentStore()
```
-
-
-
-
-
+
-
+The `FAISSDocumentStore` requires no external setup. Start it by simply using this line.
-The `FAISSDocumentStore` requires no external setup. Start it by simply using this line.
```python
+from haystack.document_store import FAISSDocumentStore
+
document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
```
-
-
-
-
-
+
-
+### In Memory
The `InMemoryDocumentStore()` requires no external setup. Start it by simply using this line.
+
```python
+from haystack.document_store import InMemoryDocumentStore
+
document_store = InMemoryDocumentStore()
```
-
-
-
-
-
-
-
+### SQL
The `SQLDocumentStore` requires SQLite, PostgresQL or MySQL to be installed and started.
-Note that SQLite already comes packaged with most operating systems.
+Note that SQLite already comes packaged with most operating systems.
```python
+from haystack.document_store import SQLDocumentStore
+
document_store = SQLDocumentStore()
```
-
-
+### Elasticsearch
-
-
-
-
-
-
+
+
+### Milvus
-
+**Pros:**
-**Pros:**
- Fast & accurate sparse retrieval with many tuning options
- Basic support for dense retrieval
- Production-ready
- Support also for Open Distro
-**Cons:**
+**Cons:**
+
- Slow for dense retrieval with more than ~ 1 Mio documents
-
-
-
-
-
+
-
+**Pros:**
-**Pros:**
- Scalable DocumentStore that excels at handling vectors (hence suited to dense retrieval methods like DPR)
- Encapsulates multiple ANN libraries (e.g. FAISS and ANNOY) and provides added reliability
- Runs as a separate service (e.g. a Docker container)
- Allows dynamic data management
**Cons:**
+
- No efficient sparse retrieval
-
-
-
-
-
+
-
+### FAISS
+
+**Pros:**
-**Pros:**
- Fast & accurate dense retrieval
- Highly scalable due to approximate nearest neighbour algorithms (ANN)
- Many options to tune dense retrieval via different index types (more info [here](https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index))
**Cons:**
+
- No efficient sparse retrieval
-
-
-
-
-
+### SQL
-
+### In Memory
**Pros:**
+
- Simple
- Exists already in many environments
**Cons:**
+
- Only compatible with minimal TF-IDF Retriever
- Bad retrieval performance
- Not recommended for production
-
-
-
-
-
+
-
+
**Pros:**
+
- Simple & fast to test
- No database requirements
- Supports MySQL, PostgreSQL and SQLite
-**Cons:**
+**Cons:**
+
- Not scalable
- Not persisting your data on disk
-
-
+
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/domain_adaptation.md b/docs/v0.8.0/usage/domain_adaptation.mdx
similarity index 70%
rename from src/pages/docs/versions/master/site/en/usage/usage/domain_adaptation.md
rename to docs/v0.8.0/usage/domain_adaptation.mdx
index 9bd415e61..614be50d7 100644
--- a/src/pages/docs/versions/master/site/en/usage/usage/domain_adaptation.md
+++ b/docs/v0.8.0/usage/domain_adaptation.mdx
@@ -1,12 +1,3 @@
----
-title: "Domain Adaptation"
-metaTitle: "Domain Adaptation"
-metaDescription: ""
-slug: "/docs/domain_adaptation"
-date: "2020-09-03"
-id: "domain_adaptationmd"
----
-
# Domain Adaptation
## Generalisation
@@ -17,7 +8,7 @@ Though SQuAD is composed entirely of Wikipedia articles, these models are flexib
Before trying to adapt these models to your domain, we’d recommend trying one of the off the shelf models.
We’ve found that these models are often flexible enough for a wide range of use cases.
-
#### Our Recommendations
-**Restricted environment:** Use the `InMemoryDocumentStore`, if you are just giving Haystack a quick try on a small sample and are working in a restricted environment that complicates running Elasticsearch or other databases
+**Restricted environment:** Use the `InMemoryDocumentStore`, if you are just giving Haystack a quick try on a small sample and are working in a restricted environment that complicates running Elasticsearch or other databases
**Allrounder:** Use the `ElasticSearchDocumentStore`, if you want to evaluate the performance of different retrieval options (dense vs. sparse) and are aiming for a smooth transition from PoC to production
**Vector Specialist:** Use the `MilvusDocumentStore`, if you want to focus on dense retrieval and possibly deal with larger datasets
-
\ No newline at end of file
+
+
**Intuition**
@@ -28,6 +19,7 @@ In the same way, many QA models have a good enough grasp of language to answer q
+
## Finetuning
@@ -43,7 +35,7 @@ reader.train(data_dir=train_data,
At the end of training, the finetuned model will be saved in the specified `save_dir` and can be loaded as a `Reader`.
-
+
**Recommendation**
@@ -53,57 +45,55 @@ please also check out Tutorial 5 which walks through the steps needed to perform
+
+
## Generating Labels
Using our [Haystack Annotate tool](https://annotate.deepset.ai/login) (Beta),
you can easily create a labelled dataset using your own documents featuring your own question/ answer pairs.
-
-
-
+
Features include:
+- Structured workspaces via organisations, projects and users
-* Structured workspaces via organisations, projects and users
-
-
-* Easy upload of your own documents and labels in a variety of formats (txt, pdf, SQuAD style)
+- Easy upload of your own documents and labels in a variety of formats (txt, pdf, SQuAD style)
-
-* Export of labels to be used directly in Haystack
+- Export of labels to be used directly in Haystack
Annotate also supports two different workflows:
+- Think up questions and answers while reading passages (SQuAD style)
-* Think up questions and answers while reading passages (SQuAD style)
-
+- Have a set of predefined questions and look for answers in the document (~ Natural Questions style)
-* Have a set of predefined questions and look for answers in the document (~ Natural Questions style)
+
## User Feedback
-A simpler and faster process to finetune models to your domain is to utilise user feedback.
-Dedicated annotation work can be costly and time consuming
-but user feedback is an efficient alternative since it allows for labels to be generated by users, for users,
+A simpler and faster process to finetune models to your domain is to utilise user feedback.
+Dedicated annotation work can be costly and time consuming
+but user feedback is an efficient alternative since it allows for labels to be generated by users, for users,
all while the system is already in production.
We, for example, have used a simple thumbs up / down system in our demos to allow
users to give feedback.
-
+
Through the Rest API, users can annotate each Haystack result as being either:
-* Correct
-* Correct document but wrong text
-* Wrong document and wrong text
+- Correct
+- Correct document but wrong text
+- Wrong document and wrong text
To get started, follow these steps:
-* Start up the REST API
- * The simplest way to do this is to call `docker-compose up` from the root directory of the Haystack repository
- * Alternatively, run `gunicorn rest_api.application:app -b 0.0.0.0:8000 -k uvicorn.workers.UvicornWorker -t 300`
-* Make a POST request to the `doc-qa-feedback` endpoint with the following payload:
+- Start up the REST API
+ - The simplest way to do this is to call `docker-compose up` from the root directory of the Haystack repository
+ - Alternatively, run `gunicorn rest_api.application:app -b 0.0.0.0:8000 -k uvicorn.workers.UvicornWorker -t 300`
+- Make a POST request to the `doc-qa-feedback` endpoint with the following payload:
+
```
{
"question": "string",
@@ -115,10 +105,12 @@ To get started, follow these steps:
"offset_start_in_doc": 0
}
```
+
To fill out all these values, you can use the response from an executed search request on the `doc-qa` endpoint.
-* Export your labels in SQuAD format by making a GET request to the `export-doc-qa-feedback` endpoint
-``` python
+- Export your labels in SQuAD format by making a GET request to the `export-doc-qa-feedback` endpoint
+
+```python
# SQUAD format
{
"data": [
@@ -146,12 +138,11 @@ To fill out all these values, you can use the response from an executed search r
}
```
-User feedback labelling also works with FAQ style QA.
+User feedback labelling also works with FAQ style QA.
Refer to the Swagger API documentation (http://127.0.0.1:8000/docs) for more details.
-This labelled data can then be used to
-augment the training data and enable `Reader` finetuning.
-Alternatively, they can also be used to form an evaluation set to
-measure the performance of trained models,
+This labelled data can then be used to
+augment the training data and enable `Reader` finetuning.
+Alternatively, they can also be used to form an evaluation set to
+measure the performance of trained models,
or monitor how well the model is doing in a live environment.
-
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/generator.md b/docs/v0.8.0/usage/generator.mdx
similarity index 68%
rename from src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/generator.md
rename to docs/v0.8.0/usage/generator.mdx
index 354f93f40..056a9bc47 100644
--- a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/generator.md
+++ b/docs/v0.8.0/usage/generator.mdx
@@ -1,15 +1,6 @@
----
-title: "Generator"
-metaTitle: "Generator"
-metaDescription: ""
-slug: "/docs/generator"
-date: "2020-11-05"
-id: "generatormd"
----
-
# Generator
-
+
**Example**
@@ -26,10 +17,11 @@ With the advent of Transformer based retrieval methods such as [Dense Passage Re
retriever and generator can be trained concurrently from the one loss signal.
Pros
-* More appropriately phrased answers
-* Able to syntehsize information from different texts
-* Can draw on latent knowledge stored in language model
+
+- More appropriately phrased answers
+- Able to syntehsize information from different texts
+- Can draw on latent knowledge stored in language model
Cons
-* Not easy to track what piece of information the generator is basing its response off of
+- Not easy to track what piece of information the generator is basing its response off of
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/languages.md b/docs/v0.8.0/usage/languages.mdx
similarity index 52%
rename from src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/languages.md
rename to docs/v0.8.0/usage/languages.mdx
index 4b15b0716..f6a9b62c8 100644
--- a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/languages.md
+++ b/docs/v0.8.0/usage/languages.mdx
@@ -1,17 +1,8 @@
----
-title: "Languages Other Than English"
-metaTitle: "Languages Other Than English"
-metaDescription: ""
-slug: "/docs/languages"
-date: "2020-11-05"
-id: "languagesmd"
----
-
# Languages Other Than English
Haystack is well suited to open-domain QA on languages other than English.
While our defaults are tuned for English,
-you will find some tips and tricks here for using Haystack in your language.
+you will find some tips and tricks here for using Haystack in your language.
##Retrievers
@@ -20,19 +11,19 @@ Their only requirement is that the text be split into words.
The ElasticsearchDocumentStore relies on an analyzer to impose word boundaries,
but also to handle punctuation, casing and stop words.
-The default analyzer is an English analyzer.
+The default analyzer is an English analyzer.
While it can still work decently for a large range of languages,
you will want to set it to your language's analyzer for optimal performance.
In some cases, such as with Thai, the default analyzer is completely incompatible.
-See [this page](https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-lang-analyzer.html)
+See [this page](https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-lang-analyzer.html)
for the full list of language specific analyzers.
```python
document_store = ElasticsearchDocumentStore(analyzer="thai")
```
-The models used in dense retrievers are language specific.
-Be sure to check language of the model used in your EmbeddingRetriever.
+The models used in dense retrievers are language specific.
+Be sure to check language of the model used in your EmbeddingRetriever.
The default model that is loaded in the DensePassageRetriever is for English.
We are currently working on training a German DensePassageRetriever model and know other teams who work on further languages.
If you have a language model and a question answering dataset in your own language, you can also train a DPR model using Haystack!
@@ -51,111 +42,107 @@ dense_passage_retriever.train(self,
n_epochs: int = 3)
```
-##Readers
+
+
+## Readers
While models are comparatively more performant on English,
thanks to a wealth of available English training data,
there are a couple QA models that are directly usable in Haystack.
-
+
+
-
-
+
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-```python
-reader = FARMReader("illuin/camembert-base-fquad")
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = FARMReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa")
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = FARMReader("deepset/xlm-roberta-large-squad2")
-```
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-```python
-reader = TransformersReader("illuin/camembert-base-fquad")
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = TransformersReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa")
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = TransformersReader("deepset/xlm-roberta-large-squad2")
-```
-
-
-from haystack.reader import FARMReader
+
+ reader = FARMReader("illuin/camembert-base-fquad")
+
+
+ ),
+ },
+ {
+ title: "Italian",
+ content: (
+ ++ ), + }, + { + title: "Zero-shot", + content: ( +from haystack.reader import FARMReader++ reader = + FARMReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa") ++
++ ), + }, + ]} + /> + ), + }, + { + title: "Transformers", + content: ( +from haystack.reader import FARMReader++ reader = FARMReader("deepset/xlm-roberta-large-squad2") ++
from haystack.reader import TransformersReader
+
+ reader = TransformersReader("illuin/camembert-base-fquad")
+
+
+ ),
+ },
+ {
+ title: "Italian",
+ content: (
+ ++ ), + }, + { + title: "Zero-shot", + content: ( +from haystack.reader import TransformersReader++ reader = + TransformersReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa") ++
++ ), + }, + ]} + /> + ), + }, + ]} +/> + + The **French** and **Italian models** are both monolingual language models trained on French and Italian versions of the SQuAD dataset and their authors report decent results in their model cards @@ -169,5 +156,4 @@ but still its performance lags behind that of the monolingual models. Nonetheless, if there is not yet a monolingual model for your language and it is one of the 100 supported by XLM-RoBERTa, this zero-shot model may serve as a decent first baseline. -[//]: # (Add link to Reader training, create section in reader.md on training Reader) - +[//]: # "Add link to Reader training, create section in reader.md on training Reader" diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/optimization.md b/docs/v0.8.0/usage/optimization.mdx similarity index 86% rename from src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/optimization.md rename to docs/v0.8.0/usage/optimization.mdx index 8a46a54a2..019ca80c3 100644 --- a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/optimization.md +++ b/docs/v0.8.0/usage/optimization.mdx @@ -1,17 +1,8 @@ ---- -title: "Optimization" -metaTitle: "Optimization" -metaDescription: "" -slug: "/docs/optimization" -date: "2020-11-05" -id: "optimizationmd" ---- - # Optimization ## Document Length -Document length has a very direct impact on the speed of the Reader +Document length has a very direct impact on the speed of the Reader which is why we recommend using the `PreProcessor` class to clean and split your documents. **If you halve the length of your documents, you will halve the workload placed onto your Reader.** @@ -25,13 +16,17 @@ By default, Haystack's DensePassageRetriever model has a maximum length of 256 t As such, we recommend that documents contain significantly less words. We have found decent performance with **documents around 100 words long**. + + ## Respecting Sentence Boundaries -When splitting documents, it is generally not a good idea to let document boundaries fall in the middle of sentences. -Doing so means that each document will contain incomplete sentence fragments +When splitting documents, it is generally not a good idea to let document boundaries fall in the middle of sentences. +Doing so means that each document will contain incomplete sentence fragments which maybe be hard for both retriever and reader to interpret. It is therefore recommended to set `split_respect_sentence_boundary=True` when initializing your `PreProcessor`. + + ## Choosing the Right top-k Values The `top-k` parameter in both the `Retriever` and `Reader` determine how many results they return. @@ -41,23 +36,26 @@ while `Reader` `top-k` determines how many answer candidates to show. In our experiments, we have found that **`Retriever` `top_k=10` gives decent overall performance** and so we have set this as the default in Haystack. -The choice of `Retriever` `top-k` is a trade-off between speed and accuracy, +The choice of `Retriever` `top-k` is a trade-off between speed and accuracy, especially when there is a `Reader` in the pipeline. -Setting it higher means passing more documents to the `Reader`, +Setting it higher means passing more documents to the `Reader`, thus reducing the chance that the answer-containing passage is missed. However, passing more documents to the `Reader` will create a larger workload for the component. These parameters can easily be tweaked as follows if using a `Finder`: -``` python + +```python answers = finder.get_answers(retriever_top_k=10, reader_top_k=5) ``` + or like this if directly calling the `Retriever`: -``` python + +```python retrieved_docs = retriever.retrieve(top_k=10) ``` -from haystack.reader import TransformersReader++ reader = + TransformersReader("deepset/xlm-roberta-large-squad2") ++
+
+
+ ),
+ },
+ {
+ title: "Transformers",
+ content: (
+
**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/pipelines.md b/docs/v0.8.0/usage/pipelines.mdx
similarity index 89%
rename from src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/pipelines.md
rename to docs/v0.8.0/usage/pipelines.mdx
index 5633a7c70..1d744a51c 100644
--- a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/pipelines.md
+++ b/docs/v0.8.0/usage/pipelines.mdx
@@ -1,26 +1,16 @@
----
-title: "Pipelines"
-metaTitle: "Pipelines"
-metaDescription: ""
-slug: "/docs/pipelines"
-date: "2020-11-05"
-id: "pipelinesmd"
----
-
# Pipelines
+
+
+ ),
+ },
+ {
+ title: "Transformers",
+ content: (
+
-
+
+
+ ),
+ },
+ {
+ title: "Transformers",
+ content: (
+
-
-The new Pipelines class was added in Haystack 0.6.0 to give a more flexible way of defining your processing steps.
+The new Pipelines class was added in Haystack 0.6.0 to give a more flexible way of defining your processing steps.
It replaces the Finder class which will be deprecated in the next version.
-
### Flexible Pipelines powered by DAGs
+
In order to build modern search pipelines, you need two things: powerful building blocks and a flexible way to stick them together.
-The `Pipeline` class is exactly build for this purpose and enables many search scenarios beyond QA. The core idea: you can build a Directed Acyclic Graph (DAG) where each node is one "building block" (Reader, Retriever, Generator ...). Here's a simple example for a "standard" Open-Domain QA Pipeline:
+The `Pipeline` class is exactly build for this purpose and enables many search scenarios beyond QA. The core idea: you can build a Directed Acyclic Graph (DAG) where each node is one "building block" (Reader, Retriever, Generator ...). Here's a simple example for a "standard" Open-Domain QA Pipeline:
```python
p = Pipeline()
@@ -31,13 +21,19 @@ res = p.run(query="What did Einstein work on?", top_k_retriever=1)
```
You can **draw the DAG** to better inspect what you are building:
+
```python
p.draw(path="custom_pipe.png")
```
+
+
+
### Multiple retrievers
-You can now also use multiple Retrievers and join their results:
+
+You can now also use multiple Retrievers and join their results:
+
```python
p = Pipeline()
p.add_node(component=es_retriever, name="ESRetriever", inputs=["Query"])
@@ -46,20 +42,28 @@ p.add_node(component=JoinDocuments(join_mode="concatenate"), name="JoinResults",
p.add_node(component=reader, name="QAReader", inputs=["JoinResults"])
res = p.run(query="What did Einstein work on?", top_k_retriever=1)
```
+
+
+
### Custom nodes
-You can easily build your own custom nodes. Just respect the following requirements:
+
+You can easily build your own custom nodes. Just respect the following requirements:
1. Add a method `run(self, **kwargs)` to your class. `**kwargs` will contain the output from the previous node in your graph.
2. Do whatever you want within `run()` (e.g. reformatting the query)
3. Return a tuple that contains your output data (for the next node) and the name of the outgoing edge `output_dict, "output_1`
4. Add a class attribute `outgoing_edges = 1` that defines the number of output options from your node. You only need a higher number here if you have a decision node (see below).
+
+
### Decision nodes
-Or you can add decision nodes where only one "branch" is executed afterwards. This allows, for example, to classify an incoming query and depending on the result routing it to different modules:
+
+Or you can add decision nodes where only one "branch" is executed afterwards. This allows, for example, to classify an incoming query and depending on the result routing it to different modules:
-```python
+
+```python
class QueryClassifier():
outgoing_edges = 2
@@ -80,11 +84,14 @@ Or you can add decision nodes where only one "branch" is executed afterwards. Th
res = p.run(query="What did Einstein work on?", top_k_retriever=1)
```
+
+
### Default Pipelines (replacing the "Finder")
+
Last but not least, we added some "Default Pipelines" that allow you to run standard patterns with very few lines of code.
This is replacing the `Finder` class which is now deprecated.
-```
+```python
from haystack.pipeline import DocumentSearchPipeline, ExtractiveQAPipeline, Pipeline, JoinDocuments
# Extractive QA
@@ -103,8 +110,8 @@ res = doc_pipe.run(query="Physics Einstein", top_k_retriever=1)
doc_pipe = FAQPipeline(retriever=retriever)
res = doc_pipe.run(query="How can I change my address?", top_k_retriever=3)
-```
-See also the [Pipelines API documentation](/docs/latest/apipipelinesmd) for more details.
+```
-We plan many more features around the new pipelines incl. parallelized execution, distributed execution, definition via YAML files, dry runs - so stay tuned ...
+See also the [Pipelines API documentation](/docs/latest/apipipelinesmd) for more details.
+We plan many more features around the new pipelines incl. parallelized execution, distributed execution, definition via YAML files, dry runs - so stay tuned ...
diff --git a/docs/v0.8.0/usage/preprocessing.mdx b/docs/v0.8.0/usage/preprocessing.mdx
new file mode 100644
index 000000000..4ba723af1
--- /dev/null
+++ b/docs/v0.8.0/usage/preprocessing.mdx
@@ -0,0 +1,113 @@
+# Preprocessing
+
+Haystack includes a suite of tools to:
+
+- extract text from different file types,
+- normalize white space
+- split text into smaller pieces to optimize retrieval
+
+These data preprocessing steps can have a big impact on the systems performance
+and effective handling of data is key to getting the most out of Haystack.
+
+The Document Store expects its inputs to come in the following format.
+The sections below will show you all the tools you'll need to ready your data for storing.
+
+```python
+docs = [
+ {
+ 'text': DOCUMENT_TEXT_HERE,
+ 'meta': {'name': DOCUMENT_NAME, ...}
+ }, ...
+]
+```
+
+
+
+## File Conversion
+
+There are a range of different file converters in Haystack that
+can extract text from files and cast them into the unified dictionary format shown above.
+Haystack features support for txt, pdf and docx files and there is even a converter that leverages Apache Tika.
+Please refer to [the API docs](/docs/latest/file_convertersmd) to see which converter best suits you.
+
+from haystack.file_converter import PDFToTextConverter
+
+ converter = PDFToTextConverter(remove_numeric_tables=True,
+ valid_languages=["de","en"])
+
+ doc = converter.convert(file_path=file, meta=None)
+
+ ),
+ },
+ {
+ title: "DOCX",
+ content: (
+ ++ ), + }, + { + title: "From a Directory", + content: ( +from haystack.file_converter import DocxToTextConverter++ converter = DocxToTextConverter(remove_numeric_tables=True, + valid_languages=["de","en"]) ++doc = converter.convert(file_path=file, meta=None)+
+
+ ),
+ },
+ ]}
+/>
+
+
+
+## PreProcessor
+
+While each of the above conversion methods produce documents that are already in the format expected by the Document Store,
+it is recommended that they are further processed in order to ensure optimal Retriever and Reader performance.
+The `PreProcessor` takes one of the documents created by the converter as input,
+performs various cleaning steps and splits them into multiple smaller documents.
+
+For suggestions on how best to split your documents, see [Optimization](/docs/latest/optimizationmd)
+
+```python
+doc = converter.convert(file_path=file, meta=None)
+processor = PreProcessor(
+ clean_empty_lines=True,
+ clean_whitespace=True,
+ clean_header_footer=True,
+ split_by="word",
+ split_length=200,
+ split_respect_sentence_boundary=True,
+ split_overlap=0
+)
+docs = processor.process(d)
+```
+
+- `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
+- `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
+- `clean_header_footer` will remove any long header or footer texts that are repeated on each page
+- `split_by` determines what unit the document is split by: `'word'`, `'sentence'` or `'passage'`
+- `split_length` sets a maximum number of `'word'`, `'sentence'` or `'passage'` units per output document
+- `split_respect_sentence_boundary` ensures that document boundaries do not fall in the middle of sentences
+- `split_overlap` sets the amount of overlap between two adjacent documents after a split. Setting this to a positive number essentially enables the sliding window approach.
diff --git a/docs/v0.8.0/usage/reader.mdx b/docs/v0.8.0/usage/reader.mdx
new file mode 100644
index 000000000..8502666f2
--- /dev/null
+++ b/docs/v0.8.0/usage/reader.mdx
@@ -0,0 +1,315 @@
+# Reader
+
+The Reader, also known as Open-Domain QA systems in Machine Learning speak,
+is the core component that enables Haystack to find the answers that you need.
+Haystack’s Readers are:
+
+- built on the latest transformer based language models
+
+- strong in their grasp of semantics
+
+- sensitive to syntactic structure
+
+- state-of-the-art in QA tasks like SQuAD and Natural Questions
+
++ Haystack also has a`convert_files_to_dicts()` utility function that + will convert all txt or pdf files in a given folder into this + dictionary format. +
++++ from haystack.preprocessor.utils import convert_files_to_dicts ++docs = convert_files_to_dicts(dir_path=doc_dir)+
++model = "deepset/roberta-base-squad2"+reader = FARMReader(model, use_gpu=True)+finder = Finder(reader, retriever)+
+
+ ),
+ },
+ ]}
+/>
+
+++model = "deepset/roberta-base-squad2"+reader = TransformersReader(model, use_gpu=1)+finder = Finder(reader, retriever)+
+
+**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
+
+
+
+While these models can work on CPU, it is recommended that they are run using GPUs to keep query times low.
+
+
+
+## Choosing the Right Model
+
+In Haystack, you can start using pretrained QA models simply by providing its HuggingFace Model Hub name to the Reader.
+The loading of model weights is handled by Haystack,
+and you have the option of using the QA pipeline from deepset FARM or HuggingFace Transformers (see FARM vs Transformers for details).
+
+Currently, there are a lot of different models out there and it can be rather overwhelming trying to pick the one that fits your use case.
+To get you started, we have a few recommendations for you to try out.
+
+
+
+ RoBERTa (base)
+An optimised variant of BERT and a great starting point.
+++from haystack.reader import FARMReader+reader = FARMReader("deepset/roberta-base-squad2")+
-
+
- Pro: Strong all round model +
- + Con: There are other models that are either faster or more + accurate + +
+
+ MiniLM
++ A cleverly distilled model that sacrifices a little accuracy for + speed. +
+++from haystack.reader import FARMReader+reader = FARMReader("deepset/minilm-uncased-squad2")+
-
+
- Pro: Inference speed up to 50% faster than BERT base +
- + Con: Still doesn’t match the best base sized models in accuracy + +
+
+ ALBERT (XXL)
+Large, powerful, SotA model.
+++from haystack.reader import FARMReader++ reader = FARMReader("ahotrod/albert_xxlargev1_squad2_512") ++
-
+
- + Pro: Better accuracy than any other open source model in QA + +
- + Con: The computational power needed make it impractical for most + use cases + +
+
+ ),
+ },
+ ]}
+/>
+
+
+
+
+
+ RoBERTa (base)
+An optimised variant of BERT and a great starting point.
+++from haystack.reader import TransformersReader++ reader = TransformersReader("deepset/roberta-base-squad2") ++
-
+
- Pro: Strong all round model +
- + Con: There are other models that are either faster or more + accurate + +
+
+ MiniLM
++ A cleverly distilled model that sacrifices a little accuracy for + speed. +
+++from haystack.reader import TransformersReader++ reader = TransformersReader("deepset/minilm-uncased-squad2") ++
-
+
- Pro: Inference speed up to 50% faster than BERT base +
- + Con: Still doesn’t match the best base sized models in accuracy + +
+
+ ALBERT (XXL)
+Large, powerful, SotA model.
+++from haystack.reader import TransformersReader++ reader = + TransformersReader("ahotrod/albert_xxlargev1_squad2_512") ++
-
+
- + Pro: Better accuracy than any other open source model in QA + +
- + Con: The computational power needed make it impractical for most + use cases + +
+
+**Recommendations:**
+
+**All-rounder**: In the class of base sized models trained on SQuAD, **RoBERTa** has shown better performance than BERT
+and can be capably handled by any machine equipped with a single NVidia V100 GPU.
+We recommend this as the starting point for anyone wanting to create a performant and computationally reasonable instance of Haystack.
+
+**Built for Speed**: If speed and GPU memory are more of a priority to you than accuracy,
+you should try the MiniLM model.
+It is a smaller model that is trained to mimic larger models through the distillation process,
+and it outperforms the BERT base on SQuAD even though it is about 40% smaller.
+
+
+
+**State of the Art Accuracy**: For most, **ALBERT XXL** will be too large to feasibly work with.
+But if performance is your sole concern, and you have the computational resources,
+you might like to try ALBERT XXL which has set SoTA performance on SQuAD 2.0.
+
+
+
+
+
+
+
+
+
+## Deeper Dive: FARM vs Transformers
+
+Apart from the **model weights**, Haystack Readers contain all the components found in end-to-end open domain QA systems.
+This includes **tokenization**, **embedding computation**, **span prediction** and **candidate aggregation**.
+While the handling of model weights is the same between the FARM and Transformers libraries, their QA pipelines differ in some ways.
+The major points are:
+
+- The **TransformersReader** will sometimes predict the same span twice while duplicates are removed in the **FARMReader**
+
+- The **FARMReader** currently uses the tokenizers from the HuggingFace Transformers library while the **TransformersReader** uses the tokenizers from the HuggingFace Tokenizers library
+
+- Start and end logits are normalized per passage and multiplied in the **TransformersReader** while they are summed and not normalised in the **FARMReader**
+
+If you’re interested in the finer details of these points, have a look at [this](https://github.com/deepset-ai/haystack/issues/248#issuecomment-661977237) GitHub comment.
+
+We see value in maintaining both kinds of Readers since Transformers is a very familiar library to many of Haystack’s users
+but we at deepset can more easily update and optimise the FARM pipeline for speed and performance.
+
+
+
+Haystack also has a close integration with FARM which means that you can further fine-tune your Readers on labelled data using a FARMReader.
+See our tutorials for an end-to-end example or below for a shortened example.
+
+```python
+# Initialise Reader
+model = "deepset/roberta-base-squad2"
+reader = FARMReader(model)
+
+# Perform finetuning
+train_data = "PATH/TO_YOUR/TRAIN_DATA"
+train_filename = "train.json"
+save_dir = "finetuned_model"
+reader.train(train_data, train_filename, save_dir=save_dir)
+
+# Load
+finetuned_reader = FARMReader(save_dir)
+```
+
+
+
+## Deeper Dive: From Language Model to Haystack Reader
+
+Language models form the core of most modern NLP systems and that includes the Readers in Haystack.
+They build a general understanding of language when performing training tasks such as Masked Language Modeling or Replaced Token Detection
+on large amounts of text.
+Well trained language models capture the word distribution in one or more languages
+but more importantly, convert input text into a set of word vectors that capture elements of syntax and semantics.
+
+In order to convert a language model into a Reader model, it needs first to be trained on a Question Answering dataset.
+To do so requires the addition of a question answering prediction head on top of the language model.
+The task can be thought of as a token classification task where every input token is assigned a probability of being
+either the start or end token of the correct answer.
+In cases where the answer is not contained within the passage, the prediction head is also expected to return a `no_answer` prediction.
+
+
+
+Since language models are limited in the number of tokens which they can process in a single forward pass,
+a sliding window mechanism is implemented to handle variable length documents.
+This functions by slicing the document into overlapping passages of (approximately) `max_seq_length`
+that are each offset by `doc_stride` number of tokens.
+These can be set when the Reader is initialized.
+
+++from haystack.reader import FARMReader++ reader = FARMReader(... max_seq_len=384, doc_stride=128 ...) ++
+
+ ),
+ },
+ ]}
+/>
+
+Predictions are made on each individual passage and the process of aggregation picks the best candidates across all passages.
+If you’d like to learn more about what is happening behind the scenes, have a look at [this](https://medium.com/deepset-ai/modern-question-answering-systems-explained-4d0913744097) article.
+
+
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/retriever.md b/docs/v0.8.0/usage/retriever.mdx
similarity index 74%
rename from src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/retriever.md
rename to docs/v0.8.0/usage/retriever.mdx
index 6af227178..bc0c1f458 100644
--- a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/retriever.md
+++ b/docs/v0.8.0/usage/retriever.mdx
@@ -1,41 +1,34 @@
----
-title: "Retriever"
-metaTitle: "Retriever"
-metaDescription: ""
-slug: "/docs/retriever"
-date: "2020-09-03"
-id: "retrievermd"
----
-
# Retriever
The Retriever is a lightweight filter that can quickly go through the full document store and pass a set of candidate documents to the Reader.
It is an tool for sifting out the obvious negative cases, saving the Reader from doing more work than it needs to and speeding up the querying process.
-++from haystack.reader import TransformersReader++ reader = TransformersReader(... max_seq_len=384, doc_stride=128 + ...) ++
+
**Recommendations**
-* BM25 (sparse)
+- BM25 (sparse)
-* Dense Passage Retrieval (dense)
+- Dense Passage Retrieval (dense)
+
Note that not all Retrievers can be paired with every DocumentStore.
Here are the combinations which are supported:
-| | Memory | Elasticsearch | SQL | FAISS | Milvus |
-| --- | --- | --- | ---- | ---- | ---- |
-| BM25 | N | Y | N | N | N |
-| TF-IDF | Y | Y | Y | N | N |
-| Embedding | Y | Y | N | Y | Y |
-| DPR | Y | Y | N | Y | Y |
+| | Memory | Elasticsearch | SQL | FAISS | Milvus |
+| --------- | ------ | ------------- | --- | ----- | ------ |
+| BM25 | N | Y | N | N | N |
+| TF-IDF | Y | Y | Y | N | N |
+| Embedding | Y | Y | N | Y | Y |
+| DPR | Y | Y | N | Y | Y |
See [Optimization](/docs/latest/optimizationmd) for suggestions on how to choose top-k values.
+
## TF-IDF
@@ -43,11 +36,9 @@ See [Optimization](/docs/latest/optimizationmd) for suggestions on how to choose
TF-IDF is a commonly used baseline for information retrieval that exploits two key intuitions:
+- documents that have more lexical overlap with the query are more likely to be relevant
-* documents that have more lexical overlap with the query are more likely to be relevant
-
-
-* words that occur in fewer documents are more significant than words that occur in many documents
+- words that occur in fewer documents are more significant than words that occur in many documents
Given a query, a tf-idf score is computed for each document as follows:
@@ -57,14 +48,14 @@ score = tf * idf
Where:
+- `tf` is how many times words in the query occur in that document.
-* `tf` is how many times words in the query occur in that document.
-
-
-* `idf` is the inverse of the fraction of documents containing the word.
+- `idf` is the inverse of the fraction of documents containing the word.
In practice, both terms are usually log normalised.
+
+
### Initialisation
```python
@@ -75,12 +66,14 @@ retriever = TfidfRetriever(document_store)
finder = Finder(reader, retriever)
```
-
+
**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
+
+
## BM25 (Recommended)
### Description
@@ -88,11 +81,11 @@ finder = Finder(reader, retriever)
BM25 is a variant of TF-IDF that we recommend you use if you are looking for a retrieval method that does not need a neural network for indexing.
It improves upon its predecessor in two main aspects:
+- It saturates `tf` after a set number of occurrences of the given term in the document
-* It saturates `tf` after a set number of occurrences of the given term in the document
-
+- It normalises by document length so that short documents are favoured over long documents if they have the same amount of word overlap with the query
-* It normalises by document length so that short documents are favoured over long documents if they have the same amount of word overlap with the query
+
### Initialisation
@@ -104,7 +97,7 @@ retriever = ElasticsearchRetriever(document_store)
finder = Finder(reader, retriever)
```
-
+
**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
@@ -113,6 +106,9 @@ finder = Finder(reader, retriever)
See [this](https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-and-its-variables) blog post for more details about the algorithm.
+
+
+
## Dense Passage Retrieval (Recommended)
### Description
@@ -120,39 +116,37 @@ See [this](https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-
[Dense Passage Retrieval](https://arxiv.org/abs/2004.04906) is a highly performant retrieval method that calculates relevance using dense representations.
Key features:
+- One BERT base model to encode documents
-* One BERT base model to encode documents
-
-
-* One BERT base model to encode queries
+- One BERT base model to encode queries
-
-* Ranking of documents done by dot product similarity between query and document embeddings
+- Ranking of documents done by dot product similarity between query and document embeddings
+
Indexing using DPR is comparatively expensive in terms of required computation since all documents in the database need to be processed through the transformer.
The embeddings that are created in this step can be stored in FAISS, a database optimized for vector similarity.
DPR can also work with the ElasticsearchDocumentStore or the InMemoryDocumentStore.
There are two design decisions that have made DPR particularly performant.
+- Separate encoders for document and query helps since queries are much shorter than documents
-* Separate encoders for document and query helps since queries are much shorter than documents
-
-
-* Training with ‘In-batch negatives’ (gold labels are treated as negative examples for other samples in same batch) is highly efficient
+- Training with ‘In-batch negatives’ (gold labels are treated as negative examples for other samples in same batch) is highly efficient
In Haystack, you can simply download the pretrained encoders needed to start using DPR.
If you’d like to learn how to set up a DPR based system, have a look at the [tutorial](/docs/latest/tutorial6md)!
+
+
### Initialisation
-
+
**Tip**
When using DPR, it is recommended that you use the dot product similarity function since that is how it is trained.
-To do so, simply provide `similarity="dot_product"` when initializing the DocumentStore
+To do so, simply provide `similarity="dot_product"` when initializing the DocumentStore
as is done in the code example below.
@@ -169,22 +163,23 @@ retriever = DensePassageRetriever(
finder = Finder(reader, retriever)
```
-
+
**Training DPR:** Haystack supports training of your own DPR model! Check out the [tutorial](/docs/latest/tutorial9md) to see how this is done!
-
+
**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
-
-
+
+
+
## Embedding Retrieval
### Description
@@ -197,16 +192,18 @@ They are particular suited to cases where your query input is similar in style t
i.e. when you are searching for most similar documents.
This is not inherently suited to query based search where the length, language and format of the query usually significantly differs from the searched for text.
-
+
**Tip**
-When using Sentence Transformer models, we recommend that you use a cosine similarity function.
-To do so, simply provide `similarity="cosine"` when initializing the DocumentStore
+When using Sentence Transformer models, we recommend that you use a cosine similarity function.
+To do so, simply provide `similarity="cosine"` when initializing the DocumentStore
as is done in the code example below.
+
+
### Initialisation
```python
@@ -218,12 +215,14 @@ retriever = EmbeddingRetriever(document_store=document_store,
finder = Finder(reader, retriever)
```
-
+
+
+ ),
+ },
+ {
+ title: "Editable",
+ content: (
+
**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
+
+
## Deeper Dive: Dense vs Sparse
Broadly speaking, retrieval methods can be split into two categories: **dense** and **sparse**.
@@ -231,28 +230,24 @@ Broadly speaking, retrieval methods can be split into two categories: **dense**
**Sparse** methods, like TF-IDF and BM25, operate by looking for shared keywords between the document and query.
They are:
+- simple but effective
-* simple but effective
-
-
-* don’t need to be trained
+- don’t need to be trained
-
-* work on any language
+- work on any language
More recently, **dense** approaches such as Dense Passage Retrieval (DPR) have shown even better performance than their sparse counter parts.
These methods embed both document and query into a shared embedding space using deep neural networks
and the top candidates are the nearest neighbour documents to the query.
They are:
+- powerful but computationally more expensive especially during indexing
-* powerful but computationally more expensive especially during indexing
-
+- trained using labelled datasets
-* trained using labelled datasets
+- language specific
-
-* language specific
+
### Qualitative Differences
@@ -268,6 +263,9 @@ they only care about whether they are present or absent in the text.
As such, **they handle out-of-vocabulary words with no problem**.
+
+
+
### Indexing
Dense methods perform indexing by processing all the documents through a neural network and storing the resulting vectors.
@@ -275,10 +273,14 @@ This is a much more expensive operation than the creation of the inverted-index
and will require significant computational power and time.
+
+
+
### Terminology
+
The terms **dense** and **sparse** refer to the representations that the algorithms build for each document and query.
**Sparse** methods characterise texts using vectors with one dimension corresponding to each word in the vocabulary.
Dimensions will be zero if the word is absent and non-zero if it is present.
diff --git a/docs/v0.9.0/menu.json b/docs/v0.9.0/menu.json
new file mode 100644
index 000000000..5e6d1b193
--- /dev/null
+++ b/docs/v0.9.0/menu.json
@@ -0,0 +1,167 @@
+[
+ {
+ "subMenuTitle": "Overview",
+ "pathPrefix": "/overview/",
+ "items": [
+ {
+ "slug": "get-started",
+ "title": "Get Started"
+ },
+ { "slug": "intro", "title": "What is Haystack?" },
+ { "slug": "use-cases", "title": "Use Cases" },
+ { "slug": "roadmap", "title": "Roadmap" },
+ { "slug": "faq", "title": "FAQ" },
+ { "slug": "glossary", "title": "Glossary" }
+ ]
+ },
+ {
+ "subMenuTitle": "Usage",
+ "pathPrefix": "/usage/",
+ "items": [
+ {
+ "slug": "preprocessing",
+ "title": "Preprocessing"
+ },
+ { "slug": "pipelines", "title": "Pipelines" },
+ {
+ "slug": "document-store",
+ "title": "DocumentStore"
+ },
+ { "slug": "retriever", "title": "Retriever" },
+ { "slug": "reader", "title": "Reader" },
+ { "slug": "generator", "title": "Generator" },
+ {
+ "slug": "summarizer",
+ "title": "Summarizer"
+ },
+ {
+ "slug": "translator",
+ "title": "Translator"
+ },
+ {
+ "slug": "knowledge-graph",
+ "title": "Knowledge Graph"
+ },
+ {
+ "slug": "languages",
+ "title": "Languages Other Than English"
+ },
+ {
+ "slug": "domain-adaptation",
+ "title": "Domain Adaptation"
+ },
+ {
+ "slug": "optimization",
+ "title": "Optimization"
+ },
+ {
+ "slug": "annotation",
+ "title": "Annotation Tool"
+ },
+ { "slug": "ranker", "title": "Ranker" },
+ { "slug": "query-classifier", "title": "Query Classifier" }
+ ]
+ },
+ {
+ "subMenuTitle": "Tutorials",
+ "pathPrefix": "/tutorials/",
+ "items": [
+ {
+ "slug": "first-qa-system",
+ "title": "Build Your First QA System"
+ },
+ {
+ "slug": "fine-tuning-a-model",
+ "title": "Fine-tuning a Model on Your Own Data"
+ },
+ {
+ "slug": "without-elasticsearch",
+ "title": "Build a QA System Without Elasticsearch"
+ },
+ {
+ "slug": "existing-faqs",
+ "title": "Utilizing existing FAQs for Question Answering"
+ },
+ {
+ "slug": "evaluation",
+ "title": "Evaluation of a QA System"
+ },
+ {
+ "slug": "dense-passage-retrieval",
+ "title": "Better Retrieval via \"Dense Passage Retrieval\""
+ },
+ {
+ "slug": "retrieval-augmented-generation",
+ "title": "Generative QA with \"Retrieval-Augmented Generation\""
+ },
+ {
+ "slug": "preprocessing",
+ "title": "Preprocessing your Documents"
+ },
+ { "slug": "train-dpr", "title": "How to Train DPR" },
+ {
+ "slug": "knowledge-graph",
+ "title": "Question Answering on a Knowledge Graph"
+ },
+ {
+ "slug": "pipelines",
+ "title": "How to use Pipelines"
+ },
+ {
+ "slug": "lfqa",
+ "title": "Generative QA with \"LFQA\""
+ },
+ {
+ "slug": "question-generation",
+ "title": "Question Generation"
+ },
+ {
+ "slug": "query-classifier",
+ "title": "Query Classifier"
+ }
+ ]
+ },
+ {
+ "subMenuTitle": "API Reference",
+ "pathPrefix": "/reference/",
+ "items": [
+ {
+ "slug": "document-store",
+ "title": "Document Store"
+ },
+ { "slug": "retriever", "title": "Retriever" },
+ { "slug": "reader", "title": "Reader" },
+ { "slug": "generator", "title": "Generator" },
+ {
+ "slug": "summarizer",
+ "title": "Summarizer"
+ },
+ {
+ "slug": "translator",
+ "title": "Translator"
+ },
+ {
+ "slug": "preprocessor",
+ "title": "Preprocessor"
+ },
+ {
+ "slug": "file-converters",
+ "title": "File Converters"
+ },
+ { "slug": "crawler", "title": "Crawler" },
+ {
+ "slug": "evaluation",
+ "title": "Evaluation"
+ },
+ { "slug": "pipelines", "title": "Pipelines" },
+ {
+ "slug": "knowledge-graph",
+ "title": "Knowledge Graph"
+ },
+ {
+ "slug": "graph-retriever",
+ "title": "Graph Retriever"
+ }
+ ]
+ }
+]
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/faq.md b/docs/v0.9.0/overview/faq.mdx
similarity index 75%
rename from src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/faq.md
rename to docs/v0.9.0/overview/faq.mdx
index f6ae8a8ac..a27972994 100644
--- a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/faq.md
+++ b/docs/v0.9.0/overview/faq.mdx
@@ -1,19 +1,10 @@
----
-title: "Frequently Asked Questions"
-metaTitle: "Frequently Asked Questions"
-metaDescription: ""
-slug: "/docs/faq"
-date: "2020-09-03"
-id: "faqmd"
----
+# Frequently Asked Questions
-#Frequently Asked Questions
+## Why am I seeing duplicate answers being returned?
-##Why am I seeing duplicate answers being returned?
-
-The ElasticsearchDocumentStore and MilvusDocumentStore rely on Elasticsearch and Milvus backend services which
+The ElasticsearchDocumentStore and MilvusDocumentStore rely on Elasticsearch and Milvus backend services which
persist after your Python script has finished running.
-If you rerun your script without deleting documents, you could end up with duplicate
+If you rerun your script without deleting documents, you could end up with duplicate
copies of your documents in your database.
The easiest way to avoid this is to call `DocumentStore.delete_documents()` after initialization
to ensure that you are working with an empty DocumentStore.
@@ -21,19 +12,23 @@ to ensure that you are working with an empty DocumentStore.
DocumentStores also have a `duplicate_documents` argument in their `__init__()` and `write_documents` methods
where you can define whether you'd like skip writing duplicates, overwrite existing duplicates or raise an error when there are duplicates.
-##How can I make sure that my GPU is being engaged when I use Haystack?
+
+
+## How can I make sure that my GPU is being engaged when I use Haystack?
You will want to ensure that a CUDA enabled GPU is being engaged when Haystack is running (you can check by running `nvidia-smi -l` on your command line).
Components which can be sped up by GPU have a `use_gpu` argument in their constructor which you will want to set to `True`.
-##How do I speed up my predictions?
+
+
+## How do I speed up my predictions?
There are many different ways to speed up the performance of your Haystack system.
-The Reader is usually the most computationally expensive component in a pipeline
+The Reader is usually the most computationally expensive component in a pipeline
and you can often speed up your system by using a smaller model, like `deepset/minilm-uncased-squad2` (see [benchmarks](https://huggingface.co/deepset/minilm-uncased-squad2)). This usually comes with a small trade-off in accuracy.
-You can reduce the work load on the Reader by instructing the Retriever to pass on less documents.
+You can reduce the work load on the Reader by instructing the Retriever to pass on less documents.
This is done by setting the `top_k_retriever` parameter to a lower value.
Making sure that your documents are shorter can also increase the speed of your system. You can split
@@ -42,31 +37,41 @@ your documents into smaller chunks by using the `PreProcessor` (see [tutorial](h
For more optimization suggestions, have a look at our [optimization page](https://haystack.deepset.ai/docs/v0.9.0/optimizationmd)
and also our [blogs](https://medium.com/deepset-ai)
-##How do I use Haystack for my language?
+
+
+## How do I use Haystack for my language?
The components in Haystack, such as the `Retriever` or the `Reader`, are designed in a language agnostic way. However you may
have to set certain parameters or load models pretrained for your language in order to get good performance out of Haystack.
See our [languages page](https://haystack.deepset.ai/docs/v0.9.0/languagesmd) for more details.
-##How can I add metadata to my documents so that I can apply filters?
+
+
+## How can I add metadata to my documents so that I can apply filters?
When providing your documents in the input format (see [here](https://haystack.deepset.ai/docs/v0.9.0/documentstoremd#Input-Format))
you can provide metadata information as a dictionary under the `meta` key. At query time, you can provide a `filters` argument
(most likely through `Pipelines.run()`) that specifies the accepted values for a certain metadata field
(for an example of what a `filters` dictionary might look like, please refer to [this example](https://haystack.deepset.ai/docs/v0.9.0/apiretrievermd#__init__))
-##How can I see predictions during evaluation?
+
-To see predictions during evaluation, you want to initialize the `EvalDocuments` or `EvalAnswers` with `debug=True`.
+## How can I see predictions during evaluation?
+
+To see predictions during evaluation, you want to initialize the `EvalDocuments` or `EvalAnswers` with `debug=True`.
This causes their `EvalDocuments.log` or `EvalAnswers.log` to be populated with a record of each prediction made.
-##How can I serve my Haystack model?
+
+
+## How can I serve my Haystack model?
-Haystack models can be wrapped in a REST API. For basic details on how to set this up, please refer to this section
-on our [Github page](https://github.com/deepset-ai/haystack/blob/master/README.md#7-rest-api).
+Haystack models can be wrapped in a REST API. For basic details on how to set this up, please refer to this section
+on our [Github page](https://github.com/deepset-ai/haystack/blob/master/README.md#7-rest-api).
More comprehensive documentation coming soon!
-##How can I interpret the confidence scores being returned by the Reader?
+
+
+## How can I interpret the confidence scores being returned by the Reader?
The confidence scores are in the range of 0 and 1 and reflect how confident the model is in each prediction that it makes.
Having a confidence score is particularly useful in cases where you need Haystack to work with a certain accuracy threshold.
@@ -74,17 +79,21 @@ Many of our users have built systems where predictions below a certain confidenc
For more information on model confidence and how to tune it, please refer to [this section](https://haystack.deepset.ai/docs/v0.9.0/readermd#Confidence-Scores).
-##My documents aren't showing up in my DocumentStore even though I've called `DocumentStore.write_documents()`
+
-When indexing, retrieving or querying for documents from a DocumentStore, you can specify an `index` on which to perform this action.
+## My documents aren't showing up in my DocumentStore even though I've called `DocumentStore.write_documents()`
+
+When indexing, retrieving or querying for documents from a DocumentStore, you can specify an `index` on which to perform this action.
This can be specified in almost all methods of `DocumentStore` as well as `Retriever.retrieve()`.
-Ensure that you are performing these operations on the one index!
+Ensure that you are performing these operations on the one index!
Note that this also applies at evaluation where labels are written into their own separate DocumentStore index.
-##What is the difference between the FARMReader and the TransformersReader?
+
+
+## What is the difference between the FARMReader and the TransformersReader?
-In short, the FARMReader using a QA pipeline implementation that comes from our own
-[FARM framework](https://github.com/deepset-ai/FARM) that we can more easily update and also optimize for performance.
+In short, the FARMReader using a QA pipeline implementation that comes from our own
+[FARM framework](https://github.com/deepset-ai/FARM) that we can more easily update and also optimize for performance.
By contrast, the TransformersReader uses a QA pipeline implementation that comes from HuggingFace's [Transformers](https://github.com/huggingface/transformers).
-See [this section](https://haystack.deepset.ai/docs/v0.9.0/readermd#Deeper-Dive-FARM-vs-Transformers)
-for a more details about their differences!
\ No newline at end of file
+See [this section](https://haystack.deepset.ai/docs/v0.9.0/readermd#Deeper-Dive-FARM-vs-Transformers)
+for a more details about their differences!
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/get_started.md b/docs/v0.9.0/overview/get_started.mdx
similarity index 60%
rename from src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/get_started.md
rename to docs/v0.9.0/overview/get_started.mdx
index 8463d99ca..535bafb70 100644
--- a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/get_started.md
+++ b/docs/v0.9.0/overview/get_started.mdx
@@ -1,57 +1,56 @@
----
-title: "Get Started"
-metaTitle: "Get Started"
-metaDescription: ""
-slug: "/docs/get_started"
-date: "2020-09-03"
-id: "get_startedmd"
----
-
# Get Started
## Installation
-
-
-
+
-
-
-
-
-
-The most straightforward way to install Haystack is through pip.
- -```python -$ pip install farm-haystack -``` - -
-- -```python -$ pip install farm-haystack -``` - -
-
-
-
-
-
-If you’d like to run a specific, unreleased version of Haystack, or make edits to the way Haystack runs,
-you’ll want to install it using `git` and `pip --editable`.
-This clones a copy of the repo to a local directory and runs Haystack from there.
- -```python -$ git clone https://github.com/deepset-ai/haystack.git -$ cd haystack -$ pip install --editable . -``` - -By default, this will give you the latest version of the master branch. Use regular git commands to switch between different branches and commits. -
-- -```python -$ git clone https://github.com/deepset-ai/haystack.git -$ cd haystack -$ pip install --editable . -``` - -By default, this will give you the latest version of the master branch. Use regular git commands to switch between different branches and commits. -
+ The most straightforward way to install Haystack is through pip. +
+
+ pip install farm-haystack
+
+
+
+ ),
+ },
+ ]}
+/>
Note: On Windows add the arg `-f https://download.pytorch.org/whl/torch_stable.html` to install PyTorch correctly
+
+
## The Building Blocks of Haystack
Here’s a sample of some Haystack code showing a question answering system using a retriever and a reader.
-For a working code example, check out our [starter tutorial](/docs/v0.9.0/tutorial1md).
+For a working code example, check out our [starter tutorial](/tutorials/first-qa-system).
```python
# DocumentStore: holds all your data
@@ -77,6 +76,8 @@ prediction = pipe.run(query=question)
print_answers(prediction)
```
+
+
## Loading Documents into the DocumentStore
In Haystack, DocumentStores expect Documents in a dictionary format. They are loaded as follows:
@@ -96,33 +97,30 @@ When we talk about Documents in Haystack, we are referring specifically to the i
You might want to use all the text in one file as a Document, or split it into multiple Documents.
This splitting can have a big impact on speed and performance.
-+ If you’d like to run a specific, unreleased version of Haystack, or + make edits to the way Haystack runs, you’ll want to install it using + `git` and `pip --editable`. This clones a copy of the repo to a + local directory and runs Haystack from there. +
+++git clone https://github.com/deepset-ai/haystack.git+cd haystack+pip install --editable .+
+ By default, this will give you the latest version of the master + branch. Use regular git commands to switch between different + branches and commits. +
+
-
-**Tip:** If Haystack is running very slowly, you might want to try splitting your text into smaller Documents.
-If you want an improvement to performance, you might want to try concatenating text to make larger Documents.
-See [Optimization](/docs/v0.9.0/optimizationmd) for more details.
-
-
+
+ Tip: If Haystack is running very slowly,
+ you might want to try splitting your text into smaller Documents. If you want
+ an improvement to performance, you might want to try concatenating text to
+ make larger Documents. See Optimization for
+ more details.
+
+
## Running Search Queries
There are many different flavours of search that can be created using Haystack.
-But to give just one example of what can be achieved, let's look more closely at
+But to give just one example of what can be achieved, let's look more closely at
an Open Domain Question Answering (ODQA) Pipeline.
-
**Querying** in an ODQA system involves searching for an answer to a given question within the full document store.
This process will:
+- make the Retriever filter for a small set of relevant candidate documents
-* make the Retriever filter for a small set of relevant candidate documents
-
-
-* get the Reader to process this set of candidate documents
+- get the Reader to process this set of candidate documents
-
-* return potential answers to the given question
+- return potential answers to the given question
Usually, there are tight time constraints on querying and so it needs to be a lightweight operation.
When documents are loaded, Haystack will precompute any of the results that might be useful at query time.
@@ -151,10 +149,13 @@ When the query is complete, you can expect to see results that look something li
]
```
-##Custom Search Pipelines
+
+
+## Custom Search Pipelines
Haystack providers many different building blocks for you to mix and match.
They include:
+
- Readers
- Retrievers (sparse and dense)
- DocumentStores
@@ -163,4 +164,4 @@ They include:
- Translators
These can all be combined in the configuration that you want.
-Have a look at our [Pipelines page](/docs/v0.9.0/pipelinesmd) to see what's possible!
\ No newline at end of file
+Have a look at our [Pipelines page](/usage/pipelines) to see what's possible!
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/terms.md b/docs/v0.9.0/overview/glossary.mdx
similarity index 96%
rename from src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/terms.md
rename to docs/v0.9.0/overview/glossary.mdx
index 3509c77d4..566c89d90 100644
--- a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/terms.md
+++ b/docs/v0.9.0/overview/glossary.mdx
@@ -1,12 +1,3 @@
----
-title: "Glossary"
-metaTitle: "Glossary"
-metaDescription: ""
-slug: "/docs/terms"
-date: "2020-09-03"
-id: "termsmd"
----
-
# Glossary
**BERT** - A popular, transformer based language model which has been improved upon but is still considered a common benchmark.
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/intro.md b/docs/v0.9.0/overview/intro.mdx
similarity index 65%
rename from src/pages/docs/versions/master/site/en/usage/usage/intro.md
rename to docs/v0.9.0/overview/intro.mdx
index 12b405570..d5907a188 100644
--- a/src/pages/docs/versions/master/site/en/usage/usage/intro.md
+++ b/docs/v0.9.0/overview/intro.mdx
@@ -1,37 +1,28 @@
----
-title: "What is Haystack"
-metaTitle: "What is Haystack"
-metaDescription: ""
-slug: "/docs/intro"
-date: "2020-09-03"
-id: "intromd"
----
-
# What is Haystack?
Haystack is an **open-source framework** for building **search systems** that work intelligently over large document collections.
Recent advances in NLP have enabled the application of question answering, retrieval and summarization to real world settings
and Haystack is designed to be the bridge between research and industry.
+- **Latest NLP models**: Utilize all transformer based models (BERT, RoBERTa, MiniLM, DPR ...) and smoothly switch when new ones get published
-* **Latest NLP models**: Utilize all transformer based models (BERT, RoBERTa, MiniLM, DPR ...) and smoothly switch when new ones get published
+- **Flexible databases**: Load data into and query from a range of databases such as Elasticsearch, Milvus, FAISS, SQL and more
-* **Flexible databases**: Load data into and query from a range of databases such as Elasticsearch, Milvus, FAISS, SQL and more
+- **Scalability**: Production-ready deployments that scale to millions of documents
-* **Scalability**: Production-ready deployments that scale to millions of documents
+- **End-to-End**: All tooling you need to implement, evaluate, improve and run a search system
-* **End-to-End**: All tooling you need to implement, evaluate, improve and run a search system
+- **Domain adaptation**: Fine-tune models to your own domain & improve them continuously via user feedback
-* **Domain adaptation**: Fine-tune models to your own domain & improve them continuously via user feedback
+
## Retriever-Reader
The most common system built with Haystack is the Retriever-Reader Pipeline which is designed to optimize for both
speed and performance on the task of Open Domain Question Answering.
In practice, this is a great back bone for creating a search system that can handle detailed full sentence queries.
-
-
+
**Readers**, also known as Open-Domain QA systems in Machine Learning speak,
are powerful models that do close analysis of documents and perform the core task of question answering.
@@ -40,28 +31,25 @@ However, it is not currently feasible to use the Reader directly on large collec
+
The **Retriever** assists the Reader by acting as a lightweight filter that reduces the number of documents that the Reader has to process.
It does this by:
+- Scanning through all documents in the database
-* Scanning through all documents in the database
-
+- Quickly identifying the relevant and dismissing the irrelevant
-* Quickly identifying the relevant and dismissing the irrelevant
-
-
-* Passing on only a small candidate set of documents to the Reader
+- Passing on only a small candidate set of documents to the Reader
Current methods fall into one of the two categories:
+- sparse
-* sparse
- * keyword based
- * fast indexing and querying
- * e.g. BM25
-
+ - keyword based
+ - fast indexing and querying
+ - e.g. BM25
-* dense
- * neural network based
- * computationally heavy indexing but fast querying
- * e.g. Dense Passage Retrieval
+- dense
+ - neural network based
+ - computationally heavy indexing but fast querying
+ - e.g. Dense Passage Retrieval
diff --git a/docs/v0.9.0/overview/roadmap.mdx b/docs/v0.9.0/overview/roadmap.mdx
new file mode 100644
index 000000000..2fa9c0fd9
--- /dev/null
+++ b/docs/v0.9.0/overview/roadmap.mdx
@@ -0,0 +1,49 @@
+# Open roadmap
+
+We believe open-source is more than open source code. It's a lot about people, collaboration, transparency and trust.
+Therefore, we decided to be as open as possible with our roadmap and sprint planning.
+In fact, you can see all of it in real-time on GitHub.
+We hope this helps to clarify the direction of the open-source project and inspires discussions in the community.
+
+
+
+## How to access it
+
+### Add Zenhub Plugin
+
+We decided for Zenhub, as it allows a close integration with GitHub and real-time sharing of roadmaps and sprints.
+Once you have installed the browser plugin below, you will see additional tabs and infos on the Haystack GitHub page.
+
+Zenhub Plugin: https://www.zenhub.com/extension
+
+
+
+### Roadmap
+
+_Zenhub Tab -> Roadmap (left menu)_
+
+Here you can find our most recent roadmap with the **high-level projects** that are planned for the upcoming quarters.
+We update it regularly and refine the projects as they come closer in time.
+
+
+
+
+
+### Board
+
+_Zenhub Tab -> Board (left menu)_
+
+If you are interested in the **operational tasks** and their status, you can find our agile board here.
+
+
+
+
+
+### Additional issue details & Releases
+
+_Right panel in regular Github issues_
+
+With Zenhub you can also see some additional tags in every GitHub issue.
+For those of you who wonder about the next release date: We aim for releases every ~ 4 weeks and will tag the issues that will need to be finished before a bit in advance.
+
+
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/use_cases.md b/docs/v0.9.0/overview/use_cases.mdx
similarity index 65%
rename from src/pages/docs/versions/master/site/en/usage/usage/use_cases.md
rename to docs/v0.9.0/overview/use_cases.mdx
index 76b420410..add2223c7 100644
--- a/src/pages/docs/versions/master/site/en/usage/usage/use_cases.md
+++ b/docs/v0.9.0/overview/use_cases.mdx
@@ -1,32 +1,20 @@
----
-title: "Use Cases"
-metaTitle: "Use Cases"
-metaDescription: ""
-slug: "/docs/use_cases"
-date: "2020-11-16"
-id: "use_casesmd"
----
-
-
# Use cases
## Semantic Search System
Take the leap from using keyword search on your own documents to semantic search with Haystack.
+- Store your documents in the database of your choice (Elasticsearch, SQL, in memory, FAISS)
-* Store your documents in the database of your choice (Elasticsearch, SQL, in memory, FAISS)
-
-
-* Perform question driven queries.
+- Perform question driven queries.
Expect to see results that highlight the very sentence that contains the answer to your question.
Thanks to the power of Transformer based language models, results are chosen based on compatibility in meaning
rather than lexical overlap.
+
-
-
+
## Information Extractor
@@ -34,36 +22,31 @@ Automate the extraction of relevant information from a set of documents that per
Haystack can:
+- Apply a set of standard questions to each document in a store
-* Apply a set of standard questions to each document in a store
-
-
-* Return a NO_ANSWER if a given document does not contain the answer to a question
+- Return a NO_ANSWER if a given document does not contain the answer to a question
Say you have the financial reports for different companies over different years.
You can gather a set of standard questions which are applicable to each financial report,
-like *what is the revenue forecast for 2020?* or *what are the main sources of income?*.
+like _what is the revenue forecast for 2020?_ or _what are the main sources of income?_.
Haystack will try to find an answer for each question within each document!
We’ve seen this style of application be particularly effective in the sphere of finance and patent law
but we see a lot of potential in using this to gain a better overview of academic papers and internal business documents.
-
+
+
## FAQ Style Question Answering
Leverage existing FAQ documents and semantic similarity search to answer new incoming questions.
The workflow is as follows:
+- Store a set of FAQ documents in Haystack
-* Store a set of FAQ documents in Haystack
-
-
-* The user presents a new question
-
-
-* Haystack will find the closest match to the new question in the FAQ documents
+- The user presents a new question
+- Haystack will find the closest match to the new question in the FAQ documents
-* The user will be presented with the most similar Question Answer pair
+- The user will be presented with the most similar Question Answer pair
Haystack’s flexibility allows you to give new users more dynamic access to your existing documentation.
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/annotation.md b/docs/v0.9.0/usage/annotation.mdx
similarity index 93%
rename from src/pages/docs/versions/master/site/en/usage/usage/annotation.md
rename to docs/v0.9.0/usage/annotation.mdx
index 6a61fca05..c39a38991 100644
--- a/src/pages/docs/versions/master/site/en/usage/usage/annotation.md
+++ b/docs/v0.9.0/usage/annotation.mdx
@@ -1,12 +1,3 @@
----
-title: "Annotation Tool"
-metaTitle: "Annotation Tool"
-metaDescription: ""
-slug: "/docs/annotation"
-date: "2020-09-03"
-id: "annotationmd"
----
-
# Annotation Tool
- Create labels with different techniques: Come up with questions (+ answers) while reading passages (SQuAD style) or have a set of predefined questions and look for answers in the document (~ Natural Questions).
@@ -14,12 +5,17 @@ id: "annotationmd"
- Upload your documents or import labels from an existing SQuAD-style dataset
- Export your labels in SQuAD Format
-
+
+
+
+
+## Hosted version
+
+Signup here: [Haystack Annotation Tool](https://annotate.deepset.ai/login)
-# Hosted version
- Signup here: [Haystack Annotation Tool](https://annotate.deepset.ai/login)
+
-# Local version (Docker)
+## Local version (Docker)
1. Configure credentials & database in the [`docker-compose.yml`](https://github.com/deepset-ai/haystack/blob/master/annotation_tool/docker-compose.yml):
@@ -37,13 +33,14 @@ The credentials should match in database image and application configuration.
POSTGRES_PASSWORD: "somesafepassword"
POSTGRES_DB: "databasename"
-
2. Run docker-compose by executing `docker-compose up`.
-
3. The UI should be available at `localhost:7001`.
-# Usage
+
+
+## Usage
+
The manual (of a slightly earlier version) can be found [here](https://drive.google.com/file/d/1Wv3OIC0Z7ibHIzOm9Xw_r0gjTFmpl-33/view). While it doesn't include all latest features, the basic workflow and tips for label quality are still the same.
# Annotation FAQ
@@ -75,4 +72,4 @@ The manual (of a slightly earlier version) can be found [here](https://drive.goo
- Please do not annotate this text
- You can write down what is missing, or the cause why you cannot label the text + the text number and title.
8. Which browser to use?
- - Please use the Chrome browser. The tool is not tested for other browsers.
+ - Please use the Chrome browser. The tool is not tested for other browsers.
\ No newline at end of file
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/document_store.md b/docs/v0.9.0/usage/document_store.mdx
similarity index 70%
rename from src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/document_store.md
rename to docs/v0.9.0/usage/document_store.mdx
index 9cb6684a0..523cc8436 100644
--- a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/document_store.md
+++ b/docs/v0.9.0/usage/document_store.mdx
@@ -1,16 +1,7 @@
----
-title: "Document Store"
-metaTitle: "Document Store"
-metaDescription: ""
-slug: "/docs/documentstore"
-date: "2020-09-03"
-id: "documentstoremd"
----
-
-
# DocumentStores
You can think of the DocumentStore as a "database" that:
+
- stores your texts and meta data
- provides them to the retriever at query time
@@ -20,30 +11,24 @@ There are different DocumentStores in Haystack to fit different use cases and te
Initialising a new DocumentStore within Haystack is straight forward.
-
+
-
Each DocumentStore constructor allows for arguments specifying how to connect to existing databases and the names of indexes.
See API documentation for more info.
+
+
## Input Format
DocumentStores expect Documents in dictionary form, like that below.
They are loaded using the `DocumentStore.write_documents()` method.
See [Preprocessing](/docs/v0.9.0/preprocessingmd) for more information on the cleaning and splitting steps that will help you maximize Haystack's performance.
-[//]: # (Add link to preprocessing section)
+[//]: # "Add link to preprocessing section"
```python
from haystack.document_store import ElasticsearchDocumentStore
@@ -169,6 +133,8 @@ dicts = [
document_store.write_documents(dicts)
```
+
+
## Writing Documents (Sparse Retrievers)
Haystack allows for you to write store documents in an optimised fashion so that query times can be kept low.
@@ -180,6 +146,8 @@ The creation of the inverted index which optimises querying speed is handled aut
document_store.write_documents(dicts)
```
+
+
## Writing Documents (Dense Retrievers)
For **dense** neural network based retrievers like Dense Passage Retrieval, or Embedding Retrieval,
@@ -198,117 +166,103 @@ Having GPU acceleration will significantly speed this up.
+
+
+
## Choosing the Right Document Store
The Document Stores have different characteristics. You should choose one depending on the maturity of your project, the use case and technical environment:
-
-
-
-
+
-
+### Elasticsearch
[Install](https://www.elastic.co/guide/en/elasticsearch/reference/current/install-elasticsearch.html)
Elasticsearch and then [start](https://www.elastic.co/guide/en/elasticsearch/reference/current/starting-elasticsearch.html)
-an instance.
+an instance.
If you have Docker set up, we recommend pulling the Docker image and running it.
+
```bash
docker pull docker.elastic.co/elasticsearch/elasticsearch:7.9.2
docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.9.2
```
-Note that we also have a utility function `haystack.utils.launch_es` that can start up an Elasticsearch instance.
-
Next you can initialize the Haystack object that will connect to this instance.
```python
-from haystack.document_store import ElasticsearchDocumentStore
-
document_store = ElasticsearchDocumentStore()
```
@@ -51,64 +36,48 @@ Note that we also support [Open Distro for Elasticsearch](https://opendistro.git
Follow [their documentation](https://opendistro.github.io/for-elasticsearch-docs/docs/install/)
to run it and connect to it using Haystack's `OpenDistroElasticsearchDocumentStore` class.
-We further support [AWS Elastic Search Service](https://aws.amazon.com/elasticsearch-service/) with [signed Requests](https://docs.aws.amazon.com/general/latest/gr/signature-version-4.html):
-Use e.g. [aws-requests-auth](https://github.com/davidmuller/aws-requests-auth) to create an auth object and pass it as `aws4auth` to the `ElasticsearchDocumentStore` constructor.
-
-
-
-
-
-
+
-
+### Milvus
-Follow the [official documentation](https://www.milvus.io/docs/v1.0.0/milvus_docker-cpu.md) to start a Milvus instance via Docker.
+Follow the [official documentation](https://www.milvus.io/docs/v1.0.0/milvus_docker-cpu.md) to start a Milvus instance via Docker.
Note that we also have a utility function `haystack.utils.launch_milvus` that can start up a Milvus instance.
You can initialize the Haystack object that will connect to this instance as follows:
+
```python
from haystack.document_store import MilvusDocumentStore
document_store = MilvusDocumentStore()
```
-
-
-
-
-
+
-
+### FAISS
The `FAISSDocumentStore` requires no external setup. Start it by simply using this line.
+
```python
from haystack.document_store import FAISSDocumentStore
document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
```
-
-
-
-
-
+
-
+### In Memory
The `InMemoryDocumentStore()` requires no external setup. Start it by simply using this line.
+
```python
from haystack.document_store import InMemoryDocumentStore
document_store = InMemoryDocumentStore()
```
-
-
-
-
-
-
-
+### SQL
The `SQLDocumentStore` requires SQLite, PostgresQL or MySQL to be installed and started.
Note that SQLite already comes packaged with most operating systems.
@@ -119,42 +88,37 @@ from haystack.document_store import SQLDocumentStore
document_store = SQLDocumentStore()
```
-
-
-
-
-
-
-
+
+
+### Weaviate
-The `WeaviateDocumentStore` requires a running Weaviate Server.
+The `WeaviateDocumentStore` requires a running Weaviate Server.
You can start a basic instance like this (see the [Weaviate docs](https://www.semi.technology/developers/weaviate/current/) for details):
+
```
docker run -d -p 8080:8080 --env AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED='true' --env PERSISTENCE_DATA_PATH='/var/lib/weaviate' semitechnologies/weaviate:1.4.0
```
-
+
Afterwards, you can use it in Haystack:
+
```python
from haystack.document_store import WeaviateDocumentStore
document_store = WeaviateDocumentStore()
```
-
-
-
-
-
-
-
-
-
+
-
+### Elasticsearch
**Pros:**
+
- Fast & accurate sparse retrieval with many tuning options
- Basic support for dense retrieval
- Production-ready
- Support also for Open Distro
**Cons:**
+
- Slow for dense retrieval with more than ~ 1 Mio documents
-
-
-
-
-
+
-
+### Milvus
**Pros:**
+
- Scalable DocumentStore that excels at handling vectors (hence suited to dense retrieval methods like DPR)
- Encapsulates multiple ANN libraries (e.g. FAISS and ANNOY) and provides added reliability
- Runs as a separate service (e.g. a Docker container)
- Allows dynamic data management
**Cons:**
+
- No efficient sparse retrieval
-
-
-
-
-
+
-
+### FAISS
**Pros:**
+
- Fast & accurate dense retrieval
- Highly scalable due to approximate nearest neighbour algorithms (ANN)
- Many options to tune dense retrieval via different index types (more info [here](https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index))
**Cons:**
+
- No efficient sparse retrieval
-
-
-
-
-
+### SQL
-
+### In Memory
**Pros:**
+
- Simple
- Exists already in many environments
**Cons:**
+
- Only compatible with minimal TF-IDF Retriever
- Bad retrieval performance
- Not recommended for production
-
-
-
-
-
+
-
-
+
**Pros:**
+
- Simple & fast to test
- No database requirements
- Supports MySQL, PostgreSQL and SQLite
**Cons:**
+
- Not scalable
- Not persisting your data on disk
-
-
-
-
-
-
-
+### Weaviate
**Pros:**
+
- Simple vector search
- Stores everything in one place: documents, meta data and vectors - so less network overhead when scaling this up
-- Allows combination of vector search and scalar filtering, i.e. you can filter for a certain tag and do dense retrieval on that subset
+- Allows combination of vector search and scalar filtering, i.e. you can filter for a certain tag and do dense retrieval on that subset
**Cons:**
+
- Less options for ANN algorithms than FAISS or Milvus
- No BM25 / Tf-idf retrieval
-
-
-
+
+
+
-
-
-
- {message}
-
-
-
-
-
- );
-
-};
-
-
-export default BetaPage
\ No newline at end of file
diff --git a/src/pages/community/join.scss b/src/pages/community/join.scss
deleted file mode 100644
index 07ccf2038..000000000
--- a/src/pages/community/join.scss
+++ /dev/null
@@ -1,141 +0,0 @@
-@import "../../components/include_media";
-
-.contact {
- margin-top: 100px;
- display: flex;
- justify-content: center;
- .community-signup {
- background-color: #F5F6FA;
- justify-content: center;
- align-items: center;
- text-align: center;
- background: #ffffff;
- padding: 20px;
- width: 50%;
- border-radius: 10px;
- box-shadow: rgba(0, 0, 0, 0.1) 0px 2px 8px, rgba(0, 0, 0, 0.08) 3px 10px 30px;
-
- .product-img {
- width: 100%;
- border-radius: 10px 10px 0px 0px;
- }
-
- h2 {
- margin-top: 20px !important;
- color: #1C2648;
- font-size: 42px;
- font-weight: bold;
- width: 100%;
- }
-
- .description-features {
- margin-top: 10px;
- padding-top: 5px;
- background-color: #f6f9f9 ;
- }
- .prodcuts-header {
- font-size: 1.5em;
- line-height: 1.5em;
- text-align: left;
- padding-bottom: 10px;
- }
- .product-description {
- margin-left: 20px;
- text-align: left;
- margin-top: 20px;
- font-size: 1.2em;
- font-weight: bold;
- color: #009bab;
- }
- .products-features {
- margin-top: 20px;
- margin-left: 30px;
- font-size: 1.1em;
- line-height: 2em;
- text-align: left;
- .column-left {
- float: left;
- width: 5%;
- li {
- height: 80px;
- padding-right: 5px;
- }
- }
- .column {
- float: left;
- width: 45%;
- li {
- height: 80px;
- padding-right: 5px;
- }
- }
- .row:after {
- content: "";
- display: table;
- clear: both;
- }
- }
-
- p {
- line-height: 1.5;
- margin-top: 20px;
- font-size: 1.4rem;
- }
-
- .form-control {
- width: 100%;
- margin-top: 20px;
- .MuiInputBase-input {
- height: 40px;
- }
- }
-
- .form-button {
- width: 100%;
- margin-top: 20px;
- margin-bottom: 40px;
- background-color: #08415D;
- color: #ffffff;
- font-weight: bold;
- &:hover {
- background-color: #08415D;
- }
- }
-
- .form-checkbox {
- margin-top: 20px;
- width: 100%;
- }
-
- .signup-beta {
- border: 0;
- background: none;
- box-shadow: none;
- font-size: 16px;
- font-weight: bold;
- border-radius: 4px;
- color: #ffffff;
- background-color: #009BAA;
- text-align: center;
- cursor: pointer;
- height: 50px;
- width: 100%;
- margin-top: 20px;
- }
- .check-font {
- color: #009bab;
- }
-
- }
- @include media("<=1000px") {
- .community-signup {
- width: 100%;
- h2 {
- font-size: 32px;
- }
- p {
- font-size: 1.0rem;
- }
- }
- }
-}
\ No newline at end of file
diff --git a/src/pages/docs/versions/master/latest/site/en/api/api/crawler.md b/src/pages/docs/versions/master/latest/site/en/api/api/crawler.md
deleted file mode 100644
index 33eebda75..000000000
--- a/src/pages/docs/versions/master/latest/site/en/api/api/crawler.md
+++ /dev/null
@@ -1,105 +0,0 @@
----
-title: "Crawler"
-metaTitle: "Crawler"
-metaDescription: ""
-slug: "/docs/crawlermd"
-date: "2020-09-03"
-id: "apicrawlermd"
----
-
-# Crawler
-
-
-# Module: crawler
-
-
-## Class: Crawler
-
-```python
-class Crawler(BaseComponent)
-```
-
-Crawl texts from a website so that we can use them later in Haystack as a corpus for search / question answering etc.
-
-**Example:**
-```python
-| from haystack.connector import Crawler
-|
-| crawler = Crawler()
-| # crawl Haystack docs, i.e. all pages that include haystack.deepset.ai/docs/
-| docs = crawler.crawl(urls=["https://haystack.deepset.ai/docs/latest/get_startedmd"],
-| output_dir="crawled_files",
-| filter_urls= ["haystack\.deepset\.ai\/docs\/"])
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(output_dir: str, urls: Optional[List[str]] = None, crawler_depth: int = 1, filter_urls: Optional[List] = None, overwrite_existing_files=True)
-```
-
-Init object with basic params for crawling (can be overwritten later).
-
-**Arguments**:
-
-- `output_dir`: Path for the directory to store files
-- `urls`: List of http(s) address(es) (can also be supplied later when calling crawl())
-- `crawler_depth`: How many sublinks to follow from the initial list of URLs. Current options:
- 0: Only initial list of urls
- 1: Follow links found on the initial URLs (but no further)
-- `filter_urls`: Optional list of regular expressions that the crawled URLs must comply with.
- All URLs not matching at least one of the regular expressions will be dropped.
-- `overwrite_existing_files`: Whether to overwrite existing files in output_dir with new content
-
-
-#### crawl
-
-```python
- | crawl(output_dir: Union[str, Path, None] = None, urls: Optional[List[str]] = None, crawler_depth: Optional[int] = None, filter_urls: Optional[List] = None, overwrite_existing_files: Optional[bool] = None) -> List[Path]
-```
-
-Craw URL(s), extract the text from the HTML, create a Haystack Document object out of it and save it (one JSON
-file per URL, including text and basic meta data).
-You can optionally specify via `filter_urls` to only crawl URLs that match a certain pattern.
-All parameters are optional here and only meant to overwrite instance attributes at runtime.
-If no parameters are provided to this method, the instance attributes that were passed during __init__ will be used.
-
-**Arguments**:
-
-- `output_dir`: Path for the directory to store files
-- `urls`: List of http addresses or single http address
-- `crawler_depth`: How many sublinks to follow from the initial list of URLs. Current options:
- 0: Only initial list of urls
- 1: Follow links found on the initial URLs (but no further)
-- `filter_urls`: Optional list of regular expressions that the crawled URLs must comply with.
- All URLs not matching at least one of the regular expressions will be dropped.
-- `overwrite_existing_files`: Whether to overwrite existing files in output_dir with new content
-
-**Returns**:
-
-List of paths where the crawled webpages got stored
-
-
-#### run
-
-```python
- | run(output_dir: Union[str, Path, None] = None, urls: Optional[List[str]] = None, crawler_depth: Optional[int] = None, filter_urls: Optional[List] = None, overwrite_existing_files: Optional[bool] = None, **kwargs) -> Tuple[Dict, str]
-```
-
-Method to be executed when the Crawler is used as a Node within a Haystack pipeline.
-
-**Arguments**:
-
-- `output_dir`: Path for the directory to store files
-- `urls`: List of http addresses or single http address
-- `crawler_depth`: How many sublinks to follow from the initial list of URLs. Current options:
- 0: Only initial list of urls
- 1: Follow links found on the initial URLs (but no further)
-- `filter_urls`: Optional list of regular expressions that the crawled URLs must comply with.
- All URLs not matching at least one of the regular expressions will be dropped.
-- `overwrite_existing_files`: Whether to overwrite existing files in output_dir with new content
-
-**Returns**:
-
-Tuple({"paths": List of filepaths, ...}, Name of output edge)
diff --git a/src/pages/docs/versions/master/latest/site/en/api/api/document_store.md b/src/pages/docs/versions/master/latest/site/en/api/api/document_store.md
deleted file mode 100644
index 7b85b5e54..000000000
--- a/src/pages/docs/versions/master/latest/site/en/api/api/document_store.md
+++ /dev/null
@@ -1,1478 +0,0 @@
----
-title: "Document Store"
-metaTitle: "Database"
-metaDescription: ""
-slug: "/docs/apidatabase"
-date: "2020-09-03"
-id: "apidatabasemd"
----
-
-# Document Store
-
-
-# Module: base
-
-
-## Class: BaseDocumentStore
-
-```python
-class BaseDocumentStore(BaseComponent)
-```
-
-Base class for implementing Document Stores.
-
-
-#### write\_documents
-
-```python
- | @abstractmethod
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
- For documents as dictionaries, the format is {"text": ""}.
- Optionally: Include meta data via {"text": "",
- "meta":{"name": ", "author": "somebody", ...}}
- It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: Optional name of index where the documents shall be written to.
- If None, the DocumentStore's default index (self.index) will be used.
-- `batch_size`: Number of documents that are passed to bulk function at a time.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-**Returns**:
-
-None
-
-
-#### get\_all\_documents
-
-```python
- | @abstractmethod
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Get documents from the document store.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-
-
-#### get\_all\_labels\_aggregated
-
-```python
- | get_all_labels_aggregated(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, open_domain: bool = True, aggregate_by_meta: Optional[Union[str, list]] = None) -> List[MultiLabel]
-```
-
-Return all labels in the DocumentStore, aggregated into MultiLabel objects.
-This aggregation step helps, for example, if you collected multiple possible answers for one question and you
-want now all answers bundled together in one place for evaluation.
-How they are aggregated is defined by the open_domain and aggregate_by_meta parameters.
-If the questions are being asked to a single document (i.e. SQuAD style), you should set open_domain=False to aggregate by question and document.
-If the questions are being asked to your full collection of documents, you should set open_domain=True to aggregate just by question.
-If the questions are being asked to a subslice of your document set (e.g. product review use cases),
-you should set open_domain=True and populate aggregate_by_meta with the names of Label meta fields to aggregate by question and your custom meta fields.
-For example, in a product review use case, you might set aggregate_by_meta=["product_id"] so that Labels
-with the same question but different answers from different documents are aggregated into the one MultiLabel
-object, provided that they have the same product_id (to be found in Label.meta["product_id"])
-
-**Arguments**:
-
-- `index`: Name of the index to get the labels from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the labels to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `open_domain`: When True, labels are aggregated purely based on the question text alone.
- When False, labels are aggregated in a closed domain fashion based on the question text
- and also the id of the document that the label is tied to. In this setting, this function
- might return multiple MultiLabel objects with the same question string.
-- `aggregate_by_meta`: The names of the Label meta fields by which to aggregate. For example: ["product_id"]
-
-
-#### add\_eval\_data
-
-```python
- | add_eval_data(filename: str, doc_index: str = "eval_document", label_index: str = "label", batch_size: Optional[int] = None, preprocessor: Optional[PreProcessor] = None, max_docs: Union[int, bool] = None, open_domain: bool = False)
-```
-
-Adds a SQuAD-formatted file to the DocumentStore in order to be able to perform evaluation on it.
-If a jsonl file and a batch_size is passed to the function, documents are loaded batchwise
-from disk and also indexed batchwise to the DocumentStore in order to prevent out of memory errors.
-
-**Arguments**:
-
-- `filename`: Name of the file containing evaluation data (json or jsonl)
-- `doc_index`: Elasticsearch index where evaluation documents should be stored
-- `label_index`: Elasticsearch index where labeled questions should be stored
-- `batch_size`: Optional number of documents that are loaded and processed at a time.
- When set to None (default) all documents are processed at once.
-- `preprocessor`: Optional PreProcessor to preprocess evaluation documents.
- It can be used for splitting documents into passages (and assigning labels to corresponding passages).
- Currently the PreProcessor does not support split_by sentence, cleaning nor split_overlap != 0.
- When set to None (default) preprocessing is disabled.
-- `max_docs`: Optional number of documents that will be loaded.
- When set to None (default) all available eval documents are used.
-- `open_domain`: Set this to True if your file is an open domain dataset where two different answers to the
- same question might be found in different contexts.
-
-
-# Module: elasticsearch
-
-
-## Class: ElasticsearchDocumentStore
-
-```python
-class ElasticsearchDocumentStore(BaseDocumentStore)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(host: Union[str, List[str]] = "localhost", port: Union[int, List[int]] = 9200, username: str = "", password: str = "", api_key_id: Optional[str] = None, api_key: Optional[str] = None, aws4auth=None, index: str = "document", label_index: str = "label", search_fields: Union[str, list] = "text", text_field: str = "text", name_field: str = "name", embedding_field: str = "embedding", embedding_dim: int = 768, custom_mapping: Optional[dict] = None, excluded_meta_data: Optional[list] = None, faq_question_field: Optional[str] = None, analyzer: str = "standard", scheme: str = "http", ca_certs: Optional[str] = None, verify_certs: bool = True, create_index: bool = True, refresh_type: str = "wait_for", similarity="dot_product", timeout=30, return_embedding: bool = False, duplicate_documents: str = 'overwrite')
-```
-
-A DocumentStore using Elasticsearch to store and query the documents for our search.
-
- * Keeps all the logic to store and query documents from Elastic, incl. mapping of fields, adding filters or boosts to your queries, and storing embeddings
- * You can either use an existing Elasticsearch index or create a new one via haystack
- * Retrievers operate on top of this DocumentStore to find the relevant documents for a query
-
-**Arguments**:
-
-- `host`: url(s) of elasticsearch nodes
-- `port`: port(s) of elasticsearch nodes
-- `username`: username (standard authentication via http_auth)
-- `password`: password (standard authentication via http_auth)
-- `api_key_id`: ID of the API key (altenative authentication mode to the above http_auth)
-- `api_key`: Secret value of the API key (altenative authentication mode to the above http_auth)
-- `aws4auth`: Authentication for usage with aws elasticsearch (can be generated with the requests-aws4auth package)
-- `index`: Name of index in elasticsearch to use for storing the documents that we want to search. If not existing yet, we will create one.
-- `label_index`: Name of index in elasticsearch to use for storing labels. If not existing yet, we will create one.
-- `search_fields`: Name of fields used by ElasticsearchRetriever to find matches in the docs to our incoming query (using elastic's multi_match query), e.g. ["title", "full_text"]
-- `text_field`: Name of field that might contain the answer and will therefore be passed to the Reader Model (e.g. "full_text").
- If no Reader is used (e.g. in FAQ-Style QA) the plain content of this field will just be returned.
-- `name_field`: Name of field that contains the title of the the doc
-- `embedding_field`: Name of field containing an embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `embedding_dim`: Dimensionality of embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `custom_mapping`: If you want to use your own custom mapping for creating a new index in Elasticsearch, you can supply it here as a dictionary.
-- `analyzer`: Specify the default analyzer from one of the built-ins when creating a new Elasticsearch Index.
- Elasticsearch also has built-in analyzers for different languages (e.g. impacting tokenization). More info at:
- https://www.elastic.co/guide/en/elasticsearch/reference/7.9/analysis-analyzers.html
-- `excluded_meta_data`: Name of fields in Elasticsearch that should not be returned (e.g. [field_one, field_two]).
- Helpful if you have fields with long, irrelevant content that you don't want to display in results (e.g. embedding vectors).
-- `scheme`: 'https' or 'http', protocol used to connect to your elasticsearch instance
-- `ca_certs`: Root certificates for SSL: it is a path to certificate authority (CA) certs on disk. You can use certifi package with certifi.where() to find where the CA certs file is located in your machine.
-- `verify_certs`: Whether to be strict about ca certificates
-- `create_index`: Whether to try creating a new index (If the index of that name is already existing, we will just continue in any case
-- `refresh_type`: Type of ES refresh used to control when changes made by a request (e.g. bulk) are made visible to search.
- If set to 'wait_for', continue only after changes are visible (slow, but safe).
- If set to 'false', continue directly (fast, but sometimes unintuitive behaviour when docs are not immediately available after ingestion).
- More info at https://www.elastic.co/guide/en/elasticsearch/reference/6.8/docs-refresh.html
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
- more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
-- `timeout`: Number of seconds after which an ElasticSearch request times out.
-- `return_embedding`: To return document embedding
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index: Optional[str] = None) -> List[Document]
-```
-
-Fetch documents by specifying a list of text id strings
-
-
-#### get\_metadata\_values\_by\_key
-
-```python
- | get_metadata_values_by_key(key: str, query: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> List[dict]
-```
-
-Get values associated with a metadata key. The output is in the format:
- [{"value": "my-value-1", "count": 23}, {"value": "my-value-2", "count": 12}, ... ]
-
-**Arguments**:
-
-- `key`: the meta key name to get the values for.
-- `query`: narrow down the scope to documents matching the query string.
-- `filters`: narrow down the scope to documents that match the given filters.
-- `index`: Elasticsearch index where the meta values should be searched. If not supplied,
- self.index will be used.
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None)
-```
-
-Indexes documents for later queries in Elasticsearch.
-
-Behaviour if a document with the same ID already exists in ElasticSearch:
-a) (Default) Throw Elastic's standard error message for duplicate IDs.
-b) If `self.update_existing_documents=True` for DocumentStore: Overwrite existing documents.
-(This is only relevant if you pass your own ID when initializing a `Document`.
-If don't set custom IDs for your Documents or just pass a list of dictionaries here,
-they will automatically get UUIDs assigned. See the `Document` class for details)
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
- For documents as dictionaries, the format is {"text": ""}.
- Optionally: Include meta data via {"text": "",
- "meta":{"name": ", "author": "somebody", ...}}
- It can be used for filtering and is accessible in the responses of the Finder.
- Advanced: If you are using your own Elasticsearch mapping, the key names in the dictionary
- should be changed to what you have set for self.text_field and self.name_field.
-- `index`: Elasticsearch index where the documents should be indexed. If not supplied, self.index will be used.
-- `batch_size`: Number of documents that are passed to Elasticsearch's bulk function at a time.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-**Raises**:
-
-- `DuplicateDocumentError`: Exception trigger on duplicate document
-
-**Returns**:
-
-None
-
-
-#### write\_labels
-
-```python
- | write_labels(labels: Union[List[Label], List[dict]], index: Optional[str] = None, batch_size: int = 10_000)
-```
-
-Write annotation labels into document store.
-
-**Arguments**:
-
-- `labels`: A list of Python dictionaries or a list of Haystack Label objects.
-- `batch_size`: Number of labels that are passed to Elasticsearch's bulk function at a time.
-
-
-#### update\_document\_meta
-
-```python
- | update_document_meta(id: str, meta: Dict[str, str])
-```
-
-Update the metadata dictionary of a document by specifying its string id
-
-
-#### get\_document\_count
-
-```python
- | get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None, only_documents_without_embedding: bool = False) -> int
-```
-
-Return the number of documents in the document store.
-
-
-#### get\_label\_count
-
-```python
- | get_label_count(index: Optional[str] = None) -> int
-```
-
-Return the number of labels in the document store
-
-
-#### get\_embedding\_count
-
-```python
- | get_embedding_count(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None) -> int
-```
-
-Return the count of embeddings in the document store.
-
-
-#### get\_all\_documents
-
-```python
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> List[Document]
-```
-
-Get documents from the document store.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_documents\_generator
-
-```python
- | get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> Generator[Document, None, None]
-```
-
-Get documents from the document store. Under-the-hood, documents are fetched in batches from the
-document store and yielded as individual documents. This method can be used to iteratively process
-a large number of documents without having to load all documents in memory.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_labels
-
-```python
- | get_all_labels(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, batch_size: int = 10_000) -> List[Label]
-```
-
-Return all labels in the document store
-
-
-#### query
-
-```python
- | query(query: Optional[str], filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, custom_query: Optional[str] = None, index: Optional[str] = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query as defined by the BM25 algorithm.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.ndarray, filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: Index name for storing the docs and metadata
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### describe\_documents
-
-```python
- | describe_documents(index=None)
-```
-
-Return a summary of the documents in the document store
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever, index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, update_existing_embeddings: bool = True, batch_size: int = 10_000)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever to use to update the embeddings.
-- `index`: Index name to update
-- `update_existing_embeddings`: Whether to update existing embeddings of the documents. If set to False,
- only documents without embeddings are processed. This mode can be used for
- incremental updating of embeddings, wherein, only newly indexed documents
- get processed.
-- `filters`: Optional filters to narrow down the documents for which embeddings are to be updated.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-**Returns**:
-
-None
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-#### delete\_documents
-
-```python
- | delete_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-## Class: OpenDistroElasticsearchDocumentStore
-
-```python
-class OpenDistroElasticsearchDocumentStore(ElasticsearchDocumentStore)
-```
-
-Document Store using the Open Distro for Elasticsearch. It is compatible with the AWS Elasticsearch Service.
-
-In addition to native Elasticsearch query & filtering, it provides efficient vector similarity search using
-the KNN plugin that can scale to a large number of documents.
-
-
-# Module: memory
-
-
-## Class: InMemoryDocumentStore
-
-```python
-class InMemoryDocumentStore(BaseDocumentStore)
-```
-
-In-memory document store
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(index: str = "document", label_index: str = "label", embedding_field: Optional[str] = "embedding", embedding_dim: int = 768, return_embedding: bool = False, similarity: str = "dot_product", progress_bar: bool = True, duplicate_documents: str = 'overwrite')
-```
-
-**Arguments**:
-
-- `index`: The documents are scoped to an index attribute that can be used when writing, querying,
- or deleting documents. This parameter sets the default value for document index.
-- `label_index`: The default value of index attribute for the labels.
-- `embedding_field`: Name of field containing an embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `embedding_dim`: The size of the embedding vector.
-- `return_embedding`: To return document embedding
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
- more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
-- `progress_bar`: Whether to show a tqdm progress bar or not.
- Can be helpful to disable in production deployments to keep the logs clean.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, duplicate_documents: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
- For documents as dictionaries, the format is {"text": ""}.
- Optionally: Include meta data via {"text": "",
- "meta": {"name": ", "author": "somebody", ...}}
- It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: write documents to a custom namespace. For instance, documents for evaluation can be indexed in a
- separate index than the documents for search.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-**Raises**:
-
-- `DuplicateDocumentError`: Exception trigger on duplicate document
-
-**Returns**:
-
-None
-
-
-#### write\_labels
-
-```python
- | write_labels(labels: Union[List[dict], List[Label]], index: Optional[str] = None)
-```
-
-Write annotation labels into document store.
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index: Optional[str] = None) -> List[Document]
-```
-
-Fetch documents by specifying a list of text id strings
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.ndarray, filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: Index name for storing the docs and metadata
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, update_existing_embeddings: bool = True, batch_size: int = 10_000)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever to use to get embeddings for text
-- `index`: Index name for which embeddings are to be updated. If set to None, the default self.index is used.
-- `update_existing_embeddings`: Whether to update existing embeddings of the documents. If set to False,
- only documents without embeddings are processed. This mode can be used for
- incremental updating of embeddings, wherein, only newly indexed documents
- get processed.
-- `filters`: Optional filters to narrow down the documents for which embeddings are to be updated.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-**Returns**:
-
-None
-
-
-#### get\_document\_count
-
-```python
- | get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
-```
-
-Return the number of documents in the document store.
-
-
-#### get\_embedding\_count
-
-```python
- | get_embedding_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
-```
-
-Return the count of embeddings in the document store.
-
-
-#### get\_label\_count
-
-```python
- | get_label_count(index: Optional[str] = None) -> int
-```
-
-Return the number of labels in the document store
-
-
-#### get\_all\_documents\_generator
-
-```python
- | get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> Generator[Document, None, None]
-```
-
-Get all documents from the document store. The methods returns a Python Generator that yields individual
-documents.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-
-
-#### get\_all\_labels
-
-```python
- | get_all_labels(index: str = None, filters: Optional[Dict[str, List[str]]] = None) -> List[Label]
-```
-
-Return all labels in the document store
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-#### delete\_documents
-
-```python
- | delete_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-# Module: sql
-
-
-## Class: SQLDocumentStore
-
-```python
-class SQLDocumentStore(BaseDocumentStore)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(url: str = "sqlite://", index: str = "document", label_index: str = "label", duplicate_documents: str = "overwrite")
-```
-
-An SQL backed DocumentStore. Currently supports SQLite, PostgreSQL and MySQL backends.
-
-**Arguments**:
-
-- `url`: URL for SQL database as expected by SQLAlchemy. More info here: https://docs.sqlalchemy.org/en/13/core/engines.html#database-urls
-- `index`: The documents are scoped to an index attribute that can be used when writing, querying, or deleting documents.
- This parameter sets the default value for document index.
-- `label_index`: The default value of index attribute for the labels.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index: Optional[str] = None, batch_size: int = 10_000) -> List[Document]
-```
-
-Fetch documents by specifying a list of text id strings
-
-
-#### get\_documents\_by\_vector\_ids
-
-```python
- | get_documents_by_vector_ids(vector_ids: List[str], index: Optional[str] = None, batch_size: int = 10_000)
-```
-
-Fetch documents by specifying a list of text vector id strings
-
-
-#### get\_all\_documents\_generator
-
-```python
- | get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> Generator[Document, None, None]
-```
-
-Get documents from the document store. Under-the-hood, documents are fetched in batches from the
-document store and yielded as individual documents. This method can be used to iteratively process
-a large number of documents without having to load all documents in memory.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_labels
-
-```python
- | get_all_labels(index=None, filters: Optional[dict] = None)
-```
-
-Return all labels in the document store
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
- For documents as dictionaries, the format is {"text": ""}.
- Optionally: Include meta data via {"text": "",
- "meta":{"name": ", "author": "somebody", ...}}
- It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: add an optional index attribute to documents. It can be later used for filtering. For instance,
- documents for evaluation can be indexed in a separate index than the documents for search.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-**Returns**:
-
-None
-
-
-#### write\_labels
-
-```python
- | write_labels(labels, index=None)
-```
-
-Write annotation labels into document store.
-
-
-#### update\_vector\_ids
-
-```python
- | update_vector_ids(vector_id_map: Dict[str, str], index: Optional[str] = None, batch_size: int = 10_000)
-```
-
-Update vector_ids for given document_ids.
-
-**Arguments**:
-
-- `vector_id_map`: dict containing mapping of document_id -> vector_id.
-- `index`: filter documents by the optional index attribute for documents in database.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### reset\_vector\_ids
-
-```python
- | reset_vector_ids(index: Optional[str] = None)
-```
-
-Set vector IDs for all documents as None
-
-
-#### update\_document\_meta
-
-```python
- | update_document_meta(id: str, meta: Dict[str, str])
-```
-
-Update the metadata dictionary of a document by specifying its string id
-
-
-#### get\_document\_count
-
-```python
- | get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
-```
-
-Return the number of documents in the document store.
-
-
-#### get\_label\_count
-
-```python
- | get_label_count(index: Optional[str] = None) -> int
-```
-
-Return the number of labels in the document store
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-#### delete\_documents
-
-```python
- | delete_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-# Module: faiss
-
-
-## Class: FAISSDocumentStore
-
-```python
-class FAISSDocumentStore(SQLDocumentStore)
-```
-
-Document store for very large scale embedding based dense retrievers like the DPR.
-
-It implements the FAISS library(https://github.com/facebookresearch/faiss)
-to perform similarity search on vectors.
-
-The document text and meta-data (for filtering) are stored using the SQLDocumentStore, while
-the vector embeddings are indexed in a FAISS Index.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(sql_url: str = "sqlite:///", vector_dim: int = 768, faiss_index_factory_str: str = "Flat", faiss_index: Optional["faiss.swigfaiss.Index"] = None, return_embedding: bool = False, index: str = "document", similarity: str = "dot_product", embedding_field: str = "embedding", progress_bar: bool = True, duplicate_documents: str = 'overwrite', **kwargs, ,)
-```
-
-**Arguments**:
-
-- `sql_url`: SQL connection URL for database. It defaults to local file based SQLite DB. For large scale
- deployment, Postgres is recommended.
-- `vector_dim`: the embedding vector size.
-- `faiss_index_factory_str`: Create a new FAISS index of the specified type.
- The type is determined from the given string following the conventions
- of the original FAISS index factory.
- Recommended options:
- - "Flat" (default): Best accuracy (= exact). Becomes slow and RAM intense for > 1 Mio docs.
- - "HNSW": Graph-based heuristic. If not further specified,
- we use the following config:
- HNSW64, efConstruction=80 and efSearch=20
- - "IVFx,Flat": Inverted Index. Replace x with the number of centroids aka nlist.
- Rule of thumb: nlist = 10 * sqrt (num_docs) is a good starting point.
- For more details see:
- - Overview of indices https://github.com/facebookresearch/faiss/wiki/Faiss-indexes
- - Guideline for choosing an index https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
- - FAISS Index factory https://github.com/facebookresearch/faiss/wiki/The-index-factory
- Benchmarks: XXX
-- `faiss_index`: Pass an existing FAISS Index, i.e. an empty one that you configured manually
- or one with docs that you used in Haystack before and want to load again.
-- `return_embedding`: To return document embedding
-- `index`: Name of index in document store to use.
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
- more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
-- `embedding_field`: Name of field containing an embedding vector.
-- `progress_bar`: Whether to show a tqdm progress bar or not.
- Can be helpful to disable in production deployments to keep the logs clean.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None)
-```
-
-Add new documents to the DocumentStore.
-
-**Arguments**:
-
-- `documents`: List of `Dicts` or List of `Documents`. If they already contain the embeddings, we'll index
- them right away in FAISS. If not, you can later call update_embeddings() to create & index them.
-- `index`: (SQL) index name for storing the docs and metadata
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-**Raises**:
-
-- `DuplicateDocumentError`: Exception trigger on duplicate document
-
-**Returns**:
-
-
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None, update_existing_embeddings: bool = True, filters: Optional[Dict[str, List[str]]] = None, batch_size: int = 10_000)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever to use to get embeddings for text
-- `index`: Index name for which embeddings are to be updated. If set to None, the default self.index is used.
-- `update_existing_embeddings`: Whether to update existing embeddings of the documents. If set to False,
- only documents without embeddings are processed. This mode can be used for
- incremental updating of embeddings, wherein, only newly indexed documents
- get processed.
-- `filters`: Optional filters to narrow down the documents for which embeddings are to be updated.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-**Returns**:
-
-None
-
-
-#### get\_all\_documents\_generator
-
-```python
- | get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> Generator[Document, None, None]
-```
-
-Get all documents from the document store. Under-the-hood, documents are fetched in batches from the
-document store and yielded as individual documents. This method can be used to iteratively process
-a large number of documents without having to load all documents in memory.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_embedding\_count
-
-```python
- | get_embedding_count(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None) -> int
-```
-
-Return the count of embeddings in the document store.
-
-
-#### train\_index
-
-```python
- | train_index(documents: Optional[Union[List[dict], List[Document]]], embeddings: Optional[np.ndarray] = None, index: Optional[str] = None)
-```
-
-Some FAISS indices (e.g. IVF) require initial "training" on a sample of vectors before you can add your final vectors.
-The train vectors should come from the same distribution as your final ones.
-You can pass either documents (incl. embeddings) or just the plain embeddings that the index shall be trained on.
-
-**Arguments**:
-
-- `documents`: Documents (incl. the embeddings)
-- `embeddings`: Plain embeddings
-- `index`: Name of the index to train. If None, the DocumentStore's default index (self.index) will be used.
-
-**Returns**:
-
-None
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete all documents from the document store.
-
-
-#### delete\_documents
-
-```python
- | delete_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete all documents from the document store.
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.ndarray, filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: Index name to query the document from.
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### save
-
-```python
- | save(file_path: Union[str, Path])
-```
-
-Save FAISS Index to the specified file.
-
-**Arguments**:
-
-- `file_path`: Path to save to.
-
-**Returns**:
-
-None
-
-
-#### load
-
-```python
- | @classmethod
- | load(cls, faiss_file_path: Union[str, Path], sql_url: str, index: str)
-```
-
-Load a saved FAISS index from a file and connect to the SQL database.
-Note: In order to have a correct mapping from FAISS to SQL,
- make sure to use the same SQL DB that you used when calling `save()`.
-
-**Arguments**:
-
-- `faiss_file_path`: Stored FAISS index file. Can be created via calling `save()`
-- `sql_url`: Connection string to the SQL database that contains your docs and metadata.
-- `index`: Index name to load the FAISS index as. It must match the index name used for
- when creating the FAISS index.
-
-**Returns**:
-
-
-
-
-# Module: milvus
-
-
-## Class: MilvusDocumentStore
-
-```python
-class MilvusDocumentStore(SQLDocumentStore)
-```
-
-Milvus (https://milvus.io/) is a highly reliable, scalable Document Store specialized on storing and processing vectors.
-Therefore, it is particularly suited for Haystack users that work with dense retrieval methods (like DPR).
-In contrast to FAISS, Milvus ...
- - runs as a separate service (e.g. a Docker container) and can scale easily in a distributed environment
- - allows dynamic data management (i.e. you can insert/delete vectors without recreating the whole index)
- - encapsulates multiple ANN libraries (FAISS, ANNOY ...)
-
-This class uses Milvus for all vector related storage, processing and querying.
-The meta-data (e.g. for filtering) and the document text are however stored in a separate SQL Database as Milvus
-does not allow these data types (yet).
-
-Usage:
-1. Start a Milvus server (see https://milvus.io/docs/v1.0.0/install_milvus.md)
-2. Init a MilvusDocumentStore in Haystack
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(sql_url: str = "sqlite:///", milvus_url: str = "tcp://localhost:19530", connection_pool: str = "SingletonThread", index: str = "document", vector_dim: int = 768, index_file_size: int = 1024, similarity: str = "dot_product", index_type: IndexType = IndexType.FLAT, index_param: Optional[Dict[str, Any]] = None, search_param: Optional[Dict[str, Any]] = None, return_embedding: bool = False, embedding_field: str = "embedding", progress_bar: bool = True, duplicate_documents: str = 'overwrite', **kwargs, ,)
-```
-
-**Arguments**:
-
-- `sql_url`: SQL connection URL for storing document texts and metadata. It defaults to a local, file based SQLite DB. For large scale
- deployment, Postgres is recommended. If using MySQL then same server can also be used for
- Milvus metadata. For more details see https://milvus.io/docs/v1.0.0/data_manage.md.
-- `milvus_url`: Milvus server connection URL for storing and processing vectors.
- Protocol, host and port will automatically be inferred from the URL.
- See https://milvus.io/docs/v1.0.0/install_milvus.md for instructions to start a Milvus instance.
-- `connection_pool`: Connection pool type to connect with Milvus server. Default: "SingletonThread".
-- `index`: Index name for text, embedding and metadata (in Milvus terms, this is the "collection name").
-- `vector_dim`: The embedding vector size. Default: 768.
-- `index_file_size`: Specifies the size of each segment file that is stored by Milvus and its default value is 1024 MB.
-When the size of newly inserted vectors reaches the specified volume, Milvus packs these vectors into a new segment.
-Milvus creates one index file for each segment. When conducting a vector search, Milvus searches all index files one by one.
-As a rule of thumb, we would see a 30% ~ 50% increase in the search performance after changing the value of index_file_size from 1024 to 2048.
-Note that an overly large index_file_size value may cause failure to load a segment into the memory or graphics memory.
-(From https://milvus.io/docs/v1.0.0/performance_faq.md#How-can-I-get-the-best-performance-from-Milvus-through-setting-index_file_size)
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default and recommended for DPR embeddings.
- 'cosine' is recommended for Sentence Transformers, but is not directly supported by Milvus.
- However, you can normalize your embeddings and use `dot_product` to get the same results.
- See https://milvus.io/docs/v1.0.0/metric.md?Inner-product-(IP)`floating`.
-- `index_type`: Type of approximate nearest neighbour (ANN) index used. The choice here determines your tradeoff between speed and accuracy.
- Some popular options:
- - FLAT (default): Exact method, slow
- - IVF_FLAT, inverted file based heuristic, fast
- - HSNW: Graph based, fast
- - ANNOY: Tree based, fast
- See: https://milvus.io/docs/v1.0.0/index.md
-- `index_param`: Configuration parameters for the chose index_type needed at indexing time.
- For example: {"nlist": 16384} as the number of cluster units to create for index_type IVF_FLAT.
- See https://milvus.io/docs/v1.0.0/index.md
-- `search_param`: Configuration parameters for the chose index_type needed at query time
- For example: {"nprobe": 10} as the number of cluster units to query for index_type IVF_FLAT.
- See https://milvus.io/docs/v1.0.0/index.md
-- `return_embedding`: To return document embedding.
-- `embedding_field`: Name of field containing an embedding vector.
-- `progress_bar`: Whether to show a tqdm progress bar or not.
- Can be helpful to disable in production deployments to keep the logs clean.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None, index_param: Optional[Dict[str, Any]] = None)
-```
-
-Add new documents to the DocumentStore.
-
-**Arguments**:
-
-- `documents`: List of `Dicts` or List of `Documents`. If they already contain the embeddings, we'll index
- them right away in Milvus. If not, you can later call update_embeddings() to create & index them.
-- `index`: (SQL) index name for storing the docs and metadata
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-**Raises**:
-
-- `DuplicateDocumentError`: Exception trigger on duplicate document
-
-**Returns**:
-
-
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None, batch_size: int = 10_000, update_existing_embeddings: bool = True, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever to use to get embeddings for text
-- `index`: (SQL) index name for storing the docs and metadata
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-- `update_existing_embeddings`: Whether to update existing embeddings of the documents. If set to False,
- only documents without embeddings are processed. This mode can be used for
- incremental updating of embeddings, wherein, only newly indexed documents
- get processed.
-- `filters`: Optional filters to narrow down the documents for which embeddings are to be updated.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-
-**Returns**:
-
-None
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.ndarray, filters: Optional[dict] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: (SQL) index name for storing the docs and metadata
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete all documents (from SQL AND Milvus).
-
-**Arguments**:
-
-- `index`: (SQL) index name for storing the docs and metadata
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-
-**Returns**:
-
-None
-
-
-#### delete\_documents
-
-```python
- | delete_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete all documents (from SQL AND Milvus).
-
-**Arguments**:
-
-- `index`: (SQL) index name for storing the docs and metadata
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-
-**Returns**:
-
-None
-
-
-#### get\_all\_documents\_generator
-
-```python
- | get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> Generator[Document, None, None]
-```
-
-Get all documents from the document store. Under-the-hood, documents are fetched in batches from the
-document store and yielded as individual documents. This method can be used to iteratively process
-a large number of documents without having to load all documents in memory.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_documents
-
-```python
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> List[Document]
-```
-
-Get documents from the document store (optionally using filter criteria).
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-**Arguments**:
-
-- `id`: ID of the document
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index: Optional[str] = None, batch_size: int = 10_000) -> List[Document]
-```
-
-Fetch multiple documents by specifying their IDs (strings)
-
-**Arguments**:
-
-- `ids`: List of IDs of the documents
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_vectors
-
-```python
- | get_all_vectors(index: Optional[str] = None) -> List[np.ndarray]
-```
-
-Helper function to dump all vectors stored in Milvus server.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-
-**Returns**:
-
-List[np.array]: List of vectors.
-
-
-#### get\_embedding\_count
-
-```python
- | get_embedding_count(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None) -> int
-```
-
-Return the count of embeddings in the document store.
diff --git a/src/pages/docs/versions/master/latest/site/en/api/api/evaluation.md b/src/pages/docs/versions/master/latest/site/en/api/api/evaluation.md
deleted file mode 100644
index 6dce91425..000000000
--- a/src/pages/docs/versions/master/latest/site/en/api/api/evaluation.md
+++ /dev/null
@@ -1,104 +0,0 @@
----
-title: "Evaluation"
-metaTitle: "Evaluation"
-metaDescription: ""
-slug: "/docs/evaluationmd"
-date: "2020-09-03"
-id: "apievaluationmd"
----
-
-# Evaluation
-
-
-# Module: eval
-
-
-## Class: EvalDocuments
-
-```python
-class EvalDocuments()
-```
-
-This is a pipeline node that should be placed after a node that returns a List of Document, e.g., Retriever or
-Ranker, in order to assess its performance. Performance metrics are stored in this class and updated as each
-sample passes through it. To view the results of the evaluation, call EvalDocuments.print(). Note that results
-from this Node may differ from that when calling Retriever.eval() since that is a closed domain evaluation. Have
-a look at our evaluation tutorial for more info about open vs closed domain eval (
-https://haystack.deepset.ai/docs/latest/tutorial5md).
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(debug: bool = False, open_domain: bool = True, top_k_eval_documents: int = 10, name="EvalDocuments")
-```
-
-**Arguments**:
-
-- `open_domain`: When True, a document is considered correctly retrieved so long as the answer string can be found within it.
- When False, correct retrieval is evaluated based on document_id.
-- `debug`: When True, a record of each sample and its evaluation will be stored in EvalDocuments.log
-- `top_k`: calculate eval metrics for top k results, e.g., recall@k
-
-
-#### run
-
-```python
- | run(documents, labels: dict, top_k_eval_documents: Optional[int] = None, **kwargs)
-```
-
-Run this node on one sample and its labels
-
-
-#### print
-
-```python
- | print()
-```
-
-Print the evaluation results
-
-
-## Class: EvalAnswers
-
-```python
-class EvalAnswers()
-```
-
-This is a pipeline node that should be placed after a Reader in order to assess the performance of the Reader
-individually or to assess the extractive QA performance of the whole pipeline. Performance metrics are stored in
-this class and updated as each sample passes through it. To view the results of the evaluation, call EvalAnswers.print().
-Note that results from this Node may differ from that when calling Reader.eval()
-since that is a closed domain evaluation. Have a look at our evaluation tutorial for more info about
-open vs closed domain eval (https://haystack.deepset.ai/docs/latest/tutorial5md).
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(skip_incorrect_retrieval: bool = True, open_domain: bool = True, debug: bool = False)
-```
-
-**Arguments**:
-
-- `skip_incorrect_retrieval`: When set to True, this eval will ignore the cases where the retriever returned no correct documents
-- `open_domain`: When True, extracted answers are evaluated purely on string similarity rather than the position of the extracted answer
-- `debug`: When True, a record of each sample and its evaluation will be stored in EvalAnswers.log
-
-
-#### run
-
-```python
- | run(labels, answers, **kwargs)
-```
-
-Run this node on one sample and its labels
-
-
-#### print
-
-```python
- | print(mode)
-```
-
-Print the evaluation results
diff --git a/src/pages/docs/versions/master/latest/site/en/api/api/file_converter.md b/src/pages/docs/versions/master/latest/site/en/api/api/file_converter.md
deleted file mode 100644
index 16b6c0eca..000000000
--- a/src/pages/docs/versions/master/latest/site/en/api/api/file_converter.md
+++ /dev/null
@@ -1,284 +0,0 @@
----
-title: "File Converters"
-metaTitle: "File Converters"
-metaDescription: ""
-slug: "/docs/file_converters"
-date: "2020-09-03"
-id: "file_convertersmd"
----
-
-# File Converters
-
-
-# Module: base
-
-
-## Class: BaseConverter
-
-```python
-class BaseConverter(BaseComponent)
-```
-
-Base class for implementing file converts to transform input documents to text format for ingestion in DocumentStore.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(remove_numeric_tables: bool = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-
-
-#### convert
-
-```python
- | @abstractmethod
- | convert(file_path: Path, meta: Optional[Dict[str, str]], remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "utf-8") -> Dict[str, Any]
-```
-
-Convert a file to a dictionary containing the text and any associated meta data.
-
-File converters may extract file meta like name or size. In addition to it, user
-supplied meta data like author, url, external IDs can be supplied as a dictionary.
-
-**Arguments**:
-
-- `file_path`: path of the file to convert
-- `meta`: dictionary of meta data key-value pairs to append in the returned document.
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-- `encoding`: Select the file encoding (default is `utf-8`)
-
-
-#### validate\_language
-
-```python
- | validate_language(text: str) -> bool
-```
-
-Validate if the language of the text is one of valid languages.
-
-
-## Class: FileTypeClassifier
-
-```python
-class FileTypeClassifier(BaseComponent)
-```
-
-Route files in an Indexing Pipeline to corresponding file converters.
-
-
-# Module: txt
-
-
-## Class: TextConverter
-
-```python
-class TextConverter(BaseConverter)
-```
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "utf-8") -> Dict[str, Any]
-```
-
-Reads text from a txt file and executes optional preprocessing steps.
-
-**Arguments**:
-
-- `file_path`: path of the file to convert
-- `meta`: dictionary of meta data key-value pairs to append in the returned document.
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-- `encoding`: Select the file encoding (default is `utf-8`)
-
-**Returns**:
-
-Dict of format {"text": "The text from file", "meta": meta}}
-
-
-# Module: docx
-
-
-## Class: DocxToTextConverter
-
-```python
-class DocxToTextConverter(BaseConverter)
-```
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = None) -> Dict[str, Any]
-```
-
-Extract text from a .docx file.
-Note: As docx doesn't contain "page" information, we actually extract and return a list of paragraphs here.
-For compliance with other converters we nevertheless opted for keeping the methods name.
-
-**Arguments**:
-
-- `file_path`: Path to the .docx file you want to convert
-- `meta`: dictionary of meta data key-value pairs to append in the returned document.
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-- `encoding`: Not applicable
-
-
-# Module: tika
-
-
-## Class: TikaConverter
-
-```python
-class TikaConverter(BaseConverter)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(tika_url: str = "http://localhost:9998/tika", remove_numeric_tables: bool = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `tika_url`: URL of the Tika server
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = None) -> Dict[str, Any]
-```
-
-**Arguments**:
-
-- `file_path`: path of the file to convert
-- `meta`: dictionary of meta data key-value pairs to append in the returned document.
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-- `encoding`: Not applicable
-
-**Returns**:
-
-a list of pages and the extracted meta data of the file.
-
-
-# Module: pdf
-
-
-## Class: PDFToTextConverter
-
-```python
-class PDFToTextConverter(BaseConverter)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(remove_numeric_tables: bool = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "Latin1") -> Dict[str, Any]
-```
-
-Extract text from a .pdf file using the pdftotext library (https://www.xpdfreader.com/pdftotext-man.html)
-
-**Arguments**:
-
-- `file_path`: Path to the .pdf file you want to convert
-- `meta`: Optional dictionary with metadata that shall be attached to all resulting documents.
- Can be any custom keys and values.
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-- `encoding`: Encoding that will be passed as -enc parameter to pdftotext. "Latin 1" is the default encoding
- of pdftotext. While this works well on many PDFs, it might be needed to switch to "UTF-8" or
- others if your doc contains special characters (e.g. German Umlauts, Cyrillic characters ...).
- Note: With "UTF-8" we experienced cases, where a simple "fi" gets wrongly parsed as
- "xefc" (see test cases). That's why we keep "Latin 1" as default here.
- (See list of available encodings by running `pdftotext -listencodings` in the terminal)
diff --git a/src/pages/docs/versions/master/latest/site/en/api/api/generator.md b/src/pages/docs/versions/master/latest/site/en/api/api/generator.md
deleted file mode 100644
index cfd5361be..000000000
--- a/src/pages/docs/versions/master/latest/site/en/api/api/generator.md
+++ /dev/null
@@ -1,242 +0,0 @@
----
-title: "Generator"
-metaTitle: "Generator"
-metaDescription: ""
-slug: "/docs/apigeneratormd"
-date: "2020-09-03"
-id: "apigeneratormd"
----
-
-# Generator
-
-
-# Module: base
-
-
-## Class: BaseGenerator
-
-```python
-class BaseGenerator(BaseComponent)
-```
-
-Abstract class for Generators
-
-
-#### predict
-
-```python
- | @abstractmethod
- | predict(query: str, documents: List[Document], top_k: Optional[int]) -> Dict
-```
-
-Abstract method to generate answers.
-
-**Arguments**:
-
-- `query`: Query
-- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on.
-- `top_k`: Number of returned answers
-
-**Returns**:
-
-Generated answers plus additional infos in a dict
-
-
-# Module: transformers
-
-
-## Class: RAGenerator
-
-```python
-class RAGenerator(BaseGenerator)
-```
-
-Implementation of Facebook's Retrieval-Augmented Generator (https://arxiv.org/abs/2005.11401) based on
-HuggingFace's transformers (https://huggingface.co/transformers/model_doc/rag.html).
-
-Instead of "finding" the answer within a document, these models **generate** the answer.
-In that sense, RAG follows a similar approach as GPT-3 but it comes with two huge advantages
-for real-world applications:
-a) it has a manageable model size
-b) the answer generation is conditioned on retrieved documents,
-i.e. the model can easily adjust to domain documents even after training has finished
-(in contrast: GPT-3 relies on the web data seen during training)
-
-**Example**
-
-```python
-| query = "who got the first nobel prize in physics?"
-|
-| # Retrieve related documents from retriever
-| retrieved_docs = retriever.retrieve(query=query)
-|
-| # Now generate answer from query and retrieved documents
-| generator.predict(
-| query=query,
-| documents=retrieved_docs,
-| top_k=1
-| )
-|
-| # Answer
-|
-| {'query': 'who got the first nobel prize in physics',
-| 'answers':
-| [{'query': 'who got the first nobel prize in physics',
-| 'answer': ' albert einstein',
-| 'meta': { 'doc_ids': [...],
-| 'doc_scores': [80.42758 ...],
-| 'doc_probabilities': [40.71379089355469, ...
-| 'texts': ['Albert Einstein was a ...]
-| 'titles': ['"Albert Einstein"', ...]
-| }}]}
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: str = "facebook/rag-token-nq", model_version: Optional[str] = None, retriever: Optional[DensePassageRetriever] = None, generator_type: RAGeneratorType = RAGeneratorType.TOKEN, top_k: int = 2, max_length: int = 200, min_length: int = 2, num_beams: int = 2, embed_title: bool = True, prefix: Optional[str] = None, use_gpu: bool = True)
-```
-
-Load a RAG model from Transformers along with passage_embedding_model.
-See https://huggingface.co/transformers/model_doc/rag.html for more details
-
-**Arguments**:
-
-- `model_name_or_path`: Directory of a saved model or the name of a public model e.g.
- 'facebook/rag-token-nq', 'facebook/rag-sequence-nq'.
- See https://huggingface.co/models for full list of available models.
-- `model_version`: The version of model to use from the HuggingFace model hub. Can be tag name, branch name, or commit hash.
-- `retriever`: `DensePassageRetriever` used to embedded passage
-- `generator_type`: Which RAG generator implementation to use? RAG-TOKEN or RAG-SEQUENCE
-- `top_k`: Number of independently generated text to return
-- `max_length`: Maximum length of generated text
-- `min_length`: Minimum length of generated text
-- `num_beams`: Number of beams for beam search. 1 means no beam search.
-- `embed_title`: Embedded the title of passage while generating embedding
-- `prefix`: The prefix used by the generator's tokenizer.
-- `use_gpu`: Whether to use GPU (if available)
-
-
-#### predict
-
-```python
- | predict(query: str, documents: List[Document], top_k: Optional[int] = None) -> Dict
-```
-
-Generate the answer to the input query. The generation will be conditioned on the supplied documents.
-These document can for example be retrieved via the Retriever.
-
-**Arguments**:
-
-- `query`: Query
-- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on.
-- `top_k`: Number of returned answers
-
-**Returns**:
-
-Generated answers plus additional infos in a dict like this:
-
-```python
-| {'query': 'who got the first nobel prize in physics',
-| 'answers':
-| [{'query': 'who got the first nobel prize in physics',
-| 'answer': ' albert einstein',
-| 'meta': { 'doc_ids': [...],
-| 'doc_scores': [80.42758 ...],
-| 'doc_probabilities': [40.71379089355469, ...
-| 'texts': ['Albert Einstein was a ...]
-| 'titles': ['"Albert Einstein"', ...]
-| }}]}
-```
-
-
-## Class: Seq2SeqGenerator
-
-```python
-class Seq2SeqGenerator(BaseGenerator)
-```
-
-A generic sequence-to-sequence generator based on HuggingFace's transformers.
-
-Text generation is supported by so called auto-regressive language models like GPT2,
-XLNet, XLM, Bart, T5 and others. In fact, any HuggingFace language model that extends
-GenerationMixin can be used by Seq2SeqGenerator.
-
-Moreover, as language models prepare model input in their specific encoding, each model
-specified with model_name_or_path parameter in this Seq2SeqGenerator should have an
-accompanying model input converter that takes care of prefixes, separator tokens etc.
-By default, we provide model input converters for a few well-known seq2seq language models (e.g. ELI5).
-It is the responsibility of Seq2SeqGenerator user to ensure an appropriate model input converter
-is either already registered or specified on a per-model basis in the Seq2SeqGenerator constructor.
-
-For mode details on custom model input converters refer to _BartEli5Converter
-
-
-See https://huggingface.co/transformers/main_classes/model.html?transformers.generation_utils.GenerationMixin#transformers.generation_utils.GenerationMixin
-as well as https://huggingface.co/blog/how-to-generate
-
-For a list of all text-generation models see https://huggingface.co/models?pipeline_tag=text-generation
-
-**Example**
-
-```python
-| query = "Why is Dothraki language important?"
-|
-| # Retrieve related documents from retriever
-| retrieved_docs = retriever.retrieve(query=query)
-|
-| # Now generate answer from query and retrieved documents
-| generator.predict(
-| query=query,
-| documents=retrieved_docs,
-| top_k=1
-| )
-|
-| # Answer
-|
-| {'answers': [" The Dothraki language is a constructed fictional language. It's important because George R.R. Martin wrote it."],
-| 'query': 'Why is Dothraki language important?'}
-|
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: str, input_converter: Optional[Callable] = None, top_k: int = 1, max_length: int = 200, min_length: int = 2, num_beams: int = 8, use_gpu: bool = True)
-```
-
-**Arguments**:
-
-- `model_name_or_path`: a HF model name for auto-regressive language model like GPT2, XLNet, XLM, Bart, T5 etc
-- `input_converter`: an optional Callable to prepare model input for the underlying language model
- specified in model_name_or_path parameter. The required __call__ method signature for
- the Callable is:
- __call__(tokenizer: PreTrainedTokenizer, query: str, documents: List[Document],
- top_k: Optional[int] = None) -> BatchEncoding:
-- `top_k`: Number of independently generated text to return
-- `max_length`: Maximum length of generated text
-- `min_length`: Minimum length of generated text
-- `num_beams`: Number of beams for beam search. 1 means no beam search.
-- `use_gpu`: Whether to use GPU (if available)
-
-
-#### predict
-
-```python
- | predict(query: str, documents: List[Document], top_k: Optional[int] = None) -> Dict
-```
-
-Generate the answer to the input query. The generation will be conditioned on the supplied documents.
-These document can be retrieved via the Retriever or supplied directly via predict method.
-
-**Arguments**:
-
-- `query`: Query
-- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on.
-- `top_k`: Number of returned answers
-
-**Returns**:
-
-Generated answers
diff --git a/src/pages/docs/versions/master/latest/site/en/api/api/graph_retriever.md b/src/pages/docs/versions/master/latest/site/en/api/api/graph_retriever.md
deleted file mode 100644
index 640eedb17..000000000
--- a/src/pages/docs/versions/master/latest/site/en/api/api/graph_retriever.md
+++ /dev/null
@@ -1,68 +0,0 @@
----
-title: "Graph Retriever"
-metaTitle: "Graph Retriever"
-metaDescription: ""
-slug: "/docs/graph_retrievermd"
-date: "2020-09-03"
-id: "apigraphretrievermd"
----
-
-# Graph Retriever
-
-
-# Module: base
-
-
-# Module: text\_to\_sparql
-
-
-## Class: Text2SparqlRetriever
-
-```python
-class Text2SparqlRetriever(BaseGraphRetriever)
-```
-
-Graph retriever that uses a pre-trained Bart model to translate natural language questions given in text form to queries in SPARQL format.
-The generated SPARQL query is executed on a knowledge graph.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(knowledge_graph, model_name_or_path, top_k: int = 1)
-```
-
-Init the Retriever by providing a knowledge graph and a pre-trained BART model
-
-**Arguments**:
-
-- `knowledge_graph`: An instance of BaseKnowledgeGraph on which to execute SPARQL queries.
-- `model_name_or_path`: Name of or path to a pre-trained BartForConditionalGeneration model.
-- `top_k`: How many SPARQL queries to generate per text query.
-
-
-#### retrieve
-
-```python
- | retrieve(query: str, top_k: Optional[int] = None)
-```
-
-Translate a text query to SPARQL and execute it on the knowledge graph to retrieve a list of answers
-
-**Arguments**:
-
-- `query`: Text query that shall be translated to SPARQL and then executed on the knowledge graph
-- `top_k`: How many SPARQL queries to generate per text query.
-
-
-#### format\_result
-
-```python
- | format_result(result)
-```
-
-Generate formatted dictionary output with text answer and additional info
-
-**Arguments**:
-
-- `result`: The result of a SPARQL query as retrieved from the knowledge graph
diff --git a/src/pages/docs/versions/master/latest/site/en/api/api/knowledge_graph.md b/src/pages/docs/versions/master/latest/site/en/api/api/knowledge_graph.md
deleted file mode 100644
index d6e648b93..000000000
--- a/src/pages/docs/versions/master/latest/site/en/api/api/knowledge_graph.md
+++ /dev/null
@@ -1,165 +0,0 @@
----
-title: "Knowledge Graph"
-metaTitle: "Knowledge Graph"
-metaDescription: ""
-slug: "/docs/knowledge_graphmd"
-date: "2020-09-03"
-id: "apiknowledgegraphmd"
----
-
-# Knowledge Graph
-
-
-# Module: base
-
-
-# Module: graphdb
-
-
-## Class: GraphDBKnowledgeGraph
-
-```python
-class GraphDBKnowledgeGraph(BaseKnowledgeGraph)
-```
-
-Knowledge graph store that runs on a GraphDB instance
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(host: str = "localhost", port: int = 7200, username: str = "", password: str = "", index: Optional[str] = None, prefixes: str = "")
-```
-
-Init the knowledge graph by defining the settings to connect with a GraphDB instance
-
-**Arguments**:
-
-- `host`: address of server where the GraphDB instance is running
-- `port`: port where the GraphDB instance is running
-- `username`: username to login to the GraphDB instance (if any)
-- `password`: password to login to the GraphDB instance (if any)
-- `index`: name of the index (also called repository) stored in the GraphDB instance
-- `prefixes`: definitions of namespaces with a new line after each namespace, e.g., PREFIX hp: `__
-
-**Example**
-
-```python
-| docs = [Document(text="PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions."
-| "The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by"
-| "the shutoffs which were expected to last through at least midday tomorrow.")]
-|
-| # Summarize
-| summary = summarizer.predict(
-| documents=docs,
-| generate_single_summary=True
-| )
-|
-| # Show results (List of Documents, containing summary and original text)
-| print(summary)
-|
-| [
-| {
-| "text": "California's largest electricity provider has turned off power to hundreds of thousands of customers.",
-| ...
-| "meta": {
-| "context": "PGE stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. ..."
-| },
-| ...
-| },
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: str = "google/pegasus-xsum", model_version: Optional[str] = None, tokenizer: Optional[str] = None, max_length: int = 200, min_length: int = 5, use_gpu: int = 0, clean_up_tokenization_spaces: bool = True, separator_for_single_summary: str = " ", generate_single_summary: bool = False)
-```
-
-Load a Summarization model from Transformers.
-See the up-to-date list of available models at
-https://huggingface.co/models?filter=summarization
-
-**Arguments**:
-
-- `model_name_or_path`: Directory of a saved model or the name of a public model e.g.
- 'facebook/rag-token-nq', 'facebook/rag-sequence-nq'.
- See https://huggingface.co/models?filter=summarization for full list of available models.
-- `model_version`: The version of model to use from the HuggingFace model hub. Can be tag name, branch name, or commit hash.
-- `tokenizer`: Name of the tokenizer (usually the same as model)
-- `max_length`: Maximum length of summarized text
-- `min_length`: Minimum length of summarized text
-- `use_gpu`: If < 0, then use cpu. If >= 0, this is the ordinal of the gpu to use
-- `clean_up_tokenization_spaces`: Whether or not to clean up the potential extra spaces in the text output
-- `separator_for_single_summary`: If `generate_single_summary=True` in `predict()`, we need to join all docs
- into a single text. This separator appears between those subsequent docs.
-- `generate_single_summary`: Whether to generate a single summary for all documents or one summary per document.
- If set to "True", all docs will be joined to a single string that will then
- be summarized.
- Important: The summary will depend on the order of the supplied documents!
-
-
-#### predict
-
-```python
- | predict(documents: List[Document], generate_single_summary: Optional[bool] = None) -> List[Document]
-```
-
-Produce the summarization from the supplied documents.
-These document can for example be retrieved via the Retriever.
-
-**Arguments**:
-
-- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on.
-- `generate_single_summary`: Whether to generate a single summary for all documents or one summary per document.
- If set to "True", all docs will be joined to a single string that will then
- be summarized.
- Important: The summary will depend on the order of the supplied documents!
-
-**Returns**:
-
-List of Documents, where Document.text contains the summarization and Document.meta["context"]
- the original, not summarized text
diff --git a/src/pages/docs/versions/master/latest/site/en/api/api/translator.md b/src/pages/docs/versions/master/latest/site/en/api/api/translator.md
deleted file mode 100644
index e478c9f26..000000000
--- a/src/pages/docs/versions/master/latest/site/en/api/api/translator.md
+++ /dev/null
@@ -1,112 +0,0 @@
----
-title: "Translator"
-metaTitle: "Translator"
-metaDescription: ""
-slug: "/docs/translatormd"
-date: "2020-09-03"
-id: "apitranslatormd"
----
-
-# Translator
-
-
-# Module: base
-
-
-## Class: BaseTranslator
-
-```python
-class BaseTranslator(BaseComponent)
-```
-
-Abstract class for a Translator component that translates either a query or a doc from language A to language B.
-
-
-#### translate
-
-```python
- | @abstractmethod
- | translate(query: Optional[str] = None, documents: Optional[Union[List[Document], List[str], List[Dict[str, Any]]]] = None, dict_key: Optional[str] = None, **kwargs) -> Union[str, List[Document], List[str], List[Dict[str, Any]]]
-```
-
-Translate the passed query or a list of documents from language A to B.
-
-
-#### run
-
-```python
- | run(query: Optional[str] = None, documents: Optional[Union[List[Document], List[str], List[Dict[str, Any]]]] = None, answers: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, dict_key: Optional[str] = None, **kwargs)
-```
-
-Method that gets executed when this class is used as a Node in a Haystack Pipeline
-
-
-# Module: transformers
-
-
-## Class: TransformersTranslator
-
-```python
-class TransformersTranslator(BaseTranslator)
-```
-
-Translator component based on Seq2Seq models from Huggingface's transformers library.
-Exemplary use cases:
-- Translate a query from Language A to B (e.g. if you only have good models + documents in language B)
-- Translate a document from Language A to B (e.g. if you want to return results in the native language of the user)
-
-We currently recommend using OPUS models (see __init__() for details)
-
-**Example:**
-
-```python
-| DOCS = [
-| Document(text="Heinz von Foerster was an Austrian American scientist combining physics and philosophy,
-| and widely attributed as the originator of Second-order cybernetics.")
-| ]
-| translator = TransformersTranslator(model_name_or_path="Helsinki-NLP/opus-mt-en-de")
-| res = translator.translate(documents=DOCS, query=None)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: str, tokenizer_name: Optional[str] = None, max_seq_len: Optional[int] = None, clean_up_tokenization_spaces: Optional[bool] = True)
-```
-
-Initialize the translator with a model that fits your targeted languages. While we support all seq2seq
-models from Hugging Face's model hub, we recommend using the OPUS models from Helsiniki NLP. They provide plenty
-of different models, usually one model per language pair and translation direction.
-They have a pretty standardized naming that should help you find the right model:
-- "Helsinki-NLP/opus-mt-en-de" => translating from English to German
-- "Helsinki-NLP/opus-mt-de-en" => translating from German to English
-- "Helsinki-NLP/opus-mt-fr-en" => translating from French to English
-- "Helsinki-NLP/opus-mt-hi-en"=> translating from Hindi to English
-...
-
-They also have a few multilingual models that support multiple languages at once.
-
-**Arguments**:
-
-- `model_name_or_path`: Name of the seq2seq model that shall be used for translation.
- Can be a remote name from Huggingface's modelhub or a local path.
-- `tokenizer_name`: Optional tokenizer name. If not supplied, `model_name_or_path` will also be used for the
- tokenizer.
-- `max_seq_len`: The maximum sentence length the model accepts. (Optional)
-- `clean_up_tokenization_spaces`: Whether or not to clean up the tokenization spaces. (default True)
-
-
-#### translate
-
-```python
- | translate(query: Optional[str] = None, documents: Optional[Union[List[Document], List[str], List[Dict[str, Any]]]] = None, dict_key: Optional[str] = None, **kwargs) -> Union[str, List[Document], List[str], List[Dict[str, Any]]]
-```
-
-Run the actual translation. You can supply a query or a list of documents. Whatever is supplied will be translated.
-
-**Arguments**:
-
-- `query`: The query string to translate
-- `documents`: The documents to translate
-- `dict_key`:
diff --git a/src/pages/docs/versions/master/latest/site/en/img/haystack_logo_blue_banner.png b/src/pages/docs/versions/master/latest/site/en/img/haystack_logo_blue_banner.png
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diff --git a/src/pages/docs/versions/master/latest/site/en/menuStructure/menu.json b/src/pages/docs/versions/master/latest/site/en/menuStructure/menu.json
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index 04bfabd1e..000000000
--- a/src/pages/docs/versions/master/latest/site/en/menuStructure/menu.json
+++ /dev/null
@@ -1,408 +0,0 @@
-{
- "menuList": [
- {
- "id": "overview",
- "title": "Overview",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 0,
- "isMenu": true
- },
- {
- "id": "get_startedmd",
- "title": "Get Started",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "intromd",
- "title": "What is Haystack?",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "use_casesmd",
- "title": "Use Cases",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "roadmapmd",
- "title": "Roadmap",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "faqmd",
- "title": "FAQ",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "termsmd",
- "title": "Glossary",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "usage_haystack",
- "title": "Usage",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 1,
- "isMenu": true
- },
- {
- "id": "preprocessingmd",
- "title": "Preprocessing",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "pipelinesmd",
- "title": "Pipelines",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "documentstoremd",
- "title": "Document Store",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "retrievermd",
- "title": "Retriever",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "readermd",
- "title": "Reader",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "generatormd",
- "title": "Generator",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "summarizermd",
- "title": "Summarizer",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 6
- },
- {
- "id": "translatormd",
- "title": "Translator",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 7
- },
- {
- "id": "knowledgegraphmd",
- "title": "Knowledge Graph",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 8
- },
- {
- "id": "languagesmd",
- "title": "Languages Other Than English",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 9
- },
- {
- "id": "domain_adaptationmd",
- "title": "Domain Adaptation",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 10
- },
- {
- "id": "optimizationmd",
- "title": "Optimization",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 11
- },
- {
- "id": "annotationmd",
- "title": "Annotation Tool",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 12
- },
- {
- "id": "rankermd",
- "title": "Ranker",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 13
- },
- {
- "id": "query_classifiermd",
- "title": "Query Classifier",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 14
- },
- {
- "id": "tutorials_haystack",
- "title": "Tutorials",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 2,
- "isMenu": true
- },
- {
- "id": "tutorial1md",
- "title": "Build Your First QA System",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "tutorial2md",
- "title": "Fine-tuning a Model on Your Own Data",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "tutorial3md",
- "title": "Build a QA System Without Elasticsearch",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "tutorial4md",
- "title": "Utilizing existing FAQs for Question Answering",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "tutorial5md",
- "title": "Evaluation of a QA System",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "tutorial6md",
- "title": "Better Retrieval via \"Dense Passage Retrieval\"",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "tutorial7md",
- "title": "Generative QA with \"Retrieval-Augmented Generation\"",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 6
- },
- {
- "id": "tutorial8md",
- "title": "Preprocessing your Documents",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 7
- },
- {
- "id": "tutorial9md",
- "title": "How to Train DPR",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 8
- },
- {
- "id": "tutorial10md",
- "title": "Question Answering on a Knowledge Graph",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 9
- },
- {
- "id": "tutorial11md",
- "title": "How to use Pipelines",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 10
- },
- {
- "id": "tutorial12md",
- "title": "Generative QA with \"LFQA\"",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 11
- },
- {
- "id": "api_haystack",
- "title": "API reference",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 3,
- "isMenu": true
- },
- {
- "id": "apidatabasemd",
- "title": "Document Store",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "apiretrievermd",
- "title": "Retriever",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "apireadermd",
- "title": "Reader",
- "label1": "api_haystack",
- "label2": "api_haystack",
- "label3": "",
- "order": 2
- },
- {
- "id": "apigeneratormd",
- "title": "Generator",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "apisummarizermd",
- "title": "Summarizer",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "apitranslatormd",
- "title": "Translator",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "apiindexingmd",
- "title": "Preprocessor",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 6
- },
- {
- "id": "file_convertersmd",
- "title": "File Converters",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 7
- },
- {
- "id": "apicrawlermd",
- "title": "Crawler",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 8
- },
- {
- "id": "apievaluationmd",
- "title": "Evaluation",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 9
- },
- {
- "id": "apipipelinesmd",
- "title": "Pipelines",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 10
- },
- {
- "id": "apiknowledgegraphmd",
- "title": "Knowledge Graph",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 11
- },
- {
- "id": "apigraphretrievermd",
- "title": "Graph Retriever",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 12
- }
- ]
-}
\ No newline at end of file
diff --git a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/1.md b/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/1.md
deleted file mode 100644
index d00738cfa..000000000
--- a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/1.md
+++ /dev/null
@@ -1,258 +0,0 @@
----
-title: "Tutorial 1"
-metaTitle: "Build Your First QA System"
-metaDescription: ""
-slug: "/docs/tutorial1"
-date: "2020-09-03"
-id: "tutorial1md"
----
-
-# Build Your First QA System
-
-
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb)
-
-Question Answering can be used in a variety of use cases. A very common one: Using it to navigate through complex knowledge bases or long documents ("search setting").
-
-A "knowledge base" could for example be your website, an internal wiki or a collection of financial reports.
-In this tutorial we will work on a slightly different domain: "Game of Thrones".
-
-Let's see how we can use a bunch of Wikipedia articles to answer a variety of questions about the
-marvellous seven kingdoms...
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-Haystack finds answers to queries within the documents stored in a `DocumentStore`. The current implementations of `DocumentStore` include `ElasticsearchDocumentStore`, `FAISSDocumentStore`, `SQLDocumentStore`, and `InMemoryDocumentStore`.
-
-**Here:** We recommended Elasticsearch as it comes preloaded with features like [full-text queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html), [BM25 retrieval](https://www.elastic.co/elasticon/conf/2016/sf/improved-text-scoring-with-bm25), and [vector storage for text embeddings](https://www.elastic.co/guide/en/elasticsearch/reference/7.6/dense-vector.html).
-
-**Alternatives:** If you are unable to setup an Elasticsearch instance, then follow the [Tutorial 3](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb) for using SQL/InMemory document stores.
-
-**Hint**: This tutorial creates a new document store instance with Wikipedia articles on Game of Thrones. However, you can configure Haystack to work with your existing document stores.
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker via the Haystack utility function
-from haystack.utils import launch_es
-
-launch_es()
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-# Connect to Elasticsearch
-
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles about Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first fetch some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is:
-# {
-# 'text': "",
-# 'meta': {'name': "", ...}
-#}
-# (Optionally: you can also add more key-value-pairs here, that will be indexed as fields in Elasticsearch and
-# can be accessed later for filtering or shown in the responses of the Pipeline)
-
-# Let's have a look at the first 3 entries:
-print(dicts[:3])
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-## Initalize Retriever, Reader, & Pipeline
-
-### Retriever
-
-Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question could be answered.
-They use some simple but fast algorithm.
-
-**Here:** We use Elasticsearch's default BM25 algorithm
-
-**Alternatives:**
-
-- Customize the `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `DensePassageRetriever` to use different embedding models for passage and query (see Tutorial 6)
-
-
-```python
-from haystack.retriever.sparse import ElasticsearchRetriever
-retriever = ElasticsearchRetriever(document_store=document_store)
-```
-
-
-```python
-# Alternative: An in-memory TfidfRetriever based on Pandas dataframes for building quick-prototypes with SQLite document store.
-
-# from haystack.retriever.sparse import TfidfRetriever
-# retriever = TfidfRetriever(document_store=document_store)
-```
-
-### Reader
-
-A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based
-on powerful, but slower deep learning models.
-
-Haystack currently supports Readers based on the frameworks FARM and Transformers.
-With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).
-
-**Here:** a medium sized RoBERTa QA model using a Reader based on FARM (https://huggingface.co/deepset/roberta-base-squad2)
-
-**Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)
-
-**Alternatives (Models):** e.g. "distilbert-base-uncased-distilled-squad" (fast) or "deepset/bert-large-uncased-whole-word-masking-squad2" (good accuracy)
-
-**Hint:** You can adjust the model to return "no answer possible" with the no_ans_boost. Higher values mean the model prefers "no answer possible"
-
-#### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-#### TransformersReader
-
-
-```python
-# Alternative:
-# reader = TransformersReader(model_name_or_path="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1)
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import ExtractiveQAPipeline
-pipe = ExtractiveQAPipeline(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = pipe.run(query="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-# prediction = pipe.run(query="Who created the Dothraki vocabulary?", top_k_reader=5)
-# prediction = pipe.run(query="Who is the sister of Sansa?", top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
-
diff --git a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/10.md b/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/10.md
deleted file mode 100644
index d69775ab2..000000000
--- a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/10.md
+++ /dev/null
@@ -1,149 +0,0 @@
----
-title: "Tutorial 10"
-metaTitle: "Knowledge Graph QA"
-metaDescription: ""
-slug: "/docs/tutorial10"
-date: "2021-04-06"
-id: "tutorial10md"
----
-
-# Question Answering on a Knowledge Graph
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial10_Knowledge_Graph.ipynb)
-
-Haystack allows storing and querying knowledge graphs with the help of pre-trained models that translate text queries to SPARQL queries.
-This tutorial demonstrates how to load an existing knowledge graph into haystack, load a pre-trained retriever, and execute text queries on the knowledge graph.
-The training of models that translate text queries into SPARQL queries is currently not supported.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-```
-
-
-```python
-# Here are some imports that we'll need
-
-import subprocess
-import time
-from pathlib import Path
-
-from haystack.graph_retriever.text_to_sparql import Text2SparqlRetriever
-from haystack.knowledge_graph.graphdb import GraphDBKnowledgeGraph
-from haystack.preprocessor.utils import fetch_archive_from_http
-```
-
-## Downloading Knowledge Graph and Model
-
-
-```python
-# Let's first fetch some triples that we want to store in our knowledge graph
-# Here: exemplary triples from the wizarding world
-graph_dir = "../data/tutorial10_knowledge_graph/"
-s3_url = "https://fandom-qa.s3-eu-west-1.amazonaws.com/triples_and_config.zip"
-fetch_archive_from_http(url=s3_url, output_dir=graph_dir)
-
-# Fetch a pre-trained BART model that translates text queries to SPARQL queries
-model_dir = "../saved_models/tutorial10_knowledge_graph/"
-s3_url = "https://fandom-qa.s3-eu-west-1.amazonaws.com/saved_models/hp_v3.4.zip"
-fetch_archive_from_http(url=s3_url, output_dir=model_dir)
-```
-
-## Launching a GraphDB instance
-
-
-```python
-# Unfortunately, there seems to be no good way to run GraphDB in colab environments
-# In your local environment, you could start a GraphDB server with docker
-# Feel free to check GraphDB's website for the free version https://www.ontotext.com/products/graphdb/graphdb-free/
-print("Starting GraphDB ...")
-status = subprocess.run(
- ['docker run -d -p 7200:7200 --name graphdb-instance-tutorial docker-registry.ontotext.com/graphdb-free:9.4.1-adoptopenjdk11'], shell=True
-)
-if status.returncode:
- raise Exception("Failed to launch GraphDB. Maybe it is already running or you already have a container with that name that you could start?")
-time.sleep(5)
-```
-
-## Creating a new GraphDB repository (also known as index in haystack's document stores)
-
-
-```python
-# Initialize a knowledge graph connected to GraphDB and use "tutorial_10_index" as the name of the index
-kg = GraphDBKnowledgeGraph(index="tutorial_10_index")
-
-# Delete the index as it might have been already created in previous runs
-kg.delete_index()
-
-# Create the index based on a configuration file
-kg.create_index(config_path=Path(graph_dir+"repo-config.ttl"))
-
-# Import triples of subject, predicate, and object statements from a ttl file
-kg.import_from_ttl_file(index="tutorial_10_index", path=Path(graph_dir+"triples.ttl"))
-print(f"The last triple stored in the knowledge graph is: {kg.get_all_triples()[-1]}")
-print(f"There are {len(kg.get_all_triples())} triples stored in the knowledge graph.")
-```
-
-
-```python
-# Define prefixes for names of resources so that we can use shorter resource names in queries
-prefixes = """PREFIX rdf:
-PREFIX xsd:
-PREFIX hp: ?obj . }")
-print(result)
-# Paraphrased question: What is the patronus of Hermione?
-# Correct answer: Otter
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/11.md b/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/11.md
deleted file mode 100644
index 1a4f3b5cc..000000000
--- a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/11.md
+++ /dev/null
@@ -1,411 +0,0 @@
----
-title: "Tutorial 11"
-metaTitle: "Pipelines"
-metaDescription: ""
-slug: "/docs/tutorial11"
-date: "2021-04-06"
-id: "tutorial11md"
----
-
-# Pipelines Tutorial
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial11_Pipelines.ipynb)
-
-In this tutorial, you will learn how the `Pipeline` class acts as a connector between all the different
-building blocks that are found in FARM. Whether you are using a Reader, Generator, Summarizer
-or Retriever (or 2), the `Pipeline` class will help you build a Directed Acyclic Graph (DAG) that
-determines how to route the output of one component into the input of another.
-
-
-## Setting Up the Environment
-
-Let's start by ensuring we have a GPU running to ensure decent speed in this tutorial.
-In Google colab, you can change to a GPU runtime in the menu:
-- **Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-These lines are to install Haystack through pip
-
-
-```python
-# Install the latest release of Haystack in your own environment
-!pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install --upgrade git+https://github.com/deepset-ai/haystack.git
-
-# Install pygraphviz
-!apt install libgraphviz-dev
-!pip install pygraphviz
-```
-
-If running from Colab or a no Docker environment, you will want to start Elasticsearch from source
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-## Initialization
-
-Here are some core imports
-
-
-```python
-from haystack.utils import print_answers, print_documents
-```
-
-Then let's fetch some data (in this case, pages from the Game of Thrones wiki) and prepare it so that it can
-be used indexed into our `DocumentStore`
-
-
-```python
-from haystack.preprocessor.utils import fetch_archive_from_http, convert_files_to_dicts
-from haystack.preprocessor.cleaning import clean_wiki_text
-
-#Download and prepare data - 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# convert files to dicts containing documents that can be indexed to our datastore
-got_dicts = convert_files_to_dicts(
- dir_path=doc_dir,
- clean_func=clean_wiki_text,
- split_paragraphs=True
-)
-```
-
-Here we initialize the core components that we will be gluing together using the `Pipeline` class.
-We have a `DocumentStore`, an `ElasticsearchRetriever` and a `FARMReader`.
-These can be combined to create a classic Retriever-Reader pipeline that is designed
-to perform Open Domain Question Answering.
-
-
-```python
-from haystack import Pipeline
-from haystack.utils import launch_es
-from haystack.document_store import ElasticsearchDocumentStore
-from haystack.retriever.sparse import ElasticsearchRetriever
-from haystack.retriever.dense import DensePassageRetriever
-from haystack.reader import FARMReader
-
-
-# Initialize DocumentStore and index documents
-launch_es()
-document_store = ElasticsearchDocumentStore()
-document_store.delete_all_documents()
-document_store.write_documents(got_dicts)
-
-# Initialize Sparse retriever
-es_retriever = ElasticsearchRetriever(document_store=document_store)
-
-# Initialize dense retriever
-dpr_retriever = DensePassageRetriever(document_store)
-document_store.update_embeddings(dpr_retriever, update_existing_embeddings=False)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")
-```
-
-## Prebuilt Pipelines
-
-Haystack features many prebuilt pipelines that cover common tasks.
-Here we have an `ExtractiveQAPipeline` (the successor to the now deprecated `Finder` class).
-
-
-```python
-from haystack.pipeline import ExtractiveQAPipeline
-
-# Prebuilt pipeline
-p_extractive_premade = ExtractiveQAPipeline(reader=reader, retriever=es_retriever)
-res = p_extractive_premade.run(
- query="Who is the father of Arya Stark?",
- top_k_retriever=10,
- top_k_reader=5
-)
-print_answers(res, details="minimal")
-```
-
-If you want to just do the retrieval step, you can use a `DocumentSearchPipeline`
-
-
-```python
-from haystack.pipeline import DocumentSearchPipeline
-
-p_retrieval = DocumentSearchPipeline(es_retriever)
-res = p_retrieval.run(
- query="Who is the father of Arya Stark?",
- top_k_retriever=10
-)
-print_documents(res, max_text_len=200)
-```
-
-Or if you want to use a `Generator` instead of a `Reader`,
-you can initialize a `GenerativeQAPipeline` like this:
-
-
-```python
-from haystack.pipeline import GenerativeQAPipeline, FAQPipeline
-from haystack.generator import RAGenerator
-
-# We set this to True so that the document store returns document embeddings with each document
-# This is needed by the Generator
-document_store.return_embedding = True
-
-# Initialize generator
-rag_generator = RAGenerator()
-
-# Generative QA
-p_generator = GenerativeQAPipeline(generator=rag_generator, retriever=dpr_retriever)
-res = p_generator.run(
- query="Who is the father of Arya Stark?",
- top_k_retriever=10
-)
-print_answers(res, details="minimal")
-
-# We are setting this to False so that in later pipelines,
-# we get a cleaner printout
-document_store.return_embedding = False
-
-```
-
-Haystack features prebuilt pipelines to do:
-- just document search (DocumentSearchPipeline),
-- document search with summarization (SearchSummarizationPipeline)
-- generative QA (GenerativeQAPipeline)
-- FAQ style QA (FAQPipeline)
-- translated search (TranslationWrapperPipeline)
-To find out more about these pipelines, have a look at our [documentation](https://haystack.deepset.ai/docs/latest/pipelinesmd)
-
-
-With any Pipeline, whether prebuilt or custom constructed,
-you can save a diagram showing how all the components are connected.
-
-
-
-
-```python
-p_extractive_premade.draw("pipeline_extractive_premade.png")
-p_retrieval.draw("pipeline_retrieval.png")
-p_generator.draw("pipeline_generator.png")
-```
-
-## Custom Pipelines
-
-Now we are going to rebuild the `ExtractiveQAPipelines` using the generic Pipeline class.
-We do this by adding the building blocks that we initialized as nodes in the graph.
-
-
-```python
-# Custom built extractive QA pipeline
-p_extractive = Pipeline()
-p_extractive.add_node(component=es_retriever, name="Retriever", inputs=["Query"])
-p_extractive.add_node(component=reader, name="Reader", inputs=["Retriever"])
-
-# Now we can run it
-res = p_extractive.run(
- query="Who is the father of Arya Stark?",
- top_k_retriever=10,
- top_k_reader=5
-)
-print_answers(res, details="minimal")
-p_extractive.draw("pipeline_extractive.png")
-```
-
-Pipelines offer a very simple way to ensemble together different components.
-In this example, we are going to combine the power of a `DensePassageRetriever`
-with the keyword based `ElasticsearchRetriever`.
-See our [documentation](https://haystack.deepset.ai/docs/latest/retrievermd) to understand why
-we might want to combine a dense and sparse retriever.
-
-
-
-Here we use a `JoinDocuments` node so that the predictions from each retriever can be merged together.
-
-
-```python
-from haystack.pipeline import JoinDocuments
-
-# Create ensembled pipeline
-p_ensemble = Pipeline()
-p_ensemble.add_node(component=es_retriever, name="ESRetriever", inputs=["Query"])
-p_ensemble.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["Query"])
-p_ensemble.add_node(component=JoinDocuments(join_mode="concatenate"), name="JoinResults", inputs=["ESRetriever", "DPRRetriever"])
-p_ensemble.add_node(component=reader, name="Reader", inputs=["JoinResults"])
-p_ensemble.draw("pipeline_ensemble.png")
-
-# Run pipeline
-res = p_ensemble.run(
- query="Who is the father of Arya Stark?",
- top_k_retriever=5 #This is top_k per retriever
-)
-print_answers(res, details="minimal")
-```
-
-## Custom Nodes
-
-Nodes are relatively simple objects
-and we encourage our users to design their own if they don't see on that fits their use case
-
-The only requirements are:
-- Add a method run(self, **kwargs) to your class. **kwargs will contain the output from the previous node in your graph.
-- Do whatever you want within run() (e.g. reformatting the query)
-- Return a tuple that contains your output data (for the next node)
-and the name of the outgoing edge (by default "output_1" for nodes that have one output)
-- Add a class attribute outgoing_edges = 1 that defines the number of output options from your node. You only need a higher number here if you have a decision node (see below).
-
-Here we have a template for a Node:
-
-
-```python
-class NodeTemplate():
- outgoing_edges = 1
-
- def run(self, **kwargs):
- # Insert code here to manipulate the variables in kwarg
- return (kwargs, "output_1")
-```
-
-## Decision Nodes
-
-Decision Nodes help you route your data so that only certain branches of your `Pipeline` are run.
-One popular use case for such query classifiers is routing keyword queries to Elasticsearch and questions to DPR + Reader.
-With this approach you keep optimal speed and simplicity for keywords while going deep with transformers when it's most helpful.
-
-
-
-Though this looks very similar to the ensembled pipeline shown above,
-the key difference is that only one of the retrievers is run for each request.
-By contrast both retrievers are always run in the ensembled approach.
-
-Below, we define a very naive `QueryClassifier` and show how to use it:
-
-
-```python
-class QueryClassifier():
- outgoing_edges = 2
-
- def run(self, **kwargs):
- if "?" in kwargs["query"]:
- return (kwargs, "output_2")
- else:
- return (kwargs, "output_1")
-
-# Here we build the pipeline
-p_classifier = Pipeline()
-p_classifier.add_node(component=QueryClassifier(), name="QueryClassifier", inputs=["Query"])
-p_classifier.add_node(component=es_retriever, name="ESRetriever", inputs=["QueryClassifier.output_1"])
-p_classifier.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_2"])
-p_classifier.add_node(component=reader, name="QAReader", inputs=["ESRetriever", "DPRRetriever"])
-p_classifier.draw("pipeline_classifier.png")
-
-# Run only the dense retriever on the full sentence query
-res_1 = p_classifier.run(
- query="Who is the father of Arya Stark?",
- top_k_retriever=10
-)
-print("DPR Results" + "\n" + "="*15)
-print_answers(res_1)
-
-# Run only the sparse retriever on a keyword based query
-res_2 = p_classifier.run(
- query="Arya Stark father",
- top_k_retriever=10
-)
-print("ES Results" + "\n" + "="*15)
-print_answers(res_2)
-```
-
-## Evaluation Nodes
-
-We have also designed a set of nodes that can be used to evaluate the performance of a system.
-Have a look at our [tutorial](https://haystack.deepset.ai/docs/latest/tutorial5md) to get hands on with the code and learn more about Evaluation Nodes!
-
-
-## YAML Configs
-
-A full `Pipeline` can be defined in a YAML file and simply loaded.
-Having your pipeline available in a YAML is particularly useful
-when you move between experimentation and production environments.
-Just export the YAML from your notebook / IDE and import it into your production environment.
-It also helps with version control of pipelines,
-allows you to share your pipeline easily with colleagues,
-and simplifies the configuration of pipeline parameters in production.
-
-It consists of two main sections: you define all objects (e.g. a reader) in components
-and then stick them together to a pipeline in pipelines.
-You can also set one component to be multiple nodes of a pipeline or to be a node across multiple pipelines.
-It will be loaded just once in memory and therefore doesn't hurt your resources more than actually needed.
-
-The contents of a YAML file should look something like this:
-
-```yaml
-version: '0.7'
-components: # define all the building-blocks for Pipeline
-- name: MyReader # custom-name for the component; helpful for visualization & debugging
- type: FARMReader # Haystack Class name for the component
- params:
- no_ans_boost: -10
- model_name_or_path: deepset/roberta-base-squad2
-- name: MyESRetriever
- type: ElasticsearchRetriever
- params:
- document_store: MyDocumentStore # params can reference other components defined in the YAML
- custom_query: null
-- name: MyDocumentStore
- type: ElasticsearchDocumentStore
- params:
- index: haystack_test
-pipelines: # multiple Pipelines can be defined using the components from above
-- name: my_query_pipeline # a simple extractive-qa Pipeline
- nodes:
- - name: MyESRetriever
- inputs: [Query]
- - name: MyReader
- inputs: [MyESRetriever]
-```
-
-To load, simply call:
-``` python
-pipeline.load_from_yaml(Path("sample.yaml"))
-```
-
-## Conclusion
-
-The possibilities are endless with the `Pipeline` class and we hope that this tutorial will inspire you
-to build custom pipeplines that really work for your use case!
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/12.md b/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/12.md
deleted file mode 100644
index d65d1a3d6..000000000
--- a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/12.md
+++ /dev/null
@@ -1,164 +0,0 @@
----
-title: "Tutorial 12"
-metaTitle: "Generative QA with LFQA"
-metaDescription: ""
-slug: "/docs/tutorial12"
-date: "2021-04-06"
-id: "tutorial12md"
----
-
-# Long-Form Question Answering
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial12_LFQA.ipynb)
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.generator.transformers import Seq2SeqGenerator
-```
-
-### Document Store
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-The default flavour of FAISSDocumentStore is "Flat" but can also be set to "HNSW" for
-faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.
-For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-
-
-```python
-from haystack.document_store.faiss import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(vector_dim=128, faiss_index_factory_str="Flat")
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever and Reader/Generator
-
-#### Retriever
-
-**Here:** We use a `RetribertRetriever` and we invoke `update_embeddings` to index the embeddings of documents in the `FAISSDocumentStore`
-
-
-
-
-```python
-from haystack.retriever.dense import EmbeddingRetriever
-
-retriever = EmbeddingRetriever(document_store=document_store,
- embedding_model="yjernite/retribert-base-uncased",
- model_format="retribert")
-
-document_store.update_embeddings(retriever)
-```
-
-Before we blindly use the `RetribertRetriever` let's empirically test it to make sure a simple search indeed finds the relevant documents.
-
-
-```python
-from haystack.utils import print_answers, print_documents
-from haystack.pipeline import DocumentSearchPipeline
-
-p_retrieval = DocumentSearchPipeline(retriever)
-res = p_retrieval.run(
- query="Tell me something about Arya Stark?",
- top_k_retriever=5
-)
-print_documents(res, max_text_len=512)
-
-```
-
-#### Reader/Generator
-
-Similar to previous Tutorials we now initalize our reader/generator.
-
-Here we use a `Seq2SeqGenerator` with the *yjernite/bart_eli5* model (see: https://huggingface.co/yjernite/bart_eli5)
-
-
-
-
-```python
-generator = Seq2SeqGenerator(model_name_or_path="yjernite/bart_eli5")
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `GenerativeQAPipeline` that combines a retriever and a reader/generator to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import GenerativeQAPipeline
-pipe = GenerativeQAPipeline(generator, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-pipe.run(query="Why did Arya Stark's character get portrayed in a television adaptation?", top_k_retriever=1)
-```
-
-
-```python
-pipe.run(query="What kind of character does Arya Stark play?", top_k_retriever=1)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/2.md b/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/2.md
deleted file mode 100644
index ec5349afb..000000000
--- a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/2.md
+++ /dev/null
@@ -1,108 +0,0 @@
----
-title: "Tutorial 2"
-metaTitle: "Fine-tuning a model on your own data"
-metaDescription: ""
-slug: "/docs/tutorial2"
-date: "2020-09-03"
-id: "tutorial2md"
----
-
-# Fine-tuning a Model on Your Own Data
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial2_Finetune_a_model_on_your_data.ipynb)
-
-For many use cases it is sufficient to just use one of the existing public models that were trained on SQuAD or other public QA datasets (e.g. Natural Questions).
-However, if you have domain-specific questions, fine-tuning your model on custom examples will very likely boost your performance.
-While this varies by domain, we saw that ~ 2000 examples can easily increase performance by +5-20%.
-
-This tutorial shows you how to fine-tune a pretrained model on your own dataset.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.reader.farm import FARMReader
-```
-
-
-## Create Training Data
-
-There are two ways to generate training data
-
-1. **Annotation**: You can use the [annotation tool](https://github.com/deepset-ai/haystack#labeling-tool) to label your data, i.e. highlighting answers to your questions in a document. The tool supports structuring your workflow with organizations, projects, and users. The labels can be exported in SQuAD format that is compatible for training with Haystack.
-
-
-
-2. **Feedback**: For production systems, you can collect training data from direct user feedback via Haystack's [REST API interface](https://github.com/deepset-ai/haystack#rest-api). This includes a customizable user feedback API for providing feedback on the answer returned by the API. The API provides a feedback export endpoint to obtain the feedback data for fine-tuning your model further.
-
-
-## Fine-tune your model
-
-Once you have collected training data, you can fine-tune your base models.
-We initialize a reader as a base model and fine-tune it on our own custom dataset (should be in SQuAD-like format).
-We recommend using a base model that was trained on SQuAD or a similar QA dataset before to benefit from Transfer Learning effects.
-
-**Recommendation**: Run training on a GPU.
-If you are using Colab: Enable this in the menu "Runtime" > "Change Runtime type" > Select "GPU" in dropdown.
-Then change the `use_gpu` arguments below to `True`
-
-
-```python
-reader = FARMReader(model_name_or_path="distilbert-base-uncased-distilled-squad", use_gpu=True)
-data_dir = "data/squad20"
-# data_dir = "PATH/TO_YOUR/TRAIN_DATA"
-reader.train(data_dir=data_dir, train_filename="dev-v2.0.json", use_gpu=True, n_epochs=1, save_dir="my_model")
-```
-
-
-```python
-# Saving the model happens automatically at the end of training into the `save_dir` you specified
-# However, you could also save a reader manually again via:
-reader.save(directory="my_model")
-```
-
-
-```python
-# If you want to load it at a later point, just do:
-new_reader = FARMReader(model_name_or_path="my_model")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/3.md b/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/3.md
deleted file mode 100644
index 09f83ace6..000000000
--- a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/3.md
+++ /dev/null
@@ -1,202 +0,0 @@
----
-title: "Tutorial 3"
-metaTitle: "Build a QA System Without Elasticsearch"
-metaDescription: ""
-slug: "/docs/tutorial3"
-date: "2020-09-03"
-id: "tutorial3md"
----
-
-# Build a QA System Without Elasticsearch
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb)
-
-Haystack provides alternatives to Elasticsearch for developing quick prototypes.
-
-You can use an `InMemoryDocumentStore` or a `SQLDocumentStore`(with SQLite) as the document store.
-
-If you are interested in more feature-rich Elasticsearch, then please refer to the Tutorial 1.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-
-
-```python
-# In-Memory Document Store
-from haystack.document_store.memory import InMemoryDocumentStore
-document_store = InMemoryDocumentStore()
-```
-
-
-```python
-# SQLite Document Store
-# from haystack.document_store.sql import SQLDocumentStore
-# document_store = SQLDocumentStore(url="sqlite:///qa.db")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles on Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first get some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# convert files to dicts containing documents that can be indexed to our datastore
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is: {"name": ", "text": ""}
-
-# Let's have a look at the first 3 entries:
-print(dicts[:3])
-# Now, let's write the docs to our DB.
-document_store.write_documents(dicts)
-```
-
-## Initalize Retriever, Reader & Pipeline
-
-### Retriever
-
-Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question could be answered.
-
-With InMemoryDocumentStore or SQLDocumentStore, you can use the TfidfRetriever. For more retrievers, please refer to the tutorial-1.
-
-
-```python
-# An in-memory TfidfRetriever based on Pandas dataframes
-
-from haystack.retriever.sparse import TfidfRetriever
-retriever = TfidfRetriever(document_store=document_store)
-```
-
-### Reader
-
-A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based
-on powerful, but slower deep learning models.
-
-Haystack currently supports Readers based on the frameworks FARM and Transformers.
-With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).
-
-**Here:** a medium sized RoBERTa QA model using a Reader based on FARM (https://huggingface.co/deepset/roberta-base-squad2)
-
-**Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)
-
-**Alternatives (Models):** e.g. "distilbert-base-uncased-distilled-squad" (fast) or "deepset/bert-large-uncased-whole-word-masking-squad2" (good accuracy)
-
-**Hint:** You can adjust the model to return "no answer possible" with the no_ans_boost. Higher values mean the model prefers "no answer possible"
-
-#### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-#### TransformersReader
-
-
-```python
-# Alternative:
-# reader = TransformersReader(model_name_or_path="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1)
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import ExtractiveQAPipeline
-pipe = ExtractiveQAPipeline(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = pipe.run(query="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-# prediction = pipe.run(query="Who created the Dothraki vocabulary?", top_k_reader=5)
-# prediction = pipe.run(query="Who is the sister of Sansa?", top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/4.md b/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/4.md
deleted file mode 100644
index f76298305..000000000
--- a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/4.md
+++ /dev/null
@@ -1,176 +0,0 @@
----
-title: "Tutorial 4"
-metaTitle: "Utilizing existing FAQs for Question Answering"
-metaDescription: ""
-slug: "/docs/tutorial4"
-date: "2020-09-03"
-id: "tutorial4md"
----
-
-# Utilizing existing FAQs for Question Answering
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial4_FAQ_style_QA.ipynb)
-
-While *extractive Question Answering* works on pure texts and is therefore more generalizable, there's also a common alternative that utilizes existing FAQ data.
-
-**Pros**:
-
-- Very fast at inference time
-- Utilize existing FAQ data
-- Quite good control over answers
-
-**Cons**:
-
-- Generalizability: We can only answer questions that are similar to existing ones in FAQ
-
-In some use cases, a combination of extractive QA and FAQ-style can also be an interesting option.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-from haystack.retriever.dense import EmbeddingRetriever
-from haystack.utils import print_answers
-import pandas as pd
-import requests
-
-```
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker via the Haystack utility function
-from haystack.utils import launch_es
-
-launch_es()
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-
-```
-
-### Init the DocumentStore
-In contrast to Tutorial 1 (extractive QA), we:
-
-* specify the name of our `text_field` in Elasticsearch that we want to return as an answer
-* specify the name of our `embedding_field` in Elasticsearch where we'll store the embedding of our question and that is used later for calculating our similarity to the incoming user question
-* set `excluded_meta_data=["question_emb"]` so that we don't return the huge embedding vectors in our search results
-
-
-```python
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="",
- index="document",
- embedding_field="question_emb",
- embedding_dim=768,
- excluded_meta_data=["question_emb"])
-```
-
-### Create a Retriever using embeddings
-Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones).
-We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings.
-
-
-```python
-retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert", use_gpu=True)
-```
-
-### Prepare & Index FAQ data
-We create a pandas dataframe containing some FAQ data (i.e curated pairs of question + answer) and index those in elasticsearch.
-Here: We download some question-answer pairs related to COVID-19
-
-
-```python
-# Download
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/faqs/faq_covidbert.csv")
-open('small_faq_covid.csv', 'wb').write(temp.content)
-
-# Get dataframe with columns "question", "answer" and some custom metadata
-df = pd.read_csv("small_faq_covid.csv")
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-df["question"] = df["question"].apply(lambda x: x.strip())
-print(df.head())
-
-# Get embeddings for our questions from the FAQs
-questions = list(df["question"].values)
-df["question_emb"] = retriever.embed_queries(texts=questions)
-df = df.rename(columns={"question": "text"})
-
-# Convert Dataframe to list of dicts and index them in our DocumentStore
-docs_to_index = df.to_dict(orient="records")
-document_store.write_documents(docs_to_index)
-```
-
-### Ask questions
-Initialize a Pipeline (this time without a reader) and ask questions
-
-
-```python
-from haystack.pipeline import FAQPipeline
-pipe = FAQPipeline(retriever=retriever)
-```
-
-
-```python
-prediction = pipe.run(query="How is the virus spreading?", top_k_retriever=10)
-print_answers(prediction, details="all")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/5.md b/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/5.md
deleted file mode 100644
index b54d5591c..000000000
--- a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/5.md
+++ /dev/null
@@ -1,260 +0,0 @@
----
-title: "Tutorial 5"
-metaTitle: "Evaluation of a QA System"
-metaDescription: ""
-slug: "/docs/tutorial5"
-date: "2020-09-03"
-id: "tutorial5md"
----
-
-# Evaluation of a QA System
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial5_Evaluation.ipynb)
-
-To be able to make a statement about the performance of a question-answering system, it is important to evalute it. Furthermore, evaluation allows to determine which parts of the system can be improved.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-## Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-from farm.utils import initialize_device_settings
-
-device, n_gpu = initialize_device_settings(use_cuda=True)
-```
-
-
-```python
-from haystack.preprocessor.utils import fetch_archive_from_http
-
-# Download evaluation data, which is a subset of Natural Questions development set containing 50 documents
-doc_dir = "../data/nq"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/nq_dev_subset_v2.json.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-
-```python
-# make sure these indices do not collide with existing ones, the indices will be wiped clean before data is inserted
-doc_index = "tutorial5_docs"
-label_index = "tutorial5_labels"
-```
-
-
-```python
-# Connect to Elasticsearch
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-# Connect to Elasticsearch
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document",
- create_index=False, embedding_field="emb",
- embedding_dim=768, excluded_meta_data=["emb"])
-```
-
-
-```python
-from haystack.preprocessor import PreProcessor
-
-# Add evaluation data to Elasticsearch Document Store
-# We first delete the custom tutorial indices to not have duplicate elements
-# and also split our documents into shorter passages using the PreProcessor
-preprocessor = PreProcessor(
- split_length=500,
- split_overlap=0,
- split_respect_sentence_boundary=False,
- clean_empty_lines=False,
- clean_whitespace=False
-)
-document_store.delete_all_documents(index=doc_index)
-document_store.delete_all_documents(index=label_index)
-document_store.add_eval_data(
- filename="../data/nq/nq_dev_subset_v2.json",
- doc_index=doc_index,
- label_index=label_index,
- preprocessor=preprocessor
-)
-
-# Let's prepare the labels that we need for the retriever and the reader
-labels = document_store.get_all_labels_aggregated(index=label_index)
-```
-
-## Initialize components of QA-System
-
-
-```python
-# Initialize Retriever
-from haystack.retriever.sparse import ElasticsearchRetriever
-retriever = ElasticsearchRetriever(document_store=document_store)
-# Alternative: Evaluate DensePassageRetriever
-# Note, that DPR works best when you index short passages < 512 tokens as only those tokens will be used for the embedding.
-# Here, for nq_dev_subset_v2.json we have avg. num of tokens = 5220(!).
-# DPR still outperforms Elastic's BM25 by a small margin here.
-# from haystack.retriever.dense import DensePassageRetriever
-# retriever = DensePassageRetriever(document_store=document_store,
-# query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
-# passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
-# use_gpu=True,
-# embed_title=True,
-# max_seq_len=256,
-# batch_size=16,
-# remove_sep_tok_from_untitled_passages=True)
-#document_store.update_embeddings(retriever, index=doc_index)
-```
-
-
-```python
-# Initialize Reader
-from haystack.reader.farm import FARMReader
-
-reader = FARMReader("deepset/roberta-base-squad2", top_k=4, return_no_answer=True)
-
-```
-
-
-```python
-from haystack.eval import EvalAnswers, EvalDocuments
-
-# Here we initialize the nodes that perform evaluation
-eval_retriever = EvalDocuments()
-eval_reader = EvalAnswers()
-```
-
-## Evaluation of Retriever
-Here we evaluate only the retriever, based on whether the gold_label document is retrieved.
-
-
-```python
-## Evaluate Retriever on its own
-retriever_eval_results = retriever.eval(top_k=20, label_index=label_index, doc_index=doc_index)
-## Retriever Recall is the proportion of questions for which the correct document containing the answer is
-## among the correct documents
-print("Retriever Recall:", retriever_eval_results["recall"])
-## Retriever Mean Avg Precision rewards retrievers that give relevant documents a higher rank
-print("Retriever Mean Avg Precision:", retriever_eval_results["map"])
-```
-
-## Evaluation of Reader
-Here we evaluate only the reader in a closed domain fashion i.e. the reader is given one query
-and one document and metrics are calculated on whether the right position in this text is selected by
-the model as the answer span (i.e. SQuAD style)
-
-
-```python
-# Evaluate Reader on its own
-reader_eval_results = reader.eval(document_store=document_store, device=device, label_index=label_index, doc_index=doc_index)
-# Evaluation of Reader can also be done directly on a SQuAD-formatted file without passing the data to Elasticsearch
-#reader_eval_results = reader.eval_on_file("../data/nq", "nq_dev_subset_v2.json", device=device)
-
-## Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer
-print("Reader Top-N-Accuracy:", reader_eval_results["top_n_accuracy"])
-## Reader Exact Match is the proportion of questions where the predicted answer is exactly the same as the correct answer
-print("Reader Exact Match:", reader_eval_results["EM"])
-## Reader F1-Score is the average overlap between the predicted answers and the correct answers
-print("Reader F1-Score:", reader_eval_results["f1"])
-```
-
-## Evaluation of Retriever and Reader (Open Domain)
-Here we evaluate retriever and reader in open domain fashion i.e. a document is considered
-correctly retrieved if it contains the answer string within it. The reader is evaluated based purely on the
-predicted string, regardless of which document this came from and the position of the extracted span.
-
-
-```python
-from haystack import Pipeline
-
-# Here is the pipeline definition
-p = Pipeline()
-p.add_node(component=retriever, name="ESRetriever", inputs=["Query"])
-p.add_node(component=eval_retriever, name="EvalRetriever", inputs=["ESRetriever"])
-p.add_node(component=reader, name="QAReader", inputs=["EvalRetriever"])
-p.add_node(component=eval_reader, name="EvalReader", inputs=["QAReader"])
-results = []
-```
-
-
-```python
-# This is how to run the pipeline
-for l in labels:
- res = p.run(
- query=l.question,
- top_k_retriever=10,
- labels=l,
- top_k_reader=10,
- index=doc_index,
- )
- results.append(res)
-```
-
-
-```python
-# When we have run evaluation using the pipeline, we can print the results
-n_queries = len(labels)
-eval_retriever.print()
-print()
-retriever.print_time()
-print()
-eval_reader.print(mode="reader")
-print()
-reader.print_time()
-print()
-eval_reader.print(mode="pipeline")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/6.md b/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/6.md
deleted file mode 100644
index 2e2767b24..000000000
--- a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/6.md
+++ /dev/null
@@ -1,240 +0,0 @@
----
-title: "Tutorial 6"
-metaTitle: "Better retrieval via Dense Passage Retrieval"
-metaDescription: ""
-slug: "/docs/tutorial6"
-date: "2020-09-03"
-id: "tutorial6md"
----
-
-# Better Retrieval via "Dense Passage Retrieval"
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb)
-
-### Importance of Retrievers
-
-The Retriever has a huge impact on the performance of our overall search pipeline.
-
-
-### Different types of Retrievers
-#### Sparse
-Family of algorithms based on counting the occurrences of words (bag-of-words) resulting in very sparse vectors with length = vocab size.
-
-**Examples**: BM25, TF-IDF
-
-**Pros**: Simple, fast, well explainable
-
-**Cons**: Relies on exact keyword matches between query and text
-
-
-#### Dense
-These retrievers use neural network models to create "dense" embedding vectors. Within this family there are two different approaches:
-
-a) Single encoder: Use a **single model** to embed both query and passage.
-b) Dual-encoder: Use **two models**, one to embed the query and one to embed the passage
-
-Recent work suggests that dual encoders work better, likely because they can deal better with the different nature of query and passage (length, style, syntax ...).
-
-**Examples**: REALM, DPR, Sentence-Transformers
-
-**Pros**: Captures semantinc similarity instead of "word matches" (e.g. synonyms, related topics ...)
-
-**Cons**: Computationally more heavy, initial training of model
-
-
-### "Dense Passage Retrieval"
-
-In this Tutorial, we want to highlight one "Dense Dual-Encoder" called Dense Passage Retriever.
-It was introdoced by Karpukhin et al. (2020, https://arxiv.org/abs/2004.04906.
-
-Original Abstract:
-
-_"Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks."_
-
-Paper: https://arxiv.org/abs/2004.04906
-Original Code: https://fburl.com/qa-dpr
-
-
-*Use this* [link](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb) *to open the notebook in Google Colab.*
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-### Document Store
-
-#### Option 1: FAISS
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-The default flavour of FAISSDocumentStore is "Flat" but can also be set to "HNSW" for
-faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.
-For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-
-
-```python
-from haystack.document_store import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
-```
-
-#### Option 2: Milvus
-
-Milvus is an open source database library that is also optimized for vector similarity searches like FAISS.
-Like FAISS it has both a "Flat" and "HNSW" mode but it outperforms FAISS when it comes to dynamic data management.
-It does require a little more setup, however, as it is run through Docker and requires the setup of some config files.
-See [their docs](https://milvus.io/docs/v1.0.0/milvus_docker-cpu.md) for more details.
-
-
-```python
-from haystack.utils import launch_milvus
-from haystack.document_store import MilvusDocumentStore
-
-launch_milvus()
-document_store = MilvusDocumentStore()
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever, Reader & Pipeline
-
-#### Retriever
-
-**Here:** We use a `DensePassageRetriever`
-
-**Alternatives:**
-
-- The `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-
-
-```python
-from haystack.retriever.dense import DensePassageRetriever
-retriever = DensePassageRetriever(document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- max_seq_len_query=64,
- max_seq_len_passage=256,
- batch_size=16,
- use_gpu=True,
- embed_title=True,
- use_fast_tokenizers=True)
-# Important:
-# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all
-# previously indexed documents and update their embedding representation.
-# While this can be a time consuming operation (depending on corpus size), it only needs to be done once.
-# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.
-document_store.update_embeddings(retriever)
-```
-
-#### Reader
-
-Similar to previous Tutorials we now initalize our reader.
-
-Here we use a FARMReader with the *deepset/roberta-base-squad2* model (see: https://huggingface.co/deepset/roberta-base-squad2)
-
-
-
-##### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import ExtractiveQAPipeline
-pipe = ExtractiveQAPipeline(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = pipe.run(query="Who created the Dothraki vocabulary?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/7.md b/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/7.md
deleted file mode 100644
index 3e472df19..000000000
--- a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/7.md
+++ /dev/null
@@ -1,217 +0,0 @@
----
-title: "Tutorial 7"
-metaTitle: "Generative QA with RAG"
-metaDescription: ""
-slug: "/docs/tutorial7"
-date: "2020-11-12"
-id: "tutorial7md"
----
-
-# Generative QA with "Retrieval-Augmented Generation"
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial7_RAG_Generator.ipynb)
-
-While extractive QA highlights the span of text that answers a query,
-generative QA can return a novel text answer that it has composed.
-In this tutorial, you will learn how to set up a generative system using the
-[RAG model](https://arxiv.org/abs/2005.11401) which conditions the
-answer generator on a set of retrieved documents.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-Here are the packages and imports that we'll need:
-
-
-```python
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from typing import List
-import requests
-import pandas as pd
-from haystack import Document
-from haystack.document_store.faiss import FAISSDocumentStore
-from haystack.generator.transformers import RAGenerator
-from haystack.retriever.dense import DensePassageRetriever
-```
-
-Let's download a csv containing some sample text and preprocess the data.
-
-
-
-```python
-# Download sample
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/haystack/master/tutorials/small_generator_dataset.csv")
-open('small_generator_dataset.csv', 'wb').write(temp.content)
-
-# Create dataframe with columns "title" and "text"
-df = pd.read_csv("small_generator_dataset.csv", sep=',')
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-
-print(df.head())
-```
-
-We can cast our data into Haystack Document objects.
-Alternatively, we can also just use dictionaries with "text" and "meta" fields
-
-
-```python
-# Use data to initialize Document objects
-titles = list(df["title"].values)
-texts = list(df["text"].values)
-documents: List[Document] = []
-for title, text in zip(titles, texts):
- documents.append(
- Document(
- text=text,
- meta={
- "name": title or ""
- }
- )
- )
-```
-
-Here we initialize the FAISSDocumentStore, DensePassageRetriever and RAGenerator.
-FAISS is chosen here since it is optimized vector storage.
-
-
-```python
-# Initialize FAISS document store.
-# Set `return_embedding` to `True`, so generator doesn't have to perform re-embedding
-document_store = FAISSDocumentStore(
- faiss_index_factory_str="Flat",
- return_embedding=True
-)
-
-# Initialize DPR Retriever to encode documents, encode question and query documents
-retriever = DensePassageRetriever(
- document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- use_gpu=True,
- embed_title=True,
-)
-
-# Initialize RAG Generator
-generator = RAGenerator(
- model_name_or_path="facebook/rag-token-nq",
- use_gpu=True,
- top_k=1,
- max_length=200,
- min_length=2,
- embed_title=True,
- num_beams=2,
-)
-```
-
-We write documents to the DocumentStore, first by deleting any remaining documents then calling `write_documents()`.
-The `update_embeddings()` method uses the retriever to create an embedding for each document.
-
-
-
-```python
-# Delete existing documents in documents store
-document_store.delete_all_documents()
-
-# Write documents to document store
-document_store.write_documents(documents)
-
-# Add documents embeddings to index
-document_store.update_embeddings(
- retriever=retriever
-)
-```
-
-Here are our questions:
-
-
-```python
-QUESTIONS = [
- "who got the first nobel prize in physics",
- "when is the next deadpool movie being released",
- "which mode is used for short wave broadcast service",
- "who is the owner of reading football club",
- "when is the next scandal episode coming out",
- "when is the last time the philadelphia won the superbowl",
- "what is the most current adobe flash player version",
- "how many episodes are there in dragon ball z",
- "what is the first step in the evolution of the eye",
- "where is gall bladder situated in human body",
- "what is the main mineral in lithium batteries",
- "who is the president of usa right now",
- "where do the greasers live in the outsiders",
- "panda is a national animal of which country",
- "what is the name of manchester united stadium",
-]
-```
-
-Now let's run our system!
-The retriever will pick out a small subset of documents that it finds relevant.
-These are used to condition the generator as it generates the answer.
-What it should return then are novel text spans that form and answer to your question!
-
-
-```python
-# Now generate an answer for each question
-for question in QUESTIONS:
- # Retrieve related documents from retriever
- retriever_results = retriever.retrieve(
- query=question
- )
-
- # Now generate answer from question and retrieved documents
- predicted_result = generator.predict(
- query=question,
- documents=retriever_results,
- top_k=1
- )
-
- # Print you answer
- answers = predicted_result["answers"]
- print(f'Generated answer is \'{answers[0]["answer"]}\' for the question = \'{question}\'')
-```
-
-
-```python
-# Or alternatively use the Pipeline class
-from haystack.pipeline import GenerativeQAPipeline
-
-pipe = GenerativeQAPipeline(generator=generator, retriever=retriever)
-for question in QUESTIONS:
- res = pipe.run(query=question, top_k_generator=1, top_k_retriever=5)
- print(res)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/8.md b/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/8.md
deleted file mode 100644
index a53c3a030..000000000
--- a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/8.md
+++ /dev/null
@@ -1,214 +0,0 @@
----
-title: "Tutorial 8"
-metaTitle: "Preprocessing"
-metaDescription: ""
-slug: "/docs/tutorial8"
-date: "2021-01-08"
-id: "tutorial8md"
----
-
-# Preprocessing
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial8_Preprocessing.ipynb)
-
-Haystack includes a suite of tools to extract text from different file types, normalize white space
-and split text into smaller pieces to optimize retrieval.
-These data preprocessing steps can have a big impact on the systems performance and effective handling of data is key to getting the most out of Haystack.
-
-Ultimately, Haystack expects data to be provided as a list documents in the following dictionary format:
-``` python
-docs = [
- {
- 'text': DOCUMENT_TEXT_HERE,
- 'meta': {'name': DOCUMENT_NAME, ...}
- }, ...
-]
-```
-
-This tutorial will show you all the tools that Haystack provides to help you cast your data into this format.
-
-
-```python
-# Let's start by installing Haystack
-
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-!wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.03.tar.gz
-!tar -xvf xpdf-tools-linux-4.03.tar.gz && sudo cp xpdf-tools-linux-4.03/bin64/pdftotext /usr/local/bin
-```
-
-
-```python
-# Here are the imports we need
-
-from haystack.file_converter.txt import TextConverter
-from haystack.file_converter.pdf import PDFToTextConverter
-from haystack.file_converter.docx import DocxToTextConverter
-
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.preprocessor.preprocessor import PreProcessor
-```
-
-
-```python
-# This fetches some sample files to work with
-
-doc_dir = "data/preprocessing_tutorial"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/preprocessing_tutorial.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-## Converters
-
-Haystack's converter classes are designed to help you turn files on your computer into the documents
-that can be processed by the Haystack pipeline.
-There are file converters for txt, pdf, docx files as well as a converter that is powered by Apache Tika.
-
-
-```python
-# Here are some examples of how you would use file converters
-
-converter = TextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_txt = converter.convert(file_path="data/preprocessing_tutorial/classics.txt", meta=None)
-
-converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_pdf = converter.convert(file_path="data/preprocessing_tutorial/bert.pdf", meta=None)
-
-converter = DocxToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_docx = converter.convert(file_path="data/preprocessing_tutorial/heavy_metal.docx", meta=None)
-
-```
-
-
-```python
-# Haystack also has a convenience function that will automatically apply the right converter to each file in a directory.
-
-all_docs = convert_files_to_dicts(dir_path="data/preprocessing_tutorial")
-```
-
-## PreProcessor
-
-The PreProcessor class is designed to help you clean text and split text into sensible units.
-File splitting can have a very significant impact on the system's performance and is absolutely mandatory for Dense Passage Retrieval models.
-In general, we recommend you split the text from your files into small documents of around 100 words for dense retrieval methods
-and no more than 10,000 words for sparse methods.
-Have a look at the [Preprocessing](https://haystack.deepset.ai/docs/latest/preprocessingmd)
-and [Optimization](https://haystack.deepset.ai/docs/latest/optimizationmd) pages on our website for more details.
-
-
-```python
-# This is a default usage of the PreProcessor.
-# Here, it performs cleaning of consecutive whitespaces
-# and splits a single large document into smaller documents.
-# Each document is up to 1000 words long and document breaks cannot fall in the middle of sentences
-# Note how the single document passed into the document gets split into 5 smaller documents
-
-preprocessor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=False,
- split_by="word",
- split_length=100,
- split_respect_sentence_boundary=True
-)
-docs_default = preprocessor.process(doc_txt)
-print(f"n_docs_input: 1\nn_docs_output: {len(docs_default)}")
-```
-
-## Cleaning
-
-- `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
-- `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
-- `clean_header_footer` will remove any long header or footer texts that are repeated on each page
-
-## Splitting
-By default, the PreProcessor will respect sentence boundaries, meaning that documents will not start or end
-midway through a sentence.
-This will help reduce the possibility of answer phrases being split between two documents.
-This feature can be turned off by setting `split_respect_sentence_boundary=False`.
-
-
-```python
-# Not respecting sentence boundary vs respecting sentence boundary
-
-preprocessor_nrsb = PreProcessor(split_respect_sentence_boundary=False)
-docs_nrsb = preprocessor_nrsb.process(doc_txt)
-
-print("RESPECTING SENTENCE BOUNDARY")
-end_text = docs_default[0]["text"][-50:]
-print("End of document: \"..." + end_text + "\"")
-print()
-print("NOT RESPECTING SENTENCE BOUNDARY")
-end_text_nrsb = docs_nrsb[0]["text"][-50:]
-print("End of document: \"..." + end_text_nrsb + "\"")
-```
-
-A commonly used strategy to split long documents, especially in the field of Question Answering,
-is the sliding window approach. If `split_length=10` and `split_overlap=3`, your documents will look like this:
-
-- doc1 = words[0:10]
-- doc2 = words[7:17]
-- doc3 = words[14:24]
-- ...
-
-You can use this strategy by following the code below.
-
-
-```python
-# Sliding window approach
-
-preprocessor_sliding_window = PreProcessor(
- split_overlap=3,
- split_length=10,
- split_respect_sentence_boundary=False
-)
-docs_sliding_window = preprocessor_sliding_window.process(doc_txt)
-
-doc1 = docs_sliding_window[0]["text"][:200]
-doc2 = docs_sliding_window[1]["text"][:100]
-doc3 = docs_sliding_window[2]["text"][:100]
-
-print("Document 1: \"" + doc1 + "...\"")
-print("Document 2: \"" + doc2 + "...\"")
-print("Document 3: \"" + doc3 + "...\"")
-```
-
-## Bringing it all together
-
-
-```python
-all_docs = convert_files_to_dicts(dir_path="data/preprocessing_tutorial")
-preprocessor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=False,
- split_by="word",
- split_length=100,
- split_respect_sentence_boundary=True
-)
-docs = preprocessor.process(all_docs)
-
-print(f"n_files_input: {len(all_docs)}\nn_docs_output: {len(docs)}")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
-
diff --git a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/9.md b/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/9.md
deleted file mode 100644
index 366b7487e..000000000
--- a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/9.md
+++ /dev/null
@@ -1,250 +0,0 @@
----
-title: "Tutorial 9"
-metaTitle: "Training a Dense Passage Retrieval model"
-metaDescription: ""
-slug: "/docs/tutorial9"
-date: "2021-01-08"
-id: "tutorial9md"
----
-
-# Training Your Own "Dense Passage Retrieval" Model
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial9_DPR_training.ipynb)
-
-Haystack contains all the tools needed to train your own Dense Passage Retrieval model.
-This tutorial will guide you through the steps required to create a retriever that is specifically tailored to your domain.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-```
-
-
-```python
-# Here are some imports that we'll need
-
-from haystack.retriever.dense import DensePassageRetriever
-from haystack.preprocessor.utils import fetch_archive_from_http
-from haystack.document_store.memory import InMemoryDocumentStore
-```
-
-## Training Data
-
-DPR training performed using Information Retrieval data.
-More specifically, you want to feed in pairs of queries and relevant documents.
-
-To train a model, we will need a dataset that has the same format as the original DPR training data.
-Each data point in the dataset should have the following dictionary structure.
-
-``` python
- {
- "dataset": str,
- "question": str,
- "answers": list of str
- "positive_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- "negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- "hard_negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- }
-```
-
-`positive_ctxs` are context passages which are relevant to the query.
-In some datasets, queries might have more than one positive context
-in which case you can set the `num_positives` parameter to be higher than the default 1.
-Note that `num_positives` needs to be lower or equal to the minimum number of `positive_ctxs` for queries in your data.
-If you have an unequal number of positive contexts per example,
-you might want to generate some soft labels by retrieving similar contexts which contain the answer.
-
-DPR is standardly trained using a method known as in-batch negatives.
-This means that positive contexts for a given query are treated as negative contexts for the other queries in the batch.
-Doing so allows for a high degree of computational efficiency, thus allowing the model to be trained on large amounts of data.
-
-`negative_ctxs` is not actually used in Haystack's DPR training so we recommend you set it to an empty list.
-They were used by the original DPR authors in an experiment to compare it against the in-batch negatives method.
-
-`hard_negative_ctxs` are passages that are not relevant to the query.
-In the original DPR paper, these are fetched using a retriever to find the most relevant passages to the query.
-Passages which contain the answer text are filtered out.
-
-If you'd like to convert your SQuAD format data into something that can train a DPR model,
-check out the utility script at [`haystack/retriever/squad_to_dpr.py`](https://github.com/deepset-ai/haystack/blob/master/haystack/retriever/squad_to_dpr.py)
-
-## Using Question Answering Data
-
-Question Answering datasets can sometimes be used as training data.
-Google's Natural Questions dataset, is sufficiently large
-and contains enough unique passages, that it can be converted into a DPR training set.
-This is done simply by considering answer containing passages as relevant documents to the query.
-
-The SQuAD dataset, however, is not as suited to this use case since its question and answer pairs
-are created on only a very small slice of wikipedia documents.
-
-## Download Original DPR Training Data
-
-WARNING: These files are large! The train set is 7.4GB and the dev set is 800MB
-
-We can download the original DPR training data with the following cell.
-Note that this data is probably only useful if you are trying to train from scratch.
-
-
-```python
-# Download original DPR data
-# WARNING: the train set is 7.4GB and the dev set is 800MB
-
-doc_dir = "data/dpr_training/"
-
-s3_url_train = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-train.json.gz"
-s3_url_dev = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz"
-
-fetch_archive_from_http(s3_url_train, output_dir=doc_dir + "train/")
-fetch_archive_from_http(s3_url_dev, output_dir=doc_dir + "dev/")
-```
-
-## Option 1: Training DPR from Scratch
-
-The default variables that we provide below are chosen to train a DPR model from scratch.
-Here, both passage and query embedding models are initialized using BERT base
-and the model is trained using Google's Natural Questions dataset (in a format specialised for DPR).
-
-If you are working in a language other than English,
-you will want to initialize the passage and query embedding models with a language model that supports your language
-and also provide a dataset in your language.
-
-
-```python
-# Here are the variables to specify our training data, the models that we use to initialize DPR
-# and the directory where we'll be saving the model
-
-doc_dir = "data/dpr_training/"
-
-train_filename = "train/biencoder-nq-train.json"
-dev_filename = "dev/biencoder-nq-dev.json"
-
-query_model = "bert-base-uncased"
-passage_model = "bert-base-uncased"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Option 2: Finetuning DPR
-
-If you have your own domain specific question answering or information retrieval dataset,
-you might instead be interested in finetuning a pretrained DPR model.
-In this case, you would initialize both query and passage models using the original pretrained model.
-You will want to load something like this set of variables instead of the ones above
-
-
-```python
-# Here are the variables you might want to use instead of the set above
-# in order to perform pretraining
-
-doc_dir = "PATH_TO_YOUR_DATA_DIR"
-train_filename = "TRAIN_FILENAME"
-dev_filename = "DEV_FILENAME"
-
-query_model = "facebook/dpr-question_encoder-single-nq-base"
-passage_model = "facebook/dpr-ctx_encoder-single-nq-base"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Initialization
-
-Here we want to initialize our model either with plain language model weights for training from scratch
-or else with pretrained DPR weights for finetuning.
-We follow the [original DPR parameters](https://github.com/facebookresearch/DPR#best-hyperparameter-settings)
-for their max passage length but set max query length to 64 since queries are very rarely longer.
-
-
-```python
-## Initialize DPR model
-
-retriever = DensePassageRetriever(
- document_store=InMemoryDocumentStore(),
- query_embedding_model=query_model,
- passage_embedding_model=passage_model,
- max_seq_len_query=64,
- max_seq_len_passage=256
-)
-```
-
-## Training
-
-Let's start training and save our trained model!
-
-On a V100 GPU, you can fit up to batch size 16 so we set gradient accumulation steps to 8 in order
-to simulate the batch size 128 of the original DPR experiment.
-
-When `embed_title=True`, the document title is prepended to the input text sequence with a `[SEP]` token
-between it and document text.
-
-When training from scratch with the above variables, 1 epoch takes around an hour and we reached the following performance:
-
-```
-loss: 0.046580662854042276
-task_name: text_similarity
-acc: 0.992524064068483
-f1: 0.8804297774366846
-acc_and_f1: 0.9364769207525838
-average_rank: 0.19631619339984652
-report:
- precision recall f1-score support
-
-hard_negative 0.9961 0.9961 0.9961 201887
- positive 0.8804 0.8804 0.8804 6515
-
- accuracy 0.9925 208402
- macro avg 0.9383 0.9383 0.9383 208402
- weighted avg 0.9925 0.9925 0.9925 208402
-
-```
-
-
-```python
-# Start training our model and save it when it is finished
-
-retriever.train(
- data_dir=doc_dir,
- train_filename=train_filename,
- dev_filename=dev_filename,
- test_filename=dev_filename,
- n_epochs=1,
- batch_size=16,
- grad_acc_steps=8,
- save_dir=save_dir,
- evaluate_every=3000,
- embed_title=True,
- num_positives=1,
- num_hard_negatives=1
-)
-```
-
-## Loading
-
-Loading our newly trained model is simple!
-
-
-```python
-reloaded_retriever = DensePassageRetriever.load(load_dir=save_dir, document_store=None)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/annotation.md b/src/pages/docs/versions/master/latest/site/en/usage/usage/annotation.md
deleted file mode 100644
index 6a61fca05..000000000
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/annotation.md
+++ /dev/null
@@ -1,78 +0,0 @@
----
-title: "Annotation Tool"
-metaTitle: "Annotation Tool"
-metaDescription: ""
-slug: "/docs/annotation"
-date: "2020-09-03"
-id: "annotationmd"
----
-
-# Annotation Tool
-
-- Create labels with different techniques: Come up with questions (+ answers) while reading passages (SQuAD style) or have a set of predefined questions and look for answers in the document (~ Natural Questions).
-- Structure your work via organizations, projects, users
-- Upload your documents or import labels from an existing SQuAD-style dataset
-- Export your labels in SQuAD Format
-
-
-
-# Hosted version
- Signup here: [Haystack Annotation Tool](https://annotate.deepset.ai/login)
-
-# Local version (Docker)
-
-1. Configure credentials & database in the [`docker-compose.yml`](https://github.com/deepset-ai/haystack/blob/master/annotation_tool/docker-compose.yml):
-
-The credentials should match in database image and application configuration.
-
- DEFAULT_ADMIN_EMAIL: "example@example.com"
- DEFAULT_ADMIN_PASSWORD: "DEMO-PASSWORD"
-
- PROD_DB_NAME: "databasename"
- PROD_DB_USERNAME: "somesafeuser"
- PROD_DB_PASSWORD: "somesafepassword"
-
-
- POSTGRES_USER: "somesafeuser"
- POSTGRES_PASSWORD: "somesafepassword"
- POSTGRES_DB: "databasename"
-
-
-2. Run docker-compose by executing `docker-compose up`.
-
-
-3. The UI should be available at `localhost:7001`.
-
-# Usage
-The manual (of a slightly earlier version) can be found [here](https://drive.google.com/file/d/1Wv3OIC0Z7ibHIzOm9Xw_r0gjTFmpl-33/view). While it doesn't include all latest features, the basic workflow and tips for label quality are still the same.
-
-# Annotation FAQ
-
-1. What is a good question?
- - A good question is a fact-seeking question that can be answered with an entity (person, organisation, location, etc.) or explanation. A bad question is ambiguous, incomprehensible, dependent on clear false presuppositions, opinion seeking, or not clearly a request for factual information.
- - The question should ask about information present in the text passage given. It should not be answerable only with additional knowledge or your interpretation.
- - Do not copy paste answer text into the question. Good questions do not contain the exact same words as the answer or the context around the answer. The question should be a reformulation with synonyms and in different order as the context of the answer.
- - Questions should be very precise natural questions you would ask when you want information from another person.
-2. How many questions should you ask per text passage?
- - Maximally ask 20 questions per passage
- - Some text passages are not suited for 20 questions. Do not make up very constructed and complicated questions just to fill up the 20 - move on to the next text.
- - Try to ask questions covering the whole passage and focus on questions covering important information. Do not only ask questions about a single sentence in that passage.
-3. What is a good answer span?
- - Always mark whole words. Do not start or end the answer within a word.
- - For short answers: The answer should be as short and as close to a spoken human answer as possible. Do not include punctuation.
- - For long answers: Please mark whole sentences with punctuation. The sentences can also pick up parts of the question, or mark even whole text passages. Mark passages only if they are not too large (e.g. not more than 8-10 sentences).
-4. How do I differentiate long vs short answers?
- - If there is a short answer possible you should always select short answer over long answer.
- - Short precise answers like numbers or a few words are short answers.
- - Long answers include lists of possibilities or multiple sentences are needed to answer the question correctly.
-5. How to handle multiple possible answers to a single question?
- - As of now there is no functionality to mark multiple answers per single question.
- - Workaround: You can add a question with the same text but different answer selection by using the button below the question list (Button reads “custom question”)
-6. What to do with grammatically wrong or incorrectly spelled questions?
- - Include them. When users use the tool and ask questions they will likely contain grammar and spelling errors, too.
- - Exception: The question needs to be understandable without reading and interpretation of the corresponding text passage. If you do not understand the question, please mark the question as “I don’t understand the question”.
-7. What to do with text passages that are not properly converted or contain (in part) information that cannot be labelled (e.g. just lists or garbage text)?
- - Please do not annotate this text
- - You can write down what is missing, or the cause why you cannot label the text + the text number and title.
-8. Which browser to use?
- - Please use the Chrome browser. The tool is not tested for other browsers.
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/document_store.md b/src/pages/docs/versions/master/latest/site/en/usage/usage/document_store.md
deleted file mode 100644
index 85b4af4ec..000000000
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/document_store.md
+++ /dev/null
@@ -1,362 +0,0 @@
----
-title: "Document Store"
-metaTitle: "Document Store"
-metaDescription: ""
-slug: "/docs/documentstore"
-date: "2020-09-03"
-id: "documentstoremd"
----
-
-
-# DocumentStores
-
-You can think of the DocumentStore as a "database" that:
-- stores your texts and meta data
-- provides them to the retriever at query time
-
-There are different DocumentStores in Haystack to fit different use cases and tech stacks.
-
-## Initialisation
-
-Initialising a new DocumentStore within Haystack is straight forward.
-
-.
-@prefix rep: .
-@prefix sr: .
-@prefix sail: .
-@prefix owlim: .
-
-[] a rep:Repository ;
- rep:repositoryID "tutorial_10_index" ;
- rdfs:label "tutorial 10 index" ;
-...
-```
-
-GraphDBKnowledgeGraph can load an existing knowledge graph represented in the form of a .ttl file with the method ```GraphDBKnowledgeGraph.import_from_ttl_file(index, path)```, where path points to a ttl file starting with something like:
-
-```
-@prefix rdf: .
-@prefix xsd: .
-@prefix hp:
- PREFIX xsd:
- PREFIX hp: ?obj . }")```
-
-instead of using the abbreviated form:
-
-```Text2SparqlRetriever._query_kg(sparql_query="select distinct ?obj where { hp:Hermione_granger hp:patronus ?obj . }")```
-
-If you would like to translate text queries to SPARQL queries for your custom data and use ```Text2SparqlRetriever.retrieve(query)```, there is significantly more effort necessary.
-We provide an exemplary pre-trained model in our [tutorial](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial10_Knowledge_Graph.py).
-One limitation is that this pre-trained model can only generate questions about resources it has seen during training.
-Otherwise, it cannot translate the name of the resource to the identifier used in the knowledge graph.
-For example, it can translate "Harry" to "hp:Harry_potter" only because we trained it to do so.
-
-Unfortunately, our pre-trained model for translating text queries does not work with your custom data.
-Instead, you need to train your own model. It needs to be trained according to the [seq2seq example for summarization with BART in transformers](https://github.com/huggingface/transformers/tree/master/examples/legacy/seq2seq).
-Haystack currently does not support the training of text2sparql models. We dont have concrete plans to extend the funtionality, but we are more than open to contributions. Don't hesitate to reach out!
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/languages.md b/src/pages/docs/versions/master/latest/site/en/usage/usage/languages.md
deleted file mode 100644
index 848792ea5..000000000
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/languages.md
+++ /dev/null
@@ -1,333 +0,0 @@
----
-title: "Languages Other Than English"
-metaTitle: "Languages Other Than English"
-metaDescription: ""
-slug: "/docs/languages"
-date: "2020-11-05"
-id: "languagesmd"
----
-
-# Languages Other Than English
-
-Haystack is well suited to open-domain QA on languages other than English.
-While our defaults are tuned for English,
-you will find some tips and tricks here for using Haystack in your language.
-
-##Preprocessor
-
-"}.
- Optionally: Include meta data via {"text": "",
- "meta":{"name": ", "author": "somebody", ...}}
- It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: Optional name of index where the documents shall be written to.
- If None, the DocumentStore's default index (self.index) will be used.
-- `batch_size`: Number of documents that are passed to bulk function at a time.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-**Returns**:
-
-None
-
-
-#### get\_all\_documents
-
-```python
- | @abstractmethod
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Get documents from the document store.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-
-
-#### get\_all\_labels\_aggregated
-
-```python
- | get_all_labels_aggregated(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, open_domain: bool = True, aggregate_by_meta: Optional[Union[str, list]] = None) -> List[MultiLabel]
-```
-
-Return all labels in the DocumentStore, aggregated into MultiLabel objects.
-This aggregation step helps, for example, if you collected multiple possible answers for one question and you
-want now all answers bundled together in one place for evaluation.
-How they are aggregated is defined by the open_domain and aggregate_by_meta parameters.
-If the questions are being asked to a single document (i.e. SQuAD style), you should set open_domain=False to aggregate by question and document.
-If the questions are being asked to your full collection of documents, you should set open_domain=True to aggregate just by question.
-If the questions are being asked to a subslice of your document set (e.g. product review use cases),
-you should set open_domain=True and populate aggregate_by_meta with the names of Label meta fields to aggregate by question and your custom meta fields.
-For example, in a product review use case, you might set aggregate_by_meta=["product_id"] so that Labels
-with the same question but different answers from different documents are aggregated into the one MultiLabel
-object, provided that they have the same product_id (to be found in Label.meta["product_id"])
-
-**Arguments**:
-
-- `index`: Name of the index to get the labels from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the labels to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `open_domain`: When True, labels are aggregated purely based on the question text alone.
- When False, labels are aggregated in a closed domain fashion based on the question text
- and also the id of the document that the label is tied to. In this setting, this function
- might return multiple MultiLabel objects with the same question string.
-- `aggregate_by_meta`: The names of the Label meta fields by which to aggregate. For example: ["product_id"]
-
-
-#### add\_eval\_data
-
-```python
- | add_eval_data(filename: str, doc_index: str = "eval_document", label_index: str = "label", batch_size: Optional[int] = None, preprocessor: Optional[PreProcessor] = None, max_docs: Union[int, bool] = None, open_domain: bool = False)
-```
-
-Adds a SQuAD-formatted file to the DocumentStore in order to be able to perform evaluation on it.
-If a jsonl file and a batch_size is passed to the function, documents are loaded batchwise
-from disk and also indexed batchwise to the DocumentStore in order to prevent out of memory errors.
-
-**Arguments**:
-
-- `filename`: Name of the file containing evaluation data (json or jsonl)
-- `doc_index`: Elasticsearch index where evaluation documents should be stored
-- `label_index`: Elasticsearch index where labeled questions should be stored
-- `batch_size`: Optional number of documents that are loaded and processed at a time.
- When set to None (default) all documents are processed at once.
-- `preprocessor`: Optional PreProcessor to preprocess evaluation documents.
- It can be used for splitting documents into passages (and assigning labels to corresponding passages).
- Currently the PreProcessor does not support split_by sentence, cleaning nor split_overlap != 0.
- When set to None (default) preprocessing is disabled.
-- `max_docs`: Optional number of documents that will be loaded.
- When set to None (default) all available eval documents are used.
-- `open_domain`: Set this to True if your file is an open domain dataset where two different answers to the
- same question might be found in different contexts.
-
-
-# Module: elasticsearch
-
-
-## Class: ElasticsearchDocumentStore
-
-```python
-class ElasticsearchDocumentStore(BaseDocumentStore)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(host: Union[str, List[str]] = "localhost", port: Union[int, List[int]] = 9200, username: str = "", password: str = "", api_key_id: Optional[str] = None, api_key: Optional[str] = None, aws4auth=None, index: str = "document", label_index: str = "label", search_fields: Union[str, list] = "text", text_field: str = "text", name_field: str = "name", embedding_field: str = "embedding", embedding_dim: int = 768, custom_mapping: Optional[dict] = None, excluded_meta_data: Optional[list] = None, faq_question_field: Optional[str] = None, analyzer: str = "standard", scheme: str = "http", ca_certs: Optional[str] = None, verify_certs: bool = True, create_index: bool = True, refresh_type: str = "wait_for", similarity="dot_product", timeout=30, return_embedding: bool = False, duplicate_documents: str = 'overwrite')
-```
-
-A DocumentStore using Elasticsearch to store and query the documents for our search.
-
- * Keeps all the logic to store and query documents from Elastic, incl. mapping of fields, adding filters or boosts to your queries, and storing embeddings
- * You can either use an existing Elasticsearch index or create a new one via haystack
- * Retrievers operate on top of this DocumentStore to find the relevant documents for a query
-
-**Arguments**:
-
-- `host`: url(s) of elasticsearch nodes
-- `port`: port(s) of elasticsearch nodes
-- `username`: username (standard authentication via http_auth)
-- `password`: password (standard authentication via http_auth)
-- `api_key_id`: ID of the API key (altenative authentication mode to the above http_auth)
-- `api_key`: Secret value of the API key (altenative authentication mode to the above http_auth)
-- `aws4auth`: Authentication for usage with aws elasticsearch (can be generated with the requests-aws4auth package)
-- `index`: Name of index in elasticsearch to use for storing the documents that we want to search. If not existing yet, we will create one.
-- `label_index`: Name of index in elasticsearch to use for storing labels. If not existing yet, we will create one.
-- `search_fields`: Name of fields used by ElasticsearchRetriever to find matches in the docs to our incoming query (using elastic's multi_match query), e.g. ["title", "full_text"]
-- `text_field`: Name of field that might contain the answer and will therefore be passed to the Reader Model (e.g. "full_text").
- If no Reader is used (e.g. in FAQ-Style QA) the plain content of this field will just be returned.
-- `name_field`: Name of field that contains the title of the the doc
-- `embedding_field`: Name of field containing an embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `embedding_dim`: Dimensionality of embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `custom_mapping`: If you want to use your own custom mapping for creating a new index in Elasticsearch, you can supply it here as a dictionary.
-- `analyzer`: Specify the default analyzer from one of the built-ins when creating a new Elasticsearch Index.
- Elasticsearch also has built-in analyzers for different languages (e.g. impacting tokenization). More info at:
- https://www.elastic.co/guide/en/elasticsearch/reference/7.9/analysis-analyzers.html
-- `excluded_meta_data`: Name of fields in Elasticsearch that should not be returned (e.g. [field_one, field_two]).
- Helpful if you have fields with long, irrelevant content that you don't want to display in results (e.g. embedding vectors).
-- `scheme`: 'https' or 'http', protocol used to connect to your elasticsearch instance
-- `ca_certs`: Root certificates for SSL: it is a path to certificate authority (CA) certs on disk. You can use certifi package with certifi.where() to find where the CA certs file is located in your machine.
-- `verify_certs`: Whether to be strict about ca certificates
-- `create_index`: Whether to try creating a new index (If the index of that name is already existing, we will just continue in any case
-- `refresh_type`: Type of ES refresh used to control when changes made by a request (e.g. bulk) are made visible to search.
- If set to 'wait_for', continue only after changes are visible (slow, but safe).
- If set to 'false', continue directly (fast, but sometimes unintuitive behaviour when docs are not immediately available after ingestion).
- More info at https://www.elastic.co/guide/en/elasticsearch/reference/6.8/docs-refresh.html
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
- more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
-- `timeout`: Number of seconds after which an ElasticSearch request times out.
-- `return_embedding`: To return document embedding
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index: Optional[str] = None) -> List[Document]
-```
-
-Fetch documents by specifying a list of text id strings
-
-
-#### get\_metadata\_values\_by\_key
-
-```python
- | get_metadata_values_by_key(key: str, query: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> List[dict]
-```
-
-Get values associated with a metadata key. The output is in the format:
- [{"value": "my-value-1", "count": 23}, {"value": "my-value-2", "count": 12}, ... ]
-
-**Arguments**:
-
-- `key`: the meta key name to get the values for.
-- `query`: narrow down the scope to documents matching the query string.
-- `filters`: narrow down the scope to documents that match the given filters.
-- `index`: Elasticsearch index where the meta values should be searched. If not supplied,
- self.index will be used.
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None)
-```
-
-Indexes documents for later queries in Elasticsearch.
-
-Behaviour if a document with the same ID already exists in ElasticSearch:
-a) (Default) Throw Elastic's standard error message for duplicate IDs.
-b) If `self.update_existing_documents=True` for DocumentStore: Overwrite existing documents.
-(This is only relevant if you pass your own ID when initializing a `Document`.
-If don't set custom IDs for your Documents or just pass a list of dictionaries here,
-they will automatically get UUIDs assigned. See the `Document` class for details)
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
- For documents as dictionaries, the format is {"text": ""}.
- Optionally: Include meta data via {"text": "",
- "meta":{"name": ", "author": "somebody", ...}}
- It can be used for filtering and is accessible in the responses of the Finder.
- Advanced: If you are using your own Elasticsearch mapping, the key names in the dictionary
- should be changed to what you have set for self.text_field and self.name_field.
-- `index`: Elasticsearch index where the documents should be indexed. If not supplied, self.index will be used.
-- `batch_size`: Number of documents that are passed to Elasticsearch's bulk function at a time.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-**Raises**:
-
-- `DuplicateDocumentError`: Exception trigger on duplicate document
-
-**Returns**:
-
-None
-
-
-#### write\_labels
-
-```python
- | write_labels(labels: Union[List[Label], List[dict]], index: Optional[str] = None, batch_size: int = 10_000)
-```
-
-Write annotation labels into document store.
-
-**Arguments**:
-
-- `labels`: A list of Python dictionaries or a list of Haystack Label objects.
-- `batch_size`: Number of labels that are passed to Elasticsearch's bulk function at a time.
-
-
-#### update\_document\_meta
-
-```python
- | update_document_meta(id: str, meta: Dict[str, str])
-```
-
-Update the metadata dictionary of a document by specifying its string id
-
-
-#### get\_document\_count
-
-```python
- | get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None, only_documents_without_embedding: bool = False) -> int
-```
-
-Return the number of documents in the document store.
-
-
-#### get\_label\_count
-
-```python
- | get_label_count(index: Optional[str] = None) -> int
-```
-
-Return the number of labels in the document store
-
-
-#### get\_embedding\_count
-
-```python
- | get_embedding_count(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None) -> int
-```
-
-Return the count of embeddings in the document store.
-
-
-#### get\_all\_documents
-
-```python
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> List[Document]
-```
-
-Get documents from the document store.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_documents\_generator
-
-```python
- | get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> Generator[Document, None, None]
-```
-
-Get documents from the document store. Under-the-hood, documents are fetched in batches from the
-document store and yielded as individual documents. This method can be used to iteratively process
-a large number of documents without having to load all documents in memory.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_labels
-
-```python
- | get_all_labels(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, batch_size: int = 10_000) -> List[Label]
-```
-
-Return all labels in the document store
-
-
-#### query
-
-```python
- | query(query: Optional[str], filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, custom_query: Optional[str] = None, index: Optional[str] = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query as defined by the BM25 algorithm.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.ndarray, filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: Index name for storing the docs and metadata
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### describe\_documents
-
-```python
- | describe_documents(index=None)
-```
-
-Return a summary of the documents in the document store
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever, index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, update_existing_embeddings: bool = True, batch_size: int = 10_000)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever to use to update the embeddings.
-- `index`: Index name to update
-- `update_existing_embeddings`: Whether to update existing embeddings of the documents. If set to False,
- only documents without embeddings are processed. This mode can be used for
- incremental updating of embeddings, wherein, only newly indexed documents
- get processed.
-- `filters`: Optional filters to narrow down the documents for which embeddings are to be updated.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-**Returns**:
-
-None
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-#### delete\_documents
-
-```python
- | delete_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-## Class: OpenDistroElasticsearchDocumentStore
-
-```python
-class OpenDistroElasticsearchDocumentStore(ElasticsearchDocumentStore)
-```
-
-Document Store using the Open Distro for Elasticsearch. It is compatible with the AWS Elasticsearch Service.
-
-In addition to native Elasticsearch query & filtering, it provides efficient vector similarity search using
-the KNN plugin that can scale to a large number of documents.
-
-
-# Module: memory
-
-
-## Class: InMemoryDocumentStore
-
-```python
-class InMemoryDocumentStore(BaseDocumentStore)
-```
-
-In-memory document store
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(index: str = "document", label_index: str = "label", embedding_field: Optional[str] = "embedding", embedding_dim: int = 768, return_embedding: bool = False, similarity: str = "dot_product", progress_bar: bool = True, duplicate_documents: str = 'overwrite')
-```
-
-**Arguments**:
-
-- `index`: The documents are scoped to an index attribute that can be used when writing, querying,
- or deleting documents. This parameter sets the default value for document index.
-- `label_index`: The default value of index attribute for the labels.
-- `embedding_field`: Name of field containing an embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `embedding_dim`: The size of the embedding vector.
-- `return_embedding`: To return document embedding
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
- more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
-- `progress_bar`: Whether to show a tqdm progress bar or not.
- Can be helpful to disable in production deployments to keep the logs clean.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, duplicate_documents: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
- For documents as dictionaries, the format is {"text": ""}.
- Optionally: Include meta data via {"text": "",
- "meta": {"name": ", "author": "somebody", ...}}
- It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: write documents to a custom namespace. For instance, documents for evaluation can be indexed in a
- separate index than the documents for search.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-**Raises**:
-
-- `DuplicateDocumentError`: Exception trigger on duplicate document
-
-**Returns**:
-
-None
-
-
-#### write\_labels
-
-```python
- | write_labels(labels: Union[List[dict], List[Label]], index: Optional[str] = None)
-```
-
-Write annotation labels into document store.
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index: Optional[str] = None) -> List[Document]
-```
-
-Fetch documents by specifying a list of text id strings
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.ndarray, filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: Index name for storing the docs and metadata
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, update_existing_embeddings: bool = True, batch_size: int = 10_000)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever to use to get embeddings for text
-- `index`: Index name for which embeddings are to be updated. If set to None, the default self.index is used.
-- `update_existing_embeddings`: Whether to update existing embeddings of the documents. If set to False,
- only documents without embeddings are processed. This mode can be used for
- incremental updating of embeddings, wherein, only newly indexed documents
- get processed.
-- `filters`: Optional filters to narrow down the documents for which embeddings are to be updated.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-**Returns**:
-
-None
-
-
-#### get\_document\_count
-
-```python
- | get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
-```
-
-Return the number of documents in the document store.
-
-
-#### get\_embedding\_count
-
-```python
- | get_embedding_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
-```
-
-Return the count of embeddings in the document store.
-
-
-#### get\_label\_count
-
-```python
- | get_label_count(index: Optional[str] = None) -> int
-```
-
-Return the number of labels in the document store
-
-
-#### get\_all\_documents\_generator
-
-```python
- | get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> Generator[Document, None, None]
-```
-
-Get all documents from the document store. The methods returns a Python Generator that yields individual
-documents.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-
-
-#### get\_all\_labels
-
-```python
- | get_all_labels(index: str = None, filters: Optional[Dict[str, List[str]]] = None) -> List[Label]
-```
-
-Return all labels in the document store
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-#### delete\_documents
-
-```python
- | delete_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-# Module: sql
-
-
-## Class: SQLDocumentStore
-
-```python
-class SQLDocumentStore(BaseDocumentStore)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(url: str = "sqlite://", index: str = "document", label_index: str = "label", duplicate_documents: str = "overwrite")
-```
-
-An SQL backed DocumentStore. Currently supports SQLite, PostgreSQL and MySQL backends.
-
-**Arguments**:
-
-- `url`: URL for SQL database as expected by SQLAlchemy. More info here: https://docs.sqlalchemy.org/en/13/core/engines.html#database-urls
-- `index`: The documents are scoped to an index attribute that can be used when writing, querying, or deleting documents.
- This parameter sets the default value for document index.
-- `label_index`: The default value of index attribute for the labels.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index: Optional[str] = None, batch_size: int = 10_000) -> List[Document]
-```
-
-Fetch documents by specifying a list of text id strings
-
-
-#### get\_documents\_by\_vector\_ids
-
-```python
- | get_documents_by_vector_ids(vector_ids: List[str], index: Optional[str] = None, batch_size: int = 10_000)
-```
-
-Fetch documents by specifying a list of text vector id strings
-
-
-#### get\_all\_documents\_generator
-
-```python
- | get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> Generator[Document, None, None]
-```
-
-Get documents from the document store. Under-the-hood, documents are fetched in batches from the
-document store and yielded as individual documents. This method can be used to iteratively process
-a large number of documents without having to load all documents in memory.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_labels
-
-```python
- | get_all_labels(index=None, filters: Optional[dict] = None)
-```
-
-Return all labels in the document store
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
- For documents as dictionaries, the format is {"text": ""}.
- Optionally: Include meta data via {"text": "",
- "meta":{"name": ", "author": "somebody", ...}}
- It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: add an optional index attribute to documents. It can be later used for filtering. For instance,
- documents for evaluation can be indexed in a separate index than the documents for search.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-**Returns**:
-
-None
-
-
-#### write\_labels
-
-```python
- | write_labels(labels, index=None)
-```
-
-Write annotation labels into document store.
-
-
-#### update\_vector\_ids
-
-```python
- | update_vector_ids(vector_id_map: Dict[str, str], index: Optional[str] = None, batch_size: int = 10_000)
-```
-
-Update vector_ids for given document_ids.
-
-**Arguments**:
-
-- `vector_id_map`: dict containing mapping of document_id -> vector_id.
-- `index`: filter documents by the optional index attribute for documents in database.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### reset\_vector\_ids
-
-```python
- | reset_vector_ids(index: Optional[str] = None)
-```
-
-Set vector IDs for all documents as None
-
-
-#### update\_document\_meta
-
-```python
- | update_document_meta(id: str, meta: Dict[str, str])
-```
-
-Update the metadata dictionary of a document by specifying its string id
-
-
-#### get\_document\_count
-
-```python
- | get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
-```
-
-Return the number of documents in the document store.
-
-
-#### get\_label\_count
-
-```python
- | get_label_count(index: Optional[str] = None) -> int
-```
-
-Return the number of labels in the document store
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-#### delete\_documents
-
-```python
- | delete_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-# Module: faiss
-
-
-## Class: FAISSDocumentStore
-
-```python
-class FAISSDocumentStore(SQLDocumentStore)
-```
-
-Document store for very large scale embedding based dense retrievers like the DPR.
-
-It implements the FAISS library(https://github.com/facebookresearch/faiss)
-to perform similarity search on vectors.
-
-The document text and meta-data (for filtering) are stored using the SQLDocumentStore, while
-the vector embeddings are indexed in a FAISS Index.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(sql_url: str = "sqlite:///", vector_dim: int = 768, faiss_index_factory_str: str = "Flat", faiss_index: Optional["faiss.swigfaiss.Index"] = None, return_embedding: bool = False, index: str = "document", similarity: str = "dot_product", embedding_field: str = "embedding", progress_bar: bool = True, duplicate_documents: str = 'overwrite', **kwargs, ,)
-```
-
-**Arguments**:
-
-- `sql_url`: SQL connection URL for database. It defaults to local file based SQLite DB. For large scale
- deployment, Postgres is recommended.
-- `vector_dim`: the embedding vector size.
-- `faiss_index_factory_str`: Create a new FAISS index of the specified type.
- The type is determined from the given string following the conventions
- of the original FAISS index factory.
- Recommended options:
- - "Flat" (default): Best accuracy (= exact). Becomes slow and RAM intense for > 1 Mio docs.
- - "HNSW": Graph-based heuristic. If not further specified,
- we use the following config:
- HNSW64, efConstruction=80 and efSearch=20
- - "IVFx,Flat": Inverted Index. Replace x with the number of centroids aka nlist.
- Rule of thumb: nlist = 10 * sqrt (num_docs) is a good starting point.
- For more details see:
- - Overview of indices https://github.com/facebookresearch/faiss/wiki/Faiss-indexes
- - Guideline for choosing an index https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
- - FAISS Index factory https://github.com/facebookresearch/faiss/wiki/The-index-factory
- Benchmarks: XXX
-- `faiss_index`: Pass an existing FAISS Index, i.e. an empty one that you configured manually
- or one with docs that you used in Haystack before and want to load again.
-- `return_embedding`: To return document embedding
-- `index`: Name of index in document store to use.
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
- more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
-- `embedding_field`: Name of field containing an embedding vector.
-- `progress_bar`: Whether to show a tqdm progress bar or not.
- Can be helpful to disable in production deployments to keep the logs clean.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None)
-```
-
-Add new documents to the DocumentStore.
-
-**Arguments**:
-
-- `documents`: List of `Dicts` or List of `Documents`. If they already contain the embeddings, we'll index
- them right away in FAISS. If not, you can later call update_embeddings() to create & index them.
-- `index`: (SQL) index name for storing the docs and metadata
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-**Raises**:
-
-- `DuplicateDocumentError`: Exception trigger on duplicate document
-
-**Returns**:
-
-
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None, update_existing_embeddings: bool = True, filters: Optional[Dict[str, List[str]]] = None, batch_size: int = 10_000)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever to use to get embeddings for text
-- `index`: Index name for which embeddings are to be updated. If set to None, the default self.index is used.
-- `update_existing_embeddings`: Whether to update existing embeddings of the documents. If set to False,
- only documents without embeddings are processed. This mode can be used for
- incremental updating of embeddings, wherein, only newly indexed documents
- get processed.
-- `filters`: Optional filters to narrow down the documents for which embeddings are to be updated.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-**Returns**:
-
-None
-
-
-#### get\_all\_documents\_generator
-
-```python
- | get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> Generator[Document, None, None]
-```
-
-Get all documents from the document store. Under-the-hood, documents are fetched in batches from the
-document store and yielded as individual documents. This method can be used to iteratively process
-a large number of documents without having to load all documents in memory.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_embedding\_count
-
-```python
- | get_embedding_count(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None) -> int
-```
-
-Return the count of embeddings in the document store.
-
-
-#### train\_index
-
-```python
- | train_index(documents: Optional[Union[List[dict], List[Document]]], embeddings: Optional[np.ndarray] = None, index: Optional[str] = None)
-```
-
-Some FAISS indices (e.g. IVF) require initial "training" on a sample of vectors before you can add your final vectors.
-The train vectors should come from the same distribution as your final ones.
-You can pass either documents (incl. embeddings) or just the plain embeddings that the index shall be trained on.
-
-**Arguments**:
-
-- `documents`: Documents (incl. the embeddings)
-- `embeddings`: Plain embeddings
-- `index`: Name of the index to train. If None, the DocumentStore's default index (self.index) will be used.
-
-**Returns**:
-
-None
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete all documents from the document store.
-
-
-#### delete\_documents
-
-```python
- | delete_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete all documents from the document store.
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.ndarray, filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: Index name to query the document from.
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### save
-
-```python
- | save(file_path: Union[str, Path])
-```
-
-Save FAISS Index to the specified file.
-
-**Arguments**:
-
-- `file_path`: Path to save to.
-
-**Returns**:
-
-None
-
-
-#### load
-
-```python
- | @classmethod
- | load(cls, faiss_file_path: Union[str, Path], sql_url: str, index: str)
-```
-
-Load a saved FAISS index from a file and connect to the SQL database.
-Note: In order to have a correct mapping from FAISS to SQL,
- make sure to use the same SQL DB that you used when calling `save()`.
-
-**Arguments**:
-
-- `faiss_file_path`: Stored FAISS index file. Can be created via calling `save()`
-- `sql_url`: Connection string to the SQL database that contains your docs and metadata.
-- `index`: Index name to load the FAISS index as. It must match the index name used for
- when creating the FAISS index.
-
-**Returns**:
-
-
-
-
-# Module: milvus
-
-
-## Class: MilvusDocumentStore
-
-```python
-class MilvusDocumentStore(SQLDocumentStore)
-```
-
-Milvus (https://milvus.io/) is a highly reliable, scalable Document Store specialized on storing and processing vectors.
-Therefore, it is particularly suited for Haystack users that work with dense retrieval methods (like DPR).
-In contrast to FAISS, Milvus ...
- - runs as a separate service (e.g. a Docker container) and can scale easily in a distributed environment
- - allows dynamic data management (i.e. you can insert/delete vectors without recreating the whole index)
- - encapsulates multiple ANN libraries (FAISS, ANNOY ...)
-
-This class uses Milvus for all vector related storage, processing and querying.
-The meta-data (e.g. for filtering) and the document text are however stored in a separate SQL Database as Milvus
-does not allow these data types (yet).
-
-Usage:
-1. Start a Milvus server (see https://milvus.io/docs/v1.0.0/install_milvus.md)
-2. Init a MilvusDocumentStore in Haystack
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(sql_url: str = "sqlite:///", milvus_url: str = "tcp://localhost:19530", connection_pool: str = "SingletonThread", index: str = "document", vector_dim: int = 768, index_file_size: int = 1024, similarity: str = "dot_product", index_type: IndexType = IndexType.FLAT, index_param: Optional[Dict[str, Any]] = None, search_param: Optional[Dict[str, Any]] = None, return_embedding: bool = False, embedding_field: str = "embedding", progress_bar: bool = True, duplicate_documents: str = 'overwrite', **kwargs, ,)
-```
-
-**Arguments**:
-
-- `sql_url`: SQL connection URL for storing document texts and metadata. It defaults to a local, file based SQLite DB. For large scale
- deployment, Postgres is recommended. If using MySQL then same server can also be used for
- Milvus metadata. For more details see https://milvus.io/docs/v1.0.0/data_manage.md.
-- `milvus_url`: Milvus server connection URL for storing and processing vectors.
- Protocol, host and port will automatically be inferred from the URL.
- See https://milvus.io/docs/v1.0.0/install_milvus.md for instructions to start a Milvus instance.
-- `connection_pool`: Connection pool type to connect with Milvus server. Default: "SingletonThread".
-- `index`: Index name for text, embedding and metadata (in Milvus terms, this is the "collection name").
-- `vector_dim`: The embedding vector size. Default: 768.
-- `index_file_size`: Specifies the size of each segment file that is stored by Milvus and its default value is 1024 MB.
-When the size of newly inserted vectors reaches the specified volume, Milvus packs these vectors into a new segment.
-Milvus creates one index file for each segment. When conducting a vector search, Milvus searches all index files one by one.
-As a rule of thumb, we would see a 30% ~ 50% increase in the search performance after changing the value of index_file_size from 1024 to 2048.
-Note that an overly large index_file_size value may cause failure to load a segment into the memory or graphics memory.
-(From https://milvus.io/docs/v1.0.0/performance_faq.md#How-can-I-get-the-best-performance-from-Milvus-through-setting-index_file_size)
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default and recommended for DPR embeddings.
- 'cosine' is recommended for Sentence Transformers, but is not directly supported by Milvus.
- However, you can normalize your embeddings and use `dot_product` to get the same results.
- See https://milvus.io/docs/v1.0.0/metric.md?Inner-product-(IP)`floating`.
-- `index_type`: Type of approximate nearest neighbour (ANN) index used. The choice here determines your tradeoff between speed and accuracy.
- Some popular options:
- - FLAT (default): Exact method, slow
- - IVF_FLAT, inverted file based heuristic, fast
- - HSNW: Graph based, fast
- - ANNOY: Tree based, fast
- See: https://milvus.io/docs/v1.0.0/index.md
-- `index_param`: Configuration parameters for the chose index_type needed at indexing time.
- For example: {"nlist": 16384} as the number of cluster units to create for index_type IVF_FLAT.
- See https://milvus.io/docs/v1.0.0/index.md
-- `search_param`: Configuration parameters for the chose index_type needed at query time
- For example: {"nprobe": 10} as the number of cluster units to query for index_type IVF_FLAT.
- See https://milvus.io/docs/v1.0.0/index.md
-- `return_embedding`: To return document embedding.
-- `embedding_field`: Name of field containing an embedding vector.
-- `progress_bar`: Whether to show a tqdm progress bar or not.
- Can be helpful to disable in production deployments to keep the logs clean.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None, index_param: Optional[Dict[str, Any]] = None)
-```
-
-Add new documents to the DocumentStore.
-
-**Arguments**:
-
-- `documents`: List of `Dicts` or List of `Documents`. If they already contain the embeddings, we'll index
- them right away in Milvus. If not, you can later call update_embeddings() to create & index them.
-- `index`: (SQL) index name for storing the docs and metadata
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-**Raises**:
-
-- `DuplicateDocumentError`: Exception trigger on duplicate document
-
-**Returns**:
-
-
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None, batch_size: int = 10_000, update_existing_embeddings: bool = True, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever to use to get embeddings for text
-- `index`: (SQL) index name for storing the docs and metadata
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-- `update_existing_embeddings`: Whether to update existing embeddings of the documents. If set to False,
- only documents without embeddings are processed. This mode can be used for
- incremental updating of embeddings, wherein, only newly indexed documents
- get processed.
-- `filters`: Optional filters to narrow down the documents for which embeddings are to be updated.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-
-**Returns**:
-
-None
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.ndarray, filters: Optional[dict] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: (SQL) index name for storing the docs and metadata
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete all documents (from SQL AND Milvus).
-
-**Arguments**:
-
-- `index`: (SQL) index name for storing the docs and metadata
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-
-**Returns**:
-
-None
-
-
-#### delete\_documents
-
-```python
- | delete_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete all documents (from SQL AND Milvus).
-
-**Arguments**:
-
-- `index`: (SQL) index name for storing the docs and metadata
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-
-**Returns**:
-
-None
-
-
-#### get\_all\_documents\_generator
-
-```python
- | get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> Generator[Document, None, None]
-```
-
-Get all documents from the document store. Under-the-hood, documents are fetched in batches from the
-document store and yielded as individual documents. This method can be used to iteratively process
-a large number of documents without having to load all documents in memory.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_documents
-
-```python
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> List[Document]
-```
-
-Get documents from the document store (optionally using filter criteria).
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-**Arguments**:
-
-- `id`: ID of the document
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index: Optional[str] = None, batch_size: int = 10_000) -> List[Document]
-```
-
-Fetch multiple documents by specifying their IDs (strings)
-
-**Arguments**:
-
-- `ids`: List of IDs of the documents
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_vectors
-
-```python
- | get_all_vectors(index: Optional[str] = None) -> List[np.ndarray]
-```
-
-Helper function to dump all vectors stored in Milvus server.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-
-**Returns**:
-
-List[np.array]: List of vectors.
-
-
-#### get\_embedding\_count
-
-```python
- | get_embedding_count(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None) -> int
-```
-
-Return the count of embeddings in the document store.
diff --git a/src/pages/docs/versions/master/site/en/api/api/evaluation.md b/src/pages/docs/versions/master/site/en/api/api/evaluation.md
deleted file mode 100644
index 6dce91425..000000000
--- a/src/pages/docs/versions/master/site/en/api/api/evaluation.md
+++ /dev/null
@@ -1,104 +0,0 @@
----
-title: "Evaluation"
-metaTitle: "Evaluation"
-metaDescription: ""
-slug: "/docs/evaluationmd"
-date: "2020-09-03"
-id: "apievaluationmd"
----
-
-# Evaluation
-
-
-# Module: eval
-
-
-## Class: EvalDocuments
-
-```python
-class EvalDocuments()
-```
-
-This is a pipeline node that should be placed after a node that returns a List of Document, e.g., Retriever or
-Ranker, in order to assess its performance. Performance metrics are stored in this class and updated as each
-sample passes through it. To view the results of the evaluation, call EvalDocuments.print(). Note that results
-from this Node may differ from that when calling Retriever.eval() since that is a closed domain evaluation. Have
-a look at our evaluation tutorial for more info about open vs closed domain eval (
-https://haystack.deepset.ai/docs/latest/tutorial5md).
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(debug: bool = False, open_domain: bool = True, top_k_eval_documents: int = 10, name="EvalDocuments")
-```
-
-**Arguments**:
-
-- `open_domain`: When True, a document is considered correctly retrieved so long as the answer string can be found within it.
- When False, correct retrieval is evaluated based on document_id.
-- `debug`: When True, a record of each sample and its evaluation will be stored in EvalDocuments.log
-- `top_k`: calculate eval metrics for top k results, e.g., recall@k
-
-
-#### run
-
-```python
- | run(documents, labels: dict, top_k_eval_documents: Optional[int] = None, **kwargs)
-```
-
-Run this node on one sample and its labels
-
-
-#### print
-
-```python
- | print()
-```
-
-Print the evaluation results
-
-
-## Class: EvalAnswers
-
-```python
-class EvalAnswers()
-```
-
-This is a pipeline node that should be placed after a Reader in order to assess the performance of the Reader
-individually or to assess the extractive QA performance of the whole pipeline. Performance metrics are stored in
-this class and updated as each sample passes through it. To view the results of the evaluation, call EvalAnswers.print().
-Note that results from this Node may differ from that when calling Reader.eval()
-since that is a closed domain evaluation. Have a look at our evaluation tutorial for more info about
-open vs closed domain eval (https://haystack.deepset.ai/docs/latest/tutorial5md).
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(skip_incorrect_retrieval: bool = True, open_domain: bool = True, debug: bool = False)
-```
-
-**Arguments**:
-
-- `skip_incorrect_retrieval`: When set to True, this eval will ignore the cases where the retriever returned no correct documents
-- `open_domain`: When True, extracted answers are evaluated purely on string similarity rather than the position of the extracted answer
-- `debug`: When True, a record of each sample and its evaluation will be stored in EvalAnswers.log
-
-
-#### run
-
-```python
- | run(labels, answers, **kwargs)
-```
-
-Run this node on one sample and its labels
-
-
-#### print
-
-```python
- | print(mode)
-```
-
-Print the evaluation results
diff --git a/src/pages/docs/versions/master/site/en/api/api/file_converter.md b/src/pages/docs/versions/master/site/en/api/api/file_converter.md
deleted file mode 100644
index 16b6c0eca..000000000
--- a/src/pages/docs/versions/master/site/en/api/api/file_converter.md
+++ /dev/null
@@ -1,284 +0,0 @@
----
-title: "File Converters"
-metaTitle: "File Converters"
-metaDescription: ""
-slug: "/docs/file_converters"
-date: "2020-09-03"
-id: "file_convertersmd"
----
-
-# File Converters
-
-
-# Module: base
-
-
-## Class: BaseConverter
-
-```python
-class BaseConverter(BaseComponent)
-```
-
-Base class for implementing file converts to transform input documents to text format for ingestion in DocumentStore.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(remove_numeric_tables: bool = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-
-
-#### convert
-
-```python
- | @abstractmethod
- | convert(file_path: Path, meta: Optional[Dict[str, str]], remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "utf-8") -> Dict[str, Any]
-```
-
-Convert a file to a dictionary containing the text and any associated meta data.
-
-File converters may extract file meta like name or size. In addition to it, user
-supplied meta data like author, url, external IDs can be supplied as a dictionary.
-
-**Arguments**:
-
-- `file_path`: path of the file to convert
-- `meta`: dictionary of meta data key-value pairs to append in the returned document.
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-- `encoding`: Select the file encoding (default is `utf-8`)
-
-
-#### validate\_language
-
-```python
- | validate_language(text: str) -> bool
-```
-
-Validate if the language of the text is one of valid languages.
-
-
-## Class: FileTypeClassifier
-
-```python
-class FileTypeClassifier(BaseComponent)
-```
-
-Route files in an Indexing Pipeline to corresponding file converters.
-
-
-# Module: txt
-
-
-## Class: TextConverter
-
-```python
-class TextConverter(BaseConverter)
-```
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "utf-8") -> Dict[str, Any]
-```
-
-Reads text from a txt file and executes optional preprocessing steps.
-
-**Arguments**:
-
-- `file_path`: path of the file to convert
-- `meta`: dictionary of meta data key-value pairs to append in the returned document.
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-- `encoding`: Select the file encoding (default is `utf-8`)
-
-**Returns**:
-
-Dict of format {"text": "The text from file", "meta": meta}}
-
-
-# Module: docx
-
-
-## Class: DocxToTextConverter
-
-```python
-class DocxToTextConverter(BaseConverter)
-```
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = None) -> Dict[str, Any]
-```
-
-Extract text from a .docx file.
-Note: As docx doesn't contain "page" information, we actually extract and return a list of paragraphs here.
-For compliance with other converters we nevertheless opted for keeping the methods name.
-
-**Arguments**:
-
-- `file_path`: Path to the .docx file you want to convert
-- `meta`: dictionary of meta data key-value pairs to append in the returned document.
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-- `encoding`: Not applicable
-
-
-# Module: tika
-
-
-## Class: TikaConverter
-
-```python
-class TikaConverter(BaseConverter)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(tika_url: str = "http://localhost:9998/tika", remove_numeric_tables: bool = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `tika_url`: URL of the Tika server
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = None) -> Dict[str, Any]
-```
-
-**Arguments**:
-
-- `file_path`: path of the file to convert
-- `meta`: dictionary of meta data key-value pairs to append in the returned document.
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-- `encoding`: Not applicable
-
-**Returns**:
-
-a list of pages and the extracted meta data of the file.
-
-
-# Module: pdf
-
-
-## Class: PDFToTextConverter
-
-```python
-class PDFToTextConverter(BaseConverter)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(remove_numeric_tables: bool = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "Latin1") -> Dict[str, Any]
-```
-
-Extract text from a .pdf file using the pdftotext library (https://www.xpdfreader.com/pdftotext-man.html)
-
-**Arguments**:
-
-- `file_path`: Path to the .pdf file you want to convert
-- `meta`: Optional dictionary with metadata that shall be attached to all resulting documents.
- Can be any custom keys and values.
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-- `encoding`: Encoding that will be passed as -enc parameter to pdftotext. "Latin 1" is the default encoding
- of pdftotext. While this works well on many PDFs, it might be needed to switch to "UTF-8" or
- others if your doc contains special characters (e.g. German Umlauts, Cyrillic characters ...).
- Note: With "UTF-8" we experienced cases, where a simple "fi" gets wrongly parsed as
- "xefc" (see test cases). That's why we keep "Latin 1" as default here.
- (See list of available encodings by running `pdftotext -listencodings` in the terminal)
diff --git a/src/pages/docs/versions/master/site/en/api/api/generator.md b/src/pages/docs/versions/master/site/en/api/api/generator.md
deleted file mode 100644
index cfd5361be..000000000
--- a/src/pages/docs/versions/master/site/en/api/api/generator.md
+++ /dev/null
@@ -1,242 +0,0 @@
----
-title: "Generator"
-metaTitle: "Generator"
-metaDescription: ""
-slug: "/docs/apigeneratormd"
-date: "2020-09-03"
-id: "apigeneratormd"
----
-
-# Generator
-
-
-# Module: base
-
-
-## Class: BaseGenerator
-
-```python
-class BaseGenerator(BaseComponent)
-```
-
-Abstract class for Generators
-
-
-#### predict
-
-```python
- | @abstractmethod
- | predict(query: str, documents: List[Document], top_k: Optional[int]) -> Dict
-```
-
-Abstract method to generate answers.
-
-**Arguments**:
-
-- `query`: Query
-- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on.
-- `top_k`: Number of returned answers
-
-**Returns**:
-
-Generated answers plus additional infos in a dict
-
-
-# Module: transformers
-
-
-## Class: RAGenerator
-
-```python
-class RAGenerator(BaseGenerator)
-```
-
-Implementation of Facebook's Retrieval-Augmented Generator (https://arxiv.org/abs/2005.11401) based on
-HuggingFace's transformers (https://huggingface.co/transformers/model_doc/rag.html).
-
-Instead of "finding" the answer within a document, these models **generate** the answer.
-In that sense, RAG follows a similar approach as GPT-3 but it comes with two huge advantages
-for real-world applications:
-a) it has a manageable model size
-b) the answer generation is conditioned on retrieved documents,
-i.e. the model can easily adjust to domain documents even after training has finished
-(in contrast: GPT-3 relies on the web data seen during training)
-
-**Example**
-
-```python
-| query = "who got the first nobel prize in physics?"
-|
-| # Retrieve related documents from retriever
-| retrieved_docs = retriever.retrieve(query=query)
-|
-| # Now generate answer from query and retrieved documents
-| generator.predict(
-| query=query,
-| documents=retrieved_docs,
-| top_k=1
-| )
-|
-| # Answer
-|
-| {'query': 'who got the first nobel prize in physics',
-| 'answers':
-| [{'query': 'who got the first nobel prize in physics',
-| 'answer': ' albert einstein',
-| 'meta': { 'doc_ids': [...],
-| 'doc_scores': [80.42758 ...],
-| 'doc_probabilities': [40.71379089355469, ...
-| 'texts': ['Albert Einstein was a ...]
-| 'titles': ['"Albert Einstein"', ...]
-| }}]}
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: str = "facebook/rag-token-nq", model_version: Optional[str] = None, retriever: Optional[DensePassageRetriever] = None, generator_type: RAGeneratorType = RAGeneratorType.TOKEN, top_k: int = 2, max_length: int = 200, min_length: int = 2, num_beams: int = 2, embed_title: bool = True, prefix: Optional[str] = None, use_gpu: bool = True)
-```
-
-Load a RAG model from Transformers along with passage_embedding_model.
-See https://huggingface.co/transformers/model_doc/rag.html for more details
-
-**Arguments**:
-
-- `model_name_or_path`: Directory of a saved model or the name of a public model e.g.
- 'facebook/rag-token-nq', 'facebook/rag-sequence-nq'.
- See https://huggingface.co/models for full list of available models.
-- `model_version`: The version of model to use from the HuggingFace model hub. Can be tag name, branch name, or commit hash.
-- `retriever`: `DensePassageRetriever` used to embedded passage
-- `generator_type`: Which RAG generator implementation to use? RAG-TOKEN or RAG-SEQUENCE
-- `top_k`: Number of independently generated text to return
-- `max_length`: Maximum length of generated text
-- `min_length`: Minimum length of generated text
-- `num_beams`: Number of beams for beam search. 1 means no beam search.
-- `embed_title`: Embedded the title of passage while generating embedding
-- `prefix`: The prefix used by the generator's tokenizer.
-- `use_gpu`: Whether to use GPU (if available)
-
-
-#### predict
-
-```python
- | predict(query: str, documents: List[Document], top_k: Optional[int] = None) -> Dict
-```
-
-Generate the answer to the input query. The generation will be conditioned on the supplied documents.
-These document can for example be retrieved via the Retriever.
-
-**Arguments**:
-
-- `query`: Query
-- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on.
-- `top_k`: Number of returned answers
-
-**Returns**:
-
-Generated answers plus additional infos in a dict like this:
-
-```python
-| {'query': 'who got the first nobel prize in physics',
-| 'answers':
-| [{'query': 'who got the first nobel prize in physics',
-| 'answer': ' albert einstein',
-| 'meta': { 'doc_ids': [...],
-| 'doc_scores': [80.42758 ...],
-| 'doc_probabilities': [40.71379089355469, ...
-| 'texts': ['Albert Einstein was a ...]
-| 'titles': ['"Albert Einstein"', ...]
-| }}]}
-```
-
-
-## Class: Seq2SeqGenerator
-
-```python
-class Seq2SeqGenerator(BaseGenerator)
-```
-
-A generic sequence-to-sequence generator based on HuggingFace's transformers.
-
-Text generation is supported by so called auto-regressive language models like GPT2,
-XLNet, XLM, Bart, T5 and others. In fact, any HuggingFace language model that extends
-GenerationMixin can be used by Seq2SeqGenerator.
-
-Moreover, as language models prepare model input in their specific encoding, each model
-specified with model_name_or_path parameter in this Seq2SeqGenerator should have an
-accompanying model input converter that takes care of prefixes, separator tokens etc.
-By default, we provide model input converters for a few well-known seq2seq language models (e.g. ELI5).
-It is the responsibility of Seq2SeqGenerator user to ensure an appropriate model input converter
-is either already registered or specified on a per-model basis in the Seq2SeqGenerator constructor.
-
-For mode details on custom model input converters refer to _BartEli5Converter
-
-
-See https://huggingface.co/transformers/main_classes/model.html?transformers.generation_utils.GenerationMixin#transformers.generation_utils.GenerationMixin
-as well as https://huggingface.co/blog/how-to-generate
-
-For a list of all text-generation models see https://huggingface.co/models?pipeline_tag=text-generation
-
-**Example**
-
-```python
-| query = "Why is Dothraki language important?"
-|
-| # Retrieve related documents from retriever
-| retrieved_docs = retriever.retrieve(query=query)
-|
-| # Now generate answer from query and retrieved documents
-| generator.predict(
-| query=query,
-| documents=retrieved_docs,
-| top_k=1
-| )
-|
-| # Answer
-|
-| {'answers': [" The Dothraki language is a constructed fictional language. It's important because George R.R. Martin wrote it."],
-| 'query': 'Why is Dothraki language important?'}
-|
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: str, input_converter: Optional[Callable] = None, top_k: int = 1, max_length: int = 200, min_length: int = 2, num_beams: int = 8, use_gpu: bool = True)
-```
-
-**Arguments**:
-
-- `model_name_or_path`: a HF model name for auto-regressive language model like GPT2, XLNet, XLM, Bart, T5 etc
-- `input_converter`: an optional Callable to prepare model input for the underlying language model
- specified in model_name_or_path parameter. The required __call__ method signature for
- the Callable is:
- __call__(tokenizer: PreTrainedTokenizer, query: str, documents: List[Document],
- top_k: Optional[int] = None) -> BatchEncoding:
-- `top_k`: Number of independently generated text to return
-- `max_length`: Maximum length of generated text
-- `min_length`: Minimum length of generated text
-- `num_beams`: Number of beams for beam search. 1 means no beam search.
-- `use_gpu`: Whether to use GPU (if available)
-
-
-#### predict
-
-```python
- | predict(query: str, documents: List[Document], top_k: Optional[int] = None) -> Dict
-```
-
-Generate the answer to the input query. The generation will be conditioned on the supplied documents.
-These document can be retrieved via the Retriever or supplied directly via predict method.
-
-**Arguments**:
-
-- `query`: Query
-- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on.
-- `top_k`: Number of returned answers
-
-**Returns**:
-
-Generated answers
diff --git a/src/pages/docs/versions/master/site/en/api/api/graph_retriever.md b/src/pages/docs/versions/master/site/en/api/api/graph_retriever.md
deleted file mode 100644
index 640eedb17..000000000
--- a/src/pages/docs/versions/master/site/en/api/api/graph_retriever.md
+++ /dev/null
@@ -1,68 +0,0 @@
----
-title: "Graph Retriever"
-metaTitle: "Graph Retriever"
-metaDescription: ""
-slug: "/docs/graph_retrievermd"
-date: "2020-09-03"
-id: "apigraphretrievermd"
----
-
-# Graph Retriever
-
-
-# Module: base
-
-
-# Module: text\_to\_sparql
-
-
-## Class: Text2SparqlRetriever
-
-```python
-class Text2SparqlRetriever(BaseGraphRetriever)
-```
-
-Graph retriever that uses a pre-trained Bart model to translate natural language questions given in text form to queries in SPARQL format.
-The generated SPARQL query is executed on a knowledge graph.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(knowledge_graph, model_name_or_path, top_k: int = 1)
-```
-
-Init the Retriever by providing a knowledge graph and a pre-trained BART model
-
-**Arguments**:
-
-- `knowledge_graph`: An instance of BaseKnowledgeGraph on which to execute SPARQL queries.
-- `model_name_or_path`: Name of or path to a pre-trained BartForConditionalGeneration model.
-- `top_k`: How many SPARQL queries to generate per text query.
-
-
-#### retrieve
-
-```python
- | retrieve(query: str, top_k: Optional[int] = None)
-```
-
-Translate a text query to SPARQL and execute it on the knowledge graph to retrieve a list of answers
-
-**Arguments**:
-
-- `query`: Text query that shall be translated to SPARQL and then executed on the knowledge graph
-- `top_k`: How many SPARQL queries to generate per text query.
-
-
-#### format\_result
-
-```python
- | format_result(result)
-```
-
-Generate formatted dictionary output with text answer and additional info
-
-**Arguments**:
-
-- `result`: The result of a SPARQL query as retrieved from the knowledge graph
diff --git a/src/pages/docs/versions/master/site/en/api/api/knowledge_graph.md b/src/pages/docs/versions/master/site/en/api/api/knowledge_graph.md
deleted file mode 100644
index d6e648b93..000000000
--- a/src/pages/docs/versions/master/site/en/api/api/knowledge_graph.md
+++ /dev/null
@@ -1,165 +0,0 @@
----
-title: "Knowledge Graph"
-metaTitle: "Knowledge Graph"
-metaDescription: ""
-slug: "/docs/knowledge_graphmd"
-date: "2020-09-03"
-id: "apiknowledgegraphmd"
----
-
-# Knowledge Graph
-
-
-# Module: base
-
-
-# Module: graphdb
-
-
-## Class: GraphDBKnowledgeGraph
-
-```python
-class GraphDBKnowledgeGraph(BaseKnowledgeGraph)
-```
-
-Knowledge graph store that runs on a GraphDB instance
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(host: str = "localhost", port: int = 7200, username: str = "", password: str = "", index: Optional[str] = None, prefixes: str = "")
-```
-
-Init the knowledge graph by defining the settings to connect with a GraphDB instance
-
-**Arguments**:
-
-- `host`: address of server where the GraphDB instance is running
-- `port`: port where the GraphDB instance is running
-- `username`: username to login to the GraphDB instance (if any)
-- `password`: password to login to the GraphDB instance (if any)
-- `index`: name of the index (also called repository) stored in the GraphDB instance
-- `prefixes`: definitions of namespaces with a new line after each namespace, e.g., PREFIX hp: `__
-
-**Example**
-
-```python
-| docs = [Document(text="PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions."
-| "The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by"
-| "the shutoffs which were expected to last through at least midday tomorrow.")]
-|
-| # Summarize
-| summary = summarizer.predict(
-| documents=docs,
-| generate_single_summary=True
-| )
-|
-| # Show results (List of Documents, containing summary and original text)
-| print(summary)
-|
-| [
-| {
-| "text": "California's largest electricity provider has turned off power to hundreds of thousands of customers.",
-| ...
-| "meta": {
-| "context": "PGE stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. ..."
-| },
-| ...
-| },
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: str = "google/pegasus-xsum", model_version: Optional[str] = None, tokenizer: Optional[str] = None, max_length: int = 200, min_length: int = 5, use_gpu: int = 0, clean_up_tokenization_spaces: bool = True, separator_for_single_summary: str = " ", generate_single_summary: bool = False)
-```
-
-Load a Summarization model from Transformers.
-See the up-to-date list of available models at
-https://huggingface.co/models?filter=summarization
-
-**Arguments**:
-
-- `model_name_or_path`: Directory of a saved model or the name of a public model e.g.
- 'facebook/rag-token-nq', 'facebook/rag-sequence-nq'.
- See https://huggingface.co/models?filter=summarization for full list of available models.
-- `model_version`: The version of model to use from the HuggingFace model hub. Can be tag name, branch name, or commit hash.
-- `tokenizer`: Name of the tokenizer (usually the same as model)
-- `max_length`: Maximum length of summarized text
-- `min_length`: Minimum length of summarized text
-- `use_gpu`: If < 0, then use cpu. If >= 0, this is the ordinal of the gpu to use
-- `clean_up_tokenization_spaces`: Whether or not to clean up the potential extra spaces in the text output
-- `separator_for_single_summary`: If `generate_single_summary=True` in `predict()`, we need to join all docs
- into a single text. This separator appears between those subsequent docs.
-- `generate_single_summary`: Whether to generate a single summary for all documents or one summary per document.
- If set to "True", all docs will be joined to a single string that will then
- be summarized.
- Important: The summary will depend on the order of the supplied documents!
-
-
-#### predict
-
-```python
- | predict(documents: List[Document], generate_single_summary: Optional[bool] = None) -> List[Document]
-```
-
-Produce the summarization from the supplied documents.
-These document can for example be retrieved via the Retriever.
-
-**Arguments**:
-
-- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on.
-- `generate_single_summary`: Whether to generate a single summary for all documents or one summary per document.
- If set to "True", all docs will be joined to a single string that will then
- be summarized.
- Important: The summary will depend on the order of the supplied documents!
-
-**Returns**:
-
-List of Documents, where Document.text contains the summarization and Document.meta["context"]
- the original, not summarized text
diff --git a/src/pages/docs/versions/master/site/en/api/api/translator.md b/src/pages/docs/versions/master/site/en/api/api/translator.md
deleted file mode 100644
index e478c9f26..000000000
--- a/src/pages/docs/versions/master/site/en/api/api/translator.md
+++ /dev/null
@@ -1,112 +0,0 @@
----
-title: "Translator"
-metaTitle: "Translator"
-metaDescription: ""
-slug: "/docs/translatormd"
-date: "2020-09-03"
-id: "apitranslatormd"
----
-
-# Translator
-
-
-# Module: base
-
-
-## Class: BaseTranslator
-
-```python
-class BaseTranslator(BaseComponent)
-```
-
-Abstract class for a Translator component that translates either a query or a doc from language A to language B.
-
-
-#### translate
-
-```python
- | @abstractmethod
- | translate(query: Optional[str] = None, documents: Optional[Union[List[Document], List[str], List[Dict[str, Any]]]] = None, dict_key: Optional[str] = None, **kwargs) -> Union[str, List[Document], List[str], List[Dict[str, Any]]]
-```
-
-Translate the passed query or a list of documents from language A to B.
-
-
-#### run
-
-```python
- | run(query: Optional[str] = None, documents: Optional[Union[List[Document], List[str], List[Dict[str, Any]]]] = None, answers: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, dict_key: Optional[str] = None, **kwargs)
-```
-
-Method that gets executed when this class is used as a Node in a Haystack Pipeline
-
-
-# Module: transformers
-
-
-## Class: TransformersTranslator
-
-```python
-class TransformersTranslator(BaseTranslator)
-```
-
-Translator component based on Seq2Seq models from Huggingface's transformers library.
-Exemplary use cases:
-- Translate a query from Language A to B (e.g. if you only have good models + documents in language B)
-- Translate a document from Language A to B (e.g. if you want to return results in the native language of the user)
-
-We currently recommend using OPUS models (see __init__() for details)
-
-**Example:**
-
-```python
-| DOCS = [
-| Document(text="Heinz von Foerster was an Austrian American scientist combining physics and philosophy,
-| and widely attributed as the originator of Second-order cybernetics.")
-| ]
-| translator = TransformersTranslator(model_name_or_path="Helsinki-NLP/opus-mt-en-de")
-| res = translator.translate(documents=DOCS, query=None)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: str, tokenizer_name: Optional[str] = None, max_seq_len: Optional[int] = None, clean_up_tokenization_spaces: Optional[bool] = True)
-```
-
-Initialize the translator with a model that fits your targeted languages. While we support all seq2seq
-models from Hugging Face's model hub, we recommend using the OPUS models from Helsiniki NLP. They provide plenty
-of different models, usually one model per language pair and translation direction.
-They have a pretty standardized naming that should help you find the right model:
-- "Helsinki-NLP/opus-mt-en-de" => translating from English to German
-- "Helsinki-NLP/opus-mt-de-en" => translating from German to English
-- "Helsinki-NLP/opus-mt-fr-en" => translating from French to English
-- "Helsinki-NLP/opus-mt-hi-en"=> translating from Hindi to English
-...
-
-They also have a few multilingual models that support multiple languages at once.
-
-**Arguments**:
-
-- `model_name_or_path`: Name of the seq2seq model that shall be used for translation.
- Can be a remote name from Huggingface's modelhub or a local path.
-- `tokenizer_name`: Optional tokenizer name. If not supplied, `model_name_or_path` will also be used for the
- tokenizer.
-- `max_seq_len`: The maximum sentence length the model accepts. (Optional)
-- `clean_up_tokenization_spaces`: Whether or not to clean up the tokenization spaces. (default True)
-
-
-#### translate
-
-```python
- | translate(query: Optional[str] = None, documents: Optional[Union[List[Document], List[str], List[Dict[str, Any]]]] = None, dict_key: Optional[str] = None, **kwargs) -> Union[str, List[Document], List[str], List[Dict[str, Any]]]
-```
-
-Run the actual translation. You can supply a query or a list of documents. Whatever is supplied will be translated.
-
-**Arguments**:
-
-- `query`: The query string to translate
-- `documents`: The documents to translate
-- `dict_key`:
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-{
- "menuList": [
- {
- "id": "overview",
- "title": "Overview",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 0,
- "isMenu": true
- },
- {
- "id": "get_startedmd",
- "title": "Get Started",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "intromd",
- "title": "What is Haystack?",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "use_casesmd",
- "title": "Use Cases",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "roadmapmd",
- "title": "Roadmap",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "faqmd",
- "title": "FAQ",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "termsmd",
- "title": "Glossary",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "usage_haystack",
- "title": "Usage",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 1,
- "isMenu": true
- },
- {
- "id": "preprocessingmd",
- "title": "Preprocessing",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "pipelinesmd",
- "title": "Pipelines",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "documentstoremd",
- "title": "Document Store",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "retrievermd",
- "title": "Retriever",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "readermd",
- "title": "Reader",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "generatormd",
- "title": "Generator",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "summarizermd",
- "title": "Summarizer",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 6
- },
- {
- "id": "translatormd",
- "title": "Translator",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 7
- },
- {
- "id": "knowledgegraphmd",
- "title": "Knowledge Graph",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 8
- },
- {
- "id": "languagesmd",
- "title": "Languages Other Than English",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 9
- },
- {
- "id": "domain_adaptationmd",
- "title": "Domain Adaptation",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 10
- },
- {
- "id": "optimizationmd",
- "title": "Optimization",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 11
- },
- {
- "id": "annotationmd",
- "title": "Annotation Tool",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 12
- },
- {
- "id": "rankermd",
- "title": "Ranker",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 13
- },
- {
- "id": "query_classifiermd",
- "title": "Query Classifier",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 14
- },
- {
- "id": "tutorials_haystack",
- "title": "Tutorials",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 2,
- "isMenu": true
- },
- {
- "id": "tutorial1md",
- "title": "Build Your First QA System",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "tutorial2md",
- "title": "Fine-tuning a Model on Your Own Data",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "tutorial3md",
- "title": "Build a QA System Without Elasticsearch",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "tutorial4md",
- "title": "Utilizing existing FAQs for Question Answering",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "tutorial5md",
- "title": "Evaluation of a QA System",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "tutorial6md",
- "title": "Better Retrieval via \"Dense Passage Retrieval\"",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "tutorial7md",
- "title": "Generative QA with \"Retrieval-Augmented Generation\"",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 6
- },
- {
- "id": "tutorial8md",
- "title": "Preprocessing your Documents",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 7
- },
- {
- "id": "tutorial9md",
- "title": "How to Train DPR",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 8
- },
- {
- "id": "tutorial10md",
- "title": "Question Answering on a Knowledge Graph",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 9
- },
- {
- "id": "tutorial11md",
- "title": "How to use Pipelines",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 10
- },
- {
- "id": "tutorial12md",
- "title": "Generative QA with \"LFQA\"",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 11
- },
- {
- "id": "api_haystack",
- "title": "API reference",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 3,
- "isMenu": true
- },
- {
- "id": "apidatabasemd",
- "title": "Document Store",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "apiretrievermd",
- "title": "Retriever",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "apireadermd",
- "title": "Reader",
- "label1": "api_haystack",
- "label2": "api_haystack",
- "label3": "",
- "order": 2
- },
- {
- "id": "apigeneratormd",
- "title": "Generator",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "apisummarizermd",
- "title": "Summarizer",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "apitranslatormd",
- "title": "Translator",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "apiindexingmd",
- "title": "Preprocessor",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 6
- },
- {
- "id": "file_convertersmd",
- "title": "File Converters",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 7
- },
- {
- "id": "apicrawlermd",
- "title": "Crawler",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 8
- },
- {
- "id": "apievaluationmd",
- "title": "Evaluation",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 9
- },
- {
- "id": "apipipelinesmd",
- "title": "Pipelines",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 10
- },
- {
- "id": "apiknowledgegraphmd",
- "title": "Knowledge Graph",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 11
- },
- {
- "id": "apigraphretrievermd",
- "title": "Graph Retriever",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 12
- }
- ]
-}
\ No newline at end of file
diff --git a/src/pages/docs/versions/master/site/en/tutorials/tutorials/1.md b/src/pages/docs/versions/master/site/en/tutorials/tutorials/1.md
deleted file mode 100644
index d00738cfa..000000000
--- a/src/pages/docs/versions/master/site/en/tutorials/tutorials/1.md
+++ /dev/null
@@ -1,258 +0,0 @@
----
-title: "Tutorial 1"
-metaTitle: "Build Your First QA System"
-metaDescription: ""
-slug: "/docs/tutorial1"
-date: "2020-09-03"
-id: "tutorial1md"
----
-
-# Build Your First QA System
-
-
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb)
-
-Question Answering can be used in a variety of use cases. A very common one: Using it to navigate through complex knowledge bases or long documents ("search setting").
-
-A "knowledge base" could for example be your website, an internal wiki or a collection of financial reports.
-In this tutorial we will work on a slightly different domain: "Game of Thrones".
-
-Let's see how we can use a bunch of Wikipedia articles to answer a variety of questions about the
-marvellous seven kingdoms...
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-Haystack finds answers to queries within the documents stored in a `DocumentStore`. The current implementations of `DocumentStore` include `ElasticsearchDocumentStore`, `FAISSDocumentStore`, `SQLDocumentStore`, and `InMemoryDocumentStore`.
-
-**Here:** We recommended Elasticsearch as it comes preloaded with features like [full-text queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html), [BM25 retrieval](https://www.elastic.co/elasticon/conf/2016/sf/improved-text-scoring-with-bm25), and [vector storage for text embeddings](https://www.elastic.co/guide/en/elasticsearch/reference/7.6/dense-vector.html).
-
-**Alternatives:** If you are unable to setup an Elasticsearch instance, then follow the [Tutorial 3](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb) for using SQL/InMemory document stores.
-
-**Hint**: This tutorial creates a new document store instance with Wikipedia articles on Game of Thrones. However, you can configure Haystack to work with your existing document stores.
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker via the Haystack utility function
-from haystack.utils import launch_es
-
-launch_es()
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-# Connect to Elasticsearch
-
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles about Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first fetch some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is:
-# {
-# 'text': "",
-# 'meta': {'name': "", ...}
-#}
-# (Optionally: you can also add more key-value-pairs here, that will be indexed as fields in Elasticsearch and
-# can be accessed later for filtering or shown in the responses of the Pipeline)
-
-# Let's have a look at the first 3 entries:
-print(dicts[:3])
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-## Initalize Retriever, Reader, & Pipeline
-
-### Retriever
-
-Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question could be answered.
-They use some simple but fast algorithm.
-
-**Here:** We use Elasticsearch's default BM25 algorithm
-
-**Alternatives:**
-
-- Customize the `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `DensePassageRetriever` to use different embedding models for passage and query (see Tutorial 6)
-
-
-```python
-from haystack.retriever.sparse import ElasticsearchRetriever
-retriever = ElasticsearchRetriever(document_store=document_store)
-```
-
-
-```python
-# Alternative: An in-memory TfidfRetriever based on Pandas dataframes for building quick-prototypes with SQLite document store.
-
-# from haystack.retriever.sparse import TfidfRetriever
-# retriever = TfidfRetriever(document_store=document_store)
-```
-
-### Reader
-
-A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based
-on powerful, but slower deep learning models.
-
-Haystack currently supports Readers based on the frameworks FARM and Transformers.
-With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).
-
-**Here:** a medium sized RoBERTa QA model using a Reader based on FARM (https://huggingface.co/deepset/roberta-base-squad2)
-
-**Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)
-
-**Alternatives (Models):** e.g. "distilbert-base-uncased-distilled-squad" (fast) or "deepset/bert-large-uncased-whole-word-masking-squad2" (good accuracy)
-
-**Hint:** You can adjust the model to return "no answer possible" with the no_ans_boost. Higher values mean the model prefers "no answer possible"
-
-#### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-#### TransformersReader
-
-
-```python
-# Alternative:
-# reader = TransformersReader(model_name_or_path="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1)
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import ExtractiveQAPipeline
-pipe = ExtractiveQAPipeline(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = pipe.run(query="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-# prediction = pipe.run(query="Who created the Dothraki vocabulary?", top_k_reader=5)
-# prediction = pipe.run(query="Who is the sister of Sansa?", top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
-
diff --git a/src/pages/docs/versions/master/site/en/tutorials/tutorials/10.md b/src/pages/docs/versions/master/site/en/tutorials/tutorials/10.md
deleted file mode 100644
index d69775ab2..000000000
--- a/src/pages/docs/versions/master/site/en/tutorials/tutorials/10.md
+++ /dev/null
@@ -1,149 +0,0 @@
----
-title: "Tutorial 10"
-metaTitle: "Knowledge Graph QA"
-metaDescription: ""
-slug: "/docs/tutorial10"
-date: "2021-04-06"
-id: "tutorial10md"
----
-
-# Question Answering on a Knowledge Graph
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial10_Knowledge_Graph.ipynb)
-
-Haystack allows storing and querying knowledge graphs with the help of pre-trained models that translate text queries to SPARQL queries.
-This tutorial demonstrates how to load an existing knowledge graph into haystack, load a pre-trained retriever, and execute text queries on the knowledge graph.
-The training of models that translate text queries into SPARQL queries is currently not supported.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-```
-
-
-```python
-# Here are some imports that we'll need
-
-import subprocess
-import time
-from pathlib import Path
-
-from haystack.graph_retriever.text_to_sparql import Text2SparqlRetriever
-from haystack.knowledge_graph.graphdb import GraphDBKnowledgeGraph
-from haystack.preprocessor.utils import fetch_archive_from_http
-```
-
-## Downloading Knowledge Graph and Model
-
-
-```python
-# Let's first fetch some triples that we want to store in our knowledge graph
-# Here: exemplary triples from the wizarding world
-graph_dir = "../data/tutorial10_knowledge_graph/"
-s3_url = "https://fandom-qa.s3-eu-west-1.amazonaws.com/triples_and_config.zip"
-fetch_archive_from_http(url=s3_url, output_dir=graph_dir)
-
-# Fetch a pre-trained BART model that translates text queries to SPARQL queries
-model_dir = "../saved_models/tutorial10_knowledge_graph/"
-s3_url = "https://fandom-qa.s3-eu-west-1.amazonaws.com/saved_models/hp_v3.4.zip"
-fetch_archive_from_http(url=s3_url, output_dir=model_dir)
-```
-
-## Launching a GraphDB instance
-
-
-```python
-# Unfortunately, there seems to be no good way to run GraphDB in colab environments
-# In your local environment, you could start a GraphDB server with docker
-# Feel free to check GraphDB's website for the free version https://www.ontotext.com/products/graphdb/graphdb-free/
-print("Starting GraphDB ...")
-status = subprocess.run(
- ['docker run -d -p 7200:7200 --name graphdb-instance-tutorial docker-registry.ontotext.com/graphdb-free:9.4.1-adoptopenjdk11'], shell=True
-)
-if status.returncode:
- raise Exception("Failed to launch GraphDB. Maybe it is already running or you already have a container with that name that you could start?")
-time.sleep(5)
-```
-
-## Creating a new GraphDB repository (also known as index in haystack's document stores)
-
-
-```python
-# Initialize a knowledge graph connected to GraphDB and use "tutorial_10_index" as the name of the index
-kg = GraphDBKnowledgeGraph(index="tutorial_10_index")
-
-# Delete the index as it might have been already created in previous runs
-kg.delete_index()
-
-# Create the index based on a configuration file
-kg.create_index(config_path=Path(graph_dir+"repo-config.ttl"))
-
-# Import triples of subject, predicate, and object statements from a ttl file
-kg.import_from_ttl_file(index="tutorial_10_index", path=Path(graph_dir+"triples.ttl"))
-print(f"The last triple stored in the knowledge graph is: {kg.get_all_triples()[-1]}")
-print(f"There are {len(kg.get_all_triples())} triples stored in the knowledge graph.")
-```
-
-
-```python
-# Define prefixes for names of resources so that we can use shorter resource names in queries
-prefixes = """PREFIX rdf:
-PREFIX xsd:
-PREFIX hp: ?obj . }")
-print(result)
-# Paraphrased question: What is the patronus of Hermione?
-# Correct answer: Otter
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/site/en/tutorials/tutorials/11.md b/src/pages/docs/versions/master/site/en/tutorials/tutorials/11.md
deleted file mode 100644
index 1a4f3b5cc..000000000
--- a/src/pages/docs/versions/master/site/en/tutorials/tutorials/11.md
+++ /dev/null
@@ -1,411 +0,0 @@
----
-title: "Tutorial 11"
-metaTitle: "Pipelines"
-metaDescription: ""
-slug: "/docs/tutorial11"
-date: "2021-04-06"
-id: "tutorial11md"
----
-
-# Pipelines Tutorial
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial11_Pipelines.ipynb)
-
-In this tutorial, you will learn how the `Pipeline` class acts as a connector between all the different
-building blocks that are found in FARM. Whether you are using a Reader, Generator, Summarizer
-or Retriever (or 2), the `Pipeline` class will help you build a Directed Acyclic Graph (DAG) that
-determines how to route the output of one component into the input of another.
-
-
-## Setting Up the Environment
-
-Let's start by ensuring we have a GPU running to ensure decent speed in this tutorial.
-In Google colab, you can change to a GPU runtime in the menu:
-- **Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-These lines are to install Haystack through pip
-
-
-```python
-# Install the latest release of Haystack in your own environment
-!pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install --upgrade git+https://github.com/deepset-ai/haystack.git
-
-# Install pygraphviz
-!apt install libgraphviz-dev
-!pip install pygraphviz
-```
-
-If running from Colab or a no Docker environment, you will want to start Elasticsearch from source
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-## Initialization
-
-Here are some core imports
-
-
-```python
-from haystack.utils import print_answers, print_documents
-```
-
-Then let's fetch some data (in this case, pages from the Game of Thrones wiki) and prepare it so that it can
-be used indexed into our `DocumentStore`
-
-
-```python
-from haystack.preprocessor.utils import fetch_archive_from_http, convert_files_to_dicts
-from haystack.preprocessor.cleaning import clean_wiki_text
-
-#Download and prepare data - 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# convert files to dicts containing documents that can be indexed to our datastore
-got_dicts = convert_files_to_dicts(
- dir_path=doc_dir,
- clean_func=clean_wiki_text,
- split_paragraphs=True
-)
-```
-
-Here we initialize the core components that we will be gluing together using the `Pipeline` class.
-We have a `DocumentStore`, an `ElasticsearchRetriever` and a `FARMReader`.
-These can be combined to create a classic Retriever-Reader pipeline that is designed
-to perform Open Domain Question Answering.
-
-
-```python
-from haystack import Pipeline
-from haystack.utils import launch_es
-from haystack.document_store import ElasticsearchDocumentStore
-from haystack.retriever.sparse import ElasticsearchRetriever
-from haystack.retriever.dense import DensePassageRetriever
-from haystack.reader import FARMReader
-
-
-# Initialize DocumentStore and index documents
-launch_es()
-document_store = ElasticsearchDocumentStore()
-document_store.delete_all_documents()
-document_store.write_documents(got_dicts)
-
-# Initialize Sparse retriever
-es_retriever = ElasticsearchRetriever(document_store=document_store)
-
-# Initialize dense retriever
-dpr_retriever = DensePassageRetriever(document_store)
-document_store.update_embeddings(dpr_retriever, update_existing_embeddings=False)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")
-```
-
-## Prebuilt Pipelines
-
-Haystack features many prebuilt pipelines that cover common tasks.
-Here we have an `ExtractiveQAPipeline` (the successor to the now deprecated `Finder` class).
-
-
-```python
-from haystack.pipeline import ExtractiveQAPipeline
-
-# Prebuilt pipeline
-p_extractive_premade = ExtractiveQAPipeline(reader=reader, retriever=es_retriever)
-res = p_extractive_premade.run(
- query="Who is the father of Arya Stark?",
- top_k_retriever=10,
- top_k_reader=5
-)
-print_answers(res, details="minimal")
-```
-
-If you want to just do the retrieval step, you can use a `DocumentSearchPipeline`
-
-
-```python
-from haystack.pipeline import DocumentSearchPipeline
-
-p_retrieval = DocumentSearchPipeline(es_retriever)
-res = p_retrieval.run(
- query="Who is the father of Arya Stark?",
- top_k_retriever=10
-)
-print_documents(res, max_text_len=200)
-```
-
-Or if you want to use a `Generator` instead of a `Reader`,
-you can initialize a `GenerativeQAPipeline` like this:
-
-
-```python
-from haystack.pipeline import GenerativeQAPipeline, FAQPipeline
-from haystack.generator import RAGenerator
-
-# We set this to True so that the document store returns document embeddings with each document
-# This is needed by the Generator
-document_store.return_embedding = True
-
-# Initialize generator
-rag_generator = RAGenerator()
-
-# Generative QA
-p_generator = GenerativeQAPipeline(generator=rag_generator, retriever=dpr_retriever)
-res = p_generator.run(
- query="Who is the father of Arya Stark?",
- top_k_retriever=10
-)
-print_answers(res, details="minimal")
-
-# We are setting this to False so that in later pipelines,
-# we get a cleaner printout
-document_store.return_embedding = False
-
-```
-
-Haystack features prebuilt pipelines to do:
-- just document search (DocumentSearchPipeline),
-- document search with summarization (SearchSummarizationPipeline)
-- generative QA (GenerativeQAPipeline)
-- FAQ style QA (FAQPipeline)
-- translated search (TranslationWrapperPipeline)
-To find out more about these pipelines, have a look at our [documentation](https://haystack.deepset.ai/docs/latest/pipelinesmd)
-
-
-With any Pipeline, whether prebuilt or custom constructed,
-you can save a diagram showing how all the components are connected.
-
-
-
-
-```python
-p_extractive_premade.draw("pipeline_extractive_premade.png")
-p_retrieval.draw("pipeline_retrieval.png")
-p_generator.draw("pipeline_generator.png")
-```
-
-## Custom Pipelines
-
-Now we are going to rebuild the `ExtractiveQAPipelines` using the generic Pipeline class.
-We do this by adding the building blocks that we initialized as nodes in the graph.
-
-
-```python
-# Custom built extractive QA pipeline
-p_extractive = Pipeline()
-p_extractive.add_node(component=es_retriever, name="Retriever", inputs=["Query"])
-p_extractive.add_node(component=reader, name="Reader", inputs=["Retriever"])
-
-# Now we can run it
-res = p_extractive.run(
- query="Who is the father of Arya Stark?",
- top_k_retriever=10,
- top_k_reader=5
-)
-print_answers(res, details="minimal")
-p_extractive.draw("pipeline_extractive.png")
-```
-
-Pipelines offer a very simple way to ensemble together different components.
-In this example, we are going to combine the power of a `DensePassageRetriever`
-with the keyword based `ElasticsearchRetriever`.
-See our [documentation](https://haystack.deepset.ai/docs/latest/retrievermd) to understand why
-we might want to combine a dense and sparse retriever.
-
-
-
-Here we use a `JoinDocuments` node so that the predictions from each retriever can be merged together.
-
-
-```python
-from haystack.pipeline import JoinDocuments
-
-# Create ensembled pipeline
-p_ensemble = Pipeline()
-p_ensemble.add_node(component=es_retriever, name="ESRetriever", inputs=["Query"])
-p_ensemble.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["Query"])
-p_ensemble.add_node(component=JoinDocuments(join_mode="concatenate"), name="JoinResults", inputs=["ESRetriever", "DPRRetriever"])
-p_ensemble.add_node(component=reader, name="Reader", inputs=["JoinResults"])
-p_ensemble.draw("pipeline_ensemble.png")
-
-# Run pipeline
-res = p_ensemble.run(
- query="Who is the father of Arya Stark?",
- top_k_retriever=5 #This is top_k per retriever
-)
-print_answers(res, details="minimal")
-```
-
-## Custom Nodes
-
-Nodes are relatively simple objects
-and we encourage our users to design their own if they don't see on that fits their use case
-
-The only requirements are:
-- Add a method run(self, **kwargs) to your class. **kwargs will contain the output from the previous node in your graph.
-- Do whatever you want within run() (e.g. reformatting the query)
-- Return a tuple that contains your output data (for the next node)
-and the name of the outgoing edge (by default "output_1" for nodes that have one output)
-- Add a class attribute outgoing_edges = 1 that defines the number of output options from your node. You only need a higher number here if you have a decision node (see below).
-
-Here we have a template for a Node:
-
-
-```python
-class NodeTemplate():
- outgoing_edges = 1
-
- def run(self, **kwargs):
- # Insert code here to manipulate the variables in kwarg
- return (kwargs, "output_1")
-```
-
-## Decision Nodes
-
-Decision Nodes help you route your data so that only certain branches of your `Pipeline` are run.
-One popular use case for such query classifiers is routing keyword queries to Elasticsearch and questions to DPR + Reader.
-With this approach you keep optimal speed and simplicity for keywords while going deep with transformers when it's most helpful.
-
-
-
-Though this looks very similar to the ensembled pipeline shown above,
-the key difference is that only one of the retrievers is run for each request.
-By contrast both retrievers are always run in the ensembled approach.
-
-Below, we define a very naive `QueryClassifier` and show how to use it:
-
-
-```python
-class QueryClassifier():
- outgoing_edges = 2
-
- def run(self, **kwargs):
- if "?" in kwargs["query"]:
- return (kwargs, "output_2")
- else:
- return (kwargs, "output_1")
-
-# Here we build the pipeline
-p_classifier = Pipeline()
-p_classifier.add_node(component=QueryClassifier(), name="QueryClassifier", inputs=["Query"])
-p_classifier.add_node(component=es_retriever, name="ESRetriever", inputs=["QueryClassifier.output_1"])
-p_classifier.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_2"])
-p_classifier.add_node(component=reader, name="QAReader", inputs=["ESRetriever", "DPRRetriever"])
-p_classifier.draw("pipeline_classifier.png")
-
-# Run only the dense retriever on the full sentence query
-res_1 = p_classifier.run(
- query="Who is the father of Arya Stark?",
- top_k_retriever=10
-)
-print("DPR Results" + "\n" + "="*15)
-print_answers(res_1)
-
-# Run only the sparse retriever on a keyword based query
-res_2 = p_classifier.run(
- query="Arya Stark father",
- top_k_retriever=10
-)
-print("ES Results" + "\n" + "="*15)
-print_answers(res_2)
-```
-
-## Evaluation Nodes
-
-We have also designed a set of nodes that can be used to evaluate the performance of a system.
-Have a look at our [tutorial](https://haystack.deepset.ai/docs/latest/tutorial5md) to get hands on with the code and learn more about Evaluation Nodes!
-
-
-## YAML Configs
-
-A full `Pipeline` can be defined in a YAML file and simply loaded.
-Having your pipeline available in a YAML is particularly useful
-when you move between experimentation and production environments.
-Just export the YAML from your notebook / IDE and import it into your production environment.
-It also helps with version control of pipelines,
-allows you to share your pipeline easily with colleagues,
-and simplifies the configuration of pipeline parameters in production.
-
-It consists of two main sections: you define all objects (e.g. a reader) in components
-and then stick them together to a pipeline in pipelines.
-You can also set one component to be multiple nodes of a pipeline or to be a node across multiple pipelines.
-It will be loaded just once in memory and therefore doesn't hurt your resources more than actually needed.
-
-The contents of a YAML file should look something like this:
-
-```yaml
-version: '0.7'
-components: # define all the building-blocks for Pipeline
-- name: MyReader # custom-name for the component; helpful for visualization & debugging
- type: FARMReader # Haystack Class name for the component
- params:
- no_ans_boost: -10
- model_name_or_path: deepset/roberta-base-squad2
-- name: MyESRetriever
- type: ElasticsearchRetriever
- params:
- document_store: MyDocumentStore # params can reference other components defined in the YAML
- custom_query: null
-- name: MyDocumentStore
- type: ElasticsearchDocumentStore
- params:
- index: haystack_test
-pipelines: # multiple Pipelines can be defined using the components from above
-- name: my_query_pipeline # a simple extractive-qa Pipeline
- nodes:
- - name: MyESRetriever
- inputs: [Query]
- - name: MyReader
- inputs: [MyESRetriever]
-```
-
-To load, simply call:
-``` python
-pipeline.load_from_yaml(Path("sample.yaml"))
-```
-
-## Conclusion
-
-The possibilities are endless with the `Pipeline` class and we hope that this tutorial will inspire you
-to build custom pipeplines that really work for your use case!
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/site/en/tutorials/tutorials/12.md b/src/pages/docs/versions/master/site/en/tutorials/tutorials/12.md
deleted file mode 100644
index d65d1a3d6..000000000
--- a/src/pages/docs/versions/master/site/en/tutorials/tutorials/12.md
+++ /dev/null
@@ -1,164 +0,0 @@
----
-title: "Tutorial 12"
-metaTitle: "Generative QA with LFQA"
-metaDescription: ""
-slug: "/docs/tutorial12"
-date: "2021-04-06"
-id: "tutorial12md"
----
-
-# Long-Form Question Answering
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial12_LFQA.ipynb)
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.generator.transformers import Seq2SeqGenerator
-```
-
-### Document Store
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-The default flavour of FAISSDocumentStore is "Flat" but can also be set to "HNSW" for
-faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.
-For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-
-
-```python
-from haystack.document_store.faiss import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(vector_dim=128, faiss_index_factory_str="Flat")
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever and Reader/Generator
-
-#### Retriever
-
-**Here:** We use a `RetribertRetriever` and we invoke `update_embeddings` to index the embeddings of documents in the `FAISSDocumentStore`
-
-
-
-
-```python
-from haystack.retriever.dense import EmbeddingRetriever
-
-retriever = EmbeddingRetriever(document_store=document_store,
- embedding_model="yjernite/retribert-base-uncased",
- model_format="retribert")
-
-document_store.update_embeddings(retriever)
-```
-
-Before we blindly use the `RetribertRetriever` let's empirically test it to make sure a simple search indeed finds the relevant documents.
-
-
-```python
-from haystack.utils import print_answers, print_documents
-from haystack.pipeline import DocumentSearchPipeline
-
-p_retrieval = DocumentSearchPipeline(retriever)
-res = p_retrieval.run(
- query="Tell me something about Arya Stark?",
- top_k_retriever=5
-)
-print_documents(res, max_text_len=512)
-
-```
-
-#### Reader/Generator
-
-Similar to previous Tutorials we now initalize our reader/generator.
-
-Here we use a `Seq2SeqGenerator` with the *yjernite/bart_eli5* model (see: https://huggingface.co/yjernite/bart_eli5)
-
-
-
-
-```python
-generator = Seq2SeqGenerator(model_name_or_path="yjernite/bart_eli5")
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `GenerativeQAPipeline` that combines a retriever and a reader/generator to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import GenerativeQAPipeline
-pipe = GenerativeQAPipeline(generator, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-pipe.run(query="Why did Arya Stark's character get portrayed in a television adaptation?", top_k_retriever=1)
-```
-
-
-```python
-pipe.run(query="What kind of character does Arya Stark play?", top_k_retriever=1)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/site/en/tutorials/tutorials/2.md b/src/pages/docs/versions/master/site/en/tutorials/tutorials/2.md
deleted file mode 100644
index ec5349afb..000000000
--- a/src/pages/docs/versions/master/site/en/tutorials/tutorials/2.md
+++ /dev/null
@@ -1,108 +0,0 @@
----
-title: "Tutorial 2"
-metaTitle: "Fine-tuning a model on your own data"
-metaDescription: ""
-slug: "/docs/tutorial2"
-date: "2020-09-03"
-id: "tutorial2md"
----
-
-# Fine-tuning a Model on Your Own Data
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial2_Finetune_a_model_on_your_data.ipynb)
-
-For many use cases it is sufficient to just use one of the existing public models that were trained on SQuAD or other public QA datasets (e.g. Natural Questions).
-However, if you have domain-specific questions, fine-tuning your model on custom examples will very likely boost your performance.
-While this varies by domain, we saw that ~ 2000 examples can easily increase performance by +5-20%.
-
-This tutorial shows you how to fine-tune a pretrained model on your own dataset.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.reader.farm import FARMReader
-```
-
-
-## Create Training Data
-
-There are two ways to generate training data
-
-1. **Annotation**: You can use the [annotation tool](https://github.com/deepset-ai/haystack#labeling-tool) to label your data, i.e. highlighting answers to your questions in a document. The tool supports structuring your workflow with organizations, projects, and users. The labels can be exported in SQuAD format that is compatible for training with Haystack.
-
-
-
-2. **Feedback**: For production systems, you can collect training data from direct user feedback via Haystack's [REST API interface](https://github.com/deepset-ai/haystack#rest-api). This includes a customizable user feedback API for providing feedback on the answer returned by the API. The API provides a feedback export endpoint to obtain the feedback data for fine-tuning your model further.
-
-
-## Fine-tune your model
-
-Once you have collected training data, you can fine-tune your base models.
-We initialize a reader as a base model and fine-tune it on our own custom dataset (should be in SQuAD-like format).
-We recommend using a base model that was trained on SQuAD or a similar QA dataset before to benefit from Transfer Learning effects.
-
-**Recommendation**: Run training on a GPU.
-If you are using Colab: Enable this in the menu "Runtime" > "Change Runtime type" > Select "GPU" in dropdown.
-Then change the `use_gpu` arguments below to `True`
-
-
-```python
-reader = FARMReader(model_name_or_path="distilbert-base-uncased-distilled-squad", use_gpu=True)
-data_dir = "data/squad20"
-# data_dir = "PATH/TO_YOUR/TRAIN_DATA"
-reader.train(data_dir=data_dir, train_filename="dev-v2.0.json", use_gpu=True, n_epochs=1, save_dir="my_model")
-```
-
-
-```python
-# Saving the model happens automatically at the end of training into the `save_dir` you specified
-# However, you could also save a reader manually again via:
-reader.save(directory="my_model")
-```
-
-
-```python
-# If you want to load it at a later point, just do:
-new_reader = FARMReader(model_name_or_path="my_model")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/site/en/tutorials/tutorials/3.md b/src/pages/docs/versions/master/site/en/tutorials/tutorials/3.md
deleted file mode 100644
index 09f83ace6..000000000
--- a/src/pages/docs/versions/master/site/en/tutorials/tutorials/3.md
+++ /dev/null
@@ -1,202 +0,0 @@
----
-title: "Tutorial 3"
-metaTitle: "Build a QA System Without Elasticsearch"
-metaDescription: ""
-slug: "/docs/tutorial3"
-date: "2020-09-03"
-id: "tutorial3md"
----
-
-# Build a QA System Without Elasticsearch
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb)
-
-Haystack provides alternatives to Elasticsearch for developing quick prototypes.
-
-You can use an `InMemoryDocumentStore` or a `SQLDocumentStore`(with SQLite) as the document store.
-
-If you are interested in more feature-rich Elasticsearch, then please refer to the Tutorial 1.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-
-
-```python
-# In-Memory Document Store
-from haystack.document_store.memory import InMemoryDocumentStore
-document_store = InMemoryDocumentStore()
-```
-
-
-```python
-# SQLite Document Store
-# from haystack.document_store.sql import SQLDocumentStore
-# document_store = SQLDocumentStore(url="sqlite:///qa.db")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles on Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first get some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# convert files to dicts containing documents that can be indexed to our datastore
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is: {"name": ", "text": ""}
-
-# Let's have a look at the first 3 entries:
-print(dicts[:3])
-# Now, let's write the docs to our DB.
-document_store.write_documents(dicts)
-```
-
-## Initalize Retriever, Reader & Pipeline
-
-### Retriever
-
-Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question could be answered.
-
-With InMemoryDocumentStore or SQLDocumentStore, you can use the TfidfRetriever. For more retrievers, please refer to the tutorial-1.
-
-
-```python
-# An in-memory TfidfRetriever based on Pandas dataframes
-
-from haystack.retriever.sparse import TfidfRetriever
-retriever = TfidfRetriever(document_store=document_store)
-```
-
-### Reader
-
-A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based
-on powerful, but slower deep learning models.
-
-Haystack currently supports Readers based on the frameworks FARM and Transformers.
-With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).
-
-**Here:** a medium sized RoBERTa QA model using a Reader based on FARM (https://huggingface.co/deepset/roberta-base-squad2)
-
-**Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)
-
-**Alternatives (Models):** e.g. "distilbert-base-uncased-distilled-squad" (fast) or "deepset/bert-large-uncased-whole-word-masking-squad2" (good accuracy)
-
-**Hint:** You can adjust the model to return "no answer possible" with the no_ans_boost. Higher values mean the model prefers "no answer possible"
-
-#### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-#### TransformersReader
-
-
-```python
-# Alternative:
-# reader = TransformersReader(model_name_or_path="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1)
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import ExtractiveQAPipeline
-pipe = ExtractiveQAPipeline(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = pipe.run(query="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-# prediction = pipe.run(query="Who created the Dothraki vocabulary?", top_k_reader=5)
-# prediction = pipe.run(query="Who is the sister of Sansa?", top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/site/en/tutorials/tutorials/4.md b/src/pages/docs/versions/master/site/en/tutorials/tutorials/4.md
deleted file mode 100644
index f76298305..000000000
--- a/src/pages/docs/versions/master/site/en/tutorials/tutorials/4.md
+++ /dev/null
@@ -1,176 +0,0 @@
----
-title: "Tutorial 4"
-metaTitle: "Utilizing existing FAQs for Question Answering"
-metaDescription: ""
-slug: "/docs/tutorial4"
-date: "2020-09-03"
-id: "tutorial4md"
----
-
-# Utilizing existing FAQs for Question Answering
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial4_FAQ_style_QA.ipynb)
-
-While *extractive Question Answering* works on pure texts and is therefore more generalizable, there's also a common alternative that utilizes existing FAQ data.
-
-**Pros**:
-
-- Very fast at inference time
-- Utilize existing FAQ data
-- Quite good control over answers
-
-**Cons**:
-
-- Generalizability: We can only answer questions that are similar to existing ones in FAQ
-
-In some use cases, a combination of extractive QA and FAQ-style can also be an interesting option.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-from haystack.retriever.dense import EmbeddingRetriever
-from haystack.utils import print_answers
-import pandas as pd
-import requests
-
-```
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker via the Haystack utility function
-from haystack.utils import launch_es
-
-launch_es()
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-
-```
-
-### Init the DocumentStore
-In contrast to Tutorial 1 (extractive QA), we:
-
-* specify the name of our `text_field` in Elasticsearch that we want to return as an answer
-* specify the name of our `embedding_field` in Elasticsearch where we'll store the embedding of our question and that is used later for calculating our similarity to the incoming user question
-* set `excluded_meta_data=["question_emb"]` so that we don't return the huge embedding vectors in our search results
-
-
-```python
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="",
- index="document",
- embedding_field="question_emb",
- embedding_dim=768,
- excluded_meta_data=["question_emb"])
-```
-
-### Create a Retriever using embeddings
-Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones).
-We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings.
-
-
-```python
-retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert", use_gpu=True)
-```
-
-### Prepare & Index FAQ data
-We create a pandas dataframe containing some FAQ data (i.e curated pairs of question + answer) and index those in elasticsearch.
-Here: We download some question-answer pairs related to COVID-19
-
-
-```python
-# Download
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/faqs/faq_covidbert.csv")
-open('small_faq_covid.csv', 'wb').write(temp.content)
-
-# Get dataframe with columns "question", "answer" and some custom metadata
-df = pd.read_csv("small_faq_covid.csv")
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-df["question"] = df["question"].apply(lambda x: x.strip())
-print(df.head())
-
-# Get embeddings for our questions from the FAQs
-questions = list(df["question"].values)
-df["question_emb"] = retriever.embed_queries(texts=questions)
-df = df.rename(columns={"question": "text"})
-
-# Convert Dataframe to list of dicts and index them in our DocumentStore
-docs_to_index = df.to_dict(orient="records")
-document_store.write_documents(docs_to_index)
-```
-
-### Ask questions
-Initialize a Pipeline (this time without a reader) and ask questions
-
-
-```python
-from haystack.pipeline import FAQPipeline
-pipe = FAQPipeline(retriever=retriever)
-```
-
-
-```python
-prediction = pipe.run(query="How is the virus spreading?", top_k_retriever=10)
-print_answers(prediction, details="all")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/site/en/tutorials/tutorials/5.md b/src/pages/docs/versions/master/site/en/tutorials/tutorials/5.md
deleted file mode 100644
index b54d5591c..000000000
--- a/src/pages/docs/versions/master/site/en/tutorials/tutorials/5.md
+++ /dev/null
@@ -1,260 +0,0 @@
----
-title: "Tutorial 5"
-metaTitle: "Evaluation of a QA System"
-metaDescription: ""
-slug: "/docs/tutorial5"
-date: "2020-09-03"
-id: "tutorial5md"
----
-
-# Evaluation of a QA System
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial5_Evaluation.ipynb)
-
-To be able to make a statement about the performance of a question-answering system, it is important to evalute it. Furthermore, evaluation allows to determine which parts of the system can be improved.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-## Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-from farm.utils import initialize_device_settings
-
-device, n_gpu = initialize_device_settings(use_cuda=True)
-```
-
-
-```python
-from haystack.preprocessor.utils import fetch_archive_from_http
-
-# Download evaluation data, which is a subset of Natural Questions development set containing 50 documents
-doc_dir = "../data/nq"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/nq_dev_subset_v2.json.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-
-```python
-# make sure these indices do not collide with existing ones, the indices will be wiped clean before data is inserted
-doc_index = "tutorial5_docs"
-label_index = "tutorial5_labels"
-```
-
-
-```python
-# Connect to Elasticsearch
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-# Connect to Elasticsearch
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document",
- create_index=False, embedding_field="emb",
- embedding_dim=768, excluded_meta_data=["emb"])
-```
-
-
-```python
-from haystack.preprocessor import PreProcessor
-
-# Add evaluation data to Elasticsearch Document Store
-# We first delete the custom tutorial indices to not have duplicate elements
-# and also split our documents into shorter passages using the PreProcessor
-preprocessor = PreProcessor(
- split_length=500,
- split_overlap=0,
- split_respect_sentence_boundary=False,
- clean_empty_lines=False,
- clean_whitespace=False
-)
-document_store.delete_all_documents(index=doc_index)
-document_store.delete_all_documents(index=label_index)
-document_store.add_eval_data(
- filename="../data/nq/nq_dev_subset_v2.json",
- doc_index=doc_index,
- label_index=label_index,
- preprocessor=preprocessor
-)
-
-# Let's prepare the labels that we need for the retriever and the reader
-labels = document_store.get_all_labels_aggregated(index=label_index)
-```
-
-## Initialize components of QA-System
-
-
-```python
-# Initialize Retriever
-from haystack.retriever.sparse import ElasticsearchRetriever
-retriever = ElasticsearchRetriever(document_store=document_store)
-# Alternative: Evaluate DensePassageRetriever
-# Note, that DPR works best when you index short passages < 512 tokens as only those tokens will be used for the embedding.
-# Here, for nq_dev_subset_v2.json we have avg. num of tokens = 5220(!).
-# DPR still outperforms Elastic's BM25 by a small margin here.
-# from haystack.retriever.dense import DensePassageRetriever
-# retriever = DensePassageRetriever(document_store=document_store,
-# query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
-# passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
-# use_gpu=True,
-# embed_title=True,
-# max_seq_len=256,
-# batch_size=16,
-# remove_sep_tok_from_untitled_passages=True)
-#document_store.update_embeddings(retriever, index=doc_index)
-```
-
-
-```python
-# Initialize Reader
-from haystack.reader.farm import FARMReader
-
-reader = FARMReader("deepset/roberta-base-squad2", top_k=4, return_no_answer=True)
-
-```
-
-
-```python
-from haystack.eval import EvalAnswers, EvalDocuments
-
-# Here we initialize the nodes that perform evaluation
-eval_retriever = EvalDocuments()
-eval_reader = EvalAnswers()
-```
-
-## Evaluation of Retriever
-Here we evaluate only the retriever, based on whether the gold_label document is retrieved.
-
-
-```python
-## Evaluate Retriever on its own
-retriever_eval_results = retriever.eval(top_k=20, label_index=label_index, doc_index=doc_index)
-## Retriever Recall is the proportion of questions for which the correct document containing the answer is
-## among the correct documents
-print("Retriever Recall:", retriever_eval_results["recall"])
-## Retriever Mean Avg Precision rewards retrievers that give relevant documents a higher rank
-print("Retriever Mean Avg Precision:", retriever_eval_results["map"])
-```
-
-## Evaluation of Reader
-Here we evaluate only the reader in a closed domain fashion i.e. the reader is given one query
-and one document and metrics are calculated on whether the right position in this text is selected by
-the model as the answer span (i.e. SQuAD style)
-
-
-```python
-# Evaluate Reader on its own
-reader_eval_results = reader.eval(document_store=document_store, device=device, label_index=label_index, doc_index=doc_index)
-# Evaluation of Reader can also be done directly on a SQuAD-formatted file without passing the data to Elasticsearch
-#reader_eval_results = reader.eval_on_file("../data/nq", "nq_dev_subset_v2.json", device=device)
-
-## Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer
-print("Reader Top-N-Accuracy:", reader_eval_results["top_n_accuracy"])
-## Reader Exact Match is the proportion of questions where the predicted answer is exactly the same as the correct answer
-print("Reader Exact Match:", reader_eval_results["EM"])
-## Reader F1-Score is the average overlap between the predicted answers and the correct answers
-print("Reader F1-Score:", reader_eval_results["f1"])
-```
-
-## Evaluation of Retriever and Reader (Open Domain)
-Here we evaluate retriever and reader in open domain fashion i.e. a document is considered
-correctly retrieved if it contains the answer string within it. The reader is evaluated based purely on the
-predicted string, regardless of which document this came from and the position of the extracted span.
-
-
-```python
-from haystack import Pipeline
-
-# Here is the pipeline definition
-p = Pipeline()
-p.add_node(component=retriever, name="ESRetriever", inputs=["Query"])
-p.add_node(component=eval_retriever, name="EvalRetriever", inputs=["ESRetriever"])
-p.add_node(component=reader, name="QAReader", inputs=["EvalRetriever"])
-p.add_node(component=eval_reader, name="EvalReader", inputs=["QAReader"])
-results = []
-```
-
-
-```python
-# This is how to run the pipeline
-for l in labels:
- res = p.run(
- query=l.question,
- top_k_retriever=10,
- labels=l,
- top_k_reader=10,
- index=doc_index,
- )
- results.append(res)
-```
-
-
-```python
-# When we have run evaluation using the pipeline, we can print the results
-n_queries = len(labels)
-eval_retriever.print()
-print()
-retriever.print_time()
-print()
-eval_reader.print(mode="reader")
-print()
-reader.print_time()
-print()
-eval_reader.print(mode="pipeline")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/site/en/tutorials/tutorials/6.md b/src/pages/docs/versions/master/site/en/tutorials/tutorials/6.md
deleted file mode 100644
index 2e2767b24..000000000
--- a/src/pages/docs/versions/master/site/en/tutorials/tutorials/6.md
+++ /dev/null
@@ -1,240 +0,0 @@
----
-title: "Tutorial 6"
-metaTitle: "Better retrieval via Dense Passage Retrieval"
-metaDescription: ""
-slug: "/docs/tutorial6"
-date: "2020-09-03"
-id: "tutorial6md"
----
-
-# Better Retrieval via "Dense Passage Retrieval"
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb)
-
-### Importance of Retrievers
-
-The Retriever has a huge impact on the performance of our overall search pipeline.
-
-
-### Different types of Retrievers
-#### Sparse
-Family of algorithms based on counting the occurrences of words (bag-of-words) resulting in very sparse vectors with length = vocab size.
-
-**Examples**: BM25, TF-IDF
-
-**Pros**: Simple, fast, well explainable
-
-**Cons**: Relies on exact keyword matches between query and text
-
-
-#### Dense
-These retrievers use neural network models to create "dense" embedding vectors. Within this family there are two different approaches:
-
-a) Single encoder: Use a **single model** to embed both query and passage.
-b) Dual-encoder: Use **two models**, one to embed the query and one to embed the passage
-
-Recent work suggests that dual encoders work better, likely because they can deal better with the different nature of query and passage (length, style, syntax ...).
-
-**Examples**: REALM, DPR, Sentence-Transformers
-
-**Pros**: Captures semantinc similarity instead of "word matches" (e.g. synonyms, related topics ...)
-
-**Cons**: Computationally more heavy, initial training of model
-
-
-### "Dense Passage Retrieval"
-
-In this Tutorial, we want to highlight one "Dense Dual-Encoder" called Dense Passage Retriever.
-It was introdoced by Karpukhin et al. (2020, https://arxiv.org/abs/2004.04906.
-
-Original Abstract:
-
-_"Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks."_
-
-Paper: https://arxiv.org/abs/2004.04906
-Original Code: https://fburl.com/qa-dpr
-
-
-*Use this* [link](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb) *to open the notebook in Google Colab.*
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-### Document Store
-
-#### Option 1: FAISS
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-The default flavour of FAISSDocumentStore is "Flat" but can also be set to "HNSW" for
-faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.
-For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-
-
-```python
-from haystack.document_store import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
-```
-
-#### Option 2: Milvus
-
-Milvus is an open source database library that is also optimized for vector similarity searches like FAISS.
-Like FAISS it has both a "Flat" and "HNSW" mode but it outperforms FAISS when it comes to dynamic data management.
-It does require a little more setup, however, as it is run through Docker and requires the setup of some config files.
-See [their docs](https://milvus.io/docs/v1.0.0/milvus_docker-cpu.md) for more details.
-
-
-```python
-from haystack.utils import launch_milvus
-from haystack.document_store import MilvusDocumentStore
-
-launch_milvus()
-document_store = MilvusDocumentStore()
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever, Reader & Pipeline
-
-#### Retriever
-
-**Here:** We use a `DensePassageRetriever`
-
-**Alternatives:**
-
-- The `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-
-
-```python
-from haystack.retriever.dense import DensePassageRetriever
-retriever = DensePassageRetriever(document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- max_seq_len_query=64,
- max_seq_len_passage=256,
- batch_size=16,
- use_gpu=True,
- embed_title=True,
- use_fast_tokenizers=True)
-# Important:
-# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all
-# previously indexed documents and update their embedding representation.
-# While this can be a time consuming operation (depending on corpus size), it only needs to be done once.
-# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.
-document_store.update_embeddings(retriever)
-```
-
-#### Reader
-
-Similar to previous Tutorials we now initalize our reader.
-
-Here we use a FARMReader with the *deepset/roberta-base-squad2* model (see: https://huggingface.co/deepset/roberta-base-squad2)
-
-
-
-##### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import ExtractiveQAPipeline
-pipe = ExtractiveQAPipeline(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = pipe.run(query="Who created the Dothraki vocabulary?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/site/en/tutorials/tutorials/7.md b/src/pages/docs/versions/master/site/en/tutorials/tutorials/7.md
deleted file mode 100644
index 3e472df19..000000000
--- a/src/pages/docs/versions/master/site/en/tutorials/tutorials/7.md
+++ /dev/null
@@ -1,217 +0,0 @@
----
-title: "Tutorial 7"
-metaTitle: "Generative QA with RAG"
-metaDescription: ""
-slug: "/docs/tutorial7"
-date: "2020-11-12"
-id: "tutorial7md"
----
-
-# Generative QA with "Retrieval-Augmented Generation"
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial7_RAG_Generator.ipynb)
-
-While extractive QA highlights the span of text that answers a query,
-generative QA can return a novel text answer that it has composed.
-In this tutorial, you will learn how to set up a generative system using the
-[RAG model](https://arxiv.org/abs/2005.11401) which conditions the
-answer generator on a set of retrieved documents.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-Here are the packages and imports that we'll need:
-
-
-```python
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from typing import List
-import requests
-import pandas as pd
-from haystack import Document
-from haystack.document_store.faiss import FAISSDocumentStore
-from haystack.generator.transformers import RAGenerator
-from haystack.retriever.dense import DensePassageRetriever
-```
-
-Let's download a csv containing some sample text and preprocess the data.
-
-
-
-```python
-# Download sample
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/haystack/master/tutorials/small_generator_dataset.csv")
-open('small_generator_dataset.csv', 'wb').write(temp.content)
-
-# Create dataframe with columns "title" and "text"
-df = pd.read_csv("small_generator_dataset.csv", sep=',')
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-
-print(df.head())
-```
-
-We can cast our data into Haystack Document objects.
-Alternatively, we can also just use dictionaries with "text" and "meta" fields
-
-
-```python
-# Use data to initialize Document objects
-titles = list(df["title"].values)
-texts = list(df["text"].values)
-documents: List[Document] = []
-for title, text in zip(titles, texts):
- documents.append(
- Document(
- text=text,
- meta={
- "name": title or ""
- }
- )
- )
-```
-
-Here we initialize the FAISSDocumentStore, DensePassageRetriever and RAGenerator.
-FAISS is chosen here since it is optimized vector storage.
-
-
-```python
-# Initialize FAISS document store.
-# Set `return_embedding` to `True`, so generator doesn't have to perform re-embedding
-document_store = FAISSDocumentStore(
- faiss_index_factory_str="Flat",
- return_embedding=True
-)
-
-# Initialize DPR Retriever to encode documents, encode question and query documents
-retriever = DensePassageRetriever(
- document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- use_gpu=True,
- embed_title=True,
-)
-
-# Initialize RAG Generator
-generator = RAGenerator(
- model_name_or_path="facebook/rag-token-nq",
- use_gpu=True,
- top_k=1,
- max_length=200,
- min_length=2,
- embed_title=True,
- num_beams=2,
-)
-```
-
-We write documents to the DocumentStore, first by deleting any remaining documents then calling `write_documents()`.
-The `update_embeddings()` method uses the retriever to create an embedding for each document.
-
-
-
-```python
-# Delete existing documents in documents store
-document_store.delete_all_documents()
-
-# Write documents to document store
-document_store.write_documents(documents)
-
-# Add documents embeddings to index
-document_store.update_embeddings(
- retriever=retriever
-)
-```
-
-Here are our questions:
-
-
-```python
-QUESTIONS = [
- "who got the first nobel prize in physics",
- "when is the next deadpool movie being released",
- "which mode is used for short wave broadcast service",
- "who is the owner of reading football club",
- "when is the next scandal episode coming out",
- "when is the last time the philadelphia won the superbowl",
- "what is the most current adobe flash player version",
- "how many episodes are there in dragon ball z",
- "what is the first step in the evolution of the eye",
- "where is gall bladder situated in human body",
- "what is the main mineral in lithium batteries",
- "who is the president of usa right now",
- "where do the greasers live in the outsiders",
- "panda is a national animal of which country",
- "what is the name of manchester united stadium",
-]
-```
-
-Now let's run our system!
-The retriever will pick out a small subset of documents that it finds relevant.
-These are used to condition the generator as it generates the answer.
-What it should return then are novel text spans that form and answer to your question!
-
-
-```python
-# Now generate an answer for each question
-for question in QUESTIONS:
- # Retrieve related documents from retriever
- retriever_results = retriever.retrieve(
- query=question
- )
-
- # Now generate answer from question and retrieved documents
- predicted_result = generator.predict(
- query=question,
- documents=retriever_results,
- top_k=1
- )
-
- # Print you answer
- answers = predicted_result["answers"]
- print(f'Generated answer is \'{answers[0]["answer"]}\' for the question = \'{question}\'')
-```
-
-
-```python
-# Or alternatively use the Pipeline class
-from haystack.pipeline import GenerativeQAPipeline
-
-pipe = GenerativeQAPipeline(generator=generator, retriever=retriever)
-for question in QUESTIONS:
- res = pipe.run(query=question, top_k_generator=1, top_k_retriever=5)
- print(res)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/site/en/tutorials/tutorials/8.md b/src/pages/docs/versions/master/site/en/tutorials/tutorials/8.md
deleted file mode 100644
index a53c3a030..000000000
--- a/src/pages/docs/versions/master/site/en/tutorials/tutorials/8.md
+++ /dev/null
@@ -1,214 +0,0 @@
----
-title: "Tutorial 8"
-metaTitle: "Preprocessing"
-metaDescription: ""
-slug: "/docs/tutorial8"
-date: "2021-01-08"
-id: "tutorial8md"
----
-
-# Preprocessing
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial8_Preprocessing.ipynb)
-
-Haystack includes a suite of tools to extract text from different file types, normalize white space
-and split text into smaller pieces to optimize retrieval.
-These data preprocessing steps can have a big impact on the systems performance and effective handling of data is key to getting the most out of Haystack.
-
-Ultimately, Haystack expects data to be provided as a list documents in the following dictionary format:
-``` python
-docs = [
- {
- 'text': DOCUMENT_TEXT_HERE,
- 'meta': {'name': DOCUMENT_NAME, ...}
- }, ...
-]
-```
-
-This tutorial will show you all the tools that Haystack provides to help you cast your data into this format.
-
-
-```python
-# Let's start by installing Haystack
-
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-!wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.03.tar.gz
-!tar -xvf xpdf-tools-linux-4.03.tar.gz && sudo cp xpdf-tools-linux-4.03/bin64/pdftotext /usr/local/bin
-```
-
-
-```python
-# Here are the imports we need
-
-from haystack.file_converter.txt import TextConverter
-from haystack.file_converter.pdf import PDFToTextConverter
-from haystack.file_converter.docx import DocxToTextConverter
-
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.preprocessor.preprocessor import PreProcessor
-```
-
-
-```python
-# This fetches some sample files to work with
-
-doc_dir = "data/preprocessing_tutorial"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/preprocessing_tutorial.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-## Converters
-
-Haystack's converter classes are designed to help you turn files on your computer into the documents
-that can be processed by the Haystack pipeline.
-There are file converters for txt, pdf, docx files as well as a converter that is powered by Apache Tika.
-
-
-```python
-# Here are some examples of how you would use file converters
-
-converter = TextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_txt = converter.convert(file_path="data/preprocessing_tutorial/classics.txt", meta=None)
-
-converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_pdf = converter.convert(file_path="data/preprocessing_tutorial/bert.pdf", meta=None)
-
-converter = DocxToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_docx = converter.convert(file_path="data/preprocessing_tutorial/heavy_metal.docx", meta=None)
-
-```
-
-
-```python
-# Haystack also has a convenience function that will automatically apply the right converter to each file in a directory.
-
-all_docs = convert_files_to_dicts(dir_path="data/preprocessing_tutorial")
-```
-
-## PreProcessor
-
-The PreProcessor class is designed to help you clean text and split text into sensible units.
-File splitting can have a very significant impact on the system's performance and is absolutely mandatory for Dense Passage Retrieval models.
-In general, we recommend you split the text from your files into small documents of around 100 words for dense retrieval methods
-and no more than 10,000 words for sparse methods.
-Have a look at the [Preprocessing](https://haystack.deepset.ai/docs/latest/preprocessingmd)
-and [Optimization](https://haystack.deepset.ai/docs/latest/optimizationmd) pages on our website for more details.
-
-
-```python
-# This is a default usage of the PreProcessor.
-# Here, it performs cleaning of consecutive whitespaces
-# and splits a single large document into smaller documents.
-# Each document is up to 1000 words long and document breaks cannot fall in the middle of sentences
-# Note how the single document passed into the document gets split into 5 smaller documents
-
-preprocessor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=False,
- split_by="word",
- split_length=100,
- split_respect_sentence_boundary=True
-)
-docs_default = preprocessor.process(doc_txt)
-print(f"n_docs_input: 1\nn_docs_output: {len(docs_default)}")
-```
-
-## Cleaning
-
-- `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
-- `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
-- `clean_header_footer` will remove any long header or footer texts that are repeated on each page
-
-## Splitting
-By default, the PreProcessor will respect sentence boundaries, meaning that documents will not start or end
-midway through a sentence.
-This will help reduce the possibility of answer phrases being split between two documents.
-This feature can be turned off by setting `split_respect_sentence_boundary=False`.
-
-
-```python
-# Not respecting sentence boundary vs respecting sentence boundary
-
-preprocessor_nrsb = PreProcessor(split_respect_sentence_boundary=False)
-docs_nrsb = preprocessor_nrsb.process(doc_txt)
-
-print("RESPECTING SENTENCE BOUNDARY")
-end_text = docs_default[0]["text"][-50:]
-print("End of document: \"..." + end_text + "\"")
-print()
-print("NOT RESPECTING SENTENCE BOUNDARY")
-end_text_nrsb = docs_nrsb[0]["text"][-50:]
-print("End of document: \"..." + end_text_nrsb + "\"")
-```
-
-A commonly used strategy to split long documents, especially in the field of Question Answering,
-is the sliding window approach. If `split_length=10` and `split_overlap=3`, your documents will look like this:
-
-- doc1 = words[0:10]
-- doc2 = words[7:17]
-- doc3 = words[14:24]
-- ...
-
-You can use this strategy by following the code below.
-
-
-```python
-# Sliding window approach
-
-preprocessor_sliding_window = PreProcessor(
- split_overlap=3,
- split_length=10,
- split_respect_sentence_boundary=False
-)
-docs_sliding_window = preprocessor_sliding_window.process(doc_txt)
-
-doc1 = docs_sliding_window[0]["text"][:200]
-doc2 = docs_sliding_window[1]["text"][:100]
-doc3 = docs_sliding_window[2]["text"][:100]
-
-print("Document 1: \"" + doc1 + "...\"")
-print("Document 2: \"" + doc2 + "...\"")
-print("Document 3: \"" + doc3 + "...\"")
-```
-
-## Bringing it all together
-
-
-```python
-all_docs = convert_files_to_dicts(dir_path="data/preprocessing_tutorial")
-preprocessor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=False,
- split_by="word",
- split_length=100,
- split_respect_sentence_boundary=True
-)
-docs = preprocessor.process(all_docs)
-
-print(f"n_files_input: {len(all_docs)}\nn_docs_output: {len(docs)}")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
-
diff --git a/src/pages/docs/versions/master/site/en/tutorials/tutorials/9.md b/src/pages/docs/versions/master/site/en/tutorials/tutorials/9.md
deleted file mode 100644
index 366b7487e..000000000
--- a/src/pages/docs/versions/master/site/en/tutorials/tutorials/9.md
+++ /dev/null
@@ -1,250 +0,0 @@
----
-title: "Tutorial 9"
-metaTitle: "Training a Dense Passage Retrieval model"
-metaDescription: ""
-slug: "/docs/tutorial9"
-date: "2021-01-08"
-id: "tutorial9md"
----
-
-# Training Your Own "Dense Passage Retrieval" Model
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial9_DPR_training.ipynb)
-
-Haystack contains all the tools needed to train your own Dense Passage Retrieval model.
-This tutorial will guide you through the steps required to create a retriever that is specifically tailored to your domain.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-```
-
-
-```python
-# Here are some imports that we'll need
-
-from haystack.retriever.dense import DensePassageRetriever
-from haystack.preprocessor.utils import fetch_archive_from_http
-from haystack.document_store.memory import InMemoryDocumentStore
-```
-
-## Training Data
-
-DPR training performed using Information Retrieval data.
-More specifically, you want to feed in pairs of queries and relevant documents.
-
-To train a model, we will need a dataset that has the same format as the original DPR training data.
-Each data point in the dataset should have the following dictionary structure.
-
-``` python
- {
- "dataset": str,
- "question": str,
- "answers": list of str
- "positive_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- "negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- "hard_negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- }
-```
-
-`positive_ctxs` are context passages which are relevant to the query.
-In some datasets, queries might have more than one positive context
-in which case you can set the `num_positives` parameter to be higher than the default 1.
-Note that `num_positives` needs to be lower or equal to the minimum number of `positive_ctxs` for queries in your data.
-If you have an unequal number of positive contexts per example,
-you might want to generate some soft labels by retrieving similar contexts which contain the answer.
-
-DPR is standardly trained using a method known as in-batch negatives.
-This means that positive contexts for a given query are treated as negative contexts for the other queries in the batch.
-Doing so allows for a high degree of computational efficiency, thus allowing the model to be trained on large amounts of data.
-
-`negative_ctxs` is not actually used in Haystack's DPR training so we recommend you set it to an empty list.
-They were used by the original DPR authors in an experiment to compare it against the in-batch negatives method.
-
-`hard_negative_ctxs` are passages that are not relevant to the query.
-In the original DPR paper, these are fetched using a retriever to find the most relevant passages to the query.
-Passages which contain the answer text are filtered out.
-
-If you'd like to convert your SQuAD format data into something that can train a DPR model,
-check out the utility script at [`haystack/retriever/squad_to_dpr.py`](https://github.com/deepset-ai/haystack/blob/master/haystack/retriever/squad_to_dpr.py)
-
-## Using Question Answering Data
-
-Question Answering datasets can sometimes be used as training data.
-Google's Natural Questions dataset, is sufficiently large
-and contains enough unique passages, that it can be converted into a DPR training set.
-This is done simply by considering answer containing passages as relevant documents to the query.
-
-The SQuAD dataset, however, is not as suited to this use case since its question and answer pairs
-are created on only a very small slice of wikipedia documents.
-
-## Download Original DPR Training Data
-
-WARNING: These files are large! The train set is 7.4GB and the dev set is 800MB
-
-We can download the original DPR training data with the following cell.
-Note that this data is probably only useful if you are trying to train from scratch.
-
-
-```python
-# Download original DPR data
-# WARNING: the train set is 7.4GB and the dev set is 800MB
-
-doc_dir = "data/dpr_training/"
-
-s3_url_train = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-train.json.gz"
-s3_url_dev = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz"
-
-fetch_archive_from_http(s3_url_train, output_dir=doc_dir + "train/")
-fetch_archive_from_http(s3_url_dev, output_dir=doc_dir + "dev/")
-```
-
-## Option 1: Training DPR from Scratch
-
-The default variables that we provide below are chosen to train a DPR model from scratch.
-Here, both passage and query embedding models are initialized using BERT base
-and the model is trained using Google's Natural Questions dataset (in a format specialised for DPR).
-
-If you are working in a language other than English,
-you will want to initialize the passage and query embedding models with a language model that supports your language
-and also provide a dataset in your language.
-
-
-```python
-# Here are the variables to specify our training data, the models that we use to initialize DPR
-# and the directory where we'll be saving the model
-
-doc_dir = "data/dpr_training/"
-
-train_filename = "train/biencoder-nq-train.json"
-dev_filename = "dev/biencoder-nq-dev.json"
-
-query_model = "bert-base-uncased"
-passage_model = "bert-base-uncased"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Option 2: Finetuning DPR
-
-If you have your own domain specific question answering or information retrieval dataset,
-you might instead be interested in finetuning a pretrained DPR model.
-In this case, you would initialize both query and passage models using the original pretrained model.
-You will want to load something like this set of variables instead of the ones above
-
-
-```python
-# Here are the variables you might want to use instead of the set above
-# in order to perform pretraining
-
-doc_dir = "PATH_TO_YOUR_DATA_DIR"
-train_filename = "TRAIN_FILENAME"
-dev_filename = "DEV_FILENAME"
-
-query_model = "facebook/dpr-question_encoder-single-nq-base"
-passage_model = "facebook/dpr-ctx_encoder-single-nq-base"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Initialization
-
-Here we want to initialize our model either with plain language model weights for training from scratch
-or else with pretrained DPR weights for finetuning.
-We follow the [original DPR parameters](https://github.com/facebookresearch/DPR#best-hyperparameter-settings)
-for their max passage length but set max query length to 64 since queries are very rarely longer.
-
-
-```python
-## Initialize DPR model
-
-retriever = DensePassageRetriever(
- document_store=InMemoryDocumentStore(),
- query_embedding_model=query_model,
- passage_embedding_model=passage_model,
- max_seq_len_query=64,
- max_seq_len_passage=256
-)
-```
-
-## Training
-
-Let's start training and save our trained model!
-
-On a V100 GPU, you can fit up to batch size 16 so we set gradient accumulation steps to 8 in order
-to simulate the batch size 128 of the original DPR experiment.
-
-When `embed_title=True`, the document title is prepended to the input text sequence with a `[SEP]` token
-between it and document text.
-
-When training from scratch with the above variables, 1 epoch takes around an hour and we reached the following performance:
-
-```
-loss: 0.046580662854042276
-task_name: text_similarity
-acc: 0.992524064068483
-f1: 0.8804297774366846
-acc_and_f1: 0.9364769207525838
-average_rank: 0.19631619339984652
-report:
- precision recall f1-score support
-
-hard_negative 0.9961 0.9961 0.9961 201887
- positive 0.8804 0.8804 0.8804 6515
-
- accuracy 0.9925 208402
- macro avg 0.9383 0.9383 0.9383 208402
- weighted avg 0.9925 0.9925 0.9925 208402
-
-```
-
-
-```python
-# Start training our model and save it when it is finished
-
-retriever.train(
- data_dir=doc_dir,
- train_filename=train_filename,
- dev_filename=dev_filename,
- test_filename=dev_filename,
- n_epochs=1,
- batch_size=16,
- grad_acc_steps=8,
- save_dir=save_dir,
- evaluate_every=3000,
- embed_title=True,
- num_positives=1,
- num_hard_negatives=1
-)
-```
-
-## Loading
-
-Loading our newly trained model is simple!
-
-
-```python
-reloaded_retriever = DensePassageRetriever.load(load_dir=save_dir, document_store=None)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/document_store.md b/src/pages/docs/versions/master/site/en/usage/usage/document_store.md
deleted file mode 100644
index 85b4af4ec..000000000
--- a/src/pages/docs/versions/master/site/en/usage/usage/document_store.md
+++ /dev/null
@@ -1,362 +0,0 @@
----
-title: "Document Store"
-metaTitle: "Document Store"
-metaDescription: ""
-slug: "/docs/documentstore"
-date: "2020-09-03"
-id: "documentstoremd"
----
-
-
-# DocumentStores
-
-You can think of the DocumentStore as a "database" that:
-- stores your texts and meta data
-- provides them to the retriever at query time
-
-There are different DocumentStores in Haystack to fit different use cases and tech stacks.
-
-## Initialisation
-
-Initialising a new DocumentStore within Haystack is straight forward.
-
-.
-@prefix rep: .
-@prefix sr: .
-@prefix sail: .
-@prefix owlim: .
-
-[] a rep:Repository ;
- rep:repositoryID "tutorial_10_index" ;
- rdfs:label "tutorial 10 index" ;
-...
-```
-
-GraphDBKnowledgeGraph can load an existing knowledge graph represented in the form of a .ttl file with the method ```GraphDBKnowledgeGraph.import_from_ttl_file(index, path)```, where path points to a ttl file starting with something like:
-
-```
-@prefix rdf: .
-@prefix xsd: .
-@prefix hp:
- PREFIX xsd:
- PREFIX hp: ?obj . }")```
-
-instead of using the abbreviated form:
-
-```Text2SparqlRetriever._query_kg(sparql_query="select distinct ?obj where { hp:Hermione_granger hp:patronus ?obj . }")```
-
-If you would like to translate text queries to SPARQL queries for your custom data and use ```Text2SparqlRetriever.retrieve(query)```, there is significantly more effort necessary.
-We provide an exemplary pre-trained model in our [tutorial](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial10_Knowledge_Graph.py).
-One limitation is that this pre-trained model can only generate questions about resources it has seen during training.
-Otherwise, it cannot translate the name of the resource to the identifier used in the knowledge graph.
-For example, it can translate "Harry" to "hp:Harry_potter" only because we trained it to do so.
-
-Unfortunately, our pre-trained model for translating text queries does not work with your custom data.
-Instead, you need to train your own model. It needs to be trained according to the [seq2seq example for summarization with BART in transformers](https://github.com/huggingface/transformers/tree/master/examples/legacy/seq2seq).
-Haystack currently does not support the training of text2sparql models. We dont have concrete plans to extend the funtionality, but we are more than open to contributions. Don't hesitate to reach out!
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/languages.md b/src/pages/docs/versions/master/site/en/usage/usage/languages.md
deleted file mode 100644
index 848792ea5..000000000
--- a/src/pages/docs/versions/master/site/en/usage/usage/languages.md
+++ /dev/null
@@ -1,333 +0,0 @@
----
-title: "Languages Other Than English"
-metaTitle: "Languages Other Than English"
-metaDescription: ""
-slug: "/docs/languages"
-date: "2020-11-05"
-id: "languagesmd"
----
-
-# Languages Other Than English
-
-Haystack is well suited to open-domain QA on languages other than English.
-While our defaults are tuned for English,
-you will find some tips and tricks here for using Haystack in your language.
-
-##Preprocessor
-
-"}.
-Optionally: Include meta data via {"text": "",
-"meta":{"name": ", "author": "somebody", ...}}
-It can be used for filtering and is accessible in the responses of the Finder.
-Advanced: If you are using your own Elasticsearch mapping, the key names in the dictionary
-should be changed to what you have set for self.text_field and self.name_field.
-- `index`: Elasticsearch index where the documents should be indexed. If not supplied, self.index will be used.
-
-**Returns**:
-
-None
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever
-- `index`: Index name to update
-
-**Returns**:
-
-None
-
-
-#### add\_eval\_data
-
-```python
- | add_eval_data(filename: str, doc_index: str = "eval_document", label_index: str = "label")
-```
-
-Adds a SQuAD-formatted file to the DocumentStore in order to be able to perform evaluation on it.
-
-**Arguments**:
-
-- `filename`: Name of the file containing evaluation data
-:type filename: str
-- `doc_index`: Elasticsearch index where evaluation documents should be stored
-:type doc_index: str
-- `label_index`: Elasticsearch index where labeled questions should be stored
-:type label_index: str
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: str)
-```
-
-Delete all documents in an index.
-
-**Arguments**:
-
-- `index`: index name
-
-**Returns**:
-
-None
-
-
-# memory
-
-
-## InMemoryDocumentStore
-
-```python
-class InMemoryDocumentStore(BaseDocumentStore)
-```
-
-In-memory document store
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
-For documents as dictionaries, the format is {"text": ""}.
-Optionally: Include meta data via {"text": "",
-"meta": {"name": ", "author": "somebody", ...}}
-It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: write documents to a custom namespace. For instance, documents for evaluation can be indexed in a
-separate index than the documents for search.
-
-**Returns**:
-
-None
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever
-- `index`: Index name to update
-
-**Returns**:
-
-None
-
-
-#### add\_eval\_data
-
-```python
- | add_eval_data(filename: str, doc_index: Optional[str] = None, label_index: Optional[str] = None)
-```
-
-Adds a SQuAD-formatted file to the DocumentStore in order to be able to perform evaluation on it.
-
-**Arguments**:
-
-- `filename`: Name of the file containing evaluation data
-:type filename: str
-- `doc_index`: Elasticsearch index where evaluation documents should be stored
-:type doc_index: str
-- `label_index`: Elasticsearch index where labeled questions should be stored
-:type label_index: str
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None)
-```
-
-Delete all documents in a index.
-
-**Arguments**:
-
-- `index`: index name
-
-**Returns**:
-
-None
-
-
-# sql
-
-
-## SQLDocumentStore
-
-```python
-class SQLDocumentStore(BaseDocumentStore)
-```
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
-For documents as dictionaries, the format is {"text": ""}.
-Optionally: Include meta data via {"text": "",
-"meta":{"name": ", "author": "somebody", ...}}
-It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: add an optional index attribute to documents. It can be later used for filtering. For instance,
-documents for evaluation can be indexed in a separate index than the documents for search.
-
-**Returns**:
-
-None
-
-
-#### update\_vector\_ids
-
-```python
- | update_vector_ids(vector_id_map: Dict[str, str], index: Optional[str] = None)
-```
-
-Update vector_ids for given document_ids.
-
-**Arguments**:
-
-- `vector_id_map`: dict containing mapping of document_id -> vector_id.
-- `index`: filter documents by the optional index attribute for documents in database.
-
-
-#### add\_eval\_data
-
-```python
- | add_eval_data(filename: str, doc_index: str = "eval_document", label_index: str = "label")
-```
-
-Adds a SQuAD-formatted file to the DocumentStore in order to be able to perform evaluation on it.
-
-**Arguments**:
-
-- `filename`: Name of the file containing evaluation data
-:type filename: str
-- `doc_index`: Elasticsearch index where evaluation documents should be stored
-:type doc_index: str
-- `label_index`: Elasticsearch index where labeled questions should be stored
-:type label_index: str
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index=None)
-```
-
-Delete all documents in a index.
-
-**Arguments**:
-
-- `index`: index name
-
-**Returns**:
-
-None
-
-
-# base
-
-
-## BaseDocumentStore
-
-```python
-class BaseDocumentStore(ABC)
-```
-
-Base class for implementing Document Stores.
-
-
-#### write\_documents
-
-```python
- | @abstractmethod
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
-For documents as dictionaries, the format is {"text": ""}.
-Optionally: Include meta data via {"text": "",
-"meta":{"name": ", "author": "somebody", ...}}
-It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: Optional name of index where the documents shall be written to.
-If None, the DocumentStore's default index (self.index) will be used.
-
-**Returns**:
-
-None
-
-
-# faiss
-
-
-## FAISSDocumentStore
-
-```python
-class FAISSDocumentStore(SQLDocumentStore)
-```
-
-Document store for very large scale embedding based dense retrievers like the DPR.
-
-It implements the FAISS library(https://github.com/facebookresearch/faiss)
-to perform similarity search on vectors.
-
-The document text and meta-data(for filtering) is stored using the SQLDocumentStore, while
-the vector embeddings are indexed in a FAISS Index.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(sql_url: str = "sqlite:///", index_buffer_size: int = 10_000, vector_size: int = 768, faiss_index: Optional[IndexHNSWFlat] = None)
-```
-
-**Arguments**:
-
-- `sql_url`: SQL connection URL for database. It defaults to local file based SQLite DB. For large scale
-deployment, Postgres is recommended.
-- `index_buffer_size`: When working with large datasets, the ingestion process(FAISS + SQL) can be buffered in
-smaller chunks to reduce memory footprint.
-- `vector_size`: the embedding vector size.
-- `faiss_index`: load an existing FAISS Index.
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever to use to get embeddings for text
-- `index`: Index name to update
-
-**Returns**:
-
-None
-
-
-#### save
-
-```python
- | save(file_path: Union[str, Path])
-```
-
-Save FAISS Index to the specified file.
-
-
-#### load
-
-```python
- | @classmethod
- | load(cls, faiss_file_path: Union[str, Path], sql_url: str, index_buffer_size: int = 10_000, vector_size: int = 768)
-```
-
-Load a saved FAISS index from a file and connect to the SQL database.
diff --git a/src/pages/docs/versions/master/v0.4.0/site/en/api/api/file_converter.md b/src/pages/docs/versions/master/v0.4.0/site/en/api/api/file_converter.md
deleted file mode 100644
index 75d0311da..000000000
--- a/src/pages/docs/versions/master/v0.4.0/site/en/api/api/file_converter.md
+++ /dev/null
@@ -1,247 +0,0 @@
----
-title: "File Converters"
-metaTitle: "File Converters"
-metaDescription: ""
-slug: "/docs/file_converters"
-date: "2020-09-03"
-id: "file_convertersmd"
----
-
-# File Converters
-
-
-# txt
-
-
-## TextConverter
-
-```python
-class TextConverter(BaseConverter)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(remove_numeric_tables: Optional[bool] = False, remove_whitespace: Optional[bool] = None, remove_empty_lines: Optional[bool] = None, remove_header_footer: Optional[bool] = None, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
-The tabular structures in documents might be noise for the reader model if it
-does not have table parsing capability for finding answers. However, tables
-may also have long strings that could possible candidate for searching answers.
-The rows containing strings are thus retained in this option.
-- `remove_whitespace`: strip whitespaces before or after each line in the text.
-- `remove_empty_lines`: remove more than two empty lines in the text.
-- `remove_header_footer`: use heuristic to remove footers and headers across different pages by searching
-for the longest common string. This heuristic uses exact matches and therefore
-works well for footers like "Copyright 2019 by XXX", but won't detect "Page 3 of 4"
-or similar.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
-(https://en.wikipedia.org/wiki/ISO_639-1) format.
-This option can be used to add test for encoding errors. If the extracted text is
-not one of the valid languages, then it might likely be encoding error resulting
-in garbled text.
-
-
-# docx
-
-
-## DocxToTextConverter
-
-```python
-class DocxToTextConverter(BaseConverter)
-```
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None) -> Dict[str, Any]
-```
-
-Extract text from a .docx file.
-Note: As docx doesn't contain "page" information, we actually extract and return a list of paragraphs here.
-For compliance with other converters we nevertheless opted for keeping the methods name.
-
-**Arguments**:
-
-- `file_path`: Path to the .docx file you want to convert
-
-
-# tika
-
-
-## TikaConverter
-
-```python
-class TikaConverter(BaseConverter)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(tika_url: str = "http://localhost:9998/tika", remove_numeric_tables: Optional[bool] = False, remove_whitespace: Optional[bool] = None, remove_empty_lines: Optional[bool] = None, remove_header_footer: Optional[bool] = None, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `tika_url`: URL of the Tika server
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
-The tabular structures in documents might be noise for the reader model if it
-does not have table parsing capability for finding answers. However, tables
-may also have long strings that could possible candidate for searching answers.
-The rows containing strings are thus retained in this option.
-- `remove_whitespace`: strip whitespaces before or after each line in the text.
-- `remove_empty_lines`: remove more than two empty lines in the text.
-- `remove_header_footer`: use heuristic to remove footers and headers across different pages by searching
-for the longest common string. This heuristic uses exact matches and therefore
-works well for footers like "Copyright 2019 by XXX", but won't detect "Page 3 of 4"
-or similar.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
-(https://en.wikipedia.org/wiki/ISO_639-1) format.
-This option can be used to add test for encoding errors. If the extracted text is
-not one of the valid languages, then it might likely be encoding error resulting
-in garbled text.
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None) -> Dict[str, Any]
-```
-
-**Arguments**:
-
-- `file_path`: Path of file to be converted.
-
-**Returns**:
-
-a list of pages and the extracted meta data of the file.
-
-
-# base
-
-
-## BaseConverter
-
-```python
-class BaseConverter()
-```
-
-Base class for implementing file converts to transform input documents to text format for ingestion in DocumentStore.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(remove_numeric_tables: Optional[bool] = None, remove_header_footer: Optional[bool] = None, remove_whitespace: Optional[bool] = None, remove_empty_lines: Optional[bool] = None, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
-The tabular structures in documents might be noise for the reader model if it
-does not have table parsing capability for finding answers. However, tables
-may also have long strings that could possible candidate for searching answers.
-The rows containing strings are thus retained in this option.
-- `remove_header_footer`: use heuristic to remove footers and headers across different pages by searching
-for the longest common string. This heuristic uses exact matches and therefore
-works well for footers like "Copyright 2019 by XXX", but won't detect "Page 3 of 4"
-or similar.
-- `remove_whitespace`: strip whitespaces before or after each line in the text.
-- `remove_empty_lines`: remove more than two empty lines in the text.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
-(https://en.wikipedia.org/wiki/ISO_639-1) format.
-This option can be used to add test for encoding errors. If the extracted text is
-not one of the valid languages, then it might likely be encoding error resulting
-in garbled text.
-
-
-#### convert
-
-```python
- | @abstractmethod
- | convert(file_path: Path, meta: Optional[Dict[str, str]]) -> Dict[str, Any]
-```
-
-Convert a file to a dictionary containing the text and any associated meta data.
-
-File converters may extract file meta like name or size. In addition to it, user
-supplied meta data like author, url, external IDs can be supplied as a dictionary.
-
-**Arguments**:
-
-- `file_path`: path of the file to convert
-- `meta`: dictionary of meta data key-value pairs to append in the returned document.
-
-
-#### validate\_language
-
-```python
- | validate_language(text: str) -> bool
-```
-
-Validate if the language of the text is one of valid languages.
-
-
-#### find\_and\_remove\_header\_footer
-
-```python
- | find_and_remove_header_footer(pages: List[str], n_chars: int, n_first_pages_to_ignore: int, n_last_pages_to_ignore: int) -> Tuple[List[str], Optional[str], Optional[str]]
-```
-
-Heuristic to find footers and headers across different pages by searching for the longest common string.
-For headers we only search in the first n_chars characters (for footer: last n_chars).
-Note: This heuristic uses exact matches and therefore works well for footers like "Copyright 2019 by XXX",
-but won't detect "Page 3 of 4" or similar.
-
-**Arguments**:
-
-- `pages`: list of strings, one string per page
-- `n_chars`: number of first/last characters where the header/footer shall be searched in
-- `n_first_pages_to_ignore`: number of first pages to ignore (e.g. TOCs often don't contain footer/header)
-- `n_last_pages_to_ignore`: number of last pages to ignore
-
-**Returns**:
-
-(cleaned pages, found_header_str, found_footer_str)
-
-
-# pdf
-
-
-## PDFToTextConverter
-
-```python
-class PDFToTextConverter(BaseConverter)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(remove_numeric_tables: Optional[bool] = False, remove_whitespace: Optional[bool] = None, remove_empty_lines: Optional[bool] = None, remove_header_footer: Optional[bool] = None, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
-The tabular structures in documents might be noise for the reader model if it
-does not have table parsing capability for finding answers. However, tables
-may also have long strings that could possible candidate for searching answers.
-The rows containing strings are thus retained in this option.
-- `remove_whitespace`: strip whitespaces before or after each line in the text.
-- `remove_empty_lines`: remove more than two empty lines in the text.
-- `remove_header_footer`: use heuristic to remove footers and headers across different pages by searching
-for the longest common string. This heuristic uses exact matches and therefore
-works well for footers like "Copyright 2019 by XXX", but won't detect "Page 3 of 4"
-or similar.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
-(https://en.wikipedia.org/wiki/ISO_639-1) format.
-This option can be used to add test for encoding errors. If the extracted text is
-not one of the valid languages, then it might likely be encoding error resulting
-in garbled text.
diff --git a/src/pages/docs/versions/master/v0.4.0/site/en/api/api/preprocessor.md b/src/pages/docs/versions/master/v0.4.0/site/en/api/api/preprocessor.md
deleted file mode 100644
index 91b888406..000000000
--- a/src/pages/docs/versions/master/v0.4.0/site/en/api/api/preprocessor.md
+++ /dev/null
@@ -1,96 +0,0 @@
----
-title: "Preprocessor"
-metaTitle: "Preprocessor"
-metaDescription: ""
-slug: "/docs/apiindexing"
-date: "2020-09-03"
-id: "apiindexingmd"
----
-
-# Preprocessor
-
-
-# cleaning
-
-
-# utils
-
-
-#### eval\_data\_from\_file
-
-```python
-eval_data_from_file(filename: str) -> Tuple[List[Document], List[Label]]
-```
-
-Read Documents + Labels from a SQuAD-style file.
-Document and Labels can then be indexed to the DocumentStore and be used for evaluation.
-
-**Arguments**:
-
-- `filename`: Path to file in SQuAD format
-
-**Returns**:
-
-(List of Documents, List of Labels)
-
-
-#### convert\_files\_to\_dicts
-
-```python
-convert_files_to_dicts(dir_path: str, clean_func: Optional[Callable] = None, split_paragraphs: bool = False) -> List[dict]
-```
-
-Convert all files(.txt, .pdf) in the sub-directories of the given path to Python dicts that can be written to a
-Document Store.
-
-**Arguments**:
-
-- `dir_path`: path for the documents to be written to the DocumentStore
-- `clean_func`: a custom cleaning function that gets applied to each doc (input: str, output:str)
-- `split_paragraphs`: split text in paragraphs.
-
-**Returns**:
-
-None
-
-
-#### tika\_convert\_files\_to\_dicts
-
-```python
-tika_convert_files_to_dicts(dir_path: str, clean_func: Optional[Callable] = None, split_paragraphs: bool = False, merge_short: bool = True, merge_lowercase: bool = True) -> List[dict]
-```
-
-Convert all files(.txt, .pdf) in the sub-directories of the given path to Python dicts that can be written to a
-Document Store.
-
-**Arguments**:
-
-- `dir_path`: path for the documents to be written to the DocumentStore
-- `clean_func`: a custom cleaning function that gets applied to each doc (input: str, output:str)
-- `split_paragraphs`: split text in paragraphs.
-
-**Returns**:
-
-None
-
-
-#### fetch\_archive\_from\_http
-
-```python
-fetch_archive_from_http(url: str, output_dir: str, proxies: Optional[dict] = None)
-```
-
-Fetch an archive (zip or tar.gz) from a url via http and extract content to an output directory.
-
-**Arguments**:
-
-- `url`: http address
-:type url: str
-- `output_dir`: local path
-:type output_dir: str
-- `proxies`: proxies details as required by requests library
-:type proxies: dict
-
-**Returns**:
-
-bool if anything got fetched
diff --git a/src/pages/docs/versions/master/v0.4.0/site/en/api/api/reader.md b/src/pages/docs/versions/master/v0.4.0/site/en/api/api/reader.md
deleted file mode 100644
index ec8712999..000000000
--- a/src/pages/docs/versions/master/v0.4.0/site/en/api/api/reader.md
+++ /dev/null
@@ -1,367 +0,0 @@
----
-title: "Reader"
-metaTitle: "Reader"
-metaDescription: ""
-slug: "/docs/apireadermd"
-date: "2020-09-03"
-id: "apireadermd"
----
-
-# Reader
-
-
-# farm
-
-
-## FARMReader
-
-```python
-class FARMReader(BaseReader)
-```
-
-Transformer based model for extractive Question Answering using the FARM framework (https://github.com/deepset-ai/FARM).
-While the underlying model can vary (BERT, Roberta, DistilBERT, ...), the interface remains the same.
-
-| With a FARMReader, you can:
-
- - directly get predictions via predict()
- - fine-tune the model on QA data via train()
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: Union[str, Path], context_window_size: int = 150, batch_size: int = 50, use_gpu: bool = True, no_ans_boost: Optional[float] = None, top_k_per_candidate: int = 3, top_k_per_sample: int = 1, num_processes: Optional[int] = None, max_seq_len: int = 256, doc_stride: int = 128)
-```
-
-**Arguments**:
-
-- `model_name_or_path`: Directory of a saved model or the name of a public model e.g. 'bert-base-cased',
-'deepset/bert-base-cased-squad2', 'deepset/bert-base-cased-squad2', 'distilbert-base-uncased-distilled-squad'.
-See https://huggingface.co/models for full list of available models.
-- `context_window_size`: The size, in characters, of the window around the answer span that is used when
-displaying the context around the answer.
-- `batch_size`: Number of samples the model receives in one batch for inference.
-Memory consumption is much lower in inference mode. Recommendation: Increase the batch size
-to a value so only a single batch is used.
-- `use_gpu`: Whether to use GPU (if available)
-- `no_ans_boost`: How much the no_answer logit is boosted/increased.
-If set to None (default), disables returning "no answer" predictions.
-If a negative number, there is a lower chance of "no_answer" being predicted.
-If a positive number, there is an increased chance of "no_answer"
-- `top_k_per_candidate`: How many answers to extract for each candidate doc that is coming from the retriever (might be a long text).
-Note that this is not the number of "final answers" you will receive
-(see `top_k` in FARMReader.predict() or Finder.get_answers() for that)
-and that FARM includes no_answer in the sorted list of predictions.
-- `top_k_per_sample`: How many answers to extract from each small text passage that the model can process at once
-(one "candidate doc" is usually split into many smaller "passages").
-You usually want a very small value here, as it slows down inference
-and you don't gain much of quality by having multiple answers from one passage.
-Note that this is not the number of "final answers" you will receive
-(see `top_k` in FARMReader.predict() or Finder.get_answers() for that)
-and that FARM includes no_answer in the sorted list of predictions.
-- `num_processes`: The number of processes for `multiprocessing.Pool`. Set to value of 0 to disable
-multiprocessing. Set to None to let Inferencer determine optimum number. If you
-want to debug the Language Model, you might need to disable multiprocessing!
-- `max_seq_len`: Max sequence length of one input text for the model
-- `doc_stride`: Length of striding window for splitting long texts (used if ``len(text) > max_seq_len``)
-
-
-#### train
-
-```python
- | train(data_dir: str, train_filename: str, dev_filename: Optional[str] = None, test_filename: Optional[str] = None, use_gpu: Optional[bool] = None, batch_size: int = 10, n_epochs: int = 2, learning_rate: float = 1e-5, max_seq_len: Optional[int] = None, warmup_proportion: float = 0.2, dev_split: float = 0, evaluate_every: int = 300, save_dir: Optional[str] = None, num_processes: Optional[int] = None)
-```
-
-Fine-tune a model on a QA dataset. Options:
-
-- Take a plain language model (e.g. `bert-base-cased`) and train it for QA (e.g. on SQuAD data)
-- Take a QA model (e.g. `deepset/bert-base-cased-squad2`) and fine-tune it for your domain (e.g. using your labels collected via the haystack annotation tool)
-
-**Arguments**:
-
-- `data_dir`: Path to directory containing your training data in SQuAD style
-- `train_filename`: Filename of training data
-- `dev_filename`: Filename of dev / eval data
-- `test_filename`: Filename of test data
-- `dev_split`: Instead of specifying a dev_filename, you can also specify a ratio (e.g. 0.1) here
-that gets split off from training data for eval.
-- `use_gpu`: Whether to use GPU (if available)
-- `batch_size`: Number of samples the model receives in one batch for training
-- `n_epochs`: Number of iterations on the whole training data set
-- `learning_rate`: Learning rate of the optimizer
-- `max_seq_len`: Maximum text length (in tokens). Everything longer gets cut down.
-- `warmup_proportion`: Proportion of training steps until maximum learning rate is reached.
-Until that point LR is increasing linearly. After that it's decreasing again linearly.
-Options for different schedules are available in FARM.
-- `evaluate_every`: Evaluate the model every X steps on the hold-out eval dataset
-- `save_dir`: Path to store the final model
-- `num_processes`: The number of processes for `multiprocessing.Pool` during preprocessing.
-Set to value of 1 to disable multiprocessing. When set to 1, you cannot split away a dev set from train set.
-Set to None to use all CPU cores minus one.
-
-**Returns**:
-
-None
-
-
-#### save
-
-```python
- | save(directory: Path)
-```
-
-Saves the Reader model so that it can be reused at a later point in time.
-
-**Arguments**:
-
-- `directory`: Directory where the Reader model should be saved
-
-
-#### predict\_batch
-
-```python
- | predict_batch(question_doc_list: List[dict], top_k_per_question: int = None, batch_size: int = None)
-```
-
-Use loaded QA model to find answers for a list of questions in each question's supplied list of Document.
-
-Returns list of dictionaries containing answers sorted by (desc.) probability
-
-**Arguments**:
-
-- `question_doc_list`: List of dictionaries containing questions with their retrieved documents
-- `top_k_per_question`: The maximum number of answers to return for each question
-- `batch_size`: Number of samples the model receives in one batch for inference
-
-**Returns**:
-
-List of dictionaries containing question and answers
-
-
-#### predict
-
-```python
- | predict(question: str, documents: List[Document], top_k: Optional[int] = None)
-```
-
-Use loaded QA model to find answers for a question in the supplied list of Document.
-
-Returns dictionaries containing answers sorted by (desc.) probability.
-Example:
-
-{'question': 'Who is the father of Arya Stark?',
-'answers': [
-{'answer': 'Eddard,',
-'context': " She travels with her father, Eddard, to King's Landing when he is ",
-'offset_answer_start': 147,
-'offset_answer_end': 154,
-'probability': 0.9787139466668613,
-'score': None,
-'document_id': '1337'
-},
-...
-]
-}
-
-**Arguments**:
-
-- `question`: Question string
-- `documents`: List of Document in which to search for the answer
-- `top_k`: The maximum number of answers to return
-
-**Returns**:
-
-Dict containing question and answers
-
-
-#### eval\_on\_file
-
-```python
- | eval_on_file(data_dir: str, test_filename: str, device: str)
-```
-
-Performs evaluation on a SQuAD-formatted file.
-Returns a dict containing the following metrics:
-- "EM": exact match score
-- "f1": F1-Score
-- "top_n_accuracy": Proportion of predicted answers that overlap with correct answer
-
-**Arguments**:
-
-- `data_dir`: The directory in which the test set can be found
-:type data_dir: Path or str
-- `test_filename`: The name of the file containing the test data in SQuAD format.
-:type test_filename: str
-- `device`: The device on which the tensors should be processed. Choose from "cpu" and "cuda".
-:type device: str
-
-
-#### eval
-
-```python
- | eval(document_store: BaseDocumentStore, device: str, label_index: str = "label", doc_index: str = "eval_document", label_origin: str = "gold_label")
-```
-
-Performs evaluation on evaluation documents in the DocumentStore.
-Returns a dict containing the following metrics:
-- "EM": Proportion of exact matches of predicted answers with their corresponding correct answers
-- "f1": Average overlap between predicted answers and their corresponding correct answers
-- "top_n_accuracy": Proportion of predicted answers that overlap with correct answer
-
-**Arguments**:
-
-- `document_store`: DocumentStore containing the evaluation documents
-- `device`: The device on which the tensors should be processed. Choose from "cpu" and "cuda".
-- `label_index`: Index/Table name where labeled questions are stored
-- `doc_index`: Index/Table name where documents that are used for evaluation are stored
-
-
-#### predict\_on\_texts
-
-```python
- | predict_on_texts(question: str, texts: List[str], top_k: Optional[int] = None)
-```
-
-Use loaded QA model to find answers for a question in the supplied list of Document.
-Returns dictionaries containing answers sorted by (desc.) probability.
-Example:
-
-{
-'question': 'Who is the father of Arya Stark?',
-'answers':[
-{'answer': 'Eddard,',
-'context': " She travels with her father, Eddard, to King's Landing when he is ",
-'offset_answer_start': 147,
-'offset_answer_end': 154,
-'probability': 0.9787139466668613,
-'score': None,
-'document_id': '1337'
-},...
-]
-}
-
-**Arguments**:
-
-- `question`: Question string
-- `documents`: List of documents as string type
-- `top_k`: The maximum number of answers to return
-
-**Returns**:
-
-Dict containing question and answers
-
-
-#### convert\_to\_onnx
-
-```python
- | @classmethod
- | convert_to_onnx(cls, model_name_or_path, opset_version: int = 11, optimize_for: Optional[str] = None)
-```
-
-Convert a PyTorch BERT model to ONNX format and write to ./onnx-export dir. The converted ONNX model
-can be loaded with in the `FARMReader` using the export path as `model_name_or_path` param.
-
-Usage:
-
-`from haystack.reader.farm import FARMReader
-FARMReader.convert_to_onnx(model_name_or_path="deepset/bert-base-cased-squad2", optimize_for="gpu_tensor_core")
-FARMReader(model_name_or_path=Path("onnx-export"))`
-
-
-**Arguments**:
-
-- `opset_version`: ONNX opset version
-- `optimize_for`: Optimize the exported model for a target device. Available options
-are "gpu_tensor_core" (GPUs with tensor core like V100 or T4),
-"gpu_without_tensor_core" (most other GPUs), and "cpu".
-
-
-# transformers
-
-
-## TransformersReader
-
-```python
-class TransformersReader(BaseReader)
-```
-
-Transformer based model for extractive Question Answering using the HuggingFace's transformers framework
-(https://github.com/huggingface/transformers).
-While the underlying model can vary (BERT, Roberta, DistilBERT ...), the interface remains the same.
-
-| With the reader, you can:
-
- - directly get predictions via predict()
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model: str = "distilbert-base-uncased-distilled-squad", tokenizer: Optional[str] = None, context_window_size: int = 70, use_gpu: int = 0, top_k_per_candidate: int = 4, return_no_answers: bool = True, max_seq_len: int = 256, doc_stride: int = 128)
-```
-
-Load a QA model from Transformers.
-Available models include:
-
-- ``'distilbert-base-uncased-distilled-squad`'``
-- ``'bert-large-cased-whole-word-masking-finetuned-squad``'
-- ``'bert-large-uncased-whole-word-masking-finetuned-squad``'
-
-See https://huggingface.co/models for full list of available QA models
-
-**Arguments**:
-
-- `model`: Name of the model
-- `tokenizer`: Name of the tokenizer (usually the same as model)
-- `context_window_size`: Num of chars (before and after the answer) to return as "context" for each answer.
-The context usually helps users to understand if the answer really makes sense.
-- `use_gpu`: If < 0, then use cpu. If >= 0, this is the ordinal of the gpu to use
-- `top_k_per_candidate`: How many answers to extract for each candidate doc that is coming from the retriever (might be a long text).
-Note that this is not the number of "final answers" you will receive
-(see `top_k` in TransformersReader.predict() or Finder.get_answers() for that)
-and that no_answer can be included in the sorted list of predictions.
-- `return_no_answers`: If True, the HuggingFace Transformers model could return a "no_answer" (i.e. when there is an unanswerable question)
-If False, it cannot return a "no_answer". Note that `no_answer_boost` is unfortunately not available with TransformersReader.
-If you would like to set no_answer_boost, use a `FARMReader`.
-- `max_seq_len`: max sequence length of one input text for the model
-- `doc_stride`: length of striding window for splitting long texts (used if len(text) > max_seq_len)
-
-
-#### predict
-
-```python
- | predict(question: str, documents: List[Document], top_k: Optional[int] = None)
-```
-
-Use loaded QA model to find answers for a question in the supplied list of Document.
-
-Returns dictionaries containing answers sorted by (desc.) probability.
-Example:
-
-{'question': 'Who is the father of Arya Stark?',
-'answers': [
-{'answer': 'Eddard,',
-'context': " She travels with her father, Eddard, to King's Landing when he is ",
-'offset_answer_start': 147,
-'offset_answer_end': 154,
-'probability': 0.9787139466668613,
-'score': None,
-'document_id': '1337'
-},
-...
-]
-}
-
-**Arguments**:
-
-- `question`: Question string
-- `documents`: List of Document in which to search for the answer
-- `top_k`: The maximum number of answers to return
-
-**Returns**:
-
-Dict containing question and answers
-
-
-# base
diff --git a/src/pages/docs/versions/master/v0.4.0/site/en/api/api/retriever.md b/src/pages/docs/versions/master/v0.4.0/site/en/api/api/retriever.md
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----
-title: "Retriever"
-metaTitle: "Retriever"
-metaDescription: ""
-slug: "/docs/apiretrievermd"
-date: "2020-09-03"
-id: "apiretrievermd"
----
-
-# Retriever
-
-
-# sparse
-
-
-## ElasticsearchRetriever
-
-```python
-class ElasticsearchRetriever(BaseRetriever)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(document_store: ElasticsearchDocumentStore, custom_query: str = None)
-```
-
-**Arguments**:
-
-- `document_store`: an instance of a DocumentStore to retrieve documents from.
-- `custom_query`: query string as per Elasticsearch DSL with a mandatory question placeholder($question).
-
-Optionally, ES `filter` clause can be added where the values of `terms` are placeholders
-that get substituted during runtime. The placeholder(${filter_name_1}, ${filter_name_2}..)
-names must match with the filters dict supplied in self.retrieve().
-::
-
-An example custom_query:
-{
-"size": 10,
-"query": {
-"bool": {
-"should": [{"multi_match": {
-"query": "${question}", // mandatory $question placeholder
-"type": "most_fields",
-"fields": ["text", "title"]}}],
-"filter": [ // optional custom filters
-{"terms": {"year": "${years}"}},
-{"terms": {"quarter": "${quarters}"}},
-{"range": {"date": {"gte": "${date}"}}}
-],
-
-}
-},
-}
-
-For this custom_query, a sample retrieve() could be:
-::
-self.retrieve(query="Why did the revenue increase?",
-filters={"years": ["2019"], "quarters": ["Q1", "Q2"]})
-
-
-## ElasticsearchFilterOnlyRetriever
-
-```python
-class ElasticsearchFilterOnlyRetriever(ElasticsearchRetriever)
-```
-
-Naive "Retriever" that returns all documents that match the given filters. No impact of query at all.
-Helpful for benchmarking, testing and if you want to do QA on small documents without an "active" retriever.
-
-
-## TfidfRetriever
-
-```python
-class TfidfRetriever(BaseRetriever)
-```
-
-Read all documents from a SQL backend.
-
-Split documents into smaller units (eg, paragraphs or pages) to reduce the
-computations when text is passed on to a Reader for QA.
-
-It uses sklearn's TfidfVectorizer to compute a tf-idf matrix.
-
-
-# dense
-
-
-## DensePassageRetriever
-
-```python
-class DensePassageRetriever(BaseRetriever)
-```
-
-Retriever that uses a bi-encoder (one transformer for query, one transformer for passage).
-See the original paper for more details:
-Karpukhin, Vladimir, et al. (2020): "Dense Passage Retrieval for Open-Domain Question Answering."
-(https://arxiv.org/abs/2004.04906).
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(document_store: BaseDocumentStore, query_embedding_model: str = "facebook/dpr-question_encoder-single-nq-base", passage_embedding_model: str = "facebook/dpr-ctx_encoder-single-nq-base", max_seq_len: int = 256, use_gpu: bool = True, batch_size: int = 16, embed_title: bool = True, remove_sep_tok_from_untitled_passages: bool = True)
-```
-
-Init the Retriever incl. the two encoder models from a local or remote model checkpoint.
-The checkpoint format matches huggingface transformers' model format
-
-**Arguments**:
-
-- `document_store`: An instance of DocumentStore from which to retrieve documents.
-- `query_embedding_model`: Local path or remote name of question encoder checkpoint. The format equals the
-one used by hugging-face transformers' modelhub models
-Currently available remote names: ``"facebook/dpr-question_encoder-single-nq-base"``
-- `passage_embedding_model`: Local path or remote name of passage encoder checkpoint. The format equals the
-one used by hugging-face transformers' modelhub models
-Currently available remote names: ``"facebook/dpr-ctx_encoder-single-nq-base"``
-- `max_seq_len`: Longest length of each sequence
-- `use_gpu`: Whether to use gpu or not
-- `batch_size`: Number of questions or passages to encode at once
-- `embed_title`: Whether to concatenate title and passage to a text pair that is then used to create the embedding
-- `remove_sep_tok_from_untitled_passages`: If embed_title is ``True``, there are different strategies to deal with documents that don't have a title.
-If this param is ``True`` => Embed passage as single text, similar to embed_title = False (i.e [CLS] passage_tok1 ... [SEP]).
-If this param is ``False`` => Embed passage as text pair with empty title (i.e. [CLS] [SEP] passage_tok1 ... [SEP])
-
-
-#### embed\_queries
-
-```python
- | embed_queries(texts: List[str]) -> List[np.array]
-```
-
-Create embeddings for a list of queries using the query encoder
-
-**Arguments**:
-
-- `texts`: Queries to embed
-
-**Returns**:
-
-Embeddings, one per input queries
-
-
-#### embed\_passages
-
-```python
- | embed_passages(docs: List[Document]) -> List[np.array]
-```
-
-Create embeddings for a list of passages using the passage encoder
-
-**Arguments**:
-
-- `docs`: List of Document objects used to represent documents / passages in a standardized way within Haystack.
-
-**Returns**:
-
-Embeddings of documents / passages shape (batch_size, embedding_dim)
-
-
-## EmbeddingRetriever
-
-```python
-class EmbeddingRetriever(BaseRetriever)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(document_store: BaseDocumentStore, embedding_model: str, use_gpu: bool = True, model_format: str = "farm", pooling_strategy: str = "reduce_mean", emb_extraction_layer: int = -1)
-```
-
-**Arguments**:
-
-- `document_store`: An instance of DocumentStore from which to retrieve documents.
-- `embedding_model`: Local path or name of model in Hugging Face's model hub such as ``'deepset/sentence_bert'``
-- `use_gpu`: Whether to use gpu or not
-- `model_format`: Name of framework that was used for saving the model. Options:
-
-- ``'farm'``
-- ``'transformers'``
-- ``'sentence_transformers'``
-- `pooling_strategy`: Strategy for combining the embeddings from the model (for farm / transformers models only).
-Options:
-
-- ``'cls_token'`` (sentence vector)
-- ``'reduce_mean'`` (sentence vector)
-- ``'reduce_max'`` (sentence vector)
-- ``'per_token'`` (individual token vectors)
-- `emb_extraction_layer`: Number of layer from which the embeddings shall be extracted (for farm / transformers models only).
-Default: -1 (very last layer).
-
-
-#### embed
-
-```python
- | embed(texts: Union[List[str], str]) -> List[np.array]
-```
-
-Create embeddings for each text in a list of texts using the retrievers model (`self.embedding_model`)
-
-**Arguments**:
-
-- `texts`: Texts to embed
-
-**Returns**:
-
-List of embeddings (one per input text). Each embedding is a list of floats.
-
-
-#### embed\_queries
-
-```python
- | embed_queries(texts: List[str]) -> List[np.array]
-```
-
-Create embeddings for a list of queries. For this Retriever type: The same as calling .embed()
-
-**Arguments**:
-
-- `texts`: Queries to embed
-
-**Returns**:
-
-Embeddings, one per input queries
-
-
-#### embed\_passages
-
-```python
- | embed_passages(docs: List[Document]) -> List[np.array]
-```
-
-Create embeddings for a list of passages. For this Retriever type: The same as calling .embed()
-
-**Arguments**:
-
-- `docs`: List of documents to embed
-
-**Returns**:
-
-Embeddings, one per input passage
-
-
-# base
-
-
-## BaseRetriever
-
-```python
-class BaseRetriever(ABC)
-```
-
-
-#### retrieve
-
-```python
- | @abstractmethod
- | retrieve(query: str, filters: dict = None, top_k: int = 10, index: str = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-#### eval
-
-```python
- | eval(label_index: str = "label", doc_index: str = "eval_document", label_origin: str = "gold_label", top_k: int = 10, open_domain: bool = False) -> dict
-```
-
-Performs evaluation on the Retriever.
-Retriever is evaluated based on whether it finds the correct document given the question string and at which
-position in the ranking of documents the correct document is.
-
-| Returns a dict containing the following metrics:
-
-- "recall": Proportion of questions for which correct document is among retrieved documents
-- "mean avg precision": Mean of average precision for each question. Rewards retrievers that give relevant
-documents a higher rank.
-
-**Arguments**:
-
-- `label_index`: Index/Table in DocumentStore where labeled questions are stored
-- `doc_index`: Index/Table in DocumentStore where documents that are used for evaluation are stored
-- `top_k`: How many documents to return per question
-- `open_domain`: If ``True``, retrieval will be evaluated by checking if the answer string to a question is
-contained in the retrieved docs (common approach in open-domain QA).
-If ``False``, retrieval uses a stricter evaluation that checks if the retrieved document ids
-are within ids explicitly stated in the labels.
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-{
- "menuList": [
- {
- "id": "usage_haystack",
- "title": "Usage",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 0,
- "isMenu": true
- },
- {
- "id": "intromd",
- "title": "What is Haystack",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "get_startedmd",
- "title": "Get Started",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "databasemd",
- "title": "Document Store",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "retrievermd",
- "title": "Retriever",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "readermd",
- "title": "Reader",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "domain_adaptationmd",
- "title": "Domain Adaptation",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "termsmd",
- "title": "Glossary",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 6
- },
- {
- "id": "roadmapmd",
- "title": "Roadmap",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 7
- },
- {
- "id": "tutorials_haystack",
- "title": "Tutorials",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 1,
- "isMenu": true
- },
- {
- "id": "tutorial1md",
- "title": "Build Your First QA System",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "tutorial2md",
- "title": "Fine-tuning a Model on Your Own Data",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "tutorial3md",
- "title": "Build a QA System Without Elasticsearch",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "tutorial4md",
- "title": "Utilizing existing FAQs for Question Answering",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "tutorial5md",
- "title": "Evaluation of a QA System",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "tutorial6md",
- "title": "Better Retrieval via \"Dense Passage Retrieval\"",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "api_haystack",
- "title": "API reference",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 2,
- "isMenu": true
- },
- {
- "id": "apidatabasemd",
- "title": "Document Store",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "apiretrievermd",
- "title": "Retriever",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "apireadermd",
- "title": "Reader",
- "label1": "api_haystack",
- "label2": "api_haystack",
- "label3": "",
- "order": 2
- },
- {
- "id": "apiindexingmd",
- "title": "Preprocessor",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "file_convertersmd",
- "title": "File Converters",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- }
- ]
-}
\ No newline at end of file
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----
-title: "Tutorial 1"
-metaTitle: "Build Your First QA System"
-metaDescription: ""
-slug: "/docs/tutorial1"
-date: "2020-09-03"
-id: "tutorial1md"
----
-
-# Build Your First QA System
-
-
-
-EXECUTABLE VERSION: [*colab*](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb)
-
-Question Answering can be used in a variety of use cases. A very common one: Using it to navigate through complex knowledge bases or long documents ("search setting").
-
-A "knowledge base" could for example be your website, an internal wiki or a collection of financial reports.
-In this tutorial we will work on a slightly different domain: "Game of Thrones".
-
-Let's see how we can use a bunch of Wikipedia articles to answer a variety of questions about the
-marvellous seven kingdoms...
-
-
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack and install the version of torch that works with the colab GPUs
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install torch==1.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-Haystack finds answers to queries within the documents stored in a `DocumentStore`. The current implementations of `DocumentStore` include `ElasticsearchDocumentStore`, `FAISSDocumentStore`, `SQLDocumentStore`, and `InMemoryDocumentStore`.
-
-**Here:** We recommended Elasticsearch as it comes preloaded with features like [full-text queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html), [BM25 retrieval](https://www.elastic.co/elasticon/conf/2016/sf/improved-text-scoring-with-bm25), and [vector storage for text embeddings](https://www.elastic.co/guide/en/elasticsearch/reference/7.6/dense-vector.html).
-
-**Alternatives:** If you are unable to setup an Elasticsearch instance, then follow the [Tutorial 3](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb) for using SQL/InMemory document stores.
-
-**Hint**: This tutorial creates a new document store instance with Wikipedia articles on Game of Thrones. However, you can configure Haystack to work with your existing document stores.
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker
-#! docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.6.2
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.6.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.6.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.6.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.6.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-# Connect to Elasticsearch
-
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles about Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first fetch some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is:
-# {
-# 'text': "",
-# 'meta': {'name': "", ...}
-#}
-# (Optionally: you can also add more key-value-pairs here, that will be indexed as fields in Elasticsearch and
-# can be accessed later for filtering or shown in the responses of the Finder)
-
-# Let's have a look at the first 3 entries:
-print(dicts[:3])
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-## Initalize Retriever, Reader, & Finder
-
-### Retriever
-
-Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question could be answered.
-They use some simple but fast algorithm.
-
-**Here:** We use Elasticsearch's default BM25 algorithm
-
-**Alternatives:**
-
-- Customize the `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `DensePassageRetriever` to use different embedding models for passage and query (see Tutorial 6)
-
-
-```python
-from haystack.retriever.sparse import ElasticsearchRetriever
-retriever = ElasticsearchRetriever(document_store=document_store)
-```
-
-
-```python
-# Alternative: An in-memory TfidfRetriever based on Pandas dataframes for building quick-prototypes with SQLite document store.
-
-# from haystack.retriever.sparse import TfidfRetriever
-# retriever = TfidfRetriever(document_store=document_store)
-```
-
-### Reader
-
-A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based
-on powerful, but slower deep learning models.
-
-Haystack currently supports Readers based on the frameworks FARM and Transformers.
-With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).
-
-**Here:** a medium sized RoBERTa QA model using a Reader based on FARM (https://huggingface.co/deepset/roberta-base-squad2)
-
-**Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)
-
-**Alternatives (Models):** e.g. "distilbert-base-uncased-distilled-squad" (fast) or "deepset/bert-large-uncased-whole-word-masking-squad2" (good accuracy)
-
-**Hint:** You can adjust the model to return "no answer possible" with the no_ans_boost. Higher values mean the model prefers "no answer possible"
-
-#### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=False)
-```
-
-#### TransformersReader
-
-
-```python
-# Alternative:
-# reader = TransformersReader(model="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1)
-```
-
-### Finder
-
-The Finder sticks together reader and retriever in a pipeline to answer our actual questions.
-
-
-```python
-finder = Finder(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = finder.get_answers(question="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-# prediction = finder.get_answers(question="Who created the Dothraki vocabulary?", top_k_reader=5)
-# prediction = finder.get_answers(question="Who is the sister of Sansa?", top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
diff --git a/src/pages/docs/versions/master/v0.4.0/site/en/tutorials/tutorials/2.md b/src/pages/docs/versions/master/v0.4.0/site/en/tutorials/tutorials/2.md
deleted file mode 100644
index 2662f38be..000000000
--- a/src/pages/docs/versions/master/v0.4.0/site/en/tutorials/tutorials/2.md
+++ /dev/null
@@ -1,76 +0,0 @@
----
-title: "Tutorial 2"
-metaTitle: "Fine-tuning a model on your own data"
-metaDescription: ""
-slug: "/docs/tutorial2"
-date: "2020-09-03"
-id: "tutorial2md"
----
-
-# Fine-tuning a Model on Your Own Data
-
-EXECUTABLE VERSION: [*colab*](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial2_Finetune_a_model_on_your_data.ipynb)
-
-For many use cases it is sufficient to just use one of the existing public models that were trained on SQuAD or other public QA datasets (e.g. Natural Questions).
-However, if you have domain-specific questions, fine-tuning your model on custom examples will very likely boost your performance.
-While this varies by domain, we saw that ~ 2000 examples can easily increase performance by +5-20%.
-
-This tutorial shows you how to fine-tune a pretrained model on your own dataset.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack and install the version of torch that works with the colab GPUs
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install torch==1.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-```
-
-
-```python
-from haystack.reader.farm import FARMReader
-```
-
-
-## Create Training Data
-
-There are two ways to generate training data
-
-1. **Annotation**: You can use the [annotation tool](https://github.com/deepset-ai/haystack#labeling-tool) to label your data, i.e. highlighting answers to your questions in a document. The tool supports structuring your workflow with organizations, projects, and users. The labels can be exported in SQuAD format that is compatible for training with Haystack.
-
-
-
-2. **Feedback**: For production systems, you can collect training data from direct user feedback via Haystack's [REST API interface](https://github.com/deepset-ai/haystack#rest-api). This includes a customizable user feedback API for providing feedback on the answer returned by the API. The API provides a feedback export endpoint to obtain the feedback data for fine-tuning your model further.
-
-
-## Fine-tune your model
-
-Once you have collected training data, you can fine-tune your base models.
-We initialize a reader as a base model and fine-tune it on our own custom dataset (should be in SQuAD-like format).
-We recommend using a base model that was trained on SQuAD or a similar QA dataset before to benefit from Transfer Learning effects.
-
-**Recommendation**: Run training on a GPU.
-If you are using Colab: Enable this in the menu "Runtime" > "Change Runtime type" > Select "GPU" in dropdown.
-Then change the `use_gpu` arguments below to `True`
-
-
-```python
-reader = FARMReader(model_name_or_path="distilbert-base-uncased-distilled-squad", use_gpu=True)
-train_data = "data/squad20"
-# train_data = "PATH/TO_YOUR/TRAIN_DATA"
-reader.train(data_dir=train_data, train_filename="dev-v2.0.json", use_gpu=True, n_epochs=1, save_dir="my_model")
-```
-
-
-```python
-# Saving the model happens automatically at the end of training into the `save_dir` you specified
-# However, you could also save a reader manually again via:
-reader.save(directory="my_model")
-```
-
-
-```python
-# If you want to load it at a later point, just do:
-new_reader = FARMReader(model_name_or_path="my_model")
-```
diff --git a/src/pages/docs/versions/master/v0.4.0/site/en/tutorials/tutorials/3.md b/src/pages/docs/versions/master/v0.4.0/site/en/tutorials/tutorials/3.md
deleted file mode 100644
index fa6f0327f..000000000
--- a/src/pages/docs/versions/master/v0.4.0/site/en/tutorials/tutorials/3.md
+++ /dev/null
@@ -1,167 +0,0 @@
----
-title: "Tutorial 3"
-metaTitle: "Build a QA System Without Elasticsearch"
-metaDescription: ""
-slug: "/docs/tutorial3"
-date: "2020-09-03"
-id: "tutorial3md"
----
-
-# Build a QA System Without Elasticsearch
-
-EXECUTABLE VERSION: [*colab*](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb)
-
-Haystack provides alternatives to Elasticsearch for developing quick prototypes.
-
-You can use an `InMemoryDocumentStore` or a `SQLDocumentStore`(with SQLite) as the document store.
-
-If you are interested in more feature-rich Elasticsearch, then please refer to the Tutorial 1.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack and install the version of torch that works with the colab GPUs
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install torch==1.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-
-
-```python
-# In-Memory Document Store
-from haystack.document_store.memory import InMemoryDocumentStore
-document_store = InMemoryDocumentStore()
-```
-
-
-```python
-# SQLite Document Store
-# from haystack.document_store.sql import SQLDocumentStore
-# document_store = SQLDocumentStore(url="sqlite:///qa.db")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles on Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first get some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# convert files to dicts containing documents that can be indexed to our datastore
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is: {"name": ", "text": ""}
-
-# Let's have a look at the first 3 entries:
-print(dicts[:3])
-# Now, let's write the docs to our DB.
-document_store.write_documents(dicts)
-```
-
-## Initalize Retriever, Reader, & Finder
-
-### Retriever
-
-Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question could be answered.
-
-With InMemoryDocumentStore or SQLDocumentStore, you can use the TfidfRetriever. For more retrievers, please refer to the tutorial-1.
-
-
-```python
-# An in-memory TfidfRetriever based on Pandas dataframes
-
-from haystack.retriever.sparse import TfidfRetriever
-retriever = TfidfRetriever(document_store=document_store)
-```
-
-### Reader
-
-A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based
-on powerful, but slower deep learning models.
-
-Haystack currently supports Readers based on the frameworks FARM and Transformers.
-With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).
-
-**Here:** a medium sized RoBERTa QA model using a Reader based on FARM (https://huggingface.co/deepset/roberta-base-squad2)
-
-**Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)
-
-**Alternatives (Models):** e.g. "distilbert-base-uncased-distilled-squad" (fast) or "deepset/bert-large-uncased-whole-word-masking-squad2" (good accuracy)
-
-**Hint:** You can adjust the model to return "no answer possible" with the no_ans_boost. Higher values mean the model prefers "no answer possible"
-
-#### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=False)
-```
-
-#### TransformersReader
-
-
-```python
-# Alternative:
-# reader = TransformersReader(model="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1)
-```
-
-### Finder
-
-The Finder sticks together reader and retriever in a pipeline to answer our actual questions.
-
-
-```python
-finder = Finder(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = finder.get_answers(question="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-# prediction = finder.get_answers(question="Who created the Dothraki vocabulary?", top_k_reader=5)
-# prediction = finder.get_answers(question="Who is the sister of Sansa?", top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
diff --git a/src/pages/docs/versions/master/v0.4.0/site/en/tutorials/tutorials/4.md b/src/pages/docs/versions/master/v0.4.0/site/en/tutorials/tutorials/4.md
deleted file mode 100644
index 9923e0b69..000000000
--- a/src/pages/docs/versions/master/v0.4.0/site/en/tutorials/tutorials/4.md
+++ /dev/null
@@ -1,140 +0,0 @@
----
-title: "Tutorial 4"
-metaTitle: "Utilizing existing FAQs for Question Answering"
-metaDescription: ""
-slug: "/docs/tutorial4"
-date: "2020-09-03"
-id: "tutorial4md"
----
-
-# Utilizing existing FAQs for Question Answering
-
-EXECUTABLE VERSION: [colab](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial4_Tutorial4_FAQ_style_QA.ipynb)
-
-While *extractive Question Answering* works on pure texts and is therefore more generalizable, there's also a common alternative that utilizes existing FAQ data.
-
-**Pros**:
-
-- Very fast at inference time
-- Utilize existing FAQ data
-- Quite good control over answers
-
-**Cons**:
-
-- Generalizability: We can only answer questions that are similar to existing ones in FAQ
-
-In some use cases, a combination of extractive QA and FAQ-style can also be an interesting option.
-
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack and install the version of torch that works with the colab GPUs
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install torch==1.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-```
-
-
-```python
-from haystack import Finder
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-from haystack.retriever.dense import EmbeddingRetriever
-from haystack.utils import print_answers
-import pandas as pd
-import requests
-
-```
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker
-# ! docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.6.2
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.6.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.6.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.6.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.6.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-
-```
-
-### Init the DocumentStore
-In contrast to Tutorial 1 (extractive QA), we:
-
-* specify the name of our `text_field` in Elasticsearch that we want to return as an answer
-* specify the name of our `embedding_field` in Elasticsearch where we'll store the embedding of our question and that is used later for calculating our similarity to the incoming user question
-* set `excluded_meta_data=["question_emb"]` so that we don't return the huge embedding vectors in our search results
-
-
-```python
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="",
- index="document",
- embedding_field="question_emb",
- embedding_dim=768,
- excluded_meta_data=["question_emb"])
-```
-
-### Create a Retriever using embeddings
-Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones).
-We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings.
-
-
-```python
-retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert", use_gpu=False)
-```
-
-### Prepare & Index FAQ data
-We create a pandas dataframe containing some FAQ data (i.e curated pairs of question + answer) and index those in elasticsearch.
-Here: We download some question-answer pairs related to COVID-19
-
-
-```python
-# Download
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/faqs/faq_covidbert.csv")
-open('small_faq_covid.csv', 'wb').write(temp.content)
-
-# Get dataframe with columns "question", "answer" and some custom metadata
-df = pd.read_csv("small_faq_covid.csv")
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-df["question"] = df["question"].apply(lambda x: x.strip())
-print(df.head())
-
-# Get embeddings for our questions from the FAQs
-questions = list(df["question"].values)
-df["question_emb"] = retriever.embed_queries(texts=questions)
-df["question_emb"] = df["question_emb"].apply(list) # convert from numpy to list for ES ingestion
-df = df.rename(columns={"answer": "text"})
-
-# Convert Dataframe to list of dicts and index them in our DocumentStore
-docs_to_index = df.to_dict(orient="records")
-document_store.write_documents(docs_to_index)
-```
-
-### Ask questions
-Initialize a Finder (this time without a reader) and ask questions
-
-
-```python
-finder = Finder(reader=None, retriever=retriever)
-prediction = finder.get_answers_via_similar_questions(question="How is the virus spreading?", top_k_retriever=10)
-print_answers(prediction, details="all")
-```
diff --git a/src/pages/docs/versions/master/v0.4.0/site/en/tutorials/tutorials/5.md b/src/pages/docs/versions/master/v0.4.0/site/en/tutorials/tutorials/5.md
deleted file mode 100644
index b32fb6bc0..000000000
--- a/src/pages/docs/versions/master/v0.4.0/site/en/tutorials/tutorials/5.md
+++ /dev/null
@@ -1,172 +0,0 @@
----
-title: "Tutorial 5"
-metaTitle: "Evaluation of a QA System"
-metaDescription: ""
-slug: "/docs/tutorial5"
-date: "2020-09-03"
-id: "tutorial5md"
----
-
-# Evaluation of a QA System
-
-EXECUTABLE VERSION: [colab](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial5_Evaluation.ipynb)
-
-To be able to make a statement about the performance of a question-answering system, it is important to evalute it. Furthermore, evaluation allows to determine which parts of the system can be improved.
-
-## Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack and install the version of torch that works with the colab GPUs
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install torch==1.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.6.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.6.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.6.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.6.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-from farm.utils import initialize_device_settings
-
-device, n_gpu = initialize_device_settings(use_cuda=True)
-```
-
-
-```python
-from haystack.preprocessor.utils import fetch_archive_from_http
-
-# Download evaluation data, which is a subset of Natural Questions development set containing 50 documents
-doc_dir = "../data/nq"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/nq_dev_subset_v2.json.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-
-```python
-# make sure these indices do not collide with existing ones, the indices will be wiped clean before data is inserted
-doc_index = "tutorial5_docs"
-label_index = "tutorial5_labels"
-```
-
-
-```python
-# Connect to Elasticsearch
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-# Connect to Elasticsearch
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document",
- create_index=False, embedding_field="emb",
- embedding_dim=768, excluded_meta_data=["emb"])
-```
-
-
-```python
-# Add evaluation data to Elasticsearch Document Store
-# We first delete the custom tutorial indices to not have duplicate elements
-document_store.delete_all_documents(index=doc_index)
-document_store.delete_all_documents(index=label_index)
-document_store.add_eval_data(filename="../data/nq/nq_dev_subset_v2.json", doc_index=doc_index, label_index=label_index)
-```
-
-## Initialize components of QA-System
-
-
-```python
-# Initialize Retriever
-from haystack.retriever.sparse import ElasticsearchRetriever
-retriever = ElasticsearchRetriever(document_store=document_store)
-# Alternative: Evaluate DensePassageRetriever
-# Note, that DPR works best when you index short passages < 512 tokens as only those tokens will be used for the embedding.
-# Here, for nq_dev_subset_v2.json we have avg. num of tokens = 5220(!).
-# DPR still outperforms Elastic's BM25 by a small margin here.
-# from haystack.retriever.dense import DensePassageRetriever
-# retriever = DensePassageRetriever(document_store=document_store,
-# query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
-# passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
-# use_gpu=True,
-# embed_title=True,
-# max_seq_len=256,
-# batch_size=16,
-# remove_sep_tok_from_untitled_passages=True)
-#document_store.update_embeddings(retriever, index=doc_index)
-```
-
-
-```python
-# Initialize Reader
-from haystack.reader.farm import FARMReader
-
-reader = FARMReader("deepset/roberta-base-squad2", top_k_per_candidate=4)
-```
-
-
-```python
-# Initialize Finder which sticks together Reader and Retriever
-from haystack.finder import Finder
-
-finder = Finder(reader, retriever)
-```
-
-## Evaluation of Retriever
-
-
-```python
-## Evaluate Retriever on its own
-retriever_eval_results = retriever.eval(top_k=20, label_index=label_index, doc_index=doc_index)
-## Retriever Recall is the proportion of questions for which the correct document containing the answer is
-## among the correct documents
-print("Retriever Recall:", retriever_eval_results["recall"])
-## Retriever Mean Avg Precision rewards retrievers that give relevant documents a higher rank
-print("Retriever Mean Avg Precision:", retriever_eval_results["map"])
-```
-
-## Evaluation of Reader
-
-
-```python
-# Evaluate Reader on its own
-reader_eval_results = reader.eval(document_store=document_store, device=device, label_index=label_index, doc_index=doc_index)
-# Evaluation of Reader can also be done directly on a SQuAD-formatted file without passing the data to Elasticsearch
-#reader_eval_results = reader.eval_on_file("../data/nq", "nq_dev_subset_v2.json", device=device)
-
-## Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer
-print("Reader Top-N-Accuracy:", reader_eval_results["top_n_accuracy"])
-## Reader Exact Match is the proportion of questions where the predicted answer is exactly the same as the correct answer
-print("Reader Exact Match:", reader_eval_results["EM"])
-## Reader F1-Score is the average overlap between the predicted answers and the correct answers
-print("Reader F1-Score:", reader_eval_results["f1"])
-```
-
-## Evaluation of Finder
-
-
-```python
-# Evaluate combination of Reader and Retriever through Finder
-# Evaluate combination of Reader and Retriever through Finder
-finder_eval_results = finder.eval(top_k_retriever=1, top_k_reader=10, label_index=label_index, doc_index=doc_index)
-finder.print_eval_results(finder_eval_results)
-```
-
-
-```python
-
-```
diff --git a/src/pages/docs/versions/master/v0.4.0/site/en/tutorials/tutorials/6.md b/src/pages/docs/versions/master/v0.4.0/site/en/tutorials/tutorials/6.md
deleted file mode 100644
index ccd88e261..000000000
--- a/src/pages/docs/versions/master/v0.4.0/site/en/tutorials/tutorials/6.md
+++ /dev/null
@@ -1,205 +0,0 @@
----
-title: "Tutorial 6"
-metaTitle: "Better retrieval via Dense Passage Retrieval"
-metaDescription: ""
-slug: "/docs/tutorial6"
-date: "2020-09-03"
-id: "tutorial6md"
----
-
-# Better Retrieval via "Dense Passage Retrieval"
-
-EXECUTABLE VERSION: [colab](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb)
-
-### Importance of Retrievers
-
-The Retriever has a huge impact on the performance of our overall search pipeline.
-
-
-### Different types of Retrievers
-#### Sparse
-Family of algorithms based on counting the occurrences of words (bag-of-words) resulting in very sparse vectors with length = vocab size.
-
-**Examples**: BM25, TF-IDF
-
-**Pros**: Simple, fast, well explainable
-
-**Cons**: Relies on exact keyword matches between query and text
-
-
-#### Dense
-These retrievers use neural network models to create "dense" embedding vectors. Within this family there are two different approaches:
-
-a) Single encoder: Use a **single model** to embed both query and passage.
-b) Dual-encoder: Use **two models**, one to embed the query and one to embed the passage
-
-Recent work suggests that dual encoders work better, likely because they can deal better with the different nature of query and passage (length, style, syntax ...).
-
-**Examples**: REALM, DPR, Sentence-Transformers
-
-**Pros**: Captures semantinc similarity instead of "word matches" (e.g. synonyms, related topics ...)
-
-**Cons**: Computationally more heavy, initial training of model
-
-
-### "Dense Passage Retrieval"
-
-In this Tutorial, we want to highlight one "Dense Dual-Encoder" called Dense Passage Retriever.
-It was introdoced by Karpukhin et al. (2020, https://arxiv.org/abs/2004.04906.
-
-Original Abstract:
-
-_"Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks."_
-
-Paper: https://arxiv.org/abs/2004.04906
-Original Code: https://fburl.com/qa-dpr
-
-
-*Use this* [link](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb) *to open the notebook in Google Colab.*
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack and install the version of torch that works with the colab GPUs
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install torch==1.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-### Document Store
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-
-
-```python
-from haystack.document_store.faiss import FAISSDocumentStore
-
-document_store = FAISSDocumentStore()
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever, Reader, & Finder
-
-#### Retriever
-
-**Here:** We use a `DensePassageRetriever`
-
-**Alternatives:**
-
-- The `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-
-
-```python
-from haystack.retriever.dense import DensePassageRetriever
-retriever = DensePassageRetriever(document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- use_gpu=True,
- embed_title=True,
- max_seq_len=256,
- batch_size=16,
- remove_sep_tok_from_untitled_passages=True)
-# Important:
-# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all
-# previously indexed documents and update their embedding representation.
-# While this can be a time consuming operation (depending on corpus size), it only needs to be done once.
-# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.
-document_store.update_embeddings(retriever)
-```
-
-#### Reader
-
-Similar to previous Tutorials we now initalize our reader.
-
-Here we use a FARMReader with the *deepset/roberta-base-squad2* model (see: https://huggingface.co/deepset/roberta-base-squad2)
-
-
-
-##### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-#### Finder
-
-The Finder sticks together reader and retriever in a pipeline to answer our actual questions.
-
-
-```python
-finder = Finder(reader, retriever)
-```
-
-### Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = finder.get_answers(question="Who created the Dothraki vocabulary?", top_k_retriever=10, top_k_reader=5)
-
-#prediction = finder.get_answers(question="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-#prediction = finder.get_answers(question="Who is the sister of Sansa?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
-
-
-```python
-
-```
diff --git a/src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/reader.md b/src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/reader.md
deleted file mode 100644
index 1e013f35a..000000000
--- a/src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/reader.md
+++ /dev/null
@@ -1,439 +0,0 @@
----
-title: "Reader"
-metaTitle: "Reader"
-metaDescription: ""
-slug: "/docs/reader"
-date: "2020-09-03"
-id: "readermd"
----
-
-# Reader
-
-The Reader, also known as Open-Domain QA systems in Machine Learning speak,
-is the core component that enables Haystack to find the answers that you need.
-Haystack’s Readers are:
-
-
-* built on the latest transformer based language models
-
-
-* strong in their grasp of semantics
-
-
-* sensitive to syntactic structure
-
-
-* state-of-the-art in QA tasks like SQuAD and Natural Questions
-
-"}.
-Optionally: Include meta data via {"text": "",
-"meta": {"name": ", "author": "somebody", ...}}
-It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: write documents to a custom namespace. For instance, documents for evaluation can be indexed in a
-separate index than the documents for search.
-
-**Returns**:
-
-None
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever
-- `index`: Index name to update
-
-**Returns**:
-
-None
-
-
-#### add\_eval\_data
-
-```python
- | add_eval_data(filename: str, doc_index: Optional[str] = None, label_index: Optional[str] = None)
-```
-
-Adds a SQuAD-formatted file to the DocumentStore in order to be able to perform evaluation on it.
-
-**Arguments**:
-
-- `filename`: Name of the file containing evaluation data
-:type filename: str
-- `doc_index`: Elasticsearch index where evaluation documents should be stored
-:type doc_index: str
-- `label_index`: Elasticsearch index where labeled questions should be stored
-:type label_index: str
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None)
-```
-
-Delete all documents in a index.
-
-**Arguments**:
-
-- `index`: index name
-
-**Returns**:
-
-None
-
-
-# faiss
-
-
-## FAISSDocumentStore
-
-```python
-class FAISSDocumentStore(SQLDocumentStore)
-```
-
-Document store for very large scale embedding based dense retrievers like the DPR.
-
-It implements the FAISS library(https://github.com/facebookresearch/faiss)
-to perform similarity search on vectors.
-
-The document text and meta-data (for filtering) are stored using the SQLDocumentStore, while
-the vector embeddings are indexed in a FAISS Index.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(sql_url: str = "sqlite:///", index_buffer_size: int = 10_000, vector_dim: int = 768, faiss_index_factory_str: str = "Flat", faiss_index: Optional[faiss.swigfaiss.Index] = None, return_embedding: Optional[bool] = True, **kwargs, ,)
-```
-
-**Arguments**:
-
-- `sql_url`: SQL connection URL for database. It defaults to local file based SQLite DB. For large scale
-deployment, Postgres is recommended.
-- `index_buffer_size`: When working with large datasets, the ingestion process(FAISS + SQL) can be buffered in
-smaller chunks to reduce memory footprint.
-- `vector_dim`: the embedding vector size.
-- `faiss_index_factory_str`: Create a new FAISS index of the specified type.
-The type is determined from the given string following the conventions
-of the original FAISS index factory.
-Recommended options:
-- "Flat" (default): Best accuracy (= exact). Becomes slow and RAM intense for > 1 Mio docs.
-- "HNSW": Graph-based heuristic. If not further specified,
-we use a RAM intense, but more accurate config:
-HNSW256, efConstruction=256 and efSearch=256
-- "IVFx,Flat": Inverted Index. Replace x with the number of centroids aka nlist.
-Rule of thumb: nlist = 10 * sqrt (num_docs) is a good starting point.
-For more details see:
-- Overview of indices https://github.com/facebookresearch/faiss/wiki/Faiss-indexes
-- Guideline for choosing an index https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-- FAISS Index factory https://github.com/facebookresearch/faiss/wiki/The-index-factory
-Benchmarks: XXX
-- `faiss_index`: Pass an existing FAISS Index, i.e. an empty one that you configured manually
-or one with docs that you used in Haystack before and want to load again.
-- `return_embedding`: To return document embedding
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
-```
-
-Add new documents to the DocumentStore.
-
-**Arguments**:
-
-- `documents`: List of `Dicts` or List of `Documents`. If they already contain the embeddings, we'll index
-them right away in FAISS. If not, you can later call update_embeddings() to create & index them.
-- `index`: (SQL) index name for storing the docs and metadata
-
-**Returns**:
-
-
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever to use to get embeddings for text
-- `index`: (SQL) index name for storing the docs and metadata
-
-**Returns**:
-
-None
-
-
-#### train\_index
-
-```python
- | train_index(documents: Optional[Union[List[dict], List[Document]]], embeddings: Optional[np.array] = None)
-```
-
-Some FAISS indices (e.g. IVF) require initial "training" on a sample of vectors before you can add your final vectors.
-The train vectors should come from the same distribution as your final ones.
-You can pass either documents (incl. embeddings) or just the plain embeddings that the index shall be trained on.
-
-**Arguments**:
-
-- `documents`: Documents (incl. the embeddings)
-- `embeddings`: Plain embeddings
-
-**Returns**:
-
-None
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.array, filters: Optional[dict] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
-Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: (SQL) index name for storing the docs and metadata
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### save
-
-```python
- | save(file_path: Union[str, Path])
-```
-
-Save FAISS Index to the specified file.
-
-**Arguments**:
-
-- `file_path`: Path to save to.
-
-**Returns**:
-
-None
-
-
-#### load
-
-```python
- | @classmethod
- | load(cls, faiss_file_path: Union[str, Path], sql_url: str, index_buffer_size: int = 10_000)
-```
-
-Load a saved FAISS index from a file and connect to the SQL database.
-Note: In order to have a correct mapping from FAISS to SQL,
-make sure to use the same SQL DB that you used when calling `save()`.
-
-**Arguments**:
-
-- `faiss_file_path`: Stored FAISS index file. Can be created via calling `save()`
-- `sql_url`: Connection string to the SQL database that contains your docs and metadata.
-- `index_buffer_size`: When working with large datasets, the ingestion process(FAISS + SQL) can be buffered in
-smaller chunks to reduce memory footprint.
-
-**Returns**:
-
-
-
-
-# elasticsearch
-
-
-## ElasticsearchDocumentStore
-
-```python
-class ElasticsearchDocumentStore(BaseDocumentStore)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(host: str = "localhost", port: int = 9200, username: str = "", password: str = "", index: str = "document", label_index: str = "label", search_fields: Union[str, list] = "text", text_field: str = "text", name_field: str = "name", embedding_field: str = "embedding", embedding_dim: int = 768, custom_mapping: Optional[dict] = None, excluded_meta_data: Optional[list] = None, faq_question_field: Optional[str] = None, analyzer: str = "standard", scheme: str = "http", ca_certs: bool = False, verify_certs: bool = True, create_index: bool = True, update_existing_documents: bool = False, refresh_type: str = "wait_for", similarity="dot_product", timeout=30, return_embedding: Optional[bool] = True)
-```
-
-A DocumentStore using Elasticsearch to store and query the documents for our search.
-
-* Keeps all the logic to store and query documents from Elastic, incl. mapping of fields, adding filters or boosts to your queries, and storing embeddings
-* You can either use an existing Elasticsearch index or create a new one via haystack
-* Retrievers operate on top of this DocumentStore to find the relevant documents for a query
-
-**Arguments**:
-
-- `host`: url of elasticsearch
-- `port`: port of elasticsearch
-- `username`: username
-- `password`: password
-- `index`: Name of index in elasticsearch to use. If not existing yet, we will create one.
-- `search_fields`: Name of fields used by ElasticsearchRetriever to find matches in the docs to our incoming query (using elastic's multi_match query), e.g. ["title", "full_text"]
-- `text_field`: Name of field that might contain the answer and will therefore be passed to the Reader Model (e.g. "full_text").
-If no Reader is used (e.g. in FAQ-Style QA) the plain content of this field will just be returned.
-- `name_field`: Name of field that contains the title of the the doc
-- `embedding_field`: Name of field containing an embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `embedding_dim`: Dimensionality of embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `custom_mapping`: If you want to use your own custom mapping for creating a new index in Elasticsearch, you can supply it here as a dictionary.
-- `analyzer`: Specify the default analyzer from one of the built-ins when creating a new Elasticsearch Index.
-Elasticsearch also has built-in analyzers for different languages (e.g. impacting tokenization). More info at:
-https://www.elastic.co/guide/en/elasticsearch/reference/7.9/analysis-analyzers.html
-- `excluded_meta_data`: Name of fields in Elasticsearch that should not be returned (e.g. [field_one, field_two]).
-Helpful if you have fields with long, irrelevant content that you don't want to display in results (e.g. embedding vectors).
-- `scheme`: 'https' or 'http', protocol used to connect to your elasticsearch instance
-- `ca_certs`: Root certificates for SSL
-- `verify_certs`: Whether to be strict about ca certificates
-- `create_index`: Whether to try creating a new index (If the index of that name is already existing, we will just continue in any case)
-- `update_existing_documents`: Whether to update any existing documents with the same ID when adding
-documents. When set as True, any document with an existing ID gets updated.
-If set to False, an error is raised if the document ID of the document being
-added already exists.
-- `refresh_type`: Type of ES refresh used to control when changes made by a request (e.g. bulk) are made visible to search.
-Values:
-- 'wait_for' => continue only after changes are visible (slow, but safe)
-- 'false' => continue directly (fast, but sometimes unintuitive behaviour when docs are not immediately available after ingestion)
-More info at https://www.elastic.co/guide/en/elasticsearch/reference/6.8/docs-refresh.html
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
-more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
-- `timeout`: Number of seconds after which an ElasticSearch request times out.
-- `return_embedding`: To return document embedding
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
-```
-
-Indexes documents for later queries in Elasticsearch.
-
-Behaviour if a document with the same ID already exists in ElasticSearch:
-a) (Default) Throw Elastic's standard error message for duplicate IDs.
-b) If `self.update_existing_documents=True` for DocumentStore: Overwrite existing documents.
-(This is only relevant if you pass your own ID when initializing a `Document`.
-If don't set custom IDs for your Documents or just pass a list of dictionaries here,
-they will automatically get UUIDs assigned. See the `Document` class for details)
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
-For documents as dictionaries, the format is {"text": ""}.
-Optionally: Include meta data via {"text": "",
-"meta":{"name": ", "author": "somebody", ...}}
-It can be used for filtering and is accessible in the responses of the Finder.
-Advanced: If you are using your own Elasticsearch mapping, the key names in the dictionary
-should be changed to what you have set for self.text_field and self.name_field.
-- `index`: Elasticsearch index where the documents should be indexed. If not supplied, self.index will be used.
-
-**Returns**:
-
-None
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever
-- `index`: Index name to update
-
-**Returns**:
-
-None
-
-
-#### add\_eval\_data
-
-```python
- | add_eval_data(filename: str, doc_index: str = "eval_document", label_index: str = "label")
-```
-
-Adds a SQuAD-formatted file to the DocumentStore in order to be able to perform evaluation on it.
-
-**Arguments**:
-
-- `filename`: Name of the file containing evaluation data
-:type filename: str
-- `doc_index`: Elasticsearch index where evaluation documents should be stored
-:type doc_index: str
-- `label_index`: Elasticsearch index where labeled questions should be stored
-:type label_index: str
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: str)
-```
-
-Delete all documents in an index.
-
-**Arguments**:
-
-- `index`: index name
-
-**Returns**:
-
-None
-
-
-# sql
-
-
-## SQLDocumentStore
-
-```python
-class SQLDocumentStore(BaseDocumentStore)
-```
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
-For documents as dictionaries, the format is {"text": ""}.
-Optionally: Include meta data via {"text": "",
-"meta":{"name": ", "author": "somebody", ...}}
-It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: add an optional index attribute to documents. It can be later used for filtering. For instance,
-documents for evaluation can be indexed in a separate index than the documents for search.
-
-**Returns**:
-
-None
-
-
-#### update\_vector\_ids
-
-```python
- | update_vector_ids(vector_id_map: Dict[str, str], index: Optional[str] = None)
-```
-
-Update vector_ids for given document_ids.
-
-**Arguments**:
-
-- `vector_id_map`: dict containing mapping of document_id -> vector_id.
-- `index`: filter documents by the optional index attribute for documents in database.
-
-
-#### add\_eval\_data
-
-```python
- | add_eval_data(filename: str, doc_index: str = "eval_document", label_index: str = "label")
-```
-
-Adds a SQuAD-formatted file to the DocumentStore in order to be able to perform evaluation on it.
-
-**Arguments**:
-
-- `filename`: Name of the file containing evaluation data
-:type filename: str
-- `doc_index`: Elasticsearch index where evaluation documents should be stored
-:type doc_index: str
-- `label_index`: Elasticsearch index where labeled questions should be stored
-:type label_index: str
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index=None)
-```
-
-Delete all documents in a index.
-
-**Arguments**:
-
-- `index`: index name
-
-**Returns**:
-
-None
-
-
-# base
-
-
-## BaseDocumentStore
-
-```python
-class BaseDocumentStore(ABC)
-```
-
-Base class for implementing Document Stores.
-
-
-#### write\_documents
-
-```python
- | @abstractmethod
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
-For documents as dictionaries, the format is {"text": ""}.
-Optionally: Include meta data via {"text": "",
-"meta":{"name": ", "author": "somebody", ...}}
-It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: Optional name of index where the documents shall be written to.
-If None, the DocumentStore's default index (self.index) will be used.
-
-**Returns**:
-
-None
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/api/api/file_converter.md b/src/pages/docs/versions/master/v0.5.0/site/en/api/api/file_converter.md
deleted file mode 100644
index a3bb5fa1d..000000000
--- a/src/pages/docs/versions/master/v0.5.0/site/en/api/api/file_converter.md
+++ /dev/null
@@ -1,219 +0,0 @@
----
-title: "File Converters"
-metaTitle: "File Converters"
-metaDescription: ""
-slug: "/docs/file_converters"
-date: "2020-09-03"
-id: "file_convertersmd"
----
-
-# File Converters
-
-
-# pdf
-
-
-## PDFToTextConverter
-
-```python
-class PDFToTextConverter(BaseConverter)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(remove_numeric_tables: Optional[bool] = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
-The tabular structures in documents might be noise for the reader model if it
-does not have table parsing capability for finding answers. However, tables
-may also have long strings that could possible candidate for searching answers.
-The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
-(https://en.wikipedia.org/wiki/ISO_639-1) format.
-This option can be used to add test for encoding errors. If the extracted text is
-not one of the valid languages, then it might likely be encoding error resulting
-in garbled text.
-
-
-# txt
-
-
-## TextConverter
-
-```python
-class TextConverter(BaseConverter)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(remove_numeric_tables: Optional[bool] = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
-The tabular structures in documents might be noise for the reader model if it
-does not have table parsing capability for finding answers. However, tables
-may also have long strings that could possible candidate for searching answers.
-The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
-(https://en.wikipedia.org/wiki/ISO_639-1) format.
-This option can be used to add test for encoding errors. If the extracted text is
-not one of the valid languages, then it might likely be encoding error resulting
-in garbled text.
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None, encoding: str = "utf-8") -> Dict[str, Any]
-```
-
-Reads text from a txt file and executes optional preprocessing steps.
-
-**Arguments**:
-
-- `file_path`: Path of the file to convert
-- `meta`: Optional meta data that should be associated with the the document (e.g. name)
-- `encoding`: Encoding of the file
-
-**Returns**:
-
-Dict of format {"text": "The text from file", "meta": meta}}
-
-
-# tika
-
-
-## TikaConverter
-
-```python
-class TikaConverter(BaseConverter)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(tika_url: str = "http://localhost:9998/tika", remove_numeric_tables: Optional[bool] = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `tika_url`: URL of the Tika server
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
-The tabular structures in documents might be noise for the reader model if it
-does not have table parsing capability for finding answers. However, tables
-may also have long strings that could possible candidate for searching answers.
-The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
-(https://en.wikipedia.org/wiki/ISO_639-1) format.
-This option can be used to add test for encoding errors. If the extracted text is
-not one of the valid languages, then it might likely be encoding error resulting
-in garbled text.
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None) -> Dict[str, Any]
-```
-
-**Arguments**:
-
-- `file_path`: Path of file to be converted.
-
-**Returns**:
-
-a list of pages and the extracted meta data of the file.
-
-
-# docx
-
-
-## DocxToTextConverter
-
-```python
-class DocxToTextConverter(BaseConverter)
-```
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None) -> Dict[str, Any]
-```
-
-Extract text from a .docx file.
-Note: As docx doesn't contain "page" information, we actually extract and return a list of paragraphs here.
-For compliance with other converters we nevertheless opted for keeping the methods name.
-
-**Arguments**:
-
-- `file_path`: Path to the .docx file you want to convert
-
-
-# base
-
-
-## BaseConverter
-
-```python
-class BaseConverter()
-```
-
-Base class for implementing file converts to transform input documents to text format for ingestion in DocumentStore.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
-The tabular structures in documents might be noise for the reader model if it
-does not have table parsing capability for finding answers. However, tables
-may also have long strings that could possible candidate for searching answers.
-The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
-(https://en.wikipedia.org/wiki/ISO_639-1) format.
-This option can be used to add test for encoding errors. If the extracted text is
-not one of the valid languages, then it might likely be encoding error resulting
-in garbled text.
-
-
-#### convert
-
-```python
- | @abstractmethod
- | convert(file_path: Path, meta: Optional[Dict[str, str]]) -> Dict[str, Any]
-```
-
-Convert a file to a dictionary containing the text and any associated meta data.
-
-File converters may extract file meta like name or size. In addition to it, user
-supplied meta data like author, url, external IDs can be supplied as a dictionary.
-
-**Arguments**:
-
-- `file_path`: path of the file to convert
-- `meta`: dictionary of meta data key-value pairs to append in the returned document.
-
-
-#### validate\_language
-
-```python
- | validate_language(text: str) -> bool
-```
-
-Validate if the language of the text is one of valid languages.
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/api/api/preprocessor.md b/src/pages/docs/versions/master/v0.5.0/site/en/api/api/preprocessor.md
deleted file mode 100644
index 30a02b23f..000000000
--- a/src/pages/docs/versions/master/v0.5.0/site/en/api/api/preprocessor.md
+++ /dev/null
@@ -1,138 +0,0 @@
----
-title: "Preprocessor"
-metaTitle: "Preprocessor"
-metaDescription: ""
-slug: "/docs/apiindexing"
-date: "2020-09-03"
-id: "apiindexingmd"
----
-
-# Preprocessor
-
-
-# utils
-
-
-#### eval\_data\_from\_file
-
-```python
-eval_data_from_file(filename: str, max_docs: Union[int, bool] = None) -> Tuple[List[Document], List[Label]]
-```
-
-Read Documents + Labels from a SQuAD-style file.
-Document and Labels can then be indexed to the DocumentStore and be used for evaluation.
-
-**Arguments**:
-
-- `filename`: Path to file in SQuAD format
-- `max_docs`: This sets the number of documents that will be loaded. By default, this is set to None, thus reading in all available eval documents.
-
-**Returns**:
-
-(List of Documents, List of Labels)
-
-
-#### convert\_files\_to\_dicts
-
-```python
-convert_files_to_dicts(dir_path: str, clean_func: Optional[Callable] = None, split_paragraphs: bool = False) -> List[dict]
-```
-
-Convert all files(.txt, .pdf, .docx) in the sub-directories of the given path to Python dicts that can be written to a
-Document Store.
-
-**Arguments**:
-
-- `dir_path`: path for the documents to be written to the DocumentStore
-- `clean_func`: a custom cleaning function that gets applied to each doc (input: str, output:str)
-- `split_paragraphs`: split text in paragraphs.
-
-**Returns**:
-
-None
-
-
-#### tika\_convert\_files\_to\_dicts
-
-```python
-tika_convert_files_to_dicts(dir_path: str, clean_func: Optional[Callable] = None, split_paragraphs: bool = False, merge_short: bool = True, merge_lowercase: bool = True) -> List[dict]
-```
-
-Convert all files(.txt, .pdf) in the sub-directories of the given path to Python dicts that can be written to a
-Document Store.
-
-**Arguments**:
-
-- `merge_lowercase`: allow conversion of merged paragraph to lowercase
-- `merge_short`: allow merging of short paragraphs
-- `dir_path`: path for the documents to be written to the DocumentStore
-- `clean_func`: a custom cleaning function that gets applied to each doc (input: str, output:str)
-- `split_paragraphs`: split text in paragraphs.
-
-**Returns**:
-
-None
-
-
-#### fetch\_archive\_from\_http
-
-```python
-fetch_archive_from_http(url: str, output_dir: str, proxies: Optional[dict] = None)
-```
-
-Fetch an archive (zip or tar.gz) from a url via http and extract content to an output directory.
-
-**Arguments**:
-
-- `url`: http address
-:type url: str
-- `output_dir`: local path
-:type output_dir: str
-- `proxies`: proxies details as required by requests library
-:type proxies: dict
-
-**Returns**:
-
-bool if anything got fetched
-
-
-# preprocessor
-
-
-## PreProcessor
-
-```python
-class PreProcessor(BasePreProcessor)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(clean_whitespace: Optional[bool] = True, clean_header_footer: Optional[bool] = False, clean_empty_lines: Optional[bool] = True, split_by: Optional[str] = "word", split_length: Optional[int] = 1000, split_stride: Optional[int] = None, split_respect_sentence_boundary: Optional[bool] = True)
-```
-
-**Arguments**:
-
-- `clean_header_footer`: Use heuristic to remove footers and headers across different pages by searching
-for the longest common string. This heuristic uses exact matches and therefore
-works well for footers like "Copyright 2019 by XXX", but won't detect "Page 3 of 4"
-or similar.
-- `clean_whitespace`: Strip whitespaces before or after each line in the text.
-- `clean_empty_lines`: Remove more than two empty lines in the text.
-- `split_by`: Unit for splitting the document. Can be "word", "sentence", or "passage". Set to None to disable splitting.
-- `split_length`: Max. number of the above split unit (e.g. words) that are allowed in one document. For instance, if n -> 10 & split_by ->
-"sentence", then each output document will have 10 sentences.
-- `split_stride`: Length of striding window over the splits. For example, if split_by -> `word`,
-split_length -> 5 & split_stride -> 2, then the splits would be like:
-[w1 w2 w3 w4 w5, w4 w5 w6 w7 w8, w7 w8 w10 w11 w12].
-Set the value to None to disable striding behaviour.
-- `split_respect_sentence_boundary`: Whether to split in partial sentences if split_by -> `word`. If set
-to True, the individual split will always have complete sentences &
-the number of words will be <= split_length.
-
-
-# base
-
-
-# cleaning
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/api/api/reader.md b/src/pages/docs/versions/master/v0.5.0/site/en/api/api/reader.md
deleted file mode 100644
index a3bbf1566..000000000
--- a/src/pages/docs/versions/master/v0.5.0/site/en/api/api/reader.md
+++ /dev/null
@@ -1,382 +0,0 @@
----
-title: "Reader"
-metaTitle: "Reader"
-metaDescription: ""
-slug: "/docs/apireadermd"
-date: "2020-09-03"
-id: "apireadermd"
----
-
-# Reader
-
-
-# farm
-
-
-## FARMReader
-
-```python
-class FARMReader(BaseReader)
-```
-
-Transformer based model for extractive Question Answering using the FARM framework (https://github.com/deepset-ai/FARM).
-While the underlying model can vary (BERT, Roberta, DistilBERT, ...), the interface remains the same.
-
-| With a FARMReader, you can:
-
- - directly get predictions via predict()
- - fine-tune the model on QA data via train()
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: Union[str, Path], context_window_size: int = 150, batch_size: int = 50, use_gpu: bool = True, no_ans_boost: Optional[float] = None, top_k_per_candidate: int = 3, top_k_per_sample: int = 1, num_processes: Optional[int] = None, max_seq_len: int = 256, doc_stride: int = 128)
-```
-
-**Arguments**:
-
-- `model_name_or_path`: Directory of a saved model or the name of a public model e.g. 'bert-base-cased',
-'deepset/bert-base-cased-squad2', 'deepset/bert-base-cased-squad2', 'distilbert-base-uncased-distilled-squad'.
-See https://huggingface.co/models for full list of available models.
-- `context_window_size`: The size, in characters, of the window around the answer span that is used when
-displaying the context around the answer.
-- `batch_size`: Number of samples the model receives in one batch for inference.
-Memory consumption is much lower in inference mode. Recommendation: Increase the batch size
-to a value so only a single batch is used.
-- `use_gpu`: Whether to use GPU (if available)
-- `no_ans_boost`: How much the no_answer logit is boosted/increased.
-If set to None (default), disables returning "no answer" predictions.
-If a negative number, there is a lower chance of "no_answer" being predicted.
-If a positive number, there is an increased chance of "no_answer"
-- `top_k_per_candidate`: How many answers to extract for each candidate doc that is coming from the retriever (might be a long text).
-Note that this is not the number of "final answers" you will receive
-(see `top_k` in FARMReader.predict() or Finder.get_answers() for that)
-and that FARM includes no_answer in the sorted list of predictions.
-- `top_k_per_sample`: How many answers to extract from each small text passage that the model can process at once
-(one "candidate doc" is usually split into many smaller "passages").
-You usually want a very small value here, as it slows down inference
-and you don't gain much of quality by having multiple answers from one passage.
-Note that this is not the number of "final answers" you will receive
-(see `top_k` in FARMReader.predict() or Finder.get_answers() for that)
-and that FARM includes no_answer in the sorted list of predictions.
-- `num_processes`: The number of processes for `multiprocessing.Pool`. Set to value of 0 to disable
-multiprocessing. Set to None to let Inferencer determine optimum number. If you
-want to debug the Language Model, you might need to disable multiprocessing!
-- `max_seq_len`: Max sequence length of one input text for the model
-- `doc_stride`: Length of striding window for splitting long texts (used if ``len(text) > max_seq_len``)
-
-
-#### train
-
-```python
- | train(data_dir: str, train_filename: str, dev_filename: Optional[str] = None, test_filename: Optional[str] = None, use_gpu: Optional[bool] = None, batch_size: int = 10, n_epochs: int = 2, learning_rate: float = 1e-5, max_seq_len: Optional[int] = None, warmup_proportion: float = 0.2, dev_split: float = 0, evaluate_every: int = 300, save_dir: Optional[str] = None, num_processes: Optional[int] = None, use_amp: str = None)
-```
-
-Fine-tune a model on a QA dataset. Options:
-
-- Take a plain language model (e.g. `bert-base-cased`) and train it for QA (e.g. on SQuAD data)
-- Take a QA model (e.g. `deepset/bert-base-cased-squad2`) and fine-tune it for your domain (e.g. using your labels collected via the haystack annotation tool)
-
-**Arguments**:
-
-- `data_dir`: Path to directory containing your training data in SQuAD style
-- `train_filename`: Filename of training data
-- `dev_filename`: Filename of dev / eval data
-- `test_filename`: Filename of test data
-- `dev_split`: Instead of specifying a dev_filename, you can also specify a ratio (e.g. 0.1) here
-that gets split off from training data for eval.
-- `use_gpu`: Whether to use GPU (if available)
-- `batch_size`: Number of samples the model receives in one batch for training
-- `n_epochs`: Number of iterations on the whole training data set
-- `learning_rate`: Learning rate of the optimizer
-- `max_seq_len`: Maximum text length (in tokens). Everything longer gets cut down.
-- `warmup_proportion`: Proportion of training steps until maximum learning rate is reached.
-Until that point LR is increasing linearly. After that it's decreasing again linearly.
-Options for different schedules are available in FARM.
-- `evaluate_every`: Evaluate the model every X steps on the hold-out eval dataset
-- `save_dir`: Path to store the final model
-- `num_processes`: The number of processes for `multiprocessing.Pool` during preprocessing.
-Set to value of 1 to disable multiprocessing. When set to 1, you cannot split away a dev set from train set.
-Set to None to use all CPU cores minus one.
-- `use_amp`: Optimization level of NVIDIA's automatic mixed precision (AMP). The higher the level, the faster the model.
-Available options:
-None (Don't use AMP)
-"O0" (Normal FP32 training)
-"O1" (Mixed Precision => Recommended)
-"O2" (Almost FP16)
-"O3" (Pure FP16).
-See details on: https://nvidia.github.io/apex/amp.html
-
-**Returns**:
-
-None
-
-
-#### save
-
-```python
- | save(directory: Path)
-```
-
-Saves the Reader model so that it can be reused at a later point in time.
-
-**Arguments**:
-
-- `directory`: Directory where the Reader model should be saved
-
-
-#### predict\_batch
-
-```python
- | predict_batch(question_doc_list: List[dict], top_k_per_question: int = None, batch_size: int = None)
-```
-
-Use loaded QA model to find answers for a list of questions in each question's supplied list of Document.
-
-Returns list of dictionaries containing answers sorted by (desc.) probability
-
-**Arguments**:
-
-- `question_doc_list`: List of dictionaries containing questions with their retrieved documents
-- `top_k_per_question`: The maximum number of answers to return for each question
-- `batch_size`: Number of samples the model receives in one batch for inference
-
-**Returns**:
-
-List of dictionaries containing question and answers
-
-
-#### predict
-
-```python
- | predict(question: str, documents: List[Document], top_k: Optional[int] = None)
-```
-
-Use loaded QA model to find answers for a question in the supplied list of Document.
-
-Returns dictionaries containing answers sorted by (desc.) probability.
-Example:
-
-{'question': 'Who is the father of Arya Stark?',
-'answers': [
-{'answer': 'Eddard,',
-'context': " She travels with her father, Eddard, to King's Landing when he is ",
-'offset_answer_start': 147,
-'offset_answer_end': 154,
-'probability': 0.9787139466668613,
-'score': None,
-'document_id': '1337'
-},
-...
-]
-}
-
-**Arguments**:
-
-- `question`: Question string
-- `documents`: List of Document in which to search for the answer
-- `top_k`: The maximum number of answers to return
-
-**Returns**:
-
-Dict containing question and answers
-
-
-#### eval\_on\_file
-
-```python
- | eval_on_file(data_dir: str, test_filename: str, device: str)
-```
-
-Performs evaluation on a SQuAD-formatted file.
-Returns a dict containing the following metrics:
-- "EM": exact match score
-- "f1": F1-Score
-- "top_n_accuracy": Proportion of predicted answers that overlap with correct answer
-
-**Arguments**:
-
-- `data_dir`: The directory in which the test set can be found
-:type data_dir: Path or str
-- `test_filename`: The name of the file containing the test data in SQuAD format.
-:type test_filename: str
-- `device`: The device on which the tensors should be processed. Choose from "cpu" and "cuda".
-:type device: str
-
-
-#### eval
-
-```python
- | eval(document_store: BaseDocumentStore, device: str, label_index: str = "label", doc_index: str = "eval_document", label_origin: str = "gold_label")
-```
-
-Performs evaluation on evaluation documents in the DocumentStore.
-Returns a dict containing the following metrics:
-- "EM": Proportion of exact matches of predicted answers with their corresponding correct answers
-- "f1": Average overlap between predicted answers and their corresponding correct answers
-- "top_n_accuracy": Proportion of predicted answers that overlap with correct answer
-
-**Arguments**:
-
-- `document_store`: DocumentStore containing the evaluation documents
-- `device`: The device on which the tensors should be processed. Choose from "cpu" and "cuda".
-- `label_index`: Index/Table name where labeled questions are stored
-- `doc_index`: Index/Table name where documents that are used for evaluation are stored
-
-
-#### predict\_on\_texts
-
-```python
- | predict_on_texts(question: str, texts: List[str], top_k: Optional[int] = None)
-```
-
-Use loaded QA model to find answers for a question in the supplied list of Document.
-Returns dictionaries containing answers sorted by (desc.) probability.
-Example:
-
-{
-'question': 'Who is the father of Arya Stark?',
-'answers':[
-{'answer': 'Eddard,',
-'context': " She travels with her father, Eddard, to King's Landing when he is ",
-'offset_answer_start': 147,
-'offset_answer_end': 154,
-'probability': 0.9787139466668613,
-'score': None,
-'document_id': '1337'
-},...
-]
-}
-
-**Arguments**:
-
-- `question`: Question string
-- `documents`: List of documents as string type
-- `top_k`: The maximum number of answers to return
-
-**Returns**:
-
-Dict containing question and answers
-
-
-#### convert\_to\_onnx
-
-```python
- | @classmethod
- | convert_to_onnx(cls, model_name: str, output_path: Path, convert_to_float16: bool = False, quantize: bool = False, task_type: str = "question_answering", opset_version: int = 11)
-```
-
-Convert a PyTorch BERT model to ONNX format and write to ./onnx-export dir. The converted ONNX model
-can be loaded with in the `FARMReader` using the export path as `model_name_or_path` param.
-
-Usage:
-
-`from haystack.reader.farm import FARMReader
-from pathlib import Path
-onnx_model_path = Path("roberta-onnx-model")
-FARMReader.convert_to_onnx(model_name="deepset/bert-base-cased-squad2", output_path=onnx_model_path)
-reader = FARMReader(onnx_model_path)`
-
-**Arguments**:
-
-- `model_name`: transformers model name
-- `output_path`: Path to output the converted model
-- `convert_to_float16`: Many models use float32 precision by default. With the half precision of float16,
-inference is faster on Nvidia GPUs with Tensor core like T4 or V100. On older GPUs,
-float32 could still be be more performant.
-- `quantize`: convert floating point number to integers
-- `task_type`: Type of task for the model. Available options: "question_answering" or "embeddings".
-- `opset_version`: ONNX opset version
-
-
-# transformers
-
-
-## TransformersReader
-
-```python
-class TransformersReader(BaseReader)
-```
-
-Transformer based model for extractive Question Answering using the HuggingFace's transformers framework
-(https://github.com/huggingface/transformers).
-While the underlying model can vary (BERT, Roberta, DistilBERT ...), the interface remains the same.
-
-| With the reader, you can:
-
- - directly get predictions via predict()
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: str = "distilbert-base-uncased-distilled-squad", tokenizer: Optional[str] = None, context_window_size: int = 70, use_gpu: int = 0, top_k_per_candidate: int = 4, return_no_answers: bool = True, max_seq_len: int = 256, doc_stride: int = 128)
-```
-
-Load a QA model from Transformers.
-Available models include:
-
-- ``'distilbert-base-uncased-distilled-squad`'``
-- ``'bert-large-cased-whole-word-masking-finetuned-squad``'
-- ``'bert-large-uncased-whole-word-masking-finetuned-squad``'
-
-See https://huggingface.co/models for full list of available QA models
-
-**Arguments**:
-
-- `model_name_or_path`: Directory of a saved model or the name of a public model e.g. 'bert-base-cased',
-'deepset/bert-base-cased-squad2', 'deepset/bert-base-cased-squad2', 'distilbert-base-uncased-distilled-squad'.
-See https://huggingface.co/models for full list of available models.
-- `tokenizer`: Name of the tokenizer (usually the same as model)
-- `context_window_size`: Num of chars (before and after the answer) to return as "context" for each answer.
-The context usually helps users to understand if the answer really makes sense.
-- `use_gpu`: If < 0, then use cpu. If >= 0, this is the ordinal of the gpu to use
-- `top_k_per_candidate`: How many answers to extract for each candidate doc that is coming from the retriever (might be a long text).
-Note that this is not the number of "final answers" you will receive
-(see `top_k` in TransformersReader.predict() or Finder.get_answers() for that)
-and that no_answer can be included in the sorted list of predictions.
-- `return_no_answers`: If True, the HuggingFace Transformers model could return a "no_answer" (i.e. when there is an unanswerable question)
-If False, it cannot return a "no_answer". Note that `no_answer_boost` is unfortunately not available with TransformersReader.
-If you would like to set no_answer_boost, use a `FARMReader`.
-- `max_seq_len`: max sequence length of one input text for the model
-- `doc_stride`: length of striding window for splitting long texts (used if len(text) > max_seq_len)
-
-
-#### predict
-
-```python
- | predict(question: str, documents: List[Document], top_k: Optional[int] = None)
-```
-
-Use loaded QA model to find answers for a question in the supplied list of Document.
-
-Returns dictionaries containing answers sorted by (desc.) probability.
-Example:
-
-{'question': 'Who is the father of Arya Stark?',
-'answers': [
-{'answer': 'Eddard,',
-'context': " She travels with her father, Eddard, to King's Landing when he is ",
-'offset_answer_start': 147,
-'offset_answer_end': 154,
-'probability': 0.9787139466668613,
-'score': None,
-'document_id': '1337'
-},
-...
-]
-}
-
-**Arguments**:
-
-- `question`: Question string
-- `documents`: List of Document in which to search for the answer
-- `top_k`: The maximum number of answers to return
-
-**Returns**:
-
-Dict containing question and answers
-
-
-# base
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/api/api/retriever.md b/src/pages/docs/versions/master/v0.5.0/site/en/api/api/retriever.md
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----
-title: "Retriever"
-metaTitle: "Retriever"
-metaDescription: ""
-slug: "/docs/apiretrievermd"
-date: "2020-09-03"
-id: "apiretrievermd"
----
-
-# Retriever
-
-
-# sparse
-
-
-## ElasticsearchRetriever
-
-```python
-class ElasticsearchRetriever(BaseRetriever)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(document_store: ElasticsearchDocumentStore, custom_query: str = None)
-```
-
-**Arguments**:
-
-- `document_store`: an instance of a DocumentStore to retrieve documents from.
-- `custom_query`: query string as per Elasticsearch DSL with a mandatory question placeholder($question).
-
-Optionally, ES `filter` clause can be added where the values of `terms` are placeholders
-that get substituted during runtime. The placeholder(${filter_name_1}, ${filter_name_2}..)
-names must match with the filters dict supplied in self.retrieve().
-::
-
-**An example custom_query:**
-```python
-{
-> "size": 10,
-> "query": {
-> "bool": {
-> "should": [{"multi_match": {
-> "query": "${question}", // mandatory $question placeholder
-> "type": "most_fields",
-> "fields": ["text", "title"]}}],
-> "filter": [ // optional custom filters
-> {"terms": {"year": "${years}"}},
-> {"terms": {"quarter": "${quarters}"}},
-> {"range": {"date": {"gte": "${date}"}}}
-> ],
-> }
-> },
-}
-```
-
-**For this custom_query, a sample retrieve() could be:**
-```python
-self.retrieve(query="Why did the revenue increase?",
-> filters={"years": ["2019"], "quarters": ["Q1", "Q2"]})
-```
-
-
-## ElasticsearchFilterOnlyRetriever
-
-```python
-class ElasticsearchFilterOnlyRetriever(ElasticsearchRetriever)
-```
-
-Naive "Retriever" that returns all documents that match the given filters. No impact of query at all.
-Helpful for benchmarking, testing and if you want to do QA on small documents without an "active" retriever.
-
-
-## TfidfRetriever
-
-```python
-class TfidfRetriever(BaseRetriever)
-```
-
-Read all documents from a SQL backend.
-
-Split documents into smaller units (eg, paragraphs or pages) to reduce the
-computations when text is passed on to a Reader for QA.
-
-It uses sklearn's TfidfVectorizer to compute a tf-idf matrix.
-
-
-# dense
-
-
-## DensePassageRetriever
-
-```python
-class DensePassageRetriever(BaseRetriever)
-```
-
-Retriever that uses a bi-encoder (one transformer for query, one transformer for passage).
-See the original paper for more details:
-Karpukhin, Vladimir, et al. (2020): "Dense Passage Retrieval for Open-Domain Question Answering."
-(https://arxiv.org/abs/2004.04906).
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(document_store: BaseDocumentStore, query_embedding_model: Union[Path, str] = "facebook/dpr-question_encoder-single-nq-base", passage_embedding_model: Union[Path, str] = "facebook/dpr-ctx_encoder-single-nq-base", max_seq_len_query: int = 64, max_seq_len_passage: int = 256, use_gpu: bool = True, batch_size: int = 16, embed_title: bool = True, use_fast_tokenizers: bool = True, similarity_function: str = "dot_product")
-```
-
-Init the Retriever incl. the two encoder models from a local or remote model checkpoint.
-The checkpoint format matches huggingface transformers' model format
-
-**Example:**
-
-```python
-# remote model from FAIR
-DensePassageRetriever(document_store=your_doc_store,
-> query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
-> passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base")
-# or from local path
-DensePassageRetriever(document_store=your_doc_store,
-> query_embedding_model="model_directory/question-encoder",
-> passage_embedding_model="model_directory/context-encoder")
-```
-
-**Arguments**:
-
-- `document_store`: An instance of DocumentStore from which to retrieve documents.
-- `query_embedding_model`: Local path or remote name of question encoder checkpoint. The format equals the
-one used by hugging-face transformers' modelhub models
-Currently available remote names: ``"facebook/dpr-question_encoder-single-nq-base"``
-- `passage_embedding_model`: Local path or remote name of passage encoder checkpoint. The format equals the
-one used by hugging-face transformers' modelhub models
-Currently available remote names: ``"facebook/dpr-ctx_encoder-single-nq-base"``
-- `max_seq_len_query`: Longest length of each query sequence. Maximum number of tokens for the query text. Longer ones will be cut down."
-- `max_seq_len_passage`: Longest length of each passage/context sequence. Maximum number of tokens for the passage text. Longer ones will be cut down."
-- `use_gpu`: Whether to use gpu or not
-- `batch_size`: Number of questions or passages to encode at once
-- `embed_title`: Whether to concatenate title and passage to a text pair that is then used to create the embedding.
-This is the approach used in the original paper and is likely to improve performance if your
-titles contain meaningful information for retrieval (topic, entities etc.) .
-The title is expected to be present in doc.meta["name"] and can be supplied in the documents
-before writing them to the DocumentStore like this:
-{"text": "my text", "meta": {"name": "my title"}}.
-
-
-#### embed\_queries
-
-```python
- | embed_queries(texts: List[str]) -> List[np.array]
-```
-
-Create embeddings for a list of queries using the query encoder
-
-**Arguments**:
-
-- `texts`: Queries to embed
-
-**Returns**:
-
-Embeddings, one per input queries
-
-
-#### embed\_passages
-
-```python
- | embed_passages(docs: List[Document]) -> List[np.array]
-```
-
-Create embeddings for a list of passages using the passage encoder
-
-**Arguments**:
-
-- `docs`: List of Document objects used to represent documents / passages in a standardized way within Haystack.
-
-**Returns**:
-
-Embeddings of documents / passages shape (batch_size, embedding_dim)
-
-
-#### train
-
-```python
- | train(data_dir: str, train_filename: str, dev_filename: str = None, test_filename: str = None, batch_size: int = 2, embed_title: bool = True, num_hard_negatives: int = 1, num_negatives: int = 0, n_epochs: int = 3, evaluate_every: int = 1000, n_gpu: int = 1, learning_rate: float = 1e-5, epsilon: float = 1e-08, weight_decay: float = 0.0, num_warmup_steps: int = 100, grad_acc_steps: int = 1, optimizer_name: str = "TransformersAdamW", optimizer_correct_bias: bool = True, save_dir: str = "../saved_models/dpr-tutorial", query_encoder_save_dir: str = "lm1", passage_encoder_save_dir: str = "lm2")
-```
-
-train a DensePassageRetrieval model
-
-**Arguments**:
-
-- `data_dir`: Directory where training file, dev file and test file are present
-- `train_filename`: training filename
-- `dev_filename`: development set filename, file to be used by model in eval step of training
-- `test_filename`: test set filename, file to be used by model in test step after training
-- `batch_size`: total number of samples in 1 batch of data
-- `embed_title`: whether to concatenate passage title with each passage. The default setting in official DPR embeds passage title with the corresponding passage
-- `num_hard_negatives`: number of hard negative passages(passages which are very similar(high score by BM25) to query but do not contain the answer
-- `num_negatives`: number of negative passages(any random passage from dataset which do not contain answer to query)
-- `n_epochs`: number of epochs to train the model on
-- `evaluate_every`: number of training steps after evaluation is run
-- `n_gpu`: number of gpus to train on
-- `learning_rate`: learning rate of optimizer
-- `epsilon`: epsilon parameter of optimizer
-- `weight_decay`: weight decay parameter of optimizer
-- `grad_acc_steps`: number of steps to accumulate gradient over before back-propagation is done
-- `optimizer_name`: what optimizer to use (default: TransformersAdamW)
-- `num_warmup_steps`: number of warmup steps
-- `optimizer_correct_bias`: Whether to correct bias in optimizer
-- `save_dir`: directory where models are saved
-- `query_encoder_save_dir`: directory inside save_dir where query_encoder model files are saved
-- `passage_encoder_save_dir`: directory inside save_dir where passage_encoder model files are saved
-
-
-## EmbeddingRetriever
-
-```python
-class EmbeddingRetriever(BaseRetriever)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(document_store: BaseDocumentStore, embedding_model: str, use_gpu: bool = True, model_format: str = "farm", pooling_strategy: str = "reduce_mean", emb_extraction_layer: int = -1)
-```
-
-**Arguments**:
-
-- `document_store`: An instance of DocumentStore from which to retrieve documents.
-- `embedding_model`: Local path or name of model in Hugging Face's model hub such as ``'deepset/sentence_bert'``
-- `use_gpu`: Whether to use gpu or not
-- `model_format`: Name of framework that was used for saving the model. Options:
-
-- ``'farm'``
-- ``'transformers'``
-- ``'sentence_transformers'``
-- `pooling_strategy`: Strategy for combining the embeddings from the model (for farm / transformers models only).
-Options:
-
-- ``'cls_token'`` (sentence vector)
-- ``'reduce_mean'`` (sentence vector)
-- ``'reduce_max'`` (sentence vector)
-- ``'per_token'`` (individual token vectors)
-- `emb_extraction_layer`: Number of layer from which the embeddings shall be extracted (for farm / transformers models only).
-Default: -1 (very last layer).
-
-
-#### embed
-
-```python
- | embed(texts: Union[List[str], str]) -> List[np.array]
-```
-
-Create embeddings for each text in a list of texts using the retrievers model (`self.embedding_model`)
-
-**Arguments**:
-
-- `texts`: Texts to embed
-
-**Returns**:
-
-List of embeddings (one per input text). Each embedding is a list of floats.
-
-
-#### embed\_queries
-
-```python
- | embed_queries(texts: List[str]) -> List[np.array]
-```
-
-Create embeddings for a list of queries. For this Retriever type: The same as calling .embed()
-
-**Arguments**:
-
-- `texts`: Queries to embed
-
-**Returns**:
-
-Embeddings, one per input queries
-
-
-#### embed\_passages
-
-```python
- | embed_passages(docs: List[Document]) -> List[np.array]
-```
-
-Create embeddings for a list of passages. For this Retriever type: The same as calling .embed()
-
-**Arguments**:
-
-- `docs`: List of documents to embed
-
-**Returns**:
-
-Embeddings, one per input passage
-
-
-# base
-
-
-## BaseRetriever
-
-```python
-class BaseRetriever(ABC)
-```
-
-
-#### retrieve
-
-```python
- | @abstractmethod
- | retrieve(query: str, filters: dict = None, top_k: int = 10, index: str = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-#### eval
-
-```python
- | eval(label_index: str = "label", doc_index: str = "eval_document", label_origin: str = "gold_label", top_k: int = 10, open_domain: bool = False, return_preds: bool = False) -> dict
-```
-
-Performs evaluation on the Retriever.
-Retriever is evaluated based on whether it finds the correct document given the question string and at which
-position in the ranking of documents the correct document is.
-
-| Returns a dict containing the following metrics:
-
-- "recall": Proportion of questions for which correct document is among retrieved documents
-- "mrr": Mean of reciprocal rank. Rewards retrievers that give relevant documents a higher rank.
-Only considers the highest ranked relevant document.
-- "map": Mean of average precision for each question. Rewards retrievers that give relevant
-documents a higher rank. Considers all retrieved relevant documents. (only with ``open_domain=False``)
-
-**Arguments**:
-
-- `label_index`: Index/Table in DocumentStore where labeled questions are stored
-- `doc_index`: Index/Table in DocumentStore where documents that are used for evaluation are stored
-- `top_k`: How many documents to return per question
-- `open_domain`: If ``True``, retrieval will be evaluated by checking if the answer string to a question is
-contained in the retrieved docs (common approach in open-domain QA).
-If ``False``, retrieval uses a stricter evaluation that checks if the retrieved document ids
-are within ids explicitly stated in the labels.
-- `return_preds`: Whether to add predictions in the returned dictionary. If True, the returned dictionary
-contains the keys "predictions" and "metrics".
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-{
- "menuList": [
- {
- "id": "usage_haystack",
- "title": "Usage",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 0,
- "isMenu": true
- },
- {
- "id": "intromd",
- "title": "What is Haystack",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "get_startedmd",
- "title": "Get Started",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "documentstoremd",
- "title": "Document Store",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "retrievermd",
- "title": "Retriever",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "readermd",
- "title": "Reader",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "preprocessingmd",
- "title": "Preprocessing",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "languagesmd",
- "title": "Languages Other Than English",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 6
- },
- {
- "id": "domain_adaptationmd",
- "title": "Domain Adaptation",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 7
- },
- {
- "id": "termsmd",
- "title": "Glossary",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 8
- },
- {
- "id": "roadmapmd",
- "title": "Roadmap",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 9
- },
- {
- "id": "annotationmd",
- "title": "Annotation Tool",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 10
- },
- {
- "id": "tutorials_haystack",
- "title": "Tutorials",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 1,
- "isMenu": true
- },
- {
- "id": "tutorial1md",
- "title": "Build Your First QA System",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "tutorial2md",
- "title": "Fine-tuning a Model on Your Own Data",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "tutorial3md",
- "title": "Build a QA System Without Elasticsearch",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "tutorial4md",
- "title": "Utilizing existing FAQs for Question Answering",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "tutorial5md",
- "title": "Evaluation of a QA System",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "tutorial6md",
- "title": "Better Retrieval via \"Dense Passage Retrieval\"",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "api_haystack",
- "title": "API reference",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 2,
- "isMenu": true
- },
- {
- "id": "apidatabasemd",
- "title": "Document Store",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "apiretrievermd",
- "title": "Retriever",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "apireadermd",
- "title": "Reader",
- "label1": "api_haystack",
- "label2": "api_haystack",
- "label3": "",
- "order": 2
- },
- {
- "id": "apiindexingmd",
- "title": "Preprocessor",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "file_convertersmd",
- "title": "File Converters",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- }
- ]
-}
\ No newline at end of file
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/tutorials/tutorials/1.md b/src/pages/docs/versions/master/v0.5.0/site/en/tutorials/tutorials/1.md
deleted file mode 100644
index e452a8de3..000000000
--- a/src/pages/docs/versions/master/v0.5.0/site/en/tutorials/tutorials/1.md
+++ /dev/null
@@ -1,221 +0,0 @@
----
-title: "Tutorial 1"
-metaTitle: "Build Your First QA System"
-metaDescription: ""
-slug: "/docs/tutorial1"
-date: "2020-09-03"
-id: "tutorial1md"
----
-
-# Build Your First QA System
-
-
-
-EXECUTABLE VERSION: [*colab*](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb)
-
-Question Answering can be used in a variety of use cases. A very common one: Using it to navigate through complex knowledge bases or long documents ("search setting").
-
-A "knowledge base" could for example be your website, an internal wiki or a collection of financial reports.
-In this tutorial we will work on a slightly different domain: "Game of Thrones".
-
-Let's see how we can use a bunch of Wikipedia articles to answer a variety of questions about the
-marvellous seven kingdoms...
-
-
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack and install the version of torch that works with the colab GPUs
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install torch==1.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-Haystack finds answers to queries within the documents stored in a `DocumentStore`. The current implementations of `DocumentStore` include `ElasticsearchDocumentStore`, `FAISSDocumentStore`, `SQLDocumentStore`, and `InMemoryDocumentStore`.
-
-**Here:** We recommended Elasticsearch as it comes preloaded with features like [full-text queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html), [BM25 retrieval](https://www.elastic.co/elasticon/conf/2016/sf/improved-text-scoring-with-bm25), and [vector storage for text embeddings](https://www.elastic.co/guide/en/elasticsearch/reference/7.6/dense-vector.html).
-
-**Alternatives:** If you are unable to setup an Elasticsearch instance, then follow the [Tutorial 3](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb) for using SQL/InMemory document stores.
-
-**Hint**: This tutorial creates a new document store instance with Wikipedia articles on Game of Thrones. However, you can configure Haystack to work with your existing document stores.
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker
-#! docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.6.2
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.6.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.6.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.6.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.6.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-# Connect to Elasticsearch
-
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles about Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first fetch some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is:
-# {
-# 'text': "",
-# 'meta': {'name': "", ...}
-#}
-# (Optionally: you can also add more key-value-pairs here, that will be indexed as fields in Elasticsearch and
-# can be accessed later for filtering or shown in the responses of the Finder)
-
-# Let's have a look at the first 3 entries:
-print(dicts[:3])
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-## Initalize Retriever, Reader, & Finder
-
-### Retriever
-
-Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question could be answered.
-They use some simple but fast algorithm.
-
-**Here:** We use Elasticsearch's default BM25 algorithm
-
-**Alternatives:**
-
-- Customize the `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `DensePassageRetriever` to use different embedding models for passage and query (see Tutorial 6)
-
-
-```python
-from haystack.retriever.sparse import ElasticsearchRetriever
-retriever = ElasticsearchRetriever(document_store=document_store)
-```
-
-
-```python
-# Alternative: An in-memory TfidfRetriever based on Pandas dataframes for building quick-prototypes with SQLite document store.
-
-# from haystack.retriever.sparse import TfidfRetriever
-# retriever = TfidfRetriever(document_store=document_store)
-```
-
-### Reader
-
-A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based
-on powerful, but slower deep learning models.
-
-Haystack currently supports Readers based on the frameworks FARM and Transformers.
-With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).
-
-**Here:** a medium sized RoBERTa QA model using a Reader based on FARM (https://huggingface.co/deepset/roberta-base-squad2)
-
-**Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)
-
-**Alternatives (Models):** e.g. "distilbert-base-uncased-distilled-squad" (fast) or "deepset/bert-large-uncased-whole-word-masking-squad2" (good accuracy)
-
-**Hint:** You can adjust the model to return "no answer possible" with the no_ans_boost. Higher values mean the model prefers "no answer possible"
-
-#### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=False)
-```
-
-#### TransformersReader
-
-
-```python
-# Alternative:
-# reader = TransformersReader(model_name_or_path="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1)
-```
-
-### Finder
-
-The Finder sticks together reader and retriever in a pipeline to answer our actual questions.
-
-
-```python
-finder = Finder(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = finder.get_answers(question="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-# prediction = finder.get_answers(question="Who created the Dothraki vocabulary?", top_k_reader=5)
-# prediction = finder.get_answers(question="Who is the sister of Sansa?", top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/tutorials/tutorials/2.md b/src/pages/docs/versions/master/v0.5.0/site/en/tutorials/tutorials/2.md
deleted file mode 100644
index 2662f38be..000000000
--- a/src/pages/docs/versions/master/v0.5.0/site/en/tutorials/tutorials/2.md
+++ /dev/null
@@ -1,76 +0,0 @@
----
-title: "Tutorial 2"
-metaTitle: "Fine-tuning a model on your own data"
-metaDescription: ""
-slug: "/docs/tutorial2"
-date: "2020-09-03"
-id: "tutorial2md"
----
-
-# Fine-tuning a Model on Your Own Data
-
-EXECUTABLE VERSION: [*colab*](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial2_Finetune_a_model_on_your_data.ipynb)
-
-For many use cases it is sufficient to just use one of the existing public models that were trained on SQuAD or other public QA datasets (e.g. Natural Questions).
-However, if you have domain-specific questions, fine-tuning your model on custom examples will very likely boost your performance.
-While this varies by domain, we saw that ~ 2000 examples can easily increase performance by +5-20%.
-
-This tutorial shows you how to fine-tune a pretrained model on your own dataset.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack and install the version of torch that works with the colab GPUs
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install torch==1.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-```
-
-
-```python
-from haystack.reader.farm import FARMReader
-```
-
-
-## Create Training Data
-
-There are two ways to generate training data
-
-1. **Annotation**: You can use the [annotation tool](https://github.com/deepset-ai/haystack#labeling-tool) to label your data, i.e. highlighting answers to your questions in a document. The tool supports structuring your workflow with organizations, projects, and users. The labels can be exported in SQuAD format that is compatible for training with Haystack.
-
-
-
-2. **Feedback**: For production systems, you can collect training data from direct user feedback via Haystack's [REST API interface](https://github.com/deepset-ai/haystack#rest-api). This includes a customizable user feedback API for providing feedback on the answer returned by the API. The API provides a feedback export endpoint to obtain the feedback data for fine-tuning your model further.
-
-
-## Fine-tune your model
-
-Once you have collected training data, you can fine-tune your base models.
-We initialize a reader as a base model and fine-tune it on our own custom dataset (should be in SQuAD-like format).
-We recommend using a base model that was trained on SQuAD or a similar QA dataset before to benefit from Transfer Learning effects.
-
-**Recommendation**: Run training on a GPU.
-If you are using Colab: Enable this in the menu "Runtime" > "Change Runtime type" > Select "GPU" in dropdown.
-Then change the `use_gpu` arguments below to `True`
-
-
-```python
-reader = FARMReader(model_name_or_path="distilbert-base-uncased-distilled-squad", use_gpu=True)
-train_data = "data/squad20"
-# train_data = "PATH/TO_YOUR/TRAIN_DATA"
-reader.train(data_dir=train_data, train_filename="dev-v2.0.json", use_gpu=True, n_epochs=1, save_dir="my_model")
-```
-
-
-```python
-# Saving the model happens automatically at the end of training into the `save_dir` you specified
-# However, you could also save a reader manually again via:
-reader.save(directory="my_model")
-```
-
-
-```python
-# If you want to load it at a later point, just do:
-new_reader = FARMReader(model_name_or_path="my_model")
-```
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/tutorials/tutorials/3.md b/src/pages/docs/versions/master/v0.5.0/site/en/tutorials/tutorials/3.md
deleted file mode 100644
index 72bcbb544..000000000
--- a/src/pages/docs/versions/master/v0.5.0/site/en/tutorials/tutorials/3.md
+++ /dev/null
@@ -1,167 +0,0 @@
----
-title: "Tutorial 3"
-metaTitle: "Build a QA System Without Elasticsearch"
-metaDescription: ""
-slug: "/docs/tutorial3"
-date: "2020-09-03"
-id: "tutorial3md"
----
-
-# Build a QA System Without Elasticsearch
-
-EXECUTABLE VERSION: [*colab*](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb)
-
-Haystack provides alternatives to Elasticsearch for developing quick prototypes.
-
-You can use an `InMemoryDocumentStore` or a `SQLDocumentStore`(with SQLite) as the document store.
-
-If you are interested in more feature-rich Elasticsearch, then please refer to the Tutorial 1.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack and install the version of torch that works with the colab GPUs
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install torch==1.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-
-
-```python
-# In-Memory Document Store
-from haystack.document_store.memory import InMemoryDocumentStore
-document_store = InMemoryDocumentStore()
-```
-
-
-```python
-# SQLite Document Store
-# from haystack.document_store.sql import SQLDocumentStore
-# document_store = SQLDocumentStore(url="sqlite:///qa.db")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles on Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first get some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# convert files to dicts containing documents that can be indexed to our datastore
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is: {"name": ", "text": ""}
-
-# Let's have a look at the first 3 entries:
-print(dicts[:3])
-# Now, let's write the docs to our DB.
-document_store.write_documents(dicts)
-```
-
-## Initalize Retriever, Reader, & Finder
-
-### Retriever
-
-Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question could be answered.
-
-With InMemoryDocumentStore or SQLDocumentStore, you can use the TfidfRetriever. For more retrievers, please refer to the tutorial-1.
-
-
-```python
-# An in-memory TfidfRetriever based on Pandas dataframes
-
-from haystack.retriever.sparse import TfidfRetriever
-retriever = TfidfRetriever(document_store=document_store)
-```
-
-### Reader
-
-A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based
-on powerful, but slower deep learning models.
-
-Haystack currently supports Readers based on the frameworks FARM and Transformers.
-With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).
-
-**Here:** a medium sized RoBERTa QA model using a Reader based on FARM (https://huggingface.co/deepset/roberta-base-squad2)
-
-**Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)
-
-**Alternatives (Models):** e.g. "distilbert-base-uncased-distilled-squad" (fast) or "deepset/bert-large-uncased-whole-word-masking-squad2" (good accuracy)
-
-**Hint:** You can adjust the model to return "no answer possible" with the no_ans_boost. Higher values mean the model prefers "no answer possible"
-
-#### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=False)
-```
-
-#### TransformersReader
-
-
-```python
-# Alternative:
-# reader = TransformersReader(model_name_or_path="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1)
-```
-
-### Finder
-
-The Finder sticks together reader and retriever in a pipeline to answer our actual questions.
-
-
-```python
-finder = Finder(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = finder.get_answers(question="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-# prediction = finder.get_answers(question="Who created the Dothraki vocabulary?", top_k_reader=5)
-# prediction = finder.get_answers(question="Who is the sister of Sansa?", top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/tutorials/tutorials/4.md b/src/pages/docs/versions/master/v0.5.0/site/en/tutorials/tutorials/4.md
deleted file mode 100644
index 9923e0b69..000000000
--- a/src/pages/docs/versions/master/v0.5.0/site/en/tutorials/tutorials/4.md
+++ /dev/null
@@ -1,140 +0,0 @@
----
-title: "Tutorial 4"
-metaTitle: "Utilizing existing FAQs for Question Answering"
-metaDescription: ""
-slug: "/docs/tutorial4"
-date: "2020-09-03"
-id: "tutorial4md"
----
-
-# Utilizing existing FAQs for Question Answering
-
-EXECUTABLE VERSION: [colab](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial4_Tutorial4_FAQ_style_QA.ipynb)
-
-While *extractive Question Answering* works on pure texts and is therefore more generalizable, there's also a common alternative that utilizes existing FAQ data.
-
-**Pros**:
-
-- Very fast at inference time
-- Utilize existing FAQ data
-- Quite good control over answers
-
-**Cons**:
-
-- Generalizability: We can only answer questions that are similar to existing ones in FAQ
-
-In some use cases, a combination of extractive QA and FAQ-style can also be an interesting option.
-
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack and install the version of torch that works with the colab GPUs
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install torch==1.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-```
-
-
-```python
-from haystack import Finder
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-from haystack.retriever.dense import EmbeddingRetriever
-from haystack.utils import print_answers
-import pandas as pd
-import requests
-
-```
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker
-# ! docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.6.2
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.6.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.6.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.6.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.6.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-
-```
-
-### Init the DocumentStore
-In contrast to Tutorial 1 (extractive QA), we:
-
-* specify the name of our `text_field` in Elasticsearch that we want to return as an answer
-* specify the name of our `embedding_field` in Elasticsearch where we'll store the embedding of our question and that is used later for calculating our similarity to the incoming user question
-* set `excluded_meta_data=["question_emb"]` so that we don't return the huge embedding vectors in our search results
-
-
-```python
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="",
- index="document",
- embedding_field="question_emb",
- embedding_dim=768,
- excluded_meta_data=["question_emb"])
-```
-
-### Create a Retriever using embeddings
-Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones).
-We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings.
-
-
-```python
-retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert", use_gpu=False)
-```
-
-### Prepare & Index FAQ data
-We create a pandas dataframe containing some FAQ data (i.e curated pairs of question + answer) and index those in elasticsearch.
-Here: We download some question-answer pairs related to COVID-19
-
-
-```python
-# Download
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/faqs/faq_covidbert.csv")
-open('small_faq_covid.csv', 'wb').write(temp.content)
-
-# Get dataframe with columns "question", "answer" and some custom metadata
-df = pd.read_csv("small_faq_covid.csv")
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-df["question"] = df["question"].apply(lambda x: x.strip())
-print(df.head())
-
-# Get embeddings for our questions from the FAQs
-questions = list(df["question"].values)
-df["question_emb"] = retriever.embed_queries(texts=questions)
-df["question_emb"] = df["question_emb"].apply(list) # convert from numpy to list for ES ingestion
-df = df.rename(columns={"answer": "text"})
-
-# Convert Dataframe to list of dicts and index them in our DocumentStore
-docs_to_index = df.to_dict(orient="records")
-document_store.write_documents(docs_to_index)
-```
-
-### Ask questions
-Initialize a Finder (this time without a reader) and ask questions
-
-
-```python
-finder = Finder(reader=None, retriever=retriever)
-prediction = finder.get_answers_via_similar_questions(question="How is the virus spreading?", top_k_retriever=10)
-print_answers(prediction, details="all")
-```
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/tutorials/tutorials/5.md b/src/pages/docs/versions/master/v0.5.0/site/en/tutorials/tutorials/5.md
deleted file mode 100644
index b32fb6bc0..000000000
--- a/src/pages/docs/versions/master/v0.5.0/site/en/tutorials/tutorials/5.md
+++ /dev/null
@@ -1,172 +0,0 @@
----
-title: "Tutorial 5"
-metaTitle: "Evaluation of a QA System"
-metaDescription: ""
-slug: "/docs/tutorial5"
-date: "2020-09-03"
-id: "tutorial5md"
----
-
-# Evaluation of a QA System
-
-EXECUTABLE VERSION: [colab](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial5_Evaluation.ipynb)
-
-To be able to make a statement about the performance of a question-answering system, it is important to evalute it. Furthermore, evaluation allows to determine which parts of the system can be improved.
-
-## Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack and install the version of torch that works with the colab GPUs
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install torch==1.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.6.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.6.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.6.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.6.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-from farm.utils import initialize_device_settings
-
-device, n_gpu = initialize_device_settings(use_cuda=True)
-```
-
-
-```python
-from haystack.preprocessor.utils import fetch_archive_from_http
-
-# Download evaluation data, which is a subset of Natural Questions development set containing 50 documents
-doc_dir = "../data/nq"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/nq_dev_subset_v2.json.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-
-```python
-# make sure these indices do not collide with existing ones, the indices will be wiped clean before data is inserted
-doc_index = "tutorial5_docs"
-label_index = "tutorial5_labels"
-```
-
-
-```python
-# Connect to Elasticsearch
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-# Connect to Elasticsearch
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document",
- create_index=False, embedding_field="emb",
- embedding_dim=768, excluded_meta_data=["emb"])
-```
-
-
-```python
-# Add evaluation data to Elasticsearch Document Store
-# We first delete the custom tutorial indices to not have duplicate elements
-document_store.delete_all_documents(index=doc_index)
-document_store.delete_all_documents(index=label_index)
-document_store.add_eval_data(filename="../data/nq/nq_dev_subset_v2.json", doc_index=doc_index, label_index=label_index)
-```
-
-## Initialize components of QA-System
-
-
-```python
-# Initialize Retriever
-from haystack.retriever.sparse import ElasticsearchRetriever
-retriever = ElasticsearchRetriever(document_store=document_store)
-# Alternative: Evaluate DensePassageRetriever
-# Note, that DPR works best when you index short passages < 512 tokens as only those tokens will be used for the embedding.
-# Here, for nq_dev_subset_v2.json we have avg. num of tokens = 5220(!).
-# DPR still outperforms Elastic's BM25 by a small margin here.
-# from haystack.retriever.dense import DensePassageRetriever
-# retriever = DensePassageRetriever(document_store=document_store,
-# query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
-# passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
-# use_gpu=True,
-# embed_title=True,
-# max_seq_len=256,
-# batch_size=16,
-# remove_sep_tok_from_untitled_passages=True)
-#document_store.update_embeddings(retriever, index=doc_index)
-```
-
-
-```python
-# Initialize Reader
-from haystack.reader.farm import FARMReader
-
-reader = FARMReader("deepset/roberta-base-squad2", top_k_per_candidate=4)
-```
-
-
-```python
-# Initialize Finder which sticks together Reader and Retriever
-from haystack.finder import Finder
-
-finder = Finder(reader, retriever)
-```
-
-## Evaluation of Retriever
-
-
-```python
-## Evaluate Retriever on its own
-retriever_eval_results = retriever.eval(top_k=20, label_index=label_index, doc_index=doc_index)
-## Retriever Recall is the proportion of questions for which the correct document containing the answer is
-## among the correct documents
-print("Retriever Recall:", retriever_eval_results["recall"])
-## Retriever Mean Avg Precision rewards retrievers that give relevant documents a higher rank
-print("Retriever Mean Avg Precision:", retriever_eval_results["map"])
-```
-
-## Evaluation of Reader
-
-
-```python
-# Evaluate Reader on its own
-reader_eval_results = reader.eval(document_store=document_store, device=device, label_index=label_index, doc_index=doc_index)
-# Evaluation of Reader can also be done directly on a SQuAD-formatted file without passing the data to Elasticsearch
-#reader_eval_results = reader.eval_on_file("../data/nq", "nq_dev_subset_v2.json", device=device)
-
-## Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer
-print("Reader Top-N-Accuracy:", reader_eval_results["top_n_accuracy"])
-## Reader Exact Match is the proportion of questions where the predicted answer is exactly the same as the correct answer
-print("Reader Exact Match:", reader_eval_results["EM"])
-## Reader F1-Score is the average overlap between the predicted answers and the correct answers
-print("Reader F1-Score:", reader_eval_results["f1"])
-```
-
-## Evaluation of Finder
-
-
-```python
-# Evaluate combination of Reader and Retriever through Finder
-# Evaluate combination of Reader and Retriever through Finder
-finder_eval_results = finder.eval(top_k_retriever=1, top_k_reader=10, label_index=label_index, doc_index=doc_index)
-finder.print_eval_results(finder_eval_results)
-```
-
-
-```python
-
-```
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/tutorials/tutorials/6.md b/src/pages/docs/versions/master/v0.5.0/site/en/tutorials/tutorials/6.md
deleted file mode 100644
index ccd88e261..000000000
--- a/src/pages/docs/versions/master/v0.5.0/site/en/tutorials/tutorials/6.md
+++ /dev/null
@@ -1,205 +0,0 @@
----
-title: "Tutorial 6"
-metaTitle: "Better retrieval via Dense Passage Retrieval"
-metaDescription: ""
-slug: "/docs/tutorial6"
-date: "2020-09-03"
-id: "tutorial6md"
----
-
-# Better Retrieval via "Dense Passage Retrieval"
-
-EXECUTABLE VERSION: [colab](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb)
-
-### Importance of Retrievers
-
-The Retriever has a huge impact on the performance of our overall search pipeline.
-
-
-### Different types of Retrievers
-#### Sparse
-Family of algorithms based on counting the occurrences of words (bag-of-words) resulting in very sparse vectors with length = vocab size.
-
-**Examples**: BM25, TF-IDF
-
-**Pros**: Simple, fast, well explainable
-
-**Cons**: Relies on exact keyword matches between query and text
-
-
-#### Dense
-These retrievers use neural network models to create "dense" embedding vectors. Within this family there are two different approaches:
-
-a) Single encoder: Use a **single model** to embed both query and passage.
-b) Dual-encoder: Use **two models**, one to embed the query and one to embed the passage
-
-Recent work suggests that dual encoders work better, likely because they can deal better with the different nature of query and passage (length, style, syntax ...).
-
-**Examples**: REALM, DPR, Sentence-Transformers
-
-**Pros**: Captures semantinc similarity instead of "word matches" (e.g. synonyms, related topics ...)
-
-**Cons**: Computationally more heavy, initial training of model
-
-
-### "Dense Passage Retrieval"
-
-In this Tutorial, we want to highlight one "Dense Dual-Encoder" called Dense Passage Retriever.
-It was introdoced by Karpukhin et al. (2020, https://arxiv.org/abs/2004.04906.
-
-Original Abstract:
-
-_"Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks."_
-
-Paper: https://arxiv.org/abs/2004.04906
-Original Code: https://fburl.com/qa-dpr
-
-
-*Use this* [link](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb) *to open the notebook in Google Colab.*
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack and install the version of torch that works with the colab GPUs
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install torch==1.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-### Document Store
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-
-
-```python
-from haystack.document_store.faiss import FAISSDocumentStore
-
-document_store = FAISSDocumentStore()
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever, Reader, & Finder
-
-#### Retriever
-
-**Here:** We use a `DensePassageRetriever`
-
-**Alternatives:**
-
-- The `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-
-
-```python
-from haystack.retriever.dense import DensePassageRetriever
-retriever = DensePassageRetriever(document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- use_gpu=True,
- embed_title=True,
- max_seq_len=256,
- batch_size=16,
- remove_sep_tok_from_untitled_passages=True)
-# Important:
-# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all
-# previously indexed documents and update their embedding representation.
-# While this can be a time consuming operation (depending on corpus size), it only needs to be done once.
-# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.
-document_store.update_embeddings(retriever)
-```
-
-#### Reader
-
-Similar to previous Tutorials we now initalize our reader.
-
-Here we use a FARMReader with the *deepset/roberta-base-squad2* model (see: https://huggingface.co/deepset/roberta-base-squad2)
-
-
-
-##### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-#### Finder
-
-The Finder sticks together reader and retriever in a pipeline to answer our actual questions.
-
-
-```python
-finder = Finder(reader, retriever)
-```
-
-### Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = finder.get_answers(question="Who created the Dothraki vocabulary?", top_k_retriever=10, top_k_reader=5)
-
-#prediction = finder.get_answers(question="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-#prediction = finder.get_answers(question="Who is the sister of Sansa?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
-
-
-```python
-
-```
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/reader.md b/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/reader.md
deleted file mode 100644
index fb6812e4f..000000000
--- a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/reader.md
+++ /dev/null
@@ -1,316 +0,0 @@
----
-title: "Reader"
-metaTitle: "Reader"
-metaDescription: ""
-slug: "/docs/reader"
-date: "2020-09-03"
-id: "readermd"
----
-
-# Reader
-
-The Reader, also known as Open-Domain QA systems in Machine Learning speak,
-is the core component that enables Haystack to find the answers that you need.
-Haystack’s Readers are:
-
-
-* built on the latest transformer based language models
-
-
-* strong in their grasp of semantics
-
-
-* sensitive to syntactic structure
-
-
-* state-of-the-art in QA tasks like SQuAD and Natural Questions
-
-"}.
-Optionally: Include meta data via {"text": "",
-"meta":{"name": ", "author": "somebody", ...}}
-It can be used for filtering and is accessible in the responses of the Finder.
-Advanced: If you are using your own Elasticsearch mapping, the key names in the dictionary
-should be changed to what you have set for self.text_field and self.name_field.
-- `index`: Elasticsearch index where the documents should be indexed. If not supplied, self.index will be used.
-
-**Returns**:
-
-None
-
-
-#### write\_labels
-
-```python
- | write_labels(labels: Union[List[Label], List[dict]], index: Optional[str] = None)
-```
-
-Write annotation labels into document store.
-
-
-#### update\_document\_meta
-
-```python
- | update_document_meta(id: str, meta: Dict[str, str])
-```
-
-Update the metadata dictionary of a document by specifying its string id
-
-
-#### get\_document\_count
-
-```python
- | get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
-```
-
-Return the number of documents in the document store.
-
-
-#### get\_label\_count
-
-```python
- | get_label_count(index: Optional[str] = None) -> int
-```
-
-Return the number of labels in the document store
-
-
-#### get\_all\_documents
-
-```python
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Get documents from the document store.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
-DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
-Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-
-
-#### get\_all\_labels
-
-```python
- | get_all_labels(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None) -> List[Label]
-```
-
-Return all labels in the document store
-
-
-#### get\_all\_documents\_in\_index
-
-```python
- | get_all_documents_in_index(index: str, filters: Optional[Dict[str, List[str]]] = None) -> List[dict]
-```
-
-Return all documents in a specific index in the document store
-
-
-#### query
-
-```python
- | query(query: Optional[str], filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, custom_query: Optional[str] = None, index: Optional[str] = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query as defined by the BM25 algorithm.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.array, filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
-Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: Index name for storing the docs and metadata
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### describe\_documents
-
-```python
- | describe_documents(index=None)
-```
-
-Return a summary of the documents in the document store
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever
-- `index`: Index name to update
-
-**Returns**:
-
-None
-
-
-#### add\_eval\_data
-
-```python
- | add_eval_data(filename: str, doc_index: str = "eval_document", label_index: str = "label")
-```
-
-Adds a SQuAD-formatted file to the DocumentStore in order to be able to perform evaluation on it.
-
-**Arguments**:
-
-- `filename`: Name of the file containing evaluation data
-:type filename: str
-- `doc_index`: Elasticsearch index where evaluation documents should be stored
-:type doc_index: str
-- `label_index`: Elasticsearch index where labeled questions should be stored
-:type label_index: str
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: str, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-## Class: OpenDistroElasticsearchDocumentStore
-
-```python
-class OpenDistroElasticsearchDocumentStore(ElasticsearchDocumentStore)
-```
-
-Document Store using the Open Distro for Elasticsearch. It is compatible with the AWS Elasticsearch Service.
-
-In addition to native Elasticsearch query & filtering, it provides efficient vector similarity search using
-the KNN plugin that can scale to a large number of documents.
-
-
-# Module: memory
-
-
-## Class: InMemoryDocumentStore
-
-```python
-class InMemoryDocumentStore(BaseDocumentStore)
-```
-
-In-memory document store
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(embedding_field: Optional[str] = "embedding", return_embedding: bool = False, similarity="dot_product")
-```
-
-**Arguments**:
-
-- `embedding_field`: Name of field containing an embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `return_embedding`: To return document embedding
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
-more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
-For documents as dictionaries, the format is {"text": ""}.
-Optionally: Include meta data via {"text": "",
-"meta": {"name": ", "author": "somebody", ...}}
-It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: write documents to a custom namespace. For instance, documents for evaluation can be indexed in a
-separate index than the documents for search.
-
-**Returns**:
-
-None
-
-
-#### write\_labels
-
-```python
- | write_labels(labels: Union[List[dict], List[Label]], index: Optional[str] = None)
-```
-
-Write annotation labels into document store.
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index: Optional[str] = None) -> List[Document]
-```
-
-Fetch documents by specifying a list of text id strings
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: List[float], filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
-Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: Index name for storing the docs and metadata
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever
-- `index`: Index name to update
-
-**Returns**:
-
-None
-
-
-#### get\_document\_count
-
-```python
- | get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
-```
-
-Return the number of documents in the document store.
-
-
-#### get\_label\_count
-
-```python
- | get_label_count(index: Optional[str] = None) -> int
-```
-
-Return the number of labels in the document store
-
-
-#### get\_all\_documents
-
-```python
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Get documents from the document store.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
-DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
-Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-
-
-#### get\_all\_labels
-
-```python
- | get_all_labels(index: str = None, filters: Optional[Dict[str, List[str]]] = None) -> List[Label]
-```
-
-Return all labels in the document store
-
-
-#### add\_eval\_data
-
-```python
- | add_eval_data(filename: str, doc_index: Optional[str] = None, label_index: Optional[str] = None)
-```
-
-Adds a SQuAD-formatted file to the DocumentStore in order to be able to perform evaluation on it.
-
-**Arguments**:
-
-- `filename`: Name of the file containing evaluation data
-:type filename: str
-- `doc_index`: Elasticsearch index where evaluation documents should be stored
-:type doc_index: str
-- `label_index`: Elasticsearch index where labeled questions should be stored
-:type label_index: str
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-# Module: sql
-
-
-## Class: SQLDocumentStore
-
-```python
-class SQLDocumentStore(BaseDocumentStore)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(url: str = "sqlite://", index: str = "document", label_index: str = "label", update_existing_documents: bool = False)
-```
-
-An SQL backed DocumentStore. Currently supports SQLite, PostgreSQL and MySQL backends.
-
-**Arguments**:
-
-- `url`: URL for SQL database as expected by SQLAlchemy. More info here: https://docs.sqlalchemy.org/en/13/core/engines.html#database-urls
-- `index`: The documents are scoped to an index attribute that can be used when writing, querying, or deleting documents.
-This parameter sets the default value for document index.
-- `label_index`: The default value of index attribute for the labels.
-- `update_existing_documents`: Whether to update any existing documents with the same ID when adding
-documents. When set as True, any document with an existing ID gets updated.
-If set to False, an error is raised if the document ID of the document being
-added already exists. Using this parameter coud cause performance degradation for document insertion.
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index: Optional[str] = None) -> List[Document]
-```
-
-Fetch documents by specifying a list of text id strings
-
-
-#### get\_documents\_by\_vector\_ids
-
-```python
- | get_documents_by_vector_ids(vector_ids: List[str], index: Optional[str] = None)
-```
-
-Fetch documents by specifying a list of text vector id strings
-
-
-#### get\_all\_documents
-
-```python
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Get documents from the document store.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
-DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
-Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-
-
-#### get\_all\_labels
-
-```python
- | get_all_labels(index=None, filters: Optional[dict] = None)
-```
-
-Return all labels in the document store
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
-For documents as dictionaries, the format is {"text": ""}.
-Optionally: Include meta data via {"text": "",
-"meta":{"name": ", "author": "somebody", ...}}
-It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: add an optional index attribute to documents. It can be later used for filtering. For instance,
-documents for evaluation can be indexed in a separate index than the documents for search.
-
-**Returns**:
-
-None
-
-
-#### write\_labels
-
-```python
- | write_labels(labels, index=None)
-```
-
-Write annotation labels into document store.
-
-
-#### update\_vector\_ids
-
-```python
- | update_vector_ids(vector_id_map: Dict[str, str], index: Optional[str] = None)
-```
-
-Update vector_ids for given document_ids.
-
-**Arguments**:
-
-- `vector_id_map`: dict containing mapping of document_id -> vector_id.
-- `index`: filter documents by the optional index attribute for documents in database.
-
-
-#### update\_document\_meta
-
-```python
- | update_document_meta(id: str, meta: Dict[str, str])
-```
-
-Update the metadata dictionary of a document by specifying its string id
-
-
-#### add\_eval\_data
-
-```python
- | add_eval_data(filename: str, doc_index: str = "eval_document", label_index: str = "label")
-```
-
-Adds a SQuAD-formatted file to the DocumentStore in order to be able to perform evaluation on it.
-
-**Arguments**:
-
-- `filename`: Name of the file containing evaluation data
-:type filename: str
-- `doc_index`: Elasticsearch index where evaluation documents should be stored
-:type doc_index: str
-- `label_index`: Elasticsearch index where labeled questions should be stored
-:type label_index: str
-
-
-#### get\_document\_count
-
-```python
- | get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
-```
-
-Return the number of documents in the document store.
-
-
-#### get\_label\_count
-
-```python
- | get_label_count(index: Optional[str] = None) -> int
-```
-
-Return the number of labels in the document store
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-# Module: base
-
-
-## Class: BaseDocumentStore
-
-```python
-class BaseDocumentStore(ABC)
-```
-
-Base class for implementing Document Stores.
-
-
-#### write\_documents
-
-```python
- | @abstractmethod
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
-For documents as dictionaries, the format is {"text": ""}.
-Optionally: Include meta data via {"text": "",
-"meta":{"name": ", "author": "somebody", ...}}
-It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: Optional name of index where the documents shall be written to.
-If None, the DocumentStore's default index (self.index) will be used.
-
-**Returns**:
-
-None
-
-
-#### get\_all\_documents
-
-```python
- | @abstractmethod
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Get documents from the document store.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
-DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
-Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-
-
-# Module: faiss
-
-
-## Class: FAISSDocumentStore
-
-```python
-class FAISSDocumentStore(SQLDocumentStore)
-```
-
-Document store for very large scale embedding based dense retrievers like the DPR.
-
-It implements the FAISS library(https://github.com/facebookresearch/faiss)
-to perform similarity search on vectors.
-
-The document text and meta-data (for filtering) are stored using the SQLDocumentStore, while
-the vector embeddings are indexed in a FAISS Index.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(sql_url: str = "sqlite:///", index_buffer_size: int = 10_000, vector_dim: int = 768, faiss_index_factory_str: str = "Flat", faiss_index: Optional[faiss.swigfaiss.Index] = None, return_embedding: bool = False, update_existing_documents: bool = False, index: str = "document", similarity: str = "dot_product", **kwargs, ,)
-```
-
-**Arguments**:
-
-- `sql_url`: SQL connection URL for database. It defaults to local file based SQLite DB. For large scale
-deployment, Postgres is recommended.
-- `index_buffer_size`: When working with large datasets, the ingestion process(FAISS + SQL) can be buffered in
-smaller chunks to reduce memory footprint.
-- `vector_dim`: the embedding vector size.
-- `faiss_index_factory_str`: Create a new FAISS index of the specified type.
-The type is determined from the given string following the conventions
-of the original FAISS index factory.
-Recommended options:
-- "Flat" (default): Best accuracy (= exact). Becomes slow and RAM intense for > 1 Mio docs.
-- "HNSW": Graph-based heuristic. If not further specified,
-we use a RAM intense, but more accurate config:
-HNSW256, efConstruction=256 and efSearch=256
-- "IVFx,Flat": Inverted Index. Replace x with the number of centroids aka nlist.
-Rule of thumb: nlist = 10 * sqrt (num_docs) is a good starting point.
-For more details see:
-- Overview of indices https://github.com/facebookresearch/faiss/wiki/Faiss-indexes
-- Guideline for choosing an index https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-- FAISS Index factory https://github.com/facebookresearch/faiss/wiki/The-index-factory
-Benchmarks: XXX
-- `faiss_index`: Pass an existing FAISS Index, i.e. an empty one that you configured manually
-or one with docs that you used in Haystack before and want to load again.
-- `return_embedding`: To return document embedding
-- `update_existing_documents`: Whether to update any existing documents with the same ID when adding
-documents. When set as True, any document with an existing ID gets updated.
-If set to False, an error is raised if the document ID of the document being
-added already exists.
-- `index`: Name of index in document store to use.
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
-more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
-```
-
-Add new documents to the DocumentStore.
-
-**Arguments**:
-
-- `documents`: List of `Dicts` or List of `Documents`. If they already contain the embeddings, we'll index
-them right away in FAISS. If not, you can later call update_embeddings() to create & index them.
-- `index`: (SQL) index name for storing the docs and metadata
-
-**Returns**:
-
-
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever to use to get embeddings for text
-- `index`: (SQL) index name for storing the docs and metadata
-
-**Returns**:
-
-None
-
-
-#### get\_all\_documents
-
-```python
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Get documents from the document store.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
-DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
-Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-
-
-#### train\_index
-
-```python
- | train_index(documents: Optional[Union[List[dict], List[Document]]], embeddings: Optional[np.array] = None)
-```
-
-Some FAISS indices (e.g. IVF) require initial "training" on a sample of vectors before you can add your final vectors.
-The train vectors should come from the same distribution as your final ones.
-You can pass either documents (incl. embeddings) or just the plain embeddings that the index shall be trained on.
-
-**Arguments**:
-
-- `documents`: Documents (incl. the embeddings)
-- `embeddings`: Plain embeddings
-
-**Returns**:
-
-None
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index=None)
-```
-
-Delete all documents from the document store.
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.array, filters: Optional[dict] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
-Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: (SQL) index name for storing the docs and metadata
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### save
-
-```python
- | save(file_path: Union[str, Path])
-```
-
-Save FAISS Index to the specified file.
-
-**Arguments**:
-
-- `file_path`: Path to save to.
-
-**Returns**:
-
-None
-
-
-#### load
-
-```python
- | @classmethod
- | load(cls, faiss_file_path: Union[str, Path], sql_url: str, index_buffer_size: int = 10_000)
-```
-
-Load a saved FAISS index from a file and connect to the SQL database.
-Note: In order to have a correct mapping from FAISS to SQL,
-make sure to use the same SQL DB that you used when calling `save()`.
-
-**Arguments**:
-
-- `faiss_file_path`: Stored FAISS index file. Can be created via calling `save()`
-- `sql_url`: Connection string to the SQL database that contains your docs and metadata.
-- `index_buffer_size`: When working with large datasets, the ingestion process(FAISS + SQL) can be buffered in
-smaller chunks to reduce memory footprint.
-
-**Returns**:
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/api/api/file_converter.md b/src/pages/docs/versions/master/v0.6.0/site/en/api/api/file_converter.md
deleted file mode 100644
index 44633266d..000000000
--- a/src/pages/docs/versions/master/v0.6.0/site/en/api/api/file_converter.md
+++ /dev/null
@@ -1,232 +0,0 @@
----
-title: "File Converters"
-metaTitle: "File Converters"
-metaDescription: ""
-slug: "/docs/file_converters"
-date: "2020-09-03"
-id: "file_convertersmd"
----
-
-# File Converters
-
-
-# Module: txt
-
-
-## Class: TextConverter
-
-```python
-class TextConverter(BaseConverter)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(remove_numeric_tables: Optional[bool] = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
-The tabular structures in documents might be noise for the reader model if it
-does not have table parsing capability for finding answers. However, tables
-may also have long strings that could possible candidate for searching answers.
-The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
-(https://en.wikipedia.org/wiki/ISO_639-1) format.
-This option can be used to add test for encoding errors. If the extracted text is
-not one of the valid languages, then it might likely be encoding error resulting
-in garbled text.
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None, encoding: str = "utf-8") -> Dict[str, Any]
-```
-
-Reads text from a txt file and executes optional preprocessing steps.
-
-**Arguments**:
-
-- `file_path`: Path of the file to convert
-- `meta`: Optional meta data that should be associated with the the document (e.g. name)
-- `encoding`: Encoding of the file
-
-**Returns**:
-
-Dict of format {"text": "The text from file", "meta": meta}}
-
-
-# Module: docx
-
-
-## Class: DocxToTextConverter
-
-```python
-class DocxToTextConverter(BaseConverter)
-```
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None) -> Dict[str, Any]
-```
-
-Extract text from a .docx file.
-Note: As docx doesn't contain "page" information, we actually extract and return a list of paragraphs here.
-For compliance with other converters we nevertheless opted for keeping the methods name.
-
-**Arguments**:
-
-- `file_path`: Path to the .docx file you want to convert
-
-
-# Module: tika
-
-
-## Class: TikaConverter
-
-```python
-class TikaConverter(BaseConverter)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(tika_url: str = "http://localhost:9998/tika", remove_numeric_tables: Optional[bool] = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `tika_url`: URL of the Tika server
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
-The tabular structures in documents might be noise for the reader model if it
-does not have table parsing capability for finding answers. However, tables
-may also have long strings that could possible candidate for searching answers.
-The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
-(https://en.wikipedia.org/wiki/ISO_639-1) format.
-This option can be used to add test for encoding errors. If the extracted text is
-not one of the valid languages, then it might likely be encoding error resulting
-in garbled text.
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None) -> Dict[str, Any]
-```
-
-**Arguments**:
-
-- `file_path`: Path of file to be converted.
-
-**Returns**:
-
-a list of pages and the extracted meta data of the file.
-
-
-# Module: base
-
-
-## Class: BaseConverter
-
-```python
-class BaseConverter()
-```
-
-Base class for implementing file converts to transform input documents to text format for ingestion in DocumentStore.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
-The tabular structures in documents might be noise for the reader model if it
-does not have table parsing capability for finding answers. However, tables
-may also have long strings that could possible candidate for searching answers.
-The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
-(https://en.wikipedia.org/wiki/ISO_639-1) format.
-This option can be used to add test for encoding errors. If the extracted text is
-not one of the valid languages, then it might likely be encoding error resulting
-in garbled text.
-
-
-#### convert
-
-```python
- | @abstractmethod
- | convert(file_path: Path, meta: Optional[Dict[str, str]]) -> Dict[str, Any]
-```
-
-Convert a file to a dictionary containing the text and any associated meta data.
-
-File converters may extract file meta like name or size. In addition to it, user
-supplied meta data like author, url, external IDs can be supplied as a dictionary.
-
-**Arguments**:
-
-- `file_path`: path of the file to convert
-- `meta`: dictionary of meta data key-value pairs to append in the returned document.
-
-
-#### validate\_language
-
-```python
- | validate_language(text: str) -> bool
-```
-
-Validate if the language of the text is one of valid languages.
-
-
-# Module: pdf
-
-
-## Class: PDFToTextConverter
-
-```python
-class PDFToTextConverter(BaseConverter)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(remove_numeric_tables: Optional[bool] = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
-The tabular structures in documents might be noise for the reader model if it
-does not have table parsing capability for finding answers. However, tables
-may also have long strings that could possible candidate for searching answers.
-The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
-(https://en.wikipedia.org/wiki/ISO_639-1) format.
-This option can be used to add test for encoding errors. If the extracted text is
-not one of the valid languages, then it might likely be encoding error resulting
-in garbled text.
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None) -> Dict[str, Any]
-```
-
-Extract text from a .pdf file.
-
-**Arguments**:
-
-- `file_path`: Path to the .pdf file you want to convert
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/api/api/generator.md b/src/pages/docs/versions/master/v0.6.0/site/en/api/api/generator.md
deleted file mode 100644
index bd91c4bda..000000000
--- a/src/pages/docs/versions/master/v0.6.0/site/en/api/api/generator.md
+++ /dev/null
@@ -1,150 +0,0 @@
----
-title: "Generator"
-metaTitle: "Generator"
-metaDescription: ""
-slug: "/docs/apigeneratormd"
-date: "2020-09-03"
-id: "apigeneratormd"
----
-
-# Generator
-
-
-# Module: transformers
-
-
-## Class: RAGenerator
-
-```python
-class RAGenerator(BaseGenerator)
-```
-
-Implementation of Facebook's Retrieval-Augmented Generator (https://arxiv.org/abs/2005.11401) based on
-HuggingFace's transformers (https://huggingface.co/transformers/model_doc/rag.html).
-
-Instead of "finding" the answer within a document, these models **generate** the answer.
-In that sense, RAG follows a similar approach as GPT-3 but it comes with two huge advantages
-for real-world applications:
-a) it has a manageable model size
-b) the answer generation is conditioned on retrieved documents,
-i.e. the model can easily adjust to domain documents even after training has finished
-(in contrast: GPT-3 relies on the web data seen during training)
-
-**Example**
-
-```python
-| query = "who got the first nobel prize in physics?"
-|
-| # Retrieve related documents from retriever
-| retrieved_docs = retriever.retrieve(query=query)
-|
-| # Now generate answer from query and retrieved documents
-| generator.predict(
-| query=query,
-| documents=retrieved_docs,
-| top_k=1
-| )
-|
-| # Answer
-|
-| {'query': 'who got the first nobel prize in physics',
-| 'answers':
-| [{'query': 'who got the first nobel prize in physics',
-| 'answer': ' albert einstein',
-| 'meta': { 'doc_ids': [...],
-| 'doc_scores': [80.42758 ...],
-| 'doc_probabilities': [40.71379089355469, ...
-| 'texts': ['Albert Einstein was a ...]
-| 'titles': ['"Albert Einstein"', ...]
-| }}]}
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: str = "facebook/rag-token-nq", retriever: Optional[DensePassageRetriever] = None, generator_type: RAGeneratorType = RAGeneratorType.TOKEN, top_k_answers: int = 2, max_length: int = 200, min_length: int = 2, num_beams: int = 2, embed_title: bool = True, prefix: Optional[str] = None, use_gpu: bool = True)
-```
-
-Load a RAG model from Transformers along with passage_embedding_model.
-See https://huggingface.co/transformers/model_doc/rag.html for more details
-
-**Arguments**:
-
-- `model_name_or_path`: Directory of a saved model or the name of a public model e.g.
-'facebook/rag-token-nq', 'facebook/rag-sequence-nq'.
-See https://huggingface.co/models for full list of available models.
-- `retriever`: `DensePassageRetriever` used to embedded passage
-- `generator_type`: Which RAG generator implementation to use? RAG-TOKEN or RAG-SEQUENCE
-- `top_k_answers`: Number of independently generated text to return
-- `max_length`: Maximum length of generated text
-- `min_length`: Minimum length of generated text
-- `num_beams`: Number of beams for beam search. 1 means no beam search.
-- `embed_title`: Embedded the title of passage while generating embedding
-- `prefix`: The prefix used by the generator's tokenizer.
-- `use_gpu`: Whether to use GPU (if available)
-
-
-#### predict
-
-```python
- | predict(query: str, documents: List[Document], top_k: Optional[int] = None) -> Dict
-```
-
-Generate the answer to the input query. The generation will be conditioned on the supplied documents.
-These document can for example be retrieved via the Retriever.
-
-**Arguments**:
-
-- `query`: Query
-- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on.
-- `top_k`: Number of returned answers
-
-**Returns**:
-
-Generated answers plus additional infos in a dict like this:
-
-```python
-| {'query': 'who got the first nobel prize in physics',
-| 'answers':
-| [{'query': 'who got the first nobel prize in physics',
-| 'answer': ' albert einstein',
-| 'meta': { 'doc_ids': [...],
-| 'doc_scores': [80.42758 ...],
-| 'doc_probabilities': [40.71379089355469, ...
-| 'texts': ['Albert Einstein was a ...]
-| 'titles': ['"Albert Einstein"', ...]
-| }}]}
-```
-
-
-# Module: base
-
-
-## Class: BaseGenerator
-
-```python
-class BaseGenerator(ABC)
-```
-
-Abstract class for Generators
-
-
-#### predict
-
-```python
- | @abstractmethod
- | predict(query: str, documents: List[Document], top_k: Optional[int]) -> Dict
-```
-
-Abstract method to generate answers.
-
-**Arguments**:
-
-- `query`: Query
-- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on.
-- `top_k`: Number of returned answers
-
-**Returns**:
-
-Generated answers plus additional infos in a dict
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/api/api/pipelines.md b/src/pages/docs/versions/master/v0.6.0/site/en/api/api/pipelines.md
deleted file mode 100644
index e86f40f95..000000000
--- a/src/pages/docs/versions/master/v0.6.0/site/en/api/api/pipelines.md
+++ /dev/null
@@ -1,269 +0,0 @@
----
-title: "Pipelines"
-metaTitle: "Pipelines"
-metaDescription: ""
-slug: "/docs/apipipelinesmd"
-date: "2020-09-03"
-id: "apipipelinesmd"
----
-
-# Pipelines
-
-
-# Module: pipeline
-
-
-## Class: Pipeline
-
-```python
-class Pipeline()
-```
-
-Pipeline brings together building blocks to build a complex search pipeline with Haystack & user-defined components.
-
-Under-the-hood, a pipeline is represented as a directed acyclic graph of component nodes. It enables custom query
-flows with options to branch queries(eg, extractive qa vs keyword match query), merge candidate documents for a
-Reader from multiple Retrievers, or re-ranking of candidate documents.
-
-
-#### add\_node
-
-```python
- | add_node(component, name: str, inputs: List[str])
-```
-
-Add a new node to the pipeline.
-
-**Arguments**:
-
-- `component`: The object to be called when the data is passed to the node. It can be a Haystack component
-(like Retriever, Reader, or Generator) or a user-defined object that implements a run()
-method to process incoming data from predecessor node.
-- `name`: The name for the node. It must not contain any dots.
-- `inputs`: A list of inputs to the node. If the predecessor node has a single outgoing edge, just the name
-of node is sufficient. For instance, a 'ElasticsearchRetriever' node would always output a single
-edge with a list of documents. It can be represented as ["ElasticsearchRetriever"].
-
-In cases when the predecessor node has multiple outputs, e.g., a "QueryClassifier", the output
-must be specified explicitly as "QueryClassifier.output_2".
-
-
-#### get\_node
-
-```python
- | get_node(name: str)
-```
-
-Get a node from the Pipeline.
-
-**Arguments**:
-
-- `name`: The name of the node.
-
-
-#### set\_node
-
-```python
- | set_node(name: str, component)
-```
-
-Set the component for a node in the Pipeline.
-
-**Arguments**:
-
-- `name`: The name of the node.
-- `component`: The component object to be set at the node.
-
-
-#### draw
-
-```python
- | draw(path: Path = Path("pipeline.png"))
-```
-
-Create a Graphviz visualization of the pipeline.
-
-**Arguments**:
-
-- `path`: the path to save the image.
-
-
-## Class: BaseStandardPipeline
-
-```python
-class BaseStandardPipeline()
-```
-
-
-#### add\_node
-
-```python
- | add_node(component, name: str, inputs: List[str])
-```
-
-Add a new node to the pipeline.
-
-**Arguments**:
-
-- `component`: The object to be called when the data is passed to the node. It can be a Haystack component
-(like Retriever, Reader, or Generator) or a user-defined object that implements a run()
-method to process incoming data from predecessor node.
-- `name`: The name for the node. It must not contain any dots.
-- `inputs`: A list of inputs to the node. If the predecessor node has a single outgoing edge, just the name
-of node is sufficient. For instance, a 'ElasticsearchRetriever' node would always output a single
-edge with a list of documents. It can be represented as ["ElasticsearchRetriever"].
-
-In cases when the predecessor node has multiple outputs, e.g., a "QueryClassifier", the output
-must be specified explicitly as "QueryClassifier.output_2".
-
-
-#### get\_node
-
-```python
- | get_node(name: str)
-```
-
-Get a node from the Pipeline.
-
-**Arguments**:
-
-- `name`: The name of the node.
-
-
-#### set\_node
-
-```python
- | set_node(name: str, component)
-```
-
-Set the component for a node in the Pipeline.
-
-**Arguments**:
-
-- `name`: The name of the node.
-- `component`: The component object to be set at the node.
-
-
-#### draw
-
-```python
- | draw(path: Path = Path("pipeline.png"))
-```
-
-Create a Graphviz visualization of the pipeline.
-
-**Arguments**:
-
-- `path`: the path to save the image.
-
-
-## Class: ExtractiveQAPipeline
-
-```python
-class ExtractiveQAPipeline(BaseStandardPipeline)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(reader: BaseReader, retriever: BaseRetriever)
-```
-
-Initialize a Pipeline for Extractive Question Answering.
-
-**Arguments**:
-
-- `reader`: Reader instance
-- `retriever`: Retriever instance
-
-
-## Class: DocumentSearchPipeline
-
-```python
-class DocumentSearchPipeline(BaseStandardPipeline)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(retriever: BaseRetriever)
-```
-
-Initialize a Pipeline for semantic document search.
-
-**Arguments**:
-
-- `retriever`: Retriever instance
-
-
-## Class: GenerativeQAPipeline
-
-```python
-class GenerativeQAPipeline(BaseStandardPipeline)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(generator: BaseGenerator, retriever: BaseRetriever)
-```
-
-Initialize a Pipeline for Generative Question Answering.
-
-**Arguments**:
-
-- `generator`: Generator instance
-- `retriever`: Retriever instance
-
-
-## Class: FAQPipeline
-
-```python
-class FAQPipeline(BaseStandardPipeline)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(retriever: BaseRetriever)
-```
-
-Initialize a Pipeline for finding similar FAQs using semantic document search.
-
-**Arguments**:
-
-- `retriever`: Retriever instance
-
-
-## Class: JoinDocuments
-
-```python
-class JoinDocuments()
-```
-
-A node to join documents outputted by multiple retriever nodes.
-
-The node allows multiple join modes:
-* concatenate: combine the documents from multiple nodes. Any duplicate documents are discarded.
-* merge: merge scores of documents from multiple nodes. Optionally, each input score can be given a different
- `weight` & a `top_k` limit can be set. This mode can also be used for "reranking" retrieved documents.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(join_mode: str = "concatenate", weights: Optional[List[float]] = None, top_k_join: Optional[int] = None)
-```
-
-**Arguments**:
-
-- `join_mode`: `concatenate` to combine documents from multiple retrievers or `merge` to aggregate scores of
-individual documents.
-- `weights`: A node-wise list(length of list must be equal to the number of input nodes) of weights for
-adjusting document scores when using the `merge` join_mode. By default, equal weight is given
-to each retriever score. This param is not compatible with the `concatenate` join_mode.
-- `top_k_join`: Limit documents to top_k based on the resulting scores of the join.
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/api/api/preprocessor.md b/src/pages/docs/versions/master/v0.6.0/site/en/api/api/preprocessor.md
deleted file mode 100644
index 49d2509af..000000000
--- a/src/pages/docs/versions/master/v0.6.0/site/en/api/api/preprocessor.md
+++ /dev/null
@@ -1,185 +0,0 @@
----
-title: "Preprocessor"
-metaTitle: "Preprocessor"
-metaDescription: ""
-slug: "/docs/apiindexing"
-date: "2020-09-03"
-id: "apiindexingmd"
----
-
-# Preprocessor
-
-
-# Module: preprocessor
-
-
-## Class: PreProcessor
-
-```python
-class PreProcessor(BasePreProcessor)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(clean_whitespace: Optional[bool] = True, clean_header_footer: Optional[bool] = False, clean_empty_lines: Optional[bool] = True, split_by: Optional[str] = "word", split_length: Optional[int] = 1000, split_stride: Optional[int] = None, split_respect_sentence_boundary: Optional[bool] = True)
-```
-
-**Arguments**:
-
-- `clean_header_footer`: Use heuristic to remove footers and headers across different pages by searching
-for the longest common string. This heuristic uses exact matches and therefore
-works well for footers like "Copyright 2019 by XXX", but won't detect "Page 3 of 4"
-or similar.
-- `clean_whitespace`: Strip whitespaces before or after each line in the text.
-- `clean_empty_lines`: Remove more than two empty lines in the text.
-- `split_by`: Unit for splitting the document. Can be "word", "sentence", or "passage". Set to None to disable splitting.
-- `split_length`: Max. number of the above split unit (e.g. words) that are allowed in one document. For instance, if n -> 10 & split_by ->
-"sentence", then each output document will have 10 sentences.
-- `split_stride`: Length of striding window over the splits. For example, if split_by -> `word`,
-split_length -> 5 & split_stride -> 2, then the splits would be like:
-[w1 w2 w3 w4 w5, w4 w5 w6 w7 w8, w7 w8 w10 w11 w12].
-Set the value to None to disable striding behaviour.
-- `split_respect_sentence_boundary`: Whether to split in partial sentences if split_by -> `word`. If set
-to True, the individual split will always have complete sentences &
-the number of words will be <= split_length.
-
-
-#### clean
-
-```python
- | clean(document: dict) -> dict
-```
-
-Perform document cleaning on a single document and return a single document. This method will deal with whitespaces, headers, footers
-and empty lines. Its exact functionality is defined by the parameters passed into PreProcessor.__init__().
-
-
-#### split
-
-```python
- | split(document: dict) -> List[dict]
-```
-
-Perform document splitting on a single document. This method can split on different units, at different lengths,
-with different strides. It can also respect sectence boundaries. Its exact functionality is defined by
-the parameters passed into PreProcessor.__init__(). Takes a single document as input and returns a list of documents.
-
-
-# Module: cleaning
-
-
-#### clean\_wiki\_text
-
-```python
-clean_wiki_text(text: str) -> str
-```
-
-Clean wikipedia text by removing multiple new lines, removing extremely short lines,
-adding paragraph breaks and removing empty paragraphs
-
-
-# Module: utils
-
-
-#### eval\_data\_from\_file
-
-```python
-eval_data_from_file(filename: str, max_docs: Union[int, bool] = None) -> Tuple[List[Document], List[Label]]
-```
-
-Read Documents + Labels from a SQuAD-style file.
-Document and Labels can then be indexed to the DocumentStore and be used for evaluation.
-
-**Arguments**:
-
-- `filename`: Path to file in SQuAD format
-- `max_docs`: This sets the number of documents that will be loaded. By default, this is set to None, thus reading in all available eval documents.
-
-**Returns**:
-
-(List of Documents, List of Labels)
-
-
-#### convert\_files\_to\_dicts
-
-```python
-convert_files_to_dicts(dir_path: str, clean_func: Optional[Callable] = None, split_paragraphs: bool = False) -> List[dict]
-```
-
-Convert all files(.txt, .pdf, .docx) in the sub-directories of the given path to Python dicts that can be written to a
-Document Store.
-
-**Arguments**:
-
-- `dir_path`: path for the documents to be written to the DocumentStore
-- `clean_func`: a custom cleaning function that gets applied to each doc (input: str, output:str)
-- `split_paragraphs`: split text in paragraphs.
-
-**Returns**:
-
-None
-
-
-#### tika\_convert\_files\_to\_dicts
-
-```python
-tika_convert_files_to_dicts(dir_path: str, clean_func: Optional[Callable] = None, split_paragraphs: bool = False, merge_short: bool = True, merge_lowercase: bool = True) -> List[dict]
-```
-
-Convert all files(.txt, .pdf) in the sub-directories of the given path to Python dicts that can be written to a
-Document Store.
-
-**Arguments**:
-
-- `merge_lowercase`: allow conversion of merged paragraph to lowercase
-- `merge_short`: allow merging of short paragraphs
-- `dir_path`: path for the documents to be written to the DocumentStore
-- `clean_func`: a custom cleaning function that gets applied to each doc (input: str, output:str)
-- `split_paragraphs`: split text in paragraphs.
-
-**Returns**:
-
-None
-
-
-#### fetch\_archive\_from\_http
-
-```python
-fetch_archive_from_http(url: str, output_dir: str, proxies: Optional[dict] = None)
-```
-
-Fetch an archive (zip or tar.gz) from a url via http and extract content to an output directory.
-
-**Arguments**:
-
-- `url`: http address
-:type url: str
-- `output_dir`: local path
-:type output_dir: str
-- `proxies`: proxies details as required by requests library
-:type proxies: dict
-
-**Returns**:
-
-bool if anything got fetched
-
-
-# Module: base
-
-
-## Class: BasePreProcessor
-
-```python
-class BasePreProcessor()
-```
-
-
-#### process
-
-```python
- | process(document: dict) -> List[dict]
-```
-
-Perform document cleaning and splitting. Takes a single document as input and returns a list of documents.
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/api/api/reader.md b/src/pages/docs/versions/master/v0.6.0/site/en/api/api/reader.md
deleted file mode 100644
index 47d3b5c22..000000000
--- a/src/pages/docs/versions/master/v0.6.0/site/en/api/api/reader.md
+++ /dev/null
@@ -1,393 +0,0 @@
----
-title: "Reader"
-metaTitle: "Reader"
-metaDescription: ""
-slug: "/docs/apireadermd"
-date: "2020-09-03"
-id: "apireadermd"
----
-
-# Reader
-
-
-# Module: farm
-
-
-## Class: FARMReader
-
-```python
-class FARMReader(BaseReader)
-```
-
-Transformer based model for extractive Question Answering using the FARM framework (https://github.com/deepset-ai/FARM).
-While the underlying model can vary (BERT, Roberta, DistilBERT, ...), the interface remains the same.
-
-| With a FARMReader, you can:
-
- - directly get predictions via predict()
- - fine-tune the model on QA data via train()
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: Union[str, Path], context_window_size: int = 150, batch_size: int = 50, use_gpu: bool = True, no_ans_boost: float = 0.0, return_no_answer: bool = False, top_k_per_candidate: int = 3, top_k_per_sample: int = 1, num_processes: Optional[int] = None, max_seq_len: int = 256, doc_stride: int = 128)
-```
-
-**Arguments**:
-
-- `model_name_or_path`: Directory of a saved model or the name of a public model e.g. 'bert-base-cased',
-'deepset/bert-base-cased-squad2', 'deepset/bert-base-cased-squad2', 'distilbert-base-uncased-distilled-squad'.
-See https://huggingface.co/models for full list of available models.
-- `context_window_size`: The size, in characters, of the window around the answer span that is used when
-displaying the context around the answer.
-- `batch_size`: Number of samples the model receives in one batch for inference.
-Memory consumption is much lower in inference mode. Recommendation: Increase the batch size
-to a value so only a single batch is used.
-- `use_gpu`: Whether to use GPU (if available)
-- `no_ans_boost`: How much the no_answer logit is boosted/increased.
-If set to 0 (default), the no_answer logit is not changed.
-If a negative number, there is a lower chance of "no_answer" being predicted.
-If a positive number, there is an increased chance of "no_answer"
-- `return_no_answer`: Whether to include no_answer predictions in the results.
-- `top_k_per_candidate`: How many answers to extract for each candidate doc that is coming from the retriever (might be a long text).
-Note that this is not the number of "final answers" you will receive
-(see `top_k` in FARMReader.predict() or Finder.get_answers() for that)
-and that FARM includes no_answer in the sorted list of predictions.
-- `top_k_per_sample`: How many answers to extract from each small text passage that the model can process at once
-(one "candidate doc" is usually split into many smaller "passages").
-You usually want a very small value here, as it slows down inference
-and you don't gain much of quality by having multiple answers from one passage.
-Note that this is not the number of "final answers" you will receive
-(see `top_k` in FARMReader.predict() or Finder.get_answers() for that)
-and that FARM includes no_answer in the sorted list of predictions.
-- `num_processes`: The number of processes for `multiprocessing.Pool`. Set to value of 0 to disable
-multiprocessing. Set to None to let Inferencer determine optimum number. If you
-want to debug the Language Model, you might need to disable multiprocessing!
-- `max_seq_len`: Max sequence length of one input text for the model
-- `doc_stride`: Length of striding window for splitting long texts (used if ``len(text) > max_seq_len``)
-
-
-#### train
-
-```python
- | train(data_dir: str, train_filename: str, dev_filename: Optional[str] = None, test_filename: Optional[str] = None, use_gpu: Optional[bool] = None, batch_size: int = 10, n_epochs: int = 2, learning_rate: float = 1e-5, max_seq_len: Optional[int] = None, warmup_proportion: float = 0.2, dev_split: float = 0, evaluate_every: int = 300, save_dir: Optional[str] = None, num_processes: Optional[int] = None, use_amp: str = None)
-```
-
-Fine-tune a model on a QA dataset. Options:
-
-- Take a plain language model (e.g. `bert-base-cased`) and train it for QA (e.g. on SQuAD data)
-- Take a QA model (e.g. `deepset/bert-base-cased-squad2`) and fine-tune it for your domain (e.g. using your labels collected via the haystack annotation tool)
-
-**Arguments**:
-
-- `data_dir`: Path to directory containing your training data in SQuAD style
-- `train_filename`: Filename of training data
-- `dev_filename`: Filename of dev / eval data
-- `test_filename`: Filename of test data
-- `dev_split`: Instead of specifying a dev_filename, you can also specify a ratio (e.g. 0.1) here
-that gets split off from training data for eval.
-- `use_gpu`: Whether to use GPU (if available)
-- `batch_size`: Number of samples the model receives in one batch for training
-- `n_epochs`: Number of iterations on the whole training data set
-- `learning_rate`: Learning rate of the optimizer
-- `max_seq_len`: Maximum text length (in tokens). Everything longer gets cut down.
-- `warmup_proportion`: Proportion of training steps until maximum learning rate is reached.
-Until that point LR is increasing linearly. After that it's decreasing again linearly.
-Options for different schedules are available in FARM.
-- `evaluate_every`: Evaluate the model every X steps on the hold-out eval dataset
-- `save_dir`: Path to store the final model
-- `num_processes`: The number of processes for `multiprocessing.Pool` during preprocessing.
-Set to value of 1 to disable multiprocessing. When set to 1, you cannot split away a dev set from train set.
-Set to None to use all CPU cores minus one.
-- `use_amp`: Optimization level of NVIDIA's automatic mixed precision (AMP). The higher the level, the faster the model.
-Available options:
-None (Don't use AMP)
-"O0" (Normal FP32 training)
-"O1" (Mixed Precision => Recommended)
-"O2" (Almost FP16)
-"O3" (Pure FP16).
-See details on: https://nvidia.github.io/apex/amp.html
-
-**Returns**:
-
-None
-
-
-#### update\_parameters
-
-```python
- | update_parameters(context_window_size: Optional[int] = None, no_ans_boost: Optional[float] = None, return_no_answer: Optional[bool] = None, max_seq_len: Optional[int] = None, doc_stride: Optional[int] = None)
-```
-
-Hot update parameters of a loaded Reader. It may not to be safe when processing concurrent requests.
-
-
-#### save
-
-```python
- | save(directory: Path)
-```
-
-Saves the Reader model so that it can be reused at a later point in time.
-
-**Arguments**:
-
-- `directory`: Directory where the Reader model should be saved
-
-
-#### predict\_batch
-
-```python
- | predict_batch(query_doc_list: List[dict], top_k: int = None, batch_size: int = None)
-```
-
-Use loaded QA model to find answers for a list of queries in each query's supplied list of Document.
-
-Returns list of dictionaries containing answers sorted by (desc.) probability
-
-**Arguments**:
-
-- `query_doc_list`: List of dictionaries containing queries with their retrieved documents
-- `top_k`: The maximum number of answers to return for each query
-- `batch_size`: Number of samples the model receives in one batch for inference
-
-**Returns**:
-
-List of dictionaries containing query and answers
-
-
-#### predict
-
-```python
- | predict(query: str, documents: List[Document], top_k: Optional[int] = None)
-```
-
-Use loaded QA model to find answers for a query in the supplied list of Document.
-
-Returns dictionaries containing answers sorted by (desc.) probability.
-Example:
-```python
-|{
-| 'query': 'Who is the father of Arya Stark?',
-| 'answers':[
-| {'answer': 'Eddard,',
-| 'context': " She travels with her father, Eddard, to King's Landing when he is ",
-| 'offset_answer_start': 147,
-| 'offset_answer_end': 154,
-| 'probability': 0.9787139466668613,
-| 'score': None,
-| 'document_id': '1337'
-| },...
-| ]
-|}
-```
-
-**Arguments**:
-
-- `query`: Query string
-- `documents`: List of Document in which to search for the answer
-- `top_k`: The maximum number of answers to return
-
-**Returns**:
-
-Dict containing query and answers
-
-
-#### eval\_on\_file
-
-```python
- | eval_on_file(data_dir: str, test_filename: str, device: str)
-```
-
-Performs evaluation on a SQuAD-formatted file.
-Returns a dict containing the following metrics:
-- "EM": exact match score
-- "f1": F1-Score
-- "top_n_accuracy": Proportion of predicted answers that overlap with correct answer
-
-**Arguments**:
-
-- `data_dir`: The directory in which the test set can be found
-:type data_dir: Path or str
-- `test_filename`: The name of the file containing the test data in SQuAD format.
-:type test_filename: str
-- `device`: The device on which the tensors should be processed. Choose from "cpu" and "cuda".
-:type device: str
-
-
-#### eval
-
-```python
- | eval(document_store: BaseDocumentStore, device: str, label_index: str = "label", doc_index: str = "eval_document", label_origin: str = "gold_label")
-```
-
-Performs evaluation on evaluation documents in the DocumentStore.
-Returns a dict containing the following metrics:
-- "EM": Proportion of exact matches of predicted answers with their corresponding correct answers
-- "f1": Average overlap between predicted answers and their corresponding correct answers
-- "top_n_accuracy": Proportion of predicted answers that overlap with correct answer
-
-**Arguments**:
-
-- `document_store`: DocumentStore containing the evaluation documents
-- `device`: The device on which the tensors should be processed. Choose from "cpu" and "cuda".
-- `label_index`: Index/Table name where labeled questions are stored
-- `doc_index`: Index/Table name where documents that are used for evaluation are stored
-
-
-#### predict\_on\_texts
-
-```python
- | predict_on_texts(question: str, texts: List[str], top_k: Optional[int] = None)
-```
-
-Use loaded QA model to find answers for a question in the supplied list of Document.
-Returns dictionaries containing answers sorted by (desc.) probability.
-Example:
-```python
-|{
-| 'question': 'Who is the father of Arya Stark?',
-| 'answers':[
-| {'answer': 'Eddard,',
-| 'context': " She travels with her father, Eddard, to King's Landing when he is ",
-| 'offset_answer_start': 147,
-| 'offset_answer_end': 154,
-| 'probability': 0.9787139466668613,
-| 'score': None,
-| 'document_id': '1337'
-| },...
-| ]
-|}
-```
-
-**Arguments**:
-
-- `question`: Question string
-- `documents`: List of documents as string type
-- `top_k`: The maximum number of answers to return
-
-**Returns**:
-
-Dict containing question and answers
-
-
-#### convert\_to\_onnx
-
-```python
- | @classmethod
- | convert_to_onnx(cls, model_name: str, output_path: Path, convert_to_float16: bool = False, quantize: bool = False, task_type: str = "question_answering", opset_version: int = 11)
-```
-
-Convert a PyTorch BERT model to ONNX format and write to ./onnx-export dir. The converted ONNX model
-can be loaded with in the `FARMReader` using the export path as `model_name_or_path` param.
-
-Usage:
-
-`from haystack.reader.farm import FARMReader
-from pathlib import Path
-onnx_model_path = Path("roberta-onnx-model")
-FARMReader.convert_to_onnx(model_name="deepset/bert-base-cased-squad2", output_path=onnx_model_path)
-reader = FARMReader(onnx_model_path)`
-
-**Arguments**:
-
-- `model_name`: transformers model name
-- `output_path`: Path to output the converted model
-- `convert_to_float16`: Many models use float32 precision by default. With the half precision of float16,
-inference is faster on Nvidia GPUs with Tensor core like T4 or V100. On older GPUs,
-float32 could still be be more performant.
-- `quantize`: convert floating point number to integers
-- `task_type`: Type of task for the model. Available options: "question_answering" or "embeddings".
-- `opset_version`: ONNX opset version
-
-
-# Module: transformers
-
-
-## Class: TransformersReader
-
-```python
-class TransformersReader(BaseReader)
-```
-
-Transformer based model for extractive Question Answering using the HuggingFace's transformers framework
-(https://github.com/huggingface/transformers).
-While the underlying model can vary (BERT, Roberta, DistilBERT ...), the interface remains the same.
-With this reader, you can directly get predictions via predict()
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: str = "distilbert-base-uncased-distilled-squad", tokenizer: Optional[str] = None, context_window_size: int = 70, use_gpu: int = 0, top_k_per_candidate: int = 4, return_no_answers: bool = True, max_seq_len: int = 256, doc_stride: int = 128)
-```
-
-Load a QA model from Transformers.
-Available models include:
-
-- ``'distilbert-base-uncased-distilled-squad`'``
-- ``'bert-large-cased-whole-word-masking-finetuned-squad``'
-- ``'bert-large-uncased-whole-word-masking-finetuned-squad``'
-
-See https://huggingface.co/models for full list of available QA models
-
-**Arguments**:
-
-- `model_name_or_path`: Directory of a saved model or the name of a public model e.g. 'bert-base-cased',
-'deepset/bert-base-cased-squad2', 'deepset/bert-base-cased-squad2', 'distilbert-base-uncased-distilled-squad'.
-See https://huggingface.co/models for full list of available models.
-- `tokenizer`: Name of the tokenizer (usually the same as model)
-- `context_window_size`: Num of chars (before and after the answer) to return as "context" for each answer.
-The context usually helps users to understand if the answer really makes sense.
-- `use_gpu`: If < 0, then use cpu. If >= 0, this is the ordinal of the gpu to use
-- `top_k_per_candidate`: How many answers to extract for each candidate doc that is coming from the retriever (might be a long text).
-Note that this is not the number of "final answers" you will receive
-(see `top_k` in TransformersReader.predict() or Finder.get_answers() for that)
-and that no_answer can be included in the sorted list of predictions.
-- `return_no_answers`: If True, the HuggingFace Transformers model could return a "no_answer" (i.e. when there is an unanswerable question)
-If False, it cannot return a "no_answer". Note that `no_answer_boost` is unfortunately not available with TransformersReader.
-If you would like to set no_answer_boost, use a `FARMReader`.
-- `max_seq_len`: max sequence length of one input text for the model
-- `doc_stride`: length of striding window for splitting long texts (used if len(text) > max_seq_len)
-
-
-#### predict
-
-```python
- | predict(query: str, documents: List[Document], top_k: Optional[int] = None)
-```
-
-Use loaded QA model to find answers for a query in the supplied list of Document.
-
-Returns dictionaries containing answers sorted by (desc.) probability.
-Example:
-
-```python
-|{
-| 'query': 'Who is the father of Arya Stark?',
-| 'answers':[
-| {'answer': 'Eddard,',
-| 'context': " She travels with her father, Eddard, to King's Landing when he is ",
-| 'offset_answer_start': 147,
-| 'offset_answer_end': 154,
-| 'probability': 0.9787139466668613,
-| 'score': None,
-| 'document_id': '1337'
-| },...
-| ]
-|}
-```
-
-**Arguments**:
-
-- `query`: Query string
-- `documents`: List of Document in which to search for the answer
-- `top_k`: The maximum number of answers to return
-
-**Returns**:
-
-Dict containing query and answers
-
-
-# Module: base
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/api/api/retriever.md b/src/pages/docs/versions/master/v0.6.0/site/en/api/api/retriever.md
deleted file mode 100644
index 13b2b264f..000000000
--- a/src/pages/docs/versions/master/v0.6.0/site/en/api/api/retriever.md
+++ /dev/null
@@ -1,491 +0,0 @@
----
-title: "Retriever"
-metaTitle: "Retriever"
-metaDescription: ""
-slug: "/docs/apiretrievermd"
-date: "2020-09-03"
-id: "apiretrievermd"
----
-
-# Retriever
-
-
-# Module: sparse
-
-
-## Class: ElasticsearchRetriever
-
-```python
-class ElasticsearchRetriever(BaseRetriever)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(document_store: ElasticsearchDocumentStore, custom_query: str = None)
-```
-
-**Arguments**:
-
-- `document_store`: an instance of a DocumentStore to retrieve documents from.
-- `custom_query`: query string as per Elasticsearch DSL with a mandatory query placeholder(query).
-
-Optionally, ES `filter` clause can be added where the values of `terms` are placeholders
-that get substituted during runtime. The placeholder(${filter_name_1}, ${filter_name_2}..)
-names must match with the filters dict supplied in self.retrieve().
-::
-
-**An example custom_query:**
-```python
-| {
-| "size": 10,
-| "query": {
-| "bool": {
-| "should": [{"multi_match": {
-| "query": "${query}", // mandatory query placeholder
-| "type": "most_fields",
-| "fields": ["text", "title"]}}],
-| "filter": [ // optional custom filters
-| {"terms": {"year": "${years}"}},
-| {"terms": {"quarter": "${quarters}"}},
-| {"range": {"date": {"gte": "${date}"}}}
-| ],
-| }
-| },
-| }
-```
-
-**For this custom_query, a sample retrieve() could be:**
-```python
-| self.retrieve(query="Why did the revenue increase?",
-| filters={"years": ["2019"], "quarters": ["Q1", "Q2"]})
-```
-
-
-#### retrieve
-
-```python
- | retrieve(query: str, filters: dict = None, top_k: int = 10, index: str = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-## Class: ElasticsearchFilterOnlyRetriever
-
-```python
-class ElasticsearchFilterOnlyRetriever(ElasticsearchRetriever)
-```
-
-Naive "Retriever" that returns all documents that match the given filters. No impact of query at all.
-Helpful for benchmarking, testing and if you want to do QA on small documents without an "active" retriever.
-
-
-#### retrieve
-
-```python
- | retrieve(query: str, filters: dict = None, top_k: int = 10, index: str = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-## Class: TfidfRetriever
-
-```python
-class TfidfRetriever(BaseRetriever)
-```
-
-Read all documents from a SQL backend.
-
-Split documents into smaller units (eg, paragraphs or pages) to reduce the
-computations when text is passed on to a Reader for QA.
-
-It uses sklearn's TfidfVectorizer to compute a tf-idf matrix.
-
-
-#### retrieve
-
-```python
- | retrieve(query: str, filters: dict = None, top_k: int = 10, index: str = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-#### fit
-
-```python
- | fit()
-```
-
-Performing training on this class according to the TF-IDF algorithm.
-
-
-# Module: dense
-
-
-## Class: DensePassageRetriever
-
-```python
-class DensePassageRetriever(BaseRetriever)
-```
-
-Retriever that uses a bi-encoder (one transformer for query, one transformer for passage).
-See the original paper for more details:
-Karpukhin, Vladimir, et al. (2020): "Dense Passage Retrieval for Open-Domain Question Answering."
-(https://arxiv.org/abs/2004.04906).
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(document_store: BaseDocumentStore, query_embedding_model: Union[Path, str] = "facebook/dpr-question_encoder-single-nq-base", passage_embedding_model: Union[Path, str] = "facebook/dpr-ctx_encoder-single-nq-base", max_seq_len_query: int = 64, max_seq_len_passage: int = 256, use_gpu: bool = True, batch_size: int = 16, embed_title: bool = True, use_fast_tokenizers: bool = True, similarity_function: str = "dot_product")
-```
-
-Init the Retriever incl. the two encoder models from a local or remote model checkpoint.
-The checkpoint format matches huggingface transformers' model format
-
-**Example:**
-
-```python
-| # remote model from FAIR
-| DensePassageRetriever(document_store=your_doc_store,
-| query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
-| passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base")
-| # or from local path
-| DensePassageRetriever(document_store=your_doc_store,
-| query_embedding_model="model_directory/question-encoder",
-| passage_embedding_model="model_directory/context-encoder")
-```
-
-**Arguments**:
-
-- `document_store`: An instance of DocumentStore from which to retrieve documents.
-- `query_embedding_model`: Local path or remote name of question encoder checkpoint. The format equals the
-one used by hugging-face transformers' modelhub models
-Currently available remote names: ``"facebook/dpr-question_encoder-single-nq-base"``
-- `passage_embedding_model`: Local path or remote name of passage encoder checkpoint. The format equals the
-one used by hugging-face transformers' modelhub models
-Currently available remote names: ``"facebook/dpr-ctx_encoder-single-nq-base"``
-- `max_seq_len_query`: Longest length of each query sequence. Maximum number of tokens for the query text. Longer ones will be cut down."
-- `max_seq_len_passage`: Longest length of each passage/context sequence. Maximum number of tokens for the passage text. Longer ones will be cut down."
-- `use_gpu`: Whether to use gpu or not
-- `batch_size`: Number of questions or passages to encode at once
-- `embed_title`: Whether to concatenate title and passage to a text pair that is then used to create the embedding.
-This is the approach used in the original paper and is likely to improve performance if your
-titles contain meaningful information for retrieval (topic, entities etc.) .
-The title is expected to be present in doc.meta["name"] and can be supplied in the documents
-before writing them to the DocumentStore like this:
-{"text": "my text", "meta": {"name": "my title"}}.
-
-
-#### retrieve
-
-```python
- | retrieve(query: str, filters: dict = None, top_k: int = 10, index: str = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-#### embed\_queries
-
-```python
- | embed_queries(texts: List[str]) -> List[np.array]
-```
-
-Create embeddings for a list of queries using the query encoder
-
-**Arguments**:
-
-- `texts`: Queries to embed
-
-**Returns**:
-
-Embeddings, one per input queries
-
-
-#### embed\_passages
-
-```python
- | embed_passages(docs: List[Document]) -> List[np.array]
-```
-
-Create embeddings for a list of passages using the passage encoder
-
-**Arguments**:
-
-- `docs`: List of Document objects used to represent documents / passages in a standardized way within Haystack.
-
-**Returns**:
-
-Embeddings of documents / passages shape (batch_size, embedding_dim)
-
-
-#### train
-
-```python
- | train(data_dir: str, train_filename: str, dev_filename: str = None, test_filename: str = None, batch_size: int = 2, embed_title: bool = True, num_hard_negatives: int = 1, num_positives: int = 1, n_epochs: int = 3, evaluate_every: int = 1000, n_gpu: int = 1, learning_rate: float = 1e-5, epsilon: float = 1e-08, weight_decay: float = 0.0, num_warmup_steps: int = 100, grad_acc_steps: int = 1, optimizer_name: str = "TransformersAdamW", optimizer_correct_bias: bool = True, save_dir: str = "../saved_models/dpr", query_encoder_save_dir: str = "query_encoder", passage_encoder_save_dir: str = "passage_encoder")
-```
-
-train a DensePassageRetrieval model
-
-**Arguments**:
-
-- `data_dir`: Directory where training file, dev file and test file are present
-- `train_filename`: training filename
-- `dev_filename`: development set filename, file to be used by model in eval step of training
-- `test_filename`: test set filename, file to be used by model in test step after training
-- `batch_size`: total number of samples in 1 batch of data
-- `embed_title`: whether to concatenate passage title with each passage. The default setting in official DPR embeds passage title with the corresponding passage
-- `num_hard_negatives`: number of hard negative passages(passages which are very similar(high score by BM25) to query but do not contain the answer
-- `num_positives`: number of positive passages
-- `n_epochs`: number of epochs to train the model on
-- `evaluate_every`: number of training steps after evaluation is run
-- `n_gpu`: number of gpus to train on
-- `learning_rate`: learning rate of optimizer
-- `epsilon`: epsilon parameter of optimizer
-- `weight_decay`: weight decay parameter of optimizer
-- `grad_acc_steps`: number of steps to accumulate gradient over before back-propagation is done
-- `optimizer_name`: what optimizer to use (default: TransformersAdamW)
-- `num_warmup_steps`: number of warmup steps
-- `optimizer_correct_bias`: Whether to correct bias in optimizer
-- `save_dir`: directory where models are saved
-- `query_encoder_save_dir`: directory inside save_dir where query_encoder model files are saved
-- `passage_encoder_save_dir`: directory inside save_dir where passage_encoder model files are saved
-
-
-#### save
-
-```python
- | save(save_dir: Union[Path, str], query_encoder_dir: str = "query_encoder", passage_encoder_dir: str = "passage_encoder")
-```
-
-Save DensePassageRetriever to the specified directory.
-
-**Arguments**:
-
-- `save_dir`: Directory to save to.
-- `query_encoder_dir`: Directory in save_dir that contains query encoder model.
-- `passage_encoder_dir`: Directory in save_dir that contains passage encoder model.
-
-**Returns**:
-
-None
-
-
-#### load
-
-```python
- | @classmethod
- | load(cls, load_dir: Union[Path, str], document_store: BaseDocumentStore, max_seq_len_query: int = 64, max_seq_len_passage: int = 256, use_gpu: bool = True, batch_size: int = 16, embed_title: bool = True, use_fast_tokenizers: bool = True, similarity_function: str = "dot_product", query_encoder_dir: str = "query_encoder", passage_encoder_dir: str = "passage_encoder")
-```
-
-Load DensePassageRetriever from the specified directory.
-
-
-## Class: EmbeddingRetriever
-
-```python
-class EmbeddingRetriever(BaseRetriever)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(document_store: BaseDocumentStore, embedding_model: str, use_gpu: bool = True, model_format: str = "farm", pooling_strategy: str = "reduce_mean", emb_extraction_layer: int = -1)
-```
-
-**Arguments**:
-
-- `document_store`: An instance of DocumentStore from which to retrieve documents.
-- `embedding_model`: Local path or name of model in Hugging Face's model hub such as ``'deepset/sentence_bert'``
-- `use_gpu`: Whether to use gpu or not
-- `model_format`: Name of framework that was used for saving the model. Options:
-
-- ``'farm'``
-- ``'transformers'``
-- ``'sentence_transformers'``
-- `pooling_strategy`: Strategy for combining the embeddings from the model (for farm / transformers models only).
-Options:
-
-- ``'cls_token'`` (sentence vector)
-- ``'reduce_mean'`` (sentence vector)
-- ``'reduce_max'`` (sentence vector)
-- ``'per_token'`` (individual token vectors)
-- `emb_extraction_layer`: Number of layer from which the embeddings shall be extracted (for farm / transformers models only).
-Default: -1 (very last layer).
-
-
-#### retrieve
-
-```python
- | retrieve(query: str, filters: dict = None, top_k: int = 10, index: str = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-#### embed
-
-```python
- | embed(texts: Union[List[str], str]) -> List[np.array]
-```
-
-Create embeddings for each text in a list of texts using the retrievers model (`self.embedding_model`)
-
-**Arguments**:
-
-- `texts`: Texts to embed
-
-**Returns**:
-
-List of embeddings (one per input text). Each embedding is a list of floats.
-
-
-#### embed\_queries
-
-```python
- | embed_queries(texts: List[str]) -> List[np.array]
-```
-
-Create embeddings for a list of queries. For this Retriever type: The same as calling .embed()
-
-**Arguments**:
-
-- `texts`: Queries to embed
-
-**Returns**:
-
-Embeddings, one per input queries
-
-
-#### embed\_passages
-
-```python
- | embed_passages(docs: List[Document]) -> List[np.array]
-```
-
-Create embeddings for a list of passages. For this Retriever type: The same as calling .embed()
-
-**Arguments**:
-
-- `docs`: List of documents to embed
-
-**Returns**:
-
-Embeddings, one per input passage
-
-
-# Module: base
-
-
-## Class: BaseRetriever
-
-```python
-class BaseRetriever(ABC)
-```
-
-
-#### retrieve
-
-```python
- | @abstractmethod
- | retrieve(query: str, filters: dict = None, top_k: int = 10, index: str = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-#### timing
-
-```python
- | timing(fn)
-```
-
-Wrapper method used to time functions.
-
-
-#### eval
-
-```python
- | eval(label_index: str = "label", doc_index: str = "eval_document", label_origin: str = "gold_label", top_k: int = 10, open_domain: bool = False, return_preds: bool = False) -> dict
-```
-
-Performs evaluation on the Retriever.
-Retriever is evaluated based on whether it finds the correct document given the query string and at which
-position in the ranking of documents the correct document is.
-
-| Returns a dict containing the following metrics:
-
-- "recall": Proportion of questions for which correct document is among retrieved documents
-- "mrr": Mean of reciprocal rank. Rewards retrievers that give relevant documents a higher rank.
-Only considers the highest ranked relevant document.
-- "map": Mean of average precision for each question. Rewards retrievers that give relevant
-documents a higher rank. Considers all retrieved relevant documents. If ``open_domain=True``,
-average precision is normalized by the number of retrieved relevant documents per query.
-If ``open_domain=False``, average precision is normalized by the number of all relevant documents
-per query.
-
-**Arguments**:
-
-- `label_index`: Index/Table in DocumentStore where labeled questions are stored
-- `doc_index`: Index/Table in DocumentStore where documents that are used for evaluation are stored
-- `top_k`: How many documents to return per query
-- `open_domain`: If ``True``, retrieval will be evaluated by checking if the answer string to a question is
-contained in the retrieved docs (common approach in open-domain QA).
-If ``False``, retrieval uses a stricter evaluation that checks if the retrieved document ids
-are within ids explicitly stated in the labels.
-- `return_preds`: Whether to add predictions in the returned dictionary. If True, the returned dictionary
-contains the keys "predictions" and "metrics".
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--- a/src/pages/docs/versions/master/v0.6.0/site/en/menuStructure/menu.json
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@@ -1,272 +0,0 @@
-{
- "menuList": [
- {
- "id": "overview",
- "title": "Overview",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 0,
- "isMenu": true
- },
- {
- "id": "get_startedmd",
- "title": "Get Started",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "intromd",
- "title": "What is Haystack?",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "use_casesmd",
- "title": "Use Cases",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "roadmapmd",
- "title": "Roadmap",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "termsmd",
- "title": "Glossary",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "usage_haystack",
- "title": "Usage",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 1,
- "isMenu": true
- },
- {
- "id": "preprocessingmd",
- "title": "Preprocessing",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "documentstoremd",
- "title": "Document Store",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "retrievermd",
- "title": "Retriever",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "readermd",
- "title": "Reader",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "generatormd",
- "title": "Generator",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "pipelinesmd",
- "title": "Pipelines",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "languagesmd",
- "title": "Languages Other Than English",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 6
- },
- {
- "id": "domain_adaptationmd",
- "title": "Domain Adaptation",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 7
- },
- {
- "id": "optimizationmd",
- "title": "Optimization",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 8
- },
- {
- "id": "annotationmd",
- "title": "Annotation Tool",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 9
- },
- {
- "id": "tutorials_haystack",
- "title": "Tutorials",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 2,
- "isMenu": true
- },
- {
- "id": "tutorial1md",
- "title": "Build Your First QA System",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "tutorial2md",
- "title": "Fine-tuning a Model on Your Own Data",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "tutorial3md",
- "title": "Build a QA System Without Elasticsearch",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "tutorial4md",
- "title": "Utilizing existing FAQs for Question Answering",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "tutorial5md",
- "title": "Evaluation of a QA System",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "tutorial6md",
- "title": "Better Retrieval via \"Dense Passage Retrieval\"",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "tutorial7md",
- "title": "Generative QA with \"Retrieval-Augmented Generation\"",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 6
- },
- {
- "id": "api_haystack",
- "title": "API reference",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 3,
- "isMenu": true
- },
- {
- "id": "apidatabasemd",
- "title": "Document Store",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "apiretrievermd",
- "title": "Retriever",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "apireadermd",
- "title": "Reader",
- "label1": "api_haystack",
- "label2": "api_haystack",
- "label3": "",
- "order": 2
- },
- {
- "id": "apigeneratormd",
- "title": "Generator",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "apiindexingmd",
- "title": "Preprocessor",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "file_convertersmd",
- "title": "File Converters",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "apipipelinesmd",
- "title": "Pipelines",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 6
- }
- ]
-}
\ No newline at end of file
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/1.md b/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/1.md
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index 2eda9fc44..000000000
--- a/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/1.md
+++ /dev/null
@@ -1,223 +0,0 @@
----
-title: "Tutorial 1"
-metaTitle: "Build Your First QA System"
-metaDescription: ""
-slug: "/docs/tutorial1"
-date: "2020-09-03"
-id: "tutorial1md"
----
-
-# Build Your First QA System
-
-
-
-EXECUTABLE VERSION: [*colab*](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb)
-
-Question Answering can be used in a variety of use cases. A very common one: Using it to navigate through complex knowledge bases or long documents ("search setting").
-
-A "knowledge base" could for example be your website, an internal wiki or a collection of financial reports.
-In this tutorial we will work on a slightly different domain: "Game of Thrones".
-
-Let's see how we can use a bunch of Wikipedia articles to answer a variety of questions about the
-marvellous seven kingdoms...
-
-
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-Haystack finds answers to queries within the documents stored in a `DocumentStore`. The current implementations of `DocumentStore` include `ElasticsearchDocumentStore`, `FAISSDocumentStore`, `SQLDocumentStore`, and `InMemoryDocumentStore`.
-
-**Here:** We recommended Elasticsearch as it comes preloaded with features like [full-text queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html), [BM25 retrieval](https://www.elastic.co/elasticon/conf/2016/sf/improved-text-scoring-with-bm25), and [vector storage for text embeddings](https://www.elastic.co/guide/en/elasticsearch/reference/7.6/dense-vector.html).
-
-**Alternatives:** If you are unable to setup an Elasticsearch instance, then follow the [Tutorial 3](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb) for using SQL/InMemory document stores.
-
-**Hint**: This tutorial creates a new document store instance with Wikipedia articles on Game of Thrones. However, you can configure Haystack to work with your existing document stores.
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker
-#! docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.6.2
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.6.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.6.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.6.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.6.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-# Connect to Elasticsearch
-
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles about Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first fetch some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is:
-# {
-# 'text': "",
-# 'meta': {'name': "", ...}
-#}
-# (Optionally: you can also add more key-value-pairs here, that will be indexed as fields in Elasticsearch and
-# can be accessed later for filtering or shown in the responses of the Finder)
-
-# Let's have a look at the first 3 entries:
-print(dicts[:3])
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-## Initalize Retriever, Reader, & Finder
-
-### Retriever
-
-Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question could be answered.
-They use some simple but fast algorithm.
-
-**Here:** We use Elasticsearch's default BM25 algorithm
-
-**Alternatives:**
-
-- Customize the `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `DensePassageRetriever` to use different embedding models for passage and query (see Tutorial 6)
-
-
-```python
-from haystack.retriever.sparse import ElasticsearchRetriever
-retriever = ElasticsearchRetriever(document_store=document_store)
-```
-
-
-```python
-# Alternative: An in-memory TfidfRetriever based on Pandas dataframes for building quick-prototypes with SQLite document store.
-
-# from haystack.retriever.sparse import TfidfRetriever
-# retriever = TfidfRetriever(document_store=document_store)
-```
-
-### Reader
-
-A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based
-on powerful, but slower deep learning models.
-
-Haystack currently supports Readers based on the frameworks FARM and Transformers.
-With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).
-
-**Here:** a medium sized RoBERTa QA model using a Reader based on FARM (https://huggingface.co/deepset/roberta-base-squad2)
-
-**Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)
-
-**Alternatives (Models):** e.g. "distilbert-base-uncased-distilled-squad" (fast) or "deepset/bert-large-uncased-whole-word-masking-squad2" (good accuracy)
-
-**Hint:** You can adjust the model to return "no answer possible" with the no_ans_boost. Higher values mean the model prefers "no answer possible"
-
-#### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=False)
-```
-
-#### TransformersReader
-
-
-```python
-# Alternative:
-# reader = TransformersReader(model_name_or_path="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1)
-```
-
-### Finder
-
-The Finder sticks together reader and retriever in a pipeline to answer our actual questions.
-
-
-```python
-finder = Finder(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = finder.get_answers(question="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-# prediction = finder.get_answers(question="Who created the Dothraki vocabulary?", top_k_reader=5)
-# prediction = finder.get_answers(question="Who is the sister of Sansa?", top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/2.md b/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/2.md
deleted file mode 100644
index 103c33a4f..000000000
--- a/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/2.md
+++ /dev/null
@@ -1,78 +0,0 @@
----
-title: "Tutorial 2"
-metaTitle: "Fine-tuning a model on your own data"
-metaDescription: ""
-slug: "/docs/tutorial2"
-date: "2020-09-03"
-id: "tutorial2md"
----
-
-# Fine-tuning a Model on Your Own Data
-
-EXECUTABLE VERSION: [*colab*](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial2_Finetune_a_model_on_your_data.ipynb)
-
-For many use cases it is sufficient to just use one of the existing public models that were trained on SQuAD or other public QA datasets (e.g. Natural Questions).
-However, if you have domain-specific questions, fine-tuning your model on custom examples will very likely boost your performance.
-While this varies by domain, we saw that ~ 2000 examples can easily increase performance by +5-20%.
-
-This tutorial shows you how to fine-tune a pretrained model on your own dataset.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-from haystack.reader.farm import FARMReader
-```
-
-
-## Create Training Data
-
-There are two ways to generate training data
-
-1. **Annotation**: You can use the [annotation tool](https://github.com/deepset-ai/haystack#labeling-tool) to label your data, i.e. highlighting answers to your questions in a document. The tool supports structuring your workflow with organizations, projects, and users. The labels can be exported in SQuAD format that is compatible for training with Haystack.
-
-
-
-2. **Feedback**: For production systems, you can collect training data from direct user feedback via Haystack's [REST API interface](https://github.com/deepset-ai/haystack#rest-api). This includes a customizable user feedback API for providing feedback on the answer returned by the API. The API provides a feedback export endpoint to obtain the feedback data for fine-tuning your model further.
-
-
-## Fine-tune your model
-
-Once you have collected training data, you can fine-tune your base models.
-We initialize a reader as a base model and fine-tune it on our own custom dataset (should be in SQuAD-like format).
-We recommend using a base model that was trained on SQuAD or a similar QA dataset before to benefit from Transfer Learning effects.
-
-**Recommendation**: Run training on a GPU.
-If you are using Colab: Enable this in the menu "Runtime" > "Change Runtime type" > Select "GPU" in dropdown.
-Then change the `use_gpu` arguments below to `True`
-
-
-```python
-reader = FARMReader(model_name_or_path="distilbert-base-uncased-distilled-squad", use_gpu=True)
-train_data = "data/squad20"
-# train_data = "PATH/TO_YOUR/TRAIN_DATA"
-reader.train(data_dir=train_data, train_filename="dev-v2.0.json", use_gpu=True, n_epochs=1, save_dir="my_model")
-```
-
-
-```python
-# Saving the model happens automatically at the end of training into the `save_dir` you specified
-# However, you could also save a reader manually again via:
-reader.save(directory="my_model")
-```
-
-
-```python
-# If you want to load it at a later point, just do:
-new_reader = FARMReader(model_name_or_path="my_model")
-```
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/3.md b/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/3.md
deleted file mode 100644
index 7e938ad84..000000000
--- a/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/3.md
+++ /dev/null
@@ -1,169 +0,0 @@
----
-title: "Tutorial 3"
-metaTitle: "Build a QA System Without Elasticsearch"
-metaDescription: ""
-slug: "/docs/tutorial3"
-date: "2020-09-03"
-id: "tutorial3md"
----
-
-# Build a QA System Without Elasticsearch
-
-EXECUTABLE VERSION: [*colab*](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb)
-
-Haystack provides alternatives to Elasticsearch for developing quick prototypes.
-
-You can use an `InMemoryDocumentStore` or a `SQLDocumentStore`(with SQLite) as the document store.
-
-If you are interested in more feature-rich Elasticsearch, then please refer to the Tutorial 1.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-
-
-```python
-# In-Memory Document Store
-from haystack.document_store.memory import InMemoryDocumentStore
-document_store = InMemoryDocumentStore()
-```
-
-
-```python
-# SQLite Document Store
-# from haystack.document_store.sql import SQLDocumentStore
-# document_store = SQLDocumentStore(url="sqlite:///qa.db")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles on Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first get some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# convert files to dicts containing documents that can be indexed to our datastore
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is: {"name": ", "text": ""}
-
-# Let's have a look at the first 3 entries:
-print(dicts[:3])
-# Now, let's write the docs to our DB.
-document_store.write_documents(dicts)
-```
-
-## Initalize Retriever, Reader, & Finder
-
-### Retriever
-
-Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question could be answered.
-
-With InMemoryDocumentStore or SQLDocumentStore, you can use the TfidfRetriever. For more retrievers, please refer to the tutorial-1.
-
-
-```python
-# An in-memory TfidfRetriever based on Pandas dataframes
-
-from haystack.retriever.sparse import TfidfRetriever
-retriever = TfidfRetriever(document_store=document_store)
-```
-
-### Reader
-
-A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based
-on powerful, but slower deep learning models.
-
-Haystack currently supports Readers based on the frameworks FARM and Transformers.
-With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).
-
-**Here:** a medium sized RoBERTa QA model using a Reader based on FARM (https://huggingface.co/deepset/roberta-base-squad2)
-
-**Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)
-
-**Alternatives (Models):** e.g. "distilbert-base-uncased-distilled-squad" (fast) or "deepset/bert-large-uncased-whole-word-masking-squad2" (good accuracy)
-
-**Hint:** You can adjust the model to return "no answer possible" with the no_ans_boost. Higher values mean the model prefers "no answer possible"
-
-#### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=False)
-```
-
-#### TransformersReader
-
-
-```python
-# Alternative:
-# reader = TransformersReader(model_name_or_path="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1)
-```
-
-### Finder
-
-The Finder sticks together reader and retriever in a pipeline to answer our actual questions.
-
-
-```python
-finder = Finder(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = finder.get_answers(question="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-# prediction = finder.get_answers(question="Who created the Dothraki vocabulary?", top_k_reader=5)
-# prediction = finder.get_answers(question="Who is the sister of Sansa?", top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/4.md b/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/4.md
deleted file mode 100644
index a18c5b741..000000000
--- a/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/4.md
+++ /dev/null
@@ -1,141 +0,0 @@
----
-title: "Tutorial 4"
-metaTitle: "Utilizing existing FAQs for Question Answering"
-metaDescription: ""
-slug: "/docs/tutorial4"
-date: "2020-09-03"
-id: "tutorial4md"
----
-
-# Utilizing existing FAQs for Question Answering
-
-EXECUTABLE VERSION: [colab](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial4_Tutorial4_FAQ_style_QA.ipynb)
-
-While *extractive Question Answering* works on pure texts and is therefore more generalizable, there's also a common alternative that utilizes existing FAQ data.
-
-**Pros**:
-
-- Very fast at inference time
-- Utilize existing FAQ data
-- Quite good control over answers
-
-**Cons**:
-
-- Generalizability: We can only answer questions that are similar to existing ones in FAQ
-
-In some use cases, a combination of extractive QA and FAQ-style can also be an interesting option.
-
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-from haystack import Finder
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-from haystack.retriever.dense import EmbeddingRetriever
-from haystack.utils import print_answers
-import pandas as pd
-import requests
-
-```
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker
-# ! docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.9.2
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-
-```
-
-### Init the DocumentStore
-In contrast to Tutorial 1 (extractive QA), we:
-
-* specify the name of our `text_field` in Elasticsearch that we want to return as an answer
-* specify the name of our `embedding_field` in Elasticsearch where we'll store the embedding of our question and that is used later for calculating our similarity to the incoming user question
-* set `excluded_meta_data=["question_emb"]` so that we don't return the huge embedding vectors in our search results
-
-
-```python
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="",
- index="document",
- embedding_field="question_emb",
- embedding_dim=768,
- excluded_meta_data=["question_emb"])
-```
-
-### Create a Retriever using embeddings
-Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones).
-We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings.
-
-
-```python
-retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert", use_gpu=True)
-```
-
-### Prepare & Index FAQ data
-We create a pandas dataframe containing some FAQ data (i.e curated pairs of question + answer) and index those in elasticsearch.
-Here: We download some question-answer pairs related to COVID-19
-
-
-```python
-# Download
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/faqs/faq_covidbert.csv")
-open('small_faq_covid.csv', 'wb').write(temp.content)
-
-# Get dataframe with columns "question", "answer" and some custom metadata
-df = pd.read_csv("small_faq_covid.csv")
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-df["question"] = df["question"].apply(lambda x: x.strip())
-print(df.head())
-
-# Get embeddings for our questions from the FAQs
-questions = list(df["question"].values)
-df["question_emb"] = retriever.embed_queries(texts=questions)
-df = df.rename(columns={"answer": "text"})
-
-# Convert Dataframe to list of dicts and index them in our DocumentStore
-docs_to_index = df.to_dict(orient="records")
-document_store.write_documents(docs_to_index)
-```
-
-### Ask questions
-Initialize a Finder (this time without a reader) and ask questions
-
-
-```python
-finder = Finder(reader=None, retriever=retriever)
-prediction = finder.get_answers_via_similar_questions(question="How is the virus spreading?", top_k_retriever=10)
-print_answers(prediction, details="all")
-```
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/5.md b/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/5.md
deleted file mode 100644
index fc0f2360e..000000000
--- a/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/5.md
+++ /dev/null
@@ -1,174 +0,0 @@
----
-title: "Tutorial 5"
-metaTitle: "Evaluation of a QA System"
-metaDescription: ""
-slug: "/docs/tutorial5"
-date: "2020-09-03"
-id: "tutorial5md"
----
-
-# Evaluation of a QA System
-
-EXECUTABLE VERSION: [colab](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial5_Evaluation.ipynb)
-
-To be able to make a statement about the performance of a question-answering system, it is important to evalute it. Furthermore, evaluation allows to determine which parts of the system can be improved.
-
-## Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-from farm.utils import initialize_device_settings
-
-device, n_gpu = initialize_device_settings(use_cuda=True)
-```
-
-
-```python
-from haystack.preprocessor.utils import fetch_archive_from_http
-
-# Download evaluation data, which is a subset of Natural Questions development set containing 50 documents
-doc_dir = "../data/nq"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/nq_dev_subset_v2.json.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-
-```python
-# make sure these indices do not collide with existing ones, the indices will be wiped clean before data is inserted
-doc_index = "tutorial5_docs"
-label_index = "tutorial5_labels"
-```
-
-
-```python
-# Connect to Elasticsearch
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-# Connect to Elasticsearch
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document",
- create_index=False, embedding_field="emb",
- embedding_dim=768, excluded_meta_data=["emb"])
-```
-
-
-```python
-# Add evaluation data to Elasticsearch Document Store
-# We first delete the custom tutorial indices to not have duplicate elements
-document_store.delete_all_documents(index=doc_index)
-document_store.delete_all_documents(index=label_index)
-document_store.add_eval_data(filename="../data/nq/nq_dev_subset_v2.json", doc_index=doc_index, label_index=label_index)
-```
-
-## Initialize components of QA-System
-
-
-```python
-# Initialize Retriever
-from haystack.retriever.sparse import ElasticsearchRetriever
-retriever = ElasticsearchRetriever(document_store=document_store)
-# Alternative: Evaluate DensePassageRetriever
-# Note, that DPR works best when you index short passages < 512 tokens as only those tokens will be used for the embedding.
-# Here, for nq_dev_subset_v2.json we have avg. num of tokens = 5220(!).
-# DPR still outperforms Elastic's BM25 by a small margin here.
-# from haystack.retriever.dense import DensePassageRetriever
-# retriever = DensePassageRetriever(document_store=document_store,
-# query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
-# passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
-# use_gpu=True,
-# embed_title=True,
-# max_seq_len=256,
-# batch_size=16,
-# remove_sep_tok_from_untitled_passages=True)
-#document_store.update_embeddings(retriever, index=doc_index)
-```
-
-
-```python
-# Initialize Reader
-from haystack.reader.farm import FARMReader
-
-reader = FARMReader("deepset/roberta-base-squad2", top_k_per_candidate=4)
-```
-
-
-```python
-# Initialize Finder which sticks together Reader and Retriever
-from haystack.finder import Finder
-
-finder = Finder(reader, retriever)
-```
-
-## Evaluation of Retriever
-
-
-```python
-## Evaluate Retriever on its own
-retriever_eval_results = retriever.eval(top_k=20, label_index=label_index, doc_index=doc_index)
-## Retriever Recall is the proportion of questions for which the correct document containing the answer is
-## among the correct documents
-print("Retriever Recall:", retriever_eval_results["recall"])
-## Retriever Mean Avg Precision rewards retrievers that give relevant documents a higher rank
-print("Retriever Mean Avg Precision:", retriever_eval_results["map"])
-```
-
-## Evaluation of Reader
-
-
-```python
-# Evaluate Reader on its own
-reader_eval_results = reader.eval(document_store=document_store, device=device, label_index=label_index, doc_index=doc_index)
-# Evaluation of Reader can also be done directly on a SQuAD-formatted file without passing the data to Elasticsearch
-#reader_eval_results = reader.eval_on_file("../data/nq", "nq_dev_subset_v2.json", device=device)
-
-## Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer
-print("Reader Top-N-Accuracy:", reader_eval_results["top_n_accuracy"])
-## Reader Exact Match is the proportion of questions where the predicted answer is exactly the same as the correct answer
-print("Reader Exact Match:", reader_eval_results["EM"])
-## Reader F1-Score is the average overlap between the predicted answers and the correct answers
-print("Reader F1-Score:", reader_eval_results["f1"])
-```
-
-## Evaluation of Finder
-
-
-```python
-# Evaluate combination of Reader and Retriever through Finder
-# Evaluate combination of Reader and Retriever through Finder
-finder_eval_results = finder.eval(top_k_retriever=1, top_k_reader=10, label_index=label_index, doc_index=doc_index)
-finder.print_eval_results(finder_eval_results)
-```
-
-
-```python
-
-```
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/6.md b/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/6.md
deleted file mode 100644
index 445886403..000000000
--- a/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/6.md
+++ /dev/null
@@ -1,211 +0,0 @@
----
-title: "Tutorial 6"
-metaTitle: "Better retrieval via Dense Passage Retrieval"
-metaDescription: ""
-slug: "/docs/tutorial6"
-date: "2020-09-03"
-id: "tutorial6md"
----
-
-# Better Retrieval via "Dense Passage Retrieval"
-
-EXECUTABLE VERSION: [colab](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb)
-
-### Importance of Retrievers
-
-The Retriever has a huge impact on the performance of our overall search pipeline.
-
-
-### Different types of Retrievers
-#### Sparse
-Family of algorithms based on counting the occurrences of words (bag-of-words) resulting in very sparse vectors with length = vocab size.
-
-**Examples**: BM25, TF-IDF
-
-**Pros**: Simple, fast, well explainable
-
-**Cons**: Relies on exact keyword matches between query and text
-
-
-#### Dense
-These retrievers use neural network models to create "dense" embedding vectors. Within this family there are two different approaches:
-
-a) Single encoder: Use a **single model** to embed both query and passage.
-b) Dual-encoder: Use **two models**, one to embed the query and one to embed the passage
-
-Recent work suggests that dual encoders work better, likely because they can deal better with the different nature of query and passage (length, style, syntax ...).
-
-**Examples**: REALM, DPR, Sentence-Transformers
-
-**Pros**: Captures semantinc similarity instead of "word matches" (e.g. synonyms, related topics ...)
-
-**Cons**: Computationally more heavy, initial training of model
-
-
-### "Dense Passage Retrieval"
-
-In this Tutorial, we want to highlight one "Dense Dual-Encoder" called Dense Passage Retriever.
-It was introdoced by Karpukhin et al. (2020, https://arxiv.org/abs/2004.04906.
-
-Original Abstract:
-
-_"Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks."_
-
-Paper: https://arxiv.org/abs/2004.04906
-Original Code: https://fburl.com/qa-dpr
-
-
-*Use this* [link](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb) *to open the notebook in Google Colab.*
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-### Document Store
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-The default flavour of FAISSDocumentStore is "Flat" but can also be set to "HNSW" for
-faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.
-For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-
-
-```python
-from haystack.document_store.faiss import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever, Reader, & Finder
-
-#### Retriever
-
-**Here:** We use a `DensePassageRetriever`
-
-**Alternatives:**
-
-- The `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-
-
-```python
-from haystack.retriever.dense import DensePassageRetriever
-retriever = DensePassageRetriever(document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- max_seq_len_query=64,
- max_seq_len_passage=256,
- batch_size=16,
- use_gpu=True,
- embed_title=True,
- use_fast_tokenizers=True)
-# Important:
-# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all
-# previously indexed documents and update their embedding representation.
-# While this can be a time consuming operation (depending on corpus size), it only needs to be done once.
-# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.
-document_store.update_embeddings(retriever)
-```
-
-#### Reader
-
-Similar to previous Tutorials we now initalize our reader.
-
-Here we use a FARMReader with the *deepset/roberta-base-squad2* model (see: https://huggingface.co/deepset/roberta-base-squad2)
-
-
-
-##### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-#### Finder
-
-The Finder sticks together reader and retriever in a pipeline to answer our actual questions.
-
-
-```python
-finder = Finder(reader, retriever)
-```
-
-### Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = finder.get_answers(question="Who created the Dothraki vocabulary?", top_k_retriever=10, top_k_reader=5)
-
-#prediction = finder.get_answers(question="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-#prediction = finder.get_answers(question="Who is the sister of Sansa?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
-
-
-```python
-
-```
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/7.md b/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/7.md
deleted file mode 100644
index c9c4abf21..000000000
--- a/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/7.md
+++ /dev/null
@@ -1,174 +0,0 @@
----
-title: "Tutorial 7"
-metaTitle: "Generative QA"
-metaDescription: ""
-slug: "/docs/tutorial7"
-date: "2020-11-12"
-id: "tutorial7md"
----
-
-# Generative QA with "Retrieval-Augmented Generation"
-
-While extractive QA highlights the span of text that answers a query,
-generative QA can return a novel text answer that it has composed.
-In this tutorial, you will learn how to set up a generative system using the
-[RAG model](https://arxiv.org/abs/2005.11401) which conditions the
-answer generator on a set of retrieved documents.
-
-Here are the packages and imports that we'll need:
-
-
-```python
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-from typing import List
-import requests
-import pandas as pd
-from haystack import Document
-from haystack.document_store.faiss import FAISSDocumentStore
-from haystack.generator.transformers import RAGenerator
-from haystack.retriever.dense import DensePassageRetriever
-```
-
-Let's download a csv containing some sample text and preprocess the data.
-
-
-
-```python
-# Download sample
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/haystack/master/tutorials/small_generator_dataset.csv")
-open('small_generator_dataset.csv', 'wb').write(temp.content)
-
-# Create dataframe with columns "title" and "text"
-df = pd.read_csv("small_generator_dataset.csv", sep=',')
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-
-print(df.head())
-```
-
-We can cast our data into Haystack Document objects.
-Alternatively, we can also just use dictionaries with "text" and "meta" fields
-
-
-```python
-# Use data to initialize Document objects
-titles = list(df["title"].values)
-texts = list(df["text"].values)
-documents: List[Document] = []
-for title, text in zip(titles, texts):
- documents.append(
- Document(
- text=text,
- meta={
- "name": title or ""
- }
- )
- )
-```
-
-Here we initialize the FAISSDocumentStore, DensePassageRetriever and RAGenerator.
-FAISS is chosen here since it is optimized vector storage.
-
-
-```python
-# Initialize FAISS document store.
-# Set `return_embedding` to `True`, so generator doesn't have to perform re-embedding
-document_store = FAISSDocumentStore(
- faiss_index_factory_str="Flat",
- return_embedding=True
-)
-
-# Initialize DPR Retriever to encode documents, encode question and query documents
-retriever = DensePassageRetriever(
- document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- use_gpu=False,
- embed_title=True,
-)
-
-# Initialize RAG Generator
-generator = RAGenerator(
- model_name_or_path="facebook/rag-token-nq",
- use_gpu=False,
- top_k_answers=1,
- max_length=200,
- min_length=2,
- embed_title=True,
- num_beams=2,
-)
-```
-
-We write documents to the DocumentStore, first by deleting any remaining documents then calling `write_documents()`.
-The `update_embeddings()` method uses the retriever to create an embedding for each document.
-
-
-
-```python
-# Delete existing documents in documents store
-document_store.delete_all_documents()
-
-# Write documents to document store
-document_store.write_documents(documents)
-
-# Add documents embeddings to index
-document_store.update_embeddings(
- retriever=retriever
-)
-```
-
-Here are our questions:
-
-
-```python
-QUESTIONS = [
- "who got the first nobel prize in physics",
- "when is the next deadpool movie being released",
- "which mode is used for short wave broadcast service",
- "who is the owner of reading football club",
- "when is the next scandal episode coming out",
- "when is the last time the philadelphia won the superbowl",
- "what is the most current adobe flash player version",
- "how many episodes are there in dragon ball z",
- "what is the first step in the evolution of the eye",
- "where is gall bladder situated in human body",
- "what is the main mineral in lithium batteries",
- "who is the president of usa right now",
- "where do the greasers live in the outsiders",
- "panda is a national animal of which country",
- "what is the name of manchester united stadium",
-]
-```
-
-Now let's run our system!
-The retriever will pick out a small subset of documents that it finds relevant.
-These are used to condition the generator as it generates the answer.
-What it should return then are novel text spans that form and answer to your question!
-
-
-```python
-# Now generate an answer for each question
-for question in QUESTIONS:
- # Retrieve related documents from retriever
- retriever_results = retriever.retrieve(
- query=question
- )
-
- # Now generate answer from question and retrieved documents
- predicted_result = generator.predict(
- query=question,
- documents=retriever_results,
- top_k=1
- )
-
- # Print you answer
- answers = predicted_result["answers"]
- print(f'Generated answer is \'{answers[0]["answer"]}\' for the question = \'{question}\'')
-```
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/preprocessing.md b/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/preprocessing.md
deleted file mode 100644
index 6924f3d39..000000000
--- a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/preprocessing.md
+++ /dev/null
@@ -1,111 +0,0 @@
----
-title: "Preprocessing"
-metaTitle: "Preprocessing"
-metaDescription: ""
-slug: "/docs/preprocessing"
-date: "2020-09-03"
-id: "preprocessingmd"
----
-
-# Preprocessing
-
-Haystack includes a suite of tools to:
-
-* extract text from different file types,
-* normalize white space
-* split text into smaller pieces to optimize retrieval
-
-These data preprocessing steps can have a big impact on the systems performance
-and effective handling of data is key to getting the most out of Haystack.
-
-The Document Store expects its inputs to come in the following format.
-The sections below will show you all the tools you'll need to ready your data for storing.
-
-```python
-docs = [
- {
- 'text': DOCUMENT_TEXT_HERE,
- 'meta': {'name': DOCUMENT_NAME, ...}
- }, ...
-]
-```
-
-## File Conversion
-
-There are a range of different file converters in Haystack that
-can extract text from files and cast them into the unified dictionary format shown above.
-Haystack features support for txt, pdf and docx files and there is even a converter that leverages Apache Tika.
-Please refer to [the API docs](/docs/latest/file_convertersmd) to see which converter best suits you.
-
-"}.
-Optionally: Include meta data via {"text": "",
-"meta":{"name": ", "author": "somebody", ...}}
-It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: Optional name of index where the documents shall be written to.
-If None, the DocumentStore's default index (self.index) will be used.
-
-**Returns**:
-
-None
-
-
-#### get\_all\_documents
-
-```python
- | @abstractmethod
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Get documents from the document store.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
-DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
-Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-
-
-# Module: elasticsearch
-
-
-## Class: ElasticsearchDocumentStore
-
-```python
-class ElasticsearchDocumentStore(BaseDocumentStore)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(host: str = "localhost", port: int = 9200, username: str = "", password: str = "", index: str = "document", label_index: str = "label", search_fields: Union[str, list] = "text", text_field: str = "text", name_field: str = "name", embedding_field: str = "embedding", embedding_dim: int = 768, custom_mapping: Optional[dict] = None, excluded_meta_data: Optional[list] = None, faq_question_field: Optional[str] = None, analyzer: str = "standard", scheme: str = "http", ca_certs: bool = False, verify_certs: bool = True, create_index: bool = True, update_existing_documents: bool = False, refresh_type: str = "wait_for", similarity="dot_product", timeout=30, return_embedding: bool = False)
-```
-
-A DocumentStore using Elasticsearch to store and query the documents for our search.
-
-* Keeps all the logic to store and query documents from Elastic, incl. mapping of fields, adding filters or boosts to your queries, and storing embeddings
-* You can either use an existing Elasticsearch index or create a new one via haystack
-* Retrievers operate on top of this DocumentStore to find the relevant documents for a query
-
-**Arguments**:
-
-- `host`: url of elasticsearch
-- `port`: port of elasticsearch
-- `username`: username
-- `password`: password
-- `index`: Name of index in elasticsearch to use. If not existing yet, we will create one.
-- `search_fields`: Name of fields used by ElasticsearchRetriever to find matches in the docs to our incoming query (using elastic's multi_match query), e.g. ["title", "full_text"]
-- `text_field`: Name of field that might contain the answer and will therefore be passed to the Reader Model (e.g. "full_text").
-If no Reader is used (e.g. in FAQ-Style QA) the plain content of this field will just be returned.
-- `name_field`: Name of field that contains the title of the the doc
-- `embedding_field`: Name of field containing an embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `embedding_dim`: Dimensionality of embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `custom_mapping`: If you want to use your own custom mapping for creating a new index in Elasticsearch, you can supply it here as a dictionary.
-- `analyzer`: Specify the default analyzer from one of the built-ins when creating a new Elasticsearch Index.
-Elasticsearch also has built-in analyzers for different languages (e.g. impacting tokenization). More info at:
-https://www.elastic.co/guide/en/elasticsearch/reference/7.9/analysis-analyzers.html
-- `excluded_meta_data`: Name of fields in Elasticsearch that should not be returned (e.g. [field_one, field_two]).
-Helpful if you have fields with long, irrelevant content that you don't want to display in results (e.g. embedding vectors).
-- `scheme`: 'https' or 'http', protocol used to connect to your elasticsearch instance
-- `ca_certs`: Root certificates for SSL
-- `verify_certs`: Whether to be strict about ca certificates
-- `create_index`: Whether to try creating a new index (If the index of that name is already existing, we will just continue in any case)
-- `update_existing_documents`: Whether to update any existing documents with the same ID when adding
-documents. When set as True, any document with an existing ID gets updated.
-If set to False, an error is raised if the document ID of the document being
-added already exists.
-- `refresh_type`: Type of ES refresh used to control when changes made by a request (e.g. bulk) are made visible to search.
-If set to 'wait_for', continue only after changes are visible (slow, but safe).
-If set to 'false', continue directly (fast, but sometimes unintuitive behaviour when docs are not immediately available after ingestion).
-More info at https://www.elastic.co/guide/en/elasticsearch/reference/6.8/docs-refresh.html
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
-more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
-- `timeout`: Number of seconds after which an ElasticSearch request times out.
-- `return_embedding`: To return document embedding
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index=None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index=None) -> List[Document]
-```
-
-Fetch documents by specifying a list of text id strings
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
-```
-
-Indexes documents for later queries in Elasticsearch.
-
-Behaviour if a document with the same ID already exists in ElasticSearch:
-a) (Default) Throw Elastic's standard error message for duplicate IDs.
-b) If `self.update_existing_documents=True` for DocumentStore: Overwrite existing documents.
-(This is only relevant if you pass your own ID when initializing a `Document`.
-If don't set custom IDs for your Documents or just pass a list of dictionaries here,
-they will automatically get UUIDs assigned. See the `Document` class for details)
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
-For documents as dictionaries, the format is {"text": ""}.
-Optionally: Include meta data via {"text": "",
-"meta":{"name": ", "author": "somebody", ...}}
-It can be used for filtering and is accessible in the responses of the Finder.
-Advanced: If you are using your own Elasticsearch mapping, the key names in the dictionary
-should be changed to what you have set for self.text_field and self.name_field.
-- `index`: Elasticsearch index where the documents should be indexed. If not supplied, self.index will be used.
-
-**Returns**:
-
-None
-
-
-#### write\_labels
-
-```python
- | write_labels(labels: Union[List[Label], List[dict]], index: Optional[str] = None)
-```
-
-Write annotation labels into document store.
-
-
-#### update\_document\_meta
-
-```python
- | update_document_meta(id: str, meta: Dict[str, str])
-```
-
-Update the metadata dictionary of a document by specifying its string id
-
-
-#### get\_document\_count
-
-```python
- | get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
-```
-
-Return the number of documents in the document store.
-
-
-#### get\_label\_count
-
-```python
- | get_label_count(index: Optional[str] = None) -> int
-```
-
-Return the number of labels in the document store
-
-
-#### get\_all\_documents
-
-```python
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Get documents from the document store.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
-DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
-Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-
-
-#### get\_all\_labels
-
-```python
- | get_all_labels(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None) -> List[Label]
-```
-
-Return all labels in the document store
-
-
-#### get\_all\_documents\_in\_index
-
-```python
- | get_all_documents_in_index(index: str, filters: Optional[Dict[str, List[str]]] = None) -> List[dict]
-```
-
-Return all documents in a specific index in the document store
-
-
-#### query
-
-```python
- | query(query: Optional[str], filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, custom_query: Optional[str] = None, index: Optional[str] = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query as defined by the BM25 algorithm.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.array, filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
-Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: Index name for storing the docs and metadata
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### describe\_documents
-
-```python
- | describe_documents(index=None)
-```
-
-Return a summary of the documents in the document store
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever
-- `index`: Index name to update
-
-**Returns**:
-
-None
-
-
-#### add\_eval\_data
-
-```python
- | add_eval_data(filename: str, doc_index: str = "eval_document", label_index: str = "label")
-```
-
-Adds a SQuAD-formatted file to the DocumentStore in order to be able to perform evaluation on it.
-
-**Arguments**:
-
-- `filename`: Name of the file containing evaluation data
-:type filename: str
-- `doc_index`: Elasticsearch index where evaluation documents should be stored
-:type doc_index: str
-- `label_index`: Elasticsearch index where labeled questions should be stored
-:type label_index: str
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: str, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-## Class: OpenDistroElasticsearchDocumentStore
-
-```python
-class OpenDistroElasticsearchDocumentStore(ElasticsearchDocumentStore)
-```
-
-Document Store using the Open Distro for Elasticsearch. It is compatible with the AWS Elasticsearch Service.
-
-In addition to native Elasticsearch query & filtering, it provides efficient vector similarity search using
-the KNN plugin that can scale to a large number of documents.
-
-
-# Module: memory
-
-
-## Class: InMemoryDocumentStore
-
-```python
-class InMemoryDocumentStore(BaseDocumentStore)
-```
-
-In-memory document store
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(embedding_field: Optional[str] = "embedding", return_embedding: bool = False, similarity="dot_product")
-```
-
-**Arguments**:
-
-- `embedding_field`: Name of field containing an embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `return_embedding`: To return document embedding
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
-more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
-For documents as dictionaries, the format is {"text": ""}.
-Optionally: Include meta data via {"text": "",
-"meta": {"name": ", "author": "somebody", ...}}
-It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: write documents to a custom namespace. For instance, documents for evaluation can be indexed in a
-separate index than the documents for search.
-
-**Returns**:
-
-None
-
-
-#### write\_labels
-
-```python
- | write_labels(labels: Union[List[dict], List[Label]], index: Optional[str] = None)
-```
-
-Write annotation labels into document store.
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index: Optional[str] = None) -> List[Document]
-```
-
-Fetch documents by specifying a list of text id strings
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: List[float], filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
-Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: Index name for storing the docs and metadata
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever
-- `index`: Index name to update
-
-**Returns**:
-
-None
-
-
-#### get\_document\_count
-
-```python
- | get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
-```
-
-Return the number of documents in the document store.
-
-
-#### get\_label\_count
-
-```python
- | get_label_count(index: Optional[str] = None) -> int
-```
-
-Return the number of labels in the document store
-
-
-#### get\_all\_documents
-
-```python
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Get documents from the document store.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
-DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
-Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-
-
-#### get\_all\_labels
-
-```python
- | get_all_labels(index: str = None, filters: Optional[Dict[str, List[str]]] = None) -> List[Label]
-```
-
-Return all labels in the document store
-
-
-#### add\_eval\_data
-
-```python
- | add_eval_data(filename: str, doc_index: Optional[str] = None, label_index: Optional[str] = None)
-```
-
-Adds a SQuAD-formatted file to the DocumentStore in order to be able to perform evaluation on it.
-
-**Arguments**:
-
-- `filename`: Name of the file containing evaluation data
-:type filename: str
-- `doc_index`: Elasticsearch index where evaluation documents should be stored
-:type doc_index: str
-- `label_index`: Elasticsearch index where labeled questions should be stored
-:type label_index: str
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-# Module: sql
-
-
-## Class: SQLDocumentStore
-
-```python
-class SQLDocumentStore(BaseDocumentStore)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(url: str = "sqlite://", index: str = "document", label_index: str = "label", update_existing_documents: bool = False, batch_size: int = 32766)
-```
-
-An SQL backed DocumentStore. Currently supports SQLite, PostgreSQL and MySQL backends.
-
-**Arguments**:
-
-- `url`: URL for SQL database as expected by SQLAlchemy. More info here: https://docs.sqlalchemy.org/en/13/core/engines.html#database-urls
-- `index`: The documents are scoped to an index attribute that can be used when writing, querying, or deleting documents.
-This parameter sets the default value for document index.
-- `label_index`: The default value of index attribute for the labels.
-- `update_existing_documents`: Whether to update any existing documents with the same ID when adding
-documents. When set as True, any document with an existing ID gets updated.
-If set to False, an error is raised if the document ID of the document being
-added already exists. Using this parameter could cause performance degradation
-for document insertion.
-- `batch_size`: Maximum number of variable parameters and rows fetched in a single SQL statement,
-to help in excessive memory allocations. In most methods of the DocumentStore this means number of documents fetched in one query.
-Tune this value based on host machine main memory.
-For SQLite versions prior to v3.32.0 keep this value less than 1000.
-More info refer: https://www.sqlite.org/limits.html
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index: Optional[str] = None) -> List[Document]
-```
-
-Fetch documents by specifying a list of text id strings
-
-
-#### get\_documents\_by\_vector\_ids
-
-```python
- | get_documents_by_vector_ids(vector_ids: List[str], index: Optional[str] = None)
-```
-
-Fetch documents by specifying a list of text vector id strings
-
-
-#### get\_all\_documents
-
-```python
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Get documents from the document store.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
-DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
-Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-
-
-#### get\_all\_labels
-
-```python
- | get_all_labels(index=None, filters: Optional[dict] = None)
-```
-
-Return all labels in the document store
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
-For documents as dictionaries, the format is {"text": ""}.
-Optionally: Include meta data via {"text": "",
-"meta":{"name": ", "author": "somebody", ...}}
-It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: add an optional index attribute to documents. It can be later used for filtering. For instance,
-documents for evaluation can be indexed in a separate index than the documents for search.
-
-**Returns**:
-
-None
-
-
-#### write\_labels
-
-```python
- | write_labels(labels, index=None)
-```
-
-Write annotation labels into document store.
-
-
-#### update\_vector\_ids
-
-```python
- | update_vector_ids(vector_id_map: Dict[str, str], index: Optional[str] = None)
-```
-
-Update vector_ids for given document_ids.
-
-**Arguments**:
-
-- `vector_id_map`: dict containing mapping of document_id -> vector_id.
-- `index`: filter documents by the optional index attribute for documents in database.
-
-
-#### update\_document\_meta
-
-```python
- | update_document_meta(id: str, meta: Dict[str, str])
-```
-
-Update the metadata dictionary of a document by specifying its string id
-
-
-#### add\_eval\_data
-
-```python
- | add_eval_data(filename: str, doc_index: str = "eval_document", label_index: str = "label")
-```
-
-Adds a SQuAD-formatted file to the DocumentStore in order to be able to perform evaluation on it.
-
-**Arguments**:
-
-- `filename`: Name of the file containing evaluation data
-:type filename: str
-- `doc_index`: Elasticsearch index where evaluation documents should be stored
-:type doc_index: str
-- `label_index`: Elasticsearch index where labeled questions should be stored
-:type label_index: str
-
-
-#### get\_document\_count
-
-```python
- | get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
-```
-
-Return the number of documents in the document store.
-
-
-#### get\_label\_count
-
-```python
- | get_label_count(index: Optional[str] = None) -> int
-```
-
-Return the number of labels in the document store
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-# Module: faiss
-
-
-## Class: FAISSDocumentStore
-
-```python
-class FAISSDocumentStore(SQLDocumentStore)
-```
-
-Document store for very large scale embedding based dense retrievers like the DPR.
-
-It implements the FAISS library(https://github.com/facebookresearch/faiss)
-to perform similarity search on vectors.
-
-The document text and meta-data (for filtering) are stored using the SQLDocumentStore, while
-the vector embeddings are indexed in a FAISS Index.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(sql_url: str = "sqlite:///", index_buffer_size: int = 10_000, vector_dim: int = 768, faiss_index_factory_str: str = "Flat", faiss_index: Optional[faiss.swigfaiss.Index] = None, return_embedding: bool = False, update_existing_documents: bool = False, index: str = "document", similarity: str = "dot_product", **kwargs, ,)
-```
-
-**Arguments**:
-
-- `sql_url`: SQL connection URL for database. It defaults to local file based SQLite DB. For large scale
-deployment, Postgres is recommended.
-- `index_buffer_size`: When working with large datasets, the ingestion process(FAISS + SQL) can be buffered in
-smaller chunks to reduce memory footprint.
-- `vector_dim`: the embedding vector size.
-- `faiss_index_factory_str`: Create a new FAISS index of the specified type.
-The type is determined from the given string following the conventions
-of the original FAISS index factory.
-Recommended options:
-- "Flat" (default): Best accuracy (= exact). Becomes slow and RAM intense for > 1 Mio docs.
-- "HNSW": Graph-based heuristic. If not further specified,
-we use a RAM intense, but more accurate config:
-HNSW256, efConstruction=256 and efSearch=256
-- "IVFx,Flat": Inverted Index. Replace x with the number of centroids aka nlist.
-Rule of thumb: nlist = 10 * sqrt (num_docs) is a good starting point.
-For more details see:
-- Overview of indices https://github.com/facebookresearch/faiss/wiki/Faiss-indexes
-- Guideline for choosing an index https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-- FAISS Index factory https://github.com/facebookresearch/faiss/wiki/The-index-factory
-Benchmarks: XXX
-- `faiss_index`: Pass an existing FAISS Index, i.e. an empty one that you configured manually
-or one with docs that you used in Haystack before and want to load again.
-- `return_embedding`: To return document embedding
-- `update_existing_documents`: Whether to update any existing documents with the same ID when adding
-documents. When set as True, any document with an existing ID gets updated.
-If set to False, an error is raised if the document ID of the document being
-added already exists.
-- `index`: Name of index in document store to use.
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
-more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
-```
-
-Add new documents to the DocumentStore.
-
-**Arguments**:
-
-- `documents`: List of `Dicts` or List of `Documents`. If they already contain the embeddings, we'll index
-them right away in FAISS. If not, you can later call update_embeddings() to create & index them.
-- `index`: (SQL) index name for storing the docs and metadata
-
-**Returns**:
-
-
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever to use to get embeddings for text
-- `index`: (SQL) index name for storing the docs and metadata
-
-**Returns**:
-
-None
-
-
-#### get\_all\_documents
-
-```python
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Get documents from the document store.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
-DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
-Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-
-
-#### train\_index
-
-```python
- | train_index(documents: Optional[Union[List[dict], List[Document]]], embeddings: Optional[np.array] = None)
-```
-
-Some FAISS indices (e.g. IVF) require initial "training" on a sample of vectors before you can add your final vectors.
-The train vectors should come from the same distribution as your final ones.
-You can pass either documents (incl. embeddings) or just the plain embeddings that the index shall be trained on.
-
-**Arguments**:
-
-- `documents`: Documents (incl. the embeddings)
-- `embeddings`: Plain embeddings
-
-**Returns**:
-
-None
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index=None)
-```
-
-Delete all documents from the document store.
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.array, filters: Optional[dict] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
-Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: (SQL) index name for storing the docs and metadata
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### save
-
-```python
- | save(file_path: Union[str, Path])
-```
-
-Save FAISS Index to the specified file.
-
-**Arguments**:
-
-- `file_path`: Path to save to.
-
-**Returns**:
-
-None
-
-
-#### load
-
-```python
- | @classmethod
- | load(cls, faiss_file_path: Union[str, Path], sql_url: str, index_buffer_size: int = 10_000)
-```
-
-Load a saved FAISS index from a file and connect to the SQL database.
-Note: In order to have a correct mapping from FAISS to SQL,
-make sure to use the same SQL DB that you used when calling `save()`.
-
-**Arguments**:
-
-- `faiss_file_path`: Stored FAISS index file. Can be created via calling `save()`
-- `sql_url`: Connection string to the SQL database that contains your docs and metadata.
-- `index_buffer_size`: When working with large datasets, the ingestion process(FAISS + SQL) can be buffered in
-smaller chunks to reduce memory footprint.
-
-**Returns**:
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/api/api/file_converter.md b/src/pages/docs/versions/master/v0.7.0/site/en/api/api/file_converter.md
deleted file mode 100644
index 5e27e5042..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/api/api/file_converter.md
+++ /dev/null
@@ -1,232 +0,0 @@
----
-title: "File Converters"
-metaTitle: "File Converters"
-metaDescription: ""
-slug: "/docs/file_converters"
-date: "2020-09-03"
-id: "file_convertersmd"
----
-
-# File Converters
-
-
-# Module: base
-
-
-## Class: BaseConverter
-
-```python
-class BaseConverter()
-```
-
-Base class for implementing file converts to transform input documents to text format for ingestion in DocumentStore.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
-The tabular structures in documents might be noise for the reader model if it
-does not have table parsing capability for finding answers. However, tables
-may also have long strings that could possible candidate for searching answers.
-The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
-(https://en.wikipedia.org/wiki/ISO_639-1) format.
-This option can be used to add test for encoding errors. If the extracted text is
-not one of the valid languages, then it might likely be encoding error resulting
-in garbled text.
-
-
-#### convert
-
-```python
- | @abstractmethod
- | convert(file_path: Path, meta: Optional[Dict[str, str]]) -> Dict[str, Any]
-```
-
-Convert a file to a dictionary containing the text and any associated meta data.
-
-File converters may extract file meta like name or size. In addition to it, user
-supplied meta data like author, url, external IDs can be supplied as a dictionary.
-
-**Arguments**:
-
-- `file_path`: path of the file to convert
-- `meta`: dictionary of meta data key-value pairs to append in the returned document.
-
-
-#### validate\_language
-
-```python
- | validate_language(text: str) -> bool
-```
-
-Validate if the language of the text is one of valid languages.
-
-
-# Module: txt
-
-
-## Class: TextConverter
-
-```python
-class TextConverter(BaseConverter)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(remove_numeric_tables: Optional[bool] = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
-The tabular structures in documents might be noise for the reader model if it
-does not have table parsing capability for finding answers. However, tables
-may also have long strings that could possible candidate for searching answers.
-The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
-(https://en.wikipedia.org/wiki/ISO_639-1) format.
-This option can be used to add test for encoding errors. If the extracted text is
-not one of the valid languages, then it might likely be encoding error resulting
-in garbled text.
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None, encoding: str = "utf-8") -> Dict[str, Any]
-```
-
-Reads text from a txt file and executes optional preprocessing steps.
-
-**Arguments**:
-
-- `file_path`: Path of the file to convert
-- `meta`: Optional meta data that should be associated with the the document (e.g. name)
-- `encoding`: Encoding of the file
-
-**Returns**:
-
-Dict of format {"text": "The text from file", "meta": meta}}
-
-
-# Module: docx
-
-
-## Class: DocxToTextConverter
-
-```python
-class DocxToTextConverter(BaseConverter)
-```
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None) -> Dict[str, Any]
-```
-
-Extract text from a .docx file.
-Note: As docx doesn't contain "page" information, we actually extract and return a list of paragraphs here.
-For compliance with other converters we nevertheless opted for keeping the methods name.
-
-**Arguments**:
-
-- `file_path`: Path to the .docx file you want to convert
-
-
-# Module: tika
-
-
-## Class: TikaConverter
-
-```python
-class TikaConverter(BaseConverter)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(tika_url: str = "http://localhost:9998/tika", remove_numeric_tables: Optional[bool] = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `tika_url`: URL of the Tika server
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
-The tabular structures in documents might be noise for the reader model if it
-does not have table parsing capability for finding answers. However, tables
-may also have long strings that could possible candidate for searching answers.
-The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
-(https://en.wikipedia.org/wiki/ISO_639-1) format.
-This option can be used to add test for encoding errors. If the extracted text is
-not one of the valid languages, then it might likely be encoding error resulting
-in garbled text.
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None) -> Dict[str, Any]
-```
-
-**Arguments**:
-
-- `file_path`: Path of file to be converted.
-
-**Returns**:
-
-a list of pages and the extracted meta data of the file.
-
-
-# Module: pdf
-
-
-## Class: PDFToTextConverter
-
-```python
-class PDFToTextConverter(BaseConverter)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(remove_numeric_tables: Optional[bool] = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
-The tabular structures in documents might be noise for the reader model if it
-does not have table parsing capability for finding answers. However, tables
-may also have long strings that could possible candidate for searching answers.
-The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
-(https://en.wikipedia.org/wiki/ISO_639-1) format.
-This option can be used to add test for encoding errors. If the extracted text is
-not one of the valid languages, then it might likely be encoding error resulting
-in garbled text.
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None) -> Dict[str, Any]
-```
-
-Extract text from a .pdf file.
-
-**Arguments**:
-
-- `file_path`: Path to the .pdf file you want to convert
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/api/api/generator.md b/src/pages/docs/versions/master/v0.7.0/site/en/api/api/generator.md
deleted file mode 100644
index fc670c18b..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/api/api/generator.md
+++ /dev/null
@@ -1,150 +0,0 @@
----
-title: "Generator"
-metaTitle: "Generator"
-metaDescription: ""
-slug: "/docs/apigeneratormd"
-date: "2020-09-03"
-id: "apigeneratormd"
----
-
-# Generator
-
-
-# Module: base
-
-
-## Class: BaseGenerator
-
-```python
-class BaseGenerator(ABC)
-```
-
-Abstract class for Generators
-
-
-#### predict
-
-```python
- | @abstractmethod
- | predict(query: str, documents: List[Document], top_k: Optional[int]) -> Dict
-```
-
-Abstract method to generate answers.
-
-**Arguments**:
-
-- `query`: Query
-- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on.
-- `top_k`: Number of returned answers
-
-**Returns**:
-
-Generated answers plus additional infos in a dict
-
-
-# Module: transformers
-
-
-## Class: RAGenerator
-
-```python
-class RAGenerator(BaseGenerator)
-```
-
-Implementation of Facebook's Retrieval-Augmented Generator (https://arxiv.org/abs/2005.11401) based on
-HuggingFace's transformers (https://huggingface.co/transformers/model_doc/rag.html).
-
-Instead of "finding" the answer within a document, these models **generate** the answer.
-In that sense, RAG follows a similar approach as GPT-3 but it comes with two huge advantages
-for real-world applications:
-a) it has a manageable model size
-b) the answer generation is conditioned on retrieved documents,
-i.e. the model can easily adjust to domain documents even after training has finished
-(in contrast: GPT-3 relies on the web data seen during training)
-
-**Example**
-
-```python
-| query = "who got the first nobel prize in physics?"
-|
-| # Retrieve related documents from retriever
-| retrieved_docs = retriever.retrieve(query=query)
-|
-| # Now generate answer from query and retrieved documents
-| generator.predict(
-| query=query,
-| documents=retrieved_docs,
-| top_k=1
-| )
-|
-| # Answer
-|
-| {'query': 'who got the first nobel prize in physics',
-| 'answers':
-| [{'query': 'who got the first nobel prize in physics',
-| 'answer': ' albert einstein',
-| 'meta': { 'doc_ids': [...],
-| 'doc_scores': [80.42758 ...],
-| 'doc_probabilities': [40.71379089355469, ...
-| 'texts': ['Albert Einstein was a ...]
-| 'titles': ['"Albert Einstein"', ...]
-| }}]}
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: str = "facebook/rag-token-nq", retriever: Optional[DensePassageRetriever] = None, generator_type: RAGeneratorType = RAGeneratorType.TOKEN, top_k_answers: int = 2, max_length: int = 200, min_length: int = 2, num_beams: int = 2, embed_title: bool = True, prefix: Optional[str] = None, use_gpu: bool = True)
-```
-
-Load a RAG model from Transformers along with passage_embedding_model.
-See https://huggingface.co/transformers/model_doc/rag.html for more details
-
-**Arguments**:
-
-- `model_name_or_path`: Directory of a saved model or the name of a public model e.g.
-'facebook/rag-token-nq', 'facebook/rag-sequence-nq'.
-See https://huggingface.co/models for full list of available models.
-- `retriever`: `DensePassageRetriever` used to embedded passage
-- `generator_type`: Which RAG generator implementation to use? RAG-TOKEN or RAG-SEQUENCE
-- `top_k_answers`: Number of independently generated text to return
-- `max_length`: Maximum length of generated text
-- `min_length`: Minimum length of generated text
-- `num_beams`: Number of beams for beam search. 1 means no beam search.
-- `embed_title`: Embedded the title of passage while generating embedding
-- `prefix`: The prefix used by the generator's tokenizer.
-- `use_gpu`: Whether to use GPU (if available)
-
-
-#### predict
-
-```python
- | predict(query: str, documents: List[Document], top_k: Optional[int] = None) -> Dict
-```
-
-Generate the answer to the input query. The generation will be conditioned on the supplied documents.
-These document can for example be retrieved via the Retriever.
-
-**Arguments**:
-
-- `query`: Query
-- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on.
-- `top_k`: Number of returned answers
-
-**Returns**:
-
-Generated answers plus additional infos in a dict like this:
-
-```python
-| {'query': 'who got the first nobel prize in physics',
-| 'answers':
-| [{'query': 'who got the first nobel prize in physics',
-| 'answer': ' albert einstein',
-| 'meta': { 'doc_ids': [...],
-| 'doc_scores': [80.42758 ...],
-| 'doc_probabilities': [40.71379089355469, ...
-| 'texts': ['Albert Einstein was a ...]
-| 'titles': ['"Albert Einstein"', ...]
-| }}]}
-```
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/api/api/pipelines.md b/src/pages/docs/versions/master/v0.7.0/site/en/api/api/pipelines.md
deleted file mode 100644
index ba089d3ae..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/api/api/pipelines.md
+++ /dev/null
@@ -1,307 +0,0 @@
----
-title: "Pipelines"
-metaTitle: "Pipelines"
-metaDescription: ""
-slug: "/docs/apipipelinesmd"
-date: "2020-09-03"
-id: "apipipelinesmd"
----
-
-# Pipelines
-
-
-# Module: pipeline
-
-
-## Class: Pipeline
-
-```python
-class Pipeline()
-```
-
-Pipeline brings together building blocks to build a complex search pipeline with Haystack & user-defined components.
-
-Under-the-hood, a pipeline is represented as a directed acyclic graph of component nodes. It enables custom query
-flows with options to branch queries(eg, extractive qa vs keyword match query), merge candidate documents for a
-Reader from multiple Retrievers, or re-ranking of candidate documents.
-
-
-#### add\_node
-
-```python
- | add_node(component, name: str, inputs: List[str])
-```
-
-Add a new node to the pipeline.
-
-**Arguments**:
-
-- `component`: The object to be called when the data is passed to the node. It can be a Haystack component
-(like Retriever, Reader, or Generator) or a user-defined object that implements a run()
-method to process incoming data from predecessor node.
-- `name`: The name for the node. It must not contain any dots.
-- `inputs`: A list of inputs to the node. If the predecessor node has a single outgoing edge, just the name
-of node is sufficient. For instance, a 'ElasticsearchRetriever' node would always output a single
-edge with a list of documents. It can be represented as ["ElasticsearchRetriever"].
-
-In cases when the predecessor node has multiple outputs, e.g., a "QueryClassifier", the output
-must be specified explicitly as "QueryClassifier.output_2".
-
-
-#### get\_node
-
-```python
- | get_node(name: str)
-```
-
-Get a node from the Pipeline.
-
-**Arguments**:
-
-- `name`: The name of the node.
-
-
-#### set\_node
-
-```python
- | set_node(name: str, component)
-```
-
-Set the component for a node in the Pipeline.
-
-**Arguments**:
-
-- `name`: The name of the node.
-- `component`: The component object to be set at the node.
-
-
-#### draw
-
-```python
- | draw(path: Path = Path("pipeline.png"))
-```
-
-Create a Graphviz visualization of the pipeline.
-
-**Arguments**:
-
-- `path`: the path to save the image.
-
-
-## Class: BaseStandardPipeline
-
-```python
-class BaseStandardPipeline()
-```
-
-
-#### add\_node
-
-```python
- | add_node(component, name: str, inputs: List[str])
-```
-
-Add a new node to the pipeline.
-
-**Arguments**:
-
-- `component`: The object to be called when the data is passed to the node. It can be a Haystack component
-(like Retriever, Reader, or Generator) or a user-defined object that implements a run()
-method to process incoming data from predecessor node.
-- `name`: The name for the node. It must not contain any dots.
-- `inputs`: A list of inputs to the node. If the predecessor node has a single outgoing edge, just the name
-of node is sufficient. For instance, a 'ElasticsearchRetriever' node would always output a single
-edge with a list of documents. It can be represented as ["ElasticsearchRetriever"].
-
-In cases when the predecessor node has multiple outputs, e.g., a "QueryClassifier", the output
-must be specified explicitly as "QueryClassifier.output_2".
-
-
-#### get\_node
-
-```python
- | get_node(name: str)
-```
-
-Get a node from the Pipeline.
-
-**Arguments**:
-
-- `name`: The name of the node.
-
-
-#### set\_node
-
-```python
- | set_node(name: str, component)
-```
-
-Set the component for a node in the Pipeline.
-
-**Arguments**:
-
-- `name`: The name of the node.
-- `component`: The component object to be set at the node.
-
-
-#### draw
-
-```python
- | draw(path: Path = Path("pipeline.png"))
-```
-
-Create a Graphviz visualization of the pipeline.
-
-**Arguments**:
-
-- `path`: the path to save the image.
-
-
-## Class: ExtractiveQAPipeline
-
-```python
-class ExtractiveQAPipeline(BaseStandardPipeline)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(reader: BaseReader, retriever: BaseRetriever)
-```
-
-Initialize a Pipeline for Extractive Question Answering.
-
-**Arguments**:
-
-- `reader`: Reader instance
-- `retriever`: Retriever instance
-
-
-## Class: DocumentSearchPipeline
-
-```python
-class DocumentSearchPipeline(BaseStandardPipeline)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(retriever: BaseRetriever)
-```
-
-Initialize a Pipeline for semantic document search.
-
-**Arguments**:
-
-- `retriever`: Retriever instance
-
-
-## Class: GenerativeQAPipeline
-
-```python
-class GenerativeQAPipeline(BaseStandardPipeline)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(generator: BaseGenerator, retriever: BaseRetriever)
-```
-
-Initialize a Pipeline for Generative Question Answering.
-
-**Arguments**:
-
-- `generator`: Generator instance
-- `retriever`: Retriever instance
-
-
-## Class: SearchSummarizationPipeline
-
-```python
-class SearchSummarizationPipeline(BaseStandardPipeline)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(summarizer: BaseSummarizer, retriever: BaseRetriever)
-```
-
-Initialize a Pipeline that retrieves documents for a query and then summarizes those documents.
-
-**Arguments**:
-
-- `summarizer`: Summarizer instance
-- `retriever`: Retriever instance
-
-
-#### run
-
-```python
- | run(query: str, filters: Optional[Dict] = None, top_k_retriever: int = 10, generate_single_summary: bool = False, return_in_answer_format=False)
-```
-
-**Arguments**:
-
-- `query`: Your search query
-- `filters`:
-- `top_k_retriever`: Number of top docs the retriever should pass to the summarizer.
-The higher this value, the slower your pipeline.
-- `generate_single_summary`: Whether to generate single summary from all retrieved docs (True) or one per doc (False).
-- `return_in_answer_format`: Whether the results should be returned as documents (False) or in the answer format used in other QA pipelines (True).
-With the latter, you can use this pipeline as a "drop-in replacement" for other QA pipelines.
-
-
-## Class: FAQPipeline
-
-```python
-class FAQPipeline(BaseStandardPipeline)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(retriever: BaseRetriever)
-```
-
-Initialize a Pipeline for finding similar FAQs using semantic document search.
-
-**Arguments**:
-
-- `retriever`: Retriever instance
-
-
-## Class: JoinDocuments
-
-```python
-class JoinDocuments()
-```
-
-A node to join documents outputted by multiple retriever nodes.
-
-The node allows multiple join modes:
-* concatenate: combine the documents from multiple nodes. Any duplicate documents are discarded.
-* merge: merge scores of documents from multiple nodes. Optionally, each input score can be given a different
- `weight` & a `top_k` limit can be set. This mode can also be used for "reranking" retrieved documents.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(join_mode: str = "concatenate", weights: Optional[List[float]] = None, top_k_join: Optional[int] = None)
-```
-
-**Arguments**:
-
-- `join_mode`: `concatenate` to combine documents from multiple retrievers or `merge` to aggregate scores of
-individual documents.
-- `weights`: A node-wise list(length of list must be equal to the number of input nodes) of weights for
-adjusting document scores when using the `merge` join_mode. By default, equal weight is given
-to each retriever score. This param is not compatible with the `concatenate` join_mode.
-- `top_k_join`: Limit documents to top_k based on the resulting scores of the join.
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/api/api/preprocessor.md b/src/pages/docs/versions/master/v0.7.0/site/en/api/api/preprocessor.md
deleted file mode 100644
index d5d2425af..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/api/api/preprocessor.md
+++ /dev/null
@@ -1,187 +0,0 @@
----
-title: "Preprocessor"
-metaTitle: "Preprocessor"
-metaDescription: ""
-slug: "/docs/apiindexing"
-date: "2020-09-03"
-id: "apiindexingmd"
----
-
-# Preprocessor
-
-
-# Module: base
-
-
-## Class: BasePreProcessor
-
-```python
-class BasePreProcessor()
-```
-
-
-#### process
-
-```python
- | process(document: dict) -> List[dict]
-```
-
-Perform document cleaning and splitting. Takes a single document as input and returns a list of documents.
-
-
-# Module: preprocessor
-
-
-## Class: PreProcessor
-
-```python
-class PreProcessor(BasePreProcessor)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(clean_whitespace: Optional[bool] = True, clean_header_footer: Optional[bool] = False, clean_empty_lines: Optional[bool] = True, split_by: Optional[str] = "word", split_length: Optional[int] = 1000, split_overlap: Optional[int] = None, split_respect_sentence_boundary: Optional[bool] = True)
-```
-
-**Arguments**:
-
-- `clean_header_footer`: Use heuristic to remove footers and headers across different pages by searching
-for the longest common string. This heuristic uses exact matches and therefore
-works well for footers like "Copyright 2019 by XXX", but won't detect "Page 3 of 4"
-or similar.
-- `clean_whitespace`: Strip whitespaces before or after each line in the text.
-- `clean_empty_lines`: Remove more than two empty lines in the text.
-- `split_by`: Unit for splitting the document. Can be "word", "sentence", or "passage". Set to None to disable splitting.
-- `split_length`: Max. number of the above split unit (e.g. words) that are allowed in one document. For instance, if n -> 10 & split_by ->
-"sentence", then each output document will have 10 sentences.
-- `split_overlap`: Word overlap between two adjacent documents after a split.
-Setting this to a positive number essentially enables the sliding window approach.
-For example, if split_by -> `word`,
-split_length -> 5 & split_overlap -> 2, then the splits would be like:
-[w1 w2 w3 w4 w5, w4 w5 w6 w7 w8, w7 w8 w10 w11 w12].
-Set the value to None to ensure there is no overlap among the documents after splitting.
-- `split_respect_sentence_boundary`: Whether to split in partial sentences if split_by -> `word`. If set
-to True, the individual split will always have complete sentences &
-the number of words will be <= split_length.
-
-
-#### clean
-
-```python
- | clean(document: dict) -> dict
-```
-
-Perform document cleaning on a single document and return a single document. This method will deal with whitespaces, headers, footers
-and empty lines. Its exact functionality is defined by the parameters passed into PreProcessor.__init__().
-
-
-#### split
-
-```python
- | split(document: dict) -> List[dict]
-```
-
-Perform document splitting on a single document. This method can split on different units, at different lengths,
-with different strides. It can also respect sentence boundaries. Its exact functionality is defined by
-the parameters passed into PreProcessor.__init__(). Takes a single document as input and returns a list of documents.
-
-
-# Module: utils
-
-
-#### eval\_data\_from\_file
-
-```python
-eval_data_from_file(filename: str, max_docs: Union[int, bool] = None) -> Tuple[List[Document], List[Label]]
-```
-
-Read Documents + Labels from a SQuAD-style file.
-Document and Labels can then be indexed to the DocumentStore and be used for evaluation.
-
-**Arguments**:
-
-- `filename`: Path to file in SQuAD format
-- `max_docs`: This sets the number of documents that will be loaded. By default, this is set to None, thus reading in all available eval documents.
-
-**Returns**:
-
-(List of Documents, List of Labels)
-
-
-#### convert\_files\_to\_dicts
-
-```python
-convert_files_to_dicts(dir_path: str, clean_func: Optional[Callable] = None, split_paragraphs: bool = False) -> List[dict]
-```
-
-Convert all files(.txt, .pdf, .docx) in the sub-directories of the given path to Python dicts that can be written to a
-Document Store.
-
-**Arguments**:
-
-- `dir_path`: path for the documents to be written to the DocumentStore
-- `clean_func`: a custom cleaning function that gets applied to each doc (input: str, output:str)
-- `split_paragraphs`: split text in paragraphs.
-
-**Returns**:
-
-None
-
-
-#### tika\_convert\_files\_to\_dicts
-
-```python
-tika_convert_files_to_dicts(dir_path: str, clean_func: Optional[Callable] = None, split_paragraphs: bool = False, merge_short: bool = True, merge_lowercase: bool = True) -> List[dict]
-```
-
-Convert all files(.txt, .pdf) in the sub-directories of the given path to Python dicts that can be written to a
-Document Store.
-
-**Arguments**:
-
-- `merge_lowercase`: allow conversion of merged paragraph to lowercase
-- `merge_short`: allow merging of short paragraphs
-- `dir_path`: path for the documents to be written to the DocumentStore
-- `clean_func`: a custom cleaning function that gets applied to each doc (input: str, output:str)
-- `split_paragraphs`: split text in paragraphs.
-
-**Returns**:
-
-None
-
-
-#### fetch\_archive\_from\_http
-
-```python
-fetch_archive_from_http(url: str, output_dir: str, proxies: Optional[dict] = None)
-```
-
-Fetch an archive (zip or tar.gz) from a url via http and extract content to an output directory.
-
-**Arguments**:
-
-- `url`: http address
-:type url: str
-- `output_dir`: local path
-:type output_dir: str
-- `proxies`: proxies details as required by requests library
-:type proxies: dict
-
-**Returns**:
-
-bool if anything got fetched
-
-
-# Module: cleaning
-
-
-#### clean\_wiki\_text
-
-```python
-clean_wiki_text(text: str) -> str
-```
-
-Clean wikipedia text by removing multiple new lines, removing extremely short lines,
-adding paragraph breaks and removing empty paragraphs
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/api/api/reader.md b/src/pages/docs/versions/master/v0.7.0/site/en/api/api/reader.md
deleted file mode 100644
index 601cf7f7e..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/api/api/reader.md
+++ /dev/null
@@ -1,393 +0,0 @@
----
-title: "Reader"
-metaTitle: "Reader"
-metaDescription: ""
-slug: "/docs/apireadermd"
-date: "2020-09-03"
-id: "apireadermd"
----
-
-# Reader
-
-
-# Module: base
-
-
-# Module: farm
-
-
-## Class: FARMReader
-
-```python
-class FARMReader(BaseReader)
-```
-
-Transformer based model for extractive Question Answering using the FARM framework (https://github.com/deepset-ai/FARM).
-While the underlying model can vary (BERT, Roberta, DistilBERT, ...), the interface remains the same.
-
-| With a FARMReader, you can:
-
- - directly get predictions via predict()
- - fine-tune the model on QA data via train()
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: Union[str, Path], context_window_size: int = 150, batch_size: int = 50, use_gpu: bool = True, no_ans_boost: float = 0.0, return_no_answer: bool = False, top_k_per_candidate: int = 3, top_k_per_sample: int = 1, num_processes: Optional[int] = None, max_seq_len: int = 256, doc_stride: int = 128)
-```
-
-**Arguments**:
-
-- `model_name_or_path`: Directory of a saved model or the name of a public model e.g. 'bert-base-cased',
-'deepset/bert-base-cased-squad2', 'deepset/bert-base-cased-squad2', 'distilbert-base-uncased-distilled-squad'.
-See https://huggingface.co/models for full list of available models.
-- `context_window_size`: The size, in characters, of the window around the answer span that is used when
-displaying the context around the answer.
-- `batch_size`: Number of samples the model receives in one batch for inference.
-Memory consumption is much lower in inference mode. Recommendation: Increase the batch size
-to a value so only a single batch is used.
-- `use_gpu`: Whether to use GPU (if available)
-- `no_ans_boost`: How much the no_answer logit is boosted/increased.
-If set to 0 (default), the no_answer logit is not changed.
-If a negative number, there is a lower chance of "no_answer" being predicted.
-If a positive number, there is an increased chance of "no_answer"
-- `return_no_answer`: Whether to include no_answer predictions in the results.
-- `top_k_per_candidate`: How many answers to extract for each candidate doc that is coming from the retriever (might be a long text).
-Note that this is not the number of "final answers" you will receive
-(see `top_k` in FARMReader.predict() or Finder.get_answers() for that)
-and that FARM includes no_answer in the sorted list of predictions.
-- `top_k_per_sample`: How many answers to extract from each small text passage that the model can process at once
-(one "candidate doc" is usually split into many smaller "passages").
-You usually want a very small value here, as it slows down inference
-and you don't gain much of quality by having multiple answers from one passage.
-Note that this is not the number of "final answers" you will receive
-(see `top_k` in FARMReader.predict() or Finder.get_answers() for that)
-and that FARM includes no_answer in the sorted list of predictions.
-- `num_processes`: The number of processes for `multiprocessing.Pool`. Set to value of 0 to disable
-multiprocessing. Set to None to let Inferencer determine optimum number. If you
-want to debug the Language Model, you might need to disable multiprocessing!
-- `max_seq_len`: Max sequence length of one input text for the model
-- `doc_stride`: Length of striding window for splitting long texts (used if ``len(text) > max_seq_len``)
-
-
-#### train
-
-```python
- | train(data_dir: str, train_filename: str, dev_filename: Optional[str] = None, test_filename: Optional[str] = None, use_gpu: Optional[bool] = None, batch_size: int = 10, n_epochs: int = 2, learning_rate: float = 1e-5, max_seq_len: Optional[int] = None, warmup_proportion: float = 0.2, dev_split: float = 0, evaluate_every: int = 300, save_dir: Optional[str] = None, num_processes: Optional[int] = None, use_amp: str = None)
-```
-
-Fine-tune a model on a QA dataset. Options:
-
-- Take a plain language model (e.g. `bert-base-cased`) and train it for QA (e.g. on SQuAD data)
-- Take a QA model (e.g. `deepset/bert-base-cased-squad2`) and fine-tune it for your domain (e.g. using your labels collected via the haystack annotation tool)
-
-**Arguments**:
-
-- `data_dir`: Path to directory containing your training data in SQuAD style
-- `train_filename`: Filename of training data
-- `dev_filename`: Filename of dev / eval data
-- `test_filename`: Filename of test data
-- `dev_split`: Instead of specifying a dev_filename, you can also specify a ratio (e.g. 0.1) here
-that gets split off from training data for eval.
-- `use_gpu`: Whether to use GPU (if available)
-- `batch_size`: Number of samples the model receives in one batch for training
-- `n_epochs`: Number of iterations on the whole training data set
-- `learning_rate`: Learning rate of the optimizer
-- `max_seq_len`: Maximum text length (in tokens). Everything longer gets cut down.
-- `warmup_proportion`: Proportion of training steps until maximum learning rate is reached.
-Until that point LR is increasing linearly. After that it's decreasing again linearly.
-Options for different schedules are available in FARM.
-- `evaluate_every`: Evaluate the model every X steps on the hold-out eval dataset
-- `save_dir`: Path to store the final model
-- `num_processes`: The number of processes for `multiprocessing.Pool` during preprocessing.
-Set to value of 1 to disable multiprocessing. When set to 1, you cannot split away a dev set from train set.
-Set to None to use all CPU cores minus one.
-- `use_amp`: Optimization level of NVIDIA's automatic mixed precision (AMP). The higher the level, the faster the model.
-Available options:
-None (Don't use AMP)
-"O0" (Normal FP32 training)
-"O1" (Mixed Precision => Recommended)
-"O2" (Almost FP16)
-"O3" (Pure FP16).
-See details on: https://nvidia.github.io/apex/amp.html
-
-**Returns**:
-
-None
-
-
-#### update\_parameters
-
-```python
- | update_parameters(context_window_size: Optional[int] = None, no_ans_boost: Optional[float] = None, return_no_answer: Optional[bool] = None, max_seq_len: Optional[int] = None, doc_stride: Optional[int] = None)
-```
-
-Hot update parameters of a loaded Reader. It may not to be safe when processing concurrent requests.
-
-
-#### save
-
-```python
- | save(directory: Path)
-```
-
-Saves the Reader model so that it can be reused at a later point in time.
-
-**Arguments**:
-
-- `directory`: Directory where the Reader model should be saved
-
-
-#### predict\_batch
-
-```python
- | predict_batch(query_doc_list: List[dict], top_k: int = None, batch_size: int = None)
-```
-
-Use loaded QA model to find answers for a list of queries in each query's supplied list of Document.
-
-Returns list of dictionaries containing answers sorted by (desc.) probability
-
-**Arguments**:
-
-- `query_doc_list`: List of dictionaries containing queries with their retrieved documents
-- `top_k`: The maximum number of answers to return for each query
-- `batch_size`: Number of samples the model receives in one batch for inference
-
-**Returns**:
-
-List of dictionaries containing query and answers
-
-
-#### predict
-
-```python
- | predict(query: str, documents: List[Document], top_k: Optional[int] = None)
-```
-
-Use loaded QA model to find answers for a query in the supplied list of Document.
-
-Returns dictionaries containing answers sorted by (desc.) probability.
-Example:
-```python
-|{
-| 'query': 'Who is the father of Arya Stark?',
-| 'answers':[
-| {'answer': 'Eddard,',
-| 'context': " She travels with her father, Eddard, to King's Landing when he is ",
-| 'offset_answer_start': 147,
-| 'offset_answer_end': 154,
-| 'probability': 0.9787139466668613,
-| 'score': None,
-| 'document_id': '1337'
-| },...
-| ]
-|}
-```
-
-**Arguments**:
-
-- `query`: Query string
-- `documents`: List of Document in which to search for the answer
-- `top_k`: The maximum number of answers to return
-
-**Returns**:
-
-Dict containing query and answers
-
-
-#### eval\_on\_file
-
-```python
- | eval_on_file(data_dir: str, test_filename: str, device: str)
-```
-
-Performs evaluation on a SQuAD-formatted file.
-Returns a dict containing the following metrics:
-- "EM": exact match score
-- "f1": F1-Score
-- "top_n_accuracy": Proportion of predicted answers that overlap with correct answer
-
-**Arguments**:
-
-- `data_dir`: The directory in which the test set can be found
-:type data_dir: Path or str
-- `test_filename`: The name of the file containing the test data in SQuAD format.
-:type test_filename: str
-- `device`: The device on which the tensors should be processed. Choose from "cpu" and "cuda".
-:type device: str
-
-
-#### eval
-
-```python
- | eval(document_store: BaseDocumentStore, device: str, label_index: str = "label", doc_index: str = "eval_document", label_origin: str = "gold_label")
-```
-
-Performs evaluation on evaluation documents in the DocumentStore.
-Returns a dict containing the following metrics:
-- "EM": Proportion of exact matches of predicted answers with their corresponding correct answers
-- "f1": Average overlap between predicted answers and their corresponding correct answers
-- "top_n_accuracy": Proportion of predicted answers that overlap with correct answer
-
-**Arguments**:
-
-- `document_store`: DocumentStore containing the evaluation documents
-- `device`: The device on which the tensors should be processed. Choose from "cpu" and "cuda".
-- `label_index`: Index/Table name where labeled questions are stored
-- `doc_index`: Index/Table name where documents that are used for evaluation are stored
-
-
-#### predict\_on\_texts
-
-```python
- | predict_on_texts(question: str, texts: List[str], top_k: Optional[int] = None)
-```
-
-Use loaded QA model to find answers for a question in the supplied list of Document.
-Returns dictionaries containing answers sorted by (desc.) probability.
-Example:
-```python
-|{
-| 'question': 'Who is the father of Arya Stark?',
-| 'answers':[
-| {'answer': 'Eddard,',
-| 'context': " She travels with her father, Eddard, to King's Landing when he is ",
-| 'offset_answer_start': 147,
-| 'offset_answer_end': 154,
-| 'probability': 0.9787139466668613,
-| 'score': None,
-| 'document_id': '1337'
-| },...
-| ]
-|}
-```
-
-**Arguments**:
-
-- `question`: Question string
-- `documents`: List of documents as string type
-- `top_k`: The maximum number of answers to return
-
-**Returns**:
-
-Dict containing question and answers
-
-
-#### convert\_to\_onnx
-
-```python
- | @classmethod
- | convert_to_onnx(cls, model_name: str, output_path: Path, convert_to_float16: bool = False, quantize: bool = False, task_type: str = "question_answering", opset_version: int = 11)
-```
-
-Convert a PyTorch BERT model to ONNX format and write to ./onnx-export dir. The converted ONNX model
-can be loaded with in the `FARMReader` using the export path as `model_name_or_path` param.
-
-Usage:
-
-`from haystack.reader.farm import FARMReader
-from pathlib import Path
-onnx_model_path = Path("roberta-onnx-model")
-FARMReader.convert_to_onnx(model_name="deepset/bert-base-cased-squad2", output_path=onnx_model_path)
-reader = FARMReader(onnx_model_path)`
-
-**Arguments**:
-
-- `model_name`: transformers model name
-- `output_path`: Path to output the converted model
-- `convert_to_float16`: Many models use float32 precision by default. With the half precision of float16,
-inference is faster on Nvidia GPUs with Tensor core like T4 or V100. On older GPUs,
-float32 could still be be more performant.
-- `quantize`: convert floating point number to integers
-- `task_type`: Type of task for the model. Available options: "question_answering" or "embeddings".
-- `opset_version`: ONNX opset version
-
-
-# Module: transformers
-
-
-## Class: TransformersReader
-
-```python
-class TransformersReader(BaseReader)
-```
-
-Transformer based model for extractive Question Answering using the HuggingFace's transformers framework
-(https://github.com/huggingface/transformers).
-While the underlying model can vary (BERT, Roberta, DistilBERT ...), the interface remains the same.
-With this reader, you can directly get predictions via predict()
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: str = "distilbert-base-uncased-distilled-squad", tokenizer: Optional[str] = None, context_window_size: int = 70, use_gpu: int = 0, top_k_per_candidate: int = 4, return_no_answers: bool = True, max_seq_len: int = 256, doc_stride: int = 128)
-```
-
-Load a QA model from Transformers.
-Available models include:
-
-- ``'distilbert-base-uncased-distilled-squad`'``
-- ``'bert-large-cased-whole-word-masking-finetuned-squad``'
-- ``'bert-large-uncased-whole-word-masking-finetuned-squad``'
-
-See https://huggingface.co/models for full list of available QA models
-
-**Arguments**:
-
-- `model_name_or_path`: Directory of a saved model or the name of a public model e.g. 'bert-base-cased',
-'deepset/bert-base-cased-squad2', 'deepset/bert-base-cased-squad2', 'distilbert-base-uncased-distilled-squad'.
-See https://huggingface.co/models for full list of available models.
-- `tokenizer`: Name of the tokenizer (usually the same as model)
-- `context_window_size`: Num of chars (before and after the answer) to return as "context" for each answer.
-The context usually helps users to understand if the answer really makes sense.
-- `use_gpu`: If < 0, then use cpu. If >= 0, this is the ordinal of the gpu to use
-- `top_k_per_candidate`: How many answers to extract for each candidate doc that is coming from the retriever (might be a long text).
-Note that this is not the number of "final answers" you will receive
-(see `top_k` in TransformersReader.predict() or Finder.get_answers() for that)
-and that no_answer can be included in the sorted list of predictions.
-- `return_no_answers`: If True, the HuggingFace Transformers model could return a "no_answer" (i.e. when there is an unanswerable question)
-If False, it cannot return a "no_answer". Note that `no_answer_boost` is unfortunately not available with TransformersReader.
-If you would like to set no_answer_boost, use a `FARMReader`.
-- `max_seq_len`: max sequence length of one input text for the model
-- `doc_stride`: length of striding window for splitting long texts (used if len(text) > max_seq_len)
-
-
-#### predict
-
-```python
- | predict(query: str, documents: List[Document], top_k: Optional[int] = None)
-```
-
-Use loaded QA model to find answers for a query in the supplied list of Document.
-
-Returns dictionaries containing answers sorted by (desc.) probability.
-Example:
-
-```python
-|{
-| 'query': 'Who is the father of Arya Stark?',
-| 'answers':[
-| {'answer': 'Eddard,',
-| 'context': " She travels with her father, Eddard, to King's Landing when he is ",
-| 'offset_answer_start': 147,
-| 'offset_answer_end': 154,
-| 'probability': 0.9787139466668613,
-| 'score': None,
-| 'document_id': '1337'
-| },...
-| ]
-|}
-```
-
-**Arguments**:
-
-- `query`: Query string
-- `documents`: List of Document in which to search for the answer
-- `top_k`: The maximum number of answers to return
-
-**Returns**:
-
-Dict containing query and answers
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/api/api/retriever.md b/src/pages/docs/versions/master/v0.7.0/site/en/api/api/retriever.md
deleted file mode 100644
index 96411630f..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/api/api/retriever.md
+++ /dev/null
@@ -1,491 +0,0 @@
----
-title: "Retriever"
-metaTitle: "Retriever"
-metaDescription: ""
-slug: "/docs/apiretrievermd"
-date: "2020-09-03"
-id: "apiretrievermd"
----
-
-# Retriever
-
-
-# Module: base
-
-
-## Class: BaseRetriever
-
-```python
-class BaseRetriever(ABC)
-```
-
-
-#### retrieve
-
-```python
- | @abstractmethod
- | retrieve(query: str, filters: dict = None, top_k: int = 10, index: str = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-#### timing
-
-```python
- | timing(fn)
-```
-
-Wrapper method used to time functions.
-
-
-#### eval
-
-```python
- | eval(label_index: str = "label", doc_index: str = "eval_document", label_origin: str = "gold_label", top_k: int = 10, open_domain: bool = False, return_preds: bool = False) -> dict
-```
-
-Performs evaluation on the Retriever.
-Retriever is evaluated based on whether it finds the correct document given the query string and at which
-position in the ranking of documents the correct document is.
-
-| Returns a dict containing the following metrics:
-
-- "recall": Proportion of questions for which correct document is among retrieved documents
-- "mrr": Mean of reciprocal rank. Rewards retrievers that give relevant documents a higher rank.
-Only considers the highest ranked relevant document.
-- "map": Mean of average precision for each question. Rewards retrievers that give relevant
-documents a higher rank. Considers all retrieved relevant documents. If ``open_domain=True``,
-average precision is normalized by the number of retrieved relevant documents per query.
-If ``open_domain=False``, average precision is normalized by the number of all relevant documents
-per query.
-
-**Arguments**:
-
-- `label_index`: Index/Table in DocumentStore where labeled questions are stored
-- `doc_index`: Index/Table in DocumentStore where documents that are used for evaluation are stored
-- `top_k`: How many documents to return per query
-- `open_domain`: If ``True``, retrieval will be evaluated by checking if the answer string to a question is
-contained in the retrieved docs (common approach in open-domain QA).
-If ``False``, retrieval uses a stricter evaluation that checks if the retrieved document ids
-are within ids explicitly stated in the labels.
-- `return_preds`: Whether to add predictions in the returned dictionary. If True, the returned dictionary
-contains the keys "predictions" and "metrics".
-
-
-# Module: sparse
-
-
-## Class: ElasticsearchRetriever
-
-```python
-class ElasticsearchRetriever(BaseRetriever)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(document_store: ElasticsearchDocumentStore, custom_query: str = None)
-```
-
-**Arguments**:
-
-- `document_store`: an instance of a DocumentStore to retrieve documents from.
-- `custom_query`: query string as per Elasticsearch DSL with a mandatory query placeholder(query).
-
-Optionally, ES `filter` clause can be added where the values of `terms` are placeholders
-that get substituted during runtime. The placeholder(${filter_name_1}, ${filter_name_2}..)
-names must match with the filters dict supplied in self.retrieve().
-::
-
-**An example custom_query:**
-```python
-| {
-| "size": 10,
-| "query": {
-| "bool": {
-| "should": [{"multi_match": {
-| "query": "${query}", // mandatory query placeholder
-| "type": "most_fields",
-| "fields": ["text", "title"]}}],
-| "filter": [ // optional custom filters
-| {"terms": {"year": "${years}"}},
-| {"terms": {"quarter": "${quarters}"}},
-| {"range": {"date": {"gte": "${date}"}}}
-| ],
-| }
-| },
-| }
-```
-
-**For this custom_query, a sample retrieve() could be:**
-```python
-| self.retrieve(query="Why did the revenue increase?",
-| filters={"years": ["2019"], "quarters": ["Q1", "Q2"]})
-```
-
-
-#### retrieve
-
-```python
- | retrieve(query: str, filters: dict = None, top_k: int = 10, index: str = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-## Class: ElasticsearchFilterOnlyRetriever
-
-```python
-class ElasticsearchFilterOnlyRetriever(ElasticsearchRetriever)
-```
-
-Naive "Retriever" that returns all documents that match the given filters. No impact of query at all.
-Helpful for benchmarking, testing and if you want to do QA on small documents without an "active" retriever.
-
-
-#### retrieve
-
-```python
- | retrieve(query: str, filters: dict = None, top_k: int = 10, index: str = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-## Class: TfidfRetriever
-
-```python
-class TfidfRetriever(BaseRetriever)
-```
-
-Read all documents from a SQL backend.
-
-Split documents into smaller units (eg, paragraphs or pages) to reduce the
-computations when text is passed on to a Reader for QA.
-
-It uses sklearn's TfidfVectorizer to compute a tf-idf matrix.
-
-
-#### retrieve
-
-```python
- | retrieve(query: str, filters: dict = None, top_k: int = 10, index: str = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-#### fit
-
-```python
- | fit()
-```
-
-Performing training on this class according to the TF-IDF algorithm.
-
-
-# Module: dense
-
-
-## Class: DensePassageRetriever
-
-```python
-class DensePassageRetriever(BaseRetriever)
-```
-
-Retriever that uses a bi-encoder (one transformer for query, one transformer for passage).
-See the original paper for more details:
-Karpukhin, Vladimir, et al. (2020): "Dense Passage Retrieval for Open-Domain Question Answering."
-(https://arxiv.org/abs/2004.04906).
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(document_store: BaseDocumentStore, query_embedding_model: Union[Path, str] = "facebook/dpr-question_encoder-single-nq-base", passage_embedding_model: Union[Path, str] = "facebook/dpr-ctx_encoder-single-nq-base", max_seq_len_query: int = 64, max_seq_len_passage: int = 256, use_gpu: bool = True, batch_size: int = 16, embed_title: bool = True, use_fast_tokenizers: bool = True, similarity_function: str = "dot_product")
-```
-
-Init the Retriever incl. the two encoder models from a local or remote model checkpoint.
-The checkpoint format matches huggingface transformers' model format
-
-**Example:**
-
-```python
-| # remote model from FAIR
-| DensePassageRetriever(document_store=your_doc_store,
-| query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
-| passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base")
-| # or from local path
-| DensePassageRetriever(document_store=your_doc_store,
-| query_embedding_model="model_directory/question-encoder",
-| passage_embedding_model="model_directory/context-encoder")
-```
-
-**Arguments**:
-
-- `document_store`: An instance of DocumentStore from which to retrieve documents.
-- `query_embedding_model`: Local path or remote name of question encoder checkpoint. The format equals the
-one used by hugging-face transformers' modelhub models
-Currently available remote names: ``"facebook/dpr-question_encoder-single-nq-base"``
-- `passage_embedding_model`: Local path or remote name of passage encoder checkpoint. The format equals the
-one used by hugging-face transformers' modelhub models
-Currently available remote names: ``"facebook/dpr-ctx_encoder-single-nq-base"``
-- `max_seq_len_query`: Longest length of each query sequence. Maximum number of tokens for the query text. Longer ones will be cut down."
-- `max_seq_len_passage`: Longest length of each passage/context sequence. Maximum number of tokens for the passage text. Longer ones will be cut down."
-- `use_gpu`: Whether to use gpu or not
-- `batch_size`: Number of questions or passages to encode at once
-- `embed_title`: Whether to concatenate title and passage to a text pair that is then used to create the embedding.
-This is the approach used in the original paper and is likely to improve performance if your
-titles contain meaningful information for retrieval (topic, entities etc.) .
-The title is expected to be present in doc.meta["name"] and can be supplied in the documents
-before writing them to the DocumentStore like this:
-{"text": "my text", "meta": {"name": "my title"}}.
-
-
-#### retrieve
-
-```python
- | retrieve(query: str, filters: dict = None, top_k: int = 10, index: str = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-#### embed\_queries
-
-```python
- | embed_queries(texts: List[str]) -> List[np.array]
-```
-
-Create embeddings for a list of queries using the query encoder
-
-**Arguments**:
-
-- `texts`: Queries to embed
-
-**Returns**:
-
-Embeddings, one per input queries
-
-
-#### embed\_passages
-
-```python
- | embed_passages(docs: List[Document]) -> List[np.array]
-```
-
-Create embeddings for a list of passages using the passage encoder
-
-**Arguments**:
-
-- `docs`: List of Document objects used to represent documents / passages in a standardized way within Haystack.
-
-**Returns**:
-
-Embeddings of documents / passages shape (batch_size, embedding_dim)
-
-
-#### train
-
-```python
- | train(data_dir: str, train_filename: str, dev_filename: str = None, test_filename: str = None, batch_size: int = 2, embed_title: bool = True, num_hard_negatives: int = 1, num_positives: int = 1, n_epochs: int = 3, evaluate_every: int = 1000, n_gpu: int = 1, learning_rate: float = 1e-5, epsilon: float = 1e-08, weight_decay: float = 0.0, num_warmup_steps: int = 100, grad_acc_steps: int = 1, optimizer_name: str = "TransformersAdamW", optimizer_correct_bias: bool = True, save_dir: str = "../saved_models/dpr", query_encoder_save_dir: str = "query_encoder", passage_encoder_save_dir: str = "passage_encoder")
-```
-
-train a DensePassageRetrieval model
-
-**Arguments**:
-
-- `data_dir`: Directory where training file, dev file and test file are present
-- `train_filename`: training filename
-- `dev_filename`: development set filename, file to be used by model in eval step of training
-- `test_filename`: test set filename, file to be used by model in test step after training
-- `batch_size`: total number of samples in 1 batch of data
-- `embed_title`: whether to concatenate passage title with each passage. The default setting in official DPR embeds passage title with the corresponding passage
-- `num_hard_negatives`: number of hard negative passages(passages which are very similar(high score by BM25) to query but do not contain the answer
-- `num_positives`: number of positive passages
-- `n_epochs`: number of epochs to train the model on
-- `evaluate_every`: number of training steps after evaluation is run
-- `n_gpu`: number of gpus to train on
-- `learning_rate`: learning rate of optimizer
-- `epsilon`: epsilon parameter of optimizer
-- `weight_decay`: weight decay parameter of optimizer
-- `grad_acc_steps`: number of steps to accumulate gradient over before back-propagation is done
-- `optimizer_name`: what optimizer to use (default: TransformersAdamW)
-- `num_warmup_steps`: number of warmup steps
-- `optimizer_correct_bias`: Whether to correct bias in optimizer
-- `save_dir`: directory where models are saved
-- `query_encoder_save_dir`: directory inside save_dir where query_encoder model files are saved
-- `passage_encoder_save_dir`: directory inside save_dir where passage_encoder model files are saved
-
-
-#### save
-
-```python
- | save(save_dir: Union[Path, str], query_encoder_dir: str = "query_encoder", passage_encoder_dir: str = "passage_encoder")
-```
-
-Save DensePassageRetriever to the specified directory.
-
-**Arguments**:
-
-- `save_dir`: Directory to save to.
-- `query_encoder_dir`: Directory in save_dir that contains query encoder model.
-- `passage_encoder_dir`: Directory in save_dir that contains passage encoder model.
-
-**Returns**:
-
-None
-
-
-#### load
-
-```python
- | @classmethod
- | load(cls, load_dir: Union[Path, str], document_store: BaseDocumentStore, max_seq_len_query: int = 64, max_seq_len_passage: int = 256, use_gpu: bool = True, batch_size: int = 16, embed_title: bool = True, use_fast_tokenizers: bool = True, similarity_function: str = "dot_product", query_encoder_dir: str = "query_encoder", passage_encoder_dir: str = "passage_encoder")
-```
-
-Load DensePassageRetriever from the specified directory.
-
-
-## Class: EmbeddingRetriever
-
-```python
-class EmbeddingRetriever(BaseRetriever)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(document_store: BaseDocumentStore, embedding_model: str, use_gpu: bool = True, model_format: str = "farm", pooling_strategy: str = "reduce_mean", emb_extraction_layer: int = -1)
-```
-
-**Arguments**:
-
-- `document_store`: An instance of DocumentStore from which to retrieve documents.
-- `embedding_model`: Local path or name of model in Hugging Face's model hub such as ``'deepset/sentence_bert'``
-- `use_gpu`: Whether to use gpu or not
-- `model_format`: Name of framework that was used for saving the model. Options:
-
-- ``'farm'``
-- ``'transformers'``
-- ``'sentence_transformers'``
-- `pooling_strategy`: Strategy for combining the embeddings from the model (for farm / transformers models only).
-Options:
-
-- ``'cls_token'`` (sentence vector)
-- ``'reduce_mean'`` (sentence vector)
-- ``'reduce_max'`` (sentence vector)
-- ``'per_token'`` (individual token vectors)
-- `emb_extraction_layer`: Number of layer from which the embeddings shall be extracted (for farm / transformers models only).
-Default: -1 (very last layer).
-
-
-#### retrieve
-
-```python
- | retrieve(query: str, filters: dict = None, top_k: int = 10, index: str = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-#### embed
-
-```python
- | embed(texts: Union[List[str], str]) -> List[np.array]
-```
-
-Create embeddings for each text in a list of texts using the retrievers model (`self.embedding_model`)
-
-**Arguments**:
-
-- `texts`: Texts to embed
-
-**Returns**:
-
-List of embeddings (one per input text). Each embedding is a list of floats.
-
-
-#### embed\_queries
-
-```python
- | embed_queries(texts: List[str]) -> List[np.array]
-```
-
-Create embeddings for a list of queries. For this Retriever type: The same as calling .embed()
-
-**Arguments**:
-
-- `texts`: Queries to embed
-
-**Returns**:
-
-Embeddings, one per input queries
-
-
-#### embed\_passages
-
-```python
- | embed_passages(docs: List[Document]) -> List[np.array]
-```
-
-Create embeddings for a list of passages. For this Retriever type: The same as calling .embed()
-
-**Arguments**:
-
-- `docs`: List of documents to embed
-
-**Returns**:
-
-Embeddings, one per input passage
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--- a/src/pages/docs/versions/master/v0.7.0/site/en/menuStructure/menu.json
+++ /dev/null
@@ -1,288 +0,0 @@
-{
- "menuList": [
- {
- "id": "overview",
- "title": "Overview",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 0,
- "isMenu": true
- },
- {
- "id": "get_startedmd",
- "title": "Get Started",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "intromd",
- "title": "What is Haystack?",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "use_casesmd",
- "title": "Use Cases",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "roadmapmd",
- "title": "Roadmap",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "termsmd",
- "title": "Glossary",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "usage_haystack",
- "title": "Usage",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 1,
- "isMenu": true
- },
- {
- "id": "preprocessingmd",
- "title": "Preprocessing",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "documentstoremd",
- "title": "Document Store",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "retrievermd",
- "title": "Retriever",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "readermd",
- "title": "Reader",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "generatormd",
- "title": "Generator",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "pipelinesmd",
- "title": "Pipelines",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "languagesmd",
- "title": "Languages Other Than English",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 6
- },
- {
- "id": "domain_adaptationmd",
- "title": "Domain Adaptation",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 7
- },
- {
- "id": "optimizationmd",
- "title": "Optimization",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 8
- },
- {
- "id": "annotationmd",
- "title": "Annotation Tool",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 9
- },
- {
- "id": "tutorials_haystack",
- "title": "Tutorials",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 2,
- "isMenu": true
- },
- {
- "id": "tutorial1md",
- "title": "Build Your First QA System",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "tutorial2md",
- "title": "Fine-tuning a Model on Your Own Data",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "tutorial3md",
- "title": "Build a QA System Without Elasticsearch",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "tutorial4md",
- "title": "Utilizing existing FAQs for Question Answering",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "tutorial5md",
- "title": "Evaluation of a QA System",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "tutorial6md",
- "title": "Better Retrieval via \"Dense Passage Retrieval\"",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "tutorial7md",
- "title": "Generative QA with \"Retrieval-Augmented Generation\"",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 6
- },
- {
- "id": "tutorial8md",
- "title": "Preprocessing your Documents",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 7
- },
- {
- "id": "tutorial9md",
- "title": "How to Train DPR",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 8
- },
- {
- "id": "api_haystack",
- "title": "API reference",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 3,
- "isMenu": true
- },
- {
- "id": "apidatabasemd",
- "title": "Document Store",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "apiretrievermd",
- "title": "Retriever",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "apireadermd",
- "title": "Reader",
- "label1": "api_haystack",
- "label2": "api_haystack",
- "label3": "",
- "order": 2
- },
- {
- "id": "apigeneratormd",
- "title": "Generator",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "apiindexingmd",
- "title": "Preprocessor",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "file_convertersmd",
- "title": "File Converters",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "apipipelinesmd",
- "title": "Pipelines",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 6
- }
- ]
-}
\ No newline at end of file
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/1.md b/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/1.md
deleted file mode 100644
index 0d2f5a663..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/1.md
+++ /dev/null
@@ -1,236 +0,0 @@
----
-title: "Tutorial 1"
-metaTitle: "Build Your First QA System"
-metaDescription: ""
-slug: "/docs/tutorial1"
-date: "2020-09-03"
-id: "tutorial1md"
----
-
-# Build Your First QA System
-
-
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb)
-
-Question Answering can be used in a variety of use cases. A very common one: Using it to navigate through complex knowledge bases or long documents ("search setting").
-
-A "knowledge base" could for example be your website, an internal wiki or a collection of financial reports.
-In this tutorial we will work on a slightly different domain: "Game of Thrones".
-
-Let's see how we can use a bunch of Wikipedia articles to answer a variety of questions about the
-marvellous seven kingdoms...
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-Haystack finds answers to queries within the documents stored in a `DocumentStore`. The current implementations of `DocumentStore` include `ElasticsearchDocumentStore`, `FAISSDocumentStore`, `SQLDocumentStore`, and `InMemoryDocumentStore`.
-
-**Here:** We recommended Elasticsearch as it comes preloaded with features like [full-text queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html), [BM25 retrieval](https://www.elastic.co/elasticon/conf/2016/sf/improved-text-scoring-with-bm25), and [vector storage for text embeddings](https://www.elastic.co/guide/en/elasticsearch/reference/7.6/dense-vector.html).
-
-**Alternatives:** If you are unable to setup an Elasticsearch instance, then follow the [Tutorial 3](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb) for using SQL/InMemory document stores.
-
-**Hint**: This tutorial creates a new document store instance with Wikipedia articles on Game of Thrones. However, you can configure Haystack to work with your existing document stores.
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker
-#! docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.6.2
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.6.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.6.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.6.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.6.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-# Connect to Elasticsearch
-
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles about Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first fetch some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is:
-# {
-# 'text': "",
-# 'meta': {'name': "", ...}
-#}
-# (Optionally: you can also add more key-value-pairs here, that will be indexed as fields in Elasticsearch and
-# can be accessed later for filtering or shown in the responses of the Finder)
-
-# Let's have a look at the first 3 entries:
-print(dicts[:3])
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-## Initalize Retriever, Reader, & Finder
-
-### Retriever
-
-Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question could be answered.
-They use some simple but fast algorithm.
-
-**Here:** We use Elasticsearch's default BM25 algorithm
-
-**Alternatives:**
-
-- Customize the `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `DensePassageRetriever` to use different embedding models for passage and query (see Tutorial 6)
-
-
-```python
-from haystack.retriever.sparse import ElasticsearchRetriever
-retriever = ElasticsearchRetriever(document_store=document_store)
-```
-
-
-```python
-# Alternative: An in-memory TfidfRetriever based on Pandas dataframes for building quick-prototypes with SQLite document store.
-
-# from haystack.retriever.sparse import TfidfRetriever
-# retriever = TfidfRetriever(document_store=document_store)
-```
-
-### Reader
-
-A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based
-on powerful, but slower deep learning models.
-
-Haystack currently supports Readers based on the frameworks FARM and Transformers.
-With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).
-
-**Here:** a medium sized RoBERTa QA model using a Reader based on FARM (https://huggingface.co/deepset/roberta-base-squad2)
-
-**Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)
-
-**Alternatives (Models):** e.g. "distilbert-base-uncased-distilled-squad" (fast) or "deepset/bert-large-uncased-whole-word-masking-squad2" (good accuracy)
-
-**Hint:** You can adjust the model to return "no answer possible" with the no_ans_boost. Higher values mean the model prefers "no answer possible"
-
-#### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-#### TransformersReader
-
-
-```python
-# Alternative:
-# reader = TransformersReader(model_name_or_path="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1)
-```
-
-### Finder
-
-The Finder sticks together reader and retriever in a pipeline to answer our actual questions.
-
-
-```python
-finder = Finder(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = finder.get_answers(question="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-# prediction = finder.get_answers(question="Who created the Dothraki vocabulary?", top_k_reader=5)
-# prediction = finder.get_answers(question="Who is the sister of Sansa?", top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/2.md b/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/2.md
deleted file mode 100644
index 261c3caac..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/2.md
+++ /dev/null
@@ -1,92 +0,0 @@
----
-title: "Tutorial 2"
-metaTitle: "Fine-tuning a model on your own data"
-metaDescription: ""
-slug: "/docs/tutorial2"
-date: "2020-09-03"
-id: "tutorial2md"
----
-
-# Fine-tuning a Model on Your Own Data
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial2_Finetune_a_model_on_your_data.ipynb)
-
-For many use cases it is sufficient to just use one of the existing public models that were trained on SQuAD or other public QA datasets (e.g. Natural Questions).
-However, if you have domain-specific questions, fine-tuning your model on custom examples will very likely boost your performance.
-While this varies by domain, we saw that ~ 2000 examples can easily increase performance by +5-20%.
-
-This tutorial shows you how to fine-tune a pretrained model on your own dataset.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-from haystack.reader.farm import FARMReader
-```
-
-
-## Create Training Data
-
-There are two ways to generate training data
-
-1. **Annotation**: You can use the [annotation tool](https://github.com/deepset-ai/haystack#labeling-tool) to label your data, i.e. highlighting answers to your questions in a document. The tool supports structuring your workflow with organizations, projects, and users. The labels can be exported in SQuAD format that is compatible for training with Haystack.
-
-
-
-2. **Feedback**: For production systems, you can collect training data from direct user feedback via Haystack's [REST API interface](https://github.com/deepset-ai/haystack#rest-api). This includes a customizable user feedback API for providing feedback on the answer returned by the API. The API provides a feedback export endpoint to obtain the feedback data for fine-tuning your model further.
-
-
-## Fine-tune your model
-
-Once you have collected training data, you can fine-tune your base models.
-We initialize a reader as a base model and fine-tune it on our own custom dataset (should be in SQuAD-like format).
-We recommend using a base model that was trained on SQuAD or a similar QA dataset before to benefit from Transfer Learning effects.
-
-**Recommendation**: Run training on a GPU.
-If you are using Colab: Enable this in the menu "Runtime" > "Change Runtime type" > Select "GPU" in dropdown.
-Then change the `use_gpu` arguments below to `True`
-
-
-```python
-reader = FARMReader(model_name_or_path="distilbert-base-uncased-distilled-squad", use_gpu=True)
-train_data = "data/squad20"
-# train_data = "PATH/TO_YOUR/TRAIN_DATA"
-reader.train(data_dir=train_data, train_filename="dev-v2.0.json", use_gpu=True, n_epochs=1, save_dir="my_model")
-```
-
-
-```python
-# Saving the model happens automatically at the end of training into the `save_dir` you specified
-# However, you could also save a reader manually again via:
-reader.save(directory="my_model")
-```
-
-
-```python
-# If you want to load it at a later point, just do:
-new_reader = FARMReader(model_name_or_path="my_model")
-```
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/3.md b/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/3.md
deleted file mode 100644
index 1740bcf2b..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/3.md
+++ /dev/null
@@ -1,183 +0,0 @@
----
-title: "Tutorial 3"
-metaTitle: "Build a QA System Without Elasticsearch"
-metaDescription: ""
-slug: "/docs/tutorial3"
-date: "2020-09-03"
-id: "tutorial3md"
----
-
-# Build a QA System Without Elasticsearch
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb)
-
-Haystack provides alternatives to Elasticsearch for developing quick prototypes.
-
-You can use an `InMemoryDocumentStore` or a `SQLDocumentStore`(with SQLite) as the document store.
-
-If you are interested in more feature-rich Elasticsearch, then please refer to the Tutorial 1.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-
-
-```python
-# In-Memory Document Store
-from haystack.document_store.memory import InMemoryDocumentStore
-document_store = InMemoryDocumentStore()
-```
-
-
-```python
-# SQLite Document Store
-# from haystack.document_store.sql import SQLDocumentStore
-# document_store = SQLDocumentStore(url="sqlite:///qa.db")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles on Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first get some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# convert files to dicts containing documents that can be indexed to our datastore
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is: {"name": ", "text": ""}
-
-# Let's have a look at the first 3 entries:
-print(dicts[:3])
-# Now, let's write the docs to our DB.
-document_store.write_documents(dicts)
-```
-
-## Initalize Retriever, Reader, & Finder
-
-### Retriever
-
-Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question could be answered.
-
-With InMemoryDocumentStore or SQLDocumentStore, you can use the TfidfRetriever. For more retrievers, please refer to the tutorial-1.
-
-
-```python
-# An in-memory TfidfRetriever based on Pandas dataframes
-
-from haystack.retriever.sparse import TfidfRetriever
-retriever = TfidfRetriever(document_store=document_store)
-```
-
-### Reader
-
-A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based
-on powerful, but slower deep learning models.
-
-Haystack currently supports Readers based on the frameworks FARM and Transformers.
-With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).
-
-**Here:** a medium sized RoBERTa QA model using a Reader based on FARM (https://huggingface.co/deepset/roberta-base-squad2)
-
-**Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)
-
-**Alternatives (Models):** e.g. "distilbert-base-uncased-distilled-squad" (fast) or "deepset/bert-large-uncased-whole-word-masking-squad2" (good accuracy)
-
-**Hint:** You can adjust the model to return "no answer possible" with the no_ans_boost. Higher values mean the model prefers "no answer possible"
-
-#### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-#### TransformersReader
-
-
-```python
-# Alternative:
-# reader = TransformersReader(model_name_or_path="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1)
-```
-
-### Finder
-
-The Finder sticks together reader and retriever in a pipeline to answer our actual questions.
-
-
-```python
-finder = Finder(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = finder.get_answers(question="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-# prediction = finder.get_answers(question="Who created the Dothraki vocabulary?", top_k_reader=5)
-# prediction = finder.get_answers(question="Who is the sister of Sansa?", top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/4.md b/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/4.md
deleted file mode 100644
index 31fa84c4f..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/4.md
+++ /dev/null
@@ -1,154 +0,0 @@
----
-title: "Tutorial 4"
-metaTitle: "Utilizing existing FAQs for Question Answering"
-metaDescription: ""
-slug: "/docs/tutorial4"
-date: "2020-09-03"
-id: "tutorial4md"
----
-
-# Utilizing existing FAQs for Question Answering
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial4_FAQ_style_QA.ipynb)
-
-While *extractive Question Answering* works on pure texts and is therefore more generalizable, there's also a common alternative that utilizes existing FAQ data.
-
-**Pros**:
-
-- Very fast at inference time
-- Utilize existing FAQ data
-- Quite good control over answers
-
-**Cons**:
-
-- Generalizability: We can only answer questions that are similar to existing ones in FAQ
-
-In some use cases, a combination of extractive QA and FAQ-style can also be an interesting option.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-from haystack import Finder
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-from haystack.retriever.dense import EmbeddingRetriever
-from haystack.utils import print_answers
-import pandas as pd
-import requests
-
-```
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker
-# ! docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.9.2
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-
-```
-
-### Init the DocumentStore
-In contrast to Tutorial 1 (extractive QA), we:
-
-* specify the name of our `text_field` in Elasticsearch that we want to return as an answer
-* specify the name of our `embedding_field` in Elasticsearch where we'll store the embedding of our question and that is used later for calculating our similarity to the incoming user question
-* set `excluded_meta_data=["question_emb"]` so that we don't return the huge embedding vectors in our search results
-
-
-```python
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="",
- index="document",
- embedding_field="question_emb",
- embedding_dim=768,
- excluded_meta_data=["question_emb"])
-```
-
-### Create a Retriever using embeddings
-Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones).
-We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings.
-
-
-```python
-retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert", use_gpu=True)
-```
-
-### Prepare & Index FAQ data
-We create a pandas dataframe containing some FAQ data (i.e curated pairs of question + answer) and index those in elasticsearch.
-Here: We download some question-answer pairs related to COVID-19
-
-
-```python
-# Download
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/faqs/faq_covidbert.csv")
-open('small_faq_covid.csv', 'wb').write(temp.content)
-
-# Get dataframe with columns "question", "answer" and some custom metadata
-df = pd.read_csv("small_faq_covid.csv")
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-df["question"] = df["question"].apply(lambda x: x.strip())
-print(df.head())
-
-# Get embeddings for our questions from the FAQs
-questions = list(df["question"].values)
-df["question_emb"] = retriever.embed_queries(texts=questions)
-df = df.rename(columns={"answer": "text"})
-
-# Convert Dataframe to list of dicts and index them in our DocumentStore
-docs_to_index = df.to_dict(orient="records")
-document_store.write_documents(docs_to_index)
-```
-
-### Ask questions
-Initialize a Finder (this time without a reader) and ask questions
-
-
-```python
-finder = Finder(reader=None, retriever=retriever)
-prediction = finder.get_answers_via_similar_questions(question="How is the virus spreading?", top_k_retriever=10)
-print_answers(prediction, details="all")
-```
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/5.md b/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/5.md
deleted file mode 100644
index 0f3ed45fe..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/5.md
+++ /dev/null
@@ -1,188 +0,0 @@
----
-title: "Tutorial 5"
-metaTitle: "Evaluation of a QA System"
-metaDescription: ""
-slug: "/docs/tutorial5"
-date: "2020-09-03"
-id: "tutorial5md"
----
-
-# Evaluation of a QA System
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial5_Evaluation.ipynb)
-
-To be able to make a statement about the performance of a question-answering system, it is important to evalute it. Furthermore, evaluation allows to determine which parts of the system can be improved.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-## Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-from farm.utils import initialize_device_settings
-
-device, n_gpu = initialize_device_settings(use_cuda=True)
-```
-
-
-```python
-from haystack.preprocessor.utils import fetch_archive_from_http
-
-# Download evaluation data, which is a subset of Natural Questions development set containing 50 documents
-doc_dir = "../data/nq"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/nq_dev_subset_v2.json.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-
-```python
-# make sure these indices do not collide with existing ones, the indices will be wiped clean before data is inserted
-doc_index = "tutorial5_docs"
-label_index = "tutorial5_labels"
-```
-
-
-```python
-# Connect to Elasticsearch
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-# Connect to Elasticsearch
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document",
- create_index=False, embedding_field="emb",
- embedding_dim=768, excluded_meta_data=["emb"])
-```
-
-
-```python
-# Add evaluation data to Elasticsearch Document Store
-# We first delete the custom tutorial indices to not have duplicate elements
-document_store.delete_all_documents(index=doc_index)
-document_store.delete_all_documents(index=label_index)
-document_store.add_eval_data(filename="../data/nq/nq_dev_subset_v2.json", doc_index=doc_index, label_index=label_index)
-```
-
-## Initialize components of QA-System
-
-
-```python
-# Initialize Retriever
-from haystack.retriever.sparse import ElasticsearchRetriever
-retriever = ElasticsearchRetriever(document_store=document_store)
-# Alternative: Evaluate DensePassageRetriever
-# Note, that DPR works best when you index short passages < 512 tokens as only those tokens will be used for the embedding.
-# Here, for nq_dev_subset_v2.json we have avg. num of tokens = 5220(!).
-# DPR still outperforms Elastic's BM25 by a small margin here.
-# from haystack.retriever.dense import DensePassageRetriever
-# retriever = DensePassageRetriever(document_store=document_store,
-# query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
-# passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
-# use_gpu=True,
-# embed_title=True,
-# max_seq_len=256,
-# batch_size=16,
-# remove_sep_tok_from_untitled_passages=True)
-#document_store.update_embeddings(retriever, index=doc_index)
-```
-
-
-```python
-# Initialize Reader
-from haystack.reader.farm import FARMReader
-
-reader = FARMReader("deepset/roberta-base-squad2", top_k_per_candidate=4)
-```
-
-
-```python
-# Initialize Finder which sticks together Reader and Retriever
-from haystack.finder import Finder
-
-finder = Finder(reader, retriever)
-```
-
-## Evaluation of Retriever
-
-
-```python
-## Evaluate Retriever on its own
-retriever_eval_results = retriever.eval(top_k=20, label_index=label_index, doc_index=doc_index)
-## Retriever Recall is the proportion of questions for which the correct document containing the answer is
-## among the correct documents
-print("Retriever Recall:", retriever_eval_results["recall"])
-## Retriever Mean Avg Precision rewards retrievers that give relevant documents a higher rank
-print("Retriever Mean Avg Precision:", retriever_eval_results["map"])
-```
-
-## Evaluation of Reader
-
-
-```python
-# Evaluate Reader on its own
-reader_eval_results = reader.eval(document_store=document_store, device=device, label_index=label_index, doc_index=doc_index)
-# Evaluation of Reader can also be done directly on a SQuAD-formatted file without passing the data to Elasticsearch
-#reader_eval_results = reader.eval_on_file("../data/nq", "nq_dev_subset_v2.json", device=device)
-
-## Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer
-print("Reader Top-N-Accuracy:", reader_eval_results["top_n_accuracy"])
-## Reader Exact Match is the proportion of questions where the predicted answer is exactly the same as the correct answer
-print("Reader Exact Match:", reader_eval_results["EM"])
-## Reader F1-Score is the average overlap between the predicted answers and the correct answers
-print("Reader F1-Score:", reader_eval_results["f1"])
-```
-
-## Evaluation of Finder
-
-
-```python
-# Evaluate combination of Reader and Retriever through Finder
-# Evaluate combination of Reader and Retriever through Finder
-finder_eval_results = finder.eval(top_k_retriever=1, top_k_reader=10, label_index=label_index, doc_index=doc_index)
-finder.print_eval_results(finder_eval_results)
-```
-
-
-```python
-
-```
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/6.md b/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/6.md
deleted file mode 100644
index 147ed7c10..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/6.md
+++ /dev/null
@@ -1,211 +0,0 @@
----
-title: "Tutorial 6"
-metaTitle: "Better retrieval via Dense Passage Retrieval"
-metaDescription: ""
-slug: "/docs/tutorial6"
-date: "2020-09-03"
-id: "tutorial6md"
----
-
-# Better Retrieval via "Dense Passage Retrieval"
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb)
-
-### Importance of Retrievers
-
-The Retriever has a huge impact on the performance of our overall search pipeline.
-
-
-### Different types of Retrievers
-#### Sparse
-Family of algorithms based on counting the occurrences of words (bag-of-words) resulting in very sparse vectors with length = vocab size.
-
-**Examples**: BM25, TF-IDF
-
-**Pros**: Simple, fast, well explainable
-
-**Cons**: Relies on exact keyword matches between query and text
-
-
-#### Dense
-These retrievers use neural network models to create "dense" embedding vectors. Within this family there are two different approaches:
-
-a) Single encoder: Use a **single model** to embed both query and passage.
-b) Dual-encoder: Use **two models**, one to embed the query and one to embed the passage
-
-Recent work suggests that dual encoders work better, likely because they can deal better with the different nature of query and passage (length, style, syntax ...).
-
-**Examples**: REALM, DPR, Sentence-Transformers
-
-**Pros**: Captures semantinc similarity instead of "word matches" (e.g. synonyms, related topics ...)
-
-**Cons**: Computationally more heavy, initial training of model
-
-
-### "Dense Passage Retrieval"
-
-In this Tutorial, we want to highlight one "Dense Dual-Encoder" called Dense Passage Retriever.
-It was introdoced by Karpukhin et al. (2020, https://arxiv.org/abs/2004.04906.
-
-Original Abstract:
-
-_"Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks."_
-
-Paper: https://arxiv.org/abs/2004.04906
-Original Code: https://fburl.com/qa-dpr
-
-
-*Use this* [link](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb) *to open the notebook in Google Colab.*
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-### Document Store
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-The default flavour of FAISSDocumentStore is "Flat" but can also be set to "HNSW" for
-faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.
-For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-
-
-```python
-from haystack.document_store.faiss import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever, Reader, & Finder
-
-#### Retriever
-
-**Here:** We use a `DensePassageRetriever`
-
-**Alternatives:**
-
-- The `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-
-
-```python
-from haystack.retriever.dense import DensePassageRetriever
-retriever = DensePassageRetriever(document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- max_seq_len_query=64,
- max_seq_len_passage=256,
- batch_size=16,
- use_gpu=True,
- embed_title=True,
- use_fast_tokenizers=True)
-# Important:
-# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all
-# previously indexed documents and update their embedding representation.
-# While this can be a time consuming operation (depending on corpus size), it only needs to be done once.
-# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.
-document_store.update_embeddings(retriever)
-```
-
-#### Reader
-
-Similar to previous Tutorials we now initalize our reader.
-
-Here we use a FARMReader with the *deepset/roberta-base-squad2* model (see: https://huggingface.co/deepset/roberta-base-squad2)
-
-
-
-##### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-#### Finder
-
-The Finder sticks together reader and retriever in a pipeline to answer our actual questions.
-
-
-```python
-finder = Finder(reader, retriever)
-```
-
-### Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = finder.get_answers(question="Who created the Dothraki vocabulary?", top_k_retriever=10, top_k_reader=5)
-
-#prediction = finder.get_answers(question="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-#prediction = finder.get_answers(question="Who is the sister of Sansa?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
-
-
-```python
-
-```
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/7.md b/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/7.md
deleted file mode 100644
index 5ae360451..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/7.md
+++ /dev/null
@@ -1,190 +0,0 @@
----
-title: "Tutorial 7"
-metaTitle: "Generative QA"
-metaDescription: ""
-slug: "/docs/tutorial7"
-date: "2020-11-12"
-id: "tutorial7md"
----
-
-# Generative QA with "Retrieval-Augmented Generation"
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial7_RAG_Generator.ipynb)
-
-While extractive QA highlights the span of text that answers a query,
-generative QA can return a novel text answer that it has composed.
-In this tutorial, you will learn how to set up a generative system using the
-[RAG model](https://arxiv.org/abs/2005.11401) which conditions the
-answer generator on a set of retrieved documents.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-Here are the packages and imports that we'll need:
-
-
-```python
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-from typing import List
-import requests
-import pandas as pd
-from haystack import Document
-from haystack.document_store.faiss import FAISSDocumentStore
-from haystack.generator.transformers import RAGenerator
-from haystack.retriever.dense import DensePassageRetriever
-```
-
-Let's download a csv containing some sample text and preprocess the data.
-
-
-
-```python
-# Download sample
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/haystack/master/tutorials/small_generator_dataset.csv")
-open('small_generator_dataset.csv', 'wb').write(temp.content)
-
-# Create dataframe with columns "title" and "text"
-df = pd.read_csv("small_generator_dataset.csv", sep=',')
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-
-print(df.head())
-```
-
-We can cast our data into Haystack Document objects.
-Alternatively, we can also just use dictionaries with "text" and "meta" fields
-
-
-```python
-# Use data to initialize Document objects
-titles = list(df["title"].values)
-texts = list(df["text"].values)
-documents: List[Document] = []
-for title, text in zip(titles, texts):
- documents.append(
- Document(
- text=text,
- meta={
- "name": title or ""
- }
- )
- )
-```
-
-Here we initialize the FAISSDocumentStore, DensePassageRetriever and RAGenerator.
-FAISS is chosen here since it is optimized vector storage.
-
-
-```python
-# Initialize FAISS document store.
-# Set `return_embedding` to `True`, so generator doesn't have to perform re-embedding
-document_store = FAISSDocumentStore(
- faiss_index_factory_str="Flat",
- return_embedding=True
-)
-
-# Initialize DPR Retriever to encode documents, encode question and query documents
-retriever = DensePassageRetriever(
- document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- use_gpu=True,
- embed_title=True,
-)
-
-# Initialize RAG Generator
-generator = RAGenerator(
- model_name_or_path="facebook/rag-token-nq",
- use_gpu=True,
- top_k_answers=1,
- max_length=200,
- min_length=2,
- embed_title=True,
- num_beams=2,
-)
-```
-
-We write documents to the DocumentStore, first by deleting any remaining documents then calling `write_documents()`.
-The `update_embeddings()` method uses the retriever to create an embedding for each document.
-
-
-
-```python
-# Delete existing documents in documents store
-document_store.delete_all_documents()
-
-# Write documents to document store
-document_store.write_documents(documents)
-
-# Add documents embeddings to index
-document_store.update_embeddings(
- retriever=retriever
-)
-```
-
-Here are our questions:
-
-
-```python
-QUESTIONS = [
- "who got the first nobel prize in physics",
- "when is the next deadpool movie being released",
- "which mode is used for short wave broadcast service",
- "who is the owner of reading football club",
- "when is the next scandal episode coming out",
- "when is the last time the philadelphia won the superbowl",
- "what is the most current adobe flash player version",
- "how many episodes are there in dragon ball z",
- "what is the first step in the evolution of the eye",
- "where is gall bladder situated in human body",
- "what is the main mineral in lithium batteries",
- "who is the president of usa right now",
- "where do the greasers live in the outsiders",
- "panda is a national animal of which country",
- "what is the name of manchester united stadium",
-]
-```
-
-Now let's run our system!
-The retriever will pick out a small subset of documents that it finds relevant.
-These are used to condition the generator as it generates the answer.
-What it should return then are novel text spans that form and answer to your question!
-
-
-```python
-# Now generate an answer for each question
-for question in QUESTIONS:
- # Retrieve related documents from retriever
- retriever_results = retriever.retrieve(
- query=question
- )
-
- # Now generate answer from question and retrieved documents
- predicted_result = generator.predict(
- query=question,
- documents=retriever_results,
- top_k=1
- )
-
- # Print you answer
- answers = predicted_result["answers"]
- print(f'Generated answer is \'{answers[0]["answer"]}\' for the question = \'{question}\'')
-```
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/8.md b/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/8.md
deleted file mode 100644
index 678b6c621..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/8.md
+++ /dev/null
@@ -1,202 +0,0 @@
----
-title: "Tutorial 8"
-metaTitle: "Preprocessing"
-metaDescription: ""
-slug: "/docs/tutorial8"
-date: "2021-01-08"
-id: "tutorial8md"
----
-
-# Preprocessing
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial9_DPR_training.ipynb)
-
-Haystack includes a suite of tools to extract text from different file types, normalize white space
-and split text into smaller pieces to optimize retrieval.
-These data preprocessing steps can have a big impact on the systems performance and effective handling of data is key to getting the most out of Haystack.
-
-Ultimately, Haystack expects data to be provided as a list documents in the following dictionary format:
-``` python
-docs = [
- {
- 'text': DOCUMENT_TEXT_HERE,
- 'meta': {'name': DOCUMENT_NAME, ...}
- }, ...
-]
-```
-
-This tutorial will show you all the tools that Haystack provides to help you cast your data into this format.
-
-
-```python
-# Let's start by installing Haystack
-
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-!wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.02.tar.gz
-!tar -xvf xpdf-tools-linux-4.02.tar.gz && sudo cp xpdf-tools-linux-4.02/bin64/pdftotext /usr/local/bin
-```
-
-
-```python
-# Here are the imports we need
-
-from haystack.file_converter.txt import TextConverter
-from haystack.file_converter.pdf import PDFToTextConverter
-from haystack.file_converter.docx import DocxToTextConverter
-
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.preprocessor.preprocessor import PreProcessor
-```
-
-
-```python
-# This fetches some sample files to work with
-
-doc_dir = "data/preprocessing_tutorial"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/preprocessing_tutorial.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-## Converters
-
-Haystack's converter classes are designed to help you turn files on your computer into the documents
-that can be processed by the Haystack pipeline.
-There are file converters for txt, pdf, docx files as well as a converter that is powered by Apache Tika.
-
-
-```python
-# Here are some examples of how you would use file converters
-
-converter = TextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_txt = converter.convert(file_path="data/preprocessing_tutorial/classics.txt", meta=None)
-
-converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_pdf = converter.convert(file_path="data/preprocessing_tutorial/bert.pdf", meta=None)
-
-converter = DocxToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_docx = converter.convert(file_path="data/preprocessing_tutorial/heavy_metal.docx", meta=None)
-
-```
-
-
-```python
-# Haystack also has a convenience function that will automatically apply the right converter to each file in a directory.
-
-all_docs = convert_files_to_dicts(dir_path="data/preprocessing_tutorial")
-```
-
-## PreProcessor
-
-The PreProcessor class is designed to help you clean text and split text into sensible units.
-File splitting can have a very significant impact on the system's performance and is absolutely mandatory for Dense Passage Retrieval models.
-In general, we recommend you split the text from your files into small documents of around 100 words for dense retrieval methods
-and no more than 10,000 words for sparse methods.
-Have a look at the [Preprocessing](https://haystack.deepset.ai/docs/latest/preprocessingmd)
-and [Optimization](https://haystack.deepset.ai/docs/latest/optimizationmd) pages on our website for more details.
-
-
-```python
-# This is a default usage of the PreProcessor.
-# Here, it performs cleaning of consecutive whitespaces
-# and splits a single large document into smaller documents.
-# Each document is up to 1000 words long and document breaks cannot fall in the middle of sentences
-# Note how the single document passed into the document gets split into 5 smaller documents
-
-preprocessor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=False,
- split_by="word",
- split_length=100,
- split_respect_sentence_boundary=True
-)
-docs_default = preprocessor.process(doc_txt)
-print(f"n_docs_input: 1\nn_docs_output: {len(docs_default)}")
-```
-
-## Cleaning
-
-- `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
-- `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
-- `clean_header_footer` will remove any long header or footer texts that are repeated on each page
-
-## Splitting
-By default, the PreProcessor will respect sentence boundaries, meaning that documents will not start or end
-midway through a sentence.
-This will help reduce the possibility of answer phrases being split between two documents.
-This feature can be turned off by setting `split_respect_sentence_boundary=False`.
-
-
-```python
-# Not respecting sentence boundary vs respecting sentence boundary
-
-preprocessor_nrsb = PreProcessor(split_respect_sentence_boundary=False)
-docs_nrsb = preprocessor_nrsb.process(doc_txt)
-
-print("RESPECTING SENTENCE BOUNDARY")
-end_text = docs_default[0]["text"][-50:]
-print("End of document: \"..." + end_text + "\"")
-print()
-print("NOT RESPECTING SENTENCE BOUNDARY")
-end_text_nrsb = docs_nrsb[0]["text"][-50:]
-print("End of document: \"..." + end_text_nrsb + "\"")
-```
-
-A commonly used strategy to split long documents, especially in the field of Question Answering,
-is the sliding window approach. If `split_length=10` and `split_overlap=3`, your documents will look like this:
-
-- doc1 = words[0:10]
-- doc2 = words[7:17]
-- doc3 = words[14:24]
-- ...
-
-You can use this strategy by following the code below.
-
-
-```python
-# Sliding window approach
-
-preprocessor_sliding_window = PreProcessor(
- split_overlap=3,
- split_length=10,
- split_respect_sentence_boundary=False
-)
-docs_sliding_window = preprocessor_sliding_window.process(doc_txt)
-
-doc1 = docs_sliding_window[0]["text"][:200]
-doc2 = docs_sliding_window[1]["text"][:100]
-doc3 = docs_sliding_window[2]["text"][:100]
-
-print("Document 1: \"" + doc1 + "...\"")
-print("Document 2: \"" + doc2 + "...\"")
-print("Document 3: \"" + doc3 + "...\"")
-```
-
-## Bringing it all together
-
-
-```python
-all_docs = convert_files_to_dicts(dir_path="data/preprocessing_tutorial")
-preprocessor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=False,
- split_by="word",
- split_length=100,
- split_respect_sentence_boundary=True
-)
-nested_docs = [preprocessor.process(d) for d in all_docs]
-docs = [d for x in nested_docs for d in x]
-
-print(f"n_files_input: {len(all_docs)}\nn_docs_output: {len(docs)}")
-```
-
-
-```python
-
-```
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/9.md b/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/9.md
deleted file mode 100644
index 4dd76c175..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/9.md
+++ /dev/null
@@ -1,211 +0,0 @@
----
-title: "Tutorial 9"
-metaTitle: "Training a Dense Passage Retrieval model"
-metaDescription: ""
-slug: "/docs/tutorial9"
-date: "2021-01-08"
-id: "tutorial9md"
----
-
-# Training Your Own "Dense Passage Retrieval" Model
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial9_DPR_training.ipynb)
-
-Haystack contains all the tools needed to train your own Dense Passage Retrieval model.
-This tutorial will guide you through the steps required to create a retriever that is specifically tailored to your domain.
-
-
-
-```python
-# Here are some imports that we'll need
-
-from haystack.retriever.dense import DensePassageRetriever
-from haystack.preprocessor.utils import fetch_archive_from_http
-```
-
-## Training Data
-
-DPR training performed using Information Retrieval data.
-More specifically, you want to feed in pairs of queries and relevant documents.
-
-To train a model, we will need a dataset that has the same format as the original DPR training data.
-Each data point in the dataset should have the following dictionary structure.
-
-``` python
- {
- "dataset": str,
- "question": str,
- "answers": list of str
- "positive_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- "negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- "hard_negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- }
-```
-
-`positive_ctxs` are context passages which are relevant to the query.
-In some datasets, queries might have more than one positive context
-in which case you can set the `num_positives` parameter to be higher than the default 1.
-Note that `num_positives` needs to be lower or equal to the minimum number of `positive_ctxs` for queries in your data.
-If you have an unequal number of positive contexts per example,
-you might want to generate some soft labels retrieving similar contexts which contain the answer.
-
-DPR is standardly trained using a method known as in-batch negatives.
-This means that positive contexts for given query are treated as negative contexts for the other queries in the batch.
-Doing so allows for a high degree of computational efficiency, thus allowing the model to be trained on large amounts of data.
-
-`negative_ctxs` is not actually used in Haystack's DPR training so we recommend you set it to an empty list.
-They were used by the original DPR authors in an experiment to compare it against the in-batch negatives method.
-
-`hard_negative_ctxs` are passages that are not relevant to the query.
-In the original DPR paper, these are fetched using a retriever to find the most similar passages to the positive passage.
-Passages which contain the answer text are filtered out.
-
-We are [currently working](https://github.com/deepset-ai/haystack/issues/705) on a script that will convert SQuAD format data into a DPR dataset!
-
-## Using Question Answering Data
-
-Question Answering datasets can sometimes be used as training data.
-Google's Natural Questions dataset, is sufficiently large
-and contains enough unique passages, that it can be converted into a DPR training set.
-This is done simply by considering answer containing passages as relevant documents to the query.
-
-The SQuAD dataset, however, is not as suited to this use case since its question and answer pairs
-are created on only a very small slice of wikipedia documents.
-
-## Download Original DPR Training Data
-
-WARNING: These files are large! The train set is 7.4GB and the dev set is 800MB
-
-We can download the original DPR training data with the following cell.
-Note that this data is probably only useful if you are trying to train from scratch.
-
-
-```python
-# Download original DPR data
-# WARNING: the train set is 7.4GB and the dev set is 800MB
-
-doc_dir = "data/dpr_training/"
-
-s3_url_train = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-train.json.gz"
-s3_url_dev = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz"
-
-fetch_archive_from_http(s3_url_train, output_dir=doc_dir + "train/")
-fetch_archive_from_http(s3_url_dev, output_dir=doc_dir + "dev/")
-```
-
-## Option 1: Training DPR from Scratch
-
-The default variables that we provide below are chosen to train a DPR model from scratch.
-Here, both passage and query embedding models are initialized using BERT base
-and the model is trained using Google's Natural Questions dataset (in a format specialised for DPR).
-
-If you are working in a language other than English,
-you will want to initialize the passage and query embedding models with a language model that supports your language
-and also provide a dataset in your language.
-
-
-```python
-# Here are the variables to specify our training data, the models that we use to initialize DPR
-# and the directory where we'll be saving the model
-
-doc_dir = "data/dpr_training/"
-
-train_filename = "train/biencoder-nq-train.json"
-dev_filename = "dev/biencoder-nq-dev.json"
-
-query_model = "bert-base-uncased"
-passage_model = "bert-base-uncased"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Option 2: Finetuning DPR
-
-If you have your own domain specific question answering or information retrieval dataset,
-you might instead be interested in finetuning a pretrained DPR model.
-In this case, you would initialize both query and passage models using the original pretrained model.
-You will want to load something like this set of variables instead of the ones above
-
-
-```python
-# Here are the variables you might want to use instead of the set above
-# in order to perform pretraining
-
-doc_dir = "PATH_TO_YOUR_DATA_DIR"
-train_filename = "TRAIN_FILENAME"
-dev_filename = "DEV_FILENAME"
-
-query_model = "facebook/dpr-question_encoder-single-nq-base"
-passage_model = "facebook/dpr-ctx_encoder-single-nq-base"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Initialization
-
-Here we want to initialize our model either with plain language model weights for training from scratch
-or else with pretrained DPR weights for finetuning.
-We follow the [original DPR parameters](https://github.com/facebookresearch/DPR#best-hyperparameter-settings)
-for their max passage length but set max query length to 64 since queries are very rarely longer.
-
-
-```python
-## Initialize DPR model
-
-retriever = DensePassageRetriever(
- document_store=None,
- query_embedding_model=query_model,
- passage_embedding_model=passage_model,
- max_seq_len_query=64,
- max_seq_len_passage=256
-)
-```
-
-## Training
-
-Let's start training and save our trained model!
-
-On a V100 GPU, you can fit up to batch size 4 so we set gradient accumulation steps to 4 in order
-to simulate the batch size 16 of the original DPR experiment.
-
-When `embed_title=True`, the document title is prepended to the input text sequence with a `[SEP]` token
-between it and document text.
-
-When training from scratch with the above variables, 1 epoch takes around an hour and we reached the following performance:
-
-```
-loss: 0.09334952129693501
-acc: 0.984035000191887
-f1: 0.936147352264006
-acc_and_f1: 0.9600911762279465
-average_rank: 0.07075978511128166
-```
-
-
-```python
-# Start training our model and save it when it is finished
-
-retriever.train(
- data_dir=doc_dir,
- train_filename=dev_filename,
- dev_filename=dev_filename,
- test_filename=dev_filename,
- n_epochs=1,
- batch_size=4,
- grad_acc_steps=4,
- save_dir=save_dir,
- evaluate_every=3000,
- embed_title=True,
- num_positives=1,
- num_hard_negatives=1
-)
-```
-
-## Loading
-
-Loading our newly trained model is simple!
-
-
-```python
-reloaded_retriever = DensePassageRetriever.load(load_dir=save_dir, document_store=None)
-```
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/preprocessing.md b/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/preprocessing.md
deleted file mode 100644
index 943bee6a3..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/preprocessing.md
+++ /dev/null
@@ -1,116 +0,0 @@
----
-title: "Preprocessing"
-metaTitle: "Preprocessing"
-metaDescription: ""
-slug: "/docs/preprocessing"
-date: "2020-09-03"
-id: "preprocessingmd"
----
-
-# Preprocessing
-
-Haystack includes a suite of tools to:
-
-* extract text from different file types,
-* normalize white space
-* split text into smaller pieces to optimize retrieval
-
-These data preprocessing steps can have a big impact on the systems performance
-and effective handling of data is key to getting the most out of Haystack.
-
-The Document Store expects its inputs to come in the following format.
-The sections below will show you all the tools you'll need to ready your data for storing.
-
-```python
-docs = [
- {
- 'text': DOCUMENT_TEXT_HERE,
- 'meta': {'name': DOCUMENT_NAME, ...}
- }, ...
-]
-```
-
-## File Conversion
-
-There are a range of different file converters in Haystack that
-can extract text from files and cast them into the unified dictionary format shown above.
-Haystack features support for txt, pdf and docx files and there is even a converter that leverages Apache Tika.
-Please refer to [the API docs](/docs/latest/file_convertersmd) to see which converter best suits you.
-
-"}.
- Optionally: Include meta data via {"text": "",
- "meta":{"name": ", "author": "somebody", ...}}
- It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: Optional name of index where the documents shall be written to.
- If None, the DocumentStore's default index (self.index) will be used.
-
-**Returns**:
-
-None
-
-
-#### get\_all\_documents
-
-```python
- | @abstractmethod
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Get documents from the document store.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-
-
-#### add\_eval\_data
-
-```python
- | add_eval_data(filename: str, doc_index: str = "eval_document", label_index: str = "label", batch_size: Optional[int] = None, preprocessor: Optional[PreProcessor] = None, max_docs: Union[int, bool] = None, open_domain: bool = False)
-```
-
-Adds a SQuAD-formatted file to the DocumentStore in order to be able to perform evaluation on it.
-If a jsonl file and a batch_size is passed to the function, documents are loaded batchwise
-from disk and also indexed batchwise to the DocumentStore in order to prevent out of memory errors.
-
-**Arguments**:
-
-- `filename`: Name of the file containing evaluation data (json or jsonl)
-- `doc_index`: Elasticsearch index where evaluation documents should be stored
-- `label_index`: Elasticsearch index where labeled questions should be stored
-- `batch_size`: Optional number of documents that are loaded and processed at a time.
- When set to None (default) all documents are processed at once.
-- `preprocessor`: Optional PreProcessor to preprocess evaluation documents.
- It can be used for splitting documents into passages (and assigning labels to corresponding passages).
- Currently the PreProcessor does not support split_by sentence, cleaning nor split_overlap != 0.
- When set to None (default) preprocessing is disabled.
-- `max_docs`: Optional number of documents that will be loaded.
- When set to None (default) all available eval documents are used.
-- `open_domain`: Set this to True if your file is an open domain dataset where two different answers to the
- same question might be found in different contexts.
-
-
-# Module: elasticsearch
-
-
-## Class: ElasticsearchDocumentStore
-
-```python
-class ElasticsearchDocumentStore(BaseDocumentStore)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(host: Union[str, List[str]] = "localhost", port: Union[int, List[int]] = 9200, username: str = "", password: str = "", api_key_id: Optional[str] = None, api_key: Optional[str] = None, index: str = "document", label_index: str = "label", search_fields: Union[str, list] = "text", text_field: str = "text", name_field: str = "name", embedding_field: str = "embedding", embedding_dim: int = 768, custom_mapping: Optional[dict] = None, excluded_meta_data: Optional[list] = None, faq_question_field: Optional[str] = None, analyzer: str = "standard", scheme: str = "http", ca_certs: Optional[str] = None, verify_certs: bool = True, create_index: bool = True, update_existing_documents: bool = False, refresh_type: str = "wait_for", similarity="dot_product", timeout=30, return_embedding: bool = False)
-```
-
-A DocumentStore using Elasticsearch to store and query the documents for our search.
-
- * Keeps all the logic to store and query documents from Elastic, incl. mapping of fields, adding filters or boosts to your queries, and storing embeddings
- * You can either use an existing Elasticsearch index or create a new one via haystack
- * Retrievers operate on top of this DocumentStore to find the relevant documents for a query
-
-**Arguments**:
-
-- `host`: url(s) of elasticsearch nodes
-- `port`: port(s) of elasticsearch nodes
-- `username`: username (standard authentication via http_auth)
-- `password`: password (standard authentication via http_auth)
-- `api_key_id`: ID of the API key (altenative authentication mode to the above http_auth)
-- `api_key`: Secret value of the API key (altenative authentication mode to the above http_auth)
-- `index`: Name of index in elasticsearch to use for storing the documents that we want to search. If not existing yet, we will create one.
-- `label_index`: Name of index in elasticsearch to use for storing labels. If not existing yet, we will create one.
-- `search_fields`: Name of fields used by ElasticsearchRetriever to find matches in the docs to our incoming query (using elastic's multi_match query), e.g. ["title", "full_text"]
-- `text_field`: Name of field that might contain the answer and will therefore be passed to the Reader Model (e.g. "full_text").
- If no Reader is used (e.g. in FAQ-Style QA) the plain content of this field will just be returned.
-- `name_field`: Name of field that contains the title of the the doc
-- `embedding_field`: Name of field containing an embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `embedding_dim`: Dimensionality of embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `custom_mapping`: If you want to use your own custom mapping for creating a new index in Elasticsearch, you can supply it here as a dictionary.
-- `analyzer`: Specify the default analyzer from one of the built-ins when creating a new Elasticsearch Index.
- Elasticsearch also has built-in analyzers for different languages (e.g. impacting tokenization). More info at:
- https://www.elastic.co/guide/en/elasticsearch/reference/7.9/analysis-analyzers.html
-- `excluded_meta_data`: Name of fields in Elasticsearch that should not be returned (e.g. [field_one, field_two]).
- Helpful if you have fields with long, irrelevant content that you don't want to display in results (e.g. embedding vectors).
-- `scheme`: 'https' or 'http', protocol used to connect to your elasticsearch instance
-- `ca_certs`: Root certificates for SSL: it is a path to certificate authority (CA) certs on disk. You can use certifi package with certifi.where() to find where the CA certs file is located in your machine.
-- `verify_certs`: Whether to be strict about ca certificates
-- `create_index`: Whether to try creating a new index (If the index of that name is already existing, we will just continue in any case)
-- `update_existing_documents`: Whether to update any existing documents with the same ID when adding
- documents. When set as True, any document with an existing ID gets updated.
- If set to False, an error is raised if the document ID of the document being
- added already exists.
-- `refresh_type`: Type of ES refresh used to control when changes made by a request (e.g. bulk) are made visible to search.
- If set to 'wait_for', continue only after changes are visible (slow, but safe).
- If set to 'false', continue directly (fast, but sometimes unintuitive behaviour when docs are not immediately available after ingestion).
- More info at https://www.elastic.co/guide/en/elasticsearch/reference/6.8/docs-refresh.html
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
- more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
-- `timeout`: Number of seconds after which an ElasticSearch request times out.
-- `return_embedding`: To return document embedding
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index: Optional[str] = None) -> List[Document]
-```
-
-Fetch documents by specifying a list of text id strings
-
-
-#### get\_metadata\_values\_by\_key
-
-```python
- | get_metadata_values_by_key(key: str, query: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> List[dict]
-```
-
-Get values associated with a metadata key. The output is in the format:
- [{"value": "my-value-1", "count": 23}, {"value": "my-value-2", "count": 12}, ... ]
-
-**Arguments**:
-
-- `key`: the meta key name to get the values for.
-- `query`: narrow down the scope to documents matching the query string.
-- `filters`: narrow down the scope to documents that match the given filters.
-- `index`: Elasticsearch index where the meta values should be searched. If not supplied,
- self.index will be used.
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000)
-```
-
-Indexes documents for later queries in Elasticsearch.
-
-Behaviour if a document with the same ID already exists in ElasticSearch:
-a) (Default) Throw Elastic's standard error message for duplicate IDs.
-b) If `self.update_existing_documents=True` for DocumentStore: Overwrite existing documents.
-(This is only relevant if you pass your own ID when initializing a `Document`.
-If don't set custom IDs for your Documents or just pass a list of dictionaries here,
-they will automatically get UUIDs assigned. See the `Document` class for details)
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
- For documents as dictionaries, the format is {"text": ""}.
- Optionally: Include meta data via {"text": "",
- "meta":{"name": ", "author": "somebody", ...}}
- It can be used for filtering and is accessible in the responses of the Finder.
- Advanced: If you are using your own Elasticsearch mapping, the key names in the dictionary
- should be changed to what you have set for self.text_field and self.name_field.
-- `index`: Elasticsearch index where the documents should be indexed. If not supplied, self.index will be used.
-- `batch_size`: Number of documents that are passed to Elasticsearch's bulk function at a time.
-
-**Returns**:
-
-None
-
-
-#### write\_labels
-
-```python
- | write_labels(labels: Union[List[Label], List[dict]], index: Optional[str] = None, batch_size: int = 10_000)
-```
-
-Write annotation labels into document store.
-
-**Arguments**:
-
-- `labels`: A list of Python dictionaries or a list of Haystack Label objects.
-- `batch_size`: Number of labels that are passed to Elasticsearch's bulk function at a time.
-
-
-#### update\_document\_meta
-
-```python
- | update_document_meta(id: str, meta: Dict[str, str])
-```
-
-Update the metadata dictionary of a document by specifying its string id
-
-
-#### get\_document\_count
-
-```python
- | get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
-```
-
-Return the number of documents in the document store.
-
-
-#### get\_label\_count
-
-```python
- | get_label_count(index: Optional[str] = None) -> int
-```
-
-Return the number of labels in the document store
-
-
-#### get\_all\_documents
-
-```python
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> List[Document]
-```
-
-Get documents from the document store.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_documents\_generator
-
-```python
- | get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> Generator[Document, None, None]
-```
-
-Get documents from the document store. Under-the-hood, documents are fetched in batches from the
-document store and yielded as individual documents. This method can be used to iteratively process
-a large number of documents without having to load all documents in memory.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_labels
-
-```python
- | get_all_labels(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, batch_size: int = 10_000) -> List[Label]
-```
-
-Return all labels in the document store
-
-
-#### query
-
-```python
- | query(query: Optional[str], filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, custom_query: Optional[str] = None, index: Optional[str] = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query as defined by the BM25 algorithm.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.ndarray, filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: Index name for storing the docs and metadata
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### describe\_documents
-
-```python
- | describe_documents(index=None)
-```
-
-Return a summary of the documents in the document store
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever, index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, update_existing_embeddings: bool = True, batch_size: int = 10_000)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever to use to update the embeddings.
-- `index`: Index name to update
-- `update_existing_embeddings`: Whether to update existing embeddings of the documents. If set to False,
- only documents without embeddings are processed. This mode can be used for
- incremental updating of embeddings, wherein, only newly indexed documents
- get processed.
-- `filters`: Optional filters to narrow down the documents for which embeddings are to be updated.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-**Returns**:
-
-None
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-## Class: OpenDistroElasticsearchDocumentStore
-
-```python
-class OpenDistroElasticsearchDocumentStore(ElasticsearchDocumentStore)
-```
-
-Document Store using the Open Distro for Elasticsearch. It is compatible with the AWS Elasticsearch Service.
-
-In addition to native Elasticsearch query & filtering, it provides efficient vector similarity search using
-the KNN plugin that can scale to a large number of documents.
-
-
-# Module: memory
-
-
-## Class: InMemoryDocumentStore
-
-```python
-class InMemoryDocumentStore(BaseDocumentStore)
-```
-
-In-memory document store
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(index: str = "document", label_index: str = "label", embedding_field: Optional[str] = "embedding", embedding_dim: int = 768, return_embedding: bool = False, similarity: str = "dot_product", progress_bar: bool = True)
-```
-
-**Arguments**:
-
-- `index`: The documents are scoped to an index attribute that can be used when writing, querying,
- or deleting documents. This parameter sets the default value for document index.
-- `label_index`: The default value of index attribute for the labels.
-- `embedding_field`: Name of field containing an embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `embedding_dim`: The size of the embedding vector.
-- `return_embedding`: To return document embedding
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
- more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
-- `progress_bar`: Whether to show a tqdm progress bar or not.
- Can be helpful to disable in production deployments to keep the logs clean.
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
- For documents as dictionaries, the format is {"text": ""}.
- Optionally: Include meta data via {"text": "",
- "meta": {"name": ", "author": "somebody", ...}}
- It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: write documents to a custom namespace. For instance, documents for evaluation can be indexed in a
- separate index than the documents for search.
-
-**Returns**:
-
-None
-
-
-#### write\_labels
-
-```python
- | write_labels(labels: Union[List[dict], List[Label]], index: Optional[str] = None)
-```
-
-Write annotation labels into document store.
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index: Optional[str] = None) -> List[Document]
-```
-
-Fetch documents by specifying a list of text id strings
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.ndarray, filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: Index name for storing the docs and metadata
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, update_existing_embeddings: bool = True, batch_size: int = 10_000)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever to use to get embeddings for text
-- `index`: Index name for which embeddings are to be updated. If set to None, the default self.index is used.
-- `update_existing_embeddings`: Whether to update existing embeddings of the documents. If set to False,
- only documents without embeddings are processed. This mode can be used for
- incremental updating of embeddings, wherein, only newly indexed documents
- get processed.
-- `filters`: Optional filters to narrow down the documents for which embeddings are to be updated.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-**Returns**:
-
-None
-
-
-#### get\_document\_count
-
-```python
- | get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
-```
-
-Return the number of documents in the document store.
-
-
-#### get\_label\_count
-
-```python
- | get_label_count(index: Optional[str] = None) -> int
-```
-
-Return the number of labels in the document store
-
-
-#### get\_all\_documents\_generator
-
-```python
- | get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> Generator[Document, None, None]
-```
-
-Get all documents from the document store. The methods returns a Python Generator that yields individual
-documents.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-
-
-#### get\_all\_labels
-
-```python
- | get_all_labels(index: str = None, filters: Optional[Dict[str, List[str]]] = None) -> List[Label]
-```
-
-Return all labels in the document store
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-# Module: sql
-
-
-## Class: SQLDocumentStore
-
-```python
-class SQLDocumentStore(BaseDocumentStore)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(url: str = "sqlite://", index: str = "document", label_index: str = "label", update_existing_documents: bool = False)
-```
-
-An SQL backed DocumentStore. Currently supports SQLite, PostgreSQL and MySQL backends.
-
-**Arguments**:
-
-- `url`: URL for SQL database as expected by SQLAlchemy. More info here: https://docs.sqlalchemy.org/en/13/core/engines.html#database-urls
-- `index`: The documents are scoped to an index attribute that can be used when writing, querying, or deleting documents.
- This parameter sets the default value for document index.
-- `label_index`: The default value of index attribute for the labels.
-- `update_existing_documents`: Whether to update any existing documents with the same ID when adding
- documents. When set as True, any document with an existing ID gets updated.
- If set to False, an error is raised if the document ID of the document being
- added already exists. Using this parameter could cause performance degradation
- for document insertion.
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index: Optional[str] = None, batch_size: int = 10_000) -> List[Document]
-```
-
-Fetch documents by specifying a list of text id strings
-
-
-#### get\_documents\_by\_vector\_ids
-
-```python
- | get_documents_by_vector_ids(vector_ids: List[str], index: Optional[str] = None, batch_size: int = 10_000)
-```
-
-Fetch documents by specifying a list of text vector id strings
-
-**Arguments**:
-
-- `vector_ids`: List of vector_id strings.
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_documents\_generator
-
-```python
- | get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> Generator[Document, None, None]
-```
-
-Get documents from the document store. Under-the-hood, documents are fetched in batches from the
-document store and yielded as individual documents. This method can be used to iteratively process
-a large number of documents without having to load all documents in memory.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_labels
-
-```python
- | get_all_labels(index=None, filters: Optional[dict] = None)
-```
-
-Return all labels in the document store
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000)
-```
-
-Indexes documents for later queries.
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
- For documents as dictionaries, the format is {"text": ""}.
- Optionally: Include meta data via {"text": "",
- "meta":{"name": ", "author": "somebody", ...}}
- It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: add an optional index attribute to documents. It can be later used for filtering. For instance,
- documents for evaluation can be indexed in a separate index than the documents for search.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-**Returns**:
-
-None
-
-
-#### write\_labels
-
-```python
- | write_labels(labels, index=None)
-```
-
-Write annotation labels into document store.
-
-
-#### update\_vector\_ids
-
-```python
- | update_vector_ids(vector_id_map: Dict[str, str], index: Optional[str] = None, batch_size: int = 10_000)
-```
-
-Update vector_ids for given document_ids.
-
-**Arguments**:
-
-- `vector_id_map`: dict containing mapping of document_id -> vector_id.
-- `index`: filter documents by the optional index attribute for documents in database.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### reset\_vector\_ids
-
-```python
- | reset_vector_ids(index: Optional[str] = None)
-```
-
-Set vector IDs for all documents as None
-
-
-#### update\_document\_meta
-
-```python
- | update_document_meta(id: str, meta: Dict[str, str])
-```
-
-Update the metadata dictionary of a document by specifying its string id
-
-
-#### get\_document\_count
-
-```python
- | get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
-```
-
-Return the number of documents in the document store.
-
-
-#### get\_label\_count
-
-```python
- | get_label_count(index: Optional[str] = None) -> int
-```
-
-Return the number of labels in the document store
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-# Module: faiss
-
-
-## Class: FAISSDocumentStore
-
-```python
-class FAISSDocumentStore(SQLDocumentStore)
-```
-
-Document store for very large scale embedding based dense retrievers like the DPR.
-
-It implements the FAISS library(https://github.com/facebookresearch/faiss)
-to perform similarity search on vectors.
-
-The document text and meta-data (for filtering) are stored using the SQLDocumentStore, while
-the vector embeddings are indexed in a FAISS Index.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(sql_url: str = "sqlite:///", vector_dim: int = 768, faiss_index_factory_str: str = "Flat", faiss_index: Optional[faiss.swigfaiss.Index] = None, return_embedding: bool = False, update_existing_documents: bool = False, index: str = "document", similarity: str = "dot_product", embedding_field: str = "embedding", progress_bar: bool = True, **kwargs, ,)
-```
-
-**Arguments**:
-
-- `sql_url`: SQL connection URL for database. It defaults to local file based SQLite DB. For large scale
- deployment, Postgres is recommended.
-- `vector_dim`: the embedding vector size.
-- `faiss_index_factory_str`: Create a new FAISS index of the specified type.
- The type is determined from the given string following the conventions
- of the original FAISS index factory.
- Recommended options:
- - "Flat" (default): Best accuracy (= exact). Becomes slow and RAM intense for > 1 Mio docs.
- - "HNSW": Graph-based heuristic. If not further specified,
- we use the following config:
- HNSW64, efConstruction=80 and efSearch=20
-
- - "IVFx,Flat": Inverted Index. Replace x with the number of centroids aka nlist.
- Rule of thumb: nlist = 10 * sqrt (num_docs) is a good starting point.
- For more details see:
- - Overview of indices https://github.com/facebookresearch/faiss/wiki/Faiss-indexes
- - Guideline for choosing an index https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
- - FAISS Index factory https://github.com/facebookresearch/faiss/wiki/The-index-factory
- Benchmarks: XXX
-- `faiss_index`: Pass an existing FAISS Index, i.e. an empty one that you configured manually
- or one with docs that you used in Haystack before and want to load again.
-- `return_embedding`: To return document embedding
-- `update_existing_documents`: Whether to update any existing documents with the same ID when adding
- documents. When set as True, any document with an existing ID gets updated.
- If set to False, an error is raised if the document ID of the document being
- added already exists.
-- `index`: Name of index in document store to use.
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
- more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
-- `embedding_field`: Name of field containing an embedding vector.
-- `progress_bar`: Whether to show a tqdm progress bar or not.
- Can be helpful to disable in production deployments to keep the logs clean.
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000)
-```
-
-Add new documents to the DocumentStore.
-
-**Arguments**:
-
-- `documents`: List of `Dicts` or List of `Documents`. If they already contain the embeddings, we'll index
- them right away in FAISS. If not, you can later call update_embeddings() to create & index them.
-- `index`: (SQL) index name for storing the docs and metadata
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-**Returns**:
-
-
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None, update_existing_embeddings: bool = True, filters: Optional[Dict[str, List[str]]] = None, batch_size: int = 10_000)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever to use to get embeddings for text
-- `index`: Index name for which embeddings are to be updated. If set to None, the default self.index is used.
-- `update_existing_embeddings`: Whether to update existing embeddings of the documents. If set to False,
- only documents without embeddings are processed. This mode can be used for
- incremental updating of embeddings, wherein, only newly indexed documents
- get processed.
-- `filters`: Optional filters to narrow down the documents for which embeddings are to be updated.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-**Returns**:
-
-None
-
-
-#### get\_all\_documents\_generator
-
-```python
- | get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> Generator[Document, None, None]
-```
-
-Get all documents from the document store. Under-the-hood, documents are fetched in batches from the
-document store and yielded as individual documents. This method can be used to iteratively process
-a large number of documents without having to load all documents in memory.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### train\_index
-
-```python
- | train_index(documents: Optional[Union[List[dict], List[Document]]], embeddings: Optional[np.ndarray] = None, index: Optional[str] = None)
-```
-
-Some FAISS indices (e.g. IVF) require initial "training" on a sample of vectors before you can add your final vectors.
-The train vectors should come from the same distribution as your final ones.
-You can pass either documents (incl. embeddings) or just the plain embeddings that the index shall be trained on.
-
-**Arguments**:
-
-- `documents`: Documents (incl. the embeddings)
-- `embeddings`: Plain embeddings
-- `index`: Name of the index to train. If None, the DocumentStore's default index (self.index) will be used.
-
-**Returns**:
-
-None
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete all documents from the document store.
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.ndarray, filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: Index name to query the document from.
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### save
-
-```python
- | save(file_path: Union[str, Path])
-```
-
-Save FAISS Index to the specified file.
-
-**Arguments**:
-
-- `file_path`: Path to save to.
-
-**Returns**:
-
-None
-
-
-#### load
-
-```python
- | @classmethod
- | load(cls, faiss_file_path: Union[str, Path], sql_url: str, index: str)
-```
-
-Load a saved FAISS index from a file and connect to the SQL database.
-Note: In order to have a correct mapping from FAISS to SQL,
- make sure to use the same SQL DB that you used when calling `save()`.
-
-**Arguments**:
-
-- `faiss_file_path`: Stored FAISS index file. Can be created via calling `save()`
-- `sql_url`: Connection string to the SQL database that contains your docs and metadata.
-- `index`: Index name to load the FAISS index as. It must match the index name used for
- when creating the FAISS index.
-
-**Returns**:
-
-
-
-
-# Module: milvus
-
-
-## Class: MilvusDocumentStore
-
-```python
-class MilvusDocumentStore(SQLDocumentStore)
-```
-
-Milvus (https://milvus.io/) is a highly reliable, scalable Document Store specialized on storing and processing vectors.
-Therefore, it is particularly suited for Haystack users that work with dense retrieval methods (like DPR).
-In contrast to FAISS, Milvus ...
- - runs as a separate service (e.g. a Docker container) and can scale easily in a distributed environment
- - allows dynamic data management (i.e. you can insert/delete vectors without recreating the whole index)
- - encapsulates multiple ANN libraries (FAISS, ANNOY ...)
-
-This class uses Milvus for all vector related storage, processing and querying.
-The meta-data (e.g. for filtering) and the document text are however stored in a separate SQL Database as Milvus
-does not allow these data types (yet).
-
-Usage:
-1. Start a Milvus server (see https://milvus.io/docs/v1.0.0/install_milvus.md)
-2. Init a MilvusDocumentStore in Haystack
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(sql_url: str = "sqlite:///", milvus_url: str = "tcp://localhost:19530", connection_pool: str = "SingletonThread", index: str = "document", vector_dim: int = 768, index_file_size: int = 1024, similarity: str = "dot_product", index_type: IndexType = IndexType.FLAT, index_param: Optional[Dict[str, Any]] = None, search_param: Optional[Dict[str, Any]] = None, update_existing_documents: bool = False, return_embedding: bool = False, embedding_field: str = "embedding", progress_bar: bool = True, **kwargs, ,)
-```
-
-**Arguments**:
-
-- `sql_url`: SQL connection URL for storing document texts and metadata. It defaults to a local, file based SQLite DB. For large scale
- deployment, Postgres is recommended. If using MySQL then same server can also be used for
- Milvus metadata. For more details see https://milvus.io/docs/v1.0.0/data_manage.md.
-- `milvus_url`: Milvus server connection URL for storing and processing vectors.
- Protocol, host and port will automatically be inferred from the URL.
- See https://milvus.io/docs/v1.0.0/install_milvus.md for instructions to start a Milvus instance.
-- `connection_pool`: Connection pool type to connect with Milvus server. Default: "SingletonThread".
-- `index`: Index name for text, embedding and metadata (in Milvus terms, this is the "collection name").
-- `vector_dim`: The embedding vector size. Default: 768.
-- `index_file_size`: Specifies the size of each segment file that is stored by Milvus and its default value is 1024 MB.
-When the size of newly inserted vectors reaches the specified volume, Milvus packs these vectors into a new segment.
-Milvus creates one index file for each segment. When conducting a vector search, Milvus searches all index files one by one.
-As a rule of thumb, we would see a 30% ~ 50% increase in the search performance after changing the value of index_file_size from 1024 to 2048.
-Note that an overly large index_file_size value may cause failure to load a segment into the memory or graphics memory.
-(From https://milvus.io/docs/v1.0.0/performance_faq.md#How-can-I-get-the-best-performance-from-Milvus-through-setting-index_file_size)
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default and recommended for DPR embeddings.
- 'cosine' is recommended for Sentence Transformers, but is not directly supported by Milvus.
- However, you can normalize your embeddings and use `dot_product` to get the same results.
- See https://milvus.io/docs/v1.0.0/metric.md?Inner-product-(IP)`floating`.
-- `index_type`: Type of approximate nearest neighbour (ANN) index used. The choice here determines your tradeoff between speed and accuracy.
- Some popular options:
- - FLAT (default): Exact method, slow
- - IVF_FLAT, inverted file based heuristic, fast
- - HSNW: Graph based, fast
- - ANNOY: Tree based, fast
- See: https://milvus.io/docs/v1.0.0/index.md
-- `index_param`: Configuration parameters for the chose index_type needed at indexing time.
- For example: {"nlist": 16384} as the number of cluster units to create for index_type IVF_FLAT.
- See https://milvus.io/docs/v1.0.0/index.md
-- `search_param`: Configuration parameters for the chose index_type needed at query time
- For example: {"nprobe": 10} as the number of cluster units to query for index_type IVF_FLAT.
- See https://milvus.io/docs/v1.0.0/index.md
-- `update_existing_documents`: Whether to update any existing documents with the same ID when adding
- documents. When set as True, any document with an existing ID gets updated.
- If set to False, an error is raised if the document ID of the document being
- added already exists.
-- `return_embedding`: To return document embedding.
-- `embedding_field`: Name of field containing an embedding vector.
-- `progress_bar`: Whether to show a tqdm progress bar or not.
- Can be helpful to disable in production deployments to keep the logs clean.
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000)
-```
-
-Add new documents to the DocumentStore.
-
-**Arguments**:
-
-- `documents`: List of `Dicts` or List of `Documents`. If they already contain the embeddings, we'll index
- them right away in Milvus. If not, you can later call update_embeddings() to create & index them.
-- `index`: (SQL) index name for storing the docs and metadata
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-**Returns**:
-
-
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None, batch_size: int = 10_000, update_existing_embeddings: bool = True, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever to use to get embeddings for text
-- `index`: (SQL) index name for storing the docs and metadata
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-- `update_existing_embeddings`: Whether to update existing embeddings of the documents. If set to False,
- only documents without embeddings are processed. This mode can be used for
- incremental updating of embeddings, wherein, only newly indexed documents
- get processed.
-- `filters`: Optional filters to narrow down the documents for which embeddings are to be updated.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-
-**Returns**:
-
-None
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.ndarray, filters: Optional[dict] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: (SQL) index name for storing the docs and metadata
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete all documents (from SQL AND Milvus).
-
-**Arguments**:
-
-- `index`: (SQL) index name for storing the docs and metadata
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-
-**Returns**:
-
-None
-
-
-#### get\_all\_documents\_generator
-
-```python
- | get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> Generator[Document, None, None]
-```
-
-Get all documents from the document store. Under-the-hood, documents are fetched in batches from the
-document store and yielded as individual documents. This method can be used to iteratively process
-a large number of documents without having to load all documents in memory.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_documents
-
-```python
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> List[Document]
-```
-
-Get documents from the document store (optionally using filter criteria).
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-**Arguments**:
-
-- `id`: ID of the document
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index: Optional[str] = None, batch_size: int = 10_000) -> List[Document]
-```
-
-Fetch multiple documents by specifying their IDs (strings)
-
-**Arguments**:
-
-- `ids`: List of IDs of the documents
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_vectors
-
-```python
- | get_all_vectors(index: Optional[str] = None) -> List[np.ndarray]
-```
-
-Helper function to dump all vectors stored in Milvus server.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-
-**Returns**:
-
-List[np.array]: List of vectors.
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/api/api/file_converter.md b/src/pages/docs/versions/master/v0.8.0/site/en/api/api/file_converter.md
deleted file mode 100644
index 6fb57174c..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/api/api/file_converter.md
+++ /dev/null
@@ -1,304 +0,0 @@
----
-title: "File Converters"
-metaTitle: "File Converters"
-metaDescription: ""
-slug: "/docs/file_converters"
-date: "2020-09-03"
-id: "file_convertersmd"
----
-
-# File Converters
-
-
-# Module: base
-
-
-## Class: BaseConverter
-
-```python
-class BaseConverter(BaseComponent)
-```
-
-Base class for implementing file converts to transform input documents to text format for ingestion in DocumentStore.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(remove_numeric_tables: bool = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-
-
-#### convert
-
-```python
- | @abstractmethod
- | convert(file_path: Path, meta: Optional[Dict[str, str]], remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "utf-8") -> Dict[str, Any]
-```
-
-Convert a file to a dictionary containing the text and any associated meta data.
-
-File converters may extract file meta like name or size. In addition to it, user
-supplied meta data like author, url, external IDs can be supplied as a dictionary.
-
-**Arguments**:
-
-- `file_path`: path of the file to convert
-- `meta`: dictionary of meta data key-value pairs to append in the returned document.
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-- `encoding`: Select the file encoding (default is `utf-8`)
-
-
-#### validate\_language
-
-```python
- | validate_language(text: str) -> bool
-```
-
-Validate if the language of the text is one of valid languages.
-
-
-## Class: FileTypeClassifier
-
-```python
-class FileTypeClassifier(BaseComponent)
-```
-
-Route files in an Indexing Pipeline to corresponding file converters.
-
-
-# Module: txt
-
-
-## Class: TextConverter
-
-```python
-class TextConverter(BaseConverter)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(remove_numeric_tables: bool = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "utf-8") -> Dict[str, Any]
-```
-
-Reads text from a txt file and executes optional preprocessing steps.
-
-**Arguments**:
-
-- `file_path`: path of the file to convert
-- `meta`: dictionary of meta data key-value pairs to append in the returned document.
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-- `encoding`: Select the file encoding (default is `utf-8`)
-
-**Returns**:
-
-Dict of format {"text": "The text from file", "meta": meta}}
-
-
-# Module: docx
-
-
-## Class: DocxToTextConverter
-
-```python
-class DocxToTextConverter(BaseConverter)
-```
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = None) -> Dict[str, Any]
-```
-
-Extract text from a .docx file.
-Note: As docx doesn't contain "page" information, we actually extract and return a list of paragraphs here.
-For compliance with other converters we nevertheless opted for keeping the methods name.
-
-**Arguments**:
-
-- `file_path`: Path to the .docx file you want to convert
-- `meta`: dictionary of meta data key-value pairs to append in the returned document.
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-- `encoding`: Not applicable
-
-
-# Module: tika
-
-
-## Class: TikaConverter
-
-```python
-class TikaConverter(BaseConverter)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(tika_url: str = "http://localhost:9998/tika", remove_numeric_tables: bool = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `tika_url`: URL of the Tika server
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = None) -> Dict[str, Any]
-```
-
-**Arguments**:
-
-- `file_path`: path of the file to convert
-- `meta`: dictionary of meta data key-value pairs to append in the returned document.
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-- `encoding`: Not applicable
-
-**Returns**:
-
-a list of pages and the extracted meta data of the file.
-
-
-# Module: pdf
-
-
-## Class: PDFToTextConverter
-
-```python
-class PDFToTextConverter(BaseConverter)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(remove_numeric_tables: bool = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "Latin1") -> Dict[str, Any]
-```
-
-Extract text from a .pdf file using the pdftotext library (https://www.xpdfreader.com/pdftotext-man.html)
-
-**Arguments**:
-
-- `file_path`: Path to the .pdf file you want to convert
-- `meta`: Optional dictionary with metadata that shall be attached to all resulting documents.
- Can be any custom keys and values.
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-- `encoding`: Encoding that will be passed as -enc parameter to pdftotext. "Latin 1" is the default encoding
- of pdftotext. While this works well on many PDFs, it might be needed to switch to "UTF-8" or
- others if your doc contains special characters (e.g. German Umlauts, Cyrillic characters ...).
- Note: With "UTF-8" we experienced cases, where a simple "fi" gets wrongly parsed as
- "xefc" (see test cases). That's why we keep "Latin 1" as default here.
- (See list of available encodings by running `pdftotext -listencodings` in the terminal)
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/api/api/generator.md b/src/pages/docs/versions/master/v0.8.0/site/en/api/api/generator.md
deleted file mode 100644
index ec5c5dc8f..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/api/api/generator.md
+++ /dev/null
@@ -1,151 +0,0 @@
----
-title: "Generator"
-metaTitle: "Generator"
-metaDescription: ""
-slug: "/docs/apigeneratormd"
-date: "2020-09-03"
-id: "apigeneratormd"
----
-
-# Generator
-
-
-# Module: base
-
-
-## Class: BaseGenerator
-
-```python
-class BaseGenerator(BaseComponent)
-```
-
-Abstract class for Generators
-
-
-#### predict
-
-```python
- | @abstractmethod
- | predict(query: str, documents: List[Document], top_k: Optional[int]) -> Dict
-```
-
-Abstract method to generate answers.
-
-**Arguments**:
-
-- `query`: Query
-- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on.
-- `top_k`: Number of returned answers
-
-**Returns**:
-
-Generated answers plus additional infos in a dict
-
-
-# Module: transformers
-
-
-## Class: RAGenerator
-
-```python
-class RAGenerator(BaseGenerator)
-```
-
-Implementation of Facebook's Retrieval-Augmented Generator (https://arxiv.org/abs/2005.11401) based on
-HuggingFace's transformers (https://huggingface.co/transformers/model_doc/rag.html).
-
-Instead of "finding" the answer within a document, these models **generate** the answer.
-In that sense, RAG follows a similar approach as GPT-3 but it comes with two huge advantages
-for real-world applications:
-a) it has a manageable model size
-b) the answer generation is conditioned on retrieved documents,
-i.e. the model can easily adjust to domain documents even after training has finished
-(in contrast: GPT-3 relies on the web data seen during training)
-
-**Example**
-
-```python
-| query = "who got the first nobel prize in physics?"
-|
-| # Retrieve related documents from retriever
-| retrieved_docs = retriever.retrieve(query=query)
-|
-| # Now generate answer from query and retrieved documents
-| generator.predict(
-| query=query,
-| documents=retrieved_docs,
-| top_k=1
-| )
-|
-| # Answer
-|
-| {'query': 'who got the first nobel prize in physics',
-| 'answers':
-| [{'query': 'who got the first nobel prize in physics',
-| 'answer': ' albert einstein',
-| 'meta': { 'doc_ids': [...],
-| 'doc_scores': [80.42758 ...],
-| 'doc_probabilities': [40.71379089355469, ...
-| 'texts': ['Albert Einstein was a ...]
-| 'titles': ['"Albert Einstein"', ...]
-| }}]}
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: str = "facebook/rag-token-nq", model_version: Optional[str] = None, retriever: Optional[DensePassageRetriever] = None, generator_type: RAGeneratorType = RAGeneratorType.TOKEN, top_k: int = 2, max_length: int = 200, min_length: int = 2, num_beams: int = 2, embed_title: bool = True, prefix: Optional[str] = None, use_gpu: bool = True)
-```
-
-Load a RAG model from Transformers along with passage_embedding_model.
-See https://huggingface.co/transformers/model_doc/rag.html for more details
-
-**Arguments**:
-
-- `model_name_or_path`: Directory of a saved model or the name of a public model e.g.
- 'facebook/rag-token-nq', 'facebook/rag-sequence-nq'.
- See https://huggingface.co/models for full list of available models.
-- `model_version`: The version of model to use from the HuggingFace model hub. Can be tag name, branch name, or commit hash.
-- `retriever`: `DensePassageRetriever` used to embedded passage
-- `generator_type`: Which RAG generator implementation to use? RAG-TOKEN or RAG-SEQUENCE
-- `top_k`: Number of independently generated text to return
-- `max_length`: Maximum length of generated text
-- `min_length`: Minimum length of generated text
-- `num_beams`: Number of beams for beam search. 1 means no beam search.
-- `embed_title`: Embedded the title of passage while generating embedding
-- `prefix`: The prefix used by the generator's tokenizer.
-- `use_gpu`: Whether to use GPU (if available)
-
-
-#### predict
-
-```python
- | predict(query: str, documents: List[Document], top_k: Optional[int] = None) -> Dict
-```
-
-Generate the answer to the input query. The generation will be conditioned on the supplied documents.
-These document can for example be retrieved via the Retriever.
-
-**Arguments**:
-
-- `query`: Query
-- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on.
-- `top_k`: Number of returned answers
-
-**Returns**:
-
-Generated answers plus additional infos in a dict like this:
-
-```python
-| {'query': 'who got the first nobel prize in physics',
-| 'answers':
-| [{'query': 'who got the first nobel prize in physics',
-| 'answer': ' albert einstein',
-| 'meta': { 'doc_ids': [...],
-| 'doc_scores': [80.42758 ...],
-| 'doc_probabilities': [40.71379089355469, ...
-| 'texts': ['Albert Einstein was a ...]
-| 'titles': ['"Albert Einstein"', ...]
-| }}]}
-```
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/api/api/pipelines.md b/src/pages/docs/versions/master/v0.8.0/site/en/api/api/pipelines.md
deleted file mode 100644
index 585dfc552..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/api/api/pipelines.md
+++ /dev/null
@@ -1,384 +0,0 @@
----
-title: "Pipelines"
-metaTitle: "Pipelines"
-metaDescription: ""
-slug: "/docs/apipipelinesmd"
-date: "2020-09-03"
-id: "apipipelinesmd"
----
-
-# Pipelines
-
-
-# Module: pipeline
-
-
-## Class: Pipeline
-
-```python
-class Pipeline(ABC)
-```
-
-Pipeline brings together building blocks to build a complex search pipeline with Haystack & user-defined components.
-
-Under-the-hood, a pipeline is represented as a directed acyclic graph of component nodes. It enables custom query
-flows with options to branch queries(eg, extractive qa vs keyword match query), merge candidate documents for a
-Reader from multiple Retrievers, or re-ranking of candidate documents.
-
-
-#### add\_node
-
-```python
- | add_node(component, name: str, inputs: List[str])
-```
-
-Add a new node to the pipeline.
-
-**Arguments**:
-
-- `component`: The object to be called when the data is passed to the node. It can be a Haystack component
- (like Retriever, Reader, or Generator) or a user-defined object that implements a run()
- method to process incoming data from predecessor node.
-- `name`: The name for the node. It must not contain any dots.
-- `inputs`: A list of inputs to the node. If the predecessor node has a single outgoing edge, just the name
- of node is sufficient. For instance, a 'ElasticsearchRetriever' node would always output a single
- edge with a list of documents. It can be represented as ["ElasticsearchRetriever"].
-
- In cases when the predecessor node has multiple outputs, e.g., a "QueryClassifier", the output
- must be specified explicitly as "QueryClassifier.output_2".
-
-
-#### get\_node
-
-```python
- | get_node(name: str) -> Optional[BaseComponent]
-```
-
-Get a node from the Pipeline.
-
-**Arguments**:
-
-- `name`: The name of the node.
-
-
-#### set\_node
-
-```python
- | set_node(name: str, component)
-```
-
-Set the component for a node in the Pipeline.
-
-**Arguments**:
-
-- `name`: The name of the node.
-- `component`: The component object to be set at the node.
-
-
-#### draw
-
-```python
- | draw(path: Path = Path("pipeline.png"))
-```
-
-Create a Graphviz visualization of the pipeline.
-
-**Arguments**:
-
-- `path`: the path to save the image.
-
-
-#### load\_from\_yaml
-
-```python
- | @classmethod
- | load_from_yaml(cls, path: Path, pipeline_name: Optional[str] = None, overwrite_with_env_variables: bool = True)
-```
-
-Load Pipeline from a YAML file defining the individual components and how they're tied together to form
-a Pipeline. A single YAML can declare multiple Pipelines, in which case an explicit `pipeline_name` must
-be passed.
-
-Here's a sample configuration:
-
- ```yaml
- | version: '0.7'
- |
- | components: # define all the building-blocks for Pipeline
- | - name: MyReader # custom-name for the component; helpful for visualization & debugging
- | type: FARMReader # Haystack Class name for the component
- | params:
- | no_ans_boost: -10
- | model_name_or_path: deepset/roberta-base-squad2
- | - name: MyESRetriever
- | type: ElasticsearchRetriever
- | params:
- | document_store: MyDocumentStore # params can reference other components defined in the YAML
- | custom_query: null
- | - name: MyDocumentStore
- | type: ElasticsearchDocumentStore
- | params:
- | index: haystack_test
- |
- | pipelines: # multiple Pipelines can be defined using the components from above
- | - name: my_query_pipeline # a simple extractive-qa Pipeline
- | nodes:
- | - name: MyESRetriever
- | inputs: [Query]
- | - name: MyReader
- | inputs: [MyESRetriever]
- ```
-
-**Arguments**:
-
-- `path`: path of the YAML file.
-- `pipeline_name`: if the YAML contains multiple pipelines, the pipeline_name to load must be set.
-- `overwrite_with_env_variables`: Overwrite the YAML configuration with environment variables. For example,
- to change index name param for an ElasticsearchDocumentStore, an env
- variable 'MYDOCSTORE_PARAMS_INDEX=documents-2021' can be set. Note that an
- `_` sign must be used to specify nested hierarchical properties.
-
-
-## Class: BaseStandardPipeline
-
-```python
-class BaseStandardPipeline(ABC)
-```
-
-
-#### add\_node
-
-```python
- | add_node(component, name: str, inputs: List[str])
-```
-
-Add a new node to the pipeline.
-
-**Arguments**:
-
-- `component`: The object to be called when the data is passed to the node. It can be a Haystack component
- (like Retriever, Reader, or Generator) or a user-defined object that implements a run()
- method to process incoming data from predecessor node.
-- `name`: The name for the node. It must not contain any dots.
-- `inputs`: A list of inputs to the node. If the predecessor node has a single outgoing edge, just the name
- of node is sufficient. For instance, a 'ElasticsearchRetriever' node would always output a single
- edge with a list of documents. It can be represented as ["ElasticsearchRetriever"].
-
- In cases when the predecessor node has multiple outputs, e.g., a "QueryClassifier", the output
- must be specified explicitly as "QueryClassifier.output_2".
-
-
-#### get\_node
-
-```python
- | get_node(name: str)
-```
-
-Get a node from the Pipeline.
-
-**Arguments**:
-
-- `name`: The name of the node.
-
-
-#### set\_node
-
-```python
- | set_node(name: str, component)
-```
-
-Set the component for a node in the Pipeline.
-
-**Arguments**:
-
-- `name`: The name of the node.
-- `component`: The component object to be set at the node.
-
-
-#### draw
-
-```python
- | draw(path: Path = Path("pipeline.png"))
-```
-
-Create a Graphviz visualization of the pipeline.
-
-**Arguments**:
-
-- `path`: the path to save the image.
-
-
-## Class: ExtractiveQAPipeline
-
-```python
-class ExtractiveQAPipeline(BaseStandardPipeline)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(reader: BaseReader, retriever: BaseRetriever)
-```
-
-Initialize a Pipeline for Extractive Question Answering.
-
-**Arguments**:
-
-- `reader`: Reader instance
-- `retriever`: Retriever instance
-
-
-## Class: DocumentSearchPipeline
-
-```python
-class DocumentSearchPipeline(BaseStandardPipeline)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(retriever: BaseRetriever)
-```
-
-Initialize a Pipeline for semantic document search.
-
-**Arguments**:
-
-- `retriever`: Retriever instance
-
-
-## Class: GenerativeQAPipeline
-
-```python
-class GenerativeQAPipeline(BaseStandardPipeline)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(generator: BaseGenerator, retriever: BaseRetriever)
-```
-
-Initialize a Pipeline for Generative Question Answering.
-
-**Arguments**:
-
-- `generator`: Generator instance
-- `retriever`: Retriever instance
-
-
-## Class: SearchSummarizationPipeline
-
-```python
-class SearchSummarizationPipeline(BaseStandardPipeline)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(summarizer: BaseSummarizer, retriever: BaseRetriever)
-```
-
-Initialize a Pipeline that retrieves documents for a query and then summarizes those documents.
-
-**Arguments**:
-
-- `summarizer`: Summarizer instance
-- `retriever`: Retriever instance
-
-
-#### run
-
-```python
- | run(query: str, filters: Optional[Dict] = None, top_k_retriever: Optional[int] = None, generate_single_summary: Optional[bool] = None, return_in_answer_format: bool = False)
-```
-
-**Arguments**:
-
-- `query`: Your search query
-- `filters`:
-- `top_k_retriever`: Number of top docs the retriever should pass to the summarizer.
- The higher this value, the slower your pipeline.
-- `generate_single_summary`: Whether to generate single summary from all retrieved docs (True) or one per doc (False).
-- `return_in_answer_format`: Whether the results should be returned as documents (False) or in the answer format used in other QA pipelines (True).
- With the latter, you can use this pipeline as a "drop-in replacement" for other QA pipelines.
-
-
-## Class: FAQPipeline
-
-```python
-class FAQPipeline(BaseStandardPipeline)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(retriever: BaseRetriever)
-```
-
-Initialize a Pipeline for finding similar FAQs using semantic document search.
-
-**Arguments**:
-
-- `retriever`: Retriever instance
-
-
-## Class: TranslationWrapperPipeline
-
-```python
-class TranslationWrapperPipeline(BaseStandardPipeline)
-```
-
-Takes an existing search pipeline and adds one "input translation node" after the Query and one
-"output translation" node just before returning the results
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(input_translator: BaseTranslator, output_translator: BaseTranslator, pipeline: BaseStandardPipeline)
-```
-
-Wrap a given `pipeline` with the `input_translator` and `output_translator`.
-
-**Arguments**:
-
-- `input_translator`: A Translator node that shall translate the input query from language A to B
-- `output_translator`: A Translator node that shall translate the pipeline results from language B to A
-- `pipeline`: The pipeline object (e.g. ExtractiveQAPipeline) you want to "wrap".
- Note that pipelines with split or merge nodes are currently not supported.
-
-
-## Class: JoinDocuments
-
-```python
-class JoinDocuments(BaseComponent)
-```
-
-A node to join documents outputted by multiple retriever nodes.
-
-The node allows multiple join modes:
-* concatenate: combine the documents from multiple nodes. Any duplicate documents are discarded.
-* merge: merge scores of documents from multiple nodes. Optionally, each input score can be given a different
- `weight` & a `top_k` limit can be set. This mode can also be used for "reranking" retrieved documents.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(join_mode: str = "concatenate", weights: Optional[List[float]] = None, top_k_join: Optional[int] = None)
-```
-
-**Arguments**:
-
-- `join_mode`: `concatenate` to combine documents from multiple retrievers or `merge` to aggregate scores of
- individual documents.
-- `weights`: A node-wise list(length of list must be equal to the number of input nodes) of weights for
- adjusting document scores when using the `merge` join_mode. By default, equal weight is given
- to each retriever score. This param is not compatible with the `concatenate` join_mode.
-- `top_k_join`: Limit documents to top_k based on the resulting scores of the join.
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/api/api/preprocessor.md b/src/pages/docs/versions/master/v0.8.0/site/en/api/api/preprocessor.md
deleted file mode 100644
index 1539bd463..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/api/api/preprocessor.md
+++ /dev/null
@@ -1,237 +0,0 @@
----
-title: "Preprocessor"
-metaTitle: "Preprocessor"
-metaDescription: ""
-slug: "/docs/apiindexing"
-date: "2020-09-03"
-id: "apiindexingmd"
----
-
-# Preprocessor
-
-
-# Module: base
-
-
-## Class: BasePreProcessor
-
-```python
-class BasePreProcessor(BaseComponent)
-```
-
-
-#### process
-
-```python
- | process(document: dict, clean_whitespace: Optional[bool] = True, clean_header_footer: Optional[bool] = False, clean_empty_lines: Optional[bool] = True, split_by: Optional[str] = "word", split_length: Optional[int] = 1000, split_overlap: Optional[int] = None, split_respect_sentence_boundary: Optional[bool] = True) -> List[dict]
-```
-
-Perform document cleaning and splitting. Takes a single document as input and returns a list of documents.
-
-
-# Module: preprocessor
-
-
-## Class: PreProcessor
-
-```python
-class PreProcessor(BasePreProcessor)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(clean_whitespace: bool = True, clean_header_footer: bool = False, clean_empty_lines: bool = True, split_by: str = "word", split_length: int = 1000, split_overlap: int = 0, split_respect_sentence_boundary: bool = True)
-```
-
-**Arguments**:
-
-- `clean_header_footer`: Use heuristic to remove footers and headers across different pages by searching
- for the longest common string. This heuristic uses exact matches and therefore
- works well for footers like "Copyright 2019 by XXX", but won't detect "Page 3 of 4"
- or similar.
-- `clean_whitespace`: Strip whitespaces before or after each line in the text.
-- `clean_empty_lines`: Remove more than two empty lines in the text.
-- `split_by`: Unit for splitting the document. Can be "word", "sentence", or "passage". Set to None to disable splitting.
-- `split_length`: Max. number of the above split unit (e.g. words) that are allowed in one document. For instance, if n -> 10 & split_by ->
- "sentence", then each output document will have 10 sentences.
-- `split_overlap`: Word overlap between two adjacent documents after a split.
- Setting this to a positive number essentially enables the sliding window approach.
- For example, if split_by -> `word`,
- split_length -> 5 & split_overlap -> 2, then the splits would be like:
- [w1 w2 w3 w4 w5, w4 w5 w6 w7 w8, w7 w8 w10 w11 w12].
- Set the value to 0 to ensure there is no overlap among the documents after splitting.
-- `split_respect_sentence_boundary`: Whether to split in partial sentences if split_by -> `word`. If set
- to True, the individual split will always have complete sentences &
- the number of words will be <= split_length.
-
-
-#### process
-
-```python
- | process(document: dict, clean_whitespace: Optional[bool] = None, clean_header_footer: Optional[bool] = None, clean_empty_lines: Optional[bool] = None, split_by: Optional[str] = None, split_length: Optional[int] = None, split_overlap: Optional[int] = None, split_respect_sentence_boundary: Optional[bool] = None) -> List[dict]
-```
-
-Perform document cleaning and splitting. Takes a single document as input and returns a list of documents.
-
-
-#### clean
-
-```python
- | clean(document: dict, clean_whitespace: bool, clean_header_footer: bool, clean_empty_lines: bool) -> dict
-```
-
-Perform document cleaning on a single document and return a single document. This method will deal with whitespaces, headers, footers
-and empty lines. Its exact functionality is defined by the parameters passed into PreProcessor.__init__().
-
-
-#### split
-
-```python
- | split(document: dict, split_by: str, split_length: int, split_overlap: int, split_respect_sentence_boundary: bool) -> List[dict]
-```
-
-Perform document splitting on a single document. This method can split on different units, at different lengths,
-with different strides. It can also respect sentence boundaries. Its exact functionality is defined by
-the parameters passed into PreProcessor.__init__(). Takes a single document as input and returns a list of documents.
-
-
-# Module: utils
-
-
-#### eval\_data\_from\_json
-
-```python
-eval_data_from_json(filename: str, max_docs: Union[int, bool] = None, preprocessor: PreProcessor = None, open_domain: bool = False) -> Tuple[List[Document], List[Label]]
-```
-
-Read Documents + Labels from a SQuAD-style file.
-Document and Labels can then be indexed to the DocumentStore and be used for evaluation.
-
-**Arguments**:
-
-- `filename`: Path to file in SQuAD format
-- `max_docs`: This sets the number of documents that will be loaded. By default, this is set to None, thus reading in all available eval documents.
-- `open_domain`: Set this to True if your file is an open domain dataset where two different answers to the same question might be found in different contexts.
-
-**Returns**:
-
-(List of Documents, List of Labels)
-
-
-#### eval\_data\_from\_jsonl
-
-```python
-eval_data_from_jsonl(filename: str, batch_size: Optional[int] = None, max_docs: Union[int, bool] = None, preprocessor: PreProcessor = None, open_domain: bool = False) -> Generator[Tuple[List[Document], List[Label]], None, None]
-```
-
-Read Documents + Labels from a SQuAD-style file in jsonl format, i.e. one document per line.
-Document and Labels can then be indexed to the DocumentStore and be used for evaluation.
-
-This is a generator which will yield one tuple per iteration containing a list
-of batch_size documents and a list with the documents' labels.
-If batch_size is set to None, this method will yield all documents and labels.
-
-**Arguments**:
-
-- `filename`: Path to file in SQuAD format
-- `max_docs`: This sets the number of documents that will be loaded. By default, this is set to None, thus reading in all available eval documents.
-- `open_domain`: Set this to True if your file is an open domain dataset where two different answers to the same question might be found in different contexts.
-
-**Returns**:
-
-(List of Documents, List of Labels)
-
-
-#### convert\_files\_to\_dicts
-
-```python
-convert_files_to_dicts(dir_path: str, clean_func: Optional[Callable] = None, split_paragraphs: bool = False) -> List[dict]
-```
-
-Convert all files(.txt, .pdf, .docx) in the sub-directories of the given path to Python dicts that can be written to a
-Document Store.
-
-**Arguments**:
-
-- `dir_path`: path for the documents to be written to the DocumentStore
-- `clean_func`: a custom cleaning function that gets applied to each doc (input: str, output:str)
-- `split_paragraphs`: split text in paragraphs.
-
-**Returns**:
-
-None
-
-
-#### tika\_convert\_files\_to\_dicts
-
-```python
-tika_convert_files_to_dicts(dir_path: str, clean_func: Optional[Callable] = None, split_paragraphs: bool = False, merge_short: bool = True, merge_lowercase: bool = True) -> List[dict]
-```
-
-Convert all files(.txt, .pdf) in the sub-directories of the given path to Python dicts that can be written to a
-Document Store.
-
-**Arguments**:
-
-- `merge_lowercase`: allow conversion of merged paragraph to lowercase
-- `merge_short`: allow merging of short paragraphs
-- `dir_path`: path for the documents to be written to the DocumentStore
-- `clean_func`: a custom cleaning function that gets applied to each doc (input: str, output:str)
-- `split_paragraphs`: split text in paragraphs.
-
-**Returns**:
-
-None
-
-
-#### fetch\_archive\_from\_http
-
-```python
-fetch_archive_from_http(url: str, output_dir: str, proxies: Optional[dict] = None)
-```
-
-Fetch an archive (zip or tar.gz) from a url via http and extract content to an output directory.
-
-**Arguments**:
-
-- `url`: http address
-:type url: str
-- `output_dir`: local path
-:type output_dir: str
-- `proxies`: proxies details as required by requests library
-:type proxies: dict
-
-**Returns**:
-
-bool if anything got fetched
-
-
-#### squad\_json\_to\_jsonl
-
-```python
-squad_json_to_jsonl(squad_file: str, output_file: str)
-```
-
-Converts a SQuAD-json-file into jsonl format with one document per line.
-
-**Arguments**:
-
-- `squad_file`: SQuAD-file in json format.
-:type squad_file: str
-- `output_file`: Name of output file (SQuAD in jsonl format)
-:type output_file: str
-
-
-# Module: cleaning
-
-
-#### clean\_wiki\_text
-
-```python
-clean_wiki_text(text: str) -> str
-```
-
-Clean wikipedia text by removing multiple new lines, removing extremely short lines,
-adding paragraph breaks and removing empty paragraphs
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/api/api/reader.md b/src/pages/docs/versions/master/v0.8.0/site/en/api/api/reader.md
deleted file mode 100644
index d93933dea..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/api/api/reader.md
+++ /dev/null
@@ -1,415 +0,0 @@
----
-title: "Reader"
-metaTitle: "Reader"
-metaDescription: ""
-slug: "/docs/apireadermd"
-date: "2020-09-03"
-id: "apireadermd"
----
-
-# Reader
-
-
-# Module: base
-
-
-## Class: BaseReader
-
-```python
-class BaseReader(BaseComponent)
-```
-
-
-#### timing
-
-```python
- | timing(fn, attr_name)
-```
-
-Wrapper method used to time functions.
-
-
-# Module: farm
-
-
-## Class: FARMReader
-
-```python
-class FARMReader(BaseReader)
-```
-
-Transformer based model for extractive Question Answering using the FARM framework (https://github.com/deepset-ai/FARM).
-While the underlying model can vary (BERT, Roberta, DistilBERT, ...), the interface remains the same.
-
-| With a FARMReader, you can:
-
- - directly get predictions via predict()
- - fine-tune the model on QA data via train()
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: Union[str, Path], model_version: Optional[str] = None, context_window_size: int = 150, batch_size: int = 50, use_gpu: bool = True, no_ans_boost: float = 0.0, return_no_answer: bool = False, top_k: int = 10, top_k_per_candidate: int = 3, top_k_per_sample: int = 1, num_processes: Optional[int] = None, max_seq_len: int = 256, doc_stride: int = 128, progress_bar: bool = True)
-```
-
-**Arguments**:
-
-- `model_name_or_path`: Directory of a saved model or the name of a public model e.g. 'bert-base-cased',
-'deepset/bert-base-cased-squad2', 'deepset/bert-base-cased-squad2', 'distilbert-base-uncased-distilled-squad'.
-See https://huggingface.co/models for full list of available models.
-- `model_version`: The version of model to use from the HuggingFace model hub. Can be tag name, branch name, or commit hash.
-- `context_window_size`: The size, in characters, of the window around the answer span that is used when
- displaying the context around the answer.
-- `batch_size`: Number of samples the model receives in one batch for inference.
- Memory consumption is much lower in inference mode. Recommendation: Increase the batch size
- to a value so only a single batch is used.
-- `use_gpu`: Whether to use GPU (if available)
-- `no_ans_boost`: How much the no_answer logit is boosted/increased.
-If set to 0 (default), the no_answer logit is not changed.
-If a negative number, there is a lower chance of "no_answer" being predicted.
-If a positive number, there is an increased chance of "no_answer"
-- `return_no_answer`: Whether to include no_answer predictions in the results.
-- `top_k`: The maximum number of answers to return
-- `top_k_per_candidate`: How many answers to extract for each candidate doc that is coming from the retriever (might be a long text).
-Note that this is not the number of "final answers" you will receive
-(see `top_k` in FARMReader.predict() or Finder.get_answers() for that)
-and that FARM includes no_answer in the sorted list of predictions.
-- `top_k_per_sample`: How many answers to extract from each small text passage that the model can process at once
-(one "candidate doc" is usually split into many smaller "passages").
-You usually want a very small value here, as it slows down inference
-and you don't gain much of quality by having multiple answers from one passage.
-Note that this is not the number of "final answers" you will receive
-(see `top_k` in FARMReader.predict() or Finder.get_answers() for that)
-and that FARM includes no_answer in the sorted list of predictions.
-- `num_processes`: The number of processes for `multiprocessing.Pool`. Set to value of 0 to disable
- multiprocessing. Set to None to let Inferencer determine optimum number. If you
- want to debug the Language Model, you might need to disable multiprocessing!
-- `max_seq_len`: Max sequence length of one input text for the model
-- `doc_stride`: Length of striding window for splitting long texts (used if ``len(text) > max_seq_len``)
-- `progress_bar`: Whether to show a tqdm progress bar or not.
- Can be helpful to disable in production deployments to keep the logs clean.
-
-
-#### train
-
-```python
- | train(data_dir: str, train_filename: str, dev_filename: Optional[str] = None, test_filename: Optional[str] = None, use_gpu: Optional[bool] = None, batch_size: int = 10, n_epochs: int = 2, learning_rate: float = 1e-5, max_seq_len: Optional[int] = None, warmup_proportion: float = 0.2, dev_split: float = 0, evaluate_every: int = 300, save_dir: Optional[str] = None, num_processes: Optional[int] = None, use_amp: str = None)
-```
-
-Fine-tune a model on a QA dataset. Options:
-
-- Take a plain language model (e.g. `bert-base-cased`) and train it for QA (e.g. on SQuAD data)
-- Take a QA model (e.g. `deepset/bert-base-cased-squad2`) and fine-tune it for your domain (e.g. using your labels collected via the haystack annotation tool)
-
-**Arguments**:
-
-- `data_dir`: Path to directory containing your training data in SQuAD style
-- `train_filename`: Filename of training data
-- `dev_filename`: Filename of dev / eval data
-- `test_filename`: Filename of test data
-- `dev_split`: Instead of specifying a dev_filename, you can also specify a ratio (e.g. 0.1) here
- that gets split off from training data for eval.
-- `use_gpu`: Whether to use GPU (if available)
-- `batch_size`: Number of samples the model receives in one batch for training
-- `n_epochs`: Number of iterations on the whole training data set
-- `learning_rate`: Learning rate of the optimizer
-- `max_seq_len`: Maximum text length (in tokens). Everything longer gets cut down.
-- `warmup_proportion`: Proportion of training steps until maximum learning rate is reached.
- Until that point LR is increasing linearly. After that it's decreasing again linearly.
- Options for different schedules are available in FARM.
-- `evaluate_every`: Evaluate the model every X steps on the hold-out eval dataset
-- `save_dir`: Path to store the final model
-- `num_processes`: The number of processes for `multiprocessing.Pool` during preprocessing.
- Set to value of 1 to disable multiprocessing. When set to 1, you cannot split away a dev set from train set.
- Set to None to use all CPU cores minus one.
-- `use_amp`: Optimization level of NVIDIA's automatic mixed precision (AMP). The higher the level, the faster the model.
- Available options:
- None (Don't use AMP)
- "O0" (Normal FP32 training)
- "O1" (Mixed Precision => Recommended)
- "O2" (Almost FP16)
- "O3" (Pure FP16).
- See details on: https://nvidia.github.io/apex/amp.html
-
-**Returns**:
-
-None
-
-
-#### update\_parameters
-
-```python
- | update_parameters(context_window_size: Optional[int] = None, no_ans_boost: Optional[float] = None, return_no_answer: Optional[bool] = None, max_seq_len: Optional[int] = None, doc_stride: Optional[int] = None)
-```
-
-Hot update parameters of a loaded Reader. It may not to be safe when processing concurrent requests.
-
-
-#### save
-
-```python
- | save(directory: Path)
-```
-
-Saves the Reader model so that it can be reused at a later point in time.
-
-**Arguments**:
-
-- `directory`: Directory where the Reader model should be saved
-
-
-#### predict\_batch
-
-```python
- | predict_batch(query_doc_list: List[dict], top_k: int = None, batch_size: int = None)
-```
-
-Use loaded QA model to find answers for a list of queries in each query's supplied list of Document.
-
-Returns list of dictionaries containing answers sorted by (desc.) probability
-
-**Arguments**:
-
-- `query_doc_list`: List of dictionaries containing queries with their retrieved documents
-- `top_k`: The maximum number of answers to return for each query
-- `batch_size`: Number of samples the model receives in one batch for inference
-
-**Returns**:
-
-List of dictionaries containing query and answers
-
-
-#### predict
-
-```python
- | predict(query: str, documents: List[Document], top_k: Optional[int] = None)
-```
-
-Use loaded QA model to find answers for a query in the supplied list of Document.
-
-Returns dictionaries containing answers sorted by (desc.) probability.
-Example:
- ```python
- |{
- | 'query': 'Who is the father of Arya Stark?',
- | 'answers':[
- | {'answer': 'Eddard,',
- | 'context': " She travels with her father, Eddard, to King's Landing when he is ",
- | 'offset_answer_start': 147,
- | 'offset_answer_end': 154,
- | 'probability': 0.9787139466668613,
- | 'score': None,
- | 'document_id': '1337'
- | },...
- | ]
- |}
-```
-
-**Arguments**:
-
-- `query`: Query string
-- `documents`: List of Document in which to search for the answer
-- `top_k`: The maximum number of answers to return
-
-**Returns**:
-
-Dict containing query and answers
-
-
-#### eval\_on\_file
-
-```python
- | eval_on_file(data_dir: str, test_filename: str, device: str)
-```
-
-Performs evaluation on a SQuAD-formatted file.
-Returns a dict containing the following metrics:
- - "EM": exact match score
- - "f1": F1-Score
- - "top_n_accuracy": Proportion of predicted answers that overlap with correct answer
-
-**Arguments**:
-
-- `data_dir`: The directory in which the test set can be found
-:type data_dir: Path or str
-- `test_filename`: The name of the file containing the test data in SQuAD format.
-:type test_filename: str
-- `device`: The device on which the tensors should be processed. Choose from "cpu" and "cuda".
-:type device: str
-
-
-#### eval
-
-```python
- | eval(document_store: BaseDocumentStore, device: str, label_index: str = "label", doc_index: str = "eval_document", label_origin: str = "gold_label")
-```
-
-Performs evaluation on evaluation documents in the DocumentStore.
-Returns a dict containing the following metrics:
- - "EM": Proportion of exact matches of predicted answers with their corresponding correct answers
- - "f1": Average overlap between predicted answers and their corresponding correct answers
- - "top_n_accuracy": Proportion of predicted answers that overlap with correct answer
-
-**Arguments**:
-
-- `document_store`: DocumentStore containing the evaluation documents
-- `device`: The device on which the tensors should be processed. Choose from "cpu" and "cuda".
-- `label_index`: Index/Table name where labeled questions are stored
-- `doc_index`: Index/Table name where documents that are used for evaluation are stored
-
-
-#### predict\_on\_texts
-
-```python
- | predict_on_texts(question: str, texts: List[str], top_k: Optional[int] = None)
-```
-
-Use loaded QA model to find answers for a question in the supplied list of Document.
-Returns dictionaries containing answers sorted by (desc.) probability.
-Example:
- ```python
- |{
- | 'question': 'Who is the father of Arya Stark?',
- | 'answers':[
- | {'answer': 'Eddard,',
- | 'context': " She travels with her father, Eddard, to King's Landing when he is ",
- | 'offset_answer_start': 147,
- | 'offset_answer_end': 154,
- | 'probability': 0.9787139466668613,
- | 'score': None,
- | 'document_id': '1337'
- | },...
- | ]
- |}
-```
-
-**Arguments**:
-
-- `question`: Question string
-- `documents`: List of documents as string type
-- `top_k`: The maximum number of answers to return
-
-**Returns**:
-
-Dict containing question and answers
-
-
-#### convert\_to\_onnx
-
-```python
- | @classmethod
- | convert_to_onnx(cls, model_name: str, output_path: Path, convert_to_float16: bool = False, quantize: bool = False, task_type: str = "question_answering", opset_version: int = 11)
-```
-
-Convert a PyTorch BERT model to ONNX format and write to ./onnx-export dir. The converted ONNX model
-can be loaded with in the `FARMReader` using the export path as `model_name_or_path` param.
-
-Usage:
-
- `from haystack.reader.farm import FARMReader
- from pathlib import Path
- onnx_model_path = Path("roberta-onnx-model")
- FARMReader.convert_to_onnx(model_name="deepset/bert-base-cased-squad2", output_path=onnx_model_path)
- reader = FARMReader(onnx_model_path)`
-
-**Arguments**:
-
-- `model_name`: transformers model name
-- `output_path`: Path to output the converted model
-- `convert_to_float16`: Many models use float32 precision by default. With the half precision of float16,
- inference is faster on Nvidia GPUs with Tensor core like T4 or V100. On older GPUs,
- float32 could still be be more performant.
-- `quantize`: convert floating point number to integers
-- `task_type`: Type of task for the model. Available options: "question_answering" or "embeddings".
-- `opset_version`: ONNX opset version
-
-
-# Module: transformers
-
-
-## Class: TransformersReader
-
-```python
-class TransformersReader(BaseReader)
-```
-
-Transformer based model for extractive Question Answering using the HuggingFace's transformers framework
-(https://github.com/huggingface/transformers).
-While the underlying model can vary (BERT, Roberta, DistilBERT ...), the interface remains the same.
-With this reader, you can directly get predictions via predict()
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: str = "distilbert-base-uncased-distilled-squad", model_version: Optional[str] = None, tokenizer: Optional[str] = None, context_window_size: int = 70, use_gpu: int = 0, top_k: int = 10, top_k_per_candidate: int = 4, return_no_answers: bool = True, max_seq_len: int = 256, doc_stride: int = 128)
-```
-
-Load a QA model from Transformers.
-Available models include:
-
-- ``'distilbert-base-uncased-distilled-squad`'``
-- ``'bert-large-cased-whole-word-masking-finetuned-squad``'
-- ``'bert-large-uncased-whole-word-masking-finetuned-squad``'
-
-See https://huggingface.co/models for full list of available QA models
-
-**Arguments**:
-
-- `model_name_or_path`: Directory of a saved model or the name of a public model e.g. 'bert-base-cased',
-'deepset/bert-base-cased-squad2', 'deepset/bert-base-cased-squad2', 'distilbert-base-uncased-distilled-squad'.
-See https://huggingface.co/models for full list of available models.
-- `model_version`: The version of model to use from the HuggingFace model hub. Can be tag name, branch name, or commit hash.
-- `tokenizer`: Name of the tokenizer (usually the same as model)
-- `context_window_size`: Num of chars (before and after the answer) to return as "context" for each answer.
- The context usually helps users to understand if the answer really makes sense.
-- `use_gpu`: If < 0, then use cpu. If >= 0, this is the ordinal of the gpu to use
-- `top_k`: The maximum number of answers to return
-- `top_k_per_candidate`: How many answers to extract for each candidate doc that is coming from the retriever (might be a long text).
-Note that this is not the number of "final answers" you will receive
-(see `top_k` in TransformersReader.predict() or Finder.get_answers() for that)
-and that no_answer can be included in the sorted list of predictions.
-- `return_no_answers`: If True, the HuggingFace Transformers model could return a "no_answer" (i.e. when there is an unanswerable question)
-If False, it cannot return a "no_answer". Note that `no_answer_boost` is unfortunately not available with TransformersReader.
-If you would like to set no_answer_boost, use a `FARMReader`.
-- `max_seq_len`: max sequence length of one input text for the model
-- `doc_stride`: length of striding window for splitting long texts (used if len(text) > max_seq_len)
-
-
-#### predict
-
-```python
- | predict(query: str, documents: List[Document], top_k: Optional[int] = None)
-```
-
-Use loaded QA model to find answers for a query in the supplied list of Document.
-
-Returns dictionaries containing answers sorted by (desc.) probability.
-Example:
-
- ```python
- |{
- | 'query': 'Who is the father of Arya Stark?',
- | 'answers':[
- | {'answer': 'Eddard,',
- | 'context': " She travels with her father, Eddard, to King's Landing when he is ",
- | 'offset_answer_start': 147,
- | 'offset_answer_end': 154,
- | 'probability': 0.9787139466668613,
- | 'score': None,
- | 'document_id': '1337'
- | },...
- | ]
- |}
-```
-
-**Arguments**:
-
-- `query`: Query string
-- `documents`: List of Document in which to search for the answer
-- `top_k`: The maximum number of answers to return
-
-**Returns**:
-
-Dict containing query and answers
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/api/api/retriever.md b/src/pages/docs/versions/master/v0.8.0/site/en/api/api/retriever.md
deleted file mode 100644
index 01bc26e1b..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/api/api/retriever.md
+++ /dev/null
@@ -1,521 +0,0 @@
----
-title: "Retriever"
-metaTitle: "Retriever"
-metaDescription: ""
-slug: "/docs/apiretrievermd"
-date: "2020-09-03"
-id: "apiretrievermd"
----
-
-# Retriever
-
-
-# Module: base
-
-
-## Class: BaseRetriever
-
-```python
-class BaseRetriever(BaseComponent)
-```
-
-
-#### retrieve
-
-```python
- | @abstractmethod
- | retrieve(query: str, filters: dict = None, top_k: Optional[int] = None, index: str = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-#### timing
-
-```python
- | timing(fn, attr_name)
-```
-
-Wrapper method used to time functions.
-
-
-#### eval
-
-```python
- | eval(label_index: str = "label", doc_index: str = "eval_document", label_origin: str = "gold_label", top_k: int = 10, open_domain: bool = False, return_preds: bool = False) -> dict
-```
-
-Performs evaluation on the Retriever.
-Retriever is evaluated based on whether it finds the correct document given the query string and at which
-position in the ranking of documents the correct document is.
-
-| Returns a dict containing the following metrics:
-
- - "recall": Proportion of questions for which correct document is among retrieved documents
- - "mrr": Mean of reciprocal rank. Rewards retrievers that give relevant documents a higher rank.
- Only considers the highest ranked relevant document.
- - "map": Mean of average precision for each question. Rewards retrievers that give relevant
- documents a higher rank. Considers all retrieved relevant documents. If ``open_domain=True``,
- average precision is normalized by the number of retrieved relevant documents per query.
- If ``open_domain=False``, average precision is normalized by the number of all relevant documents
- per query.
-
-**Arguments**:
-
-- `label_index`: Index/Table in DocumentStore where labeled questions are stored
-- `doc_index`: Index/Table in DocumentStore where documents that are used for evaluation are stored
-- `top_k`: How many documents to return per query
-- `open_domain`: If ``True``, retrieval will be evaluated by checking if the answer string to a question is
- contained in the retrieved docs (common approach in open-domain QA).
- If ``False``, retrieval uses a stricter evaluation that checks if the retrieved document ids
- are within ids explicitly stated in the labels.
-- `return_preds`: Whether to add predictions in the returned dictionary. If True, the returned dictionary
- contains the keys "predictions" and "metrics".
-
-
-# Module: sparse
-
-
-## Class: ElasticsearchRetriever
-
-```python
-class ElasticsearchRetriever(BaseRetriever)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(document_store: ElasticsearchDocumentStore, top_k: int = 10, custom_query: str = None)
-```
-
-**Arguments**:
-
-- `document_store`: an instance of a DocumentStore to retrieve documents from.
-- `custom_query`: query string as per Elasticsearch DSL with a mandatory query placeholder(query).
-
- Optionally, ES `filter` clause can be added where the values of `terms` are placeholders
- that get substituted during runtime. The placeholder(${filter_name_1}, ${filter_name_2}..)
- names must match with the filters dict supplied in self.retrieve().
- ::
-
- **An example custom_query:**
- ```python
- | {
- | "size": 10,
- | "query": {
- | "bool": {
- | "should": [{"multi_match": {
- | "query": ${query}, // mandatory query placeholder
- | "type": "most_fields",
- | "fields": ["text", "title"]}}],
- | "filter": [ // optional custom filters
- | {"terms": {"year": ${years}}},
- | {"terms": {"quarter": ${quarters}}},
- | {"range": {"date": {"gte": ${date}}}}
- | ],
- | }
- | },
- | }
- ```
-
- **For this custom_query, a sample retrieve() could be:**
- ```python
- | self.retrieve(query="Why did the revenue increase?",
- | filters={"years": ["2019"], "quarters": ["Q1", "Q2"]})
- ```
-- `top_k`: How many documents to return per query.
-
-
-#### retrieve
-
-```python
- | retrieve(query: str, filters: dict = None, top_k: Optional[int] = None, index: str = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-## Class: ElasticsearchFilterOnlyRetriever
-
-```python
-class ElasticsearchFilterOnlyRetriever(ElasticsearchRetriever)
-```
-
-Naive "Retriever" that returns all documents that match the given filters. No impact of query at all.
-Helpful for benchmarking, testing and if you want to do QA on small documents without an "active" retriever.
-
-
-#### retrieve
-
-```python
- | retrieve(query: str, filters: dict = None, top_k: Optional[int] = None, index: str = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-## Class: TfidfRetriever
-
-```python
-class TfidfRetriever(BaseRetriever)
-```
-
-Read all documents from a SQL backend.
-
-Split documents into smaller units (eg, paragraphs or pages) to reduce the
-computations when text is passed on to a Reader for QA.
-
-It uses sklearn's TfidfVectorizer to compute a tf-idf matrix.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(document_store: BaseDocumentStore, top_k: int = 10)
-```
-
-**Arguments**:
-
-- `document_store`: an instance of a DocumentStore to retrieve documents from.
-- `top_k`: How many documents to return per query.
-
-
-#### retrieve
-
-```python
- | retrieve(query: str, filters: dict = None, top_k: Optional[int] = None, index: str = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-#### fit
-
-```python
- | fit()
-```
-
-Performing training on this class according to the TF-IDF algorithm.
-
-
-# Module: dense
-
-
-## Class: DensePassageRetriever
-
-```python
-class DensePassageRetriever(BaseRetriever)
-```
-
-Retriever that uses a bi-encoder (one transformer for query, one transformer for passage).
-See the original paper for more details:
-Karpukhin, Vladimir, et al. (2020): "Dense Passage Retrieval for Open-Domain Question Answering."
-(https://arxiv.org/abs/2004.04906).
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(document_store: BaseDocumentStore, query_embedding_model: Union[Path, str] = "facebook/dpr-question_encoder-single-nq-base", passage_embedding_model: Union[Path, str] = "facebook/dpr-ctx_encoder-single-nq-base", single_model_path: Optional[Union[Path, str]] = None, model_version: Optional[str] = None, max_seq_len_query: int = 64, max_seq_len_passage: int = 256, top_k: int = 10, use_gpu: bool = True, batch_size: int = 16, embed_title: bool = True, use_fast_tokenizers: bool = True, infer_tokenizer_classes: bool = False, similarity_function: str = "dot_product", progress_bar: bool = True)
-```
-
-Init the Retriever incl. the two encoder models from a local or remote model checkpoint.
-The checkpoint format matches huggingface transformers' model format
-
-**Example:**
-
- ```python
- | # remote model from FAIR
- | DensePassageRetriever(document_store=your_doc_store,
- | query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- | passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base")
- | # or from local path
- | DensePassageRetriever(document_store=your_doc_store,
- | query_embedding_model="model_directory/question-encoder",
- | passage_embedding_model="model_directory/context-encoder")
- ```
-
-**Arguments**:
-
-- `document_store`: An instance of DocumentStore from which to retrieve documents.
-- `query_embedding_model`: Local path or remote name of question encoder checkpoint. The format equals the
- one used by hugging-face transformers' modelhub models
- Currently available remote names: ``"facebook/dpr-question_encoder-single-nq-base"``
-- `passage_embedding_model`: Local path or remote name of passage encoder checkpoint. The format equals the
- one used by hugging-face transformers' modelhub models
- Currently available remote names: ``"facebook/dpr-ctx_encoder-single-nq-base"``
-- `single_model_path`: Local path or remote name of a query and passage embedder in one single model. Those
- models are typically trained within FARM.
- Currently available remote names: TODO add FARM DPR model to HF modelhub
-- `model_version`: The version of model to use from the HuggingFace model hub. Can be tag name, branch name, or commit hash.
-- `max_seq_len_query`: Longest length of each query sequence. Maximum number of tokens for the query text. Longer ones will be cut down."
-- `max_seq_len_passage`: Longest length of each passage/context sequence. Maximum number of tokens for the passage text. Longer ones will be cut down."
-- `top_k`: How many documents to return per query.
-- `use_gpu`: Whether to use gpu or not
-- `batch_size`: Number of questions or passages to encode at once
-- `embed_title`: Whether to concatenate title and passage to a text pair that is then used to create the embedding.
- This is the approach used in the original paper and is likely to improve performance if your
- titles contain meaningful information for retrieval (topic, entities etc.) .
- The title is expected to be present in doc.meta["name"] and can be supplied in the documents
- before writing them to the DocumentStore like this:
- {"text": "my text", "meta": {"name": "my title"}}.
-- `use_fast_tokenizers`: Whether to use fast Rust tokenizers
-- `infer_tokenizer_classes`: Whether to infer tokenizer class from the model config / name.
- If `False`, the class always loads `DPRQuestionEncoderTokenizer` and `DPRContextEncoderTokenizer`.
-- `similarity_function`: Which function to apply for calculating the similarity of query and passage embeddings during training.
- Options: `dot_product` (Default) or `cosine`
-- `progress_bar`: Whether to show a tqdm progress bar or not.
- Can be helpful to disable in production deployments to keep the logs clean.
-
-
-#### retrieve
-
-```python
- | retrieve(query: str, filters: dict = None, top_k: Optional[int] = None, index: str = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-#### embed\_queries
-
-```python
- | embed_queries(texts: List[str]) -> List[np.ndarray]
-```
-
-Create embeddings for a list of queries using the query encoder
-
-**Arguments**:
-
-- `texts`: Queries to embed
-
-**Returns**:
-
-Embeddings, one per input queries
-
-
-#### embed\_passages
-
-```python
- | embed_passages(docs: List[Document]) -> List[np.ndarray]
-```
-
-Create embeddings for a list of passages using the passage encoder
-
-**Arguments**:
-
-- `docs`: List of Document objects used to represent documents / passages in a standardized way within Haystack.
-
-**Returns**:
-
-Embeddings of documents / passages shape (batch_size, embedding_dim)
-
-
-#### train
-
-```python
- | train(data_dir: str, train_filename: str, dev_filename: str = None, test_filename: str = None, max_processes: int = 128, dev_split: float = 0, batch_size: int = 2, embed_title: bool = True, num_hard_negatives: int = 1, num_positives: int = 1, n_epochs: int = 3, evaluate_every: int = 1000, n_gpu: int = 1, learning_rate: float = 1e-5, epsilon: float = 1e-08, weight_decay: float = 0.0, num_warmup_steps: int = 100, grad_acc_steps: int = 1, optimizer_name: str = "TransformersAdamW", optimizer_correct_bias: bool = True, save_dir: str = "../saved_models/dpr", query_encoder_save_dir: str = "query_encoder", passage_encoder_save_dir: str = "passage_encoder")
-```
-
-train a DensePassageRetrieval model
-
-**Arguments**:
-
-- `data_dir`: Directory where training file, dev file and test file are present
-- `train_filename`: training filename
-- `dev_filename`: development set filename, file to be used by model in eval step of training
-- `test_filename`: test set filename, file to be used by model in test step after training
-- `max_processes`: the maximum number of processes to spawn in the multiprocessing.Pool used in DataSilo.
- It can be set to 1 to disable the use of multiprocessing or make debugging easier.
-- `dev_split`: The proportion of the train set that will sliced. Only works if dev_filename is set to None
-- `batch_size`: total number of samples in 1 batch of data
-- `embed_title`: whether to concatenate passage title with each passage. The default setting in official DPR embeds passage title with the corresponding passage
-- `num_hard_negatives`: number of hard negative passages(passages which are very similar(high score by BM25) to query but do not contain the answer
-- `num_positives`: number of positive passages
-- `n_epochs`: number of epochs to train the model on
-- `evaluate_every`: number of training steps after evaluation is run
-- `n_gpu`: number of gpus to train on
-- `learning_rate`: learning rate of optimizer
-- `epsilon`: epsilon parameter of optimizer
-- `weight_decay`: weight decay parameter of optimizer
-- `grad_acc_steps`: number of steps to accumulate gradient over before back-propagation is done
-- `optimizer_name`: what optimizer to use (default: TransformersAdamW)
-- `num_warmup_steps`: number of warmup steps
-- `optimizer_correct_bias`: Whether to correct bias in optimizer
-- `save_dir`: directory where models are saved
-- `query_encoder_save_dir`: directory inside save_dir where query_encoder model files are saved
-- `passage_encoder_save_dir`: directory inside save_dir where passage_encoder model files are saved
-
-
-#### save
-
-```python
- | save(save_dir: Union[Path, str], query_encoder_dir: str = "query_encoder", passage_encoder_dir: str = "passage_encoder")
-```
-
-Save DensePassageRetriever to the specified directory.
-
-**Arguments**:
-
-- `save_dir`: Directory to save to.
-- `query_encoder_dir`: Directory in save_dir that contains query encoder model.
-- `passage_encoder_dir`: Directory in save_dir that contains passage encoder model.
-
-**Returns**:
-
-None
-
-
-#### load
-
-```python
- | @classmethod
- | load(cls, load_dir: Union[Path, str], document_store: BaseDocumentStore, max_seq_len_query: int = 64, max_seq_len_passage: int = 256, use_gpu: bool = True, batch_size: int = 16, embed_title: bool = True, use_fast_tokenizers: bool = True, similarity_function: str = "dot_product", query_encoder_dir: str = "query_encoder", passage_encoder_dir: str = "passage_encoder")
-```
-
-Load DensePassageRetriever from the specified directory.
-
-
-## Class: EmbeddingRetriever
-
-```python
-class EmbeddingRetriever(BaseRetriever)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(document_store: BaseDocumentStore, embedding_model: str, model_version: Optional[str] = None, use_gpu: bool = True, model_format: str = "farm", pooling_strategy: str = "reduce_mean", emb_extraction_layer: int = -1, top_k: int = 10)
-```
-
-**Arguments**:
-
-- `document_store`: An instance of DocumentStore from which to retrieve documents.
-- `embedding_model`: Local path or name of model in Hugging Face's model hub such as ``'deepset/sentence_bert'``
-- `model_version`: The version of model to use from the HuggingFace model hub. Can be tag name, branch name, or commit hash.
-- `use_gpu`: Whether to use gpu or not
-- `model_format`: Name of framework that was used for saving the model. Options:
-
- - ``'farm'``
- - ``'transformers'``
- - ``'sentence_transformers'``
-- `pooling_strategy`: Strategy for combining the embeddings from the model (for farm / transformers models only).
- Options:
-
- - ``'cls_token'`` (sentence vector)
- - ``'reduce_mean'`` (sentence vector)
- - ``'reduce_max'`` (sentence vector)
- - ``'per_token'`` (individual token vectors)
-- `emb_extraction_layer`: Number of layer from which the embeddings shall be extracted (for farm / transformers models only).
- Default: -1 (very last layer).
-- `top_k`: How many documents to return per query.
-
-
-#### retrieve
-
-```python
- | retrieve(query: str, filters: dict = None, top_k: Optional[int] = None, index: str = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-#### embed
-
-```python
- | embed(texts: Union[List[List[str]], List[str], str]) -> List[np.ndarray]
-```
-
-Create embeddings for each text in a list of texts using the retrievers model (`self.embedding_model`)
-
-**Arguments**:
-
-- `texts`: Texts to embed
-
-**Returns**:
-
-List of embeddings (one per input text). Each embedding is a list of floats.
-
-
-#### embed\_queries
-
-```python
- | embed_queries(texts: List[str]) -> List[np.ndarray]
-```
-
-Create embeddings for a list of queries. For this Retriever type: The same as calling .embed()
-
-**Arguments**:
-
-- `texts`: Queries to embed
-
-**Returns**:
-
-Embeddings, one per input queries
-
-
-#### embed\_passages
-
-```python
- | embed_passages(docs: List[Document]) -> Union[List[str], List[List[str]]]
-```
-
-Create embeddings for a list of passages. For this Retriever type: The same as calling .embed()
-
-**Arguments**:
-
-- `docs`: List of documents to embed
-
-**Returns**:
-
-Embeddings, one per input passage
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--- a/src/pages/docs/versions/master/v0.8.0/site/en/menuStructure/menu.json
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@@ -1,296 +0,0 @@
-{
- "menuList": [
- {
- "id": "overview",
- "title": "Overview",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 0,
- "isMenu": true
- },
- {
- "id": "get_startedmd",
- "title": "Get Started",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "intromd",
- "title": "What is Haystack?",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "use_casesmd",
- "title": "Use Cases",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "roadmapmd",
- "title": "Roadmap",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "termsmd",
- "title": "Glossary",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "usage_haystack",
- "title": "Usage",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 1,
- "isMenu": true
- },
- {
- "id": "preprocessingmd",
- "title": "Preprocessing",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "documentstoremd",
- "title": "Document Store",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "retrievermd",
- "title": "Retriever",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "readermd",
- "title": "Reader",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "generatormd",
- "title": "Generator",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "pipelinesmd",
- "title": "Pipelines",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "languagesmd",
- "title": "Languages Other Than English",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 6
- },
- {
- "id": "domain_adaptationmd",
- "title": "Domain Adaptation",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 7
- },
- {
- "id": "optimizationmd",
- "title": "Optimization",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 8
- },
- {
- "id": "annotationmd",
- "title": "Annotation Tool",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 9
- },
- {
- "id": "tutorials_haystack",
- "title": "Tutorials",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 2,
- "isMenu": true
- },
- {
- "id": "tutorial1md",
- "title": "Build Your First QA System",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "tutorial2md",
- "title": "Fine-tuning a Model on Your Own Data",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "tutorial3md",
- "title": "Build a QA System Without Elasticsearch",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "tutorial4md",
- "title": "Utilizing existing FAQs for Question Answering",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "tutorial5md",
- "title": "Evaluation of a QA System",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "tutorial6md",
- "title": "Better Retrieval via \"Dense Passage Retrieval\"",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "tutorial7md",
- "title": "Generative QA with \"Retrieval-Augmented Generation\"",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 6
- },
- {
- "id": "tutorial8md",
- "title": "Preprocessing your Documents",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 7
- },
- {
- "id": "tutorial9md",
- "title": "How to Train DPR",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 8
- },
- {
- "id": "tutorial10md",
- "title": "Question Answering on a Knowledge Graph",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 9
- },
- {
- "id": "api_haystack",
- "title": "API reference",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 3,
- "isMenu": true
- },
- {
- "id": "apidatabasemd",
- "title": "Document Store",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "apiretrievermd",
- "title": "Retriever",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "apireadermd",
- "title": "Reader",
- "label1": "api_haystack",
- "label2": "api_haystack",
- "label3": "",
- "order": 2
- },
- {
- "id": "apigeneratormd",
- "title": "Generator",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "apiindexingmd",
- "title": "Preprocessor",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "file_convertersmd",
- "title": "File Converters",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "apipipelinesmd",
- "title": "Pipelines",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 6
- }
- ]
-}
\ No newline at end of file
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/1.md b/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/1.md
deleted file mode 100644
index affd3c05e..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/1.md
+++ /dev/null
@@ -1,238 +0,0 @@
----
-title: "Tutorial 1"
-metaTitle: "Build Your First QA System"
-metaDescription: ""
-slug: "/docs/tutorial1"
-date: "2020-09-03"
-id: "tutorial1md"
----
-
-# Build Your First QA System
-
-
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb)
-
-Question Answering can be used in a variety of use cases. A very common one: Using it to navigate through complex knowledge bases or long documents ("search setting").
-
-A "knowledge base" could for example be your website, an internal wiki or a collection of financial reports.
-In this tutorial we will work on a slightly different domain: "Game of Thrones".
-
-Let's see how we can use a bunch of Wikipedia articles to answer a variety of questions about the
-marvellous seven kingdoms...
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-Haystack finds answers to queries within the documents stored in a `DocumentStore`. The current implementations of `DocumentStore` include `ElasticsearchDocumentStore`, `FAISSDocumentStore`, `SQLDocumentStore`, and `InMemoryDocumentStore`.
-
-**Here:** We recommended Elasticsearch as it comes preloaded with features like [full-text queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html), [BM25 retrieval](https://www.elastic.co/elasticon/conf/2016/sf/improved-text-scoring-with-bm25), and [vector storage for text embeddings](https://www.elastic.co/guide/en/elasticsearch/reference/7.6/dense-vector.html).
-
-**Alternatives:** If you are unable to setup an Elasticsearch instance, then follow the [Tutorial 3](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb) for using SQL/InMemory document stores.
-
-**Hint**: This tutorial creates a new document store instance with Wikipedia articles on Game of Thrones. However, you can configure Haystack to work with your existing document stores.
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker
-#! docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.9.2
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-# Connect to Elasticsearch
-
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles about Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first fetch some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is:
-# {
-# 'text': "",
-# 'meta': {'name': "", ...}
-#}
-# (Optionally: you can also add more key-value-pairs here, that will be indexed as fields in Elasticsearch and
-# can be accessed later for filtering or shown in the responses of the Finder)
-
-# Let's have a look at the first 3 entries:
-print(dicts[:3])
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-## Initalize Retriever, Reader, & Finder
-
-### Retriever
-
-Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question could be answered.
-They use some simple but fast algorithm.
-
-**Here:** We use Elasticsearch's default BM25 algorithm
-
-**Alternatives:**
-
-- Customize the `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `DensePassageRetriever` to use different embedding models for passage and query (see Tutorial 6)
-
-
-```python
-from haystack.retriever.sparse import ElasticsearchRetriever
-retriever = ElasticsearchRetriever(document_store=document_store)
-```
-
-
-```python
-# Alternative: An in-memory TfidfRetriever based on Pandas dataframes for building quick-prototypes with SQLite document store.
-
-# from haystack.retriever.sparse import TfidfRetriever
-# retriever = TfidfRetriever(document_store=document_store)
-```
-
-### Reader
-
-A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based
-on powerful, but slower deep learning models.
-
-Haystack currently supports Readers based on the frameworks FARM and Transformers.
-With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).
-
-**Here:** a medium sized RoBERTa QA model using a Reader based on FARM (https://huggingface.co/deepset/roberta-base-squad2)
-
-**Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)
-
-**Alternatives (Models):** e.g. "distilbert-base-uncased-distilled-squad" (fast) or "deepset/bert-large-uncased-whole-word-masking-squad2" (good accuracy)
-
-**Hint:** You can adjust the model to return "no answer possible" with the no_ans_boost. Higher values mean the model prefers "no answer possible"
-
-#### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-#### TransformersReader
-
-
-```python
-# Alternative:
-# reader = TransformersReader(model_name_or_path="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1)
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import ExtractiveQAPipeline
-pipe = ExtractiveQAPipeline(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = pipe.run(query="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-# prediction = pipe.run(query="Who created the Dothraki vocabulary?", top_k_reader=5)
-# prediction = pipe.run(query="Who is the sister of Sansa?", top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/10.md b/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/10.md
deleted file mode 100644
index efad092d3..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/10.md
+++ /dev/null
@@ -1,131 +0,0 @@
----
-title: "Tutorial 10"
-metaTitle: "Knowledge Graph QA"
-metaDescription: ""
-slug: "/docs/tutorial10"
-date: "2021-04-06"
-id: "tutorial10md"
----
-
-# Question Answering on a Knowledge Graph
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial10_Knowledge_Graph.ipynb)
-
-Haystack allows storing and querying knowledge graphs with the help of pre-trained models that translate text queries to SPARQL queries.
-This tutorial demonstrates how to load an existing knowledge graph into haystack, load a pre-trained retriever, and execute text queries on the knowledge graph.
-The training of models that translate text queries into SPARQL queries is currently not supported.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-```
-
-
-```python
-# Here are some imports that we'll need
-
-import subprocess
-import time
-from pathlib import Path
-
-from haystack.graph_retriever.text_to_sparql import Text2SparqlRetriever
-from haystack.knowledge_graph.graphdb import GraphDBKnowledgeGraph
-from haystack.preprocessor.utils import fetch_archive_from_http
-```
-
-## Downloading Knowledge Graph and Model
-
-
-```python
-# Let's first fetch some triples that we want to store in our knowledge graph
-# Here: exemplary triples from the wizarding world
-graph_dir = "../data/tutorial10_knowledge_graph/"
-s3_url = "https://fandom-qa.s3-eu-west-1.amazonaws.com/triples_and_config.zip"
-fetch_archive_from_http(url=s3_url, output_dir=graph_dir)
-
-# Fetch a pre-trained BART model that translates text queries to SPARQL queries
-model_dir = "../saved_models/tutorial10_knowledge_graph/"
-s3_url = "https://fandom-qa.s3-eu-west-1.amazonaws.com/saved_models/hp_v3.4.zip"
-fetch_archive_from_http(url=s3_url, output_dir=model_dir)
-```
-
-## Launching a GraphDB instance
-
-
-```python
-# Unfortunately, there seems to be no good way to run GraphDB in colab environments
-# In your local environment, you could start a GraphDB server with docker
-# Feel free to check GraphDB's website for the free version https://www.ontotext.com/products/graphdb/graphdb-free/
-print("Starting GraphDB ...")
-status = subprocess.run(
- ['docker run -d -p 7200:7200 --name graphdb-instance-tutorial docker-registry.ontotext.com/graphdb-free:9.4.1-adoptopenjdk11'], shell=True
-)
-if status.returncode:
- raise Exception("Failed to launch GraphDB. Maybe it is already running or you already have a container with that name that you could start?")
-time.sleep(5)
-```
-
-## Creating a new GraphDB repository (also known as index in haystack's document stores)
-
-
-```python
-# Initialize a knowledge graph connected to GraphDB and use "tutorial_10_index" as the name of the index
-kg = GraphDBKnowledgeGraph(index="tutorial_10_index")
-
-# Delete the index as it might have been already created in previous runs
-kg.delete_index()
-
-# Create the index based on a configuration file
-kg.create_index(config_path=Path(graph_dir+"repo-config.ttl"))
-
-# Import triples of subject, predicate, and object statements from a ttl file
-kg.import_from_ttl_file(index="tutorial_10_index", path=Path(graph_dir+"triples.ttl"))
-print(f"The last triple stored in the knowledge graph is: {kg.get_all_triples()[-1]}")
-print(f"There are {len(kg.get_all_triples())} triples stored in the knowledge graph.")
-```
-
-
-```python
-# Define prefixes for names of resources so that we can use shorter resource names in queries
-prefixes = """PREFIX rdf:
-PREFIX xsd:
-PREFIX hp: ?obj . }")
-print(result)
-# Paraphrased question: What is the patronus of Hermione?
-# Correct answer: Otter
-```
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/2.md b/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/2.md
deleted file mode 100644
index 0623ff5cd..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/2.md
+++ /dev/null
@@ -1,90 +0,0 @@
----
-title: "Tutorial 2"
-metaTitle: "Fine-tuning a model on your own data"
-metaDescription: ""
-slug: "/docs/tutorial2"
-date: "2020-09-03"
-id: "tutorial2md"
----
-
-# Fine-tuning a Model on Your Own Data
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial2_Finetune_a_model_on_your_data.ipynb)
-
-For many use cases it is sufficient to just use one of the existing public models that were trained on SQuAD or other public QA datasets (e.g. Natural Questions).
-However, if you have domain-specific questions, fine-tuning your model on custom examples will very likely boost your performance.
-While this varies by domain, we saw that ~ 2000 examples can easily increase performance by +5-20%.
-
-This tutorial shows you how to fine-tune a pretrained model on your own dataset.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-```
-
-
-```python
-from haystack.reader.farm import FARMReader
-```
-
-
-## Create Training Data
-
-There are two ways to generate training data
-
-1. **Annotation**: You can use the [annotation tool](https://github.com/deepset-ai/haystack#labeling-tool) to label your data, i.e. highlighting answers to your questions in a document. The tool supports structuring your workflow with organizations, projects, and users. The labels can be exported in SQuAD format that is compatible for training with Haystack.
-
-
-
-2. **Feedback**: For production systems, you can collect training data from direct user feedback via Haystack's [REST API interface](https://github.com/deepset-ai/haystack#rest-api). This includes a customizable user feedback API for providing feedback on the answer returned by the API. The API provides a feedback export endpoint to obtain the feedback data for fine-tuning your model further.
-
-
-## Fine-tune your model
-
-Once you have collected training data, you can fine-tune your base models.
-We initialize a reader as a base model and fine-tune it on our own custom dataset (should be in SQuAD-like format).
-We recommend using a base model that was trained on SQuAD or a similar QA dataset before to benefit from Transfer Learning effects.
-
-**Recommendation**: Run training on a GPU.
-If you are using Colab: Enable this in the menu "Runtime" > "Change Runtime type" > Select "GPU" in dropdown.
-Then change the `use_gpu` arguments below to `True`
-
-
-```python
-reader = FARMReader(model_name_or_path="distilbert-base-uncased-distilled-squad", use_gpu=True)
-train_data = "data/squad20"
-# train_data = "PATH/TO_YOUR/TRAIN_DATA"
-reader.train(data_dir=train_data, train_filename="dev-v2.0.json", use_gpu=True, n_epochs=1, save_dir="my_model")
-```
-
-
-```python
-# Saving the model happens automatically at the end of training into the `save_dir` you specified
-# However, you could also save a reader manually again via:
-reader.save(directory="my_model")
-```
-
-
-```python
-# If you want to load it at a later point, just do:
-new_reader = FARMReader(model_name_or_path="my_model")
-```
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/3.md b/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/3.md
deleted file mode 100644
index ee79c1215..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/3.md
+++ /dev/null
@@ -1,185 +0,0 @@
----
-title: "Tutorial 3"
-metaTitle: "Build a QA System Without Elasticsearch"
-metaDescription: ""
-slug: "/docs/tutorial3"
-date: "2020-09-03"
-id: "tutorial3md"
----
-
-# Build a QA System Without Elasticsearch
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb)
-
-Haystack provides alternatives to Elasticsearch for developing quick prototypes.
-
-You can use an `InMemoryDocumentStore` or a `SQLDocumentStore`(with SQLite) as the document store.
-
-If you are interested in more feature-rich Elasticsearch, then please refer to the Tutorial 1.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-
-
-```python
-# In-Memory Document Store
-from haystack.document_store.memory import InMemoryDocumentStore
-document_store = InMemoryDocumentStore()
-```
-
-
-```python
-# SQLite Document Store
-# from haystack.document_store.sql import SQLDocumentStore
-# document_store = SQLDocumentStore(url="sqlite:///qa.db")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles on Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first get some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# convert files to dicts containing documents that can be indexed to our datastore
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is: {"name": ", "text": ""}
-
-# Let's have a look at the first 3 entries:
-print(dicts[:3])
-# Now, let's write the docs to our DB.
-document_store.write_documents(dicts)
-```
-
-## Initalize Retriever, Reader, & Finder
-
-### Retriever
-
-Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question could be answered.
-
-With InMemoryDocumentStore or SQLDocumentStore, you can use the TfidfRetriever. For more retrievers, please refer to the tutorial-1.
-
-
-```python
-# An in-memory TfidfRetriever based on Pandas dataframes
-
-from haystack.retriever.sparse import TfidfRetriever
-retriever = TfidfRetriever(document_store=document_store)
-```
-
-### Reader
-
-A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based
-on powerful, but slower deep learning models.
-
-Haystack currently supports Readers based on the frameworks FARM and Transformers.
-With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).
-
-**Here:** a medium sized RoBERTa QA model using a Reader based on FARM (https://huggingface.co/deepset/roberta-base-squad2)
-
-**Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)
-
-**Alternatives (Models):** e.g. "distilbert-base-uncased-distilled-squad" (fast) or "deepset/bert-large-uncased-whole-word-masking-squad2" (good accuracy)
-
-**Hint:** You can adjust the model to return "no answer possible" with the no_ans_boost. Higher values mean the model prefers "no answer possible"
-
-#### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-#### TransformersReader
-
-
-```python
-# Alternative:
-# reader = TransformersReader(model_name_or_path="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1)
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import ExtractiveQAPipeline
-pipe = ExtractiveQAPipeline(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = pipe.run(query="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-# prediction = pipe.run(query="Who created the Dothraki vocabulary?", top_k_reader=5)
-# prediction = pipe.run(query="Who is the sister of Sansa?", top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/4.md b/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/4.md
deleted file mode 100644
index 330f93bb0..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/4.md
+++ /dev/null
@@ -1,159 +0,0 @@
----
-title: "Tutorial 4"
-metaTitle: "Utilizing existing FAQs for Question Answering"
-metaDescription: ""
-slug: "/docs/tutorial4"
-date: "2020-09-03"
-id: "tutorial4md"
----
-
-# Utilizing existing FAQs for Question Answering
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial4_FAQ_style_QA.ipynb)
-
-While *extractive Question Answering* works on pure texts and is therefore more generalizable, there's also a common alternative that utilizes existing FAQ data.
-
-**Pros**:
-
-- Very fast at inference time
-- Utilize existing FAQ data
-- Quite good control over answers
-
-**Cons**:
-
-- Generalizability: We can only answer questions that are similar to existing ones in FAQ
-
-In some use cases, a combination of extractive QA and FAQ-style can also be an interesting option.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-```
-
-
-```python
-from haystack import Finder
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-from haystack.retriever.dense import EmbeddingRetriever
-from haystack.utils import print_answers
-import pandas as pd
-import requests
-
-```
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker
-# ! docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.9.2
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-
-```
-
-### Init the DocumentStore
-In contrast to Tutorial 1 (extractive QA), we:
-
-* specify the name of our `text_field` in Elasticsearch that we want to return as an answer
-* specify the name of our `embedding_field` in Elasticsearch where we'll store the embedding of our question and that is used later for calculating our similarity to the incoming user question
-* set `excluded_meta_data=["question_emb"]` so that we don't return the huge embedding vectors in our search results
-
-
-```python
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="",
- index="document",
- embedding_field="question_emb",
- embedding_dim=768,
- excluded_meta_data=["question_emb"])
-```
-
-### Create a Retriever using embeddings
-Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones).
-We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings.
-
-
-```python
-retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert", use_gpu=True)
-```
-
-### Prepare & Index FAQ data
-We create a pandas dataframe containing some FAQ data (i.e curated pairs of question + answer) and index those in elasticsearch.
-Here: We download some question-answer pairs related to COVID-19
-
-
-```python
-# Download
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/faqs/faq_covidbert.csv")
-open('small_faq_covid.csv', 'wb').write(temp.content)
-
-# Get dataframe with columns "question", "answer" and some custom metadata
-df = pd.read_csv("small_faq_covid.csv")
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-df["question"] = df["question"].apply(lambda x: x.strip())
-print(df.head())
-
-# Get embeddings for our questions from the FAQs
-questions = list(df["question"].values)
-df["question_emb"] = retriever.embed_queries(texts=questions)
-df = df.rename(columns={"question": "text"})
-
-# Convert Dataframe to list of dicts and index them in our DocumentStore
-docs_to_index = df.to_dict(orient="records")
-document_store.write_documents(docs_to_index)
-```
-
-### Ask questions
-Initialize a Pipeline (this time without a reader) and ask questions
-
-
-```python
-from haystack.pipeline import FAQPipeline
-pipe = FAQPipeline(retriever=retriever)
-```
-
-
-```python
-prediction = pipe.run(query="How is the virus spreading?", top_k_retriever=10)
-print_answers(prediction, details="all")
-
-
-```
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/5.md b/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/5.md
deleted file mode 100644
index 236f205f3..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/5.md
+++ /dev/null
@@ -1,248 +0,0 @@
----
-title: "Tutorial 5"
-metaTitle: "Evaluation of a QA System"
-metaDescription: ""
-slug: "/docs/tutorial5"
-date: "2020-09-03"
-id: "tutorial5md"
----
-
-# Evaluation of a QA System
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial5_Evaluation.ipynb)
-
-To be able to make a statement about the performance of a question-answering system, it is important to evalute it. Furthermore, evaluation allows to determine which parts of the system can be improved.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-## Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-from farm.utils import initialize_device_settings
-
-device, n_gpu = initialize_device_settings(use_cuda=True)
-```
-
-
-```python
-from haystack.preprocessor.utils import fetch_archive_from_http
-
-# Download evaluation data, which is a subset of Natural Questions development set containing 50 documents
-doc_dir = "../data/nq"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/nq_dev_subset_v2.json.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-
-```python
-# make sure these indices do not collide with existing ones, the indices will be wiped clean before data is inserted
-doc_index = "tutorial5_docs"
-label_index = "tutorial5_labels"
-```
-
-
-```python
-# Connect to Elasticsearch
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-# Connect to Elasticsearch
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document",
- create_index=False, embedding_field="emb",
- embedding_dim=768, excluded_meta_data=["emb"])
-```
-
-
-```python
-from haystack.preprocessor import PreProcessor
-
-# Add evaluation data to Elasticsearch Document Store
-# We first delete the custom tutorial indices to not have duplicate elements
-# and also split our documents into shorter passages using the PreProcessor
-preprocessor = PreProcessor(
- split_length=500,
- split_overlap=0,
- split_respect_sentence_boundary=False,
- clean_empty_lines=False,
- clean_whitespace=False
-)
-document_store.delete_all_documents(index=doc_index)
-document_store.delete_all_documents(index=label_index)
-document_store.add_eval_data(
- filename="../data/nq/nq_dev_subset_v2.json",
- doc_index=doc_index,
- label_index=label_index,
- preprocessor=preprocessor
-)
-
-# Let's prepare the labels that we need for the retriever and the reader
-labels = document_store.get_all_labels_aggregated(index=label_index)
-q_to_l_dict = {
- l.question: {
- "retriever": l,
- "reader": l
- } for l in labels
-}
-```
-
-## Initialize components of QA-System
-
-
-```python
-# Initialize Retriever
-from haystack.retriever.sparse import ElasticsearchRetriever
-retriever = ElasticsearchRetriever(document_store=document_store)
-# Alternative: Evaluate DensePassageRetriever
-# Note, that DPR works best when you index short passages < 512 tokens as only those tokens will be used for the embedding.
-# Here, for nq_dev_subset_v2.json we have avg. num of tokens = 5220(!).
-# DPR still outperforms Elastic's BM25 by a small margin here.
-# from haystack.retriever.dense import DensePassageRetriever
-# retriever = DensePassageRetriever(document_store=document_store,
-# query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
-# passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
-# use_gpu=True,
-# embed_title=True,
-# max_seq_len=256,
-# batch_size=16,
-# remove_sep_tok_from_untitled_passages=True)
-#document_store.update_embeddings(retriever, index=doc_index)
-```
-
-
-```python
-# Initialize Reader
-from haystack.reader.farm import FARMReader
-
-reader = FARMReader("deepset/roberta-base-squad2", top_k_per_candidate=4, return_no_answer=True)
-
-```
-
-```python
-from haystack.eval import EvalAnswers, EvalDocuments
-
-# Here we initialize the nodes that perform evaluation
-eval_retriever = EvalRetriever()
-eval_reader = EvalReader()
-```
-
-## Evaluation of Retriever
-Here we evaluate only the retriever, based on whether the gold_label document is retrieved.
-
-
-```python
-## Evaluate Retriever on its own
-retriever_eval_results = retriever.eval(top_k=20, label_index=label_index, doc_index=doc_index)
-## Retriever Recall is the proportion of questions for which the correct document containing the answer is
-## among the correct documents
-print("Retriever Recall:", retriever_eval_results["recall"])
-## Retriever Mean Avg Precision rewards retrievers that give relevant documents a higher rank
-print("Retriever Mean Avg Precision:", retriever_eval_results["map"])
-```
-
-## Evaluation of Reader
-Here we evaluate only the reader in a closed domain fashion i.e. the reader is given one query
-and one document and metrics are calculated on whether the right position in this text is selected by
-the model as the answer span (i.e. SQuAD style)
-
-
-```python
-# Evaluate Reader on its own
-reader_eval_results = reader.eval(document_store=document_store, device=device, label_index=label_index, doc_index=doc_index)
-# Evaluation of Reader can also be done directly on a SQuAD-formatted file without passing the data to Elasticsearch
-#reader_eval_results = reader.eval_on_file("../data/nq", "nq_dev_subset_v2.json", device=device)
-
-## Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer
-print("Reader Top-N-Accuracy:", reader_eval_results["top_n_accuracy"])
-## Reader Exact Match is the proportion of questions where the predicted answer is exactly the same as the correct answer
-print("Reader Exact Match:", reader_eval_results["EM"])
-## Reader F1-Score is the average overlap between the predicted answers and the correct answers
-print("Reader F1-Score:", reader_eval_results["f1"])
-```
-
-## Evaluation of Retriever and Reader (Open Domain)
-Here we evaluate retriever and reader in open domain fashion i.e. a document is considered
-correctly retrieved if it contains the answer string within it. The reader is evaluated based purely on the
-predicted string, regardless of which document this came from and the position of the extracted span.
-
-
-```python
-from haystack import Pipeline
-
-# Here is the pipeline definition
-p = Pipeline()
-p.add_node(component=retriever, name="ESRetriever", inputs=["Query"])
-p.add_node(component=eval_retriever, name="EvalDocuments", inputs=["ESRetriever"])
-p.add_node(component=reader, name="QAReader", inputs=["EvalDocuments"])
-p.add_node(component=eval_reader, name="EvalAnswers", inputs=["QAReader"])
-results = []
-```
-
-
-```python
-# This is how to run the pipeline
-for q, l in q_to_l_dict.items():
- res = p.run(
- query=q,
- top_k_retriever=10,
- labels=l,
- top_k_reader=10,
- index=doc_index,
- )
- results.append(res)
-```
-
-
-```python
-# When we have run evaluation using the pipeline, we can print the results
-n_queries = len(labels)
-eval_retriever.print()
-print()
-retriever.print_time()
-print()
-eval_reader.print(mode="reader")
-print()
-reader.print_time()
-print()
-eval_reader.print(mode="pipeline")
-
-```
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/6.md b/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/6.md
deleted file mode 100644
index 8385df887..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/6.md
+++ /dev/null
@@ -1,207 +0,0 @@
----
-title: "Tutorial 6"
-metaTitle: "Better retrieval via Dense Passage Retrieval"
-metaDescription: ""
-slug: "/docs/tutorial6"
-date: "2020-09-03"
-id: "tutorial6md"
----
-
-# Better Retrieval via "Dense Passage Retrieval"
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb)
-
-### Importance of Retrievers
-
-The Retriever has a huge impact on the performance of our overall search pipeline.
-
-
-### Different types of Retrievers
-#### Sparse
-Family of algorithms based on counting the occurrences of words (bag-of-words) resulting in very sparse vectors with length = vocab size.
-
-**Examples**: BM25, TF-IDF
-
-**Pros**: Simple, fast, well explainable
-
-**Cons**: Relies on exact keyword matches between query and text
-
-
-#### Dense
-These retrievers use neural network models to create "dense" embedding vectors. Within this family there are two different approaches:
-
-a) Single encoder: Use a **single model** to embed both query and passage.
-b) Dual-encoder: Use **two models**, one to embed the query and one to embed the passage
-
-Recent work suggests that dual encoders work better, likely because they can deal better with the different nature of query and passage (length, style, syntax ...).
-
-**Examples**: REALM, DPR, Sentence-Transformers
-
-**Pros**: Captures semantinc similarity instead of "word matches" (e.g. synonyms, related topics ...)
-
-**Cons**: Computationally more heavy, initial training of model
-
-
-### "Dense Passage Retrieval"
-
-In this Tutorial, we want to highlight one "Dense Dual-Encoder" called Dense Passage Retriever.
-It was introdoced by Karpukhin et al. (2020, https://arxiv.org/abs/2004.04906.
-
-Original Abstract:
-
-_"Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks."_
-
-Paper: https://arxiv.org/abs/2004.04906
-Original Code: https://fburl.com/qa-dpr
-
-
-*Use this* [link](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb) *to open the notebook in Google Colab.*
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-### Document Store
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-The default flavour of FAISSDocumentStore is "Flat" but can also be set to "HNSW" for
-faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.
-For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-
-
-```python
-from haystack.document_store.faiss import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever, Reader, & Finder
-
-#### Retriever
-
-**Here:** We use a `DensePassageRetriever`
-
-**Alternatives:**
-
-- The `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-
-
-```python
-from haystack.retriever.dense import DensePassageRetriever
-retriever = DensePassageRetriever(document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- max_seq_len_query=64,
- max_seq_len_passage=256,
- batch_size=16,
- use_gpu=True,
- embed_title=True,
- use_fast_tokenizers=True)
-# Important:
-# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all
-# previously indexed documents and update their embedding representation.
-# While this can be a time consuming operation (depending on corpus size), it only needs to be done once.
-# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.
-document_store.update_embeddings(retriever)
-```
-
-#### Reader
-
-Similar to previous Tutorials we now initalize our reader.
-
-Here we use a FARMReader with the *deepset/roberta-base-squad2* model (see: https://huggingface.co/deepset/roberta-base-squad2)
-
-
-
-##### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import ExtractiveQAPipeline
-pipe = ExtractiveQAPipeline(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = pipe.run(query="Who created the Dothraki vocabulary?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-
-
-```
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/7.md b/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/7.md
deleted file mode 100644
index fc73caf6b..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/7.md
+++ /dev/null
@@ -1,199 +0,0 @@
----
-title: "Tutorial 7"
-metaTitle: "Generative QA"
-metaDescription: ""
-slug: "/docs/tutorial7"
-date: "2020-11-12"
-id: "tutorial7md"
----
-
-# Generative QA with "Retrieval-Augmented Generation"
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial7_RAG_Generator.ipynb)
-
-While extractive QA highlights the span of text that answers a query,
-generative QA can return a novel text answer that it has composed.
-In this tutorial, you will learn how to set up a generative system using the
-[RAG model](https://arxiv.org/abs/2005.11401) which conditions the
-answer generator on a set of retrieved documents.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-Here are the packages and imports that we'll need:
-
-
-```python
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-```
-
-
-```python
-from typing import List
-import requests
-import pandas as pd
-from haystack import Document
-from haystack.document_store.faiss import FAISSDocumentStore
-from haystack.generator.transformers import RAGenerator
-from haystack.retriever.dense import DensePassageRetriever
-```
-
-Let's download a csv containing some sample text and preprocess the data.
-
-
-
-```python
-# Download sample
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/haystack/master/tutorials/small_generator_dataset.csv")
-open('small_generator_dataset.csv', 'wb').write(temp.content)
-
-# Create dataframe with columns "title" and "text"
-df = pd.read_csv("small_generator_dataset.csv", sep=',')
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-
-print(df.head())
-```
-
-We can cast our data into Haystack Document objects.
-Alternatively, we can also just use dictionaries with "text" and "meta" fields
-
-
-```python
-# Use data to initialize Document objects
-titles = list(df["title"].values)
-texts = list(df["text"].values)
-documents: List[Document] = []
-for title, text in zip(titles, texts):
- documents.append(
- Document(
- text=text,
- meta={
- "name": title or ""
- }
- )
- )
-```
-
-Here we initialize the FAISSDocumentStore, DensePassageRetriever and RAGenerator.
-FAISS is chosen here since it is optimized vector storage.
-
-
-```python
-# Initialize FAISS document store.
-# Set `return_embedding` to `True`, so generator doesn't have to perform re-embedding
-document_store = FAISSDocumentStore(
- faiss_index_factory_str="Flat",
- return_embedding=True
-)
-
-# Initialize DPR Retriever to encode documents, encode question and query documents
-retriever = DensePassageRetriever(
- document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- use_gpu=True,
- embed_title=True,
-)
-
-# Initialize RAG Generator
-generator = RAGenerator(
- model_name_or_path="facebook/rag-token-nq",
- use_gpu=True,
- top_k=1,
- max_length=200,
- min_length=2,
- embed_title=True,
- num_beams=2,
-)
-```
-
-We write documents to the DocumentStore, first by deleting any remaining documents then calling `write_documents()`.
-The `update_embeddings()` method uses the retriever to create an embedding for each document.
-
-
-
-```python
-# Delete existing documents in documents store
-document_store.delete_all_documents()
-
-# Write documents to document store
-document_store.write_documents(documents)
-
-# Add documents embeddings to index
-document_store.update_embeddings(
- retriever=retriever
-)
-```
-
-Here are our questions:
-
-
-```python
-QUESTIONS = [
- "who got the first nobel prize in physics",
- "when is the next deadpool movie being released",
- "which mode is used for short wave broadcast service",
- "who is the owner of reading football club",
- "when is the next scandal episode coming out",
- "when is the last time the philadelphia won the superbowl",
- "what is the most current adobe flash player version",
- "how many episodes are there in dragon ball z",
- "what is the first step in the evolution of the eye",
- "where is gall bladder situated in human body",
- "what is the main mineral in lithium batteries",
- "who is the president of usa right now",
- "where do the greasers live in the outsiders",
- "panda is a national animal of which country",
- "what is the name of manchester united stadium",
-]
-```
-
-Now let's run our system!
-The retriever will pick out a small subset of documents that it finds relevant.
-These are used to condition the generator as it generates the answer.
-What it should return then are novel text spans that form and answer to your question!
-
-
-```python
-# Now generate an answer for each question
-for question in QUESTIONS:
- # Retrieve related documents from retriever
- retriever_results = retriever.retrieve(
- query=question
- )
-
- # Now generate answer from question and retrieved documents
- predicted_result = generator.predict(
- query=question,
- documents=retriever_results,
- top_k=1
- )
-
- # Print you answer
- answers = predicted_result["answers"]
- print(f'Generated answer is \'{answers[0]["answer"]}\' for the question = \'{question}\'')
-```
-
-
-```python
-# Or alternatively use the Pipeline class
-from haystack.pipeline import GenerativeQAPipeline
-
-pipe = GenerativeQAPipeline(generator=generator, retriever=retriever)
-for question in QUESTIONS:
- res = pipe.run(query=question, top_k_generator=1, top_k_retriever=5)
- print(res)
-```
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/8.md b/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/8.md
deleted file mode 100644
index b2ee61786..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/8.md
+++ /dev/null
@@ -1,201 +0,0 @@
----
-title: "Tutorial 8"
-metaTitle: "Preprocessing"
-metaDescription: ""
-slug: "/docs/tutorial8"
-date: "2021-01-08"
-id: "tutorial8md"
----
-
-# Preprocessing
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial8_Preprocessing.ipynb)
-
-Haystack includes a suite of tools to extract text from different file types, normalize white space
-and split text into smaller pieces to optimize retrieval.
-These data preprocessing steps can have a big impact on the systems performance and effective handling of data is key to getting the most out of Haystack.
-
-Ultimately, Haystack expects data to be provided as a list documents in the following dictionary format:
-``` python
-docs = [
- {
- 'text': DOCUMENT_TEXT_HERE,
- 'meta': {'name': DOCUMENT_NAME, ...}
- }, ...
-]
-```
-
-This tutorial will show you all the tools that Haystack provides to help you cast your data into this format.
-
-
-```python
-# Let's start by installing Haystack
-
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.03.tar.gz
-!tar -xvf xpdf-tools-linux-4.03.tar.gz && sudo cp xpdf-tools-linux-4.03/bin64/pdftotext /usr/local/bin
-```
-
-
-```python
-# Here are the imports we need
-
-from haystack.file_converter.txt import TextConverter
-from haystack.file_converter.pdf import PDFToTextConverter
-from haystack.file_converter.docx import DocxToTextConverter
-
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.preprocessor.preprocessor import PreProcessor
-```
-
-
-```python
-# This fetches some sample files to work with
-
-doc_dir = "data/preprocessing_tutorial"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/preprocessing_tutorial.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-## Converters
-
-Haystack's converter classes are designed to help you turn files on your computer into the documents
-that can be processed by the Haystack pipeline.
-There are file converters for txt, pdf, docx files as well as a converter that is powered by Apache Tika.
-
-
-```python
-# Here are some examples of how you would use file converters
-
-converter = TextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_txt = converter.convert(file_path="data/preprocessing_tutorial/classics.txt", meta=None)
-
-converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_pdf = converter.convert(file_path="data/preprocessing_tutorial/bert.pdf", meta=None)
-
-converter = DocxToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_docx = converter.convert(file_path="data/preprocessing_tutorial/heavy_metal.docx", meta=None)
-
-```
-
-
-```python
-# Haystack also has a convenience function that will automatically apply the right converter to each file in a directory.
-
-all_docs = convert_files_to_dicts(dir_path="data/preprocessing_tutorial")
-```
-
-## PreProcessor
-
-The PreProcessor class is designed to help you clean text and split text into sensible units.
-File splitting can have a very significant impact on the system's performance and is absolutely mandatory for Dense Passage Retrieval models.
-In general, we recommend you split the text from your files into small documents of around 100 words for dense retrieval methods
-and no more than 10,000 words for sparse methods.
-Have a look at the [Preprocessing](https://haystack.deepset.ai/docs/latest/preprocessingmd)
-and [Optimization](https://haystack.deepset.ai/docs/latest/optimizationmd) pages on our website for more details.
-
-
-```python
-# This is a default usage of the PreProcessor.
-# Here, it performs cleaning of consecutive whitespaces
-# and splits a single large document into smaller documents.
-# Each document is up to 1000 words long and document breaks cannot fall in the middle of sentences
-# Note how the single document passed into the document gets split into 5 smaller documents
-
-preprocessor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=False,
- split_by="word",
- split_length=100,
- split_respect_sentence_boundary=True
-)
-docs_default = preprocessor.process(doc_txt)
-print(f"n_docs_input: 1\nn_docs_output: {len(docs_default)}")
-```
-
-## Cleaning
-
-- `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
-- `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
-- `clean_header_footer` will remove any long header or footer texts that are repeated on each page
-
-## Splitting
-By default, the PreProcessor will respect sentence boundaries, meaning that documents will not start or end
-midway through a sentence.
-This will help reduce the possibility of answer phrases being split between two documents.
-This feature can be turned off by setting `split_respect_sentence_boundary=False`.
-
-
-```python
-# Not respecting sentence boundary vs respecting sentence boundary
-
-preprocessor_nrsb = PreProcessor(split_respect_sentence_boundary=False)
-docs_nrsb = preprocessor_nrsb.process(doc_txt)
-
-print("RESPECTING SENTENCE BOUNDARY")
-end_text = docs_default[0]["text"][-50:]
-print("End of document: \"..." + end_text + "\"")
-print()
-print("NOT RESPECTING SENTENCE BOUNDARY")
-end_text_nrsb = docs_nrsb[0]["text"][-50:]
-print("End of document: \"..." + end_text_nrsb + "\"")
-```
-
-A commonly used strategy to split long documents, especially in the field of Question Answering,
-is the sliding window approach. If `split_length=10` and `split_overlap=3`, your documents will look like this:
-
-- doc1 = words[0:10]
-- doc2 = words[7:17]
-- doc3 = words[14:24]
-- ...
-
-You can use this strategy by following the code below.
-
-
-```python
-# Sliding window approach
-
-preprocessor_sliding_window = PreProcessor(
- split_overlap=3,
- split_length=10,
- split_respect_sentence_boundary=False
-)
-docs_sliding_window = preprocessor_sliding_window.process(doc_txt)
-
-doc1 = docs_sliding_window[0]["text"][:200]
-doc2 = docs_sliding_window[1]["text"][:100]
-doc3 = docs_sliding_window[2]["text"][:100]
-
-print("Document 1: \"" + doc1 + "...\"")
-print("Document 2: \"" + doc2 + "...\"")
-print("Document 3: \"" + doc3 + "...\"")
-```
-
-## Bringing it all together
-
-
-```python
-all_docs = convert_files_to_dicts(dir_path="data/preprocessing_tutorial")
-preprocessor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=False,
- split_by="word",
- split_length=100,
- split_respect_sentence_boundary=True
-)
-nested_docs = [preprocessor.process(d) for d in all_docs]
-docs = [d for x in nested_docs for d in x]
-
-print(f"n_files_input: {len(all_docs)}\nn_docs_output: {len(docs)}")
-```
-
-
-```python
-
-```
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/9.md b/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/9.md
deleted file mode 100644
index 193abe908..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/9.md
+++ /dev/null
@@ -1,231 +0,0 @@
----
-title: "Tutorial 9"
-metaTitle: "Training a Dense Passage Retrieval model"
-metaDescription: ""
-slug: "/docs/tutorial9"
-date: "2021-01-08"
-id: "tutorial9md"
----
-
-# Training Your Own "Dense Passage Retrieval" Model
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial9_DPR_training.ipynb)
-
-Haystack contains all the tools needed to train your own Dense Passage Retrieval model.
-This tutorial will guide you through the steps required to create a retriever that is specifically tailored to your domain.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-```
-
-
-```python
-# Here are some imports that we'll need
-
-from haystack.retriever.dense import DensePassageRetriever
-from haystack.preprocessor.utils import fetch_archive_from_http
-from haystack.document_store.memory import InMemoryDocumentStore
-```
-
-## Training Data
-
-DPR training performed using Information Retrieval data.
-More specifically, you want to feed in pairs of queries and relevant documents.
-
-To train a model, we will need a dataset that has the same format as the original DPR training data.
-Each data point in the dataset should have the following dictionary structure.
-
-``` python
- {
- "dataset": str,
- "question": str,
- "answers": list of str
- "positive_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- "negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- "hard_negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- }
-```
-
-`positive_ctxs` are context passages which are relevant to the query.
-In some datasets, queries might have more than one positive context
-in which case you can set the `num_positives` parameter to be higher than the default 1.
-Note that `num_positives` needs to be lower or equal to the minimum number of `positive_ctxs` for queries in your data.
-If you have an unequal number of positive contexts per example,
-you might want to generate some soft labels by retrieving similar contexts which contain the answer.
-
-DPR is standardly trained using a method known as in-batch negatives.
-This means that positive contexts for a given query are treated as negative contexts for the other queries in the batch.
-Doing so allows for a high degree of computational efficiency, thus allowing the model to be trained on large amounts of data.
-
-`negative_ctxs` is not actually used in Haystack's DPR training so we recommend you set it to an empty list.
-They were used by the original DPR authors in an experiment to compare it against the in-batch negatives method.
-
-`hard_negative_ctxs` are passages that are not relevant to the query.
-In the original DPR paper, these are fetched using a retriever to find the most relevant passages to the query.
-Passages which contain the answer text are filtered out.
-
-We are [currently working](https://github.com/deepset-ai/haystack/issues/705) on a script that will convert SQuAD format data into a DPR dataset!
-
-## Using Question Answering Data
-
-Question Answering datasets can sometimes be used as training data.
-Google's Natural Questions dataset, is sufficiently large
-and contains enough unique passages, that it can be converted into a DPR training set.
-This is done simply by considering answer containing passages as relevant documents to the query.
-
-The SQuAD dataset, however, is not as suited to this use case since its question and answer pairs
-are created on only a very small slice of wikipedia documents.
-
-## Download Original DPR Training Data
-
-WARNING: These files are large! The train set is 7.4GB and the dev set is 800MB
-
-We can download the original DPR training data with the following cell.
-Note that this data is probably only useful if you are trying to train from scratch.
-
-
-```python
-# Download original DPR data
-# WARNING: the train set is 7.4GB and the dev set is 800MB
-
-doc_dir = "data/dpr_training/"
-
-s3_url_train = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-train.json.gz"
-s3_url_dev = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz"
-
-fetch_archive_from_http(s3_url_train, output_dir=doc_dir + "train/")
-fetch_archive_from_http(s3_url_dev, output_dir=doc_dir + "dev/")
-```
-
-## Option 1: Training DPR from Scratch
-
-The default variables that we provide below are chosen to train a DPR model from scratch.
-Here, both passage and query embedding models are initialized using BERT base
-and the model is trained using Google's Natural Questions dataset (in a format specialised for DPR).
-
-If you are working in a language other than English,
-you will want to initialize the passage and query embedding models with a language model that supports your language
-and also provide a dataset in your language.
-
-
-```python
-# Here are the variables to specify our training data, the models that we use to initialize DPR
-# and the directory where we'll be saving the model
-
-doc_dir = "data/dpr_training/"
-
-train_filename = "train/biencoder-nq-train.json"
-dev_filename = "dev/biencoder-nq-dev.json"
-
-query_model = "bert-base-uncased"
-passage_model = "bert-base-uncased"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Option 2: Finetuning DPR
-
-If you have your own domain specific question answering or information retrieval dataset,
-you might instead be interested in finetuning a pretrained DPR model.
-In this case, you would initialize both query and passage models using the original pretrained model.
-You will want to load something like this set of variables instead of the ones above
-
-
-```python
-# Here are the variables you might want to use instead of the set above
-# in order to perform pretraining
-
-doc_dir = "PATH_TO_YOUR_DATA_DIR"
-train_filename = "TRAIN_FILENAME"
-dev_filename = "DEV_FILENAME"
-
-query_model = "facebook/dpr-question_encoder-single-nq-base"
-passage_model = "facebook/dpr-ctx_encoder-single-nq-base"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Initialization
-
-Here we want to initialize our model either with plain language model weights for training from scratch
-or else with pretrained DPR weights for finetuning.
-We follow the [original DPR parameters](https://github.com/facebookresearch/DPR#best-hyperparameter-settings)
-for their max passage length but set max query length to 64 since queries are very rarely longer.
-
-
-```python
-## Initialize DPR model
-
-retriever = DensePassageRetriever(
- document_store=InMemoryDocumentStore(),
- query_embedding_model=query_model,
- passage_embedding_model=passage_model,
- max_seq_len_query=64,
- max_seq_len_passage=256
-)
-```
-
-## Training
-
-Let's start training and save our trained model!
-
-On a V100 GPU, you can fit up to batch size 16 so we set gradient accumulation steps to 8 in order
-to simulate the batch size 128 of the original DPR experiment.
-
-When `embed_title=True`, the document title is prepended to the input text sequence with a `[SEP]` token
-between it and document text.
-
-When training from scratch with the above variables, 1 epoch takes around an hour and we reached the following performance:
-
-```
-loss: 0.046580662854042276
-task_name: text_similarity
-acc: 0.992524064068483
-f1: 0.8804297774366846
-acc_and_f1: 0.9364769207525838
-average_rank: 0.19631619339984652
-report:
- precision recall f1-score support
-
-hard_negative 0.9961 0.9961 0.9961 201887
- positive 0.8804 0.8804 0.8804 6515
-
- accuracy 0.9925 208402
- macro avg 0.9383 0.9383 0.9383 208402
- weighted avg 0.9925 0.9925 0.9925 208402
-
-```
-
-
-```python
-# Start training our model and save it when it is finished
-
-retriever.train(
- data_dir=doc_dir,
- train_filename=train_filename,
- dev_filename=dev_filename,
- test_filename=dev_filename,
- n_epochs=1,
- batch_size=16,
- grad_acc_steps=8,
- save_dir=save_dir,
- evaluate_every=3000,
- embed_title=True,
- num_positives=1,
- num_hard_negatives=1
-)
-```
-
-## Loading
-
-Loading our newly trained model is simple!
-
-
-```python
-reloaded_retriever = DensePassageRetriever.load(load_dir=save_dir, document_store=None)
-```
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/domain_adaptation.md b/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/domain_adaptation.md
deleted file mode 100644
index 9bd415e61..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/domain_adaptation.md
+++ /dev/null
@@ -1,157 +0,0 @@
----
-title: "Domain Adaptation"
-metaTitle: "Domain Adaptation"
-metaDescription: ""
-slug: "/docs/domain_adaptation"
-date: "2020-09-03"
-id: "domain_adaptationmd"
----
-
-# Domain Adaptation
-
-## Generalisation
-
-In our experience, language models trained on SQuAD show very strong general question answering capabilities.
-Though SQuAD is composed entirely of Wikipedia articles, these models are flexible enough to deal with many different styles of text.
-
-Before trying to adapt these models to your domain, we’d recommend trying one of the off the shelf models.
-We’ve found that these models are often flexible enough for a wide range of use cases.
-
-"}.
- Optionally: Include meta data via {"text": "",
- "meta":{"name": ", "author": "somebody", ...}}
- It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: Optional name of index where the documents shall be written to.
- If None, the DocumentStore's default index (self.index) will be used.
-- `batch_size`: Number of documents that are passed to bulk function at a time.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-**Returns**:
-
-None
-
-
-#### get\_all\_documents
-
-```python
- | @abstractmethod
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Get documents from the document store.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-
-
-#### get\_all\_labels\_aggregated
-
-```python
- | get_all_labels_aggregated(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, open_domain: bool = True, aggregate_by_meta: Optional[Union[str, list]] = None) -> List[MultiLabel]
-```
-
-Return all labels in the DocumentStore, aggregated into MultiLabel objects.
-This aggregation step helps, for example, if you collected multiple possible answers for one question and you
-want now all answers bundled together in one place for evaluation.
-How they are aggregated is defined by the open_domain and aggregate_by_meta parameters.
-If the questions are being asked to a single document (i.e. SQuAD style), you should set open_domain=False to aggregate by question and document.
-If the questions are being asked to your full collection of documents, you should set open_domain=True to aggregate just by question.
-If the questions are being asked to a subslice of your document set (e.g. product review use cases),
-you should set open_domain=True and populate aggregate_by_meta with the names of Label meta fields to aggregate by question and your custom meta fields.
-For example, in a product review use case, you might set aggregate_by_meta=["product_id"] so that Labels
-with the same question but different answers from different documents are aggregated into the one MultiLabel
-object, provided that they have the same product_id (to be found in Label.meta["product_id"])
-
-**Arguments**:
-
-- `index`: Name of the index to get the labels from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the labels to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `open_domain`: When True, labels are aggregated purely based on the question text alone.
- When False, labels are aggregated in a closed domain fashion based on the question text
- and also the id of the document that the label is tied to. In this setting, this function
- might return multiple MultiLabel objects with the same question string.
-- `aggregate_by_meta`: The names of the Label meta fields by which to aggregate. For example: ["product_id"]
-
-
-#### add\_eval\_data
-
-```python
- | add_eval_data(filename: str, doc_index: str = "eval_document", label_index: str = "label", batch_size: Optional[int] = None, preprocessor: Optional[PreProcessor] = None, max_docs: Union[int, bool] = None, open_domain: bool = False)
-```
-
-Adds a SQuAD-formatted file to the DocumentStore in order to be able to perform evaluation on it.
-If a jsonl file and a batch_size is passed to the function, documents are loaded batchwise
-from disk and also indexed batchwise to the DocumentStore in order to prevent out of memory errors.
-
-**Arguments**:
-
-- `filename`: Name of the file containing evaluation data (json or jsonl)
-- `doc_index`: Elasticsearch index where evaluation documents should be stored
-- `label_index`: Elasticsearch index where labeled questions should be stored
-- `batch_size`: Optional number of documents that are loaded and processed at a time.
- When set to None (default) all documents are processed at once.
-- `preprocessor`: Optional PreProcessor to preprocess evaluation documents.
- It can be used for splitting documents into passages (and assigning labels to corresponding passages).
- Currently the PreProcessor does not support split_by sentence, cleaning nor split_overlap != 0.
- When set to None (default) preprocessing is disabled.
-- `max_docs`: Optional number of documents that will be loaded.
- When set to None (default) all available eval documents are used.
-- `open_domain`: Set this to True if your file is an open domain dataset where two different answers to the
- same question might be found in different contexts.
-
-
-# Module: elasticsearch
-
-
-## Class: ElasticsearchDocumentStore
-
-```python
-class ElasticsearchDocumentStore(BaseDocumentStore)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(host: Union[str, List[str]] = "localhost", port: Union[int, List[int]] = 9200, username: str = "", password: str = "", api_key_id: Optional[str] = None, api_key: Optional[str] = None, aws4auth=None, index: str = "document", label_index: str = "label", search_fields: Union[str, list] = "text", text_field: str = "text", name_field: str = "name", embedding_field: str = "embedding", embedding_dim: int = 768, custom_mapping: Optional[dict] = None, excluded_meta_data: Optional[list] = None, faq_question_field: Optional[str] = None, analyzer: str = "standard", scheme: str = "http", ca_certs: Optional[str] = None, verify_certs: bool = True, create_index: bool = True, refresh_type: str = "wait_for", similarity="dot_product", timeout=30, return_embedding: bool = False, duplicate_documents: str = 'overwrite')
-```
-
-A DocumentStore using Elasticsearch to store and query the documents for our search.
-
- * Keeps all the logic to store and query documents from Elastic, incl. mapping of fields, adding filters or boosts to your queries, and storing embeddings
- * You can either use an existing Elasticsearch index or create a new one via haystack
- * Retrievers operate on top of this DocumentStore to find the relevant documents for a query
-
-**Arguments**:
-
-- `host`: url(s) of elasticsearch nodes
-- `port`: port(s) of elasticsearch nodes
-- `username`: username (standard authentication via http_auth)
-- `password`: password (standard authentication via http_auth)
-- `api_key_id`: ID of the API key (altenative authentication mode to the above http_auth)
-- `api_key`: Secret value of the API key (altenative authentication mode to the above http_auth)
-- `aws4auth`: Authentication for usage with aws elasticsearch (can be generated with the requests-aws4auth package)
-- `index`: Name of index in elasticsearch to use for storing the documents that we want to search. If not existing yet, we will create one.
-- `label_index`: Name of index in elasticsearch to use for storing labels. If not existing yet, we will create one.
-- `search_fields`: Name of fields used by ElasticsearchRetriever to find matches in the docs to our incoming query (using elastic's multi_match query), e.g. ["title", "full_text"]
-- `text_field`: Name of field that might contain the answer and will therefore be passed to the Reader Model (e.g. "full_text").
- If no Reader is used (e.g. in FAQ-Style QA) the plain content of this field will just be returned.
-- `name_field`: Name of field that contains the title of the the doc
-- `embedding_field`: Name of field containing an embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `embedding_dim`: Dimensionality of embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `custom_mapping`: If you want to use your own custom mapping for creating a new index in Elasticsearch, you can supply it here as a dictionary.
-- `analyzer`: Specify the default analyzer from one of the built-ins when creating a new Elasticsearch Index.
- Elasticsearch also has built-in analyzers for different languages (e.g. impacting tokenization). More info at:
- https://www.elastic.co/guide/en/elasticsearch/reference/7.9/analysis-analyzers.html
-- `excluded_meta_data`: Name of fields in Elasticsearch that should not be returned (e.g. [field_one, field_two]).
- Helpful if you have fields with long, irrelevant content that you don't want to display in results (e.g. embedding vectors).
-- `scheme`: 'https' or 'http', protocol used to connect to your elasticsearch instance
-- `ca_certs`: Root certificates for SSL: it is a path to certificate authority (CA) certs on disk. You can use certifi package with certifi.where() to find where the CA certs file is located in your machine.
-- `verify_certs`: Whether to be strict about ca certificates
-- `create_index`: Whether to try creating a new index (If the index of that name is already existing, we will just continue in any case
-- `refresh_type`: Type of ES refresh used to control when changes made by a request (e.g. bulk) are made visible to search.
- If set to 'wait_for', continue only after changes are visible (slow, but safe).
- If set to 'false', continue directly (fast, but sometimes unintuitive behaviour when docs are not immediately available after ingestion).
- More info at https://www.elastic.co/guide/en/elasticsearch/reference/6.8/docs-refresh.html
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
- more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
-- `timeout`: Number of seconds after which an ElasticSearch request times out.
-- `return_embedding`: To return document embedding
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index: Optional[str] = None) -> List[Document]
-```
-
-Fetch documents by specifying a list of text id strings
-
-
-#### get\_metadata\_values\_by\_key
-
-```python
- | get_metadata_values_by_key(key: str, query: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> List[dict]
-```
-
-Get values associated with a metadata key. The output is in the format:
- [{"value": "my-value-1", "count": 23}, {"value": "my-value-2", "count": 12}, ... ]
-
-**Arguments**:
-
-- `key`: the meta key name to get the values for.
-- `query`: narrow down the scope to documents matching the query string.
-- `filters`: narrow down the scope to documents that match the given filters.
-- `index`: Elasticsearch index where the meta values should be searched. If not supplied,
- self.index will be used.
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None)
-```
-
-Indexes documents for later queries in Elasticsearch.
-
-Behaviour if a document with the same ID already exists in ElasticSearch:
-a) (Default) Throw Elastic's standard error message for duplicate IDs.
-b) If `self.update_existing_documents=True` for DocumentStore: Overwrite existing documents.
-(This is only relevant if you pass your own ID when initializing a `Document`.
-If don't set custom IDs for your Documents or just pass a list of dictionaries here,
-they will automatically get UUIDs assigned. See the `Document` class for details)
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
- For documents as dictionaries, the format is {"text": ""}.
- Optionally: Include meta data via {"text": "",
- "meta":{"name": ", "author": "somebody", ...}}
- It can be used for filtering and is accessible in the responses of the Finder.
- Advanced: If you are using your own Elasticsearch mapping, the key names in the dictionary
- should be changed to what you have set for self.text_field and self.name_field.
-- `index`: Elasticsearch index where the documents should be indexed. If not supplied, self.index will be used.
-- `batch_size`: Number of documents that are passed to Elasticsearch's bulk function at a time.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-**Raises**:
-
-- `DuplicateDocumentError`: Exception trigger on duplicate document
-
-**Returns**:
-
-None
-
-
-#### write\_labels
-
-```python
- | write_labels(labels: Union[List[Label], List[dict]], index: Optional[str] = None, batch_size: int = 10_000)
-```
-
-Write annotation labels into document store.
-
-**Arguments**:
-
-- `labels`: A list of Python dictionaries or a list of Haystack Label objects.
-- `batch_size`: Number of labels that are passed to Elasticsearch's bulk function at a time.
-
-
-#### update\_document\_meta
-
-```python
- | update_document_meta(id: str, meta: Dict[str, str])
-```
-
-Update the metadata dictionary of a document by specifying its string id
-
-
-#### get\_document\_count
-
-```python
- | get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None, only_documents_without_embedding: bool = False) -> int
-```
-
-Return the number of documents in the document store.
-
-
-#### get\_label\_count
-
-```python
- | get_label_count(index: Optional[str] = None) -> int
-```
-
-Return the number of labels in the document store
-
-
-#### get\_embedding\_count
-
-```python
- | get_embedding_count(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None) -> int
-```
-
-Return the count of embeddings in the document store.
-
-
-#### get\_all\_documents
-
-```python
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> List[Document]
-```
-
-Get documents from the document store.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_documents\_generator
-
-```python
- | get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> Generator[Document, None, None]
-```
-
-Get documents from the document store. Under-the-hood, documents are fetched in batches from the
-document store and yielded as individual documents. This method can be used to iteratively process
-a large number of documents without having to load all documents in memory.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_labels
-
-```python
- | get_all_labels(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, batch_size: int = 10_000) -> List[Label]
-```
-
-Return all labels in the document store
-
-
-#### query
-
-```python
- | query(query: Optional[str], filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, custom_query: Optional[str] = None, index: Optional[str] = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query as defined by the BM25 algorithm.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.ndarray, filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: Index name for storing the docs and metadata
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### describe\_documents
-
-```python
- | describe_documents(index=None)
-```
-
-Return a summary of the documents in the document store
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever, index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, update_existing_embeddings: bool = True, batch_size: int = 10_000)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever to use to update the embeddings.
-- `index`: Index name to update
-- `update_existing_embeddings`: Whether to update existing embeddings of the documents. If set to False,
- only documents without embeddings are processed. This mode can be used for
- incremental updating of embeddings, wherein, only newly indexed documents
- get processed.
-- `filters`: Optional filters to narrow down the documents for which embeddings are to be updated.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-**Returns**:
-
-None
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-#### delete\_documents
-
-```python
- | delete_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-## Class: OpenDistroElasticsearchDocumentStore
-
-```python
-class OpenDistroElasticsearchDocumentStore(ElasticsearchDocumentStore)
-```
-
-Document Store using the Open Distro for Elasticsearch. It is compatible with the AWS Elasticsearch Service.
-
-In addition to native Elasticsearch query & filtering, it provides efficient vector similarity search using
-the KNN plugin that can scale to a large number of documents.
-
-
-# Module: memory
-
-
-## Class: InMemoryDocumentStore
-
-```python
-class InMemoryDocumentStore(BaseDocumentStore)
-```
-
-In-memory document store
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(index: str = "document", label_index: str = "label", embedding_field: Optional[str] = "embedding", embedding_dim: int = 768, return_embedding: bool = False, similarity: str = "dot_product", progress_bar: bool = True, duplicate_documents: str = 'overwrite')
-```
-
-**Arguments**:
-
-- `index`: The documents are scoped to an index attribute that can be used when writing, querying,
- or deleting documents. This parameter sets the default value for document index.
-- `label_index`: The default value of index attribute for the labels.
-- `embedding_field`: Name of field containing an embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `embedding_dim`: The size of the embedding vector.
-- `return_embedding`: To return document embedding
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
- more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
-- `progress_bar`: Whether to show a tqdm progress bar or not.
- Can be helpful to disable in production deployments to keep the logs clean.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, duplicate_documents: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
- For documents as dictionaries, the format is {"text": ""}.
- Optionally: Include meta data via {"text": "",
- "meta": {"name": ", "author": "somebody", ...}}
- It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: write documents to a custom namespace. For instance, documents for evaluation can be indexed in a
- separate index than the documents for search.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-**Raises**:
-
-- `DuplicateDocumentError`: Exception trigger on duplicate document
-
-**Returns**:
-
-None
-
-
-#### write\_labels
-
-```python
- | write_labels(labels: Union[List[dict], List[Label]], index: Optional[str] = None)
-```
-
-Write annotation labels into document store.
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index: Optional[str] = None) -> List[Document]
-```
-
-Fetch documents by specifying a list of text id strings
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.ndarray, filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: Index name for storing the docs and metadata
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, update_existing_embeddings: bool = True, batch_size: int = 10_000)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever to use to get embeddings for text
-- `index`: Index name for which embeddings are to be updated. If set to None, the default self.index is used.
-- `update_existing_embeddings`: Whether to update existing embeddings of the documents. If set to False,
- only documents without embeddings are processed. This mode can be used for
- incremental updating of embeddings, wherein, only newly indexed documents
- get processed.
-- `filters`: Optional filters to narrow down the documents for which embeddings are to be updated.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-**Returns**:
-
-None
-
-
-#### get\_document\_count
-
-```python
- | get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
-```
-
-Return the number of documents in the document store.
-
-
-#### get\_embedding\_count
-
-```python
- | get_embedding_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
-```
-
-Return the count of embeddings in the document store.
-
-
-#### get\_label\_count
-
-```python
- | get_label_count(index: Optional[str] = None) -> int
-```
-
-Return the number of labels in the document store
-
-
-#### get\_all\_documents\_generator
-
-```python
- | get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> Generator[Document, None, None]
-```
-
-Get all documents from the document store. The methods returns a Python Generator that yields individual
-documents.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-
-
-#### get\_all\_labels
-
-```python
- | get_all_labels(index: str = None, filters: Optional[Dict[str, List[str]]] = None) -> List[Label]
-```
-
-Return all labels in the document store
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-#### delete\_documents
-
-```python
- | delete_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-# Module: sql
-
-
-## Class: SQLDocumentStore
-
-```python
-class SQLDocumentStore(BaseDocumentStore)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(url: str = "sqlite://", index: str = "document", label_index: str = "label", duplicate_documents: str = "overwrite")
-```
-
-An SQL backed DocumentStore. Currently supports SQLite, PostgreSQL and MySQL backends.
-
-**Arguments**:
-
-- `url`: URL for SQL database as expected by SQLAlchemy. More info here: https://docs.sqlalchemy.org/en/13/core/engines.html#database-urls
-- `index`: The documents are scoped to an index attribute that can be used when writing, querying, or deleting documents.
- This parameter sets the default value for document index.
-- `label_index`: The default value of index attribute for the labels.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index: Optional[str] = None, batch_size: int = 10_000) -> List[Document]
-```
-
-Fetch documents by specifying a list of text id strings
-
-
-#### get\_documents\_by\_vector\_ids
-
-```python
- | get_documents_by_vector_ids(vector_ids: List[str], index: Optional[str] = None, batch_size: int = 10_000)
-```
-
-Fetch documents by specifying a list of text vector id strings
-
-
-#### get\_all\_documents\_generator
-
-```python
- | get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> Generator[Document, None, None]
-```
-
-Get documents from the document store. Under-the-hood, documents are fetched in batches from the
-document store and yielded as individual documents. This method can be used to iteratively process
-a large number of documents without having to load all documents in memory.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_labels
-
-```python
- | get_all_labels(index=None, filters: Optional[dict] = None)
-```
-
-Return all labels in the document store
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
- For documents as dictionaries, the format is {"text": ""}.
- Optionally: Include meta data via {"text": "",
- "meta":{"name": ", "author": "somebody", ...}}
- It can be used for filtering and is accessible in the responses of the Finder.
-- `index`: add an optional index attribute to documents. It can be later used for filtering. For instance,
- documents for evaluation can be indexed in a separate index than the documents for search.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-**Returns**:
-
-None
-
-
-#### write\_labels
-
-```python
- | write_labels(labels, index=None)
-```
-
-Write annotation labels into document store.
-
-
-#### update\_vector\_ids
-
-```python
- | update_vector_ids(vector_id_map: Dict[str, str], index: Optional[str] = None, batch_size: int = 10_000)
-```
-
-Update vector_ids for given document_ids.
-
-**Arguments**:
-
-- `vector_id_map`: dict containing mapping of document_id -> vector_id.
-- `index`: filter documents by the optional index attribute for documents in database.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### reset\_vector\_ids
-
-```python
- | reset_vector_ids(index: Optional[str] = None)
-```
-
-Set vector IDs for all documents as None
-
-
-#### update\_document\_meta
-
-```python
- | update_document_meta(id: str, meta: Dict[str, str])
-```
-
-Update the metadata dictionary of a document by specifying its string id
-
-
-#### get\_document\_count
-
-```python
- | get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
-```
-
-Return the number of documents in the document store.
-
-
-#### get\_label\_count
-
-```python
- | get_label_count(index: Optional[str] = None) -> int
-```
-
-Return the number of labels in the document store
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-#### delete\_documents
-
-```python
- | delete_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
-
-
-# Module: faiss
-
-
-## Class: FAISSDocumentStore
-
-```python
-class FAISSDocumentStore(SQLDocumentStore)
-```
-
-Document store for very large scale embedding based dense retrievers like the DPR.
-
-It implements the FAISS library(https://github.com/facebookresearch/faiss)
-to perform similarity search on vectors.
-
-The document text and meta-data (for filtering) are stored using the SQLDocumentStore, while
-the vector embeddings are indexed in a FAISS Index.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(sql_url: str = "sqlite:///", vector_dim: int = 768, faiss_index_factory_str: str = "Flat", faiss_index: Optional["faiss.swigfaiss.Index"] = None, return_embedding: bool = False, index: str = "document", similarity: str = "dot_product", embedding_field: str = "embedding", progress_bar: bool = True, duplicate_documents: str = 'overwrite', **kwargs, ,)
-```
-
-**Arguments**:
-
-- `sql_url`: SQL connection URL for database. It defaults to local file based SQLite DB. For large scale
- deployment, Postgres is recommended.
-- `vector_dim`: the embedding vector size.
-- `faiss_index_factory_str`: Create a new FAISS index of the specified type.
- The type is determined from the given string following the conventions
- of the original FAISS index factory.
- Recommended options:
- - "Flat" (default): Best accuracy (= exact). Becomes slow and RAM intense for > 1 Mio docs.
- - "HNSW": Graph-based heuristic. If not further specified,
- we use the following config:
- HNSW64, efConstruction=80 and efSearch=20
- - "IVFx,Flat": Inverted Index. Replace x with the number of centroids aka nlist.
- Rule of thumb: nlist = 10 * sqrt (num_docs) is a good starting point.
- For more details see:
- - Overview of indices https://github.com/facebookresearch/faiss/wiki/Faiss-indexes
- - Guideline for choosing an index https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
- - FAISS Index factory https://github.com/facebookresearch/faiss/wiki/The-index-factory
- Benchmarks: XXX
-- `faiss_index`: Pass an existing FAISS Index, i.e. an empty one that you configured manually
- or one with docs that you used in Haystack before and want to load again.
-- `return_embedding`: To return document embedding
-- `index`: Name of index in document store to use.
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
- more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
-- `embedding_field`: Name of field containing an embedding vector.
-- `progress_bar`: Whether to show a tqdm progress bar or not.
- Can be helpful to disable in production deployments to keep the logs clean.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None)
-```
-
-Add new documents to the DocumentStore.
-
-**Arguments**:
-
-- `documents`: List of `Dicts` or List of `Documents`. If they already contain the embeddings, we'll index
- them right away in FAISS. If not, you can later call update_embeddings() to create & index them.
-- `index`: (SQL) index name for storing the docs and metadata
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-**Raises**:
-
-- `DuplicateDocumentError`: Exception trigger on duplicate document
-
-**Returns**:
-
-
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None, update_existing_embeddings: bool = True, filters: Optional[Dict[str, List[str]]] = None, batch_size: int = 10_000)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever to use to get embeddings for text
-- `index`: Index name for which embeddings are to be updated. If set to None, the default self.index is used.
-- `update_existing_embeddings`: Whether to update existing embeddings of the documents. If set to False,
- only documents without embeddings are processed. This mode can be used for
- incremental updating of embeddings, wherein, only newly indexed documents
- get processed.
-- `filters`: Optional filters to narrow down the documents for which embeddings are to be updated.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-**Returns**:
-
-None
-
-
-#### get\_all\_documents\_generator
-
-```python
- | get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> Generator[Document, None, None]
-```
-
-Get all documents from the document store. Under-the-hood, documents are fetched in batches from the
-document store and yielded as individual documents. This method can be used to iteratively process
-a large number of documents without having to load all documents in memory.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_embedding\_count
-
-```python
- | get_embedding_count(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None) -> int
-```
-
-Return the count of embeddings in the document store.
-
-
-#### train\_index
-
-```python
- | train_index(documents: Optional[Union[List[dict], List[Document]]], embeddings: Optional[np.ndarray] = None, index: Optional[str] = None)
-```
-
-Some FAISS indices (e.g. IVF) require initial "training" on a sample of vectors before you can add your final vectors.
-The train vectors should come from the same distribution as your final ones.
-You can pass either documents (incl. embeddings) or just the plain embeddings that the index shall be trained on.
-
-**Arguments**:
-
-- `documents`: Documents (incl. the embeddings)
-- `embeddings`: Plain embeddings
-- `index`: Name of the index to train. If None, the DocumentStore's default index (self.index) will be used.
-
-**Returns**:
-
-None
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete all documents from the document store.
-
-
-#### delete\_documents
-
-```python
- | delete_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete all documents from the document store.
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.ndarray, filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: Index name to query the document from.
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### save
-
-```python
- | save(file_path: Union[str, Path])
-```
-
-Save FAISS Index to the specified file.
-
-**Arguments**:
-
-- `file_path`: Path to save to.
-
-**Returns**:
-
-None
-
-
-#### load
-
-```python
- | @classmethod
- | load(cls, faiss_file_path: Union[str, Path], sql_url: str, index: str)
-```
-
-Load a saved FAISS index from a file and connect to the SQL database.
-Note: In order to have a correct mapping from FAISS to SQL,
- make sure to use the same SQL DB that you used when calling `save()`.
-
-**Arguments**:
-
-- `faiss_file_path`: Stored FAISS index file. Can be created via calling `save()`
-- `sql_url`: Connection string to the SQL database that contains your docs and metadata.
-- `index`: Index name to load the FAISS index as. It must match the index name used for
- when creating the FAISS index.
-
-**Returns**:
-
-
-
-
-# Module: milvus
-
-
-## Class: MilvusDocumentStore
-
-```python
-class MilvusDocumentStore(SQLDocumentStore)
-```
-
-Milvus (https://milvus.io/) is a highly reliable, scalable Document Store specialized on storing and processing vectors.
-Therefore, it is particularly suited for Haystack users that work with dense retrieval methods (like DPR).
-In contrast to FAISS, Milvus ...
- - runs as a separate service (e.g. a Docker container) and can scale easily in a distributed environment
- - allows dynamic data management (i.e. you can insert/delete vectors without recreating the whole index)
- - encapsulates multiple ANN libraries (FAISS, ANNOY ...)
-
-This class uses Milvus for all vector related storage, processing and querying.
-The meta-data (e.g. for filtering) and the document text are however stored in a separate SQL Database as Milvus
-does not allow these data types (yet).
-
-Usage:
-1. Start a Milvus server (see https://milvus.io/docs/v1.0.0/install_milvus.md)
-2. Init a MilvusDocumentStore in Haystack
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(sql_url: str = "sqlite:///", milvus_url: str = "tcp://localhost:19530", connection_pool: str = "SingletonThread", index: str = "document", vector_dim: int = 768, index_file_size: int = 1024, similarity: str = "dot_product", index_type: IndexType = IndexType.FLAT, index_param: Optional[Dict[str, Any]] = None, search_param: Optional[Dict[str, Any]] = None, return_embedding: bool = False, embedding_field: str = "embedding", progress_bar: bool = True, duplicate_documents: str = 'overwrite', **kwargs, ,)
-```
-
-**Arguments**:
-
-- `sql_url`: SQL connection URL for storing document texts and metadata. It defaults to a local, file based SQLite DB. For large scale
- deployment, Postgres is recommended. If using MySQL then same server can also be used for
- Milvus metadata. For more details see https://milvus.io/docs/v1.0.0/data_manage.md.
-- `milvus_url`: Milvus server connection URL for storing and processing vectors.
- Protocol, host and port will automatically be inferred from the URL.
- See https://milvus.io/docs/v1.0.0/install_milvus.md for instructions to start a Milvus instance.
-- `connection_pool`: Connection pool type to connect with Milvus server. Default: "SingletonThread".
-- `index`: Index name for text, embedding and metadata (in Milvus terms, this is the "collection name").
-- `vector_dim`: The embedding vector size. Default: 768.
-- `index_file_size`: Specifies the size of each segment file that is stored by Milvus and its default value is 1024 MB.
-When the size of newly inserted vectors reaches the specified volume, Milvus packs these vectors into a new segment.
-Milvus creates one index file for each segment. When conducting a vector search, Milvus searches all index files one by one.
-As a rule of thumb, we would see a 30% ~ 50% increase in the search performance after changing the value of index_file_size from 1024 to 2048.
-Note that an overly large index_file_size value may cause failure to load a segment into the memory or graphics memory.
-(From https://milvus.io/docs/v1.0.0/performance_faq.md#How-can-I-get-the-best-performance-from-Milvus-through-setting-index_file_size)
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default and recommended for DPR embeddings.
- 'cosine' is recommended for Sentence Transformers, but is not directly supported by Milvus.
- However, you can normalize your embeddings and use `dot_product` to get the same results.
- See https://milvus.io/docs/v1.0.0/metric.md?Inner-product-(IP)`floating`.
-- `index_type`: Type of approximate nearest neighbour (ANN) index used. The choice here determines your tradeoff between speed and accuracy.
- Some popular options:
- - FLAT (default): Exact method, slow
- - IVF_FLAT, inverted file based heuristic, fast
- - HSNW: Graph based, fast
- - ANNOY: Tree based, fast
- See: https://milvus.io/docs/v1.0.0/index.md
-- `index_param`: Configuration parameters for the chose index_type needed at indexing time.
- For example: {"nlist": 16384} as the number of cluster units to create for index_type IVF_FLAT.
- See https://milvus.io/docs/v1.0.0/index.md
-- `search_param`: Configuration parameters for the chose index_type needed at query time
- For example: {"nprobe": 10} as the number of cluster units to query for index_type IVF_FLAT.
- See https://milvus.io/docs/v1.0.0/index.md
-- `return_embedding`: To return document embedding.
-- `embedding_field`: Name of field containing an embedding vector.
-- `progress_bar`: Whether to show a tqdm progress bar or not.
- Can be helpful to disable in production deployments to keep the logs clean.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None, index_param: Optional[Dict[str, Any]] = None)
-```
-
-Add new documents to the DocumentStore.
-
-**Arguments**:
-
-- `documents`: List of `Dicts` or List of `Documents`. If they already contain the embeddings, we'll index
- them right away in Milvus. If not, you can later call update_embeddings() to create & index them.
-- `index`: (SQL) index name for storing the docs and metadata
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-**Raises**:
-
-- `DuplicateDocumentError`: Exception trigger on duplicate document
-
-**Returns**:
-
-
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever: BaseRetriever, index: Optional[str] = None, batch_size: int = 10_000, update_existing_embeddings: bool = True, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever to use to get embeddings for text
-- `index`: (SQL) index name for storing the docs and metadata
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-- `update_existing_embeddings`: Whether to update existing embeddings of the documents. If set to False,
- only documents without embeddings are processed. This mode can be used for
- incremental updating of embeddings, wherein, only newly indexed documents
- get processed.
-- `filters`: Optional filters to narrow down the documents for which embeddings are to be updated.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-
-**Returns**:
-
-None
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.ndarray, filters: Optional[dict] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: (SQL) index name for storing the docs and metadata
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete all documents (from SQL AND Milvus).
-
-**Arguments**:
-
-- `index`: (SQL) index name for storing the docs and metadata
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-
-**Returns**:
-
-None
-
-
-#### delete\_documents
-
-```python
- | delete_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete all documents (from SQL AND Milvus).
-
-**Arguments**:
-
-- `index`: (SQL) index name for storing the docs and metadata
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-
-**Returns**:
-
-None
-
-
-#### get\_all\_documents\_generator
-
-```python
- | get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> Generator[Document, None, None]
-```
-
-Get all documents from the document store. Under-the-hood, documents are fetched in batches from the
-document store and yielded as individual documents. This method can be used to iteratively process
-a large number of documents without having to load all documents in memory.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_documents
-
-```python
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> List[Document]
-```
-
-Get documents from the document store (optionally using filter criteria).
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-**Arguments**:
-
-- `id`: ID of the document
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index: Optional[str] = None, batch_size: int = 10_000) -> List[Document]
-```
-
-Fetch multiple documents by specifying their IDs (strings)
-
-**Arguments**:
-
-- `ids`: List of IDs of the documents
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_vectors
-
-```python
- | get_all_vectors(index: Optional[str] = None) -> List[np.ndarray]
-```
-
-Helper function to dump all vectors stored in Milvus server.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-
-**Returns**:
-
-List[np.array]: List of vectors.
-
-
-#### get\_embedding\_count
-
-```python
- | get_embedding_count(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None) -> int
-```
-
-Return the count of embeddings in the document store.
-
-
-# Module: weaviate
-
-
-## Class: WeaviateDocumentStore
-
-```python
-class WeaviateDocumentStore(BaseDocumentStore)
-```
-
-Weaviate is a cloud-native, modular, real-time vector search engine built to scale your machine learning models.
-(See https://www.semi.technology/developers/weaviate/current/index.html#what-is-weaviate)
-
-Some of the key differences in contrast to FAISS & Milvus:
-1. Stores everything in one place: documents, meta data and vectors - so less network overhead when scaling this up
-2. Allows combination of vector search and scalar filtering, i.e. you can filter for a certain tag and do dense retrieval on that subset
-3. Has less variety of ANN algorithms, as of now only HNSW.
-
-Weaviate python client is used to connect to the server, more details are here
-https://weaviate-python-client.readthedocs.io/en/docs/weaviate.html
-
-Usage:
-1. Start a Weaviate server (see https://www.semi.technology/developers/weaviate/current/getting-started/installation.html)
-2. Init a WeaviateDocumentStore in Haystack
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(host: Union[str, List[str]] = "http://localhost", port: Union[int, List[int]] = 8080, timeout_config: tuple = (5, 15), username: str = None, password: str = None, index: str = "Document", embedding_dim: int = 768, text_field: str = "text", name_field: str = "name", faq_question_field="question", similarity: str = "dot_product", index_type: str = "hnsw", custom_schema: Optional[dict] = None, return_embedding: bool = False, embedding_field: str = "embedding", progress_bar: bool = True, duplicate_documents: str = 'overwrite', **kwargs, ,)
-```
-
-**Arguments**:
-
-- `host`: Weaviate server connection URL for storing and processing documents and vectors.
- For more details, refer "https://www.semi.technology/developers/weaviate/current/getting-started/installation.html"
-- `port`: port of Weaviate instance
-- `timeout_config`: Weaviate Timeout config as a tuple of (retries, time out seconds).
-- `username`: username (standard authentication via http_auth)
-- `password`: password (standard authentication via http_auth)
-- `index`: Index name for document text, embedding and metadata (in Weaviate terminology, this is a "Class" in Weaviate schema).
-- `embedding_dim`: The embedding vector size. Default: 768.
-- `text_field`: Name of field that might contain the answer and will therefore be passed to the Reader Model (e.g. "full_text").
- If no Reader is used (e.g. in FAQ-Style QA) the plain content of this field will just be returned.
-- `name_field`: Name of field that contains the title of the the doc
-- `faq_question_field`: Name of field containing the question in case of FAQ-Style QA
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default.
-- `index_type`: Index type of any vector object defined in weaviate schema. The vector index type is pluggable.
- Currently, HSNW is only supported.
- See: https://www.semi.technology/developers/weaviate/current/more-resources/performance.html
-- `custom_schema`: Allows to create custom schema in Weaviate, for more details
- See https://www.semi.technology/developers/weaviate/current/data-schema/schema-configuration.html
-- `module_name`: Vectorization module to convert data into vectors. Default is "text2vec-trasnformers"
- For more details, See https://www.semi.technology/developers/weaviate/current/modules/
-- `return_embedding`: To return document embedding.
-- `embedding_field`: Name of field containing an embedding vector.
-- `progress_bar`: Whether to show a tqdm progress bar or not.
- Can be helpful to disable in production deployments to keep the logs clean.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already exists.
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index: Optional[str] = None, batch_size: int = 10_000) -> List[Document]
-```
-
-Fetch documents by specifying a list of text id strings
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None)
-```
-
-Add new documents to the DocumentStore.
-
-**Arguments**:
-
-- `documents`: List of `Dicts` or List of `Documents`. Passing an Embedding/Vector is mandatory in case weaviate is not
- configured with a module. If a module is configured, the embedding is automatically generated by Weaviate.
-- `index`: index name for storing the docs and metadata
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-- `duplicate_documents`: Handle duplicates document based on parameter options.
- Parameter options : ( 'skip','overwrite','fail')
- skip: Ignore the duplicates documents
- overwrite: Update any existing documents with the same ID when adding documents.
- fail: an error is raised if the document ID of the document being added already
- exists.
-
-**Raises**:
-
-- `DuplicateDocumentError`: Exception trigger on duplicate document
-
-**Returns**:
-
-None
-
-
-#### update\_document\_meta
-
-```python
- | update_document_meta(id: str, meta: Dict[str, str])
-```
-
-Update the metadata dictionary of a document by specifying its string id
-
-
-#### get\_document\_count
-
-```python
- | get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
-```
-
-Return the number of documents in the document store.
-
-
-#### get\_all\_documents
-
-```python
- | get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> List[Document]
-```
-
-Get documents from the document store.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### get\_all\_documents\_generator
-
-```python
- | get_all_documents_generator(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None, batch_size: int = 10_000) -> Generator[Document, None, None]
-```
-
-Get documents from the document store. Under-the-hood, documents are fetched in batches from the
-document store and yielded as individual documents. This method can be used to iteratively process
-a large number of documents without having to load all documents in memory.
-
-**Arguments**:
-
-- `index`: Name of the index to get the documents from. If None, the
- DocumentStore's default index (self.index) will be used.
-- `filters`: Optional filters to narrow down the documents to return.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `return_embedding`: Whether to return the document embeddings.
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-
-#### query
-
-```python
- | query(query: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, custom_query: Optional[str] = None, index: Optional[str] = None) -> List[Document]
-```
-
-Scan through documents in DocumentStore and return a small number documents
-that are most relevant to the query as defined by Weaviate semantic search.
-
-**Arguments**:
-
-- `query`: The query
-- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
-- `top_k`: How many documents to return per query.
-- `custom_query`: Custom query that will executed using query.raw method, for more details refer
- https://www.semi.technology/developers/weaviate/current/graphql-references/filters.html
-- `index`: The name of the index in the DocumentStore from which to retrieve documents
-
-
-#### query\_by\_embedding
-
-```python
- | query_by_embedding(query_emb: np.ndarray, filters: Optional[dict] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
-```
-
-Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
-
-**Arguments**:
-
-- `query_emb`: Embedding of the query (e.g. gathered from DPR)
-- `filters`: Optional filters to narrow down the search space.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `top_k`: How many documents to return
-- `index`: index name for storing the docs and metadata
-- `return_embedding`: To return document embedding
-
-**Returns**:
-
-
-
-
-#### update\_embeddings
-
-```python
- | update_embeddings(retriever, index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, update_existing_embeddings: bool = True, batch_size: int = 10_000)
-```
-
-Updates the embeddings in the the document store using the encoding model specified in the retriever.
-This can be useful if want to change the embeddings for your documents (e.g. after changing the retriever config).
-
-**Arguments**:
-
-- `retriever`: Retriever to use to update the embeddings.
-- `index`: Index name to update
-- `update_existing_embeddings`: Weaviate mandates an embedding while creating the document itself.
-This option must be always true for weaviate and it will update the embeddings for all the documents.
-- `filters`: Optional filters to narrow down the documents for which embeddings are to be updated.
- Example: {"name": ["some", "more"], "category": ["only_one"]}
-- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
-
-**Returns**:
-
-None
-
-
-#### delete\_all\_documents
-
-```python
- | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
-```
-
-Delete documents in an index. All documents are deleted if no filters are passed.
-
-**Arguments**:
-
-- `index`: Index name to delete the document from.
-- `filters`: Optional filters to narrow down the documents to be deleted.
-
-**Returns**:
-
-None
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/api/api/evaluation.md b/src/pages/docs/versions/master/v0.9.0/site/en/api/api/evaluation.md
deleted file mode 100644
index 6dce91425..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/api/api/evaluation.md
+++ /dev/null
@@ -1,104 +0,0 @@
----
-title: "Evaluation"
-metaTitle: "Evaluation"
-metaDescription: ""
-slug: "/docs/evaluationmd"
-date: "2020-09-03"
-id: "apievaluationmd"
----
-
-# Evaluation
-
-
-# Module: eval
-
-
-## Class: EvalDocuments
-
-```python
-class EvalDocuments()
-```
-
-This is a pipeline node that should be placed after a node that returns a List of Document, e.g., Retriever or
-Ranker, in order to assess its performance. Performance metrics are stored in this class and updated as each
-sample passes through it. To view the results of the evaluation, call EvalDocuments.print(). Note that results
-from this Node may differ from that when calling Retriever.eval() since that is a closed domain evaluation. Have
-a look at our evaluation tutorial for more info about open vs closed domain eval (
-https://haystack.deepset.ai/docs/latest/tutorial5md).
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(debug: bool = False, open_domain: bool = True, top_k_eval_documents: int = 10, name="EvalDocuments")
-```
-
-**Arguments**:
-
-- `open_domain`: When True, a document is considered correctly retrieved so long as the answer string can be found within it.
- When False, correct retrieval is evaluated based on document_id.
-- `debug`: When True, a record of each sample and its evaluation will be stored in EvalDocuments.log
-- `top_k`: calculate eval metrics for top k results, e.g., recall@k
-
-
-#### run
-
-```python
- | run(documents, labels: dict, top_k_eval_documents: Optional[int] = None, **kwargs)
-```
-
-Run this node on one sample and its labels
-
-
-#### print
-
-```python
- | print()
-```
-
-Print the evaluation results
-
-
-## Class: EvalAnswers
-
-```python
-class EvalAnswers()
-```
-
-This is a pipeline node that should be placed after a Reader in order to assess the performance of the Reader
-individually or to assess the extractive QA performance of the whole pipeline. Performance metrics are stored in
-this class and updated as each sample passes through it. To view the results of the evaluation, call EvalAnswers.print().
-Note that results from this Node may differ from that when calling Reader.eval()
-since that is a closed domain evaluation. Have a look at our evaluation tutorial for more info about
-open vs closed domain eval (https://haystack.deepset.ai/docs/latest/tutorial5md).
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(skip_incorrect_retrieval: bool = True, open_domain: bool = True, debug: bool = False)
-```
-
-**Arguments**:
-
-- `skip_incorrect_retrieval`: When set to True, this eval will ignore the cases where the retriever returned no correct documents
-- `open_domain`: When True, extracted answers are evaluated purely on string similarity rather than the position of the extracted answer
-- `debug`: When True, a record of each sample and its evaluation will be stored in EvalAnswers.log
-
-
-#### run
-
-```python
- | run(labels, answers, **kwargs)
-```
-
-Run this node on one sample and its labels
-
-
-#### print
-
-```python
- | print(mode)
-```
-
-Print the evaluation results
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/api/api/file_converter.md b/src/pages/docs/versions/master/v0.9.0/site/en/api/api/file_converter.md
deleted file mode 100644
index 16b6c0eca..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/api/api/file_converter.md
+++ /dev/null
@@ -1,284 +0,0 @@
----
-title: "File Converters"
-metaTitle: "File Converters"
-metaDescription: ""
-slug: "/docs/file_converters"
-date: "2020-09-03"
-id: "file_convertersmd"
----
-
-# File Converters
-
-
-# Module: base
-
-
-## Class: BaseConverter
-
-```python
-class BaseConverter(BaseComponent)
-```
-
-Base class for implementing file converts to transform input documents to text format for ingestion in DocumentStore.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(remove_numeric_tables: bool = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-
-
-#### convert
-
-```python
- | @abstractmethod
- | convert(file_path: Path, meta: Optional[Dict[str, str]], remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "utf-8") -> Dict[str, Any]
-```
-
-Convert a file to a dictionary containing the text and any associated meta data.
-
-File converters may extract file meta like name or size. In addition to it, user
-supplied meta data like author, url, external IDs can be supplied as a dictionary.
-
-**Arguments**:
-
-- `file_path`: path of the file to convert
-- `meta`: dictionary of meta data key-value pairs to append in the returned document.
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-- `encoding`: Select the file encoding (default is `utf-8`)
-
-
-#### validate\_language
-
-```python
- | validate_language(text: str) -> bool
-```
-
-Validate if the language of the text is one of valid languages.
-
-
-## Class: FileTypeClassifier
-
-```python
-class FileTypeClassifier(BaseComponent)
-```
-
-Route files in an Indexing Pipeline to corresponding file converters.
-
-
-# Module: txt
-
-
-## Class: TextConverter
-
-```python
-class TextConverter(BaseConverter)
-```
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "utf-8") -> Dict[str, Any]
-```
-
-Reads text from a txt file and executes optional preprocessing steps.
-
-**Arguments**:
-
-- `file_path`: path of the file to convert
-- `meta`: dictionary of meta data key-value pairs to append in the returned document.
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-- `encoding`: Select the file encoding (default is `utf-8`)
-
-**Returns**:
-
-Dict of format {"text": "The text from file", "meta": meta}}
-
-
-# Module: docx
-
-
-## Class: DocxToTextConverter
-
-```python
-class DocxToTextConverter(BaseConverter)
-```
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = None) -> Dict[str, Any]
-```
-
-Extract text from a .docx file.
-Note: As docx doesn't contain "page" information, we actually extract and return a list of paragraphs here.
-For compliance with other converters we nevertheless opted for keeping the methods name.
-
-**Arguments**:
-
-- `file_path`: Path to the .docx file you want to convert
-- `meta`: dictionary of meta data key-value pairs to append in the returned document.
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-- `encoding`: Not applicable
-
-
-# Module: tika
-
-
-## Class: TikaConverter
-
-```python
-class TikaConverter(BaseConverter)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(tika_url: str = "http://localhost:9998/tika", remove_numeric_tables: bool = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `tika_url`: URL of the Tika server
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = None) -> Dict[str, Any]
-```
-
-**Arguments**:
-
-- `file_path`: path of the file to convert
-- `meta`: dictionary of meta data key-value pairs to append in the returned document.
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-- `encoding`: Not applicable
-
-**Returns**:
-
-a list of pages and the extracted meta data of the file.
-
-
-# Module: pdf
-
-
-## Class: PDFToTextConverter
-
-```python
-class PDFToTextConverter(BaseConverter)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(remove_numeric_tables: bool = False, valid_languages: Optional[List[str]] = None)
-```
-
-**Arguments**:
-
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-
-
-#### convert
-
-```python
- | convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "Latin1") -> Dict[str, Any]
-```
-
-Extract text from a .pdf file using the pdftotext library (https://www.xpdfreader.com/pdftotext-man.html)
-
-**Arguments**:
-
-- `file_path`: Path to the .pdf file you want to convert
-- `meta`: Optional dictionary with metadata that shall be attached to all resulting documents.
- Can be any custom keys and values.
-- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
- The tabular structures in documents might be noise for the reader model if it
- does not have table parsing capability for finding answers. However, tables
- may also have long strings that could possible candidate for searching answers.
- The rows containing strings are thus retained in this option.
-- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
- (https://en.wikipedia.org/wiki/ISO_639-1) format.
- This option can be used to add test for encoding errors. If the extracted text is
- not one of the valid languages, then it might likely be encoding error resulting
- in garbled text.
-- `encoding`: Encoding that will be passed as -enc parameter to pdftotext. "Latin 1" is the default encoding
- of pdftotext. While this works well on many PDFs, it might be needed to switch to "UTF-8" or
- others if your doc contains special characters (e.g. German Umlauts, Cyrillic characters ...).
- Note: With "UTF-8" we experienced cases, where a simple "fi" gets wrongly parsed as
- "xefc" (see test cases). That's why we keep "Latin 1" as default here.
- (See list of available encodings by running `pdftotext -listencodings` in the terminal)
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/api/api/generator.md b/src/pages/docs/versions/master/v0.9.0/site/en/api/api/generator.md
deleted file mode 100644
index cfd5361be..000000000
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----
-title: "Generator"
-metaTitle: "Generator"
-metaDescription: ""
-slug: "/docs/apigeneratormd"
-date: "2020-09-03"
-id: "apigeneratormd"
----
-
-# Generator
-
-
-# Module: base
-
-
-## Class: BaseGenerator
-
-```python
-class BaseGenerator(BaseComponent)
-```
-
-Abstract class for Generators
-
-
-#### predict
-
-```python
- | @abstractmethod
- | predict(query: str, documents: List[Document], top_k: Optional[int]) -> Dict
-```
-
-Abstract method to generate answers.
-
-**Arguments**:
-
-- `query`: Query
-- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on.
-- `top_k`: Number of returned answers
-
-**Returns**:
-
-Generated answers plus additional infos in a dict
-
-
-# Module: transformers
-
-
-## Class: RAGenerator
-
-```python
-class RAGenerator(BaseGenerator)
-```
-
-Implementation of Facebook's Retrieval-Augmented Generator (https://arxiv.org/abs/2005.11401) based on
-HuggingFace's transformers (https://huggingface.co/transformers/model_doc/rag.html).
-
-Instead of "finding" the answer within a document, these models **generate** the answer.
-In that sense, RAG follows a similar approach as GPT-3 but it comes with two huge advantages
-for real-world applications:
-a) it has a manageable model size
-b) the answer generation is conditioned on retrieved documents,
-i.e. the model can easily adjust to domain documents even after training has finished
-(in contrast: GPT-3 relies on the web data seen during training)
-
-**Example**
-
-```python
-| query = "who got the first nobel prize in physics?"
-|
-| # Retrieve related documents from retriever
-| retrieved_docs = retriever.retrieve(query=query)
-|
-| # Now generate answer from query and retrieved documents
-| generator.predict(
-| query=query,
-| documents=retrieved_docs,
-| top_k=1
-| )
-|
-| # Answer
-|
-| {'query': 'who got the first nobel prize in physics',
-| 'answers':
-| [{'query': 'who got the first nobel prize in physics',
-| 'answer': ' albert einstein',
-| 'meta': { 'doc_ids': [...],
-| 'doc_scores': [80.42758 ...],
-| 'doc_probabilities': [40.71379089355469, ...
-| 'texts': ['Albert Einstein was a ...]
-| 'titles': ['"Albert Einstein"', ...]
-| }}]}
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: str = "facebook/rag-token-nq", model_version: Optional[str] = None, retriever: Optional[DensePassageRetriever] = None, generator_type: RAGeneratorType = RAGeneratorType.TOKEN, top_k: int = 2, max_length: int = 200, min_length: int = 2, num_beams: int = 2, embed_title: bool = True, prefix: Optional[str] = None, use_gpu: bool = True)
-```
-
-Load a RAG model from Transformers along with passage_embedding_model.
-See https://huggingface.co/transformers/model_doc/rag.html for more details
-
-**Arguments**:
-
-- `model_name_or_path`: Directory of a saved model or the name of a public model e.g.
- 'facebook/rag-token-nq', 'facebook/rag-sequence-nq'.
- See https://huggingface.co/models for full list of available models.
-- `model_version`: The version of model to use from the HuggingFace model hub. Can be tag name, branch name, or commit hash.
-- `retriever`: `DensePassageRetriever` used to embedded passage
-- `generator_type`: Which RAG generator implementation to use? RAG-TOKEN or RAG-SEQUENCE
-- `top_k`: Number of independently generated text to return
-- `max_length`: Maximum length of generated text
-- `min_length`: Minimum length of generated text
-- `num_beams`: Number of beams for beam search. 1 means no beam search.
-- `embed_title`: Embedded the title of passage while generating embedding
-- `prefix`: The prefix used by the generator's tokenizer.
-- `use_gpu`: Whether to use GPU (if available)
-
-
-#### predict
-
-```python
- | predict(query: str, documents: List[Document], top_k: Optional[int] = None) -> Dict
-```
-
-Generate the answer to the input query. The generation will be conditioned on the supplied documents.
-These document can for example be retrieved via the Retriever.
-
-**Arguments**:
-
-- `query`: Query
-- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on.
-- `top_k`: Number of returned answers
-
-**Returns**:
-
-Generated answers plus additional infos in a dict like this:
-
-```python
-| {'query': 'who got the first nobel prize in physics',
-| 'answers':
-| [{'query': 'who got the first nobel prize in physics',
-| 'answer': ' albert einstein',
-| 'meta': { 'doc_ids': [...],
-| 'doc_scores': [80.42758 ...],
-| 'doc_probabilities': [40.71379089355469, ...
-| 'texts': ['Albert Einstein was a ...]
-| 'titles': ['"Albert Einstein"', ...]
-| }}]}
-```
-
-
-## Class: Seq2SeqGenerator
-
-```python
-class Seq2SeqGenerator(BaseGenerator)
-```
-
-A generic sequence-to-sequence generator based on HuggingFace's transformers.
-
-Text generation is supported by so called auto-regressive language models like GPT2,
-XLNet, XLM, Bart, T5 and others. In fact, any HuggingFace language model that extends
-GenerationMixin can be used by Seq2SeqGenerator.
-
-Moreover, as language models prepare model input in their specific encoding, each model
-specified with model_name_or_path parameter in this Seq2SeqGenerator should have an
-accompanying model input converter that takes care of prefixes, separator tokens etc.
-By default, we provide model input converters for a few well-known seq2seq language models (e.g. ELI5).
-It is the responsibility of Seq2SeqGenerator user to ensure an appropriate model input converter
-is either already registered or specified on a per-model basis in the Seq2SeqGenerator constructor.
-
-For mode details on custom model input converters refer to _BartEli5Converter
-
-
-See https://huggingface.co/transformers/main_classes/model.html?transformers.generation_utils.GenerationMixin#transformers.generation_utils.GenerationMixin
-as well as https://huggingface.co/blog/how-to-generate
-
-For a list of all text-generation models see https://huggingface.co/models?pipeline_tag=text-generation
-
-**Example**
-
-```python
-| query = "Why is Dothraki language important?"
-|
-| # Retrieve related documents from retriever
-| retrieved_docs = retriever.retrieve(query=query)
-|
-| # Now generate answer from query and retrieved documents
-| generator.predict(
-| query=query,
-| documents=retrieved_docs,
-| top_k=1
-| )
-|
-| # Answer
-|
-| {'answers': [" The Dothraki language is a constructed fictional language. It's important because George R.R. Martin wrote it."],
-| 'query': 'Why is Dothraki language important?'}
-|
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: str, input_converter: Optional[Callable] = None, top_k: int = 1, max_length: int = 200, min_length: int = 2, num_beams: int = 8, use_gpu: bool = True)
-```
-
-**Arguments**:
-
-- `model_name_or_path`: a HF model name for auto-regressive language model like GPT2, XLNet, XLM, Bart, T5 etc
-- `input_converter`: an optional Callable to prepare model input for the underlying language model
- specified in model_name_or_path parameter. The required __call__ method signature for
- the Callable is:
- __call__(tokenizer: PreTrainedTokenizer, query: str, documents: List[Document],
- top_k: Optional[int] = None) -> BatchEncoding:
-- `top_k`: Number of independently generated text to return
-- `max_length`: Maximum length of generated text
-- `min_length`: Minimum length of generated text
-- `num_beams`: Number of beams for beam search. 1 means no beam search.
-- `use_gpu`: Whether to use GPU (if available)
-
-
-#### predict
-
-```python
- | predict(query: str, documents: List[Document], top_k: Optional[int] = None) -> Dict
-```
-
-Generate the answer to the input query. The generation will be conditioned on the supplied documents.
-These document can be retrieved via the Retriever or supplied directly via predict method.
-
-**Arguments**:
-
-- `query`: Query
-- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on.
-- `top_k`: Number of returned answers
-
-**Returns**:
-
-Generated answers
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/api/api/graph_retriever.md b/src/pages/docs/versions/master/v0.9.0/site/en/api/api/graph_retriever.md
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index 640eedb17..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/api/api/graph_retriever.md
+++ /dev/null
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----
-title: "Graph Retriever"
-metaTitle: "Graph Retriever"
-metaDescription: ""
-slug: "/docs/graph_retrievermd"
-date: "2020-09-03"
-id: "apigraphretrievermd"
----
-
-# Graph Retriever
-
-
-# Module: base
-
-
-# Module: text\_to\_sparql
-
-
-## Class: Text2SparqlRetriever
-
-```python
-class Text2SparqlRetriever(BaseGraphRetriever)
-```
-
-Graph retriever that uses a pre-trained Bart model to translate natural language questions given in text form to queries in SPARQL format.
-The generated SPARQL query is executed on a knowledge graph.
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(knowledge_graph, model_name_or_path, top_k: int = 1)
-```
-
-Init the Retriever by providing a knowledge graph and a pre-trained BART model
-
-**Arguments**:
-
-- `knowledge_graph`: An instance of BaseKnowledgeGraph on which to execute SPARQL queries.
-- `model_name_or_path`: Name of or path to a pre-trained BartForConditionalGeneration model.
-- `top_k`: How many SPARQL queries to generate per text query.
-
-
-#### retrieve
-
-```python
- | retrieve(query: str, top_k: Optional[int] = None)
-```
-
-Translate a text query to SPARQL and execute it on the knowledge graph to retrieve a list of answers
-
-**Arguments**:
-
-- `query`: Text query that shall be translated to SPARQL and then executed on the knowledge graph
-- `top_k`: How many SPARQL queries to generate per text query.
-
-
-#### format\_result
-
-```python
- | format_result(result)
-```
-
-Generate formatted dictionary output with text answer and additional info
-
-**Arguments**:
-
-- `result`: The result of a SPARQL query as retrieved from the knowledge graph
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/api/api/knowledge_graph.md b/src/pages/docs/versions/master/v0.9.0/site/en/api/api/knowledge_graph.md
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--- a/src/pages/docs/versions/master/v0.9.0/site/en/api/api/knowledge_graph.md
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----
-title: "Knowledge Graph"
-metaTitle: "Knowledge Graph"
-metaDescription: ""
-slug: "/docs/knowledge_graphmd"
-date: "2020-09-03"
-id: "apiknowledgegraphmd"
----
-
-# Knowledge Graph
-
-
-# Module: base
-
-
-# Module: graphdb
-
-
-## Class: GraphDBKnowledgeGraph
-
-```python
-class GraphDBKnowledgeGraph(BaseKnowledgeGraph)
-```
-
-Knowledge graph store that runs on a GraphDB instance
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(host: str = "localhost", port: int = 7200, username: str = "", password: str = "", index: Optional[str] = None, prefixes: str = "")
-```
-
-Init the knowledge graph by defining the settings to connect with a GraphDB instance
-
-**Arguments**:
-
-- `host`: address of server where the GraphDB instance is running
-- `port`: port where the GraphDB instance is running
-- `username`: username to login to the GraphDB instance (if any)
-- `password`: password to login to the GraphDB instance (if any)
-- `index`: name of the index (also called repository) stored in the GraphDB instance
-- `prefixes`: definitions of namespaces with a new line after each namespace, e.g., PREFIX hp: `__
-
-**Example**
-
-```python
-| docs = [Document(text="PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions."
-| "The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by"
-| "the shutoffs which were expected to last through at least midday tomorrow.")]
-|
-| # Summarize
-| summary = summarizer.predict(
-| documents=docs,
-| generate_single_summary=True
-| )
-|
-| # Show results (List of Documents, containing summary and original text)
-| print(summary)
-|
-| [
-| {
-| "text": "California's largest electricity provider has turned off power to hundreds of thousands of customers.",
-| ...
-| "meta": {
-| "context": "PGE stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. ..."
-| },
-| ...
-| },
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: str = "google/pegasus-xsum", model_version: Optional[str] = None, tokenizer: Optional[str] = None, max_length: int = 200, min_length: int = 5, use_gpu: int = 0, clean_up_tokenization_spaces: bool = True, separator_for_single_summary: str = " ", generate_single_summary: bool = False)
-```
-
-Load a Summarization model from Transformers.
-See the up-to-date list of available models at
-https://huggingface.co/models?filter=summarization
-
-**Arguments**:
-
-- `model_name_or_path`: Directory of a saved model or the name of a public model e.g.
- 'facebook/rag-token-nq', 'facebook/rag-sequence-nq'.
- See https://huggingface.co/models?filter=summarization for full list of available models.
-- `model_version`: The version of model to use from the HuggingFace model hub. Can be tag name, branch name, or commit hash.
-- `tokenizer`: Name of the tokenizer (usually the same as model)
-- `max_length`: Maximum length of summarized text
-- `min_length`: Minimum length of summarized text
-- `use_gpu`: If < 0, then use cpu. If >= 0, this is the ordinal of the gpu to use
-- `clean_up_tokenization_spaces`: Whether or not to clean up the potential extra spaces in the text output
-- `separator_for_single_summary`: If `generate_single_summary=True` in `predict()`, we need to join all docs
- into a single text. This separator appears between those subsequent docs.
-- `generate_single_summary`: Whether to generate a single summary for all documents or one summary per document.
- If set to "True", all docs will be joined to a single string that will then
- be summarized.
- Important: The summary will depend on the order of the supplied documents!
-
-
-#### predict
-
-```python
- | predict(documents: List[Document], generate_single_summary: Optional[bool] = None) -> List[Document]
-```
-
-Produce the summarization from the supplied documents.
-These document can for example be retrieved via the Retriever.
-
-**Arguments**:
-
-- `documents`: Related documents (e.g. coming from a retriever) that the answer shall be conditioned on.
-- `generate_single_summary`: Whether to generate a single summary for all documents or one summary per document.
- If set to "True", all docs will be joined to a single string that will then
- be summarized.
- Important: The summary will depend on the order of the supplied documents!
-
-**Returns**:
-
-List of Documents, where Document.text contains the summarization and Document.meta["context"]
- the original, not summarized text
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/api/api/translator.md b/src/pages/docs/versions/master/v0.9.0/site/en/api/api/translator.md
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----
-title: "Translator"
-metaTitle: "Translator"
-metaDescription: ""
-slug: "/docs/translatormd"
-date: "2020-09-03"
-id: "apitranslatormd"
----
-
-# Translator
-
-
-# Module: base
-
-
-## Class: BaseTranslator
-
-```python
-class BaseTranslator(BaseComponent)
-```
-
-Abstract class for a Translator component that translates either a query or a doc from language A to language B.
-
-
-#### translate
-
-```python
- | @abstractmethod
- | translate(query: Optional[str] = None, documents: Optional[Union[List[Document], List[str], List[Dict[str, Any]]]] = None, dict_key: Optional[str] = None, **kwargs) -> Union[str, List[Document], List[str], List[Dict[str, Any]]]
-```
-
-Translate the passed query or a list of documents from language A to B.
-
-
-#### run
-
-```python
- | run(query: Optional[str] = None, documents: Optional[Union[List[Document], List[str], List[Dict[str, Any]]]] = None, answers: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, dict_key: Optional[str] = None, **kwargs)
-```
-
-Method that gets executed when this class is used as a Node in a Haystack Pipeline
-
-
-# Module: transformers
-
-
-## Class: TransformersTranslator
-
-```python
-class TransformersTranslator(BaseTranslator)
-```
-
-Translator component based on Seq2Seq models from Huggingface's transformers library.
-Exemplary use cases:
-- Translate a query from Language A to B (e.g. if you only have good models + documents in language B)
-- Translate a document from Language A to B (e.g. if you want to return results in the native language of the user)
-
-We currently recommend using OPUS models (see __init__() for details)
-
-**Example:**
-
-```python
-| DOCS = [
-| Document(text="Heinz von Foerster was an Austrian American scientist combining physics and philosophy,
-| and widely attributed as the originator of Second-order cybernetics.")
-| ]
-| translator = TransformersTranslator(model_name_or_path="Helsinki-NLP/opus-mt-en-de")
-| res = translator.translate(documents=DOCS, query=None)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(model_name_or_path: str, tokenizer_name: Optional[str] = None, max_seq_len: Optional[int] = None, clean_up_tokenization_spaces: Optional[bool] = True)
-```
-
-Initialize the translator with a model that fits your targeted languages. While we support all seq2seq
-models from Hugging Face's model hub, we recommend using the OPUS models from Helsiniki NLP. They provide plenty
-of different models, usually one model per language pair and translation direction.
-They have a pretty standardized naming that should help you find the right model:
-- "Helsinki-NLP/opus-mt-en-de" => translating from English to German
-- "Helsinki-NLP/opus-mt-de-en" => translating from German to English
-- "Helsinki-NLP/opus-mt-fr-en" => translating from French to English
-- "Helsinki-NLP/opus-mt-hi-en"=> translating from Hindi to English
-...
-
-They also have a few multilingual models that support multiple languages at once.
-
-**Arguments**:
-
-- `model_name_or_path`: Name of the seq2seq model that shall be used for translation.
- Can be a remote name from Huggingface's modelhub or a local path.
-- `tokenizer_name`: Optional tokenizer name. If not supplied, `model_name_or_path` will also be used for the
- tokenizer.
-- `max_seq_len`: The maximum sentence length the model accepts. (Optional)
-- `clean_up_tokenization_spaces`: Whether or not to clean up the tokenization spaces. (default True)
-
-
-#### translate
-
-```python
- | translate(query: Optional[str] = None, documents: Optional[Union[List[Document], List[str], List[Dict[str, Any]]]] = None, dict_key: Optional[str] = None, **kwargs) -> Union[str, List[Document], List[str], List[Dict[str, Any]]]
-```
-
-Run the actual translation. You can supply a query or a list of documents. Whatever is supplied will be translated.
-
-**Arguments**:
-
-- `query`: The query string to translate
-- `documents`: The documents to translate
-- `dict_key`:
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--- a/src/pages/docs/versions/master/v0.9.0/site/en/menuStructure/menu.json
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-{
- "menuList": [
- {
- "id": "overview",
- "title": "Overview",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 0,
- "isMenu": true
- },
- {
- "id": "get_startedmd",
- "title": "Get Started",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "intromd",
- "title": "What is Haystack?",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "use_casesmd",
- "title": "Use Cases",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "roadmapmd",
- "title": "Roadmap",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "faqmd",
- "title": "FAQ",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "termsmd",
- "title": "Glossary",
- "label1": "overview",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "usage_haystack",
- "title": "Usage",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 1,
- "isMenu": true
- },
- {
- "id": "preprocessingmd",
- "title": "Preprocessing",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "pipelinesmd",
- "title": "Pipelines",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "documentstoremd",
- "title": "Document Store",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "retrievermd",
- "title": "Retriever",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "readermd",
- "title": "Reader",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "generatormd",
- "title": "Generator",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "summarizermd",
- "title": "Summarizer",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 6
- },
- {
- "id": "translatormd",
- "title": "Translator",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 7
- },
- {
- "id": "knowledgegraphmd",
- "title": "Knowledge Graph",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 8
- },
- {
- "id": "languagesmd",
- "title": "Languages Other Than English",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 9
- },
- {
- "id": "domain_adaptationmd",
- "title": "Domain Adaptation",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 10
- },
- {
- "id": "optimizationmd",
- "title": "Optimization",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 11
- },
- {
- "id": "annotationmd",
- "title": "Annotation Tool",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 12
- },
- {
- "id": "rankermd",
- "title": "Ranker",
- "label1": "usage_haystack",
- "label2": "",
- "label3": "",
- "order": 13
- },
- {
- "id": "tutorials_haystack",
- "title": "Tutorials",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 2,
- "isMenu": true
- },
- {
- "id": "tutorial1md",
- "title": "Build Your First QA System",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "tutorial2md",
- "title": "Fine-tuning a Model on Your Own Data",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "tutorial3md",
- "title": "Build a QA System Without Elasticsearch",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 2
- },
- {
- "id": "tutorial4md",
- "title": "Utilizing existing FAQs for Question Answering",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "tutorial5md",
- "title": "Evaluation of a QA System",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "tutorial6md",
- "title": "Better Retrieval via \"Dense Passage Retrieval\"",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "tutorial7md",
- "title": "Generative QA with \"Retrieval-Augmented Generation\"",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 6
- },
- {
- "id": "tutorial8md",
- "title": "Preprocessing your Documents",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 7
- },
- {
- "id": "tutorial9md",
- "title": "How to Train DPR",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 8
- },
- {
- "id": "tutorial10md",
- "title": "Question Answering on a Knowledge Graph",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 9
- },
- {
- "id": "tutorial11md",
- "title": "How to use Pipelines",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 10
- },
- {
- "id": "tutorial12md",
- "title": "Generative QA with \"LFQA\"",
- "label1": "tutorials_haystack",
- "label2": "",
- "label3": "",
- "order": 11
- },
- {
- "id": "api_haystack",
- "title": "API reference",
- "label1": "",
- "label2": "",
- "label3": "",
- "order": 3,
- "isMenu": true
- },
- {
- "id": "apidatabasemd",
- "title": "Document Store",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 0
- },
- {
- "id": "apiretrievermd",
- "title": "Retriever",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 1
- },
- {
- "id": "apireadermd",
- "title": "Reader",
- "label1": "api_haystack",
- "label2": "api_haystack",
- "label3": "",
- "order": 2
- },
- {
- "id": "apigeneratormd",
- "title": "Generator",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 3
- },
- {
- "id": "apisummarizermd",
- "title": "Summarizer",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 4
- },
- {
- "id": "apitranslatormd",
- "title": "Translator",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 5
- },
- {
- "id": "apiindexingmd",
- "title": "Preprocessor",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 6
- },
- {
- "id": "file_convertersmd",
- "title": "File Converters",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 7
- },
- {
- "id": "apicrawlermd",
- "title": "Crawler",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 8
- },
- {
- "id": "apievaluationmd",
- "title": "Evaluation",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 9
- },
- {
- "id": "apipipelinesmd",
- "title": "Pipelines",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 10
- },
- {
- "id": "apiknowledgegraphmd",
- "title": "Knowledge Graph",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 11
- },
- {
- "id": "apigraphretrievermd",
- "title": "Graph Retriever",
- "label1": "api_haystack",
- "label2": "",
- "label3": "",
- "order": 12
- }
- ]
-}
\ No newline at end of file
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/1.md b/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/1.md
deleted file mode 100644
index 74483b395..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/1.md
+++ /dev/null
@@ -1,258 +0,0 @@
----
-title: "Tutorial 1"
-metaTitle: "Build Your First QA System"
-metaDescription: ""
-slug: "/docs/tutorial1"
-date: "2020-09-03"
-id: "tutorial1md"
----
-
-# Build Your First QA System
-
-
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb)
-
-Question Answering can be used in a variety of use cases. A very common one: Using it to navigate through complex knowledge bases or long documents ("search setting").
-
-A "knowledge base" could for example be your website, an internal wiki or a collection of financial reports.
-In this tutorial we will work on a slightly different domain: "Game of Thrones".
-
-Let's see how we can use a bunch of Wikipedia articles to answer a variety of questions about the
-marvellous seven kingdoms...
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-Haystack finds answers to queries within the documents stored in a `DocumentStore`. The current implementations of `DocumentStore` include `ElasticsearchDocumentStore`, `FAISSDocumentStore`, `SQLDocumentStore`, and `InMemoryDocumentStore`.
-
-**Here:** We recommended Elasticsearch as it comes preloaded with features like [full-text queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html), [BM25 retrieval](https://www.elastic.co/elasticon/conf/2016/sf/improved-text-scoring-with-bm25), and [vector storage for text embeddings](https://www.elastic.co/guide/en/elasticsearch/reference/7.6/dense-vector.html).
-
-**Alternatives:** If you are unable to setup an Elasticsearch instance, then follow the [Tutorial 3](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb) for using SQL/InMemory document stores.
-
-**Hint**: This tutorial creates a new document store instance with Wikipedia articles on Game of Thrones. However, you can configure Haystack to work with your existing document stores.
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker via the Haystack utility function
-from haystack.utils import launch_es
-
-launch_es()
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-# Connect to Elasticsearch
-
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles about Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first fetch some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is:
-# {
-# 'text': "",
-# 'meta': {'name': "", ...}
-#}
-# (Optionally: you can also add more key-value-pairs here, that will be indexed as fields in Elasticsearch and
-# can be accessed later for filtering or shown in the responses of the Finder)
-
-# Let's have a look at the first 3 entries:
-print(dicts[:3])
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-## Initalize Retriever, Reader, & Finder
-
-### Retriever
-
-Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question could be answered.
-They use some simple but fast algorithm.
-
-**Here:** We use Elasticsearch's default BM25 algorithm
-
-**Alternatives:**
-
-- Customize the `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `DensePassageRetriever` to use different embedding models for passage and query (see Tutorial 6)
-
-
-```python
-from haystack.retriever.sparse import ElasticsearchRetriever
-retriever = ElasticsearchRetriever(document_store=document_store)
-```
-
-
-```python
-# Alternative: An in-memory TfidfRetriever based on Pandas dataframes for building quick-prototypes with SQLite document store.
-
-# from haystack.retriever.sparse import TfidfRetriever
-# retriever = TfidfRetriever(document_store=document_store)
-```
-
-### Reader
-
-A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based
-on powerful, but slower deep learning models.
-
-Haystack currently supports Readers based on the frameworks FARM and Transformers.
-With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).
-
-**Here:** a medium sized RoBERTa QA model using a Reader based on FARM (https://huggingface.co/deepset/roberta-base-squad2)
-
-**Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)
-
-**Alternatives (Models):** e.g. "distilbert-base-uncased-distilled-squad" (fast) or "deepset/bert-large-uncased-whole-word-masking-squad2" (good accuracy)
-
-**Hint:** You can adjust the model to return "no answer possible" with the no_ans_boost. Higher values mean the model prefers "no answer possible"
-
-#### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-#### TransformersReader
-
-
-```python
-# Alternative:
-# reader = TransformersReader(model_name_or_path="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1)
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import ExtractiveQAPipeline
-pipe = ExtractiveQAPipeline(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = pipe.run(query="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-# prediction = pipe.run(query="Who created the Dothraki vocabulary?", top_k_reader=5)
-# prediction = pipe.run(query="Who is the sister of Sansa?", top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
-
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/10.md b/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/10.md
deleted file mode 100644
index d69775ab2..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/10.md
+++ /dev/null
@@ -1,149 +0,0 @@
----
-title: "Tutorial 10"
-metaTitle: "Knowledge Graph QA"
-metaDescription: ""
-slug: "/docs/tutorial10"
-date: "2021-04-06"
-id: "tutorial10md"
----
-
-# Question Answering on a Knowledge Graph
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial10_Knowledge_Graph.ipynb)
-
-Haystack allows storing and querying knowledge graphs with the help of pre-trained models that translate text queries to SPARQL queries.
-This tutorial demonstrates how to load an existing knowledge graph into haystack, load a pre-trained retriever, and execute text queries on the knowledge graph.
-The training of models that translate text queries into SPARQL queries is currently not supported.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-```
-
-
-```python
-# Here are some imports that we'll need
-
-import subprocess
-import time
-from pathlib import Path
-
-from haystack.graph_retriever.text_to_sparql import Text2SparqlRetriever
-from haystack.knowledge_graph.graphdb import GraphDBKnowledgeGraph
-from haystack.preprocessor.utils import fetch_archive_from_http
-```
-
-## Downloading Knowledge Graph and Model
-
-
-```python
-# Let's first fetch some triples that we want to store in our knowledge graph
-# Here: exemplary triples from the wizarding world
-graph_dir = "../data/tutorial10_knowledge_graph/"
-s3_url = "https://fandom-qa.s3-eu-west-1.amazonaws.com/triples_and_config.zip"
-fetch_archive_from_http(url=s3_url, output_dir=graph_dir)
-
-# Fetch a pre-trained BART model that translates text queries to SPARQL queries
-model_dir = "../saved_models/tutorial10_knowledge_graph/"
-s3_url = "https://fandom-qa.s3-eu-west-1.amazonaws.com/saved_models/hp_v3.4.zip"
-fetch_archive_from_http(url=s3_url, output_dir=model_dir)
-```
-
-## Launching a GraphDB instance
-
-
-```python
-# Unfortunately, there seems to be no good way to run GraphDB in colab environments
-# In your local environment, you could start a GraphDB server with docker
-# Feel free to check GraphDB's website for the free version https://www.ontotext.com/products/graphdb/graphdb-free/
-print("Starting GraphDB ...")
-status = subprocess.run(
- ['docker run -d -p 7200:7200 --name graphdb-instance-tutorial docker-registry.ontotext.com/graphdb-free:9.4.1-adoptopenjdk11'], shell=True
-)
-if status.returncode:
- raise Exception("Failed to launch GraphDB. Maybe it is already running or you already have a container with that name that you could start?")
-time.sleep(5)
-```
-
-## Creating a new GraphDB repository (also known as index in haystack's document stores)
-
-
-```python
-# Initialize a knowledge graph connected to GraphDB and use "tutorial_10_index" as the name of the index
-kg = GraphDBKnowledgeGraph(index="tutorial_10_index")
-
-# Delete the index as it might have been already created in previous runs
-kg.delete_index()
-
-# Create the index based on a configuration file
-kg.create_index(config_path=Path(graph_dir+"repo-config.ttl"))
-
-# Import triples of subject, predicate, and object statements from a ttl file
-kg.import_from_ttl_file(index="tutorial_10_index", path=Path(graph_dir+"triples.ttl"))
-print(f"The last triple stored in the knowledge graph is: {kg.get_all_triples()[-1]}")
-print(f"There are {len(kg.get_all_triples())} triples stored in the knowledge graph.")
-```
-
-
-```python
-# Define prefixes for names of resources so that we can use shorter resource names in queries
-prefixes = """PREFIX rdf:
-PREFIX xsd:
-PREFIX hp: ?obj . }")
-print(result)
-# Paraphrased question: What is the patronus of Hermione?
-# Correct answer: Otter
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/11.md b/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/11.md
deleted file mode 100644
index ed81a5950..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/11.md
+++ /dev/null
@@ -1,411 +0,0 @@
----
-title: "Tutorial 11"
-metaTitle: "Pipelines"
-metaDescription: ""
-slug: "/docs/tutorial11"
-date: "2021-04-06"
-id: "tutorial11md"
----
-
-# Pipelines Tutorial
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial11_Pipelines.ipynb)
-
-In this tutorial, you will learn how the `Pipeline` class acts as a connector between all the different
-building blocks that are found in FARM. Whether you are using a Reader, Generator, Summarizer
-or Retriever (or 2), the `Pipeline` class will help you build a Directed Acyclic Graph (DAG) that
-determines how to route the output of one component into the input of another.
-
-
-## Setting Up the Environment
-
-Let's start by ensuring we have a GPU running to ensure decent speed in this tutorial.
-In Google colab, you can change to a GPU runtime in the menu:
-- **Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-These lines are to install Haystack through pip
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install --upgrade git+https://github.com/deepset-ai/haystack.git
-
-# Install pygraphviz
-!apt install libgraphviz-dev
-!pip install pygraphviz
-```
-
-If running from Colab or a no Docker environment, you will want to start Elasticsearch from source
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-## Initialization
-
-Here are some core imports
-
-
-```python
-from haystack.utils import print_answers, print_documents
-```
-
-Then let's fetch some data (in this case, pages from the Game of Thrones wiki) and prepare it so that it can
-be used indexed into our `DocumentStore`
-
-
-```python
-from haystack.preprocessor.utils import fetch_archive_from_http, convert_files_to_dicts
-from haystack.preprocessor.cleaning import clean_wiki_text
-
-#Download and prepare data - 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# convert files to dicts containing documents that can be indexed to our datastore
-got_dicts = convert_files_to_dicts(
- dir_path=doc_dir,
- clean_func=clean_wiki_text,
- split_paragraphs=True
-)
-```
-
-Here we initialize the core components that we will be gluing together using the `Pipeline` class.
-We have a `DocumentStore`, an `ElasticsearchRetriever` and a `FARMReader`.
-These can be combined to create a classic Retriever-Reader pipeline that is designed
-to perform Open Domain Question Answering.
-
-
-```python
-from haystack import Pipeline
-from haystack.utils import launch_es
-from haystack.document_store import ElasticsearchDocumentStore
-from haystack.retriever.sparse import ElasticsearchRetriever
-from haystack.retriever.dense import DensePassageRetriever
-from haystack.reader import FARMReader
-
-
-# Initialize DocumentStore and index documents
-launch_es()
-document_store = ElasticsearchDocumentStore()
-document_store.delete_all_documents()
-document_store.write_documents(got_dicts)
-
-# Initialize Sparse retriever
-es_retriever = ElasticsearchRetriever(document_store=document_store)
-
-# Initialize dense retriever
-dpr_retriever = DensePassageRetriever(document_store)
-document_store.update_embeddings(dpr_retriever, update_existing_embeddings=False)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")
-```
-
-## Prebuilt Pipelines
-
-Haystack features many prebuilt pipelines that cover common tasks.
-Here we have an `ExtractiveQAPipeline` (the successor to the now deprecated `Finder` class).
-
-
-```python
-from haystack.pipeline import ExtractiveQAPipeline
-
-# Prebuilt pipeline
-p_extractive_premade = ExtractiveQAPipeline(reader=reader, retriever=es_retriever)
-res = p_extractive_premade.run(
- query="Who is the father of Arya Stark?",
- top_k_retriever=10,
- top_k_reader=5
-)
-print_answers(res, details="minimal")
-```
-
-If you want to just do the retrieval step, you can use a `DocumentSearchPipeline`
-
-
-```python
-from haystack.pipeline import DocumentSearchPipeline
-
-p_retrieval = DocumentSearchPipeline(es_retriever)
-res = p_retrieval.run(
- query="Who is the father of Arya Stark?",
- top_k_retriever=10
-)
-print_documents(res, max_text_len=200)
-```
-
-Or if you want to use a `Generator` instead of a `Reader`,
-you can initialize a `GenerativeQAPipeline` like this:
-
-
-```python
-from haystack.pipeline import GenerativeQAPipeline, FAQPipeline
-from haystack.generator import RAGenerator
-
-# We set this to True so that the document store returns document embeddings with each document
-# This is needed by the Generator
-document_store.return_embedding = True
-
-# Initialize generator
-rag_generator = RAGenerator()
-
-# Generative QA
-p_generator = GenerativeQAPipeline(generator=rag_generator, retriever=dpr_retriever)
-res = p_generator.run(
- query="Who is the father of Arya Stark?",
- top_k_retriever=10
-)
-print_answers(res, details="minimal")
-
-# We are setting this to False so that in later pipelines,
-# we get a cleaner printout
-document_store.return_embedding = False
-
-```
-
-Haystack features prebuilt pipelines to do:
-- just document search (DocumentSearchPipeline),
-- document search with summarization (SearchSummarizationPipeline)
-- generative QA (GenerativeQAPipeline)
-- FAQ style QA (FAQPipeline)
-- translated search (TranslationWrapperPipeline)
-To find out more about these pipelines, have a look at our [documentation](https://haystack.deepset.ai/docs/latest/pipelinesmd)
-
-
-With any Pipeline, whether prebuilt or custom constructed,
-you can save a diagram showing how all the components are connected.
-
-
-
-
-```python
-p_extractive_premade.draw("pipeline_extractive_premade.png")
-p_retrieval.draw("pipeline_retrieval.png")
-p_generator.draw("pipeline_generator.png")
-```
-
-## Custom Pipelines
-
-Now we are going to rebuild the `ExtractiveQAPipelines` using the generic Pipeline class.
-We do this by adding the building blocks that we initialized as nodes in the graph.
-
-
-```python
-# Custom built extractive QA pipeline
-p_extractive = Pipeline()
-p_extractive.add_node(component=es_retriever, name="Retriever", inputs=["Query"])
-p_extractive.add_node(component=reader, name="Reader", inputs=["Retriever"])
-
-# Now we can run it
-res = p_extractive.run(
- query="Who is the father of Arya Stark?",
- top_k_retriever=10,
- top_k_reader=5
-)
-print_answers(res, details="minimal")
-p_extractive.draw("pipeline_extractive.png")
-```
-
-Pipelines offer a very simple way to ensemble together different components.
-In this example, we are going to combine the power of a `DensePassageRetriever`
-with the keyword based `ElasticsearchRetriever`.
-See our [documentation](https://haystack.deepset.ai/docs/latest/retrievermd) to understand why
-we might want to combine a dense and sparse retriever.
-
-
-
-Here we use a `JoinDocuments` node so that the predictions from each retriever can be merged together.
-
-
-```python
-from haystack.pipeline import JoinDocuments
-
-# Create ensembled pipeline
-p_ensemble = Pipeline()
-p_ensemble.add_node(component=es_retriever, name="ESRetriever", inputs=["Query"])
-p_ensemble.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["Query"])
-p_ensemble.add_node(component=JoinDocuments(join_mode="concatenate"), name="JoinResults", inputs=["ESRetriever", "DPRRetriever"])
-p_ensemble.add_node(component=reader, name="Reader", inputs=["JoinResults"])
-p_ensemble.draw("pipeline_ensemble.png")
-
-# Run pipeline
-res = p_ensemble.run(
- query="Who is the father of Arya Stark?",
- top_k_retriever=5 #This is top_k per retriever
-)
-print_answers(res, details="minimal")
-```
-
-## Custom Nodes
-
-Nodes are relatively simple objects
-and we encourage our users to design their own if they don't see on that fits their use case
-
-The only requirements are:
-- Add a method run(self, **kwargs) to your class. **kwargs will contain the output from the previous node in your graph.
-- Do whatever you want within run() (e.g. reformatting the query)
-- Return a tuple that contains your output data (for the next node)
-and the name of the outgoing edge (by default "output_1" for nodes that have one output)
-- Add a class attribute outgoing_edges = 1 that defines the number of output options from your node. You only need a higher number here if you have a decision node (see below).
-
-Here we have a template for a Node:
-
-
-```python
-class NodeTemplate():
- outgoing_edges = 1
-
- def run(self, **kwargs):
- # Insert code here to manipulate the variables in kwarg
- return (kwargs, "output_1")
-```
-
-## Decision Nodes
-
-Decision Nodes help you route your data so that only certain branches of your `Pipeline` are run.
-One popular use case for such query classifiers is routing keyword queries to Elasticsearch and questions to DPR + Reader.
-With this approach you keep optimal speed and simplicity for keywords while going deep with transformers when it's most helpful.
-
-
-
-Though this looks very similar to the ensembled pipeline shown above,
-the key difference is that only one of the retrievers is run for each request.
-By contrast both retrievers are always run in the ensembled approach.
-
-Below, we define a very naive `QueryClassifier` and show how to use it:
-
-
-```python
-class QueryClassifier():
- outgoing_edges = 2
-
- def run(self, **kwargs):
- if "?" in kwargs["query"]:
- return (kwargs, "output_2")
- else:
- return (kwargs, "output_1")
-
-# Here we build the pipeline
-p_classifier = Pipeline()
-p_classifier.add_node(component=QueryClassifier(), name="QueryClassifier", inputs=["Query"])
-p_classifier.add_node(component=es_retriever, name="ESRetriever", inputs=["QueryClassifier.output_1"])
-p_classifier.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_2"])
-p_classifier.add_node(component=reader, name="QAReader", inputs=["ESRetriever", "DPRRetriever"])
-p_classifier.draw("pipeline_classifier.png")
-
-# Run only the dense retriever on the full sentence query
-res_1 = p_classifier.run(
- query="Who is the father of Arya Stark?",
- top_k_retriever=10
-)
-print("DPR Results" + "\n" + "="*15)
-print_answers(res_1)
-
-# Run only the sparse retriever on a keyword based query
-res_2 = p_classifier.run(
- query="Arya Stark father",
- top_k_retriever=10
-)
-print("ES Results" + "\n" + "="*15)
-print_answers(res_2)
-```
-
-## Evaluation Nodes
-
-We have also designed a set of nodes that can be used to evaluate the performance of a system.
-Have a look at our [tutorial](https://haystack.deepset.ai/docs/latest/tutorial5md) to get hands on with the code and learn more about Evaluation Nodes!
-
-
-## YAML Configs
-
-A full `Pipeline` can be defined in a YAML file and simply loaded.
-Having your pipeline available in a YAML is particularly useful
-when you move between experimentation and production environments.
-Just export the YAML from your notebook / IDE and import it into your production environment.
-It also helps with version control of pipelines,
-allows you to share your pipeline easily with colleagues,
-and simplifies the configuration of pipeline parameters in production.
-
-It consists of two main sections: you define all objects (e.g. a reader) in components
-and then stick them together to a pipeline in pipelines.
-You can also set one component to be multiple nodes of a pipeline or to be a node across multiple pipelines.
-It will be loaded just once in memory and therefore doesn't hurt your resources more than actually needed.
-
-The contents of a YAML file should look something like this:
-
-```yaml
-version: '0.7'
-components: # define all the building-blocks for Pipeline
-- name: MyReader # custom-name for the component; helpful for visualization & debugging
- type: FARMReader # Haystack Class name for the component
- params:
- no_ans_boost: -10
- model_name_or_path: deepset/roberta-base-squad2
-- name: MyESRetriever
- type: ElasticsearchRetriever
- params:
- document_store: MyDocumentStore # params can reference other components defined in the YAML
- custom_query: null
-- name: MyDocumentStore
- type: ElasticsearchDocumentStore
- params:
- index: haystack_test
-pipelines: # multiple Pipelines can be defined using the components from above
-- name: my_query_pipeline # a simple extractive-qa Pipeline
- nodes:
- - name: MyESRetriever
- inputs: [Query]
- - name: MyReader
- inputs: [MyESRetriever]
-```
-
-To load, simply call:
-``` python
-pipeline.load_from_yaml(Path("sample.yaml"))
-```
-
-## Conclusion
-
-The possibilities are endless with the `Pipeline` class and we hope that this tutorial will inspire you
-to build custom pipeplines that really work for your use case!
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/12.md b/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/12.md
deleted file mode 100644
index d65d1a3d6..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/12.md
+++ /dev/null
@@ -1,164 +0,0 @@
----
-title: "Tutorial 12"
-metaTitle: "Generative QA with LFQA"
-metaDescription: ""
-slug: "/docs/tutorial12"
-date: "2021-04-06"
-id: "tutorial12md"
----
-
-# Long-Form Question Answering
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial12_LFQA.ipynb)
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.generator.transformers import Seq2SeqGenerator
-```
-
-### Document Store
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-The default flavour of FAISSDocumentStore is "Flat" but can also be set to "HNSW" for
-faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.
-For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-
-
-```python
-from haystack.document_store.faiss import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(vector_dim=128, faiss_index_factory_str="Flat")
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever and Reader/Generator
-
-#### Retriever
-
-**Here:** We use a `RetribertRetriever` and we invoke `update_embeddings` to index the embeddings of documents in the `FAISSDocumentStore`
-
-
-
-
-```python
-from haystack.retriever.dense import EmbeddingRetriever
-
-retriever = EmbeddingRetriever(document_store=document_store,
- embedding_model="yjernite/retribert-base-uncased",
- model_format="retribert")
-
-document_store.update_embeddings(retriever)
-```
-
-Before we blindly use the `RetribertRetriever` let's empirically test it to make sure a simple search indeed finds the relevant documents.
-
-
-```python
-from haystack.utils import print_answers, print_documents
-from haystack.pipeline import DocumentSearchPipeline
-
-p_retrieval = DocumentSearchPipeline(retriever)
-res = p_retrieval.run(
- query="Tell me something about Arya Stark?",
- top_k_retriever=5
-)
-print_documents(res, max_text_len=512)
-
-```
-
-#### Reader/Generator
-
-Similar to previous Tutorials we now initalize our reader/generator.
-
-Here we use a `Seq2SeqGenerator` with the *yjernite/bart_eli5* model (see: https://huggingface.co/yjernite/bart_eli5)
-
-
-
-
-```python
-generator = Seq2SeqGenerator(model_name_or_path="yjernite/bart_eli5")
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `GenerativeQAPipeline` that combines a retriever and a reader/generator to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import GenerativeQAPipeline
-pipe = GenerativeQAPipeline(generator, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-pipe.run(query="Why did Arya Stark's character get portrayed in a television adaptation?", top_k_retriever=1)
-```
-
-
-```python
-pipe.run(query="What kind of character does Arya Stark play?", top_k_retriever=1)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/2.md b/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/2.md
deleted file mode 100644
index d9eba3738..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/2.md
+++ /dev/null
@@ -1,108 +0,0 @@
----
-title: "Tutorial 2"
-metaTitle: "Fine-tuning a model on your own data"
-metaDescription: ""
-slug: "/docs/tutorial2"
-date: "2020-09-03"
-id: "tutorial2md"
----
-
-# Fine-tuning a Model on Your Own Data
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial2_Finetune_a_model_on_your_data.ipynb)
-
-For many use cases it is sufficient to just use one of the existing public models that were trained on SQuAD or other public QA datasets (e.g. Natural Questions).
-However, if you have domain-specific questions, fine-tuning your model on custom examples will very likely boost your performance.
-While this varies by domain, we saw that ~ 2000 examples can easily increase performance by +5-20%.
-
-This tutorial shows you how to fine-tune a pretrained model on your own dataset.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.reader.farm import FARMReader
-```
-
-
-## Create Training Data
-
-There are two ways to generate training data
-
-1. **Annotation**: You can use the [annotation tool](https://github.com/deepset-ai/haystack#labeling-tool) to label your data, i.e. highlighting answers to your questions in a document. The tool supports structuring your workflow with organizations, projects, and users. The labels can be exported in SQuAD format that is compatible for training with Haystack.
-
-
-
-2. **Feedback**: For production systems, you can collect training data from direct user feedback via Haystack's [REST API interface](https://github.com/deepset-ai/haystack#rest-api). This includes a customizable user feedback API for providing feedback on the answer returned by the API. The API provides a feedback export endpoint to obtain the feedback data for fine-tuning your model further.
-
-
-## Fine-tune your model
-
-Once you have collected training data, you can fine-tune your base models.
-We initialize a reader as a base model and fine-tune it on our own custom dataset (should be in SQuAD-like format).
-We recommend using a base model that was trained on SQuAD or a similar QA dataset before to benefit from Transfer Learning effects.
-
-**Recommendation**: Run training on a GPU.
-If you are using Colab: Enable this in the menu "Runtime" > "Change Runtime type" > Select "GPU" in dropdown.
-Then change the `use_gpu` arguments below to `True`
-
-
-```python
-reader = FARMReader(model_name_or_path="distilbert-base-uncased-distilled-squad", use_gpu=True)
-train_data = "data/squad20"
-# train_data = "PATH/TO_YOUR/TRAIN_DATA"
-reader.train(data_dir=train_data, train_filename="dev-v2.0.json", use_gpu=True, n_epochs=1, save_dir="my_model")
-```
-
-
-```python
-# Saving the model happens automatically at the end of training into the `save_dir` you specified
-# However, you could also save a reader manually again via:
-reader.save(directory="my_model")
-```
-
-
-```python
-# If you want to load it at a later point, just do:
-new_reader = FARMReader(model_name_or_path="my_model")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/3.md b/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/3.md
deleted file mode 100644
index 180d5eabd..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/3.md
+++ /dev/null
@@ -1,203 +0,0 @@
----
-title: "Tutorial 3"
-metaTitle: "Build a QA System Without Elasticsearch"
-metaDescription: ""
-slug: "/docs/tutorial3"
-date: "2020-09-03"
-id: "tutorial3md"
----
-
-# Build a QA System Without Elasticsearch
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb)
-
-Haystack provides alternatives to Elasticsearch for developing quick prototypes.
-
-You can use an `InMemoryDocumentStore` or a `SQLDocumentStore`(with SQLite) as the document store.
-
-If you are interested in more feature-rich Elasticsearch, then please refer to the Tutorial 1.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-
-
-```python
-# In-Memory Document Store
-from haystack.document_store.memory import InMemoryDocumentStore
-document_store = InMemoryDocumentStore()
-```
-
-
-```python
-# SQLite Document Store
-# from haystack.document_store.sql import SQLDocumentStore
-# document_store = SQLDocumentStore(url="sqlite:///qa.db")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles on Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first get some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# convert files to dicts containing documents that can be indexed to our datastore
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is: {"name": ", "text": ""}
-
-# Let's have a look at the first 3 entries:
-print(dicts[:3])
-# Now, let's write the docs to our DB.
-document_store.write_documents(dicts)
-```
-
-## Initalize Retriever, Reader, & Finder
-
-### Retriever
-
-Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question could be answered.
-
-With InMemoryDocumentStore or SQLDocumentStore, you can use the TfidfRetriever. For more retrievers, please refer to the tutorial-1.
-
-
-```python
-# An in-memory TfidfRetriever based on Pandas dataframes
-
-from haystack.retriever.sparse import TfidfRetriever
-retriever = TfidfRetriever(document_store=document_store)
-```
-
-### Reader
-
-A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based
-on powerful, but slower deep learning models.
-
-Haystack currently supports Readers based on the frameworks FARM and Transformers.
-With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).
-
-**Here:** a medium sized RoBERTa QA model using a Reader based on FARM (https://huggingface.co/deepset/roberta-base-squad2)
-
-**Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)
-
-**Alternatives (Models):** e.g. "distilbert-base-uncased-distilled-squad" (fast) or "deepset/bert-large-uncased-whole-word-masking-squad2" (good accuracy)
-
-**Hint:** You can adjust the model to return "no answer possible" with the no_ans_boost. Higher values mean the model prefers "no answer possible"
-
-#### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-#### TransformersReader
-
-
-```python
-# Alternative:
-# reader = TransformersReader(model_name_or_path="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1)
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import ExtractiveQAPipeline
-pipe = ExtractiveQAPipeline(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = pipe.run(query="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-# prediction = pipe.run(query="Who created the Dothraki vocabulary?", top_k_reader=5)
-# prediction = pipe.run(query="Who is the sister of Sansa?", top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/4.md b/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/4.md
deleted file mode 100644
index 57fb73518..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/4.md
+++ /dev/null
@@ -1,177 +0,0 @@
----
-title: "Tutorial 4"
-metaTitle: "Utilizing existing FAQs for Question Answering"
-metaDescription: ""
-slug: "/docs/tutorial4"
-date: "2020-09-03"
-id: "tutorial4md"
----
-
-# Utilizing existing FAQs for Question Answering
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial4_FAQ_style_QA.ipynb)
-
-While *extractive Question Answering* works on pure texts and is therefore more generalizable, there's also a common alternative that utilizes existing FAQ data.
-
-**Pros**:
-
-- Very fast at inference time
-- Utilize existing FAQ data
-- Quite good control over answers
-
-**Cons**:
-
-- Generalizability: We can only answer questions that are similar to existing ones in FAQ
-
-In some use cases, a combination of extractive QA and FAQ-style can also be an interesting option.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack import Finder
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-from haystack.retriever.dense import EmbeddingRetriever
-from haystack.utils import print_answers
-import pandas as pd
-import requests
-
-```
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker via the Haystack utility function
-from haystack.utils import launch_es
-
-launch_es()
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-
-```
-
-### Init the DocumentStore
-In contrast to Tutorial 1 (extractive QA), we:
-
-* specify the name of our `text_field` in Elasticsearch that we want to return as an answer
-* specify the name of our `embedding_field` in Elasticsearch where we'll store the embedding of our question and that is used later for calculating our similarity to the incoming user question
-* set `excluded_meta_data=["question_emb"]` so that we don't return the huge embedding vectors in our search results
-
-
-```python
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="",
- index="document",
- embedding_field="question_emb",
- embedding_dim=768,
- excluded_meta_data=["question_emb"])
-```
-
-### Create a Retriever using embeddings
-Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones).
-We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings.
-
-
-```python
-retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert", use_gpu=True)
-```
-
-### Prepare & Index FAQ data
-We create a pandas dataframe containing some FAQ data (i.e curated pairs of question + answer) and index those in elasticsearch.
-Here: We download some question-answer pairs related to COVID-19
-
-
-```python
-# Download
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/faqs/faq_covidbert.csv")
-open('small_faq_covid.csv', 'wb').write(temp.content)
-
-# Get dataframe with columns "question", "answer" and some custom metadata
-df = pd.read_csv("small_faq_covid.csv")
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-df["question"] = df["question"].apply(lambda x: x.strip())
-print(df.head())
-
-# Get embeddings for our questions from the FAQs
-questions = list(df["question"].values)
-df["question_emb"] = retriever.embed_queries(texts=questions)
-df = df.rename(columns={"question": "text"})
-
-# Convert Dataframe to list of dicts and index them in our DocumentStore
-docs_to_index = df.to_dict(orient="records")
-document_store.write_documents(docs_to_index)
-```
-
-### Ask questions
-Initialize a Pipeline (this time without a reader) and ask questions
-
-
-```python
-from haystack.pipeline import FAQPipeline
-pipe = FAQPipeline(retriever=retriever)
-```
-
-
-```python
-prediction = pipe.run(query="How is the virus spreading?", top_k_retriever=10)
-print_answers(prediction, details="all")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/5.md b/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/5.md
deleted file mode 100644
index 9ce00b1b3..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/5.md
+++ /dev/null
@@ -1,260 +0,0 @@
----
-title: "Tutorial 5"
-metaTitle: "Evaluation of a QA System"
-metaDescription: ""
-slug: "/docs/tutorial5"
-date: "2020-09-03"
-id: "tutorial5md"
----
-
-# Evaluation of a QA System
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial5_Evaluation.ipynb)
-
-To be able to make a statement about the performance of a question-answering system, it is important to evalute it. Furthermore, evaluation allows to determine which parts of the system can be improved.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-## Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-from farm.utils import initialize_device_settings
-
-device, n_gpu = initialize_device_settings(use_cuda=True)
-```
-
-
-```python
-from haystack.preprocessor.utils import fetch_archive_from_http
-
-# Download evaluation data, which is a subset of Natural Questions development set containing 50 documents
-doc_dir = "../data/nq"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/nq_dev_subset_v2.json.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-
-```python
-# make sure these indices do not collide with existing ones, the indices will be wiped clean before data is inserted
-doc_index = "tutorial5_docs"
-label_index = "tutorial5_labels"
-```
-
-
-```python
-# Connect to Elasticsearch
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-# Connect to Elasticsearch
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document",
- create_index=False, embedding_field="emb",
- embedding_dim=768, excluded_meta_data=["emb"])
-```
-
-
-```python
-from haystack.preprocessor import PreProcessor
-
-# Add evaluation data to Elasticsearch Document Store
-# We first delete the custom tutorial indices to not have duplicate elements
-# and also split our documents into shorter passages using the PreProcessor
-preprocessor = PreProcessor(
- split_length=500,
- split_overlap=0,
- split_respect_sentence_boundary=False,
- clean_empty_lines=False,
- clean_whitespace=False
-)
-document_store.delete_all_documents(index=doc_index)
-document_store.delete_all_documents(index=label_index)
-document_store.add_eval_data(
- filename="../data/nq/nq_dev_subset_v2.json",
- doc_index=doc_index,
- label_index=label_index,
- preprocessor=preprocessor
-)
-
-# Let's prepare the labels that we need for the retriever and the reader
-labels = document_store.get_all_labels_aggregated(index=label_index)
-```
-
-## Initialize components of QA-System
-
-
-```python
-# Initialize Retriever
-from haystack.retriever.sparse import ElasticsearchRetriever
-retriever = ElasticsearchRetriever(document_store=document_store)
-# Alternative: Evaluate DensePassageRetriever
-# Note, that DPR works best when you index short passages < 512 tokens as only those tokens will be used for the embedding.
-# Here, for nq_dev_subset_v2.json we have avg. num of tokens = 5220(!).
-# DPR still outperforms Elastic's BM25 by a small margin here.
-# from haystack.retriever.dense import DensePassageRetriever
-# retriever = DensePassageRetriever(document_store=document_store,
-# query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
-# passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
-# use_gpu=True,
-# embed_title=True,
-# max_seq_len=256,
-# batch_size=16,
-# remove_sep_tok_from_untitled_passages=True)
-#document_store.update_embeddings(retriever, index=doc_index)
-```
-
-
-```python
-# Initialize Reader
-from haystack.reader.farm import FARMReader
-
-reader = FARMReader("deepset/roberta-base-squad2", top_k_per_candidate=4, return_no_answer=True)
-
-```
-
-
-```python
-from haystack.eval import EvalAnswers, EvalDocuments
-
-# Here we initialize the nodes that perform evaluation
-eval_retriever = EvalDocuments()
-eval_reader = EvalAnswers()
-```
-
-## Evaluation of Retriever
-Here we evaluate only the retriever, based on whether the gold_label document is retrieved.
-
-
-```python
-## Evaluate Retriever on its own
-retriever_eval_results = retriever.eval(top_k=20, label_index=label_index, doc_index=doc_index)
-## Retriever Recall is the proportion of questions for which the correct document containing the answer is
-## among the correct documents
-print("Retriever Recall:", retriever_eval_results["recall"])
-## Retriever Mean Avg Precision rewards retrievers that give relevant documents a higher rank
-print("Retriever Mean Avg Precision:", retriever_eval_results["map"])
-```
-
-## Evaluation of Reader
-Here we evaluate only the reader in a closed domain fashion i.e. the reader is given one query
-and one document and metrics are calculated on whether the right position in this text is selected by
-the model as the answer span (i.e. SQuAD style)
-
-
-```python
-# Evaluate Reader on its own
-reader_eval_results = reader.eval(document_store=document_store, device=device, label_index=label_index, doc_index=doc_index)
-# Evaluation of Reader can also be done directly on a SQuAD-formatted file without passing the data to Elasticsearch
-#reader_eval_results = reader.eval_on_file("../data/nq", "nq_dev_subset_v2.json", device=device)
-
-## Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer
-print("Reader Top-N-Accuracy:", reader_eval_results["top_n_accuracy"])
-## Reader Exact Match is the proportion of questions where the predicted answer is exactly the same as the correct answer
-print("Reader Exact Match:", reader_eval_results["EM"])
-## Reader F1-Score is the average overlap between the predicted answers and the correct answers
-print("Reader F1-Score:", reader_eval_results["f1"])
-```
-
-## Evaluation of Retriever and Reader (Open Domain)
-Here we evaluate retriever and reader in open domain fashion i.e. a document is considered
-correctly retrieved if it contains the answer string within it. The reader is evaluated based purely on the
-predicted string, regardless of which document this came from and the position of the extracted span.
-
-
-```python
-from haystack import Pipeline
-
-# Here is the pipeline definition
-p = Pipeline()
-p.add_node(component=retriever, name="ESRetriever", inputs=["Query"])
-p.add_node(component=eval_retriever, name="EvalRetriever", inputs=["ESRetriever"])
-p.add_node(component=reader, name="QAReader", inputs=["EvalRetriever"])
-p.add_node(component=eval_reader, name="EvalReader", inputs=["QAReader"])
-results = []
-```
-
-
-```python
-# This is how to run the pipeline
-for l in labels:
- res = p.run(
- query=l.question,
- top_k_retriever=10,
- labels=l,
- top_k_reader=10,
- index=doc_index,
- )
- results.append(res)
-```
-
-
-```python
-# When we have run evaluation using the pipeline, we can print the results
-n_queries = len(labels)
-eval_retriever.print()
-print()
-retriever.print_time()
-print()
-eval_reader.print(mode="reader")
-print()
-reader.print_time()
-print()
-eval_reader.print(mode="pipeline")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/6.md b/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/6.md
deleted file mode 100644
index 4f47974eb..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/6.md
+++ /dev/null
@@ -1,241 +0,0 @@
----
-title: "Tutorial 6"
-metaTitle: "Better retrieval via Dense Passage Retrieval"
-metaDescription: ""
-slug: "/docs/tutorial6"
-date: "2020-09-03"
-id: "tutorial6md"
----
-
-# Better Retrieval via "Dense Passage Retrieval"
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb)
-
-### Importance of Retrievers
-
-The Retriever has a huge impact on the performance of our overall search pipeline.
-
-
-### Different types of Retrievers
-#### Sparse
-Family of algorithms based on counting the occurrences of words (bag-of-words) resulting in very sparse vectors with length = vocab size.
-
-**Examples**: BM25, TF-IDF
-
-**Pros**: Simple, fast, well explainable
-
-**Cons**: Relies on exact keyword matches between query and text
-
-
-#### Dense
-These retrievers use neural network models to create "dense" embedding vectors. Within this family there are two different approaches:
-
-a) Single encoder: Use a **single model** to embed both query and passage.
-b) Dual-encoder: Use **two models**, one to embed the query and one to embed the passage
-
-Recent work suggests that dual encoders work better, likely because they can deal better with the different nature of query and passage (length, style, syntax ...).
-
-**Examples**: REALM, DPR, Sentence-Transformers
-
-**Pros**: Captures semantinc similarity instead of "word matches" (e.g. synonyms, related topics ...)
-
-**Cons**: Computationally more heavy, initial training of model
-
-
-### "Dense Passage Retrieval"
-
-In this Tutorial, we want to highlight one "Dense Dual-Encoder" called Dense Passage Retriever.
-It was introdoced by Karpukhin et al. (2020, https://arxiv.org/abs/2004.04906.
-
-Original Abstract:
-
-_"Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks."_
-
-Paper: https://arxiv.org/abs/2004.04906
-Original Code: https://fburl.com/qa-dpr
-
-
-*Use this* [link](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb) *to open the notebook in Google Colab.*
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-### Document Store
-
-#### Option 1: FAISS
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-The default flavour of FAISSDocumentStore is "Flat" but can also be set to "HNSW" for
-faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.
-For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-
-
-```python
-from haystack.document_store import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
-```
-
-#### Option 2: Milvus
-
-Milvus is an open source database library that is also optimized for vector similarity searches like FAISS.
-Like FAISS it has both a "Flat" and "HNSW" mode but it outperforms FAISS when it comes to dynamic data management.
-It does require a little more setup, however, as it is run through Docker and requires the setup of some config files.
-See [their docs](https://milvus.io/docs/v1.0.0/milvus_docker-cpu.md) for more details.
-
-
-```python
-from haystack.utils import launch_milvus
-from haystack.document_store import MilvusDocumentStore
-
-launch_milvus()
-document_store = MilvusDocumentStore()
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever, Reader, & Finder
-
-#### Retriever
-
-**Here:** We use a `DensePassageRetriever`
-
-**Alternatives:**
-
-- The `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-
-
-```python
-from haystack.retriever.dense import DensePassageRetriever
-retriever = DensePassageRetriever(document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- max_seq_len_query=64,
- max_seq_len_passage=256,
- batch_size=16,
- use_gpu=True,
- embed_title=True,
- use_fast_tokenizers=True)
-# Important:
-# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all
-# previously indexed documents and update their embedding representation.
-# While this can be a time consuming operation (depending on corpus size), it only needs to be done once.
-# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.
-document_store.update_embeddings(retriever)
-```
-
-#### Reader
-
-Similar to previous Tutorials we now initalize our reader.
-
-Here we use a FARMReader with the *deepset/roberta-base-squad2* model (see: https://huggingface.co/deepset/roberta-base-squad2)
-
-
-
-##### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import ExtractiveQAPipeline
-pipe = ExtractiveQAPipeline(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = pipe.run(query="Who created the Dothraki vocabulary?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/7.md b/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/7.md
deleted file mode 100644
index 3e472df19..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/7.md
+++ /dev/null
@@ -1,217 +0,0 @@
----
-title: "Tutorial 7"
-metaTitle: "Generative QA with RAG"
-metaDescription: ""
-slug: "/docs/tutorial7"
-date: "2020-11-12"
-id: "tutorial7md"
----
-
-# Generative QA with "Retrieval-Augmented Generation"
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial7_RAG_Generator.ipynb)
-
-While extractive QA highlights the span of text that answers a query,
-generative QA can return a novel text answer that it has composed.
-In this tutorial, you will learn how to set up a generative system using the
-[RAG model](https://arxiv.org/abs/2005.11401) which conditions the
-answer generator on a set of retrieved documents.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-Here are the packages and imports that we'll need:
-
-
-```python
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from typing import List
-import requests
-import pandas as pd
-from haystack import Document
-from haystack.document_store.faiss import FAISSDocumentStore
-from haystack.generator.transformers import RAGenerator
-from haystack.retriever.dense import DensePassageRetriever
-```
-
-Let's download a csv containing some sample text and preprocess the data.
-
-
-
-```python
-# Download sample
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/haystack/master/tutorials/small_generator_dataset.csv")
-open('small_generator_dataset.csv', 'wb').write(temp.content)
-
-# Create dataframe with columns "title" and "text"
-df = pd.read_csv("small_generator_dataset.csv", sep=',')
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-
-print(df.head())
-```
-
-We can cast our data into Haystack Document objects.
-Alternatively, we can also just use dictionaries with "text" and "meta" fields
-
-
-```python
-# Use data to initialize Document objects
-titles = list(df["title"].values)
-texts = list(df["text"].values)
-documents: List[Document] = []
-for title, text in zip(titles, texts):
- documents.append(
- Document(
- text=text,
- meta={
- "name": title or ""
- }
- )
- )
-```
-
-Here we initialize the FAISSDocumentStore, DensePassageRetriever and RAGenerator.
-FAISS is chosen here since it is optimized vector storage.
-
-
-```python
-# Initialize FAISS document store.
-# Set `return_embedding` to `True`, so generator doesn't have to perform re-embedding
-document_store = FAISSDocumentStore(
- faiss_index_factory_str="Flat",
- return_embedding=True
-)
-
-# Initialize DPR Retriever to encode documents, encode question and query documents
-retriever = DensePassageRetriever(
- document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- use_gpu=True,
- embed_title=True,
-)
-
-# Initialize RAG Generator
-generator = RAGenerator(
- model_name_or_path="facebook/rag-token-nq",
- use_gpu=True,
- top_k=1,
- max_length=200,
- min_length=2,
- embed_title=True,
- num_beams=2,
-)
-```
-
-We write documents to the DocumentStore, first by deleting any remaining documents then calling `write_documents()`.
-The `update_embeddings()` method uses the retriever to create an embedding for each document.
-
-
-
-```python
-# Delete existing documents in documents store
-document_store.delete_all_documents()
-
-# Write documents to document store
-document_store.write_documents(documents)
-
-# Add documents embeddings to index
-document_store.update_embeddings(
- retriever=retriever
-)
-```
-
-Here are our questions:
-
-
-```python
-QUESTIONS = [
- "who got the first nobel prize in physics",
- "when is the next deadpool movie being released",
- "which mode is used for short wave broadcast service",
- "who is the owner of reading football club",
- "when is the next scandal episode coming out",
- "when is the last time the philadelphia won the superbowl",
- "what is the most current adobe flash player version",
- "how many episodes are there in dragon ball z",
- "what is the first step in the evolution of the eye",
- "where is gall bladder situated in human body",
- "what is the main mineral in lithium batteries",
- "who is the president of usa right now",
- "where do the greasers live in the outsiders",
- "panda is a national animal of which country",
- "what is the name of manchester united stadium",
-]
-```
-
-Now let's run our system!
-The retriever will pick out a small subset of documents that it finds relevant.
-These are used to condition the generator as it generates the answer.
-What it should return then are novel text spans that form and answer to your question!
-
-
-```python
-# Now generate an answer for each question
-for question in QUESTIONS:
- # Retrieve related documents from retriever
- retriever_results = retriever.retrieve(
- query=question
- )
-
- # Now generate answer from question and retrieved documents
- predicted_result = generator.predict(
- query=question,
- documents=retriever_results,
- top_k=1
- )
-
- # Print you answer
- answers = predicted_result["answers"]
- print(f'Generated answer is \'{answers[0]["answer"]}\' for the question = \'{question}\'')
-```
-
-
-```python
-# Or alternatively use the Pipeline class
-from haystack.pipeline import GenerativeQAPipeline
-
-pipe = GenerativeQAPipeline(generator=generator, retriever=retriever)
-for question in QUESTIONS:
- res = pipe.run(query=question, top_k_generator=1, top_k_retriever=5)
- print(res)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/8.md b/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/8.md
deleted file mode 100644
index f630573d2..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/8.md
+++ /dev/null
@@ -1,215 +0,0 @@
----
-title: "Tutorial 8"
-metaTitle: "Preprocessing"
-metaDescription: ""
-slug: "/docs/tutorial8"
-date: "2021-01-08"
-id: "tutorial8md"
----
-
-# Preprocessing
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial8_Preprocessing.ipynb)
-
-Haystack includes a suite of tools to extract text from different file types, normalize white space
-and split text into smaller pieces to optimize retrieval.
-These data preprocessing steps can have a big impact on the systems performance and effective handling of data is key to getting the most out of Haystack.
-
-Ultimately, Haystack expects data to be provided as a list documents in the following dictionary format:
-``` python
-docs = [
- {
- 'text': DOCUMENT_TEXT_HERE,
- 'meta': {'name': DOCUMENT_NAME, ...}
- }, ...
-]
-```
-
-This tutorial will show you all the tools that Haystack provides to help you cast your data into this format.
-
-
-```python
-# Let's start by installing Haystack
-
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-!wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.03.tar.gz
-!tar -xvf xpdf-tools-linux-4.03.tar.gz && sudo cp xpdf-tools-linux-4.03/bin64/pdftotext /usr/local/bin
-```
-
-
-```python
-# Here are the imports we need
-
-from haystack.file_converter.txt import TextConverter
-from haystack.file_converter.pdf import PDFToTextConverter
-from haystack.file_converter.docx import DocxToTextConverter
-
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.preprocessor.preprocessor import PreProcessor
-```
-
-
-```python
-# This fetches some sample files to work with
-
-doc_dir = "data/preprocessing_tutorial"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/preprocessing_tutorial.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-## Converters
-
-Haystack's converter classes are designed to help you turn files on your computer into the documents
-that can be processed by the Haystack pipeline.
-There are file converters for txt, pdf, docx files as well as a converter that is powered by Apache Tika.
-
-
-```python
-# Here are some examples of how you would use file converters
-
-converter = TextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_txt = converter.convert(file_path="data/preprocessing_tutorial/classics.txt", meta=None)
-
-converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_pdf = converter.convert(file_path="data/preprocessing_tutorial/bert.pdf", meta=None)
-
-converter = DocxToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_docx = converter.convert(file_path="data/preprocessing_tutorial/heavy_metal.docx", meta=None)
-
-```
-
-
-```python
-# Haystack also has a convenience function that will automatically apply the right converter to each file in a directory.
-
-all_docs = convert_files_to_dicts(dir_path="data/preprocessing_tutorial")
-```
-
-## PreProcessor
-
-The PreProcessor class is designed to help you clean text and split text into sensible units.
-File splitting can have a very significant impact on the system's performance and is absolutely mandatory for Dense Passage Retrieval models.
-In general, we recommend you split the text from your files into small documents of around 100 words for dense retrieval methods
-and no more than 10,000 words for sparse methods.
-Have a look at the [Preprocessing](https://haystack.deepset.ai/docs/latest/preprocessingmd)
-and [Optimization](https://haystack.deepset.ai/docs/latest/optimizationmd) pages on our website for more details.
-
-
-```python
-# This is a default usage of the PreProcessor.
-# Here, it performs cleaning of consecutive whitespaces
-# and splits a single large document into smaller documents.
-# Each document is up to 1000 words long and document breaks cannot fall in the middle of sentences
-# Note how the single document passed into the document gets split into 5 smaller documents
-
-preprocessor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=False,
- split_by="word",
- split_length=100,
- split_respect_sentence_boundary=True
-)
-docs_default = preprocessor.process(doc_txt)
-print(f"n_docs_input: 1\nn_docs_output: {len(docs_default)}")
-```
-
-## Cleaning
-
-- `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
-- `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
-- `clean_header_footer` will remove any long header or footer texts that are repeated on each page
-
-## Splitting
-By default, the PreProcessor will respect sentence boundaries, meaning that documents will not start or end
-midway through a sentence.
-This will help reduce the possibility of answer phrases being split between two documents.
-This feature can be turned off by setting `split_respect_sentence_boundary=False`.
-
-
-```python
-# Not respecting sentence boundary vs respecting sentence boundary
-
-preprocessor_nrsb = PreProcessor(split_respect_sentence_boundary=False)
-docs_nrsb = preprocessor_nrsb.process(doc_txt)
-
-print("RESPECTING SENTENCE BOUNDARY")
-end_text = docs_default[0]["text"][-50:]
-print("End of document: \"..." + end_text + "\"")
-print()
-print("NOT RESPECTING SENTENCE BOUNDARY")
-end_text_nrsb = docs_nrsb[0]["text"][-50:]
-print("End of document: \"..." + end_text_nrsb + "\"")
-```
-
-A commonly used strategy to split long documents, especially in the field of Question Answering,
-is the sliding window approach. If `split_length=10` and `split_overlap=3`, your documents will look like this:
-
-- doc1 = words[0:10]
-- doc2 = words[7:17]
-- doc3 = words[14:24]
-- ...
-
-You can use this strategy by following the code below.
-
-
-```python
-# Sliding window approach
-
-preprocessor_sliding_window = PreProcessor(
- split_overlap=3,
- split_length=10,
- split_respect_sentence_boundary=False
-)
-docs_sliding_window = preprocessor_sliding_window.process(doc_txt)
-
-doc1 = docs_sliding_window[0]["text"][:200]
-doc2 = docs_sliding_window[1]["text"][:100]
-doc3 = docs_sliding_window[2]["text"][:100]
-
-print("Document 1: \"" + doc1 + "...\"")
-print("Document 2: \"" + doc2 + "...\"")
-print("Document 3: \"" + doc3 + "...\"")
-```
-
-## Bringing it all together
-
-
-```python
-all_docs = convert_files_to_dicts(dir_path="data/preprocessing_tutorial")
-preprocessor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=False,
- split_by="word",
- split_length=100,
- split_respect_sentence_boundary=True
-)
-nested_docs = [preprocessor.process(d) for d in all_docs]
-docs = [d for x in nested_docs for d in x]
-
-print(f"n_files_input: {len(all_docs)}\nn_docs_output: {len(docs)}")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
-
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/9.md b/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/9.md
deleted file mode 100644
index 366b7487e..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/9.md
+++ /dev/null
@@ -1,250 +0,0 @@
----
-title: "Tutorial 9"
-metaTitle: "Training a Dense Passage Retrieval model"
-metaDescription: ""
-slug: "/docs/tutorial9"
-date: "2021-01-08"
-id: "tutorial9md"
----
-
-# Training Your Own "Dense Passage Retrieval" Model
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial9_DPR_training.ipynb)
-
-Haystack contains all the tools needed to train your own Dense Passage Retrieval model.
-This tutorial will guide you through the steps required to create a retriever that is specifically tailored to your domain.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-```
-
-
-```python
-# Here are some imports that we'll need
-
-from haystack.retriever.dense import DensePassageRetriever
-from haystack.preprocessor.utils import fetch_archive_from_http
-from haystack.document_store.memory import InMemoryDocumentStore
-```
-
-## Training Data
-
-DPR training performed using Information Retrieval data.
-More specifically, you want to feed in pairs of queries and relevant documents.
-
-To train a model, we will need a dataset that has the same format as the original DPR training data.
-Each data point in the dataset should have the following dictionary structure.
-
-``` python
- {
- "dataset": str,
- "question": str,
- "answers": list of str
- "positive_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- "negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- "hard_negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- }
-```
-
-`positive_ctxs` are context passages which are relevant to the query.
-In some datasets, queries might have more than one positive context
-in which case you can set the `num_positives` parameter to be higher than the default 1.
-Note that `num_positives` needs to be lower or equal to the minimum number of `positive_ctxs` for queries in your data.
-If you have an unequal number of positive contexts per example,
-you might want to generate some soft labels by retrieving similar contexts which contain the answer.
-
-DPR is standardly trained using a method known as in-batch negatives.
-This means that positive contexts for a given query are treated as negative contexts for the other queries in the batch.
-Doing so allows for a high degree of computational efficiency, thus allowing the model to be trained on large amounts of data.
-
-`negative_ctxs` is not actually used in Haystack's DPR training so we recommend you set it to an empty list.
-They were used by the original DPR authors in an experiment to compare it against the in-batch negatives method.
-
-`hard_negative_ctxs` are passages that are not relevant to the query.
-In the original DPR paper, these are fetched using a retriever to find the most relevant passages to the query.
-Passages which contain the answer text are filtered out.
-
-If you'd like to convert your SQuAD format data into something that can train a DPR model,
-check out the utility script at [`haystack/retriever/squad_to_dpr.py`](https://github.com/deepset-ai/haystack/blob/master/haystack/retriever/squad_to_dpr.py)
-
-## Using Question Answering Data
-
-Question Answering datasets can sometimes be used as training data.
-Google's Natural Questions dataset, is sufficiently large
-and contains enough unique passages, that it can be converted into a DPR training set.
-This is done simply by considering answer containing passages as relevant documents to the query.
-
-The SQuAD dataset, however, is not as suited to this use case since its question and answer pairs
-are created on only a very small slice of wikipedia documents.
-
-## Download Original DPR Training Data
-
-WARNING: These files are large! The train set is 7.4GB and the dev set is 800MB
-
-We can download the original DPR training data with the following cell.
-Note that this data is probably only useful if you are trying to train from scratch.
-
-
-```python
-# Download original DPR data
-# WARNING: the train set is 7.4GB and the dev set is 800MB
-
-doc_dir = "data/dpr_training/"
-
-s3_url_train = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-train.json.gz"
-s3_url_dev = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz"
-
-fetch_archive_from_http(s3_url_train, output_dir=doc_dir + "train/")
-fetch_archive_from_http(s3_url_dev, output_dir=doc_dir + "dev/")
-```
-
-## Option 1: Training DPR from Scratch
-
-The default variables that we provide below are chosen to train a DPR model from scratch.
-Here, both passage and query embedding models are initialized using BERT base
-and the model is trained using Google's Natural Questions dataset (in a format specialised for DPR).
-
-If you are working in a language other than English,
-you will want to initialize the passage and query embedding models with a language model that supports your language
-and also provide a dataset in your language.
-
-
-```python
-# Here are the variables to specify our training data, the models that we use to initialize DPR
-# and the directory where we'll be saving the model
-
-doc_dir = "data/dpr_training/"
-
-train_filename = "train/biencoder-nq-train.json"
-dev_filename = "dev/biencoder-nq-dev.json"
-
-query_model = "bert-base-uncased"
-passage_model = "bert-base-uncased"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Option 2: Finetuning DPR
-
-If you have your own domain specific question answering or information retrieval dataset,
-you might instead be interested in finetuning a pretrained DPR model.
-In this case, you would initialize both query and passage models using the original pretrained model.
-You will want to load something like this set of variables instead of the ones above
-
-
-```python
-# Here are the variables you might want to use instead of the set above
-# in order to perform pretraining
-
-doc_dir = "PATH_TO_YOUR_DATA_DIR"
-train_filename = "TRAIN_FILENAME"
-dev_filename = "DEV_FILENAME"
-
-query_model = "facebook/dpr-question_encoder-single-nq-base"
-passage_model = "facebook/dpr-ctx_encoder-single-nq-base"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Initialization
-
-Here we want to initialize our model either with plain language model weights for training from scratch
-or else with pretrained DPR weights for finetuning.
-We follow the [original DPR parameters](https://github.com/facebookresearch/DPR#best-hyperparameter-settings)
-for their max passage length but set max query length to 64 since queries are very rarely longer.
-
-
-```python
-## Initialize DPR model
-
-retriever = DensePassageRetriever(
- document_store=InMemoryDocumentStore(),
- query_embedding_model=query_model,
- passage_embedding_model=passage_model,
- max_seq_len_query=64,
- max_seq_len_passage=256
-)
-```
-
-## Training
-
-Let's start training and save our trained model!
-
-On a V100 GPU, you can fit up to batch size 16 so we set gradient accumulation steps to 8 in order
-to simulate the batch size 128 of the original DPR experiment.
-
-When `embed_title=True`, the document title is prepended to the input text sequence with a `[SEP]` token
-between it and document text.
-
-When training from scratch with the above variables, 1 epoch takes around an hour and we reached the following performance:
-
-```
-loss: 0.046580662854042276
-task_name: text_similarity
-acc: 0.992524064068483
-f1: 0.8804297774366846
-acc_and_f1: 0.9364769207525838
-average_rank: 0.19631619339984652
-report:
- precision recall f1-score support
-
-hard_negative 0.9961 0.9961 0.9961 201887
- positive 0.8804 0.8804 0.8804 6515
-
- accuracy 0.9925 208402
- macro avg 0.9383 0.9383 0.9383 208402
- weighted avg 0.9925 0.9925 0.9925 208402
-
-```
-
-
-```python
-# Start training our model and save it when it is finished
-
-retriever.train(
- data_dir=doc_dir,
- train_filename=train_filename,
- dev_filename=dev_filename,
- test_filename=dev_filename,
- n_epochs=1,
- batch_size=16,
- grad_acc_steps=8,
- save_dir=save_dir,
- evaluate_every=3000,
- embed_title=True,
- num_positives=1,
- num_hard_negatives=1
-)
-```
-
-## Loading
-
-Loading our newly trained model is simple!
-
-
-```python
-reloaded_retriever = DensePassageRetriever.load(load_dir=save_dir, document_store=None)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/annotation.md b/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/annotation.md
deleted file mode 100644
index ed29d18c8..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/annotation.md
+++ /dev/null
@@ -1,47 +0,0 @@
----
-title: "Annotation Tool"
-metaTitle: "Annotation Tool"
-metaDescription: ""
-slug: "/docs/annotation"
-date: "2020-09-03"
-id: "annotationmd"
----
-
-# Annotation Tool
-
-- Create labels with different techniques: Come up with questions (+ answers) while reading passages (SQuAD style) or have a set of predefined questions and look for answers in the document (~ Natural Questions).
-- Structure your work via organizations, projects, users
-- Upload your documents or import labels from an existing SQuAD-style dataset
-- Export your labels in SQuAD Format
-
-
-
-# Hosted version
- Signup here: [Haystack Annotation Tool](https://annotate.deepset.ai/login)
-
-# Local version (Docker)
-
-1. Configure credentials & database in the [`docker-compose.yml`](https://github.com/deepset-ai/haystack/blob/master/annotation_tool/docker-compose.yml):
-
-The credentials should match in database image and application configuration.
-
- DEFAULT_ADMIN_EMAIL: "example@example.com"
- DEFAULT_ADMIN_PASSWORD: "DEMO-PASSWORD"
-
- PROD_DB_NAME: "databasename"
- PROD_DB_USERNAME: "somesafeuser"
- PROD_DB_PASSWORD: "somesafepassword"
-
-
- POSTGRES_USER: "somesafeuser"
- POSTGRES_PASSWORD: "somesafepassword"
- POSTGRES_DB: "databasename"
-
-
-2. Run docker-compose by executing `docker-compose up`.
-
-
-3. The UI should be available at `localhost:7001`.
-
-# Usage
-The manual (of a slightly earlier version) can be found [here](https://drive.google.com/file/d/1Wv3OIC0Z7ibHIzOm9Xw_r0gjTFmpl-33/view). While it doesn't include all latest features, the basic workflow and tips for label quality are still the same.
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/domain_adaptation.md b/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/domain_adaptation.md
deleted file mode 100644
index 9bd415e61..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/domain_adaptation.md
+++ /dev/null
@@ -1,157 +0,0 @@
----
-title: "Domain Adaptation"
-metaTitle: "Domain Adaptation"
-metaDescription: ""
-slug: "/docs/domain_adaptation"
-date: "2020-09-03"
-id: "domain_adaptationmd"
----
-
-# Domain Adaptation
-
-## Generalisation
-
-In our experience, language models trained on SQuAD show very strong general question answering capabilities.
-Though SQuAD is composed entirely of Wikipedia articles, these models are flexible enough to deal with many different styles of text.
-
-Before trying to adapt these models to your domain, we’d recommend trying one of the off the shelf models.
-We’ve found that these models are often flexible enough for a wide range of use cases.
-
-
#### Our Recommendations
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/domain_adaptation.md b/docs/v0.9.0/usage/domain_adaptation.mdx
similarity index 70%
rename from src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/domain_adaptation.md
rename to docs/v0.9.0/usage/domain_adaptation.mdx
index 9bd415e61..614be50d7 100644
--- a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/domain_adaptation.md
+++ b/docs/v0.9.0/usage/domain_adaptation.mdx
@@ -1,12 +1,3 @@
----
-title: "Domain Adaptation"
-metaTitle: "Domain Adaptation"
-metaDescription: ""
-slug: "/docs/domain_adaptation"
-date: "2020-09-03"
-id: "domain_adaptationmd"
----
-
# Domain Adaptation
## Generalisation
@@ -17,7 +8,7 @@ Though SQuAD is composed entirely of Wikipedia articles, these models are flexib
Before trying to adapt these models to your domain, we’d recommend trying one of the off the shelf models.
We’ve found that these models are often flexible enough for a wide range of use cases.
-
-
-
- );
-
-};
-
-export const query = graphql`
- query BenchmarkQuery {
- allFile(filter: {absolutePath: {regex: "/(?:benchmarks)/"}}) {
- edges {
- node {
- absolutePath
- childMapJson {
- axis {
- y
- x
- }
- chart_type
- columns
- data {
- map
- n_docs
- model
- }
- description
- id
- title
- subtitle
- }
- childSpeedJson {
- axis {
- x
- y
- }
- chart_type
- columns
- data {
- model
- n_docs
- query_speed
- }
- description
- subtitle
- title
- }
- childPerformanceJson {
- axes {
- label
- time_label
- time_side
- }
- bars
- chart_type
- columns
- data {
- F1
- Model
- index_speed
- Speed
- model
- n_docs
- query_speed
- map
- }
- description
- series {
- s0
- s1
- s2
- }
- subtitle
- title
- }
- }
- }
- }
- }
- `
-
-
-export default BenchMarks
\ No newline at end of file
diff --git a/src/pages/community/join.js b/src/pages/community/join.js
deleted file mode 100644
index aecd52b55..000000000
--- a/src/pages/community/join.js
+++ /dev/null
@@ -1,271 +0,0 @@
-import React, { useState } from 'react';
-import Layout from '../../components/layout/layout';
-import './join.scss';
-import SEO from "../../components/seo";
-import Logo from "../../images/haystack_logo_blue_banner_social.png"
-
-
-import FormControl from '@material-ui/core/FormControl';
-import InputLabel from '@material-ui/core/InputLabel';
-import Input from '@material-ui/core/Input';
-import FormControlLabel from '@material-ui/core/FormControlLabel';
-import Checkbox from '@material-ui/core/Checkbox';
-import Snackbar from '@material-ui/core/Snackbar';
-import Alert from '@material-ui/lab/Alert';
-import Button from "../../components/landing-page/button";
-
-import OpenSource from "../../images/haystack_logo_blue_banner.png"
-
-const BetaPage = () => {
-
- const [email, setEmail] = useState('');
- const [github, setGithub] = useState('');
- const [twitter, setTwitter] = useState('');
- const [company, setCompany] = useState('');
- const [fname, setFName] = useState('');
- const [lname, setLName] = useState('');
- const [consentToProcess, setConsentToProcess] = useState(false);
- const [communications, setCommunications] = useState(true);
- const [success, setSuccess] = React.useState(false);
- const [message, setMessage] = React.useState("Thank you for joining our community!");
- const [severity, setSeverity] = React.useState("success");
- const vertical='top';
- const horizontal='center';
-
- const handleSubmit = (event) => {
- event.preventDefault();
-
- // Hubspot integration
- // Create the new request
- var xhr = new XMLHttpRequest();
- var url = 'https://api.hsforms.com/submissions/v3/integration/submit/4561480/28f3118d-1c2b-4a93-9c8f-f11e4454f1ac';
-
- // JSON:
- var data = {
- "submittedAt": new Date().getTime(),
- "fields": [
- {
- "name": "email",
- "value": email
- },
- {
- "name": "github",
- "value": github
- },
- {
- "name": "twitter",
- "value": twitter
- },
- {
- "name": "company",
- "value": company
- },
- {
- "name": "firstname",
- "value": fname
- },
- {
- "name": "lastname",
- "value": lname
- }
- ],
- "context": {
- "pageUri": "haystack.deepset.ai",
- "pageName": "Haystack Hub"
- },
- "legalConsentOptions":{ // Include this object when GDPR options are enabled
- "consent":{
- "consentToProcess":consentToProcess,
- "text":"I agree to allow deepset GmbH to store and process my personal data.",
- "communications":[
- {
- "value":communications,
- "subscriptionTypeId":999,
- "text":"I agree to receive marketing communications from deepset GmbH."
- }
- ]
- }
- }
- };
-
- var final_data = JSON.stringify(data);
-
- xhr.open('POST', url);
- // Sets the value of the 'Content-Type' HTTP request headers to 'application/json'
- xhr.setRequestHeader('Content-Type', 'application/json');
-
- xhr.onreadystatechange = function() {
- if(xhr.readyState === 4 && xhr.status === 200) {
- setMessage("Thank you for joining our community!");
- setSeverity("success");
- setSuccess(true);// Returns a 200 response if the submission is successful.
-
- // Send invite
- // Create the new request
- //var xhrInvite = new XMLHttpRequest();
- //var urlInvite = 'https://p3e3737mri.execute-api.eu-central-1.amazonaws.com/default/haystack-slack-invite';
- //var dataInvite = {email: email};
- //var final_dataInvite = JSON.stringify(dataInvite);
-
- //xhrInvite.open('POST', urlInvite);
-
- //xhrInvite.onreadystatechange = function() {
- //console.log(xhrInvite);
- //}
-
- // Sends the request
- //xhrInvite.send(final_dataInvite);
- } else if (xhr.readyState === 4 && xhr.status === 400){
- setMessage("An error occurred. Please check your details.");
- setSeverity("error");
- setSuccess(true);// Returns Error
- } else if (xhr.readyState === 4 && xhr.status === 403){
- setMessage("An error occurred. Please check your details.");
- setSeverity("error");
- setSuccess(true);// Returns Error
- } else if (xhr.readyState === 4 && xhr.status === 404){
- setMessage("An error occurred. Please check your details.");
- setSeverity("error");
- setSuccess(true);// Returns Error
- } else {
- setMessage("An error occurred. Please check your details.");
- setSeverity("error");
- setSuccess(true);// Returns Error
- }
- }
-
- // Sends the request
- xhr.send(final_data);
- window.open(
- 'https://join.slack.com/t/haystack-community/shared_invite/zt-n9pgt0w1-J7YB_FM4dFeubQI2t6WD8w',
- '_blank'
- );
-
- };
-
- const handleChangeEmail = (event) => {
- setEmail(event.target.value);
- };
-
- const handleChangeGithub = (event) => {
- setGithub(event.target.value);
- };
-
- const handleChangeTwitter = (event) => {
- setTwitter(event.target.value);
- };
-
- const handleChangeCompany = (event) => {
- setCompany(event.target.value);
- };
-
- const handleChangeFName = (event) => {
- setFName(event.target.value);
- };
-
- const handleChangeLName = (event) => {
- setLName(event.target.value);
- };
-
- const handleChangeConsentToProcess = (event) => {
- setConsentToProcess(event.target.checked);
- };
-
- const handleCommunication = (event) => {
- setCommunications(event.target.checked);
- };
-
- const handleClose = (event, reason) => {
- if (reason === 'clickaway') {
- return;
- }
-
- setSuccess(false);
- };
-
-
- return (
-
+Loading Chart
}
- data={mapDataSpeed[selectedVersion]}
- options={{
- subtitle: subTitleSpeed[selectedVersion],
- colors: ['#22BA99', '#63C7CA', '#49B0E4', '#FBB14B'],
- hAxis: {
- title: axisXSpeed[selectedVersion]
- },
- vAxis: {
- title: axisYSpeed[selectedVersion]
- },
- pointSize: 5,
- legend: "bottom"
- }}
- />
-
-
**Intuition**
@@ -28,6 +19,7 @@ In the same way, many QA models have a good enough grasp of language to answer q
+
## Finetuning
@@ -43,7 +35,7 @@ reader.train(data_dir=train_data,
At the end of training, the finetuned model will be saved in the specified `save_dir` and can be loaded as a `Reader`.
-
+
+
+ ),
+ },
+ {
+ title: "Transformers",
+ content: (
+
**Recommendation**
@@ -53,57 +45,55 @@ please also check out Tutorial 5 which walks through the steps needed to perform
+
+
## Generating Labels
Using our [Haystack Annotate tool](https://annotate.deepset.ai/login) (Beta),
you can easily create a labelled dataset using your own documents featuring your own question/ answer pairs.
-
-
-
+
Features include:
+- Structured workspaces via organisations, projects and users
-* Structured workspaces via organisations, projects and users
-
-
-* Easy upload of your own documents and labels in a variety of formats (txt, pdf, SQuAD style)
+- Easy upload of your own documents and labels in a variety of formats (txt, pdf, SQuAD style)
-
-* Export of labels to be used directly in Haystack
+- Export of labels to be used directly in Haystack
Annotate also supports two different workflows:
+- Think up questions and answers while reading passages (SQuAD style)
-* Think up questions and answers while reading passages (SQuAD style)
-
+- Have a set of predefined questions and look for answers in the document (~ Natural Questions style)
-* Have a set of predefined questions and look for answers in the document (~ Natural Questions style)
+
## User Feedback
-A simpler and faster process to finetune models to your domain is to utilise user feedback.
-Dedicated annotation work can be costly and time consuming
-but user feedback is an efficient alternative since it allows for labels to be generated by users, for users,
+A simpler and faster process to finetune models to your domain is to utilise user feedback.
+Dedicated annotation work can be costly and time consuming
+but user feedback is an efficient alternative since it allows for labels to be generated by users, for users,
all while the system is already in production.
We, for example, have used a simple thumbs up / down system in our demos to allow
users to give feedback.
-
+
Through the Rest API, users can annotate each Haystack result as being either:
-* Correct
-* Correct document but wrong text
-* Wrong document and wrong text
+- Correct
+- Correct document but wrong text
+- Wrong document and wrong text
To get started, follow these steps:
-* Start up the REST API
- * The simplest way to do this is to call `docker-compose up` from the root directory of the Haystack repository
- * Alternatively, run `gunicorn rest_api.application:app -b 0.0.0.0:8000 -k uvicorn.workers.UvicornWorker -t 300`
-* Make a POST request to the `doc-qa-feedback` endpoint with the following payload:
+- Start up the REST API
+ - The simplest way to do this is to call `docker-compose up` from the root directory of the Haystack repository
+ - Alternatively, run `gunicorn rest_api.application:app -b 0.0.0.0:8000 -k uvicorn.workers.UvicornWorker -t 300`
+- Make a POST request to the `doc-qa-feedback` endpoint with the following payload:
+
```
{
"question": "string",
@@ -115,10 +105,12 @@ To get started, follow these steps:
"offset_start_in_doc": 0
}
```
+
To fill out all these values, you can use the response from an executed search request on the `doc-qa` endpoint.
-* Export your labels in SQuAD format by making a GET request to the `export-doc-qa-feedback` endpoint
-``` python
+- Export your labels in SQuAD format by making a GET request to the `export-doc-qa-feedback` endpoint
+
+```python
# SQUAD format
{
"data": [
@@ -146,12 +138,11 @@ To fill out all these values, you can use the response from an executed search r
}
```
-User feedback labelling also works with FAQ style QA.
+User feedback labelling also works with FAQ style QA.
Refer to the Swagger API documentation (http://127.0.0.1:8000/docs) for more details.
-This labelled data can then be used to
-augment the training data and enable `Reader` finetuning.
-Alternatively, they can also be used to form an evaluation set to
-measure the performance of trained models,
+This labelled data can then be used to
+augment the training data and enable `Reader` finetuning.
+Alternatively, they can also be used to form an evaluation set to
+measure the performance of trained models,
or monitor how well the model is doing in a live environment.
-
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/generator.md b/docs/v0.9.0/usage/generator.mdx
similarity index 71%
rename from src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/generator.md
rename to docs/v0.9.0/usage/generator.mdx
index 96d82d54e..ea38e1888 100644
--- a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/generator.md
+++ b/docs/v0.9.0/usage/generator.mdx
@@ -1,12 +1,3 @@
----
-title: "Generator"
-metaTitle: "Generator"
-metaDescription: ""
-slug: "/docs/generator"
-date: "2020-11-05"
-id: "generatormd"
----
-
# Generator
While extractive QA highlights the span of text that answers a query,
@@ -17,7 +8,7 @@ as well as passages provided to it with a retriever (non-parametric memory).
With the advent of Transformer based retrieval methods such as [Dense Passage Retrieval](https://arxiv.org/abs/2004.04906),
retriever and generator can be trained concurrently from the one loss signal.
-
+ .
@@ -65,21 +58,27 @@ hp:Gryffindor hp:founder hp:Godric_gryffindor .
...
```
-```GraphDBKnowledgeGraph.get_all_triples()``` returns all loaded triples in the form of subject, predicate, and object. It is helpful to check whether the loading of a .ttl file was successful.
+`GraphDBKnowledgeGraph.get_all_triples()` returns all loaded triples in the form of subject, predicate, and object. It is helpful to check whether the loading of a .ttl file was successful.
+
+`GraphDBKnowledgeGraph.query(sparql_query)` executes SPARQL queries on the knowledge graph. However, we usually do not want to use this method directly but use it through a retriever.
-```GraphDBKnowledgeGraph.query(sparql_query)``` executes SPARQL queries on the knowledge graph. However, we usually do not want to use this method directly but use it through a retriever.
+
## Text2SparqlRetriever
+
Text2SparqlRetriever can execute SPARQL queries translated from text but also any other custom SPARQL queries. Currently, it is the only implementation of the BaseGraphRetriever class.
Internally, Text2SparqlRetriever uses a pre-trained BART model to translate text questions to queries in SPARQL format.
-```Text2SparqlRetriever.retrieve(query)``` can be called with a text query, which is then automatically translated to a SPARQL query.
+`Text2SparqlRetriever.retrieve(query)` can be called with a text query, which is then automatically translated to a SPARQL query.
+
+`Text2SparqlRetriever._query_kg(sparql_query)` can be called with a SPARQL query.
-```Text2SparqlRetriever._query_kg(sparql_query)``` can be called with a SPARQL query.
+
## Trying Question Answering on Knowledge Graphs with Custom Data
+
If you want to use your custom data you would first need to have your custom knowledge graph in the format of a .ttl file.
-You can load your custom graph and execute SPARQL queries with ```Text2SparqlRetriever._query_kg(sparql_query)```. To allow the use of abbreviations of namespaces, GraphDBKnowledgeGraph needs to know about them:
+You can load your custom graph and execute SPARQL queries with `Text2SparqlRetriever._query_kg(sparql_query)`. To allow the use of abbreviations of namespaces, GraphDBKnowledgeGraph needs to know about them:
```
prefixes = """PREFIX rdf:
@@ -91,13 +90,13 @@ kg.prefixes = prefixes
If you suspect you are having issues because of abbreviations of namespaces not mapped correctly, you can always try to execute a SPARQL query with the full namespace:
-```Text2SparqlRetriever._query_kg(sparql_query="select distinct ?obj where { ?obj . }")```
+`Text2SparqlRetriever._query_kg(sparql_query="select distinct ?obj where { ?obj . }")`
instead of using the abbreviated form:
-```Text2SparqlRetriever._query_kg(sparql_query="select distinct ?obj where { hp:Hermione_granger hp:patronus ?obj . }")```
+`Text2SparqlRetriever._query_kg(sparql_query="select distinct ?obj where { hp:Hermione_granger hp:patronus ?obj . }")`
-If you would like to translate text queries to SPARQL queries for your custom data and use ```Text2SparqlRetriever.retrieve(query)```, there is significantly more effort necessary.
+If you would like to translate text queries to SPARQL queries for your custom data and use `Text2SparqlRetriever.retrieve(query)`, there is significantly more effort necessary.
We provide an exemplary pre-trained model in our [tutorial](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial10_Knowledge_Graph.py).
One limitation is that this pre-trained model can only generate questions about resources it has seen during training.
Otherwise, it cannot translate the name of the resource to the identifier used in the knowledge graph.
@@ -105,4 +104,4 @@ For example, it can translate "Harry" to "hp:Harry_potter" only because we train
Unfortunately, our pre-trained model for translating text queries does not work with your custom data.
Instead, you need to train your own model. It needs to be trained according to the [seq2seq example for summarization with BART in transformers](https://github.com/huggingface/transformers/tree/master/examples/legacy/seq2seq).
-Haystack currently does not support the training of text2sparql models. We dont have concrete plans to extend the funtionality, but we are more than open to contributions. Don't hesitate to reach out!
+Haystack currently does not support the training of text2sparql models. We dont have concrete plans to extend the funtionality, but we are more than open to contributions. Don't hesitate to reach out!
diff --git a/docs/v0.9.0/usage/languages.mdx b/docs/v0.9.0/usage/languages.mdx
new file mode 100644
index 000000000..c20a45ca9
--- /dev/null
+++ b/docs/v0.9.0/usage/languages.mdx
@@ -0,0 +1,199 @@
+# Languages Other Than English
+
+Haystack is well suited to open-domain QA on languages other than English.
+While our defaults are tuned for English,
+you will find some tips and tricks here for using Haystack in your language.
+
+
+
+## Preprocessor
+
+
+
+
+
+
+ ),
+ },
+ {
+ title: "Transformers",
+ content: (
+
**Tutorial**
@@ -27,10 +18,11 @@ or with LFQA ([here](/docs/v0.9.0/tutorial12md)).
Pros
-* More appropriately phrased answers
-* Able to synthesize information from different texts
-* Can draw on latent knowledge stored in language model
+
+- More appropriately phrased answers
+- Able to synthesize information from different texts
+- Can draw on latent knowledge stored in language model
Cons
-* Not easy to track what piece of information the generator is basing its response off of
+- Not easy to track what piece of information the generator is basing its response off of
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/knowledge_graph.md b/docs/v0.9.0/usage/knowledge_graph.mdx
similarity index 63%
rename from src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/knowledge_graph.md
rename to docs/v0.9.0/usage/knowledge_graph.mdx
index 65e8bb6cd..ec5c9d9f8 100644
--- a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/knowledge_graph.md
+++ b/docs/v0.9.0/usage/knowledge_graph.mdx
@@ -1,39 +1,32 @@
----
-title: "Knowledge Graph"
-metaTitle: "Knowledge Graph"
-metaDescription: ""
-slug: "/docs/knowledgegraph"
-date: "2021-04-19"
-id: "knowledgegraphmd"
----
-
# Question Answering on a Knowledge Graph
Haystack allows loading and querying knowledge graphs. In particular, Haystack can:
-
-* Load an existing knowledge graph given as a .ttl file
-* Execute SPARQL queries on a knowledge graph
-* Execute text queries on the knowledge graph by translating them to SPARQL queries with the help of a pre-trained seq2seq model
+
+- Load an existing knowledge graph given as a .ttl file
+- Execute SPARQL queries on a knowledge graph
+- Execute text queries on the knowledge graph by translating them to SPARQL queries with the help of a pre-trained seq2seq model
Haystack's knowledge graph functionalities are still in a very early stage. Thus, don't expect our [exemplary tutorial](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial10_Knowledge_Graph.py) to work on your custom dataset out-of-the-box.
Two classes implement the functionalities: GraphDBKnowledgeGraph and Text2SparqlRetriever.
+
+
## GraphDBKnowledgeGraph
GraphDBKnowledgeGraph is a triple store similar to Haystack's document stores. Currently, it is the only implementation of the BaseKnowledgeGraph class.
GraphDBKnowledgeGraph runs on GraphDB. The licensing of GraphDB is rather complicated and it's more than unfortunate that GraphDB cannot be used right away in colab notebooks.
On your local machine, you can start a GraphDB instance by running:
-```docker run -d -p 7200:7200 --name graphdb-instance-tutorial docker-registry.ontotext.com/graphdb-free:9.4.1-adoptopenjdk11```
+`docker run -d -p 7200:7200 --name graphdb-instance-tutorial docker-registry.ontotext.com/graphdb-free:9.4.1-adoptopenjdk11`
-By default, GraphDBKnowledgeGraph connects to a GraphDB instance running on localhost at port 7200.
+By default, GraphDBKnowledgeGraph connects to a GraphDB instance running on localhost at port 7200.
Similar to Haystack's ElasticsearchDocumentStore, the only additional setting needed is an index name.
(Note that GraphDB internally calls these indices repositories.)
-```kg = GraphDBKnowledgeGraph(index="tutorial_10_index")```
+`kg = GraphDBKnowledgeGraph(index="tutorial_10_index")`
-Indices can be deleted and created with ```GraphDBKnowledgeGraph.delete_index()``` and ```GraphDBKnowledgeGraph.create_index(config_path)```.
-```config_path``` needs to point to a .ttl file that contains configuration settings (see [GraphDB documentation](https://graphdb.ontotext.com/documentation/free/configuring-a-repository.html#configure-a-repository-programmatically) for details or use the file from our [tutorial](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial10_Knowledge_Graph.py)). It starts with something like:
+Indices can be deleted and created with `GraphDBKnowledgeGraph.delete_index()` and `GraphDBKnowledgeGraph.create_index(config_path)`.
+`config_path` needs to point to a .ttl file that contains configuration settings (see [GraphDB documentation](https://graphdb.ontotext.com/documentation/free/configuring-a-repository.html#configure-a-repository-programmatically) for details or use the file from our [tutorial](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial10_Knowledge_Graph.py)). It starts with something like:
```
#
@@ -51,7 +44,7 @@ Indices can be deleted and created with ```GraphDBKnowledgeGraph.delete_index()`
...
```
-GraphDBKnowledgeGraph can load an existing knowledge graph represented in the form of a .ttl file with the method ```GraphDBKnowledgeGraph.import_from_ttl_file(index, path)```, where path points to a ttl file starting with something like:
+GraphDBKnowledgeGraph can load an existing knowledge graph represented in the form of a .ttl file with the method `GraphDBKnowledgeGraph.import_from_ttl_file(index, path)`, where path points to a ttl file starting with something like:
```
@prefix rdf:
+
+**Note**
+
+This feature will be implemented by [this PR.](https://github.com/deepset-ai/haystack/pull/1160)
+
+
+
+The PreProcessor's sentence tokenization is language specific.
+If you are using the PreProcessor on a language other than English,
+make sure to set the `language` argument when initializing it.
+
+
+
+## Retrievers
+
+The sparse retriever methods themselves(BM25, TF-IDF) are language agnostic.
+Their only requirement is that the text be split into words.
+The ElasticsearchDocumentStore relies on an analyzer to impose word boundaries,
+but also to handle punctuation, casing and stop words.
+
+The default analyzer is an English analyzer.
+While it can still work decently for a large range of languages,
+you will want to set it to your language's analyzer for optimal performance.
+In some cases, such as with Thai, the default analyzer is completely incompatible.
+See [this page](https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-lang-analyzer.html)
+for the full list of language specific analyzers.
+
+```python
+from haystack.document_store import ElasticsearchDocumentStore
+
+document_store = ElasticsearchDocumentStore(analyzer="thai")
+```
+
+The models used in dense retrievers are language specific.
+Be sure to check language of the model used in your EmbeddingRetriever.
+The default model that is loaded in the DensePassageRetriever is for English.
+
+We have created a [German DensePassageRetriever model](https://deepset.ai/germanquad) and know other teams who work on further languages.
+If you have a language model and a question answering dataset in your own language, you can also train a DPR model using Haystack!
+Below is a simplified example.
+See [our tutorial](/docs/v0.9.0/tutorial9md) and also the [API reference](/docs/v0.9.0/apiretrievermd#train) for `DensePassageRetriever.train()` for more details.
+
+```python
+from haystack.retriever import DensePassageRetriever
+
+dense_passage_retriever = DensePassageRetriever(document_store)
+dense_passage_retriever.train(self,
+ data_dir: str,
+ train_filename: str,
+ dev_filename: str = None,
+ test_filename: str = None,
+ batch_size: int = 16,
+ embed_title: bool = True,
+ num_hard_negatives: int = 1,
+ n_epochs: int = 3)
+```
+
+
+
+## Readers
+
+While models are comparatively more performant on English,
+thanks to a wealth of available English training data,
+there are a couple QA models that are directly usable in Haystack.
+
+from haystack.reader import FARMReader
+
+ reader = FARMReader("deepset/gelectra-large-germanquad")
+
+
+ ),
+ },
+ {
+ title: "Italian",
+ content: (
+ ++ ), + }, + { + title: "Chinese", + content: ( +from haystack.reader import FARMReader++ reader = + FARMReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa") ++
++ ), + }, + { + title: "Zero-shot", + content: ( +from haystack.reader import FARMReader++ reader = + FARMReader("uer/roberta-base-chinese-extractive-qa") ++# or++ reader = FARMReader("wptoux/albert-chinese-large-qa") ++
++ ), + }, + ]} + /> + ), + }, + { + title: "Transformers", + content: ( +from haystack.reader import FARMReader++ reader = FARMReader("deepset/xlm-roberta-large-squad2") ++
from haystack.reader import TransformersReader
+
+ reader = TransformersReader("illuin/camembert-base-fquad")
+
+
+ ),
+ },
+ {
+ title: "Italian",
+ content: (
+ ++ ), + }, + { + title: "Zero-shot", + content: ( +from haystack.reader import TransformersReader++ reader = + TransformersReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa") ++
++ ), + }, + ]} + /> + ), + }, + ]} +/> + + + +We are the creators of the **German** model and you can find out more about it [here](https://deepset.ai/germanquad) + +The **French**, **Italian**, **Spanish**, **Portuguese** and **Chinese** models are monolingual language models trained on versions of the SQuAD dataset in their respective languages +and their authors report decent results in their model cards +(e.g. [here](https://huggingface.co/illuin/camembert-base-fquad) and [here](https://huggingface.co/mrm8488/bert-italian-finedtuned-squadv1-it-alfa)). +There also exist Korean QA models on the model hub but their performance is not reported. + +The **zero-shot model** that is shown above is a **multilingual XLM-RoBERTa Large** that is trained on English SQuAD. +It is clear, from our [evaluations](https://huggingface.co/deepset/xlm-roberta-large-squad2#model_card), +that the model has been able to transfer some of its English QA capabilities to other languages, +but still its performance lags behind that of the monolingual models. +Nonetheless, if there is not yet a monolingual model for your language and it is one of the 100 supported by XLM-RoBERTa, +this zero-shot model may serve as a decent first baseline. diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/optimization.md b/docs/v0.9.0/usage/optimization.mdx similarity index 86% rename from src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/optimization.md rename to docs/v0.9.0/usage/optimization.mdx index 864510b79..b23b7befd 100644 --- a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/optimization.md +++ b/docs/v0.9.0/usage/optimization.mdx @@ -1,48 +1,43 @@ ---- -title: "Optimization" -metaTitle: "Optimization" -metaDescription: "" -slug: "/docs/optimization" -date: "2020-11-05" -id: "optimizationmd" ---- - # Optimization ## Speeding up Reader -In most pipelines, the Reader will be the most computationally expensive component. -If this is a step that you would like to speed up, you can opt for a smaller Reader model -that can process more passages in the same amount of time. +In most pipelines, the Reader will be the most computationally expensive component. +If this is a step that you would like to speed up, you can opt for a smaller Reader model +that can process more passages in the same amount of time. On our [benchmarks page](https://haystack.deepset.ai/bm/benchmarks), you will find a comparison of many of the common model architectures. While our default recommendation is RoBERTa, -MiniLM offers much faster processing for only a minimal drop in accuracy. +MiniLM offers much faster processing for only a minimal drop in accuracy. You can find the models that we've trained on [the HuggingFace Model Hub](https://huggingface.co/deepset) + + ## GPU acceleration -The transformer based models used in Haystack are designed to be run on a GPU enabled machine. +The transformer based models used in Haystack are designed to be run on a GPU enabled machine. The design of these models means that they greatly benefit from the parallel processing capabilities of graphics cards. If Haystack has successfully detected a graphics card, you should see these lines in your console output. ``` -INFO - farm.utils - Using device: CUDA +INFO - farm.utils - Using device: CUDA INFO - farm.utils - Number of GPUs: 1 ``` You can track the work load on your CUDA enabled Nvidia GPU by tracking the output of `nvidia-smi -l` on the command line while your Haystack program is running. + + ## Document Length -Document length has a very direct impact on the speed of the Reader +Document length has a very direct impact on the speed of the Reader which is why we recommend using the `PreProcessor` class to clean and split your documents. **If you halve the length of your documents, you will halve the workload placed onto your Reader.** For **sparse retrievers**, very long documents pose a challenge since the signal of the relevant section of text can get washed out by the rest of the document. -To get a good balance between Reader speed and Retriever performance, we splitting documents to a maximum of 500 words. +To get a good balance between Reader speed and Retriever performance, we splitting documents to a maximum of 500 words. If there is no Reader in the pipeline following the Retriever, we recommend that **documents be no longer than 10,000 words**. **Dense retrievers** are limited in the length of text that they can read in one pass. @@ -53,11 +48,13 @@ We have found decent performance with **documents around 100 words long**. ## Respecting Sentence Boundaries -When splitting documents, it is generally not a good idea to let document boundaries fall in the middle of sentences. -Doing so means that each document will contain incomplete sentence fragments +When splitting documents, it is generally not a good idea to let document boundaries fall in the middle of sentences. +Doing so means that each document will contain incomplete sentence fragments which maybe be hard for both retriever and reader to interpret. It is therefore recommended to set `split_respect_sentence_boundary=True` when initializing your `PreProcessor`. + + ## Choosing the Right top-k Values The `top-k` parameter in both the `Retriever` and `Reader` determine how many results they return. @@ -67,33 +64,38 @@ while `Reader` `top-k` determines how many answer candidates to show. In our experiments, we have found that **`Retriever` `top_k=10` gives decent overall performance** and so we have set this as the default in Haystack. -The choice of `Retriever` `top-k` is a trade-off between speed and accuracy, +The choice of `Retriever` `top-k` is a trade-off between speed and accuracy, especially when there is a `Reader` in the pipeline. -Setting it higher means passing more documents to the `Reader`, +Setting it higher means passing more documents to the `Reader`, thus reducing the chance that the answer-containing passage is missed. However, passing more documents to the `Reader` will create a larger workload for the component. These parameters can easily be tweaked as follows if using a `Finder`: -``` python + +```python answers = pipeline.run(query="What did Einstein work on?", top_k_retriever=10, top_k_reader=5) ``` + or like this if directly calling the `Retriever`: -``` python + +```python retrieved_docs = retriever.retrieve(top_k=10) ``` + + ## Metadata Filtering Metadata can be attached to the documents which you index into your DocumentStore (see the input data format [here](/docs/v0.9.0/retrievermd)). -At query time, you can apply filters based on this metadata to limit the scope of your search and ensure your answers -come from a specific slice of your data. +At query time, you can apply filters based on this metadata to limit the scope of your search and ensure your answers +come from a specific slice of your data. -For example, if you have a set of annual reports from various companies, +For example, if you have a set of annual reports from various companies, you may want to perform a search on just a specific year, or on a small selection of companies. This can reduce the work load of the retriever and also ensure that you get more relevant results. Filters are applied via the `filters` argument of the `Retriever` class. In practice, this argument will probably -be passed into the `Pipeline.run()` call, which will then route it on to the `Retriever` class +be passed into the `Pipeline.run()` call, which will then route it on to the `Retriever` class (see our the Arguments on the [Pipelines page](/docs/v0.9.0/pipelinesmd) for an explanation). ```python diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/pipelines.md b/docs/v0.9.0/usage/pipelines.mdx similarity index 79% rename from src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/pipelines.md rename to docs/v0.9.0/usage/pipelines.mdx index 1d8177859..fb2c9129f 100644 --- a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/pipelines.md +++ b/docs/v0.9.0/usage/pipelines.mdx @@ -1,19 +1,11 @@ ---- -title: "Pipelines" -metaTitle: "Pipelines" -metaDescription: "" -slug: "/docs/pipelines" -date: "2020-11-05" -id: "pipelinesmd" ---- - # Pipelines ### Flexibility powered by DAGs + In order to build modern search pipelines, you need two things: powerful building blocks and an easy way to stick them together. -The `Pipeline` class is exactly built for this purpose and enables many search scenarios beyond QA. -The core idea is that you can build a Directed Acyclic Graph (DAG) where each node is one building block (Reader, Retriever, Generator ...). -Here's a simple example for a standard Open-Domain QA Pipeline: +The `Pipeline` class is exactly built for this purpose and enables many search scenarios beyond QA. +The core idea is that you can build a Directed Acyclic Graph (DAG) where each node is one building block (Reader, Retriever, Generator ...). +Here's a simple example for a standard Open-Domain QA Pipeline: ```python from haystack import Pipeline @@ -25,16 +17,20 @@ res = p.run(query="What did Einstein work on?", top_k_retriever=1) ``` You can **draw the DAG** to better inspect what you are building: + ```python p.draw(path="custom_pipe.png") ``` +  + + ### Arguments Whatever keyword arguments are passed into the `Pipeline.run()` method will be passed on to each node in the pipeline. For example, in the code snippet below, all nodes will receive `query`, `top_k_retriever` and `top_k_reader` as argument, -even if they don't use those arguments. It is therefore very important when defining custom nodes that their +even if they don't use those arguments. It is therefore very important when defining custom nodes that their keyword argument names do not clash with the other nodes in your pipeline. ```python @@ -45,43 +41,45 @@ res = pipeline.run( ) ``` + + ### YAML File Definitions For your convenience, there is also the option of defining and loading pipelines in YAML files. -Having your pipeline available in a YAML is particularly useful when -you move between experimentation and production environments. -Just export the YAML from your notebook / IDE and import it into your production environment. -It also helps with version control of pipelines, allows you to share your pipeline easily with colleagues, +Having your pipeline available in a YAML is particularly useful when +you move between experimentation and production environments. +Just export the YAML from your notebook / IDE and import it into your production environment. +It also helps with version control of pipelines, allows you to share your pipeline easily with colleagues, and simplifies the configuration of pipeline parameters in production. For example, you can define and save a simple Retriever Reader pipeline by saving the following to a file: ```yaml -version: '0.7' - -components: # define all the building-blocks for Pipeline -- name: MyReader # custom-name for the component; helpful for visualization & debugging - type: FARMReader # Haystack Class name for the component - params: - no_ans_boost: -10 - model_name_or_path: deepset/roberta-base-squad2 -- name: MyESRetriever - type: ElasticsearchRetriever - params: - document_store: MyDocumentStore # params can reference other components defined in the YAML - custom_query: null -- name: MyDocumentStore - type: ElasticsearchDocumentStore - params: - index: haystack_test - -pipelines: # multiple Pipelines can be defined using the components from above -- name: my_query_pipeline # a simple extractive-qa Pipeline - nodes: +version: "0.7" + +components: # define all the building-blocks for Pipeline + - name: MyReader # custom-name for the component; helpful for visualization & debugging + type: FARMReader # Haystack Class name for the component + params: + no_ans_boost: -10 + model_name_or_path: deepset/roberta-base-squad2 - name: MyESRetriever - inputs: [Query] - - name: MyReader - inputs: [MyESRetriever] + type: ElasticsearchRetriever + params: + document_store: MyDocumentStore # params can reference other components defined in the YAML + custom_query: null + - name: MyDocumentStore + type: ElasticsearchDocumentStore + params: + index: haystack_test + +pipelines: # multiple Pipelines can be defined using the components from above + - name: my_query_pipeline # a simple extractive-qa Pipeline + nodes: + - name: MyESRetriever + inputs: [Query] + - name: MyReader + inputs: [MyESRetriever] ``` To load, simply call: @@ -92,8 +90,12 @@ pipeline.load_from_yaml(Path("sample.yaml")) For another example YAML config, check out [this file](https://github.com/deepset-ai/haystack/blob/master/rest_api/pipelines.yaml). + + ### Multiple retrievers -You can now also use multiple Retrievers and join their results: + +You can now also use multiple Retrievers and join their results: + ```python from haystack import Pipeline @@ -104,20 +106,28 @@ p.add_node(component=JoinDocuments(join_mode="concatenate"), name="JoinResults", p.add_node(component=reader, name="QAReader", inputs=["JoinResults"]) res = p.run(query="What did Einstein work on?", top_k_retriever=1) ``` +  + + ### Custom nodes -You can easily build your own custom nodes. Just respect the following requirements: + +You can easily build your own custom nodes. Just respect the following requirements: 1. Add a method `run(self, **kwargs)` to your class. `**kwargs` will contain the output from the previous node in your graph. 2. Do whatever you want within `run()` (e.g. reformatting the query) 3. Return a tuple that contains your output data (for the next node) and the name of the outgoing edge `output_dict, "output_1` 4. Add a class attribute `outgoing_edges = 1` that defines the number of output options from your node. You only need a higher number here if you have a decision node (see below). + + ### Decision nodes -Or you can add decision nodes where only one "branch" is executed afterwards. This allows, for example, to classify an incoming query and depending on the result routing it to different modules: + +Or you can add decision nodes where only one "branch" is executed afterwards. This allows, for example, to classify an incoming query and depending on the result routing it to different modules:  -```python + +```python class QueryClassifier(): outgoing_edges = 2 @@ -138,16 +148,21 @@ Or you can add decision nodes where only one "branch" is executed afterwards. Th res = p.run(query="What did Einstein work on?", top_k_retriever=1) ``` + + ### Evaluation nodes There are nodes in Haystack that are used to evaluate the performance of readers, retrievers and combine systems. To get hands on with this kind of node, have a look at the [evaluation tutorial](/docs/v0.9.0/tutorial5md). + + ### Default Pipelines (replacing the "Finder") + Last but not least, we added some "Default Pipelines" that allow you to run standard patterns with very few lines of code. This is replacing the `Finder` class which is now deprecated. -``` +```python from haystack.pipeline import DocumentSearchPipeline, ExtractiveQAPipeline, Pipeline, JoinDocuments # Extractive QA @@ -166,8 +181,8 @@ res = doc_pipe.run(query="Physics Einstein", top_k_retriever=1) doc_pipe = FAQPipeline(retriever=retriever) res = doc_pipe.run(query="How can I change my address?", top_k_retriever=3) -``` -See also the [Pipelines API documentation](/docs/v0.9.0/apipipelinesmd) for more details. +``` -We plan many more features around the new pipelines incl. parallelized execution, distributed execution, dry runs - so stay tuned ... +See also the [Pipelines API documentation](/docs/v0.9.0/apipipelinesmd) for more details. +We plan many more features around the new pipelines incl. parallelized execution, distributed execution, dry runs - so stay tuned ... diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/preprocessing.md b/docs/v0.9.0/usage/preprocessing.mdx similarity index 54% rename from src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/preprocessing.md rename to docs/v0.9.0/usage/preprocessing.mdx index 9059f4027..77dbfdd64 100644 --- a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/preprocessing.md +++ b/docs/v0.9.0/usage/preprocessing.mdx @@ -1,32 +1,19 @@ ---- -title: "Preprocessing" -metaTitle: "Preprocessing" -metaDescription: "" -slug: "/docs/preprocessing" -date: "2020-09-03" -id: "preprocessingmd" ---- - # Preprocessing Haystack includes a suite of tools to: - -* extract text from different file types, -* normalize white space -* split text into smaller pieces to optimize retrieval -from haystack.reader import TransformersReader++ reader = + TransformersReader("deepset/xlm-roberta-large-squad2") ++
+- extract text from different file types,
+- normalize white space
+- split text into smaller pieces to optimize retrieval
Check out our [preprocessing tutorial](/docs/v0.9.0/tutorial8md) if you'd like to start working with code examples already!
-
-
These data preprocessing steps can have a big impact on the systems performance
and effective handling of data is key to getting the most out of Haystack.
-The Document Store expects its inputs to come in the following format.
+The Document Store expects its inputs to come in the following format.
The sections below will show you all the tools you'll need to ready your data for storing.
-
+
```python
docs = [
{
@@ -36,68 +23,69 @@ docs = [
]
```
+
+
## File Conversion
-There are a range of different file converters in Haystack that
+There are a range of different file converters in Haystack that
can extract text from files and cast them into the unified dictionary format shown above.
Haystack features support for txt, pdf and docx files and there is even a converter that leverages Apache Tika.
Please refer to [the API docs](/docs/v0.9.0/file_convertersmd) to see which converter best suits you.
-
-
-
+
-
-
-
-
-
-
-```python
-from haystack.file_converter import PDFToTextConverter
-
-converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=["de","en"])
-doc = converter.convert(file_path=file, meta=None)
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.file_converter import DocxToTextConverter
-
-converter = DocxToTextConverter(remove_numeric_tables=True, valid_languages=["de","en"])
-doc = converter.convert(file_path=file, meta=None)
-```
-
-
-
-
-
-
-
-
-
-
-Haystack also has a `convert_files_to_dicts()` utility function that will convert
-all txt or pdf files in a given folder into this dictionary format.
-
-```python
-from haystack.preprocessor.utils import convert_files_to_dicts
-
-docs = convert_files_to_dicts(dir_path=doc_dir)
-```
-
-
-from haystack.file_converter import PDFToTextConverter
+
+ converter = PDFToTextConverter(remove_numeric_tables=True,
+ valid_languages=["de","en"])
+
+ doc = converter.convert(file_path=file, meta=None)
+
+ ),
+ },
+ {
+ title: "DOCX",
+ content: (
+ ++ ), + }, + { + title: "From a Directory", + content: ( +from haystack.file_converter import DocxToTextConverter++ converter = DocxToTextConverter(remove_numeric_tables=True, + valid_languages=["de","en"]) ++doc = converter.convert(file_path=file, meta=None)+
+
+ ),
+ },
+ ]}
+/>
+
+
## Web Crawler
-In Haystack, you will find a web crawler that will help you scrape text from websites and save it to file.
+In Haystack, you will find a web crawler that will help you scrape text from websites and save it to file.
See the [API documentation](https://haystack.deepset.ai/docs/v0.9.0/apicrawlermd) for more details.
```python
@@ -109,6 +97,8 @@ docs = crawler.crawl(urls=["https://haystack.deepset.ai/docs/v0.9.0/get_startedm
filter_urls= ["haystack\.deepset\.ai\/docs\/"])
```
+
+
## PreProcessor
While each of the above conversion methods produce documents that are already in the format expected by the Document Store,
@@ -134,11 +124,10 @@ processor = PreProcessor(
docs = processor.process(doc)
```
-* `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
-* `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
-* `clean_header_footer` will remove any long header or footer texts that are repeated on each page
-* `split_by` determines what unit the document is split by: `'word'`, `'sentence'` or `'passage'`
-* `split_length` sets a maximum number of `'word'`, `'sentence'` or `'passage'` units per output document
-* `split_respect_sentence_boundary` ensures that document boundaries do not fall in the middle of sentences
-* `split_overlap` sets the amount of overlap between two adjacent documents after a split. Setting this to a positive number essentially enables the sliding window approach.
-
+- `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
+- `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
+- `clean_header_footer` will remove any long header or footer texts that are repeated on each page
+- `split_by` determines what unit the document is split by: `'word'`, `'sentence'` or `'passage'`
+- `split_length` sets a maximum number of `'word'`, `'sentence'` or `'passage'` units per output document
+- `split_respect_sentence_boundary` ensures that document boundaries do not fall in the middle of sentences
+- `split_overlap` sets the amount of overlap between two adjacent documents after a split. Setting this to a positive number essentially enables the sliding window approach.
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/query_classifier.md b/docs/v0.9.0/usage/query_classifier.mdx
similarity index 96%
rename from src/pages/docs/versions/master/latest/site/en/usage/usage/query_classifier.md
rename to docs/v0.9.0/usage/query_classifier.mdx
index 26b6e2d0e..51bc8a8f9 100644
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/query_classifier.md
+++ b/docs/v0.9.0/usage/query_classifier.mdx
@@ -1,12 +1,3 @@
----
-title: "Query Classifier"
-metaTitle: "Query Classifier"
-metaDescription: ""
-slug: "/docs/query_classifier"
-date: "2021-08-17"
-id: "query_classifiermd"
----
-
# Query Classifier
Queries come in all shapes and forms. A keyword-based search differs from a question posed in natural language. In Haystack, we can account for these differences by integrating a special node into our QA pipeline: the query classifier.
@@ -19,6 +10,7 @@ Using a query classifier can potentially yield the following benefits:
* Getting better search results (e.g. by routing only proper questions to DPR / QA branches and not keyword queries)
* Less GPU costs (e.g. if 50% of your traffic is only keyword queries you could just use elastic here and save the GPU resources for the other 50% of traffic with semantic queries)
+
### Common Query types
@@ -28,6 +20,8 @@ Such queries don't have semantic meaning, merely consist of keywords and the ord
* jon snow country
* arya stark younger brothers
+
+
#### 2. Questions (Interrogative Queries):
In such queries users ask a question in a complete, "natural" sentence. Regardless of the presence of "?" in the query the goal here is to detect the intent of the user whether any question is asked or not in the query:
@@ -42,6 +36,8 @@ Such queries consist also of a regular, natural sentence with semantic relations
* Show countries that Jon snow was filmed in.
* List all brothers of Arya.
+
+
### Usage standalone: Try a Query Classifier
To test how a query classifier works before integrating it into a pipeline, you can run it just as an individual component:
@@ -72,6 +68,8 @@ for query in queries:
```
Note how the node returns two objects: the query (e.g.'Arya Stark father') and the name of the output edge (e.g. "output_2"). This information can be leveraged in a pipeline for routing the query to the next node.
+
+
### Usage in a pipeline: Use different retrievers depending on the query type
You can use a Query Classifier within a pipeline as a "decision node". Depending on the output of the classifier other parts of the pipeline will be executed. For example, we can route keyword queries to an ElasticsearchRetriever and semantic queries (questions/statements) to DPR.
@@ -134,10 +132,13 @@ res_2 = pipe.run(
```
+
### Which models are available?
The transformer classifier is more accurate than the SkLearn classifier as it can use the context and order of words. However, it requires more memory and most probably GPU for faster inference. You can mitigate those down sides by choosing a very small transformer model. The default models we trained are using a mini BERT architecture which is only about `50 MB` in size and allows relatively fast inference on CPU.
+
+
#### Transformers
Pass your own `Transformer` binary classification model from file/huggingface or use one of the following pretrained ones hosted on Huggingface:
1) Keywords vs. Questions/Statements (Default)
@@ -160,6 +161,7 @@ Pass your own `Transformer` binary classification model from file/huggingface or
[Readme](https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier_statements/readme.txt)
+
#### Sklearn
Pass your own `Sklearn` binary classification model or use one of the following pretrained Gradient boosting models:
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/ranker.md b/docs/v0.9.0/usage/ranker.mdx
similarity index 90%
rename from src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/ranker.md
rename to docs/v0.9.0/usage/ranker.mdx
index bef13c52e..0a2f4c479 100644
--- a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/ranker.md
+++ b/docs/v0.9.0/usage/ranker.mdx
@@ -1,12 +1,3 @@
----
-title: "Ranker"
-metaTitle: "Ranker"
-metaDescription: ""
-slug: "/docs/ranker"
-date: "2020-05-26"
-id: "rankermd"
----
-
# Ranker
There are pure "semantic document search" use cases that do not need question answering functionality but only document ranking.
@@ -24,6 +15,7 @@ Note that a Ranker needs to be initialised with a model trained on a text pair c
You can train the model also with the train() method of the Ranker.
Alternatively, [this example](https://github.com/deepset-ai/FARM/blob/master/examples/text_pair_classification.py) shows how to train a text pair classification model in FARM.
+
## FARMRanker
@@ -33,8 +25,11 @@ The FARMRanker consists of a Transformer-based model for document re-ranking usi
Given a text pair of query and passage, the TextPairClassifier either predicts label "1" if the pair is similar or label "0" if they are dissimilar (accompanied with a probability).
While the underlying model can vary (BERT, Roberta, DistilBERT, ...), the interface remains the same.
With a FARMRanker, you can:
-* Directly get predictions (re-ranked version of the supplied list of Document) via predict() if supplying a pre-trained model
-* Take a plain language model (e.g. `bert-base-cased`) and train it for TextPairClassification via train()
+
+- Directly get predictions (re-ranked version of the supplied list of Document) via predict() if supplying a pre-trained model
+- Take a plain language model (e.g. `bert-base-cased`) and train it for TextPairClassification via train()
+
+
### Initialisation
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/reader.md b/docs/v0.9.0/usage/reader.mdx
similarity index 53%
rename from src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/reader.md
rename to docs/v0.9.0/usage/reader.mdx
index 91b3c8c4a..8c7829077 100644
--- a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/reader.md
+++ b/docs/v0.9.0/usage/reader.mdx
@@ -1,66 +1,50 @@
----
-title: "Reader"
-metaTitle: "Reader"
-metaDescription: ""
-slug: "/docs/reader"
-date: "2020-09-03"
-id: "readermd"
----
-
# Reader
The Reader, also known as Open-Domain QA systems in Machine Learning speak,
is the core component that enables Haystack to find the answers that you need.
Haystack’s Readers are:
-
-* built on the latest transformer based language models
-
-
-* strong in their grasp of semantics
-
-
-* sensitive to syntactic structure
-
-
-* state-of-the-art in QA tasks like SQuAD and Natural Questions
-
-+ Haystack also has a`convert_files_to_dicts()` utility function that + will convert all txt or pdf files in a given folder into this + dictionary format. +
++++ from haystack.preprocessor.utils import convert_files_to_dicts ++docs = convert_files_to_dicts(dir_path=doc_dir)+
-
-
+- built on the latest transformer based language models
+
+- strong in their grasp of semantics
+
+- sensitive to syntactic structure
+
+- state-of-the-art in QA tasks like SQuAD and Natural Questions
+
+
-
-
-
-
-
-
-```python
-from haystack.reader import FARMReader
-
-model = "deepset/roberta-base-squad2"
-reader = FARMReader(model, use_gpu=True)
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import TransformersReader
-
-model = "deepset/roberta-base-squad2"
-reader = TransformersReader(model, use_gpu=1)
-```
-
-
-++from haystack.reader import FARMReader+model = "deepset/roberta-base-squad2"+reader = FARMReader(model, use_gpu=True)+
+
+ ),
+ },
+ ]}
+/>
While these models can work on CPU, it is recommended that they are run using GPUs to keep query times low.
+
+
## Choosing the Right Model
In Haystack, you can start using pretrained QA models simply by providing its HuggingFace Model Hub name to the Reader.
@@ -70,155 +54,135 @@ and you have the option of using the QA pipeline from deepset FARM or HuggingFac
Currently, there are a lot of different models out there and it can be rather overwhelming trying to pick the one that fits your use case.
To get you started, we have a few recommendations for you to try out.
-++from haystack.reader import TransformersReader+model = "deepset/roberta-base-squad2"+reader = TransformersReader(model, use_gpu=1)+
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-**An optimised variant of BERT and a great starting point.**
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("deepset/roberta-base-squad2")
-```
-
-* **Pro**: Strong all round model
-
-* **Con**: There are other models that are either faster or more accurate
-
-
-
-
-
-
-
-
-
-**A cleverly distilled model that sacrifices a little accuracy for speed.**
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("deepset/minilm-uncased-squad2")
-```
-
-* **Pro**: Inference speed up to 50% faster than BERT base
-
-* **Con**: Still doesn’t match the best base sized models in accuracy
-
-
-
-
-
-
-
-
-
-**Large, powerful, SotA model.**
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("ahotrod/albert_xxlargev1_squad2_512")
-```
-
-* **Pro**: Better accuracy than any other open source model in QA
-
-* **Con**: The computational power needed make it impractical for most use cases
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-**An optimised variant of BERT and a great starting point.**
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("deepset/roberta-base-squad2")
-```
-
-* **Pro**: Strong all round model
-
-* **Con**: There are other models that are either faster or more accurate
-
-
-
-
-
-
-
-
-
-**A cleverly distilled model that sacrifices a little accuracy for speed.**
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("deepset/minilm-uncased-squad2")
-```
-
-* **Pro**: Inference speed up to 50% faster than BERT base
-
-* **Con**: Still doesn’t match the best base sized models in accuracy
-
-
-
-
-
-
-
-
-
-**Large, powerful, SotA model.**
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("ahotrod/albert_xxlargev1_squad2_512")
-```
-
-* **Pro**: Better accuracy than any other open source model in QA
-
-* **Con**: The computational power needed make it impractical for most use cases
-
-
-
+
+
+ ),
+ },
+ {
+ title: "Transformers",
+ content: (
+
+
+ RoBERTa (base)
+An optimised variant of BERT and a great starting point.
+++from haystack.reader import FARMReader+reader = FARMReader("deepset/roberta-base-squad2")+
-
+
- Pro: Strong all round model +
- + Con: There are other models that are either faster or more + accurate + +
+
+ MiniLM
++ A cleverly distilled model that sacrifices a little accuracy for + speed. +
+++from haystack.reader import FARMReader+reader = FARMReader("deepset/minilm-uncased-squad2")+
-
+
- Pro: Inference speed up to 50% faster than BERT base +
- + Con: Still doesn’t match the best base sized models in accuracy + +
+
+ ALBERT (XXL)
+Large, powerful, SotA model.
+++from haystack.reader import FARMReader++ reader = FARMReader("ahotrod/albert_xxlargev1_squad2_512") ++
-
+
- + Pro: Better accuracy than any other open source model in QA + +
- + Con: The computational power needed make it impractical for most + use cases + +
+
+ ),
+ },
+ ]}
+/>
+
+
+
+
+
+ RoBERTa (base)
+An optimised variant of BERT and a great starting point.
+++from haystack.reader import TransformersReader++ reader = TransformersReader("deepset/roberta-base-squad2") ++
-
+
- Pro: Strong all round model +
- + Con: There are other models that are either faster or more + accurate + +
+
+ MiniLM
++ A cleverly distilled model that sacrifices a little accuracy for + speed. +
+++from haystack.reader import TransformersReader++ reader = TransformersReader("deepset/minilm-uncased-squad2") ++
-
+
- Pro: Inference speed up to 50% faster than BERT base +
- + Con: Still doesn’t match the best base sized models in accuracy + +
+
+ ALBERT (XXL)
+Large, powerful, SotA model.
+++from haystack.reader import TransformersReader++ reader = + TransformersReader("ahotrod/albert_xxlargev1_squad2_512") ++
-
+
- + Pro: Better accuracy than any other open source model in QA + +
- + Con: The computational power needed make it impractical for most + use cases + +
**Recommendations:**
@@ -232,6 +196,7 @@ It is a smaller model that is trained to mimic larger models through the distill
and it outperforms the BERT base on SQuAD even though it is about 40% smaller.
+
**State of the Art Accuracy**: For most, **ALBERT XXL** will be too large to feasibly work with.
But if performance is your sole concern, and you have the computational resources,
you might like to try ALBERT XXL which has set SoTA performance on SQuAD 2.0.
@@ -240,11 +205,12 @@ you might like to try ALBERT XXL which has set SoTA performance on SQuAD 2.0.
+
## Confidence Scores
-When printing the full results of a Reader,
-you will see that each prediction is accompanied
+When printing the full results of a Reader,
+you will see that each prediction is accompanied
by a value in the range of 0 to 1 reflecting the model's confidence in that prediction
In the output of `print_answers()`, you will find the model confidence in dictionary key called `confidence`.
@@ -256,8 +222,8 @@ print_answers(prediction, details="all")
```
```python
-{
- 'answers': [
+{
+ 'answers': [
{ 'answer': 'Eddard',
'context': 's Nymeria after a legendary warrior queen. '
'She travels with her father, Eddard, to '
@@ -271,22 +237,24 @@ print_answers(prediction, details="all")
```
In order to align this probability score with the model's accuracy, finetuning needs to be performed
-on a specific dataset.
+on a specific dataset.
To this end, the reader has a method `calibrate_confidence_scores(document_store, device, label_index, doc_index, label_origin)`.
The parameters of this method are the same as for the `eval()` method because the calibration of confidence scores is performed on a dataset that comes with gold labels.
The calibration calls the `eval()` method internally and therefore needs a DocumentStore containing labeled questions and evaluation documents.
Have a look at this [FARM tutorial](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering_confidence.py)
-to see how to compare calibrated confidence scores with uncalibrated confidence scores within FARM.
-Note that a finetuned confidence score is specific to the domain that it is finetuned on.
+to see how to compare calibrated confidence scores with uncalibrated confidence scores within FARM.
+Note that a finetuned confidence score is specific to the domain that it is finetuned on.
There is no guarantee that this performance can transfer to a new domain.
Having a confidence score is particularly useful in cases where you need Haystack to work with a certain accuracy threshold.
Many of our users have built systems where predictions below a certain confidence value are routed
on to a fallback system.
-
+
+
+
## Deeper Dive: FARM vs Transformers
Apart from the **model weights**, Haystack Readers contain all the components found in end-to-end open domain QA systems.
@@ -294,14 +262,11 @@ This includes **tokenization**, **embedding computation**, **span prediction** a
While the handling of model weights is the same between the FARM and Transformers libraries, their QA pipelines differ in some ways.
The major points are:
+- The **TransformersReader** will sometimes predict the same span twice while duplicates are removed in the **FARMReader**
-* The **TransformersReader** will sometimes predict the same span twice while duplicates are removed in the **FARMReader**
-
-
-* The **FARMReader** currently uses the tokenizers from the HuggingFace Transformers library while the **TransformersReader** uses the tokenizers from the HuggingFace Tokenizers library
+- The **FARMReader** currently uses the tokenizers from the HuggingFace Transformers library while the **TransformersReader** uses the tokenizers from the HuggingFace Tokenizers library
-
-* Start and end logits are normalized per passage and multiplied in the **TransformersReader** while they are summed and not normalised in the **FARMReader**
+- Start and end logits are normalized per passage and multiplied in the **TransformersReader** while they are summed and not normalised in the **FARMReader**
If you’re interested in the finer details of these points, have a look at [this](https://github.com/deepset-ai/haystack/issues/248#issuecomment-661977237) GitHub comment.
@@ -309,6 +274,7 @@ We see value in maintaining both kinds of Readers since Transformers is a very f
but we at deepset can more easily update and optimise the FARM pipeline for speed and performance.
+
Haystack also has a close integration with FARM which means that you can further fine-tune your Readers on labelled data using a FARMReader.
See our tutorials for an end-to-end example or below for a shortened example.
@@ -329,6 +295,8 @@ reader.train(train_data, train_filename, save_dir=save_dir)
finetuned_reader = FARMReader(save_dir)
```
+
+
## Deeper Dive: From Language Model to Haystack Reader
Language models form the core of most modern NLP systems and that includes the Readers in Haystack.
@@ -344,43 +312,44 @@ either the start or end token of the correct answer.
In cases where the answer is not contained within the passage, the prediction head is also expected to return a `no_answer` prediction.
+
Since language models are limited in the number of tokens which they can process in a single forward pass,
a sliding window mechanism is implemented to handle variable length documents.
This functions by slicing the document into overlapping passages of (approximately) `max_seq_length`
that are each offset by `doc_stride` number of tokens.
These can be set when the Reader is initialized.
-
-
-
+
-
-
-
-
-
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader(... max_seq_len=384, doc_stride=128 ...)
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader(... max_seq_len=384, doc_stride=128 ...)
-```
-
-
-++from haystack.reader import FARMReader++ reader = FARMReader(... max_seq_len=384, doc_stride=128 ...) ++
+
+ ),
+ },
+ ]}
+/>
Predictions are made on each individual passage and the process of aggregation picks the best candidates across all passages.
If you’d like to learn more about what is happening behind the scenes, have a look at [this](https://medium.com/deepset-ai/modern-question-answering-systems-explained-4d0913744097) article.
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/retriever.md b/docs/v0.9.0/usage/retriever.mdx
similarity index 78%
rename from src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/retriever.md
rename to docs/v0.9.0/usage/retriever.mdx
index 990b70062..ffc47dfab 100644
--- a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/retriever.md
+++ b/docs/v0.9.0/usage/retriever.mdx
@@ -1,41 +1,34 @@
----
-title: "Retriever"
-metaTitle: "Retriever"
-metaDescription: ""
-slug: "/docs/retriever"
-date: "2020-09-03"
-id: "retrievermd"
----
-
# Retriever
The Retriever is a lightweight filter that can quickly go through the full document store and pass on a set of candidate documents that are relevant to the query.
When used in combination with a Reader, it is a tool for sifting out the obvious negative cases, saving the Reader from doing more work than it needs to and speeding up the querying process.
-++from haystack.reader import TransformersReader++ reader = TransformersReader(... max_seq_len=384, doc_stride=128 + ...) ++
+Loading Chart
}
- data={mapDataMap[selectedVersion]}
- options={{
- subtitle: subTitleMap[selectedVersion],
- colors: ['#22BA99', '#FBB14B', '#63C7CA', '#49B0E4'],
- hAxis: {
- title: axisXMap[selectedVersion]
- },
- vAxis: {
- title: axisYMap[selectedVersion]
- },
- pointSize: 5,
- legend: "bottom"
- }}
- />
-
-
- {showLineChart2Desc && (
-
- )}
-
**Recommendations**
-* BM25 (sparse)
+- BM25 (sparse)
-* Dense Passage Retrieval (dense)
+- Dense Passage Retrieval (dense)
+
Note that not all Retrievers can be paired with every DocumentStore.
Here are the combinations which are supported:
-| | Memory | Elasticsearch | SQL | FAISS | Milvus |
-| --- | --- | --- | ---- | ---- | ---- |
-| BM25 | N | Y | N | N | N |
-| TF-IDF | Y | Y | Y | N | N |
-| Embedding | Y | Y | N | Y | Y |
-| DPR | Y | Y | N | Y | Y |
+| | Memory | Elasticsearch | SQL | FAISS | Milvus |
+| --------- | ------ | ------------- | --- | ----- | ------ |
+| BM25 | N | Y | N | N | N |
+| TF-IDF | Y | Y | Y | N | N |
+| Embedding | Y | Y | N | Y | Y |
+| DPR | Y | Y | N | Y | Y |
See [Optimization](/docs/v0.9.0/optimizationmd) for suggestions on how to choose top-k values.
+
## TF-IDF
@@ -43,10 +36,9 @@ See [Optimization](/docs/v0.9.0/optimizationmd) for suggestions on how to choose
TF-IDF is a commonly used baseline for information retrieval that exploits two key intuitions:
+- documents that have more lexical overlap with the query are more likely to be relevant
-* documents that have more lexical overlap with the query are more likely to be relevant
-
-* words that occur in fewer documents are more significant than words that occur in many documents
+- words that occur in fewer documents are more significant than words that occur in many documents
Given a query, a tf-idf score is computed for each document as follows:
@@ -56,14 +48,14 @@ score = tf * idf
Where:
+- `tf` is how many times words in the query occur in that document.
-* `tf` is how many times words in the query occur in that document.
-
-
-* `idf` is the inverse of the fraction of documents containing the word.
+- `idf` is the inverse of the fraction of documents containing the word.
In practice, both terms are usually log normalised.
+
+
### Initialisation
```python
@@ -78,6 +70,8 @@ retriever = TfidfRetriever(document_store)
p = ExtractiveQAPipeline(reader, retriever)
```
+
+
## BM25 (Recommended)
### Description
@@ -85,11 +79,11 @@ p = ExtractiveQAPipeline(reader, retriever)
BM25 is a variant of TF-IDF that we recommend you use if you are looking for a retrieval method that does not need a neural network for indexing.
It improves upon its predecessor in two main aspects:
+- It saturates `tf` after a set number of occurrences of the given term in the document
-* It saturates `tf` after a set number of occurrences of the given term in the document
-
+- It normalises by document length so that short documents are favoured over long documents if they have the same amount of word overlap with the query
-* It normalises by document length so that short documents are favoured over long documents if they have the same amount of word overlap with the query
+
### Initialisation
@@ -108,6 +102,9 @@ p = ExtractiveQAPipeline(reader, retriever)
See [this](https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-and-its-variables) blog post for more details about the algorithm.
+
+
+
## Dense Passage Retrieval (Recommended)
### Description
@@ -115,39 +112,37 @@ See [this](https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-
[Dense Passage Retrieval](https://arxiv.org/abs/2004.04906) is a highly performant retrieval method that calculates relevance using dense representations.
Key features:
+- One BERT base model to encode documents
-* One BERT base model to encode documents
-
-
-* One BERT base model to encode queries
+- One BERT base model to encode queries
-
-* Ranking of documents done by dot product similarity between query and document embeddings
+- Ranking of documents done by dot product similarity between query and document embeddings
+
Indexing using DPR is comparatively expensive in terms of required computation since all documents in the database need to be processed through the transformer.
The embeddings that are created in this step can be stored in FAISS, a database optimized for vector similarity.
DPR can also work with the ElasticsearchDocumentStore or the InMemoryDocumentStore.
There are two design decisions that have made DPR particularly performant.
+- Separate encoders for document and query helps since queries are much shorter than documents
-* Separate encoders for document and query helps since queries are much shorter than documents
-
-
-* Training with ‘In-batch negatives’ (gold labels are treated as negative examples for other samples in same batch) is highly efficient
+- Training with ‘In-batch negatives’ (gold labels are treated as negative examples for other samples in same batch) is highly efficient
In Haystack, you can simply download the pretrained encoders needed to start using DPR.
If you’d like to learn how to set up a DPR based system, have a look at the [tutorial](/docs/v0.9.0/tutorial6md)!
+
+
### Initialisation
-
+ = async ({
+ params,
+}: GetStaticPropsContext) => {
+ if (!params?.slug || !Array.isArray(params.slug)) {
+ return {
+ notFound: true,
+ };
+ }
+
+ let map: any;
+ let retriever_performance: any;
+ let reader_performance: any;
+ let retriever_speed: any;
+
+ try {
+ // Map folder
+ let directory = getDirectoryBenchmarks(
+ "map",
+ getVersionFromParams(params.slug)
+ );
+ let filenames = [];
+ let fileContent = null;
+ if (!fs.existsSync(directory))
+ return {
+ notFound: true,
+ };
+ filenames = fs.readdirSync(directory);
+
+ for (const filename of filenames) {
+ const fullPath = join(directory, filename);
+ if (!fs.existsSync(directory) || !fs.existsSync(fullPath)) {
+ return {
+ notFound: true,
+ };
+ }
+ fileContent = JSON.parse(fs.readFileSync(fullPath, "utf8"));
+ map = getRetrieverMapData(fileContent);
+ }
+
+ //Performance folder
+ directory = getDirectoryBenchmarks(
+ "performance",
+ getVersionFromParams(params.slug)
+ );
+ if (!fs.existsSync(directory))
+ return {
+ notFound: true,
+ };
+ filenames = fs.readdirSync(directory);
+
+ for (const filename of filenames) {
+ const fullPath = join(directory, filename);
+ if (!fs.existsSync(directory) || !fs.existsSync(fullPath)) {
+ return {
+ notFound: true,
+ };
+ }
+ fileContent = JSON.parse(fs.readFileSync(fullPath, "utf8"));
+ if(filename === "reader_performance.json") {
+ reader_performance = getReaderPerformanceData(fileContent);
+ } else if (filename === "retriever_performance.json") {
+ retriever_performance = getRetrieverPerformanceData(fileContent);
+ }
+ }
+
+ //Speed folder
+ directory = getDirectoryBenchmarks(
+ "speed",
+ getVersionFromParams(params.slug)
+ );
+ if (!fs.existsSync(directory))
+ return {
+ notFound: true,
+ };
+ filenames = fs.readdirSync(directory);
+
+ for (const filename of filenames) {
+ const fullPath = join(directory, filename);
+ if (!fs.existsSync(directory) || !fs.existsSync(fullPath)) {
+ return {
+ notFound: true,
+ };
+ }
+ fileContent = JSON.parse(fs.readFileSync(fullPath, "utf8"));
+ retriever_speed = getRetrieverSpeedData(fileContent);
+ }
+ return {
+ props: {
+ map,
+ retriever_performance,
+ reader_performance,
+ retriever_speed
+ },
+ revalidate: 1,
+ };
+
+ } catch (e) {
+ console.log(e);
+ return {
+ notFound: true,
+ };
+ }
+};
+
+export function getRetrieverMapData(
+ fileContent: any
+) {
+ const n_docs = [1000, 10000, 100000, 500000];
+ const title: string = fileContent.title;
+ const desc: string = fileContent.description;
+ const chartType: any = fileContent.chart_type;
+ const subTitle: string = fileContent.subtitle;
+ const axisX: string = fileContent.axis[0].x;
+ const axisY: string = fileContent.axis[0].y;
+ const dataRetriever = new Array(fileContent.columns);
+ for(let z=0; z("success");
+const vertical='top';
+const horizontal='center';
+
+const handleSubmit = (event: any) => {
+ event.preventDefault();
+
+ // Hubspot integration
+ // Create the new request
+ var xhr = new XMLHttpRequest();
+ var url = 'https://api.hsforms.com/submissions/v3/integration/submit/4561480/28f3118d-1c2b-4a93-9c8f-f11e4454f1ac';
+
+ // JSON:
+ var data = {
+ "submittedAt": new Date().getTime(),
+ "fields": [
+ {
+ "name": "email",
+ "value": email
+ },
+ {
+ "name": "github",
+ "value": github
+ },
+ {
+ "name": "twitter",
+ "value": twitter
+ },
+ {
+ "name": "company",
+ "value": company
+ },
+ {
+ "name": "firstname",
+ "value": fname
+ },
+ {
+ "name": "lastname",
+ "value": lname
+ }
+ ],
+ "context": {
+ "pageUri": "haystack.deepset.ai",
+ "pageName": "Haystack Hub"
+ },
+ "legalConsentOptions":{ // Include this object when GDPR options are enabled
+ "consent":{
+ "consentToProcess":consentToProcess,
+ "text":"I agree to allow deepset GmbH to store and process my personal data.",
+ "communications":[
+ {
+ "value":communications,
+ "subscriptionTypeId":999,
+ "text":"I agree to receive marketing communications from deepset GmbH."
+ }
+ ]
+ }
+ }
+ };
+
+ var final_data = JSON.stringify(data);
+ console.log(final_data)
+
+ xhr.open('POST', url);
+ // Sets the value of the 'Content-Type' HTTP request headers to 'application/json'
+ xhr.setRequestHeader('Content-Type', 'application/json');
+
+ xhr.onreadystatechange = function() {
+ if(xhr.readyState === 4 && xhr.status === 200) {
+ setMessage("Thank you for joining our community!");
+ setSeverity("success");
+ setSuccess(true);// Returns a 200 response if the submission is successful.
+ } else if (xhr.readyState === 4 && xhr.status === 400){
+ setMessage("An error occurred. Please check your details.");
+ setSeverity("error");
+ setSuccess(true);// Returns Error
+ } else if (xhr.readyState === 4 && xhr.status === 403){
+ setMessage("An error occurred. Please check your details.");
+ setSeverity("error");
+ setSuccess(true);// Returns Error
+ } else if (xhr.readyState === 4 && xhr.status === 404){
+ setMessage("An error occurred. Please check your details.");
+ setSeverity("error");
+ setSuccess(true);// Returns Error
+ } else {
+ setMessage("An error occurred. Please check your details.");
+ setSeverity("error");
+ setSuccess(true);// Returns Error
+ }
+ }
+
+ // Sends the request
+ xhr.send(final_data);
+ window.open(
+ 'https://join.slack.com/t/haystack-community/shared_invite/zt-n9pgt0w1-J7YB_FM4dFeubQI2t6WD8w',
+ '_blank'
+ );
+
+ };
+
+ const handleChangeEmail = (event: any) => {
+ setEmail(event.target.value);
+ };
+
+ const handleChangeGithub = (event: any) => {
+ setGithub(event.target.value);
+ };
+
+ const handleChangeTwitter = (event: any) => {
+ setTwitter(event.target.value);
+ };
+
+ const handleChangeCompany = (event: any) => {
+ setCompany(event.target.value);
+ };
+
+ const handleChangeFName = (event: any) => {
+ setFName(event.target.value);
+ };
+
+ const handleChangeLName = (event: any) => {
+ setLName(event.target.value);
+ };
+
+ const handleChangeConsentToProcess = (event: any) => {
+ setConsentToProcess(event.target.checked);
+ };
+
+ const handleCommunication = (event: any) => {
+ setCommunications(event.target.checked);
+ };
+
+ const handleClose = (reason: any) => {
+ if (reason === 'clickaway') {
+ return;
+ }
+
+ setSuccess(false);
+ };
+
+
+ return ) {
+ return (
+
+ {source && (
+
+ );
+}
+
+export const getStaticPaths: GetStaticPaths = async () => {
+ // we want to get all versions, apart from the latest one
+ const latestVersion = getLatestVersion();
+ const versions = getDocsVersions().filter((v) => v !== latestVersion);
+
+ // we initialize the paths array with the paths that will be used for the latest version (i.e. without a version in the url)
+ let paths = getSlugsFromLocalMarkdownFiles("overview").map((param) => ({
+ params: { slug: [param] },
+ }));
+
+ // we loop over all versions other than the latest one, to create paths that will include the version and the slug in the url
+ for (const version of versions) {
+ const slugs = getSlugsFromLocalMarkdownFiles("overview", version);
+ paths = [
+ ...paths,
+ ...slugs.map((param) => ({
+ params: { slug: [version, param] },
+ })),
+ ];
+ }
+
+ return {
+ paths: paths.flat(),
+ fallback: false,
+ };
+};
+
+export const getStaticProps: GetStaticProps = async ({
+ params,
+}: GetStaticPropsContext) => {
+ if (!params?.slug || !Array.isArray(params.slug)) {
+ return {
+ notFound: true,
+ };
+ }
+
+ try {
+ const docTitleSlug = params.slug?.[params.slug?.length - 1];
+ const directory = getDirectory(
+ "overview",
+ getVersionFromParams(params.slug)
+ );
+ const fullPath = join(directory, `${docTitleSlug.replace("-", "_")}.mdx`);
+
+ if (!fs.existsSync(directory) || !fs.existsSync(fullPath)) {
+ return {
+ notFound: true,
+ };
+ }
+
+ const fileContents = fs.readFileSync(fullPath, "utf8");
+
+ // remove once all markdown files have correctly formatted front matter:
+ const fileContentWithFrontMatter = fileContents
+ .replace("", "---");
+
+ const { content, data } = matter(fileContentWithFrontMatter);
+
+ const mdxSource = await serialize(content, {
+ // Optionally pass remark/rehype plugins
+ mdxOptions: {
+ // @ts-ignore
+ remarkPlugins: [remarkSlug, remarkAutolinkHeadings],
+ rehypePlugins: [],
+ },
+ scope: data,
+ });
+
+ const version = getVersionFromParams(params.slug) || getLatestVersion();
+
+ const layoutProps = await getStaticLayoutProps({
+ content,
+ version,
+ docTitleSlug,
+ });
+
+ return {
+ props: {
+ ...layoutProps,
+ source: mdxSource,
+ },
+ revalidate: 1,
+ };
+ } catch (e) {
+ console.log(e);
+ return {
+ notFound: true,
+ };
+ }
+};
diff --git a/pages/reference/[...slug].tsx b/pages/reference/[...slug].tsx
new file mode 100644
index 000000000..97ea20865
--- /dev/null
+++ b/pages/reference/[...slug].tsx
@@ -0,0 +1,130 @@
+import styles from "components/non-mdx.module.css";
+import {
+ GetStaticPaths,
+ GetStaticProps,
+ GetStaticPropsContext,
+ InferGetStaticPropsType,
+} from "next";
+import Layout from "components/Layout";
+import { getDownloadUrl } from "lib/github";
+import {
+ markdownToHtml,
+ getVersionFromParams,
+ getDocsVersions,
+ getStaticLayoutProps,
+ StaticPageProps,
+} from "lib/utils";
+import { referenceFiles } from "lib/constants";
+import matter from "gray-matter";
+
+export default function ReferenceDoc({
+ menu,
+ toc,
+ editOnGitHubLink,
+ stars,
+ source,
+}: InferGetStaticPropsType) {
+ return (
+
+
+
+ );
+}
+
+// TODO: This function can be shorter and cleaner, similar to how it's written for overview and usage pages
+export const getStaticPaths: GetStaticPaths = async () => {
+ const versions = getDocsVersions();
+
+ const paths = [
+ ...referenceFiles.items.map((item) => ({ params: { slug: [item.slug] } })),
+ ...referenceFiles.items
+ .map((item) =>
+ versions.map((version) => ({
+ params: {
+ slug: [version, item.slug],
+ },
+ }))
+ )
+ .flat(),
+ ];
+
+ return {
+ paths,
+ fallback: true,
+ };
+};
+
+export const getStaticProps: GetStaticProps = async ({
+ params,
+}: GetStaticPropsContext) => {
+ if (!params?.slug || !Array.isArray(params.slug)) {
+ return {
+ notFound: true,
+ };
+ }
+
+ try {
+ const docTitleSlug = params.slug?.[params.slug?.length - 1];
+ const item = referenceFiles.items.find(
+ (item) => item.slug === docTitleSlug
+ );
+
+ if (!item) {
+ return {
+ notFound: true,
+ };
+ }
+
+ const version = getVersionFromParams(params.slug);
+
+ const downloadUrl = await getDownloadUrl({
+ repoPath: referenceFiles.repoPath,
+ filename: item.filename,
+ version,
+ });
+
+ if (!downloadUrl) {
+ return {
+ notFound: true,
+ };
+ }
+
+ const res = await fetch(downloadUrl);
+ const fileContent = await res.text();
+
+ // remove once all markdown files have correctly formatted front matter:
+ const fileContentWithFrontMatter = fileContent
+ .replace("", "---");
+
+ const { content } = matter(fileContentWithFrontMatter);
+ const { markup } = await markdownToHtml({ content, downloadUrl });
+
+ const layoutProps = await getStaticLayoutProps({
+ content,
+ version,
+ docTitleSlug,
+ });
+
+ return {
+ props: {
+ ...layoutProps,
+ source: markup,
+ },
+ revalidate: 1,
+ };
+ } catch (e) {
+ console.log(e);
+ return {
+ notFound: true,
+ };
+ }
+};
diff --git a/pages/tutorials/[...slug].tsx b/pages/tutorials/[...slug].tsx
new file mode 100644
index 000000000..964e4362f
--- /dev/null
+++ b/pages/tutorials/[...slug].tsx
@@ -0,0 +1,128 @@
+import {
+ GetStaticPaths,
+ GetStaticProps,
+ GetStaticPropsContext,
+ InferGetStaticPropsType,
+} from "next";
+import styles from "components/non-mdx.module.css";
+import Layout from "components/Layout";
+import { getDownloadUrl } from "lib/github";
+import {
+ markdownToHtml,
+ getVersionFromParams,
+ getDocsVersions,
+ getStaticLayoutProps,
+ StaticPageProps,
+} from "lib/utils";
+import { tutorialFiles } from "lib/constants";
+import matter from "gray-matter";
+
+export default function TutorialDoc({
+ menu,
+ toc,
+ editOnGitHubLink,
+ stars,
+ source,
+}: InferGetStaticPropsType) {
+ return (
+
+
+
+ );
+}
+
+// TODO: This function can be shorter and cleaner, similar to how it's written for overview and usage pages
+export const getStaticPaths: GetStaticPaths = async () => {
+ const versions = getDocsVersions();
+
+ const paths = [
+ ...tutorialFiles.items.map((item) => ({ params: { slug: [item.slug] } })),
+ ...tutorialFiles.items
+ .map((item) =>
+ versions.map((version) => ({
+ params: {
+ slug: [version, item.slug],
+ },
+ }))
+ )
+ .flat(),
+ ];
+
+ return {
+ paths,
+ fallback: true,
+ };
+};
+
+export const getStaticProps: GetStaticProps = async ({
+ params,
+}: GetStaticPropsContext) => {
+ if (!params?.slug || !Array.isArray(params.slug)) {
+ return {
+ notFound: true,
+ };
+ }
+
+ try {
+ const docTitleSlug = params.slug?.[params.slug?.length - 1];
+ const item = tutorialFiles.items.find((item) => item.slug === docTitleSlug);
+
+ if (!item) {
+ return {
+ notFound: true,
+ };
+ }
+
+ const version = getVersionFromParams(params.slug);
+
+ const downloadUrl = await getDownloadUrl({
+ repoPath: tutorialFiles.repoPath,
+ filename: item.filename,
+ version,
+ });
+
+ if (!downloadUrl) {
+ return {
+ notFound: true,
+ };
+ }
+
+ const res = await fetch(downloadUrl);
+ const fileContent = await res.text();
+
+ // remove once all markdown files have correctly formatted front matter:
+ const fileContentWithFrontMatter = fileContent
+ .replace("", "---");
+
+ const { content } = matter(fileContentWithFrontMatter);
+ const { markup } = await markdownToHtml({ content, downloadUrl });
+
+ const layoutProps = await getStaticLayoutProps({
+ content,
+ version,
+ docTitleSlug,
+ });
+
+ return {
+ props: {
+ ...layoutProps,
+ source: markup,
+ },
+ revalidate: 1,
+ };
+ } catch (e) {
+ console.log(e);
+ return {
+ notFound: true,
+ };
+ }
+};
diff --git a/pages/usage/[...slug].tsx b/pages/usage/[...slug].tsx
new file mode 100644
index 000000000..35501f50a
--- /dev/null
+++ b/pages/usage/[...slug].tsx
@@ -0,0 +1,137 @@
+import matter from "gray-matter";
+import { MDXRemote, MDXRemoteSerializeResult } from "next-mdx-remote";
+import { serialize } from "next-mdx-remote/serialize";
+import remarkSlug from "remark-slug";
+import remarkAutolinkHeadings from "remark-autolink-headings";
+import {
+ GetStaticPaths,
+ GetStaticProps,
+ GetStaticPropsContext,
+ InferGetStaticPropsType,
+} from "next";
+import { join } from "path";
+import fs from "fs";
+import Layout from "components/Layout";
+import {
+ getSlugsFromLocalMarkdownFiles,
+ getVersionFromParams,
+ getDirectory,
+ getDocsVersions,
+ getLatestVersion,
+ getStaticLayoutProps,
+ StaticPageProps,
+} from "lib/utils";
+import { components } from "lib/mdx";
+
+export default function UsageDoc({
+ menu,
+ toc,
+ editOnGitHubLink,
+ stars,
+ source,
+}: InferGetStaticPropsType) {
+ return (
+
+ {source && (
+
+ );
+}
+
+export const getStaticPaths: GetStaticPaths = async () => {
+ // we want to get all versions, apart from the latest one
+ const latestVersion = getLatestVersion();
+ const versions = getDocsVersions().filter((v) => v !== latestVersion);
+
+ // we initialize the paths array with the paths that will be used for the latest version (i.e. without a version in the url)
+ let paths = getSlugsFromLocalMarkdownFiles("usage").map((param) => ({
+ params: { slug: [param] },
+ }));
+
+ // we loop over all versions other than the latest one, to create paths that will include the version and the slug in the url
+ for (const version of versions) {
+ const slugs = getSlugsFromLocalMarkdownFiles("usage", version);
+ paths = [
+ ...paths,
+ ...slugs.map((param) => ({
+ params: { slug: [version, param] },
+ })),
+ ];
+ }
+
+ return {
+ paths: paths.flat(),
+ fallback: false,
+ };
+};
+
+export const getStaticProps: GetStaticProps = async ({
+ params,
+}: GetStaticPropsContext) => {
+ if (!params?.slug || !Array.isArray(params.slug)) {
+ return {
+ notFound: true,
+ };
+ }
+
+ try {
+ const docTitleSlug = params.slug?.[params.slug?.length - 1];
+ const directory = getDirectory("usage", getVersionFromParams(params.slug));
+ const fullPath = join(directory, `${docTitleSlug.replace("-", "_")}.mdx`);
+
+ if (!fs.existsSync(directory) || !fs.existsSync(fullPath)) {
+ return {
+ notFound: true,
+ };
+ }
+
+ const fileContents = fs.readFileSync(fullPath, "utf8");
+
+ // remove once all markdown files have correctly formatted front matter:
+ const fileContentWithFrontMatter = fileContents
+ .replace("", "---");
+
+ const { content, data } = matter(fileContentWithFrontMatter);
+
+ const mdxSource = await serialize(content, {
+ // Optionally pass remark/rehype plugins
+ mdxOptions: {
+ // @ts-ignore
+ remarkPlugins: [remarkSlug, remarkAutolinkHeadings],
+ rehypePlugins: [],
+ },
+ scope: data,
+ });
+
+ const version = getVersionFromParams(params.slug) || getLatestVersion();
+
+ const layoutProps = await getStaticLayoutProps({
+ content,
+ version,
+ docTitleSlug,
+ });
+
+ return {
+ props: {
+ ...layoutProps,
+ source: mdxSource,
+ },
+ revalidate: 1,
+ };
+ } catch (e) {
+ console.log(e);
+ return {
+ notFound: true,
+ };
+ }
+};
diff --git a/postcss.config.js b/postcss.config.js
new file mode 100644
index 000000000..12a703d90
--- /dev/null
+++ b/postcss.config.js
@@ -0,0 +1,6 @@
+module.exports = {
+ plugins: {
+ tailwindcss: {},
+ autoprefixer: {},
+ },
+};
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diff --git a/src/pages/docs/versions/master/latest/site/en/img/zenhub_board.png b/public/img/zenhub_board.png
similarity index 100%
rename from src/pages/docs/versions/master/latest/site/en/img/zenhub_board.png
rename to public/img/zenhub_board.png
diff --git a/src/pages/docs/versions/master/latest/site/en/img/zenhub_issue.png b/public/img/zenhub_issue.png
similarity index 100%
rename from src/pages/docs/versions/master/latest/site/en/img/zenhub_issue.png
rename to public/img/zenhub_issue.png
diff --git a/src/pages/docs/versions/master/latest/site/en/img/zenhub_roadmap.png b/public/img/zenhub_roadmap.png
similarity index 100%
rename from src/pages/docs/versions/master/latest/site/en/img/zenhub_roadmap.png
rename to public/img/zenhub_roadmap.png
diff --git a/src/components/_include_media.scss b/src/components/_include_media.scss
deleted file mode 100644
index e5d24cc40..000000000
--- a/src/components/_include_media.scss
+++ /dev/null
@@ -1,601 +0,0 @@
-@charset "UTF-8";
-
-// _ _ _ _ _
-// (_) | | | | | (_)
-// _ _ __ ___| |_ _ __| | ___ _ __ ___ ___ __| |_ __ _
-// | | '_ \ / __| | | | |/ _` |/ _ \ | '_ ` _ \ / _ \/ _` | |/ _` |
-// | | | | | (__| | |_| | (_| | __/ | | | | | | __/ (_| | | (_| |
-// |_|_| |_|\___|_|\__,_|\__,_|\___| |_| |_| |_|\___|\__,_|_|\__,_|
-//
-// Simple, elegant and maintainable media queries in Sass
-// v1.4.9
-//
-// http://include-media.com
-//
-// Authors: Eduardo Boucas (@eduardoboucas)
-// Hugo Giraudel (@hugogiraudel)
-//
-// This project is licensed under the terms of the MIT license
-
-
-////
-/// include-media library public configuration
-/// @author Eduardo Boucas
-/// @access public
-////
-
-
-///
-/// Creates a list of global breakpoints
-///
-/// @example scss - Creates a single breakpoint with the label `phone`
-/// $breakpoints: ('phone': 320px);
-///
-$breakpoints: ('phone': 320px,
- 'tablet': 768px,
- 'desktop': 1024px) !default;
-
-
-///
-/// Creates a list of static expressions or media types
-///
-/// @example scss - Creates a single media type (screen)
-/// $media-expressions: ('screen': 'screen');
-///
-/// @example scss - Creates a static expression with logical disjunction (OR operator)
-/// $media-expressions: (
-/// 'retina2x': '(-webkit-min-device-pixel-ratio: 2), (min-resolution: 192dpi)'
-/// );
-///
-$media-expressions: ('screen': 'screen',
- 'print': 'print',
- 'handheld': 'handheld',
- 'landscape': '(orientation: landscape)',
- 'portrait': '(orientation: portrait)',
- 'retina2x': '(-webkit-min-device-pixel-ratio: 2), (min-resolution: 192dpi), (min-resolution: 2dppx)',
- 'retina3x': '(-webkit-min-device-pixel-ratio: 3), (min-resolution: 350dpi), (min-resolution: 3dppx)'
-) !default;
-
-
-///
-/// Defines a number to be added or subtracted from each unit when declaring breakpoints with exclusive intervals
-///
-/// @example scss - Interval for pixels is defined as `1` by default
-/// @include media('>128px') {}
-///
-/// /* Generates: */
-/// @media (min-width: 129px) {}
-///
-/// @example scss - Interval for ems is defined as `0.01` by default
-/// @include media('>20em') {}
-///
-/// /* Generates: */
-/// @media (min-width: 20.01em) {}
-///
-/// @example scss - Interval for rems is defined as `0.1` by default, to be used with `font-size: 62.5%;`
-/// @include media('>2.0rem') {}
-///
-/// /* Generates: */
-/// @media (min-width: 2.1rem) {}
-///
-$unit-intervals: ('px': 1,
- 'em': 0.01,
- 'rem': 0.1,
- '': 0) !default;
-
-///
-/// Defines whether support for media queries is available, useful for creating separate stylesheets
-/// for browsers that don't support media queries.
-///
-/// @example scss - Disables support for media queries
-/// $im-media-support: false;
-/// @include media('>=tablet') {
-/// .foo {
-/// color: tomato;
-/// }
-/// }
-///
-/// /* Generates: */
-/// .foo {
-/// color: tomato;
-/// }
-///
-$im-media-support: true !default;
-
-///
-/// Selects which breakpoint to emulate when support for media queries is disabled. Media queries that start at or
-/// intercept the breakpoint will be displayed, any others will be ignored.
-///
-/// @example scss - This media query will show because it intercepts the static breakpoint
-/// $im-media-support: false;
-/// $im-no-media-breakpoint: 'desktop';
-/// @include media('>=tablet') {
-/// .foo {
-/// color: tomato;
-/// }
-/// }
-///
-/// /* Generates: */
-/// .foo {
-/// color: tomato;
-/// }
-///
-/// @example scss - This media query will NOT show because it does not intercept the desktop breakpoint
-/// $im-media-support: false;
-/// $im-no-media-breakpoint: 'tablet';
-/// @include media('>=desktop') {
-/// .foo {
-/// color: tomato;
-/// }
-/// }
-///
-/// /* No output */
-///
-$im-no-media-breakpoint: 'desktop' !default;
-
-///
-/// Selects which media expressions are allowed in an expression for it to be used when media queries
-/// are not supported.
-///
-/// @example scss - This media query will show because it intercepts the static breakpoint and contains only accepted media expressions
-/// $im-media-support: false;
-/// $im-no-media-breakpoint: 'desktop';
-/// $im-no-media-expressions: ('screen');
-/// @include media('>=tablet', 'screen') {
-/// .foo {
-/// color: tomato;
-/// }
-/// }
-///
-/// /* Generates: */
-/// .foo {
-/// color: tomato;
-/// }
-///
-/// @example scss - This media query will NOT show because it intercepts the static breakpoint but contains a media expression that is not accepted
-/// $im-media-support: false;
-/// $im-no-media-breakpoint: 'desktop';
-/// $im-no-media-expressions: ('screen');
-/// @include media('>=tablet', 'retina2x') {
-/// .foo {
-/// color: tomato;
-/// }
-/// }
-///
-/// /* No output */
-///
-$im-no-media-expressions: ('screen', 'portrait', 'landscape') !default;
-
-////
-/// Cross-engine logging engine
-/// @author Hugo Giraudel
-/// @access private
-////
-
-
-///
-/// Log a message either with `@error` if supported
-/// else with `@warn`, using `feature-exists('at-error')`
-/// to detect support.
-///
-/// @param {String} $message - Message to log
-///
-@function im-log($message) {
- @if feature-exists('at-error') {
- @error $message;
- }
-
- @else {
- @warn $message;
- $_: noop();
- }
-
- @return $message;
-}
-
-
-///
-/// Wrapper mixin for the log function so it can be used with a more friendly
-/// API than `@if im-log('..') {}` or `$_: im-log('..')`. Basically, use the function
-/// within functions because it is not possible to include a mixin in a function
-/// and use the mixin everywhere else because it's much more elegant.
-///
-/// @param {String} $message - Message to log
-///
-@mixin log($message) {
- @if im-log($message) {}
-}
-
-
-///
-/// Function with no `@return` called next to `@warn` in Sass 3.3
-/// to trigger a compiling error and stop the process.
-///
-@function noop() {}
-
-///
-/// Determines whether a list of conditions is intercepted by the static breakpoint.
-///
-/// @param {Arglist} $conditions - Media query conditions
-///
-/// @return {Boolean} - Returns true if the conditions are intercepted by the static breakpoint
-///
-@function im-intercepts-static-breakpoint($conditions...) {
- $no-media-breakpoint-value: map-get($breakpoints, $im-no-media-breakpoint);
-
- @if not $no-media-breakpoint-value {
- @if im-log('`#{$im-no-media-breakpoint}` is not a valid breakpoint.') {}
- }
-
- @each $condition in $conditions {
- @if not map-has-key($media-expressions, $condition) {
- $operator: get-expression-operator($condition);
- $prefix: get-expression-prefix($operator);
- $value: get-expression-value($condition, $operator);
-
- @if ($prefix=='max'and $value <=$no-media-breakpoint-value) or ($prefix=='min'and $value > $no-media-breakpoint-value) {
- @return false;
- }
- }
-
- @else if not index($im-no-media-expressions, $condition) {
- @return false;
- }
- }
-
- @return true;
-}
-
-////
-/// Parsing engine
-/// @author Hugo Giraudel
-/// @access private
-////
-
-
-///
-/// Get operator of an expression
-///
-/// @param {String} $expression - Expression to extract operator from
-///
-/// @return {String} - Any of `>=`, `>`, `<=`, `<`, `≥`, `≤`
-///
-@function get-expression-operator($expression) {
- @each $operator in ('>=', '>', '<=', '<', '≥', '≤') {
- @if str-index($expression, $operator) {
- @return $operator;
- }
- }
-
- // It is not possible to include a mixin inside a function, so we have to
- // rely on the `im-log(..)` function rather than the `log(..)` mixin. Because
- // functions cannot be called anywhere in Sass, we need to hack the call in
- // a dummy variable, such as `$_`. If anybody ever raise a scoping issue with
- // Sass 3.3, change this line in `@if im-log(..) {}` instead.
- $_: im-log('No operator found in `#{$expression}`.');
-}
-
-
-///
-/// Get dimension of an expression, based on a found operator
-///
-/// @param {String} $expression - Expression to extract dimension from
-/// @param {String} $operator - Operator from `$expression`
-///
-/// @return {String} - `width` or `height` (or potentially anything else)
-///
-@function get-expression-dimension($expression, $operator) {
- $operator-index: str-index($expression, $operator);
- $parsed-dimension: str-slice($expression, 0, $operator-index - 1);
- $dimension: 'width';
-
- @if str-length($parsed-dimension)>0 {
- $dimension: $parsed-dimension;
- }
-
- @return $dimension;
-}
-
-
-///
-/// Get dimension prefix based on an operator
-///
-/// @param {String} $operator - Operator
-///
-/// @return {String} - `min` or `max`
-///
-@function get-expression-prefix($operator) {
- @return if(index(('<', '<=', '≤'), $operator), 'max', 'min');
-}
-
-
-///
-/// Get value of an expression, based on a found operator
-///
-/// @param {String} $expression - Expression to extract value from
-/// @param {String} $operator - Operator from `$expression`
-///
-/// @return {Number} - A numeric value
-///
-@function get-expression-value($expression, $operator) {
- $operator-index: str-index($expression, $operator);
- $value: str-slice($expression, $operator-index + str-length($operator));
-
- @if map-has-key($breakpoints, $value) {
- $value: map-get($breakpoints, $value);
- }
-
- @else {
- $value: to-number($value);
- }
-
- $interval: map-get($unit-intervals, unit($value));
-
- @if not $interval {
- // It is not possible to include a mixin inside a function, so we have to
- // rely on the `im-log(..)` function rather than the `log(..)` mixin. Because
- // functions cannot be called anywhere in Sass, we need to hack the call in
- // a dummy variable, such as `$_`. If anybody ever raise a scoping issue with
- // Sass 3.3, change this line in `@if im-log(..) {}` instead.
- $_: im-log('Unknown unit `#{unit($value)}`.');
- }
-
- @if $operator=='>' {
- $value: $value + $interval;
- }
-
- @else if $operator=='<' {
- $value: $value - $interval;
- }
-
- @return $value;
-}
-
-
-///
-/// Parse an expression to return a valid media-query expression
-///
-/// @param {String} $expression - Expression to parse
-///
-/// @return {String} - Valid media query
-///
-@function parse-expression($expression) {
-
- // If it is part of $media-expressions, it has no operator
- // then there is no need to go any further, just return the value
- @if map-has-key($media-expressions, $expression) {
- @return map-get($media-expressions, $expression);
- }
-
- $operator: get-expression-operator($expression);
- $dimension: get-expression-dimension($expression, $operator);
- $prefix: get-expression-prefix($operator);
- $value: get-expression-value($expression, $operator);
-
- @return '(#{$prefix}-#{$dimension}: #{$value})';
-}
-
-///
-/// Slice `$list` between `$start` and `$end` indexes
-///
-/// @access private
-///
-/// @param {List} $list - List to slice
-/// @param {Number} $start [1] - Start index
-/// @param {Number} $end [length($list)] - End index
-///
-/// @return {List} Sliced list
-///
-@function slice($list, $start: 1, $end: length($list)) {
- @if length($list) < 1 or $start>$end {
- @return ();
- }
-
- $result: ();
-
- @for $i from $start through $end {
- $result: append($result, nth($list, $i));
- }
-
- @return $result;
-}
-
-////
-/// String to number converter
-/// @author Hugo Giraudel
-/// @access private
-////
-
-
-///
-/// Casts a string into a number
-///
-/// @param {String | Number} $value - Value to be parsed
-///
-/// @return {Number}
-///
-@function to-number($value) {
- @if type-of($value)=='number' {
- @return $value;
- }
-
- @else if type-of($value) !='string' {
- $_: im-log('Value for `to-number` should be a number or a string.');
- }
-
- $first-character: str-slice($value, 1, 1);
- $result: 0;
- $digits: 0;
- $minus: ($first-character=='-');
- $numbers: ('0': 0, '1': 1, '2': 2, '3': 3, '4': 4, '5': 5, '6': 6, '7': 7, '8': 8, '9': 9);
-
- // Remove +/- sign if present at first character
- @if ($first-character=='+'or $first-character=='-') {
- $value: str-slice($value, 2);
- }
-
- @for $i from 1 through str-length($value) {
- $character: str-slice($value, $i, $i);
-
- @if not (index(map-keys($numbers), $character) or $character=='.') {
- @return to-length(if($minus, -$result, $result), str-slice($value, $i))
- }
-
- @if $character=='.' {
- $digits: 1;
- }
-
- @else if $digits==0 {
- $result: $result * 10 + map-get($numbers, $character);
- }
-
- @else {
- $digits: $digits * 10;
- $result: $result + map-get($numbers, $character) / $digits;
- }
- }
-
- @return if($minus, -$result, $result);
-}
-
-
-///
-/// Add `$unit` to `$value`
-///
-/// @param {Number} $value - Value to add unit to
-/// @param {String} $unit - String representation of the unit
-///
-/// @return {Number} - `$value` expressed in `$unit`
-///
-@function to-length($value, $unit) {
- $units: ('px': 1px, 'cm': 1cm, 'mm': 1mm, '%': 1%, 'ch': 1ch, 'pc': 1pc, 'in': 1in, 'em': 1em, 'rem': 1rem, 'pt': 1pt, 'ex': 1ex, 'vw': 1vw, 'vh': 1vh, 'vmin': 1vmin, 'vmax': 1vmax);
-
- @if not index(map-keys($units), $unit) {
- $_: im-log('Invalid unit `#{$unit}`.');
- }
-
- @return $value * map-get($units, $unit);
-}
-
-///
-/// This mixin aims at redefining the configuration just for the scope of
-/// the call. It is helpful when having a component needing an extended
-/// configuration such as custom breakpoints (referred to as tweakpoints)
-/// for instance.
-///
-/// @author Hugo Giraudel
-///
-/// @param {Map} $tweakpoints [()] - Map of tweakpoints to be merged with `$breakpoints`
-/// @param {Map} $tweak-media-expressions [()] - Map of tweaked media expressions to be merged with `$media-expression`
-///
-/// @example scss - Extend the global breakpoints with a tweakpoint
-/// @include media-context(('custom': 678px)) {
-/// .foo {
-/// @include media('>phone', '<=custom') {
-/// // ...
-/// }
-/// }
-/// }
-///
-/// @example scss - Extend the global media expressions with a custom one
-/// @include media-context($tweak-media-expressions: ('all': 'all')) {
-/// .foo {
-/// @include media('all', '>phone') {
-/// // ...
-/// }
-/// }
-/// }
-///
-/// @example scss - Extend both configuration maps
-/// @include media-context(('custom': 678px), ('all': 'all')) {
-/// .foo {
-/// @include media('all', '>phone', '<=custom') {
-/// // ...
-/// }
-/// }
-/// }
-///
-@mixin media-context($tweakpoints: (), $tweak-media-expressions: ()) {
- // Save global configuration
- $global-breakpoints: $breakpoints;
- $global-media-expressions: $media-expressions;
-
- // Update global configuration
- $breakpoints: map-merge($breakpoints, $tweakpoints) !global;
- $media-expressions: map-merge($media-expressions, $tweak-media-expressions) !global;
-
- @content;
-
- // Restore global configuration
- $breakpoints: $global-breakpoints !global;
- $media-expressions: $global-media-expressions !global;
-}
-
-////
-/// include-media public exposed API
-/// @author Eduardo Boucas
-/// @access public
-////
-
-
-///
-/// Generates a media query based on a list of conditions
-///
-/// @param {Arglist} $conditions - Media query conditions
-///
-/// @example scss - With a single set breakpoint
-/// @include media('>phone') { }
-///
-/// @example scss - With two set breakpoints
-/// @include media('>phone', '<=tablet') { }
-///
-/// @example scss - With custom values
-/// @include media('>=358px', '<850px') { }
-///
-/// @example scss - With set breakpoints with custom values
-/// @include media('>desktop', '<=1350px') { }
-///
-/// @example scss - With a static expression
-/// @include media('retina2x') { }
-///
-/// @example scss - Mixing everything
-/// @include media('>=350px', ' 0) {
- @media #{unquote(parse-expression(nth($conditions, 1)))} {
-
- // Recursive call
- @include media(slice($conditions, 2)...) {
- @content;
- }
- }
- }
-}
-
-@mixin scroll-bar {
- // &::-webkit-scrollbar {
- // width: 6px;
- // height: 6px;
- // background-color: rgba(255, 255, 255, 0.05);
- // }
-
- // /*滑块*/
- // &::-webkit-scrollbar-thumb {
- // background-color: rgba(255, 255, 255, 0.1);
- // border-radius: 10px;
- // }
-
- // &::-webkit-scrollbar-thumb:hover {
- // background-color: rgba(255, 255, 255, 0.3);
- // }
-
- // /*滑道*/
- // &::-webkit-scrollbar-track {
- // border-radius: 10px;
- // }
-}
\ No newline at end of file
diff --git a/src/components/code/code.js b/src/components/code/code.js
deleted file mode 100644
index 4bba2d86c..000000000
--- a/src/components/code/code.js
+++ /dev/null
@@ -1,89 +0,0 @@
-import React from 'react';
-import PropTypes from 'prop-types';
-import './code.scss';
-
-const Code = ({ html, content, locale }) => {
- const buttonTextMap = {
- en: {
- true: 'Copied',
- false: 'Copy',
- },
- cn: {
- true: '已复制',
- false: '复制',
- },
- };
-
- const handleButtonsContent = () => {
- document.querySelectorAll('.button-copy-text').forEach((item) => {
- const isCurrentElement =
- item.parentNode.previousSibling.innerHTML === html;
-
- // change button text
- item.textContent = buttonTextMap[locale][isCurrentElement];
- // change button icon
- item.nextSibling.className = isCurrentElement
- ? 'button-copy-icon fa fa-check'
- : 'button-copy-icon fa fa-clone';
- });
- };
-
- const formatContent = (content) => {
- const code = content
- .split('\n')
- .filter((item) => item[0] !== '#')
- .map((str) => {
- const invalidItems = ['$', '>>>'];
- return str
- .split(' ')
- .filter((s) => !invalidItems.includes(s))
- .join(' ');
- })
- .join('\n');
-
- return code;
- };
-
- const onButtonClick = () => {
- handleButtonsContent();
- const code = formatContent(content);
- copyToClipboard(code);
- };
-
- const copyToClipboard = (content) => {
- const el = document.createElement(`textarea`);
- el.value = content;
- el.setAttribute(`readonly`, ``);
- el.style.position = `absolute`;
- el.style.left = `-9999px`;
- document.body.appendChild(el);
- el.select();
- document.execCommand(`copy`);
- document.body.removeChild(el);
- };
-
- return (
- <>
-
-
-
-
-
- );
-};
-
-Code.prototypes = {
- html: PropTypes.string.isRequired,
- content: PropTypes.string.isRequired,
- locale: PropTypes.string.isRequired,
-};
-
-export default Code;
\ No newline at end of file
diff --git a/src/components/code/code.scss b/src/components/code/code.scss
deleted file mode 100644
index 0c00866a5..000000000
--- a/src/components/code/code.scss
+++ /dev/null
@@ -1,29 +0,0 @@
-.code-wrapper {
- position: relative;
- margin: 16px 0 8px;
- min-height: 24px;
- }
-
- .content {
- overflow-x: auto;
- }
-
- .button-copy {
- position: absolute;
- top: -18px;
- right: 0;
- border: none;
- border-radius: 4px;
- color: #b7b5bd;
- background-color: transparent;
- font-size: 1rem;
- cursor: pointer;
-
- &:active {
- outline: none;
- }
-
- &-icon {
- margin-left: 4px;
- }
- }
\ No newline at end of file
diff --git a/src/components/docLayout/index.jsx b/src/components/docLayout/index.jsx
deleted file mode 100644
index c1d8b9f97..000000000
--- a/src/components/docLayout/index.jsx
+++ /dev/null
@@ -1,260 +0,0 @@
-import React, { useState, useEffect, useRef } from "react";
-import Menu from "../menu";
-import Header from "../header/header";
-import Footer from "../footer/footer";
-import "./index.scss";
-import { GithubLogo } from "../../images/icon/GitHub-Mark-32px.png";
-import thumbnail from "../../images/haystack_logo_blue_banner_social.png";
-import SEO from "../../components/seo";
-import { useMobileScreen } from "../../hooks";
-
-export default (props) => {
- const {
- language,
- children,
- locale,
- menuList,
- id,
- versions,
- version,
- headings,
- current,
- wrapperClass = "doc-wrapper",
- isDocAPI = false,
- isDocHub = false,
- isBenchMark = false,
- showDoc = true,
- } = props;
- let formatHeadings = null;
-
- formatHeadings =
- headings &&
- headings.reduce((pre, cur) => {
- const copyCur = JSON.parse(JSON.stringify(cur));
- const preHead = pre[pre.length - 1];
- if (preHead && preHead.depth < cur.depth
- && pre[pre.length-1].children
- && pre[pre.length-1].children.length > 0
- && pre[pre.length-1].children[pre[pre.length-1].children.length-1].children !== undefined) {
- pre[pre.length - 1].children[pre[pre.length - 1].children.length-1].children.push(cur);
- } else if (pre.length > 0
- && pre[pre.length-1].children
- && pre[pre.length-1].children.length > 0
- && pre[pre.length-1].children[pre[pre.length-1].children.length-1].depth < cur.depth) {
- pre[pre.length-1].children[pre[pre.length-1].children.length-1].children = [];
- } else if (preHead && preHead.depth < cur.depth) {
- pre[pre.length - 1].children.push(cur);
- pre[pre.length - 1].children[pre[pre.length-1].children.length-1].children = [];
- } else {
- copyCur.children = [];
- pre = [...pre, copyCur];
- }
- return pre;
- }, []);
- const [hash, setHash] = useState(null);
- const docContainer = useRef(null);
- const [showToTopButton, setShowToTopButton] = useState(false);
- const screenWidth = useMobileScreen();
-
- useEffect(() => {
- // eslint-disable-next-line react-hooks/exhaustive-deps
- }, [screenWidth]);
-
- // star reference
- const star = useRef(null);
- useEffect(() => {
- if (!window) {
- return;
- }
- if (!star.current) {
- return;
- }
- const repoUrl = `https://api.github.com/repos/deepset-ai/haystack`;
- let latest = window.localStorage.getItem("deepset-ai.io.stargazers");
- const latestFetchTime = window.localStorage.getItem(
- "deepset-ai.io.stargazers_fetch_time"
- );
-
- if (
- !latestFetchTime ||
- Date.now() - latestFetchTime > 60000 * 60 ||
- !latest
- ) {
- // get
- fetch(repoUrl)
- .then((response) => response.json())
- .then((data) => {
- if (data) {
- if (data.stargazers_count >= latest) {
- window.localStorage.setItem(
- "deepset-ai.io.stargazers",
- data.stargazers_count
- );
- window.localStorage.setItem(
- "deepset-ai.io.stargazers_fetch_time",
- Date.now()
- );
- star.current.innerHTML = `Github ${data.stargazers_count} stars`;
- }
- }
- });
- } else {
- star.current.innerHTML = `Github ${latest} stars`;
- }
- }, []);
-
- useEffect(() => {
- const cb = () => {
- const wrapper = document.querySelector("html");
-
- const showButton = wrapper.scrollTop !== 0;
- setShowToTopButton(showButton);
- };
-
- window.addEventListener("scroll", () => {
- cb();
- });
-
- return window.removeEventListener("scroll", () => {
- cb();
- });
- }, []);
-
- const generateAnchorMenu = (headings, className, anchors = []) => {
- return headings.map((v) => {
- /* eslint-disable-next-line */
- const normalVal = v.value.replace(/[.｜,｜\/｜\'｜\?｜？｜、|，|\(|\)|:|&|!|;|:]/g, "");
- let anchor = normalVal.split(" ").join("-");
- if (anchors.includes(anchor)) {
- let filteredAnchors = anchors.filter(element => element === anchor);
- anchor = `${anchor}-${filteredAnchors.length}`
- anchors.push(anchor);
- } else {
- anchors.push(anchor);
- }
- let childDom = null;
- let childchildDom = null;
- if (v.children && v.children.length && v.children[0].depth === 2) {
- childDom = generateAnchorMenu(v.children, "child-item", anchors);
- } else if (v.children && v.children.length && v.children[0].depth === 4) {
- childchildDom = generateAnchorMenu(v.children, "child-child-item", anchors);
- }
-
- return (
- ![]()
-}
-
-export default Image
diff --git a/src/components/landing-page/button.js b/src/components/landing-page/button.js
deleted file mode 100644
index 9f5dac75c..000000000
--- a/src/components/landing-page/button.js
+++ /dev/null
@@ -1,12 +0,0 @@
-import React from 'react';
-import { Link } from "gatsby";
-
-const Button = ({ label, to, className="button", type, title, linkName }) => (
-
-
-
-);
-
-export default Button;
\ No newline at end of file
diff --git a/src/components/layout/404Layout.jsx b/src/components/layout/404Layout.jsx
deleted file mode 100644
index 8a5e3eae4..000000000
--- a/src/components/layout/404Layout.jsx
+++ /dev/null
@@ -1,29 +0,0 @@
-/**
- * Layout component that queries for data
- * with Gatsby's useStaticQuery component
- *
- * See: https://www.gatsbyjs.org/docs/use-static-query/
- */
-
-import React from "react";
-import PropTypes from "prop-types";
-import Header from "../header/header";
-import Footer from "../footer/footer";
-
-const Layout = ({ children, language, locale }) => {
- return (
- <>
-
- We use cookies to provide the best site experience.
-
-
-
- );
-};
-Layout.propTypes = {
- children: PropTypes.node.isRequired,
- language: PropTypes.object,
- locale: PropTypes.string
-};
-export default Layout;
\ No newline at end of file
diff --git a/src/components/layout/layout.scss b/src/components/layout/layout.scss
deleted file mode 100644
index 5c8b8ccbe..000000000
--- a/src/components/layout/layout.scss
+++ /dev/null
@@ -1,16 +0,0 @@
-@import "../../reset.scss";
-@import "../include_media.scss";
-
-.content-wrapper {
- width: 100%;
- box-sizing: border-box;
- background-color: #F5F6FA;
- ul,
- li {
- list-style: none;
- }
-}
-
-.cookies-button-wrapper {
- width: 100%;
-}
\ No newline at end of file
diff --git a/src/components/localizedLink/localizedLink.js b/src/components/localizedLink/localizedLink.js
deleted file mode 100644
index d62b26c08..000000000
--- a/src/components/localizedLink/localizedLink.js
+++ /dev/null
@@ -1,28 +0,0 @@
-
-import React from "react";
-import { Link } from "gatsby";
-import locales from "../../consts/locales.js";
-import "./localizedLink.scss";
-
-export default ({ locale, to, children, className = "link", scrollTo }) => {
- const language = locales[locale];
- const toMedium = locale === "en" && to.includes("blog");
- if (toMedium) {
- return (
- scrollTo({scrollTo})}
- >
- );
- }
-
- let path;
-
- language && !language.default ? (path = `/${locale}${to}`) : (path = to);
- return ;
-};
\ No newline at end of file
diff --git a/src/components/localizedLink/localizedLink.scss b/src/components/localizedLink/localizedLink.scss
deleted file mode 100644
index 9ceed9733..000000000
--- a/src/components/localizedLink/localizedLink.scss
+++ /dev/null
@@ -1,45 +0,0 @@
-@import "../include_media";
-
-$primary: #fff;
-
-a {
- text-decoration: none;
- color: #fff;
-
-
- &.primary {
- display: flex;
- align-items: center;
- justify-content: center;
- padding: 10px 14px;
- border: 2px solid $primary;
- font-weight: bold;
- letter-spacing: 2px;
- height: 48px;
- box-sizing: border-box;
- min-width: 160px;
- text-align: center;
-
- @include media("<=1000px") {
- &:lang(en) {
- letter-spacing: 2px;
- }
- }
- }
-
- &.primary-color {
- color: #000;
- background: $primary;
- border: 2px solid #000;
- }
-
- &.white-color {
- color: #000;
- border: 2px solid #000;
-
- &:hover {
- color: #08415D;
- background: #fff;
- }
- }
-}
\ No newline at end of file
diff --git a/src/components/menu/index.jsx b/src/components/menu/index.jsx
deleted file mode 100644
index 95993f4d4..000000000
--- a/src/components/menu/index.jsx
+++ /dev/null
@@ -1,255 +0,0 @@
-import React, { useEffect, useState, useRef } from "react";
-import LocalizeLink from "../localizedLink/localizedLink";
-import VersionSelector from "../selector";
-import { useMobileScreen } from "../../hooks";
-import "./index.scss";
-import { FontAwesomeIcon } from '@fortawesome/react-fontawesome';
-import { faBars } from '@fortawesome/free-solid-svg-icons';
-
-/* eslint-disable */
-const findItem = (key, value, arr) => {
- let find = undefined;
- arr.forEach((v) => {
- if (find) return;
- // because of tab id like: xxx-cpu
- // we only need string before "-" to compare
- // value already split with "-"
- const target = v[key].split("-")[0];
- if (target === value) {
- find = v;
- } else if (v.children && v.children.length) {
- find = findItem(key, value, v.children);
- }
- });
- return find;
-};
-
-const Menu = (props) => {
- let { menuList, activeDoc, version, versions, locale, isDocAPI, isDocHub } = props;
-
- if (!version || version === "") {
- version = versions[0];
- }
-
- const [menuStatus, setMenuStatus] = useState(true);
- const { isBlog } = menuList || {};
- const [realMenuList, setRealMenuList] = useState([]);
- useEffect(() => {
- const generateMenu = (list) => {
- // get all labels , make sure will generate menu from top to bottom
- const labelKeys = Object.keys(menuList.menuList[0])
- .filter((v) => v.includes("label"))
- .sort((a, b) => a[a.length - 1] - b[b.length - 1]);
- let index = 0;
- return function innerFn(formatMenu = []) {
- let copyMenu = JSON.parse(JSON.stringify(formatMenu));
- const parentLabel = index ? labelKeys[index - 1] : "";
-
- if (index && !parentLabel) {
- return copyMenu;
- }
- const generatePath = (doc, isDocAPI, isDocHub) => {
- if(isDocAPI || isDocHub) {
- return `${doc.id}`;
- } else {
- return `/docs/${version}/${doc.id}`;
- }
- };
- // find top menu by current label
- const topMenu = list.filter((v) => {
- if (!labelKeys[index] || !v[labelKeys[index]]) {
- return index > 0 ? (v[parentLabel] ? true : false) : true;
- }
- return false;
- });
-
- topMenu.forEach((v) => {
- const item = {
- ...v,
- children: [],
- showChildren: false,
- isActive: false,
- isLast: !labelKeys[index + 1],
- isBlog,
- path: generatePath(v, isDocAPI, isDocHub),
- };
- if (index === 0) {
- copyMenu.push(item);
- } else {
- const parent = findItem("id", v[parentLabel], copyMenu);
- parent && parent.children.push(item);
- }
- });
-
- index++;
- return innerFn(copyMenu);
- };
- };
-
- const checkActive = (list) => {
- if(isDocHub) {
- activeDoc = `/docs_hub/${activeDoc}`;
- } else {
- activeDoc = `${activeDoc}`;
- }
- const findDoc = findItem("id", activeDoc, list);
- if (!findDoc) {
- return;
- }
- const labelKeys = Object.keys(findDoc).filter((v) => v.includes("label"));
- findDoc.isActive = true; // here will open the right menu and give the active color
- labelKeys.forEach((label) => {
- const parentDoc = findItem("id", findDoc[label], list);
- parentDoc && (parentDoc.showChildren = true);
- });
- };
-
- const sortMenu = (list) => {
- list.sort((a, b) => {
- return a.order - b.order;
- });
- list.forEach((v) => {
- if (v.children && v.children.length) {
- sortMenu(v.children);
- }
- });
- };
- const arr = generateMenu(menuList.menuList)();
- checkActive(arr);
- sortMenu(arr);
- setRealMenuList(arr);
- // eslint-disable-next-line react-hooks/exhaustive-deps
- }, [menuList, activeDoc, version]);
-
- const screenWidth = useMobileScreen();
- useEffect(() => {
- setMenuStatus(screenWidth > 1000);
- // eslint-disable-next-line react-hooks/exhaustive-deps
- }, [screenWidth]);
-
- const menuRef = useRef(null);
-
- const handleMenuClick = (e) => {
- const menuContainer = menuRef.current;
- window.localStorage.setItem("zilliz-height", menuContainer.scrollTop);
- };
-
- useEffect(() => {
- const menuContainer = menuRef.current;
- const scrollTop = window.localStorage.getItem("zilliz-height") || 0;
- menuContainer.scrollTop = scrollTop;
- }, [props]);
-
- const generageMenuDom = (list, className = "") => {
- return list.map((doc) => (
-
- {screenWidth <= 1000 ? (
- {
- toggleMenu(false);
- }}
- >
- ) : null}
- {isDocHub || isDocAPI ? (
-
- {!menuStatus ? (
-
-
-
-
-
-
-
- );
-};
-
-QueryModal.prototype = {
- locale: PropTypes.string.isRequired,
-};
-
-export default QueryModal;
\ No newline at end of file
diff --git a/src/components/query-modal/query-modal.scss b/src/components/query-modal/query-modal.scss
deleted file mode 100644
index 57a55a322..000000000
--- a/src/components/query-modal/query-modal.scss
+++ /dev/null
@@ -1,76 +0,0 @@
-.query {
- &-wrapper {
- position: fixed;
- top: 50%;
- left: 50%;
- transform: translate(-50%, -50%);
-
- background-color: #f8f8f8;
- border: 1px solid #ddd;
- border-radius: 10px;
- padding: 32px;
-
- z-index: 10;
- }
-
- &-label {
- display: block;
- margin: 0.5rem 0;
- color: #2b4590;
- }
-
- &-input {
- display: block;
- width: 100%;
- margin-bottom: 0.1rem;
- }
-
- &-form {
- width: 30vw;
- max-width: 600px;
- }
-
- &-button {
- margin-right: 16px;
- padding: 8px;
- border: none;
- border-radius: 4px;
- background-color: #00a9e5;
- color: #fff;
- outline: none;
-
- &:hover {
- cursor: pointer;
- }
-
- &:active {
- outline: none;
- }
-
- &-wrapper {
- margin-top: 16px;
- }
-
- &-close {
- position: absolute;
- top: 2px;
- right: 2px;
-
- background-color: transparent;
- border: none;
- font-size: 24px;
- color: #ccc;
- outline: none;
-
- cursor: pointer;
-
- &:active {
- outline: none;
- }
-
- &:hover {
- color: #000;
- }
- }
- }
- }
\ No newline at end of file
diff --git a/src/components/selector/index.jsx b/src/components/selector/index.jsx
deleted file mode 100644
index ab8d33a6c..000000000
--- a/src/components/selector/index.jsx
+++ /dev/null
@@ -1,71 +0,0 @@
-import React, { useState, useEffect } from "react";
-import LocalizeLink from "../localizedLink/localizedLink";
-import { FontAwesomeIcon } from '@fortawesome/react-fontawesome';
-import { faChevronDown } from '@fortawesome/free-solid-svg-icons';
-import "./index.scss";
-/* eslint-disable */
-const Selector = (props) => {
- let {
- selected,
- options,
- locale,
- isVersion = false,
- setSelected = () => {},
- } = props;
-
- if (!selected || selected === "") {
- selected = options[0];
- }
-
- const [listStatus, setListStatus] = useState(false);
- const toggleList = (e) => {
- e.stopPropagation();
- setListStatus(!listStatus);
- };
-
- useEffect(() => {
- const cb = () => {
- setListStatus(false);
- };
- window.addEventListener("click", cb);
- return () => {
- window.removeEventListener("click", cb);
- };
- }, []);
-
- const handleSelected = (e) => {
- const value = e.target.dataset.value;
- setSelected(value);
- };
-
- return (
-
-
- );
-};
-
-export const Query = graphql`
- query F0FQuery($locale: String) {
- allFile(filter: { name: { eq: $locale } }) {
- edges {
- node {
- childLayoutJson {
- layout {
- notFound
- backtohome
- header {
- quick
- benchmarks
- why
- gui
- tutorials
- solution
- about
- doc
- blog
- try
- loading
- noresult
- tutorial
- search
- bootcamp
- }
- }
- }
- }
- }
- }
- }
-`;
-
-export default NotFoundPage;
\ No newline at end of file
diff --git a/src/pages/404.scss b/src/pages/404.scss
deleted file mode 100644
index fd2ff782b..000000000
--- a/src/pages/404.scss
+++ /dev/null
@@ -1,7 +0,0 @@
-.notfound-wrapper {
- .back {
- color: #3f9cd1;
- margin-top: 20px;
- font-size: 16px;
- }
-}
\ No newline at end of file
diff --git a/src/pages/benchmarks/versions/update_benchmarks.sh b/src/pages/benchmarks/versions/update_benchmarks.sh
deleted file mode 100755
index b657f323c..000000000
--- a/src/pages/benchmarks/versions/update_benchmarks.sh
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/bin/bash
-
-# Used branch
-echo "Haystack branch: $1"
-
-# Master data
-versionstxt=$(cat ./src/pages/docs/versions/master/versions.txt)
-versions=($versionstxt)
-
-for i in "${versions[@]}"
-do
- if [ -d "./src/pages/benchmarks/versions/${i}" ]; then
- rm ./src/pages/benchmarks/versions/${i}site/en/performance/*
- rm ./src/pages/benchmarks/versions/${i}site/en/map/*
- rm ./src/pages/benchmarks/versions/${i}site/en/speed/*
- fi
-done
-
-if [ ! -d "./src/pages/benchmarks/versions/${i}" ]; then
- mkdir -p ./src/pages/benchmarks/versions/${i}site/en/performance
- mkdir -p ./src/pages/benchmarks/versions/${i}site/en/map
- mkdir -p ./src/pages/benchmarks/versions/${i}site/en/speed
-fi
-
-for i in "${versions[@]}"
-do
- j=$i
- if [ "$i" = "latest/" ]; then
- j=''
- fi
-
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/benchmarks/reader_performance.json -O ./src/pages/benchmarks/versions/${i}site/en/performance/reader_performance.json
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/benchmarks/retriever_performance.json -O ./src/pages/benchmarks/versions/${i}site/en/performance/retriever_performance.json
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/benchmarks/retriever_map.json -O ./src/pages/benchmarks/versions/${i}site/en/map/retriever_map.json
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/benchmarks/retriever_speed.json -O ./src/pages/benchmarks/versions/${i}site/en/speed/retriever_speed.json
-done
diff --git a/src/pages/benchmarks/versions/v0.9.0/site/en/map/retriever_map.json b/src/pages/benchmarks/versions/v0.9.0/site/en/map/retriever_map.json
deleted file mode 100644
index 1d12c90f0..000000000
--- a/src/pages/benchmarks/versions/v0.9.0/site/en/map/retriever_map.json
+++ /dev/null
@@ -1,164 +0,0 @@
-{
- "chart_type": "LineChart",
- "title": "Retriever Accuracy",
- "subtitle": "mAP at different number of docs",
- "description": "Here you can see how the mean avg. precision (mAP) of the retriever decays as the number of documents increases. The set up is the same as the above querying benchmark except that a varying number of negative documents are used to fill the document store.",
- "columns": [
- "n_docs",
- "BM25 / Elasticsearch",
- "DPR / Elasticsearch",
- "DPR / FAISS (flat)",
- "DPR / FAISS (HNSW)",
- "DPR / Milvus (flat)",
- "DPR / Milvus (HNSW)",
- "Sentence Transformers / Elasticsearch"
- ],
- "axis": [
- {
- "x": "Number of docs",
- "y": "mAP"
- }
- ],
- "data": [
- {
- "model": "DPR / Elasticsearch",
- "n_docs": 1000,
- "map": 92.95105322830891
- },
- {
- "model": "DPR / Elasticsearch",
- "n_docs": 10000,
- "map": 89.87097014904354
- },
- {
- "model": "BM25 / Elasticsearch",
- "n_docs": 10000,
- "map": 66.26543444531747
- },
- {
- "model": "Sentence Transformers / Elasticsearch",
- "n_docs": 1000,
- "map": 90.06638620360428
- },
- {
- "model": "Sentence Transformers / Elasticsearch",
- "n_docs": 10000,
- "map": 87.11255142468549
- },
- {
- "model": "DPR / FAISS (flat)",
- "n_docs": 1000,
- "map": 92.95105322830891
- },
- {
- "model": "DPR / FAISS (flat)",
- "n_docs": 10000,
- "map": 89.87097014904354
- },
- {
- "model": "DPR / FAISS (HNSW)",
- "n_docs": 1000,
- "map": 92.95105322830891
- },
- {
- "model": "DPR / FAISS (HNSW)",
- "n_docs": 10000,
- "map": 89.51337675393017
- },
- {
- "model": "DPR / Milvus (flat)",
- "n_docs": 1000,
- "map": 92.95105322830891
- },
- {
- "model": "DPR / Milvus (flat)",
- "n_docs": 10000,
- "map": 89.87097014904354
- },
- {
- "model": "DPR / Milvus (HNSW)",
- "n_docs": 1000,
- "map": 92.95105322830891
- },
- {
- "model": "DPR / Milvus (HNSW)",
- "n_docs": 10000,
- "map": 88.24421129104469
- },
- {
- "model": "DPR / Elasticsearch",
- "n_docs": 100000,
- "map": 86.54606328368976
- },
- {
- "model": "DPR / Elasticsearch",
- "n_docs": 500000,
- "map": 80.86137228234091
- },
- {
- "model": "BM25 / Elasticsearch",
- "n_docs": 100000,
- "map": 56.25299537353825
- },
- {
- "model": "BM25 / Elasticsearch",
- "n_docs": 500000,
- "map": 45.595090262466535
- },
- {
- "model": "Sentence Transformers / Elasticsearch",
- "n_docs": 100000,
- "map": 82.74686664920836
- },
- {
- "model": "Sentence Transformers / Elasticsearch",
- "n_docs": 500000,
- "map": 76.49564526892904
- },
- {
- "model": "DPR / FAISS (flat)",
- "n_docs": 100000,
- "map": 86.54606328368973
- },
- {
- "model": "DPR / FAISS (flat)",
- "n_docs": 500000,
- "map": 80.86137228234091
- },
- {
- "model": "DPR / FAISS (HNSW)",
- "n_docs": 100000,
- "map": 84.33419639513305
- },
- {
- "model": "DPR / FAISS (HNSW)",
- "n_docs": 500000,
- "map": 75.73062475537202
- },
- {
- "model": "DPR / Milvus (flat)",
- "n_docs": 100000,
- "map": 86.54606328368973
- },
- {
- "model": "DPR / Milvus (flat)",
- "n_docs": 500000,
- "map": 80.86137228234091
- },
- {
- "model": "DPR / Milvus (HNSW)",
- "n_docs": 100000,
- "map": 81.63864883662649
- },
- {
- "model": "DPR / Milvus (HNSW)",
- "n_docs": 500000,
- "map": 73.57986207906387
- },
- {
- "model": "BM25 / Elasticsearch",
- "n_docs": 1000,
- "map": 74.20444712972909
- }
- ]
-}
\ No newline at end of file
diff --git a/src/pages/benchmarks/versions/v0.9.0/site/en/performance/reader_performance.json b/src/pages/benchmarks/versions/v0.9.0/site/en/performance/reader_performance.json
deleted file mode 100644
index be935fe27..000000000
--- a/src/pages/benchmarks/versions/v0.9.0/site/en/performance/reader_performance.json
+++ /dev/null
@@ -1,44 +0,0 @@
-{
- "chart_type": "BarChart",
- "title": "Reader Performance",
- "subtitle": "Time and Accuracy Benchmarks",
- "description": "Performance benchmarks of different Readers that can be used off-the-shelf in Haystack. Some models are geared towards speed, while others are more performance-focused. Accuracy is measured as F1 score and speed as passages/sec (with passages of 384 tokens). Each Reader is benchmarked using the SQuAD v2.0 development set, which contains 11866 question answer pairs. When tokenized using the BERT tokenizer and split using a sliding window approach, these become 12350 passages that are passed into the model. We set max_seq_len=384 and doc_stride=128. These benchmarking tests are run using an AWS p3.2xlarge instance with a Nvidia V100 GPU with this script. Please note that we are using the FARMReader class rather than the TransformersReader class. Also, the F1 measure that is reported here is in fact calculated on token level, rather than word level as is done in the official SQuAD script.",
- "bars": "horizontal",
- "columns": [
- "Model",
- "F1",
- "Speed (passages/sec)"
- ],
- "data": [
- {
- "F1": 82.58860575299658,
- "Speed": 125.81040525892848,
- "Model": "RoBERTa"
- },
- {
- "F1": 78.87858491007042,
- "Speed": 260.6443097981493,
- "Model": "MiniLM"
- },
- {
- "F1": 74.31182400443286,
- "Speed": 121.08066567525722,
- "Model": "BERT base"
- },
- {
- "F1": 83.26306774734308,
- "Speed": 42.21949937744112,
- "Model": "BERT large"
- },
- {
- "F1": 84.50422699207468,
- "Speed": 42.07400844838985,
- "Model": "XLM-RoBERTa"
- },
- {
- "F1": 42.31925844723574,
- "Speed": 222.91207128366702,
- "Model": "DistilBERT"
- }
- ]
-}
\ No newline at end of file
diff --git a/src/pages/benchmarks/versions/v0.9.0/site/en/performance/retriever_performance.json b/src/pages/benchmarks/versions/v0.9.0/site/en/performance/retriever_performance.json
deleted file mode 100644
index 166e0b32a..000000000
--- a/src/pages/benchmarks/versions/v0.9.0/site/en/performance/retriever_performance.json
+++ /dev/null
@@ -1,74 +0,0 @@
-{
- "chart_type": "BarChart",
- "title": "Retriever Performance",
- "subtitle": "Time and Accuracy Benchmarks",
- "description": "Comparison of the speed and accuracy of different DocumentStore / Retriever combinations on 100k documents. Indexing speed (in docs/sec) refers to how quickly Documents can be inserted into a DocumentStore. Querying speed (in queries/sec) refers to the speed at which the system returns relevant Documents when presented with a query.\n\nThe dataset used is Wikipedia, split into 100 word passages (from here)). \n\nFor querying, we use the Natural Questions development set in combination with the wiki passages. The Document Store is populated with the 100 word passages in which the answer spans occur (i.e. gold passages) as well as a random selection of 100 word passages in which the answer spans do not occur (i.e. negative passages). We take a total of 100k gold and negative passages. Query and document embedding are generated by the \"facebook/dpr-question_encoder-single-nq-base\" and \"facebook/dpr-ctx_encoder-single-nq-base\" models. The retriever returns 10 candidates and both the recall and mAP scores are calculated on these 10.\n\nFor FAISS HNSW, we use n_links=128, efSearch=20 and efConstruction=80. We use a cosine similarity function with BM25 retrievers, and dot product with DPR. Both index and query benchmarks are performed on an AWS P3.2xlarge instance which is accelerated by an Nvidia V100 GPU.",
- "bars": "horizontal",
- "columns": [
- "Model",
- "mAP",
- "Index Speed (docs/sec)",
- "Query Speed (queries/sec)"
- ],
- "series": {
- "s0": "map",
- "s1": "time",
- "s2": "time"
- },
- "axes": {
- "label": "map",
- "time_side": "top",
- "time_label": "seconds"
- },
- "data": [
- {
- "model": "DPR / Elasticsearch",
- "n_docs": 100000,
- "index_speed": 71.36964873196698,
- "query_speed": 5.192368815242574,
- "map": 86.54606328368976
- },
- {
- "model": "BM25 / Elasticsearch",
- "n_docs": 100000,
- "index_speed": 485.5602670200369,
- "query_speed": 103.0884393334727,
- "map": 56.25299537353825
- },
- {
- "model": "Sentence Transformers / Elasticsearch",
- "n_docs": 100000,
- "index_speed": 119.52937722555107,
- "query_speed": 6.385621466857457,
- "map": 82.74686664920836
- },
- {
- "model": "DPR / FAISS (flat)",
- "n_docs": 100000,
- "index_speed": 100.01184910084558,
- "query_speed": 6.6270933964840415,
- "map": 86.54606328368973
- },
- {
- "model": "DPR / FAISS (HNSW)",
- "n_docs": 100000,
- "index_speed": 89.90389306648805,
- "query_speed": 39.7839528511866,
- "map": 84.33419639513305
- },
- {
- "model": "DPR / Milvus (flat)",
- "n_docs": 100000,
- "index_speed": 116.00982709720004,
- "query_speed": 28.57264344960955,
- "map": 86.54606328368973
- },
- {
- "model": "DPR / Milvus (HNSW)",
- "n_docs": 100000,
- "index_speed": 115.61076852516383,
- "query_speed": 38.80526238789059,
- "map": 81.63864883662649
- }
- ]
-}
\ No newline at end of file
diff --git a/src/pages/benchmarks/versions/v0.9.0/site/en/speed/retriever_speed.json b/src/pages/benchmarks/versions/v0.9.0/site/en/speed/retriever_speed.json
deleted file mode 100644
index 2660ceac4..000000000
--- a/src/pages/benchmarks/versions/v0.9.0/site/en/speed/retriever_speed.json
+++ /dev/null
@@ -1,164 +0,0 @@
-{
- "chart_type": "LineChart",
- "title": "Retriever Speed",
- "subtitle": "Query Speed at different number of docs",
- "description": "Here you can see how the query speed of different Retriever / DocumentStore combinations scale as the number of documents increases. The set up is the same as the above querying benchmark except that a varying number of negative documents are used to fill the document store.",
- "columns": [
- "n_docs",
- "BM25 / Elasticsearch",
- "DPR / Elasticsearch",
- "DPR / FAISS (flat)",
- "DPR / FAISS (HNSW)",
- "DPR / Milvus (flat)",
- "DPR / Milvus (HNSW)",
- "Sentence Transformers / Elasticsearch"
- ],
- "axis": [
- {
- "x": "Number of docs",
- "y": "Queries/sec"
- }
- ],
- "data": [
- {
- "model": "DPR / Elasticsearch",
- "n_docs": 1000,
- "query_speed": 34.22768858415144
- },
- {
- "model": "DPR / Elasticsearch",
- "n_docs": 10000,
- "query_speed": 22.197089725786853
- },
- {
- "model": "BM25 / Elasticsearch",
- "n_docs": 10000,
- "query_speed": 127.11481826852273
- },
- {
- "model": "Sentence Transformers / Elasticsearch",
- "n_docs": 1000,
- "query_speed": 47.51341215808855
- },
- {
- "model": "Sentence Transformers / Elasticsearch",
- "n_docs": 10000,
- "query_speed": 29.74515869340777
- },
- {
- "model": "DPR / FAISS (flat)",
- "n_docs": 1000,
- "query_speed": 42.49634272581313
- },
- {
- "model": "DPR / FAISS (flat)",
- "n_docs": 10000,
- "query_speed": 27.684040507849826
- },
- {
- "model": "DPR / FAISS (HNSW)",
- "n_docs": 1000,
- "query_speed": 43.36685860983961
- },
- {
- "model": "DPR / FAISS (HNSW)",
- "n_docs": 10000,
- "query_speed": 41.819147130090286
- },
- {
- "model": "DPR / Milvus (flat)",
- "n_docs": 1000,
- "query_speed": 41.12204778755844
- },
- {
- "model": "DPR / Milvus (flat)",
- "n_docs": 10000,
- "query_speed": 37.86882443918513
- },
- {
- "model": "DPR / Milvus (HNSW)",
- "n_docs": 1000,
- "query_speed": 41.14803671045185
- },
- {
- "model": "DPR / Milvus (HNSW)",
- "n_docs": 10000,
- "query_speed": 40.072871546542935
- },
- {
- "model": "DPR / Elasticsearch",
- "n_docs": 100000,
- "query_speed": 5.192368815242574
- },
- {
- "model": "DPR / Elasticsearch",
- "n_docs": 500000,
- "query_speed": 1.0337466563959614
- },
- {
- "model": "BM25 / Elasticsearch",
- "n_docs": 100000,
- "query_speed": 103.0884393334727
- },
- {
- "model": "BM25 / Elasticsearch",
- "n_docs": 500000,
- "query_speed": 78.95037031647355
- },
- {
- "model": "Sentence Transformers / Elasticsearch",
- "n_docs": 100000,
- "query_speed": 6.385621466857457
- },
- {
- "model": "Sentence Transformers / Elasticsearch",
- "n_docs": 500000,
- "query_speed": 1.4175454254854258
- },
- {
- "model": "DPR / FAISS (flat)",
- "n_docs": 100000,
- "query_speed": 6.6270933964840415
- },
- {
- "model": "DPR / FAISS (flat)",
- "n_docs": 500000,
- "query_speed": 1.5394964631878052
- },
- {
- "model": "DPR / FAISS (HNSW)",
- "n_docs": 100000,
- "query_speed": 39.7839528511866
- },
- {
- "model": "DPR / FAISS (HNSW)",
- "n_docs": 500000,
- "query_speed": 39.84177061191119
- },
- {
- "model": "DPR / Milvus (flat)",
- "n_docs": 100000,
- "query_speed": 28.57264344960955
- },
- {
- "model": "DPR / Milvus (flat)",
- "n_docs": 500000,
- "query_speed": 15.645867393099733
- },
- {
- "model": "DPR / Milvus (HNSW)",
- "n_docs": 100000,
- "query_speed": 38.80526238789059
- },
- {
- "model": "DPR / Milvus (HNSW)",
- "n_docs": 500000,
- "query_speed": 37.15717318924075
- },
- {
- "model": "BM25 / Elasticsearch",
- "n_docs": 1000,
- "query_speed": 282.95914917837337
- }
- ]
-}
\ No newline at end of file
diff --git a/src/pages/bm/benchmarking.scss b/src/pages/bm/benchmarking.scss
deleted file mode 100644
index f64a0f07f..000000000
--- a/src/pages/bm/benchmarking.scss
+++ /dev/null
@@ -1,100 +0,0 @@
-@import "../../components/include_media";
-
-.thumbnail {
- position : absolute;
- top : 0 ;
- z-index : -5000;
- width : 0px;
- height : 0px;
-
- img {
- object-fit : cover;
- width : 0px;
- height : 0px;
- }
-}
-
-.benchmarking {
- display: flex;
- max-width: 900;
- justify-content: center;
- justify-items: center;
- padding-top: 100px;
- align-content: center;
- background-color: #ffffff;
- text-align: justify;
- .benchmarking-content {
- width:80%;
- .h-button {
- margin-top: 20px;
- cursor: pointer;
- border: none;
- background-color: #ffffff;
- }
- .desc-details {
- margin-top: 10px;
- margin-bottom: 10px;
- line-height: 1.5rem;
- a {
- color: #08415D;
- }
- i {
- font-style: italic;
- }
- }
- }
- h1 {
- text-align: center;
- color: #000;
- font-size: 52px !important;
- margin-bottom: 4rem;
- .version-formcontrol {
- margin-left: 20px;
- min-width: 150px;
- //border: 1px solid grey;
- //border-radius: 4px;
- //box-shadow: 0 1px 0 rgba(27, 31, 35, 0.1);
- .MuiInputBase-root {
- font-size: 42px;
- }
- .MuiSelect-select {
- margin: 0;
- padding-bottom: 0px;
- }
- }
- }
-
- h2 {
- text-align: left;
- color: #000;
- font-size: 42px !important;
- .fontawsome-icon {
- width: 20px;
- color: #000000;
- }
- }
- .setup {
- margin-top: 20px;
- margin-bottom: 20px;
- line-height: 1.5rem;
- }
- *,*:focus,*:hover{
- outline:none;
- }
- @include media("<=1000px") {
- .benchmarking-content {
- width:94%;
- }
- h1 {
- font-size: 32px !important;
- .version-formcontrol {
- .MuiInputBase-root {
- font-size: 24px;
- }
- }
- }
- h2 {
- font-size: 28px !important;;
- }
- }
-}
\ No newline at end of file
diff --git a/src/pages/bm/benchmarks.js b/src/pages/bm/benchmarks.js
deleted file mode 100644
index 91786bf28..000000000
--- a/src/pages/bm/benchmarks.js
+++ /dev/null
@@ -1,407 +0,0 @@
-import React, { useState } from "react"
-import Layout from "../../components/layout/layout";
-import { graphql } from "gatsby"
-import { useMobileScreen } from "../../hooks";
-
-import { Chart } from "react-google-charts";
-
-import { FontAwesomeIcon } from '@fortawesome/react-fontawesome';
-import { faChevronDown } from '@fortawesome/free-solid-svg-icons';
-import { faChevronUp } from '@fortawesome/free-solid-svg-icons';
-
-import "./benchmarking.scss";
-
-import SEO from "../../components/seo"
-import thumbnail from "../../images/haystack_logo_blue_banner_social.png";
-
-import Input from '@material-ui/core/Input';
-import MenuItem from '@material-ui/core/MenuItem';
-import FormControl from '@material-ui/core/FormControl';
-import Select from '@material-ui/core/Select';
-
-const BenchMarks = ({data}) => {
-
- const screenWidth = useMobileScreen();
- let graphWidth = 1200;
- let graphHeight = 600;
- if(screenWidth <= 1000) {
- graphWidth = "100%";
- }
-
- // Parse versions from path
- const findVersion = (str) => {
- const regx = /versions\/([v\d.]*)/;
- const match = str.match(regx);
- return match
- ? match[1]
- ? match[1]
- : str.includes("latest")
- ? "latest"
- : match[1]
- : "";
- };
-
- const versions = new Set();
- const n_docs = [1000, 10000, 100000, 500000];
-
- // Maps in order to connect version with loaded data
- let titleReader = new Map();
- let descReader = new Map();
- let chartTypeReader = new Map();
- let mapDataReader = new Map();
- let subTitelReader = new Map();
- let barsReader = new Map();
-
- let titleRetriever = new Map();
- let descRetriever = new Map();
- let chartTypeRetriever = new Map();
- let mapDataRetriever = new Map();
- let subTitelRetriever = new Map();
- let barsRetriever = new Map();
- let seriesS0Retriever = new Map();
- let seriesS1Retriever = new Map();
- let seriesS2Retriever = new Map();
- let labelRetriever = new Map();
- let time_sideRetriever = new Map();
- let time_labelRetriever = new Map();
-
- let titleMap = new Map();
- let descMap = new Map();
- let chartTypeMap = new Map();
- let subTitleMap = new Map();
- let axisXMap = new Map();
- let axisYMap = new Map();
- let mapDataMap = new Map();
-
- let titleSpeed = new Map();
- let descSpeed = new Map();
- let chartTypeSpeed = new Map();
- let mapDataSpeed = new Map();
- let subTitleSpeed = new Map();
- let axisXSpeed = new Map();
- let axisYSpeed = new Map();
-
- data.allFile.edges.forEach(({ node }) => {
-
- const fileAbsolutePath = node.absolutePath;
- const version = findVersion(fileAbsolutePath);
-
- // released: no -> not show , yes -> show
- // when env is latest ignore released
- if (version !== '') {
- versions.add(version);
- }
-
- let fileObject = null;
- if(node.childPerformanceJson) {
-
- fileObject = node.childPerformanceJson;
-
- if(fileAbsolutePath.includes("reader")) {
-
- titleReader[version] = fileObject.title;
- descReader[version] = fileObject.description;
- chartTypeReader[version] = fileObject.chart_type;
- let dataReader = new Array(fileObject.columns);
- for (let i=0; i {
- setSelectedVersion(event.target.value);
- };
-
- function createMarkup(desc) { return {__html: desc}; };
-
- const arrayVersions = Array.from(versions);
- const [selectedVersion, setSelectedVersion] = React.useState([arrayVersions[0]]);
-
- return (
-
-
- Loading Chart
}
- data={mapDataRetriever[selectedVersion]}
- options={{
- subtitle: subTitelRetriever[selectedVersion],
- colors: ['#22BA99', '#63C7CA', '#49B0E4', '#FBB14B'],
- bars: barsRetriever[selectedVersion], // Required for Material Bar Charts.
- series: {
- 0: { axis: seriesS0Retriever[selectedVersion] }, // Bind series 0 to an axis named 'distance'.
- 1: { axis: seriesS1Retriever[selectedVersion] }, // Bind series 1 to an axis named 'brightness'.
- 2: { axis: seriesS2Retriever[selectedVersion] } // Bind series 1 to an axis named 'brightness'.
- },
- axes: {
- x: {
- map: {label: labelRetriever[selectedVersion]}, // Bottom x-axis.
- time: {side: time_sideRetriever[selectedVersion], label: time_labelRetriever[selectedVersion]} // Top x-axis.
- }
- },
- legend: "bottom"
- }}
- />
-
-
- {showLineChart1Desc && (
-
- )}
-
**Tip**
When using DPR, it is recommended that you use the dot product similarity function since that is how it is trained.
-To do so, simply provide `similarity="dot_product"` when initializing the DocumentStore
+To do so, simply provide `similarity="dot_product"` when initializing the DocumentStore
as is done in the code example below.
@@ -168,7 +163,7 @@ retriever = DensePassageRetriever(
finder = ExtractiveQAPipeline(reader, retriever)
```
-
+
+ );
+}
+
+export const getStaticPaths: GetStaticPaths = async () => {
+
+ // we want to get all versions, apart from the latest one
+ let paths: any = [];
+ const latestVersion = getLatestVersion();
+ paths = [...paths, {params: { slug: [latestVersion] }}];
+ const versions = getDocsVersions().filter((v) => v !== latestVersion);
+ for (const version of versions) {
+ paths = [
+ ...paths,
+ {
+ params: { slug: [version] },
+ }
+ ];
+ }
+
+ return {
+ paths: paths.flat(),
+ fallback: false,
+ };
+
+};
+
+type Props = {
+ map: any,
+ retriever_performance: any,
+ reader_performance: any,
+ retriever_speed: any
+};
+
+export const getStaticProps: GetStaticProps
**Training DPR:** Haystack supports training of your own DPR model! Check out the [tutorial](/docs/v0.9.0/tutorial9md) to see how this is done!
@@ -176,6 +171,9 @@ finder = ExtractiveQAPipeline(reader, retriever)
+
+
+
## Embedding Retrieval
### Description
@@ -188,16 +186,18 @@ They are particular suited to cases where your query input is similar in style t
i.e. when you are searching for most similar documents.
This is not inherently suited to query based search where the length, language and format of the query usually significantly differs from the searched for text.
-
+
+
+
+
**Tip**
-When using Sentence Transformer models, we recommend that you use a cosine similarity function.
-To do so, simply provide `similarity="cosine"` when initializing the DocumentStore
+When using Sentence Transformer models, we recommend that you use a cosine similarity function.
+To do so, simply provide `similarity="cosine"` when initializing the DocumentStore
as is done in the code example below.
+
+
### Initialisation
```python
@@ -213,6 +213,8 @@ retriever = EmbeddingRetriever(document_store=document_store,
p = ExtractiveQAPipeline(reader, retriever)
```
+
+
## Deeper Dive: Dense vs Sparse
Broadly speaking, retrieval methods can be split into two categories: **dense** and **sparse**.
@@ -220,28 +222,24 @@ Broadly speaking, retrieval methods can be split into two categories: **dense**
**Sparse** methods, like TF-IDF and BM25, operate by looking for shared keywords between the document and query.
They are:
+- simple but effective
-* simple but effective
-
-
-* don’t need to be trained
-
+- don’t need to be trained
-* work on any language
+- work on any language
More recently, **dense** approaches such as Dense Passage Retrieval (DPR) have shown even better performance than their sparse counter parts.
These methods embed both document and query into a shared embedding space using deep neural networks
and the top candidates are the nearest neighbour documents to the query.
They are:
+- powerful but computationally more expensive especially during indexing
-* powerful but computationally more expensive especially during indexing
+- trained using labelled datasets
+- language specific
-* trained using labelled datasets
-
-
-* language specific
+
### Qualitative Differences
@@ -257,6 +255,9 @@ they only care about whether they are present or absent in the text.
As such, **they handle out-of-vocabulary words with no problem**.
+
+
+
### Indexing
Dense methods perform indexing by processing all the documents through a neural network and storing the resulting vectors.
@@ -264,10 +265,14 @@ This is a much more expensive operation than the creation of the inverted-index
and will require significant computational power and time.
+
+
+
### Terminology
+
The terms **dense** and **sparse** refer to the representations that the algorithms build for each document and query.
**Sparse** methods characterise texts using vectors with one dimension corresponding to each word in the vocabulary.
Dimensions will be zero if the word is absent and non-zero if it is present.
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/summarizer.md b/docs/v0.9.0/usage/summarizer.mdx
similarity index 92%
rename from src/pages/docs/versions/master/site/en/usage/usage/summarizer.md
rename to docs/v0.9.0/usage/summarizer.mdx
index d1aeda7ad..15266f75c 100644
--- a/src/pages/docs/versions/master/site/en/usage/usage/summarizer.md
+++ b/docs/v0.9.0/usage/summarizer.mdx
@@ -1,12 +1,3 @@
----
-title: "Summarizer"
-metaTitle: "Summarizer"
-metaDescription: "Using the Summarizer Node"
-slug: "/docs/summarizer"
-date: "2021-04-16"
-id: "summarizermd"
----
-
# Summarizer
Retrievers are excellent at returning a set of candidate documents,
@@ -53,4 +44,4 @@ p = Pipeline()
p.add_node(component=retriever, name="ESRetriever1", inputs=["Query"])
p.add_node(component=summarizer, name="Summarizer", inputs=["ESRetriever1"])
res = p.run(query="What did Einstein work on?", top_k_retriever=10)
-```
+```
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/translator.md b/docs/v0.9.0/usage/translator.mdx
similarity index 85%
rename from src/pages/docs/versions/master/latest/site/en/usage/usage/translator.md
rename to docs/v0.9.0/usage/translator.mdx
index dbdc2ccf4..0f1e84e1c 100644
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/translator.md
+++ b/docs/v0.9.0/usage/translator.mdx
@@ -1,36 +1,29 @@
----
-title: "Translator"
-metaTitle: "Translator"
-metaDescription: ""
-slug: "/docs/translator"
-date: "2021-02-10"
-id: "translatormd"
----
-
# Translator
-Texts come in different languages. This is not different for search and there are plenty of options to deal with it.
-One of them is actually to translate the incoming query, the documents or the search results.
+Texts come in different languages. This is not different for search and there are plenty of options to deal with it.
+One of them is actually to translate the incoming query, the documents or the search results.
-Let's imagine you have an English corpus of technical docs, but the mother tongue of many of your users is French.
+Let's imagine you have an English corpus of technical docs, but the mother tongue of many of your users is French.
You can use a Translator node in your pipeline to
+
1. Translate the incoming query from French to English
2. Search in your English corpus for the right document / answer
3. Translate the results back from English to French
-
+/node_modules/babel-jest",
+ },
+ transformIgnorePatterns: [
+ "/node_modules/",
+ "^.+\\.module\\.(css|sass|scss)$",
+ ],
+ moduleNameMapper: {
+ "^.+\\.module\\.(css|sass|scss)$": "identity-obj-proxy",
+ },
+};
diff --git a/lib/constants.ts b/lib/constants.ts
new file mode 100644
index 000000000..2b5f01b6c
--- /dev/null
+++ b/lib/constants.ts
@@ -0,0 +1,157 @@
+type Meta = {
+ subMenuTitle: string;
+ repoPath: string;
+ pathPrefix: string;
+ items: { filename: string; title: string; slug: string }[];
+};
+
+export const referenceFiles: Meta = {
+ subMenuTitle: "API Reference",
+ repoPath: "/_src/api/api/",
+ pathPrefix: "/reference/",
+ items: [
+ {
+ slug: "document-store",
+ filename: "document_store.md",
+ title: "Document Store",
+ },
+ {
+ slug: "retriever",
+ filename: "retriever.md",
+ title: "Retriever",
+ },
+ {
+ slug: "reader",
+ filename: "reader.md",
+ title: "Reader",
+ },
+ {
+ slug: "generator",
+ filename: "generator.md",
+ title: "Generator",
+ },
+ {
+ slug: "summarizer",
+ filename: "summarizer.md",
+ title: "Summarizer",
+ },
+ {
+ slug: "translator",
+ filename: "translator.md",
+ title: "Translator",
+ },
+ {
+ slug: "preprocessor",
+ filename: "preprocessor.md",
+ title: "Preprocessor",
+ },
+ {
+ slug: "file-converters",
+ filename: "file_converter.md",
+ title: "File Converters",
+ },
+ {
+ slug: "crawler",
+ filename: "crawler.md",
+ title: "Crawler",
+ },
+ {
+ slug: "evaluation",
+ filename: "evaluation.md",
+ title: "Evaluation",
+ },
+ {
+ slug: "pipelines",
+ filename: "pipelines.md",
+ title: "Pipelines",
+ },
+ {
+ slug: "knowledge-graph",
+ filename: "knowledge_graph.md",
+ title: "Knowledge Graph",
+ },
+ {
+ slug: "graph-retriever",
+ filename: "graph_retriever.md",
+ title: "Graph Retriever",
+ },
+ ],
+};
+
+export const tutorialFiles: Meta = {
+ subMenuTitle: "Tutorials",
+ repoPath: "/_src/tutorials/tutorials/",
+ pathPrefix: "/tutorials/",
+ items: [
+ {
+ slug: "first-qa-system",
+ filename: "1.md",
+ title: "Build Your First QA System",
+ },
+ {
+ slug: "fine-tuning-a-model",
+ filename: "2.md",
+ title: "Fine-tuning a Model on Your Own Data",
+ },
+ {
+ slug: "without-elasticsearch",
+ filename: "3.md",
+ title: "Build a QA System Without Elasticsearch",
+ },
+ {
+ slug: "existing-faqs",
+ filename: "4.md",
+ title: "Utilizing existing FAQs for Question Answering",
+ },
+ {
+ slug: "evaluation",
+ filename: "5.md",
+ title: "Evaluation of a QA System",
+ },
+ {
+ slug: "dense-passage-retrieval",
+ filename: "6.md",
+ title: 'Better Retrieval via "Dense Passage Retrieval"',
+ },
+ {
+ slug: "retrieval-augmented-generation",
+ filename: "7.md",
+ title: 'Generative QA with "Retrieval-Augmented Generation"',
+ },
+ {
+ slug: "preprocessing",
+ filename: "8.md",
+ title: "Preprocessing your Documents",
+ },
+ {
+ slug: "train-dpr",
+ filename: "9.md",
+ title: "How to Train DPR",
+ },
+ {
+ slug: "knowledge-graph",
+ filename: "10.md",
+ title: "Question Answering on a Knowledge Graph",
+ },
+ {
+ slug: "pipelines",
+ filename: "11.md",
+ title: "How to use Pipelines",
+ },
+ {
+ slug: "lfqa",
+ filename: "12.md",
+ title: 'Generative QA with "LFQA"',
+ },
+ {
+ slug: "question-generation",
+ filename: "13.md",
+ title: 'Question Generation',
+ },
+ {
+ slug: "query-classifier",
+ filename: "14.md",
+ title: 'Query Classifier',
+ },
+ ],
+};
diff --git a/lib/copyToClipboard.ts b/lib/copyToClipboard.ts
new file mode 100644
index 000000000..1595ee9ef
--- /dev/null
+++ b/lib/copyToClipboard.ts
@@ -0,0 +1,25 @@
+export const copyToClipboard = (text: string): Promise =>
+ new Promise((resolve, reject) => {
+ if (navigator?.clipboard) {
+ const cb = navigator.clipboard;
+
+ cb.writeText(text).then(resolve).catch(reject);
+ } else {
+ try {
+ const body = document.querySelector("body");
+
+ const textarea = document.createElement("textarea");
+ body?.appendChild(textarea);
+
+ textarea.value = text;
+ textarea.select();
+ document.execCommand("copy");
+
+ body?.removeChild(textarea);
+
+ resolve();
+ } catch (e) {
+ reject(e);
+ }
+ }
+ });
diff --git a/lib/github.ts b/lib/github.ts
new file mode 100644
index 000000000..2f5acd8cb
--- /dev/null
+++ b/lib/github.ts
@@ -0,0 +1,36 @@
+import { Octokit } from "octokit";
+
+export const octokit = new Octokit({
+ auth: process.env.GITHUB_PERSONAL_ACCESS_TOKEN,
+});
+
+export const getDownloadUrl = async ({
+ filename,
+ repoPath,
+ version,
+}: {
+ filename: string;
+ repoPath: string;
+ version?: string;
+}) => {
+ try {
+ const res = await octokit.rest.repos.getContent({
+ owner: "deepset-ai",
+ repo: "haystack",
+ path: `docs${version ? `/${version}` : ""}${repoPath}${filename}`,
+ });
+ if (Array.isArray(res.data)) return;
+ if (!res.data.download_url) return;
+ return res.data.download_url;
+ } catch (e) {
+ return;
+ }
+};
+
+export const getStargazersCount = async () => {
+ const res = await octokit.rest.repos.get({
+ owner: "deepset-ai",
+ repo: "haystack",
+ });
+ return res.data.stargazers_count;
+};
diff --git a/lib/mdx.ts b/lib/mdx.ts
new file mode 100644
index 000000000..07420f8e5
--- /dev/null
+++ b/lib/mdx.ts
@@ -0,0 +1,12 @@
+import dynamic from "next/dynamic";
+
+// Custom components/renderers to pass to MDX.
+// Since the MDX files aren't loaded by webpack, they have no knowledge of how
+// to handle import statements. Instead, you must include components in scope
+// here.
+export const components = {
+ Disclosures: dynamic(() => import("components/Disclosures")),
+ Tabs: dynamic(() => import("components/Tabs")),
+ pre: dynamic(() => import("components/Pre")),
+ code: dynamic(() => import("components/Code")),
+};
diff --git a/lib/utils.ts b/lib/utils.ts
new file mode 100644
index 000000000..7460a02e4
--- /dev/null
+++ b/lib/utils.ts
@@ -0,0 +1,148 @@
+import fs from "fs";
+import { join } from "path";
+import remark from "remark";
+import html from "remark-html";
+import slug from "remark-slug";
+import autolink from "remark-autolink-headings";
+import prism from "remark-prism";
+import GitHubSlugger from "github-slugger";
+import imgLinks from "@pondorasti/remark-img-links";
+import semverCompare from "semver-compare";
+import { getStargazersCount } from "./github";
+import { MDXRemoteSerializeResult } from "next-mdx-remote";
+
+export const markdownToHtml = async ({
+ content,
+ downloadUrl,
+}: {
+ content: string;
+ downloadUrl: string;
+}) => {
+ const result = await remark()
+ .use(imgLinks, {
+ absolutePath: downloadUrl,
+ })
+ // @ts-ignore
+ .use(html)
+ // @ts-ignore
+ .use(slug)
+ // @ts-ignore
+ .use(autolink)
+ .use(prism)
+ .process(content);
+
+ return {
+ markup: result.toString(),
+ };
+};
+
+export type StaticPageProps = {
+ menu: any;
+ toc: { text: string; level: number; link: string }[];
+ editOnGitHubLink: string;
+ stars: number;
+ source: MDXRemoteSerializeResult | string;
+};
+
+const slugger = new GitHubSlugger();
+
+export const getStaticLayoutProps = async ({
+ content,
+ version,
+ docTitleSlug,
+}: {
+ content: string;
+ version?: string;
+ docTitleSlug: string;
+}) => {
+ const getHeadings = () => {
+ const headingLines = content
+ .split("\n")
+ .filter((line) => line.match(/^###*\s/));
+ return headingLines.map((raw) => {
+ const text = raw.replace(/^###*\s/, "").replace(/\\/g, "");
+ const level = raw.slice(0, 3) === "###" ? 3 : 2;
+ return { text, level, link: slugger.slug(text) };
+ });
+ };
+
+ const menu = getMenu(version);
+
+ const toc = getHeadings();
+
+ const latestVersion = getLatestVersion();
+ const editOnGitHubLink = `https://github.com/deepset-ai/haystack-website/blob/source/docs/${
+ version || latestVersion
+ }/${docTitleSlug.replace("-", "_")}.mdx`;
+
+ const stars = await getStargazersCount();
+
+ return { menu, toc, editOnGitHubLink, stars };
+};
+
+export const getMenu = (version?: string) => {
+ const latestVersion = getLatestVersion();
+ const menuPath = join(
+ process.cwd(),
+ `docs/${version || latestVersion}/menu.json`
+ );
+ return JSON.parse(fs.readFileSync(menuPath, "utf8"));
+};
+
+export const getBenchmarks = (type: string, name: string, version?: string) => {
+ const latestVersion = getLatestVersion();
+ const benchmarksPath = join(
+ process.cwd(),
+ `benchmarks/${version || latestVersion}/${type}/${name}.json`
+ );
+ return JSON.parse(fs.readFileSync(benchmarksPath, "utf8"));
+};
+
+export function getDocsVersions() {
+ return fs.readdirSync(join(process.cwd(), "docs"));
+}
+
+export function getVersionFromParams(params: string[]) {
+ const versions = getDocsVersions();
+ return versions.find((version) => params.includes(version));
+}
+
+export function getLatestVersion() {
+ return getDocsVersions().sort(semverCompare).pop();
+}
+
+export function getDirectory(category: "overview" | "usage", version?: string) {
+ const latestVersion = getLatestVersion();
+ return join(process.cwd(), `docs/${version || latestVersion}/${category}`);
+}
+
+export function getDirectoryBenchmarks(
+ category: "map" | "performance" | "speed",
+ version?: string
+) {
+ const latestVersion = getLatestVersion();
+ return join(
+ process.cwd(),
+ `benchmarks/${version || latestVersion}/${category}`
+ );
+}
+
+export function getSlugsFromLocalMarkdownFiles(
+ category: "overview" | "usage",
+ version?: string
+) {
+ const directory = getDirectory(category, version);
+ if (!fs.existsSync(directory)) return [];
+ const filenames = fs.readdirSync(directory);
+ return filenames.map((file) => file.replace(/\.mdx$/, "").replace("_", "-"));
+}
+
+export function getSlugsFromLocalBenchmarksFiles(
+ category: "map" | "performance" | "speed",
+ version?: string
+) {
+ const directory = getDirectoryBenchmarks(category, version);
+ if (!fs.existsSync(directory)) return [];
+ const filenames = fs.readdirSync(directory);
+ return filenames;
+}
diff --git a/next-env.d.ts b/next-env.d.ts
new file mode 100644
index 000000000..9bc3dd46b
--- /dev/null
+++ b/next-env.d.ts
@@ -0,0 +1,6 @@
+///
"), - host: json.host, - schemes: json.schemes, - basePath: json.basePath, - produces: json.produces, - }, - } - - nodes.push(toNode(information, 'OpenApiSpec')); - paths.forEach(p => { - nodes.push(toNode(p, 'OpenApiSpecPath')) - }) - responses.forEach(r => { - nodes.push(toNode(r, 'OpenApiSpecResponse')) - }) - responses.forEach(r => { - nodes.push(toNode(r, 'OpenApiSpecComponents')) - }) - - return nodes; - - }); -} - -module.exports = openApiAggregate; \ No newline at end of file diff --git a/package-lock.json b/package-lock.json index b75ae27ae..ccdc9c77b 100644 --- a/package-lock.json +++ b/package-lock.json @@ -1,2596 +1,2909 @@ { - "name": "gatsby-starter-default", + "name": "haystack-website", "version": "0.1.0", "lockfileVersion": 2, "requires": true, "packages": { "": { - "name": "gatsby-starter-default", "version": "0.1.0", - "license": "0BSD", - "dependencies": { - "@fortawesome/fontawesome-common-types": "^0.2.34", - "@fortawesome/fontawesome-svg-core": "^1.2.34", - "@fortawesome/free-brands-svg-icons": "^5.15.2", - "@fortawesome/free-solid-svg-icons": "^5.15.2", - "@fortawesome/react-fontawesome": "^0.1.14", - "@material-ui/core": "^4.11.3", - "@material-ui/lab": "^4.0.0-alpha.57", - "@material-ui/styles": "^4.11.3", - "@sentry/gatsby": "^5.30.0", - "core-js": "^3.8.3", - "filesystem": "^1.0.1", - "firebase": "^8.2.4", - "gatsby": "^2.31.1", - "gatsby-image": "^2.10.0", - "gatsby-plugin-cname": "^1.0.0", - "gatsby-plugin-firebase": "^0.2.0-beta.4", - "gatsby-plugin-fontawesome-css": "^1.0.0", - "gatsby-plugin-glamor": "^2.9.0", - "gatsby-plugin-google-analytics": "^2.10.0", - "gatsby-plugin-google-fonts": "^1.0.1", - "gatsby-plugin-google-gtag": "^2.7.0", - "gatsby-plugin-hotjar": "^1.1.1", - "gatsby-plugin-hubspot": "^1.3.4", - "gatsby-plugin-manifest": "^2.11.0", - "gatsby-plugin-material-ui": "^2.1.10", - "gatsby-plugin-modal-routing": "^1.2.0", - "gatsby-plugin-offline": "^3.9.0", - "gatsby-plugin-postcss": "^3.6.0", - "gatsby-plugin-purgecss": "^5.0.0", - "gatsby-plugin-react-helmet": "^3.9.0", - "gatsby-plugin-react-svg": "^3.0.0", - "gatsby-plugin-robots-txt": "^1.5.5", - "gatsby-plugin-sass": "^2.8.0", - "gatsby-plugin-sharp": "^2.13.3", - "gatsby-plugin-sitemap": "^2.11.0", - "gatsby-plugin-smoothscroll": "^1.2.0", - "gatsby-remark-autolink-headers": "^2.10.0", - "gatsby-remark-check-links": "^2.1.0", - "gatsby-remark-images": "^3.10.0", - "gatsby-source-filesystem": "^2.10.0", - "gatsby-source-openapi-aggregate": "^0.3.0", - "gatsby-transformer-json": "^2.10.0", - "gatsby-transformer-remark": "^2.15.0", - "gatsby-transformer-sharp": "^2.11.0", - "glamor": "^2.20.40", - "glamorous": "^5.0.0", - "highlight.js": "^10.5.0", - "lodash.groupby": "^4.6.0", - "lodash.rest": "^4.0.5", - "mobx": "^6.1.1", - "node-fetch": "^2.6.1", - "node-sass": "^4.14.1", - "postcss": "^8.2.4", - "prop-types": "^15.7.2", - "react": "^16.14.0", - "react-cookie-consent": "^6.2.2", - "react-dom": "^16.14.0", - "react-github-button": "^0.1.11", + "dependencies": { + "@headlessui/react": "^1.4.0", + "@heroicons/react": "^1.0.3", + "@material-ui/core": "^4.12.3", + "@material-ui/lab": "^4.0.0-alpha.60", + "@pondorasti/remark-img-links": "^1.0.8", + "body-scroll-lock": "^4.0.0-beta.0", + "classnames": "^2.3.1", + "github-slugger": "^1.4.0", + "gray-matter": "^4.0.3", + "markdown-to-jsx": "^7.1.3", + "next": "^11.1.0", + "next-mdx-remote": "^3.0.4", + "next-remote-watch": "^1.0.0", + "octokit": "^1.1.0", + "prism-react-renderer": "^1.2.1", + "react": "17.0.2", + "react-dom": "17.0.2", "react-google-charts": "^3.0.15", - "react-helmet": "^6.1.0", - "react-tooltip": "^4.2.13", - "redoc": "^2.0.0-rc.48", - "remark-react": "^8.0.0", - "sharp": "^0.27.1", - "sitemap-urls": "^3.0.0", - "source": "0.0.3", - "styled-components": "^5.2.1" + "react-icons": "^4.2.0", + "remark": "^13.0.0", + "remark-autolink-headings": "^7.0.1", + "remark-html": "^13.0.1", + "remark-prism": "^1.3.6", + "remark-slug": "^7.0.0", + "semver": "^7.3.5", + "semver-compare": "^1.0.0" }, "devDependencies": { - "gh-pages": "^3.1.0", - "prettier": "2.1.0" + "@types/body-scroll-lock": "^2.6.2", + "@types/jest": "^26.0.24", + "@types/node": "^16.4.11", + "@types/react": "17.0.15", + "@types/react-dom": "^17.0.9", + "@types/semver": "^7.3.8", + "@types/semver-compare": "^1.0.1", + "autoprefixer": "^10.3.1", + "babel-jest": "^27.0.6", + "eslint": "7.32.0", + "eslint-config-next": "11.0.1", + "identity-obj-proxy": "^3.0.0", + "jest": "^27.0.6", + "postcss": "^8.3.6", + "tailwindcss": "^2.2.7", + "typescript": "4.3.5" } }, - "node_modules/@ardatan/aggregate-error": { - 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"integrity": "sha1-AI4G2AlDIMNy28L47XagymyKxBk=" - }, - "yn": { - "version": "3.1.1", - "resolved": "https://registry.npmjs.org/yn/-/yn-3.1.1.tgz", - "integrity": "sha512-Ux4ygGWsu2c7isFWe8Yu1YluJmqVhxqK2cLXNQA5AcC3QfbGNpM7fu0Y8b/z16pXLnFxZYvWhd3fhBY9DLmC6Q==" - }, - "yoga-layout-prebuilt": { - "version": "1.10.0", - "resolved": "https://registry.npmjs.org/yoga-layout-prebuilt/-/yoga-layout-prebuilt-1.10.0.tgz", - "integrity": "sha512-YnOmtSbv4MTf7RGJMK0FvZ+KD8OEe/J5BNnR0GHhD8J/XcG/Qvxgszm0Un6FTHWW4uHlTgP0IztiXQnGyIR45g==", - "requires": { - "@types/yoga-layout": "1.9.2" - } + "version": "20.2.9", + "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-20.2.9.tgz", + "integrity": "sha512-y11nGElTIV+CT3Zv9t7VKl+Q3hTQoT9a1Qzezhhl6Rp21gJ/IVTW7Z3y9EWXhuUBC2Shnf+DX0antecpAwSP8w==", + "dev": true }, - "yurnalist": { - "version": "2.1.0", - "resolved": "https://registry.npmjs.org/yurnalist/-/yurnalist-2.1.0.tgz", - "integrity": "sha512-PgrBqosQLM3gN2xBFIMDLACRTV9c365VqityKKpSTWpwR+U4LAFR3rSVyEoscWlu3EzX9+Y0I86GXUKxpHFl6w==", - "requires": { - "chalk": "^2.4.2", - "inquirer": "^7.0.0", - "is-ci": "^2.0.0", - "read": "^1.0.7", - "strip-ansi": "^5.2.0" - }, - "dependencies": { - "ansi-regex": { - "version": "4.1.0", - "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-4.1.0.tgz", - "integrity": "sha512-1apePfXM1UOSqw0o9IiFAovVz9M5S1Dg+4TrDwfMewQ6p/rmMueb7tWZjQ1rx4Loy1ArBggoqGpfqqdI4rondg==" - }, - "strip-ansi": { - "version": "5.2.0", - "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-5.2.0.tgz", - "integrity": "sha512-DuRs1gKbBqsMKIZlrffwlug8MHkcnpjs5VPmL1PAh+mA30U0DTotfDZ0d2UUsXpPmPmMMJ6W773MaA3J+lbiWA==", - "requires": { - "ansi-regex": "^4.1.0" - } - } - } + "yocto-queue": { + "version": "0.1.0", + "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz", + "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==" }, "zwitch": { "version": "1.0.5", diff --git a/package.json b/package.json index 8efb6f61a..fe0b436e0 100644 --- a/package.json +++ b/package.json @@ -1,114 +1,60 @@ { - "name": "gatsby-starter-default", - "private": true, - "description": "A simple starter to get up and developing quickly with Gatsby", + "name": "haystack-website", "version": "0.1.0", - "author": "Kyle Mathews",
+ "private": true,
+ "scripts": {
+ "dev": "next dev",
+ "dev:watch": "next-remote-watch ./docs",
+ "build": "next build",
+ "start": "next start",
+ "lint": "next lint",
+ "test": "jest --watch",
+ "test:ci": "jest --ci"
+ },
"dependencies": {
- "@fortawesome/fontawesome-common-types": "^0.2.34",
- "@fortawesome/fontawesome-svg-core": "^1.2.34",
- "@fortawesome/free-brands-svg-icons": "^5.15.2",
- "@fortawesome/free-solid-svg-icons": "^5.15.2",
- "@fortawesome/react-fontawesome": "^0.1.14",
- "@material-ui/core": "^4.11.3",
- "@material-ui/lab": "^4.0.0-alpha.57",
- "@material-ui/styles": "^4.11.3",
- "@sentry/gatsby": "^5.30.0",
- "core-js": "^3.8.3",
- "filesystem": "^1.0.1",
- "firebase": "^8.2.4",
- "gatsby": "^2.31.1",
- "gatsby-image": "^2.10.0",
- "gatsby-plugin-cname": "^1.0.0",
- "gatsby-plugin-firebase": "^0.2.0-beta.4",
- "gatsby-plugin-fontawesome-css": "^1.0.0",
- "gatsby-plugin-glamor": "^2.9.0",
- "gatsby-plugin-google-analytics": "^2.10.0",
- "gatsby-plugin-google-fonts": "^1.0.1",
- "gatsby-plugin-google-gtag": "^2.7.0",
- "gatsby-plugin-hotjar": "^1.1.1",
- "gatsby-plugin-hubspot": "^1.3.4",
- "gatsby-plugin-manifest": "^2.11.0",
- "gatsby-plugin-material-ui": "^2.1.10",
- "gatsby-plugin-modal-routing": "^1.2.0",
- "gatsby-plugin-offline": "^3.9.0",
- "gatsby-plugin-postcss": "^3.6.0",
- "gatsby-plugin-purgecss": "^5.0.0",
- "gatsby-plugin-react-helmet": "^3.9.0",
- "gatsby-plugin-react-svg": "^3.0.0",
- "gatsby-plugin-robots-txt": "^1.5.5",
- "gatsby-plugin-sass": "^2.8.0",
- "gatsby-plugin-sharp": "^2.13.3",
- "gatsby-plugin-sitemap": "^2.11.0",
- "gatsby-plugin-smoothscroll": "^1.2.0",
- "gatsby-remark-autolink-headers": "^2.10.0",
- "gatsby-remark-check-links": "^2.1.0",
- "gatsby-remark-images": "^3.10.0",
- "gatsby-source-filesystem": "^2.10.0",
- "gatsby-source-openapi-aggregate": "^0.3.0",
- "gatsby-transformer-json": "^2.10.0",
- "gatsby-transformer-remark": "^2.15.0",
- "gatsby-transformer-sharp": "^2.11.0",
- "glamor": "^2.20.40",
- "glamorous": "^5.0.0",
- "highlight.js": "^10.5.0",
- "lodash.groupby": "^4.6.0",
- "lodash.rest": "^4.0.5",
- "mobx": "^6.1.1",
- "node-fetch": "^2.6.1",
- "node-sass": "^4.14.1",
- "postcss": "^8.2.4",
- "prop-types": "^15.7.2",
- "react": "^16.14.0",
- "react-cookie-consent": "^6.2.2",
- "react-dom": "^16.14.0",
- "react-github-button": "^0.1.11",
+ "@headlessui/react": "^1.4.0",
+ "@heroicons/react": "^1.0.3",
+ "@material-ui/core": "^4.12.3",
+ "@material-ui/lab": "^4.0.0-alpha.60",
+ "@pondorasti/remark-img-links": "^1.0.8",
+ "body-scroll-lock": "^4.0.0-beta.0",
+ "classnames": "^2.3.1",
+ "github-slugger": "^1.4.0",
+ "gray-matter": "^4.0.3",
+ "markdown-to-jsx": "^7.1.3",
+ "next": "^11.1.0",
+ "next-mdx-remote": "^3.0.4",
+ "next-remote-watch": "^1.0.0",
+ "octokit": "^1.1.0",
+ "prism-react-renderer": "^1.2.1",
+ "react": "17.0.2",
+ "react-dom": "17.0.2",
"react-google-charts": "^3.0.15",
- "react-helmet": "^6.1.0",
- "react-tooltip": "^4.2.13",
- "redoc": "^2.0.0-rc.48",
- "remark-react": "^8.0.0",
- "sharp": "^0.27.1",
- "sitemap-urls": "^3.0.0",
- "source": "0.0.3",
- "styled-components": "^5.2.1"
+ "react-icons": "^4.2.0",
+ "remark": "^13.0.0",
+ "remark-autolink-headings": "^7.0.1",
+ "remark-html": "^13.0.1",
+ "remark-prism": "^1.3.6",
+ "remark-slug": "^7.0.0",
+ "semver": "^7.3.5",
+ "semver-compare": "^1.0.0"
},
"devDependencies": {
- "gh-pages": "^3.1.0",
- "prettier": "2.1.0"
- },
- "keywords": [
- "gatsby"
- ],
- "license": "0BSD",
- "scripts": {
- "build": "gatsby build",
- "develop": "gatsby develop",
- "format": "prettier --write \"**/*.{js,jsx,ts,tsx,json,md}\"",
- "start": "npm run develop",
- "serve": "gatsby serve",
- "clean": "gatsby clean",
- "test": "echo \"Write tests! -> https://gatsby.dev/unit-testing\" && exit 1",
- "deploy": "gatsby build && gh-pages -d public -b master",
- "update_apidocs": "./src/pages/docs/versions/master/update_apidocs.sh master",
- "update_tutorialsdocs": "./src/pages/docs/versions/master/update_tutorialsdocs.sh master",
- "update_usagedocs": "./src/pages/docs/versions/master/update_usagedocs.sh master",
- "update_img": "./src/pages/docs/versions/master/update_img.sh master",
- "update_apidocs_dev": "./src/pages/docs/versions/master/update_apidocs.sh",
- "update_tutorialsdocs_dev": "./src/pages/docs/versions/master/update_tutorialsdocs.sh",
- "update_usagedocs_dev": "./src/pages/docs/versions/master/update_usagedocs.sh",
- "update_img_dev": "./src/pages/docs/versions/master/update_img.sh",
- "update_benchmarks": "./src/pages/benchmarks/versions/update_benchmarks.sh master",
- "update_benchmarks_dev": "./src/pages/benchmarks/versions/update_benchmarks.sh",
- "check_links_dev": "./broken-link-checker.sh dev",
- "check_links_prod": "./broken-link-checker.sh prod",
- "update_deploy": "npm run update_benchmarks && npm run update_img && npm run update_apidocs && npm run update_tutorialsdocs && npm run update_usagedocs && gatsby build && gh-pages -d public -b master"
- },
- "repository": {
- "type": "git",
- "url": "https://github.com/gatsbyjs/gatsby-starter-default"
- },
- "bugs": {
- "url": "https://github.com/gatsbyjs/gatsby/issues"
+ "@types/body-scroll-lock": "^2.6.2",
+ "@types/jest": "^26.0.24",
+ "@types/node": "^16.4.11",
+ "@types/react": "17.0.15",
+ "@types/react-dom": "^17.0.9",
+ "@types/semver": "^7.3.8",
+ "@types/semver-compare": "^1.0.1",
+ "autoprefixer": "^10.3.1",
+ "babel-jest": "^27.0.6",
+ "eslint": "7.32.0",
+ "eslint-config-next": "11.0.1",
+ "identity-obj-proxy": "^3.0.0",
+ "jest": "^27.0.6",
+ "postcss": "^8.3.6",
+ "tailwindcss": "^2.2.7",
+ "typescript": "4.3.5"
}
}
diff --git a/pages/404.tsx b/pages/404.tsx
new file mode 100644
index 000000000..651c6cb96
--- /dev/null
+++ b/pages/404.tsx
@@ -0,0 +1,42 @@
+import Head from "next/head";
+import Header from "components/Header";
+import DesktopNav from "components/DesktopNav";
+import { GetStaticProps, InferGetStaticPropsType } from "next";
+import { getMenu } from "lib/utils";
+
+export default function Custom404({
+ menu,
+}: InferGetStaticPropsType) {
+ return (
+
+
+
+ ) {
+
+ function createMarkup(desc: string) { return {__html: desc}; };
+
+ return (
+
+
**Example (Stand-alone Translator)**
-You can use the Translator component directly to translate your query or document(s):
+You can use the Translator component directly to translate your query or document(s):
+
```python
from haystack.schema import Document
from haystack.translator import TransformersTranslator
DOCS = [
Document(
- text="""Heinz von Foerster was an Austrian American scientist
- combining physics and philosophy, and widely attributed
+ text="""Heinz von Foerster was an Austrian American scientist
+ combining physics and philosophy, and widely attributed
as the originator of Second-order cybernetics."""
)
]
@@ -38,6 +31,8 @@ translator = TransformersTranslator(model_name_or_path="Helsinki-NLP/opus-mt-en-
res = translator.translate(documents=DOCS, query=None)
```
+
+
**Example (Wrapping another Pipeline)**
You can also wrap one of your existing pipelines and "add" the translation nodes at the beginning and at the end of your pipeline.
@@ -57,5 +52,4 @@ pipeline_with_translation = TranslationWrapperPipeline(input_translator=in_trans
pipeline=pipeline)
```
-
diff --git a/gatsby-browser.js b/gatsby-browser.js
deleted file mode 100644
index af2a7335a..000000000
--- a/gatsby-browser.js
+++ /dev/null
@@ -1,8 +0,0 @@
-/**
- * Implement Gatsby's Browser APIs in this file.
- *
- * See: https://www.gatsbyjs.org/docs/browser-apis/
- */
-
-// You can delete this file if you're not using it
-import 'firebase/database';
diff --git a/gatsby-config.js b/gatsby-config.js
deleted file mode 100644
index 9b5093eaa..000000000
--- a/gatsby-config.js
+++ /dev/null
@@ -1,278 +0,0 @@
-
-module.exports = {
- siteMetadata: {
- title: `Haystack`,
- titleTemplate: "%s - Question Answering",
- siteUrl: `https://haystack.deepset.ai`,
- description: `Haystack enables Question Answering at Scale`,
- author: "deepset",
- twitterUsername: "@deepset_ai",
- keywords: ["haystack deepset","haystack question answering","haystack deep set",
- "deepset ai haystack","haystack qa","deepset haystack","deepset/roberta-base-squad2",
- "dense passage retrieval","haystack nlp", "dense passage retriever", "deepset ai",
- "question answering system", "question answering nlp", "question answering berg",
- "question answering model",
- "neural search", "semantic search"],
- },
- plugins: [
- 'gatsby-plugin-cname',
- `gatsby-plugin-fontawesome-css`,
- `gatsby-plugin-robots-txt`,
- `gatsby-plugin-sitemap`,
- `gatsby-plugin-sass`,
- `gatsby-plugin-react-helmet`,
- `gatsby-transformer-json`,
- //`gatsby-plugin-postcss`,
- /*{
- resolve: `gatsby-plugin-purgecss`,
- options: {
- printRejected: true, // Print removed selectors and processed file names
- //develop: true, // Enable while using `gatsby develop`
- // tailwind: true, // Enable tailwindcss support
- // whitelist: ['whitelist'], // Don't remove this selector
- // ignore: ['/ignored.css'], // Ignore files/folders
- // purgeOnly : ['components/', '/main.css', 'bootstrap/'], // Purge only these files/folders
- }
- },*/
- {
- resolve: `gatsby-plugin-google-fonts`,
- options: {
- fonts: [
- `Lato`, `Source Code Pro`
- ],
- },
- },
- {
- resolve: `gatsby-plugin-google-gtag`,
- options: {
- // You can add multiple tracking ids and a pageview event will be fired for all of them.
- trackingIds: [
- "G-91GYG7G9X7", // Google Analytics / GA
- //"AW-CONVERSION_ID", // Google Ads / Adwords / AW
- //"DC-FLOODIGHT_ID", // Marketing Platform advertising products (Display & Video 360, Search Ads 360, and Campaign Manager)
- ],
- // This object gets passed directly to the gtag config command
- // This config will be shared across all trackingIds
- gtagConfig: {
- //optimize_id: "OPT_CONTAINER_ID",
- anonymize_ip: true,
- cookie_expires: 0,
- },
- // This object is used for configuration specific to this plugin
- pluginConfig: {
- // Puts tracking script in the head instead of the body
- head: false,
- // Setting this parameter is also optional
- respectDNT: true,
- // Avoids sending pageview hits from custom paths
- //exclude: ["/preview/**", "/do-not-track/me/too/"],
- },
- },
- },
- {
- resolve: "gatsby-plugin-firebase",
- options: {
- credentials: {
- apiKey: "AIzaSyDdNTrQNqHP6_UNtlZ0CLYAT2H3U9lVX8Y",
- authDomain: "haystack-hub-video-views.firebaseapp.com",
- databaseURL: "https://haystack-hub-video-views-default-rtdb.europe-west1.firebasedatabase.app/",
- projectId: "haystack-hub-video-views",
- storageBucket: "haystack-hub-video-views.appspot.com",
- messagingSenderId: "391319291902",
- appId: "1:391319291902:web:ecc541bd86ec1bbab662af"
- }
- }
- },
- {
- resolve: `gatsby-plugin-hotjar`,
- options: {
- includeInDevelopment: true, // optional parameter to include script in development
- id: 2051465,
- sv: 6,
- },
- },
- {
- resolve: `gatsby-source-filesystem`,
- options: {
- name: `data`,
- path: `${__dirname}/src/i18n/`,
- },
- },
- {
- resolve: `gatsby-source-filesystem`,
- options: {
- path: `${__dirname}/src/pages/docs/versions`,
- },
- },
- {
- resolve: "gatsby-plugin-react-svg",
- options: {
- rule: {
- include: `${__dirname}/src/images/svg`,
- }
- }
- },
- {
- resolve: `gatsby-source-filesystem`,
- options: {
- name: `images`,
- path: `${__dirname}/src/images`,
- },
- },
- {
- resolve: `gatsby-source-filesystem`,
- options: {
- name: `docs`,
- path: `${__dirname}/src/pages/docs`,
- },
- },
- {
- resolve: `gatsby-source-filesystem`,
- options: {
- name: `docs`,
- path: `${__dirname}/src/pages/docs_hub`,
- },
- },
- {
- resolve: `gatsby-source-filesystem`,
- options: {
- name: `benchmarks`,
- path: `${__dirname}/src/pages/benchmarks/versions`,
- },
- },
- {
- resolve: `gatsby-transformer-remark`,
- options: {
- plugins: [
- {
- resolve: `gatsby-remark-images`,
- options: {
- // It's important to specify the maxWidth (in pixels) of
- // the content container as this plugin uses this as the
- // base for generating different widths of each image.
- maxWidth: 590,
- },
- },
- {
- resolve: "gatsby-remark-autolink-headers",
- options: {
- offsetY: `100`,
- maintainCase: true,
- enableCustomId: true,
- isIconAfterHeader: true,
- },
- },
- {
- resolve: "gatsby-remark-check-links",
- },
- ],
- },
- },
- `gatsby-transformer-sharp`,
- `gatsby-plugin-sharp`,
- {
- resolve: `gatsby-plugin-manifest`,
- options: {
- name: `gatsby-starter-default`,
- short_name: `starter`,
- start_url: `/`,
- background_color: `#663399`,
- theme_color: `#663399`,
- display: `minimal-ui`,
- icon: `src/images/HaystackIcon.png`, // This path is relative to the root of the site.
- },
- },
- /*{
- resolve: "gatsby-plugin-hubspot",
- options: {
- trackingCode: "4561480",
- respectDNT: true,
- productionOnly: false,
- },
- },*/
- {
- resolve: `gatsby-plugin-material-ui`,
- // If you want to use styled components, in conjunction to Material-UI, you should:
- // - Change the injection order
- // - Add the plugin
- options: {
- // stylesProvider: {
- // injectFirst: true,
- // },
- }
- },
- {
- resolve: `gatsby-plugin-sitemap`,
- options: {
- output: `/haystack-sitemap.xml`,
- // Exclude specific pages or groups of pages using glob parameters
- // See: https://github.com/isaacs/minimatch
- // The example below will exclude the single `path/to/page` and all routes beginning with `category`
- //exclude: [`/category/*`, `/path/to/page`],
- query: `
- {
- allSitePage {
- nodes {
- path
- }
- }
- }`,
- resolveSiteUrl: ({site, allSitePage}) => {
- //Alternatively, you may also pass in an environment variable (or any location) at the beginning of your `gatsby-config.js`.
- return `https://haystack.deepset.ai`
- },
- serialize: ({ site, allSitePage }) =>
- allSitePage.nodes.map(node => {
- return {
- url: `https://haystack.deepset.ai${node.path}`,
- changefreq: `daily`,
- priority: 0.7,
- }
- })
- }
- },
- {
- resolve: "@sentry/gatsby",
- options: {
- dsn: "https://d837502349244a4289584f23df6bd79b@o485100.ingest.sentry.io/5556528",
- autoSessionTracking: true,
- sampleRate: 0.7,
- },
- },
- //`gatsby-plugin-modal-routing`,
- {
- resolve: `gatsby-plugin-modal-routing`,
- options: {
- // A selector to set react-modal's app root to, default is `#___gatsby`
- // See http://reactcommunity.org/react-modal/accessibility/#app-element
- appElement: '#___gatsby',
-
- // Object of props that will be passed to the react-modal container
- // See http://reactcommunity.org/react-modal/#usage
- modalProps: {
- style: {
- overlay: {
- zIndex: '1001'
- },
- content: {
- height: "500px",
- inset: "100px"
- }
- }
- },
- }
- },
- /*{
- resolve: "gatsby-plugin-hubspot",
- options: {
- trackingCode: "4561480",
- respectDNT: true,
- productionOnly: true,
- },
- },*/
-
- // this (optional) plugin enables Progressive Web App + Offline functionality
- // To learn more, visit: https://gatsby.dev/offline
- // `gatsby-plugin-offline`,
- ],
-}
diff --git a/gatsby-node.js b/gatsby-node.js
deleted file mode 100644
index 2f4caa46a..000000000
--- a/gatsby-node.js
+++ /dev/null
@@ -1,488 +0,0 @@
-const path = require("path");
-const fs = require("fs");
-const ReadVersionJson = require("./walkFile");
-const GenerateOpenAPI = require("./openapi-aggregate");
-const locales = require("./src/consts/locales");
-const express = require("express");
-const env = "latest";
-const getNewestVersion = (versionInfo) => {
- const keys = Object.keys(versionInfo).filter(
- (v) =>
- v !== "master" && (versionInfo[v].released === "yes" || env === "latest")
- );
- return keys.reduce((pre, cur) => {
- const curVersion = cur
- .substring(1)
- .split(".")
- .map((v) => Number(v));
- const preVersion = pre
- .substring(1)
- .split(".")
- .map((v) => Number(v));
-
- if (curVersion[0] !== preVersion[0]) {
- pre = curVersion[0] < preVersion[0] ? pre : cur;
- } else if (curVersion[1] !== preVersion[1]) {
- pre = curVersion[1] < preVersion[1] ? pre : cur;
- } else if (curVersion[2] !== preVersion[2]) {
- pre = curVersion[2] < preVersion[2] ? pre : cur;
- } else {
- pre = cur;
- }
-
- return pre;
- }, "v0.0.0");
-};
-
-exports.onCreateDevServer = ({ app }) => {
- app.use(express.static("public"));
-};
-
-
-// the version is same for different lang, so we only need one
-const DOC_ROOT = "src/pages/docs/versions";
-const versionInfo = ReadVersionJson(DOC_ROOT);
-const newestVersion = getNewestVersion(versionInfo);
-if (env === "latest") {
- versionInfo.preview = {
- version: "latest",
- released: "no",
- };
-}
-exports.onCreatePage = ({ page, actions }) => {
- const { createPage, deletePage } = actions;
- return new Promise((resolve) => {
- deletePage(page);
- Object.keys(locales).map((lang) => {
- let localizedPath = locales[lang].default
- ? page.path
- : locales[lang].path + page.path;
-
- return createPage({
- ...page,
- path: localizedPath,
- context: {
- locale: lang,
- newestVersion,
- },
- });
- });
- resolve();
- });
-};
-
-exports.sourceNodes = async ({ actions }) => {
-
- const nodes = await GenerateOpenAPI("https://api.haystack-hub.com/openapi.json");//GenerateOpenAPI("./src/pages/docs_hub/open_api.json");
-
- const { createNode } = actions;
-
- nodes.forEach(n => {
- createNode(n);
- });
-}
-
-exports.createPages = ({ actions, graphql }) => {
- const { createPage } = actions;
-
- const docTemplate = path.resolve(`src/templates/docTemplate.js`);
-
- // isMenu outLink can be add when need to use
- const docsCore = graphql(`
- {
- allMarkdownRemark(
- filter: { fileAbsolutePath: { regex: "/(?:site/en)/" } }
- ) {
- edges {
- node {
- headings {
- value
- depth
- }
- frontmatter {
- id
- }
- fileAbsolutePath
- html
- }
- }
- }
- allFile(filter: { absolutePath: { regex: "/(?:site/en/menuStructure)/" } }) {
- edges {
- node {
- absolutePath
- childMenuStructureJson {
- menuList {
- id
- title
- label1
- label2
- label3
- order
- isMenu
- }
- }
- }
- }
- }
- }
- `).then((result) => {
- if (result.errors) {
- return Promise.reject(result.errors);
- }
-
- const findVersion = (str) => {
- const regx = /versions\/master\/([v\d\.]*)/;
- const match = str.match(regx);
- return match
- ? match[1]
- ? match[1]
- : env === "latest" && str.includes("latest")
- ? "latest"
- : match[1]
- : "";
- };
-
- // get all menuStructures
- const allMenus = result.data.allFile.edges.map(
- ({ node: { absolutePath, childMenuStructureJson } }) => {
- let lang = "en";
- const version = findVersion(absolutePath) || "master";
- const menuStructureList =
- (childMenuStructureJson && [...childMenuStructureJson.menuList]) ||
- [];
- const menuList = [...menuStructureList];
- return {
- lang,
- version,
- menuList,
- absolutePath,
- };
- }
- );
-
- // filter useless md file blog has't version
- const legalMd = result.data.allMarkdownRemark.edges;
-
- // we generate path by menu structure
- const generatePath = (
- id,
- lang,
- version,
- needLocal = true,
- ) => {
- const findMenu = allMenus.find(
- (v) => v.lang === lang && v.version === version
- );
-
- const menuList = findMenu ? findMenu.menuList : [];
- const doc = menuList.find((v) => v.id === id);
- let localizedPath = "";
- if (version && version !== "master") {
- localizedPath =
- lang === defaultLang
- ? `/docs/${version}/`
- : `${lang}/docs/${version}/`;
- } else {
- // for master branch version -> false
- localizedPath = lang === defaultLang ? `/docs/` : `${lang}/docs/`;
- }
-
- return needLocal ? `${localizedPath}${id}` : `${id}`;
- };
-
- const defaultLang = Object.keys(locales).find(
- (lang) => locales[lang].default
- );
-
- // ----- for global search begin -----
- const flatten = (arr) =>
- arr.map(({ node: { frontmatter, fileAbsolutePath, headings } }) => {
- const fileLang = "en";
-
- const version = findVersion(fileAbsolutePath) || "master";
- const headingVals = headings.map((v) => v.value);
- return {
- ...frontmatter,
- //fileLang,
- version,
- path: generatePath(
- frontmatter.id,
- fileLang,
- version,
- false
- ),
- // the value we need compare with search query
- values: [...headingVals, frontmatter.id],
- };
- });
-
- // get all version
- const versions = new Set();
- legalMd.forEach(({ node }) => {
- const fileAbsolutePath = node.fileAbsolutePath;
- const version = findVersion(fileAbsolutePath);
-
- // released: no -> not show , yes -> show
- // when env is latest ignore released
- if (version != '') {
- versions.add(version);
- }
- });
-
- legalMd.forEach(({ node }) => {
- const fileAbsolutePath = node.fileAbsolutePath;
- const fileId = node.frontmatter.id;
- let version = findVersion(fileAbsolutePath);
-
- const fileLang = "en";
-
- let editPath = fileAbsolutePath.split(
- fileLang === "en" ? "/en/" : "/zh-CN/"
- )[1];
-
- const localizedPath = generatePath(
- fileId,
- fileLang,
- version,
- true
- );
- const newHtml = node.html;
-
- // the newest doc version is master so we need to make route without version.
- // for easy link to the newest doc
- if (version === newestVersion) {
- const masterPath = generatePath(fileId, fileLang, "master");
- createPage({
- path: masterPath,
- component: docTemplate,
- context: {
- locale: fileLang,
- version: newestVersion, // get master version
- versions: Array.from(versions),
- newestVersion,
- old: fileId,
- headings: node.headings.filter((v) => v.depth < 5 && v.depth >= 1),
- fileAbsolutePath,
- isBlog,
- editPath,
- allMenus,
- isDocAPI: false,
- isDocHub: false,
- newHtml,
- }, // additional data can be passed via context
- });
- }
-
- // normal pages
- isBlog=false;
- createPage({
- path: localizedPath,
- component: docTemplate,
- context: {
- locale: fileLang,
- version: version,
- versions: Array.from(versions),
- old: fileId,
- headings: node.headings.filter((v) => v.depth < 5 && v.depth >= 1),
- fileAbsolutePath,
- newestVersion,
- editPath,
- allMenus,
- newHtml,
- }, // additional data can be passed via context
- });
- });
- });
-
- const docsHub = graphql(`
- {
- allMarkdownRemark(
- filter: { fileAbsolutePath: { regex: "/(?:docs_hub)/" } }
- ) {
- edges {
- node {
- headings {
- value
- depth
- }
- frontmatter {
- id
- }
- fileAbsolutePath
- html
- }
- }
- }
- allFile(filter: { absolutePath: { regex: "/(?:docs_hub/menuStructure)/" } }) {
- edges {
- node {
- absolutePath
- childMenuStructureJson {
- menuList {
- id
- title
- label1
- label2
- label3
- order
- isMenu
- }
- }
- }
- }
- }
- }
- `).then((result) => {
- if (result.errors) {
- return Promise.reject(result.errors);
- }
-
- // get all menuStructures
- const allMenus = result.data.allFile.edges.map(
- ({ node: { absolutePath, childMenuStructureJson } }) => {
- let lang = "en";
- const menuStructureList =
- (childMenuStructureJson && [...childMenuStructureJson.menuList]) ||
- [];
- const menuList = [...menuStructureList];
- return {
- lang,
- menuList,
- absolutePath,
- };
- }
- );
-
- // filter useless md file blog has't version
- const legalMd = result.data.allMarkdownRemark.edges;
-
- // we generate path by menu structure
- const generatePath = (
- id,
- lang,
- needLocal = true,
- ) => {
-
- localizedPath = lang === defaultLang ? `/docs_hub/` : `${lang}/docs_hub/`;
-
- return needLocal ? `${localizedPath}${id}` : `${id}`;
- };
-
- const defaultLang = Object.keys(locales).find(
- (lang) => locales[lang].default
- );
-
- legalMd.forEach(({ node }) => {
- const fileAbsolutePath = node.fileAbsolutePath;
- const fileId = node.frontmatter.id;
-
- const fileLang = "en";
-
- let editPath = fileAbsolutePath.split(
- fileLang === "en" ? "/en/" : "/zh-CN/"
- )[1];
-
- const localizedPath = generatePath(
- fileId,
- fileLang,
- true
- );
- const newHtml = node.html;
-
- // normal pages
- isBlog=false;
- createPage({
- path: localizedPath,
- component: docTemplate,
- context: {
- locale: fileLang,
- old: fileId,
- headings: node.headings.filter((v) => v.depth < 4 && v.depth >= 1),
- fileAbsolutePath,
- editPath,
- allMenus,
- isDocAPI: false,
- isDocHub: true,
- newHtml,
- }, // additional data can be passed via context
- });
- });
- });
-
-
- const docsAPI = graphql(`
- {
- allOpenApiSpec {
- edges {
- node {
- id
- name
- }
- }
- }
- allFile(filter: { absolutePath: { regex: "/(?:docs_hub/menuStructure)/" } }) {
- edges {
- node {
- absolutePath
- childMenuStructureJson {
- menuList {
- id
- title
- label1
- label2
- label3
- order
- isMenu
- }
- }
- }
- }
- }
- }
- `).then(result => {
- if (result.errors) {
- return Promise.reject(result.errors);
- }
-
- // get all menuStructures
- const allMenus = result.data.allFile.edges.map(
- ({ node: { absolutePath, childMenuStructureJson } }) => {
- let lang = "en";
- const menuStructureList =
- (childMenuStructureJson && [...childMenuStructureJson.menuList]) ||
- [];
- const menuList = [...menuStructureList];
- return {
- lang,
- menuList,
- absolutePath,
- };
- }
- );
-
- result.data.allOpenApiSpec.edges.map(({ node }) => {
-
- const fileLang = "en";
- createPage({
- path: `docs_api/${node.name}`,
- component: docTemplate,
- context: {
- locale: fileLang,
- old: node.id,
- allMenus,
- isDocAPI: true,
- isDocHub: false,
- }, // additional data can be passed via context
- });
- });
- });
-
- // Return a Promise which would wait for both the queries to resolve
- return Promise.all([docsCore, docsHub, docsAPI]);
-};
-
-
-
-const checkIsblog = (path) => path.includes("blog");
-const checkIsBenchmark = (path) => path.includes("benchmarks");
diff --git a/gatsby-ssr.js b/gatsby-ssr.js
deleted file mode 100644
index 9d7c7caac..000000000
--- a/gatsby-ssr.js
+++ /dev/null
@@ -1,8 +0,0 @@
-/**
- * Implement Gatsby's SSR (Server Side Rendering) APIs in this file.
- *
- * See: https://www.gatsbyjs.org/docs/ssr-apis/
- */
-
-// You can delete this file if you're not using it
-import 'firebase/database';
diff --git a/jest.config.js b/jest.config.js
new file mode 100644
index 000000000..2b6a67907
--- /dev/null
+++ b/jest.config.js
@@ -0,0 +1,18 @@
+module.exports = {
+ collectCoverageFrom: [
+ "**/*.{js,jsx,ts,tsx}",
+ "!**/*.d.ts",
+ "!**/node_modules/**",
+ ],
+ testPathIgnorePatterns: ["/node_modules/", "/.next/"],
+ transform: {
+ "^.+\\.(js|jsx|ts|tsx)$": ""), - host: json.host, - schemes: json.schemes, - basePath: json.basePath, - produces: json.produces, - }, - } - - nodes.push(toNode(information, 'OpenApiSpec')); - paths.forEach(p => { - nodes.push(toNode(p, 'OpenApiSpecPath')) - }) - responses.forEach(r => { - nodes.push(toNode(r, 'OpenApiSpecResponse')) - }) - responses.forEach(r => { - nodes.push(toNode(r, 'OpenApiSpecComponents')) - }) - - return nodes; - - }); -} - -module.exports = openApiAggregate; \ No newline at end of file diff --git a/package-lock.json b/package-lock.json index b75ae27ae..ccdc9c77b 100644 --- a/package-lock.json +++ b/package-lock.json @@ -1,2596 +1,2909 @@ { - "name": "gatsby-starter-default", + "name": "haystack-website", "version": "0.1.0", "lockfileVersion": 2, "requires": true, "packages": { "": { - "name": "gatsby-starter-default", "version": "0.1.0", - "license": "0BSD", - "dependencies": { - "@fortawesome/fontawesome-common-types": "^0.2.34", - "@fortawesome/fontawesome-svg-core": "^1.2.34", - "@fortawesome/free-brands-svg-icons": "^5.15.2", - "@fortawesome/free-solid-svg-icons": "^5.15.2", - "@fortawesome/react-fontawesome": "^0.1.14", - "@material-ui/core": "^4.11.3", - "@material-ui/lab": "^4.0.0-alpha.57", - "@material-ui/styles": "^4.11.3", - "@sentry/gatsby": "^5.30.0", - "core-js": "^3.8.3", - "filesystem": "^1.0.1", - "firebase": "^8.2.4", - "gatsby": "^2.31.1", - "gatsby-image": "^2.10.0", - "gatsby-plugin-cname": "^1.0.0", - "gatsby-plugin-firebase": "^0.2.0-beta.4", - "gatsby-plugin-fontawesome-css": "^1.0.0", - "gatsby-plugin-glamor": "^2.9.0", - "gatsby-plugin-google-analytics": "^2.10.0", - "gatsby-plugin-google-fonts": "^1.0.1", - "gatsby-plugin-google-gtag": "^2.7.0", - "gatsby-plugin-hotjar": "^1.1.1", - "gatsby-plugin-hubspot": "^1.3.4", - "gatsby-plugin-manifest": "^2.11.0", - "gatsby-plugin-material-ui": "^2.1.10", - "gatsby-plugin-modal-routing": "^1.2.0", - "gatsby-plugin-offline": "^3.9.0", - "gatsby-plugin-postcss": "^3.6.0", - "gatsby-plugin-purgecss": "^5.0.0", - "gatsby-plugin-react-helmet": "^3.9.0", - "gatsby-plugin-react-svg": "^3.0.0", - "gatsby-plugin-robots-txt": "^1.5.5", - "gatsby-plugin-sass": "^2.8.0", - "gatsby-plugin-sharp": "^2.13.3", - "gatsby-plugin-sitemap": "^2.11.0", - "gatsby-plugin-smoothscroll": "^1.2.0", - "gatsby-remark-autolink-headers": "^2.10.0", - "gatsby-remark-check-links": "^2.1.0", - "gatsby-remark-images": "^3.10.0", - "gatsby-source-filesystem": "^2.10.0", - "gatsby-source-openapi-aggregate": "^0.3.0", - "gatsby-transformer-json": "^2.10.0", - "gatsby-transformer-remark": "^2.15.0", - "gatsby-transformer-sharp": "^2.11.0", - "glamor": "^2.20.40", - "glamorous": "^5.0.0", - "highlight.js": "^10.5.0", - "lodash.groupby": "^4.6.0", - "lodash.rest": "^4.0.5", - "mobx": "^6.1.1", - "node-fetch": "^2.6.1", - "node-sass": "^4.14.1", - "postcss": "^8.2.4", - "prop-types": "^15.7.2", - "react": "^16.14.0", - "react-cookie-consent": "^6.2.2", - "react-dom": "^16.14.0", - "react-github-button": "^0.1.11", + "dependencies": { + "@headlessui/react": "^1.4.0", + "@heroicons/react": "^1.0.3", + "@material-ui/core": "^4.12.3", + "@material-ui/lab": "^4.0.0-alpha.60", + "@pondorasti/remark-img-links": "^1.0.8", + "body-scroll-lock": "^4.0.0-beta.0", + "classnames": "^2.3.1", + "github-slugger": "^1.4.0", + "gray-matter": "^4.0.3", + "markdown-to-jsx": "^7.1.3", + "next": "^11.1.0", + "next-mdx-remote": "^3.0.4", + "next-remote-watch": "^1.0.0", + "octokit": "^1.1.0", + "prism-react-renderer": "^1.2.1", + "react": "17.0.2", + "react-dom": "17.0.2", "react-google-charts": "^3.0.15", - "react-helmet": "^6.1.0", - "react-tooltip": "^4.2.13", - "redoc": "^2.0.0-rc.48", - "remark-react": "^8.0.0", - "sharp": "^0.27.1", - "sitemap-urls": "^3.0.0", - "source": "0.0.3", - "styled-components": "^5.2.1" + "react-icons": "^4.2.0", + "remark": "^13.0.0", + "remark-autolink-headings": "^7.0.1", + "remark-html": "^13.0.1", + "remark-prism": "^1.3.6", + "remark-slug": "^7.0.0", + "semver": "^7.3.5", + "semver-compare": "^1.0.0" }, "devDependencies": { - "gh-pages": "^3.1.0", - "prettier": "2.1.0" + "@types/body-scroll-lock": "^2.6.2", + "@types/jest": "^26.0.24", + "@types/node": "^16.4.11", + "@types/react": "17.0.15", + "@types/react-dom": "^17.0.9", + "@types/semver": "^7.3.8", + "@types/semver-compare": "^1.0.1", + "autoprefixer": "^10.3.1", + "babel-jest": "^27.0.6", + "eslint": "7.32.0", + "eslint-config-next": "11.0.1", + "identity-obj-proxy": "^3.0.0", + "jest": "^27.0.6", + "postcss": "^8.3.6", + "tailwindcss": "^2.2.7", + "typescript": "4.3.5" } }, - "node_modules/@ardatan/aggregate-error": { - 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"integrity": "sha1-AI4G2AlDIMNy28L47XagymyKxBk=" - }, - "yn": { - "version": "3.1.1", - "resolved": "https://registry.npmjs.org/yn/-/yn-3.1.1.tgz", - "integrity": "sha512-Ux4ygGWsu2c7isFWe8Yu1YluJmqVhxqK2cLXNQA5AcC3QfbGNpM7fu0Y8b/z16pXLnFxZYvWhd3fhBY9DLmC6Q==" - }, - "yoga-layout-prebuilt": { - "version": "1.10.0", - "resolved": "https://registry.npmjs.org/yoga-layout-prebuilt/-/yoga-layout-prebuilt-1.10.0.tgz", - "integrity": "sha512-YnOmtSbv4MTf7RGJMK0FvZ+KD8OEe/J5BNnR0GHhD8J/XcG/Qvxgszm0Un6FTHWW4uHlTgP0IztiXQnGyIR45g==", - "requires": { - "@types/yoga-layout": "1.9.2" - } + "version": "20.2.9", + "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-20.2.9.tgz", + "integrity": "sha512-y11nGElTIV+CT3Zv9t7VKl+Q3hTQoT9a1Qzezhhl6Rp21gJ/IVTW7Z3y9EWXhuUBC2Shnf+DX0antecpAwSP8w==", + "dev": true }, - "yurnalist": { - "version": "2.1.0", - "resolved": "https://registry.npmjs.org/yurnalist/-/yurnalist-2.1.0.tgz", - "integrity": "sha512-PgrBqosQLM3gN2xBFIMDLACRTV9c365VqityKKpSTWpwR+U4LAFR3rSVyEoscWlu3EzX9+Y0I86GXUKxpHFl6w==", - "requires": { - "chalk": "^2.4.2", - "inquirer": "^7.0.0", - "is-ci": "^2.0.0", - "read": "^1.0.7", - "strip-ansi": "^5.2.0" - }, - "dependencies": { - "ansi-regex": { - "version": "4.1.0", - "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-4.1.0.tgz", - "integrity": "sha512-1apePfXM1UOSqw0o9IiFAovVz9M5S1Dg+4TrDwfMewQ6p/rmMueb7tWZjQ1rx4Loy1ArBggoqGpfqqdI4rondg==" - }, - "strip-ansi": { - "version": "5.2.0", - "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-5.2.0.tgz", - "integrity": "sha512-DuRs1gKbBqsMKIZlrffwlug8MHkcnpjs5VPmL1PAh+mA30U0DTotfDZ0d2UUsXpPmPmMMJ6W773MaA3J+lbiWA==", - "requires": { - "ansi-regex": "^4.1.0" - } - } - } + "yocto-queue": { + "version": "0.1.0", + "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz", + "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==" }, "zwitch": { "version": "1.0.5", diff --git a/package.json b/package.json index 8efb6f61a..fe0b436e0 100644 --- a/package.json +++ b/package.json @@ -1,114 +1,60 @@ { - "name": "gatsby-starter-default", - "private": true, - "description": "A simple starter to get up and developing quickly with Gatsby", + "name": "haystack-website", "version": "0.1.0", - "author": "Kyle Mathews
+
+ Haystack Docs
+
+
+
+
+
+
+
+
+ );
+}
+
+export const getStaticProps: GetStaticProps = () => {
+ const menu = getMenu();
+ return {
+ props: {
+ menu,
+ },
+ };
+};
diff --git a/pages/_app.tsx b/pages/_app.tsx
new file mode 100644
index 000000000..0b3a67060
--- /dev/null
+++ b/pages/_app.tsx
@@ -0,0 +1,8 @@
+import "../styles/global.css";
+import "../styles/prism-theme.css";
+import type { AppProps } from "next/app";
+
+function MyApp({ Component, pageProps }: AppProps) {
+ return 404 - There's no doc for the page you requested.
+
+
+ Haystack Benchmarks
+
+
+
+
+
+
+
+
+
+ Haystack Benchmarks
+
+
+
+ {reader_performance.title}
+
+ Loading Chart
}
+ data={reader_performance.dataReader}
+ options={{
+ colors: ['#22BA99', '#63C7CA', '#49B0E4', '#FBB14B'],
+ subTitle: reader_performance.subTitle,
+ bars: reader_performance.bars,
+ legend: "bottom",
+ displayAnnotations: true,
+ annotations: {
+ textStyle: {
+ // The color of the text.
+ color: '#000000',
+ fontSize: 15
+ },
+ },
+ }}
+ />
+
+
+
+ {retriever_performance.title}
+
+ Loading Chart
}
+ data={retriever_performance.dataRetriever}
+ options={{
+ subtitle: retriever_performance.subTitle,
+ colors: ['#22BA99', '#63C7CA', '#49B0E4', '#FBB14B'],
+ bars: retriever_performance.bars, // Required for Material Bar Charts.
+ series: {
+ 0: { axis: retriever_performance.seriesS0 }, // Bind series 0 to an axis named 'distance'.
+ 1: { axis: retriever_performance.seriesS1 }, // Bind series 1 to an axis named 'brightness'.
+ 2: { axis: retriever_performance.seriesS2 } // Bind series 1 to an axis named 'brightness'.
+ },
+ axes: {
+ x: {
+ map: {label: retriever_performance.label}, // Bottom x-axis.
+ time: {side: retriever_performance.time_side, label: retriever_performance.time_label} // Top x-axis.
+ }
+ },
+ legend: "bottom"
+ }}
+ />
+
+
+
+ {map.title}
+
+ Loading Chart
}
+ data={map.dataRetriever}
+ options={{
+ subtitle: map.subTitle,
+ colors: ['#22BA99', '#FBB14B', '#63C7CA', '#49B0E4'],
+ hAxis: {
+ title: map.axisX
+ },
+ vAxis: {
+ title: map.axisY
+ },
+ pointSize: 5,
+ legend: "bottom"
+ }}
+ />
+
+
+
+ {retriever_speed.title}
+
+ Loading Chart
}
+ data={retriever_speed.dataRetrieverSpeed}
+ options={{
+ subtitle: retriever_speed.subTitle,
+ colors: ['#22BA99', '#63C7CA', '#49B0E4', '#FBB14B'],
+ hAxis: {
+ title: retriever_speed.axisX
+ },
+ vAxis: {
+ title: retriever_speed.axisY
+ },
+ pointSize: 5,
+ legend: "bottom"
+ }}
+ />
+
+
+ Haystack Benchmarks
+
+
+
+
+
+
+ {message}
+
+
+
+
+}
\ No newline at end of file
diff --git a/pages/index.tsx b/pages/index.tsx
new file mode 100644
index 000000000..8243227fc
--- /dev/null
+++ b/pages/index.tsx
@@ -0,0 +1,14 @@
+import { GetServerSideProps } from "next";
+
+export const getServerSideProps: GetServerSideProps = async () => {
+ return {
+ redirect: {
+ destination: "/overview/get-started",
+ permanent: true,
+ },
+ };
+};
+
+export default function Index() {
+ return ;
+}
diff --git a/pages/overview/[...slug].tsx b/pages/overview/[...slug].tsx
new file mode 100644
index 000000000..bbfff0963
--- /dev/null
+++ b/pages/overview/[...slug].tsx
@@ -0,0 +1,140 @@
+import matter from "gray-matter";
+import { MDXRemote, MDXRemoteSerializeResult } from "next-mdx-remote";
+import { serialize } from "next-mdx-remote/serialize";
+import remarkSlug from "remark-slug";
+import remarkAutolinkHeadings from "remark-autolink-headings";
+import {
+ GetStaticPaths,
+ GetStaticProps,
+ GetStaticPropsContext,
+ InferGetStaticPropsType,
+} from "next";
+import { join } from "path";
+import fs from "fs";
+import Layout from "components/Layout";
+import {
+ getSlugsFromLocalMarkdownFiles,
+ getVersionFromParams,
+ getDirectory,
+ getDocsVersions,
+ getLatestVersion,
+ StaticPageProps,
+ getStaticLayoutProps,
+} from "lib/utils";
+import { components } from "lib/mdx";
+
+export default function OverviewDoc({
+ menu,
+ toc,
+ editOnGitHubLink,
+ stars,
+ source,
+}: InferGetStaticPropsType
+
+
+
+
+ Join the Haystack Community on Slack
+
+
+ Learn more about what people are building with Haystack, ask questions, share knowledge, track events, meet collaborators.
+
+
+
+
- onAnchorClick(e, anchor)}
- >
- {v.value}
-
- {childDom}
- {childchildDom}
-
- );
- });
- };
-
- const onAnchorClick = (event, anchor) => {
- event.stopPropagation();
- event.preventDefault();
-
- setHash(anchor);
-
- const element = document.querySelector(`#${anchor}`);
- const offset = 62;
-
- const bodyRect = document.body.getBoundingClientRect().top;
- const elementRect = element.getBoundingClientRect().top;
- const elementPosition = elementRect - bodyRect;
- const offsetPosition = elementPosition - offset;
-
- window.scrollTo({
- top: offsetPosition,
- });
- };
-
- const onToTopClick = () => {
- window.scrollTo({
- top: 0,
- behavior: "smooth",
- });
- };
-
- return (
-
-
-
-
-
- );
-};
\ No newline at end of file
diff --git a/src/components/docLayout/index.scss b/src/components/docLayout/index.scss
deleted file mode 100644
index 61b410711..000000000
--- a/src/components/docLayout/index.scss
+++ /dev/null
@@ -1,333 +0,0 @@
-@import "../include_media.scss";
-
-html {
- height: 100%;
-}
-
-body,
-ul,
-li,
-p,
-h1,
-h2,
-h3,
-h4,
-h5,
-h6 {
- margin: 0;
- padding: 0;
-}
-
-h1,
-h2,
-h3,
-h4,
-h5,
-h6 {
- font-weight: 100;
-}
-
-#gatsby-focus-wrapper,
-#___gatsby {
- height: 100%;
-}
-
-body {
- font-size: 15px;
- background-color: #F5F6FA !important;
- color: #434548;
- @include media("<=1000px") {
- font-size: 14px;
- }
-}
-
-::selection {
- background-color: #4fc4f9;
- color: #ffffff;
-}
-
-a.btn {
- border-radius: 4px;
- border: 2px #000 solid;
- color: #000 !important;
- font-weight: bold;
- display: block;
- padding: 10px;
- margin-bottom: 10px;
- max-width: 180px;
- box-shadow: 0 1px 0 rgba(27, 31, 35, 0.1),
- inset 0 1px 0 hsla(0, 0%, 100%, 0.03);
- i {
- margin-right: 8px;
- }
- &:hover {
- //background-color: var(--color-highlight) !important;
- color: #000 !important;
- text-decoration: none;
- }
-}
-
-.svg-inline--fa {
- height: 0.8em;
-}
-
-.doc-wrapper {
- position: relative;
- display: flex;
- max-width: 1600px;
- margin: 5px auto;
- background-color: #fff;
- min-height: calc(100vh);
- padding-top: 60px;
- @include media("<1600px") {
- margin: 5px;
- }
-
- .menu-container {
- top: 60px;
- margin-top: 5px;
- padding-top: 20px;
- bottom: 0;
- width: 250px;
- overflow: auto;
-
- @include media("<=1000px") {
- position: fixed;
- top: 0;
- bottom: 0;
- left: 0;
- width: 80vw;
- max-width: 260px;
- height: 100vh;
- box-sizing: border-box;
- z-index: 1000;
- }
- }
-
- .inner-container {
- margin-left: 250px;
- max-width: 63.67%;
- min-width: 60%;
- overflow: hidden;
- &.fullwidth {
- max-width: 80%;
- min-width: 80%;
- }
- iframe {
- position: absolute;
- top: 0;
- }
- @include media("<=1000px") {
- flex: 0 0 100%;
- margin: 0;
- max-width: 100%;
-
- &.fullwidth {
- width: 100%;
- }
- }
- }
- .button-container {
- margin-top: 50px;
- }
-
- .anchor-wrapper {
- font-size: 14px;
- margin-top: 65px;
- padding-right: 10px;
- width: 33.3%;
- section {
- position: sticky;
- top: 160px;
- left: 0;
- max-width: 90%;
- overflow: hidden;
- }
- .event {
- margin-top: 20px;
- h4 {
- color: var(--color-highlight) !important;
- font-weight: bold;
- }
- img {
- display: block;
- border: 1px solid #fff;
-
- &:hover {
- border: 1px solid var(--color-highlight);
- }
- }
- }
-
- // background-color: #fff;
- @include media("<=1700px") {
- right: 2rem;
- }
-
- @include media("<1400px") {
- background-color: #fff;
- right: 0;
- }
-
- @include media("<=1000px") {
- display: none;
- }
-
- .parent-item {
- padding-left: 10px;
- padding-bottom: 5px;
- border-left: 4px solid #eee;
- }
-
- .item {
- .child-item {
- margin-left: 8px;
- padding-top: 3px;
- padding-bottom: 3px;
- }
- .child-child-item {
- margin-left: 8px;
- padding-top: 3px;
- padding-bottom: 3px;
- }
- }
-
- a {
- display: block;
- white-space: nowrap;
- overflow-x: hidden;
- line-height: 21px;
- text-overflow: ellipsis;
- color: #60606f;
-
- &:hover,
- &.active {
- color: #000;
- }
- }
- }
-
- .footer-wrapper {
- margin-bottom: 0;
- }
-}
-
- .inner-container {
- width: 1000px;
-
- @include media("<=1000px") {
- width: auto;
- }
-
- margin: 0 auto;
- }
-
-.not-found {
- background: #fff;
- height: 100%;
-}
-
-.doc-fixed-header {
- position: fixed;
- top: 0;
- left: 0;
- right: 0;
- z-index: 1000;
-}
-
-.doc-post-content {
- p {
- position: relative;
- z-index: 2;
- }
-
- // [id]::before {
- // content: "";
- // display: block;
- // height: 75px;
- // margin-top: -75px;
- // visibility: hidden;
- // position: relative;
- // z-index: 0;
- // }
-}
-.doc-post {
- position: relative;
-}
-
-.button-to-top {
- position: fixed;
- right: 15%;
- bottom: 80px;
-
- display: flex;
- justify-content: center;
- align-items: center;
-
- width: 40px;
- height: 40px;
- border-radius: 50%;
- box-shadow: 0 2px 5px 0 rgba(0, 0, 0, 0.2);
-
- background-color: #fff;
- cursor: pointer;
-
- z-index: 1000;
-
- :hover {
- color: #000;
- }
-
- @include media(">=1600px") {
- position: fixed;
- right: 20%;
- bottom: 80px;
- }
-
- @include media(">=2000px") {
- position: fixed;
- right: 25%;
- bottom: 80px;
- }
-}
-
-.search-docs-button {
- padding-top: 8px;
- margin-bottom: 10px;
- padding-left: 5px;
- margin-left: 0px;
- font-size: 16px;
- font-weight: bold;
- color: #ffffff;
- border-radius: 4px;
- background-color: #009BAA;
- border: 0;
- height: 40px;
- width: 150px;
- outline: none;
- cursor: pointer;
- &:active {
- outline: none;
- }
- .button {
- outline: none;
- &:active {
- outline: none;
- }
- }
- @include media("<1000px") {
- position: fixed;
- top: 100px;
- right: 20px;
- width: 40px;
- height: 40px;
- z-index: 1001;
- border-radius: 50%;
- padding-top: 7px;
- padding-left: 9px;
- }
-}
-
-.fontawsome-icon {
- margin-left: 0px !important;
-}
\ No newline at end of file
diff --git a/src/components/footer/footer.js b/src/components/footer/footer.js
deleted file mode 100644
index 2808729d3..000000000
--- a/src/components/footer/footer.js
+++ /dev/null
@@ -1,33 +0,0 @@
-
-import React from "react";
-import PropTypes from "prop-types";
-import "./footer.scss";
-import { FontAwesomeIcon } from '@fortawesome/react-fontawesome';
-import { faTwitter } from '@fortawesome/free-brands-svg-icons';
-import { faLinkedin } from '@fortawesome/free-brands-svg-icons';
-
-const Footer = ({ style }) => {
- // const l = locale === "cn" ? "en" : "cn";
- // const to = globalHistory.location.pathname
- // .replace("/en/", "/")
- // .replace("/cn/", "/");
- return (
-
- );
-};
-
-Footer.propTypes = {
- language: PropTypes.string,
- locale: PropTypes.string.isRequired
-};
-
-export default Footer;
\ No newline at end of file
diff --git a/src/components/footer/footer.scss b/src/components/footer/footer.scss
deleted file mode 100644
index 2577c03a7..000000000
--- a/src/components/footer/footer.scss
+++ /dev/null
@@ -1,31 +0,0 @@
-.footer-wrapper {
- background: #F5F6FA;
- color: #60606F;
- padding: 22px;
-
- .follow {
- text-align: right;
- margin-right: 30px;
- }
- .copy-right {
- text-align: center;
-
- span {
- font-size: 14px;
- }
- }
-
- .link {
- color: #60606F;
- margin-left: 20px;
- }
- a {
- color:#60606F;
- }
- }
-
- .fontawsome-icon {
- width: 20px !important;
- height: 20px !important;
- margin-left: 20px !important;
- }
\ No newline at end of file
diff --git a/src/components/header/header.js b/src/components/header/header.js
deleted file mode 100644
index 8063bbd19..000000000
--- a/src/components/header/header.js
+++ /dev/null
@@ -1,146 +0,0 @@
-import PropTypes from "prop-types"
-import React, { useState, useEffect } from "react"
-
-import LocalizedLink from "../../components/localizedLink/localizedLink";
-
-import "./header.scss";
-import Logo from "../../images/HaystackLogo.png"
-import { useMobileScreen } from "../../hooks";
-import { FontAwesomeIcon } from '@fortawesome/react-fontawesome';
-import { faBars } from '@fortawesome/free-solid-svg-icons';
-
-import Button from "../landing-page/button";
-
-const Header = ({ siteTitle, menuLinks }) => {
-
- const screenWidth = useMobileScreen();
- const [mobileNav, setMobileNav] = useState(false);
-
- useEffect(() => {
- window.addEventListener("click", () => {
- setMobileNav(false);
- });
- }, []);
-
- const handleClick = (e) => {
- e.stopPropagation();
- setMobileNav((v) => !v);
- };
-
- return (
- <>
-
- {children}
- {!isBenchMark && (
-
- )}
-
- {formatHeadings && !isBenchMark && !isDocAPI && (
-
-
- {generateAnchorMenu(formatHeadings, "parent-item")}
-
-
- )}
-
- {showToTopButton && (
-
-
- 
- 788 stars
-
-
-
-
-
- )}
-
-
-
-
- )}
-
-
-
-
- );
-
-};
-
-Header.propTypes = {
- siteTitle: PropTypes.string,
-}
-
-Header.defaultProps = {
- siteTitle: ``,
-}
-
-export default Header
diff --git a/src/components/header/header.scss b/src/components/header/header.scss
deleted file mode 100644
index 241491ec5..000000000
--- a/src/components/header/header.scss
+++ /dev/null
@@ -1,265 +0,0 @@
-@import "../include_media";
-
-.full-header-wrapper {
- background: #ffffff;
- width: 100%;
- position: fixed;
- height: 60px;
- top: 0;
- left: 0;
- right: 0;
- z-index: 100;
- box-shadow: 0px 1px 2px 0px rgba(60,64,67,0.3), 0px 2px 6px 2px rgba(60,64,67,0.15);
-}
-
-.header-wrapper {
- display: flex;
- justify-content: space-between;
- align-items: center;
- height: 60px;
- padding: 10px 0;
- z-index: 100;
- box-sizing: border-box;
- background: #ffffff;
- max-width: 1600px;
- // overflow: hidden;
- margin: 0 auto;
- box-shadow: 0px 2px 4px rgba(0, 0, 0, 0.1);
- text-transform: uppercase;
-
- .beta-button {
- height: 40px;
- width: 150px;
- margin-left: 20px;
- }
-
- .logo-wrapper {
- display: flex;
- align-items: center;
- padding-left: 20px;
-
- img {
- height: 2rem;
- }
-
- .lfai {
- height: 1rem;
- margin-left: 1rem;
- }
- }
-
- .fontawsome-icon {
- color: #009BAA;
- }
- .svg-inline--fa.fa-w-14 {
- width: 4em;
- height: 3em;
- padding-right: 5px;
- }
-
- .docs {
- position: relative;
- display: inline-block;
- margin: 0 1rem;
- color: #60677e;
- font-weight: bold;
- cursor: pointer;
-
- .docs-list {
- background-color: #fff;
- border-radius: 10px;
- position: absolute;
- top: 150%;
- right: -20px;
- box-shadow: 0px 2px 4px rgba(0, 0, 0, 0.1);
- width: 10rem;
-
- a {
- display: block;
- margin: 10px 0;
- padding: 10px;
- color: #333;
-
- &.core:hover {
- background-color: #3f9cd1;
- color: #fff;
- }
- &.hub:hover {
- background-color: #3f9cd1;
- color: #fff;
- }
- }
- }
-
- &:before {
- position: absolute;
- width: 100px;
- height: 100px;
- top: -10px;
- left: 0;
- content: "";
- }
- }
-
- .right {
- display: flex;
- justify-content: space-between;
- align-items: center;
- padding-right: 30px;
-
- .Rectangle {
- width: 2px;
- height: 32px;
- background-color: #009bab;
- }
-
- .link {
- color: #60677e;;
- font-weight: bold;
- margin: 0 1rem;
- }
-
- .a {
- color: #60677e;;
- font-weight: bold;
- }
-
- .beta-button {
- margin-left: 20px;
- font-size: 16px;
- font-weight: bold;
- color: #ffffff;
- border-radius: 4px;
- background-color: #009BAA;
- border: 0;
- height: 40px;
- width: 150px;
- cursor: pointer;
- }
- .beta-button {
- margin-left: 20px;
- width: 170px;
- }
- .beta-button-link {
- margin-left: 20px;
- font-size: 16px;
- font-weight: bold;
- color: #ffffff;
- border-radius: 4px;
- background-color: #009BAA;
- border: 0;
- height: 40px;
- width: 150px;
- cursor: pointer;
- text-align: center;
- text-decoration: none;
- padding-top: 10px;
- text-transform: none;
- }
- .beta-button-link {
- margin-left: 20px;
- width: 170px;
- }
- }
-
- @include media("<=1000px") {
- box-shadow: none;
- background: #ffffff;
- max-width: 100%;
-
- .link {
- margin: 0;
- }
-
- .logo-wrapper {
- padding-left: 10px;
-
- img {
- height: 3rem;
- }
- }
- }
-}
-
-@include media("<=1000px") {
- .header-wrapper {
- padding: 0;
- }
-}
-
-.mobile-nav {
- position: fixed;
- width: 100%;
- top: 0px;
- left: 0;
- right: 0;
- font-size: 14px;
- z-index: 100;
- transition: all 0.1s linear;
- height: 0;
- opacity: 0;
- overflow: hidden;
- box-sizing: border-box;
-
- a {
- width: 100%;
- display: block;
- background: #fff;
- color: var(--color-primary);
- height: 48px;
- line-height: 48px;
- text-align: center;
- border-bottom: 1px solid #eee;
-
- &:hover {
- background-color: var(--color-highlight);
- color: #000;
- .button {
- background-color: var(--color-highlight);
- color: #000;
- }
- }
-
- &:last-child {
- margin-bottom: 0;
- }
- }
-
- &.open {
- top: 60px;
- height: auto;
- opacity: 1;
- }
- .beta-button {
- margin-left: 20px;
- font-size: 14px;
- font-weight: bold;
- color: #0e0101;
- border-radius: 4px;
- background-color: lightgray;
- border: 0;
- height: 40px;
- width: 170px;
- cursor: pointer;
- }
- .button {
- font-size: 14px;
- padding: 5px;
- background-color: white;
- color: var(--color-primary);
- font-weight: normal;
- &:hover {
- background-color: var(--color-highlight);
- color: #000;
- }
- }
-}
-
-.gradient {
- font-weight: bold;
- background: -webkit-linear-gradient(30deg, #eee, rgba(79, 196, 249, 0.9));
- color: rgba(79, 196, 249, 0.9);
- -webkit-background-clip: text;
- background-clip: text;
- -webkit-text-fill-color: transparent;
-}
\ No newline at end of file
diff --git a/src/components/image.js b/src/components/image.js
deleted file mode 100644
index e61edb684..000000000
--- a/src/components/image.js
+++ /dev/null
@@ -1,32 +0,0 @@
-import React from "react"
-import { useStaticQuery, graphql } from "gatsby"
-import Img from "gatsby-image"
-
-/*
- * This component is built using `gatsby-image` to automatically serve optimized
- * images with lazy loading and reduced file sizes. The image is loaded using a
- * `useStaticQuery`, which allows us to load the image from directly within this
- * component, rather than having to pass the image data down from pages.
- *
- * For more information, see the docs:
- * - `gatsby-image`: https://gatsby.dev/gatsby-image
- * - `useStaticQuery`: https://www.gatsbyjs.org/docs/use-static-query/
- */
-
-const Image = () => {
- const data = useStaticQuery(graphql`
- query {
- placeholderImage: file(relativePath: { eq: "gatsby-astronaut.png" }) {
- childImageSharp {
- fluid(maxWidth: 300) {
- ...GatsbyImageSharpFluid
- }
- }
- }
- }
- `)
-
- return
-
- 
-
-
-
- {screenWidth > 1000 ? (
-
-
-
- Haystack Docs
-
-
-
- Haystack Hub Docs
-
-
-
- Benchmarks
-
-
-
-
- .
-
-
- Join Open NLP
-
-
-
- ) : (
-
- {children}
-
-
-
- );
-};
-Layout.propTypes = {
- children: PropTypes.node.isRequired,
- language: PropTypes.object.isRequired,
- locale: PropTypes.string.isRequired
-};
-export default Layout;
\ No newline at end of file
diff --git a/src/components/layout/layout.jsx b/src/components/layout/layout.jsx
deleted file mode 100644
index 0a084614a..000000000
--- a/src/components/layout/layout.jsx
+++ /dev/null
@@ -1,41 +0,0 @@
-/**
- * Layout component that queries for data
- * with Gatsby's useStaticQuery component
- *
- * See: https://www.gatsbyjs.org/docs/use-static-query/
- */
-
-import React from "react";
-import PropTypes from "prop-types";
-import Header from "../header/header";
-import Footer from "../footer/footer";
-import CookieConsent from "react-cookie-consent";
-
-import "./layout.scss";
-import "../../reset.scss";
-const Layout = ({ children, language="en", locale="en" }) => {
- return (
- <>
- {children}
-
-
- ));
- };
-
- const toggleMenuChild = (doc) => {
- const copyMenu = JSON.parse(JSON.stringify(realMenuList));
- const findDoc = findItem("title", doc.title, copyMenu);
- findDoc.showChildren = !findDoc.showChildren;
- setRealMenuList(copyMenu);
- };
-
- const toggleMenu = (status) => {
- setMenuStatus(status);
- };
-
- return (
- <>
- {
- toggleMenuChild(doc);
- }
- : handleMenuClick
- }
- style={doc.isMenu ? { cursor: "pointer" } : null}
- >
- {doc.outLink ? (
-
-
- {doc.title}
-
- ) : doc.isMenu === true ? (
- {doc.title}
- ) : (
-
- {doc.title}
-
- )}
-
- {doc.children && doc.children.length ? (
-
- ) : null}
-
-
- {doc.children && doc.children.length
- ? generageMenuDom(doc.children, "menu-child")
- : null}
-
-
-
- ) : (
- Haystack Hub
-
-
- )}
-
-
- {generageMenuDom(realMenuList, "menu-top-level border-bottom")}
- Core Version:
- {
- toggleMenu(true);
- }}
- >
-
- ) : null}
-
- );
-};
-export default Menu;
\ No newline at end of file
diff --git a/src/components/menu/index.scss b/src/components/menu/index.scss
deleted file mode 100644
index 3237cc99e..000000000
--- a/src/components/menu/index.scss
+++ /dev/null
@@ -1,244 +0,0 @@
-@import '../include_media';
-
-.mini-menu-control {
- position: fixed;
- bottom: 80px;
- left: 20px;
- width: 40px;
- height: 40px;
- line-height: 40px;
- text-align: center;
- border-radius: 50%;
- background: #fff;
- color: #000;
- z-index: 1000;
- box-shadow: 0 2px 5px 0 rgba(0, 0, 0, 0.2);
-}
-
-.menu-container {
- position: fixed;
- box-sizing: border-box;
- color: #fafafa;
- overflow: auto;
- line-height: 1.2rem;
- background: #f1f1f1; //#f7f9fa;
-
- * {
- transition: all 0.2s;
- }
-
- .select-wrapper {
- padding-left: 1rem;
- display: flex;
- .h-version {
- color: black;
- font-weight: bold;
- font-size: 18px;
- margin-right: 10px;
- padding-top: 5px;
- padding-bottom: 20px;
- }
-
- }
-
- .text {
- opacity: 0.75;
-
- &:hover {
- opacity: 1;
- }
- }
-
- @include media('<=1000px') {
- &.hide {
- transform: translate3d(-100%, 0, 0);
- }
- }
-
- @include scroll-bar;
-
- .title {
- padding: 30px 0 10px;
- font-size: 26px;
- text-shadow: 0 0 10px rgba(0, 0, 0, 0.5);
-
- @include media("<=1000px") {
- font-size: 18px;
- }
- }
-
- .close {
- position: absolute;
- top: 10px;
- right: 10px;
- font-size: 10px;
- }
-
- .menu-top-level {
- margin-bottom: 0.1rem;
- box-sizing: border-box;
- padding-bottom: 1rem;
-
- .text {
- color: #000000;
- font-size: 16px;
- font-weight: bold;
- letter-spacing: 0;
- line-height: 20px;
- }
-
- .line-separator {
- width: 100%;
- text-align: center;
- border-bottom: 1px solid grey;
- letter-spacing: 0;
- line-height: 0em;
- cursor: default;
- .separator {
- color: grey;
- font-size: 16px;
- font-weight: bold;
- background:#f1f1f1;
- padding:0 10px;
- }
- }
-
- > .menu_name-wrapper {
- padding: 0 1rem 0.25rem 1rem;
- text-transform: uppercase;
- }
-
- .menu_name-wrapper {
- font-size: 16px;
-
- @include media('<=1000px') {
- margin-bottom: 20px;
- }
- }
- }
-
- .menu-child {
- margin: 0.25rem 0rem;
- color: var(--color-primary);
- padding: 0 1rem;
-
- .text {
- margin-left: 0.5rem;
- font-weight: normal;
- }
-
- &.active {
- background-color: #49B0E4;
-
- .text {
- opacity: 1;
- }
- }
-
- .menu_name-wrapper {
- .text {
- font-size: 0.8rem;
- }
- }
- }
-
- .menu-last-level {
- padding-left: 1rem;
-
- .text {
- color: #000000;
- font-size: 12px;
- letter-spacing: 0;
- line-height: 16px;
- font-weight: normal;
- }
-
- &.active {
- background-color: #49B0E4;
-
- .menu_name-wrapper {
- .text {
- font-weight: bold;
- }
- }
- }
-
- .menu_name-wrapper {
- margin-bottom: 0px !important;
- letter-spacing: 0;
- font-size: 14px;
- }
- }
-
- .menu-child-wrapper {
- max-height: 0;
- overflow: hidden;
- transition: max-height 0.6s;
-
- &.open {
- max-height: 1000px;
- transition: max-height 0.6s;
- }
-
- .menu_name-wrapper {
- padding: 0.2rem 0;
- font-size: 1rem;
-
- @include media('<=1000px') {
- margin-bottom: 0.2rem;
-
- &:lang(en) {
- font-size: 14px;
- }
-
- &:lang(cn) {
- font-size: 14px;
- }
- }
- }
- }
-
- .menu_name-wrapper {
- display: flex;
- justify-content: space-between;
- align-items: center;
- margin-left: 0.3rem;
- }
-
- .outlink {
- display: flex;
- align-items: center;
- }
-
- .fas {
- position: relative;
- margin-right: 6px;
- font-size: 0.7rem;
- cursor: pointer;
- }
-
- .close {
- position: absolute;
- top: 20px;
- right: 20px;
- font-size: 1rem;
- color: #000;
- }
-
- .arrow {
- transform: rotate(180deg);
- color: #000;
-
- &.top {
- transform: rotate(0);
- }
-
- &:hover {
- opacity: 1;
- }
-
- @include media('<=1000px') {
- font-size: 14px;
- }
- }
-}
\ No newline at end of file
diff --git a/src/components/query-modal/query-modal.js b/src/components/query-modal/query-modal.js
deleted file mode 100644
index e8efaf80b..000000000
--- a/src/components/query-modal/query-modal.js
+++ /dev/null
@@ -1,75 +0,0 @@
-import React, { useState } from 'react';
-import PropTypes from 'prop-types';
-import './query-modal.scss';
-
-const QueryModal = ({ locale, setShowModal }) => {
- const [url, setUrl] = useState('http://localhost:8000/app/dev_tools/console');
- const [host, setHost] = useState('localhost:8000');
-
- const i18nMap = {
- en: {
- save: 'Save',
- reset: 'Reset',
- url: 'Enter the URL of the Console editor',
- host: 'cURL host',
- username: 'cURL username',
- hint: 'Or install',
- },
- cn: {
- save: '保存',
- reset: '重置',
- url: '输入Sense编辑器的URL',
- host: 'cURL主办',
- username: 'cURL用户名',
- hint: '或安装',
- },
- };
-
- const onUrlChange = (event) => setUrl(event.target.value);
- const onHostChange = (event) => setHost(event.target.value);
- const onCloseClick = () => setShowModal(false);
- const onSaveClick = () => {};
-
- return (
- <>
-
-
-
-
-
- );
-};
-
-export default Selector;
\ No newline at end of file
diff --git a/src/components/selector/index.scss b/src/components/selector/index.scss
deleted file mode 100644
index 5287b258e..000000000
--- a/src/components/selector/index.scss
+++ /dev/null
@@ -1,64 +0,0 @@
-.selector-wrapper {
- font-size: 0.8rem;
- letter-spacing: 0.1rem;
- width: 100%;
- height: 26px;
- margin-bottom: 20px;
- line-height: 26px;
- border: 1px solid #60606f;
- border-radius: 4px;
- text-align: center;
-
- &.version-wrapper {
- width: 92px;
- }
-
- .options-wrapper {
- position: relative;
- margin-top: 4px;
- max-height: 0;
- opacity: 0;
- overflow: hidden;
- transition: all 0.2s linear;
- border-radius: 4px;
- background: #28292e;
- border: 1px solid #60606f;
- box-shadow: 0 25px 50px 0 rgba(16, 16, 16, 0.5);
- z-index: 10;
- cursor: pointer;
-
- &.open {
- max-height: 1600px;
- opacity: 1;
- }
-
- li {
- .text {
- display: block;
- color: #fafafa;
- }
-
- &.active {
- // not working: highlight current version
- color: #4fc4f9;
- }
- }
- }
-
- .selected {
- padding-left: 0.4rem;
- display: flex;
- justify-content: space-between;
- align-items: center;
- padding: 0 10px;
- cursor: pointer;
- font-weight: 400;
- color: #000;
-
- .fontawsome-icon {
- width: 12px;
- color: #000000;
- }
- }
-
-}
\ No newline at end of file
diff --git a/src/components/seo.js b/src/components/seo.js
deleted file mode 100644
index a97cee769..000000000
--- a/src/components/seo.js
+++ /dev/null
@@ -1,127 +0,0 @@
-/**
- * SEO component that queries for data with
- * Gatsby's useStaticQuery React hook
- *
- * See: https://www.gatsbyjs.org/docs/use-static-query/
- */
-
-import React from "react"
-import PropTypes from "prop-types"
-import { Helmet } from "react-helmet"
-import { useStaticQuery, graphql } from "gatsby"
-
-function SEO({ description, lang, meta, image: metaImage, title, pathname }) {
- const { site } = useStaticQuery(
- graphql`
- query {
- site {
- siteMetadata {
- title
- description
- author
- titleTemplate
- twitterUsername
- siteUrl
- keywords
- }
- }
- }
- `
- )
-
- const metaDescription = description || site.siteMetadata.description
- const image =
- metaImage /*&& metaImage.src
- ? `${site.siteMetadata.siteUrl}${metaImage.src}`
- : null*/
- const canonical = pathname ? `${site.siteMetadata.siteUrl}${pathname}` : null
-
- return (
-
- {selected}
-
-
- -
- {options.map((v) => (
-
-
- {isVersion ? (
-
- {v} - - ) : ( - {v} - )} -
- ))}
-
-
-)
-
-SpecInformation.propTypes = {
- title: PropTypes.string.isRequired,
- version: PropTypes.string.isRequired,
- description: PropTypes.string.isRequired,
-}
-
-export default SpecInformation
\ No newline at end of file
diff --git a/src/components/spec/SpecPaths.js b/src/components/spec/SpecPaths.js
deleted file mode 100644
index 6bb68fb7c..000000000
--- a/src/components/spec/SpecPaths.js
+++ /dev/null
@@ -1,142 +0,0 @@
-import React, { useState } from 'react'
-import PropTypes from 'prop-types'
-
-import { FontAwesomeIcon } from '@fortawesome/react-fontawesome';
-import { faChevronDown } from '@fortawesome/free-solid-svg-icons';
-import { faChevronUp } from '@fortawesome/free-solid-svg-icons';
-
-import "../../scss/specs/_specpath.scss";
-
-const SpecPaths = ({ tag, paths }) => {
-
- const [showChild, setShowChild] = useState(false);
-
- function IterateJSON(jsonObject= {}) {
- let type = "";
- let elementArray = null;
- let element = null;
- for (let prop in jsonObject) {
- type = jsonObject[prop].type;
- if(type === "array" && jsonObject[prop]["items"] !== undefined && jsonObject[prop]["items"]["properties"] !== undefined) {
- elementArray = {title}
- -We also provide a - Swagger UI - . Please feel free to check it out.
- -API Reference
-The Haystack Hub API is organized around REST. Our API has predictable resource-oriented URLs, - accepts form-encoded request bodies, returns JSON-encoded responses, and uses standard HTTP - response codes, authentication, and verbs.
- -Authentication
-Errors
-
-
-
-
- Haystack Hub Rest API uses conventional HTTP response codes to indicate the success - or failure of an API request. In general: Codes in the 2xx range indicate - success. Codes in the 4xx range indicate an error that failed given the - information provided. Codes in the 5xx range indicate an error with Haystack Hub's servers.
-
-
-
-
-
-
-
- HTTP Status Code Summary
-
-
-
-
-
- | 200 - OK | -Everything worked as expected. | -
| 400 - Bad Request | -The request was unacceptable, often due to missing a required parameter. | -
| 401 - Unauthorized | -No valid API key provided. | -
| 402 - Request Failed | -The parameters were valid but the request failed. | -
| 403 - Forbidden | -The API key doesn't have permissions to perform the request. | -
| 404 - Not Found | -The requested resource doesn't exist. | -
| 409 - Conflict | -The request conflicts with another request (perhaps due to using the same idempotent key). | -
| 429 - Too Many Requests | -Too many requests hit the API too quickly. We recommend an exponential backoff of your requests. | -
| 500, 502, 503, 504 - Server Errors | -Something went wrong on Haystack Hub's end. | -
-
- {showChild && (
;
- } else if (type === "object" && jsonObject[prop]["properties"] !== undefined) {
- elementArray = {IterateJSON(jsonObject[prop]["items"]["properties"])}
)}
-
- {showChild && (
;
- }
- element = {IterateJSON(jsonObject[prop]["properties"])}
)}{element}
{elementArray}
;
- elementArray = null;
- }
- return {prop} {type}
{jsonObject[prop].description}
{elementArray}
{element}
;
- }
-
- return (
- {tag !== "Auth" && (
-);
-
-};
-//{iterateJSON(p.exampleDef)}
-
-SpecPaths.propTypes = {
- tag: PropTypes.string.isRequired,
- paths: PropTypes.array.isRequired,
-}
-
-export default SpecPaths
diff --git a/src/consts/locales.js b/src/consts/locales.js
deleted file mode 100644
index 924de1764..000000000
--- a/src/consts/locales.js
+++ /dev/null
@@ -1,8 +0,0 @@
-
-module.exports = {
- en: {
- path: 'en',
- locale: 'English',
- default: true
- }
- }
\ No newline at end of file
diff --git a/src/hooks/index.jsx b/src/hooks/index.jsx
deleted file mode 100644
index 33d228f7b..000000000
--- a/src/hooks/index.jsx
+++ /dev/null
@@ -1,16 +0,0 @@
-import { useEffect, useState } from "react";
-export const useMobileScreen = () => {
- const [screenWidth, setScreenWidth] = useState(null);
- useEffect(() => {
- const cb = () => {
- setScreenWidth(document.body.clientWidth);
- };
- cb();
- window.addEventListener("resize", cb);
-
- return () => {
- window.removeEventListener("resize", cb);
- };
- }, []);
- return screenWidth;
-};
\ No newline at end of file
diff --git a/src/i18n/layout/en.json b/src/i18n/layout/en.json
deleted file mode 100644
index 287daab69..000000000
--- a/src/i18n/layout/en.json
+++ /dev/null
@@ -1,23 +0,0 @@
-{
- "layout": {
- "notFound": "404 Page Not Found",
- "backtohome": "Take me back to the home page.",
- "header": {
- "quick": "Quick Start",
- "why": "Why Haystack",
- "gui": "Admin",
- "tutorials": "Tutorials",
- "solution": "Scenarios",
- "about": "About Haystack",
- "doc": "Docs",
- "blog": "Blog",
- "tutorial": "Tutorial",
- "benchmarks": "Benchmarks",
- "bootcamp": "Bootcamp",
- "try": "Try",
- "loading": "Loading...",
- "noresult": "No Result",
- "search": "Search"
- }
- }
-}
\ No newline at end of file
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index c922cc7ff..000000000
--- a/src/images/icon/github-white.svg
+++ /dev/null
@@ -1,9 +0,0 @@
-
-
\ No newline at end of file
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diff --git a/src/pages/404.js b/src/pages/404.js
deleted file mode 100644
index a32adf818..000000000
--- a/src/pages/404.js
+++ /dev/null
@@ -1,69 +0,0 @@
-import React from "react";
-import { graphql } from "gatsby";
-import Layout from "../components/layout/404Layout";
-import SEO from "../components/seo";
-import LocalizeLink from "../components/localizedLink/localizedLink";
-
-import "./404.scss";
-const NotFoundPage = ({ data, pageContext }) => {
- const language = data.allFile.edges[0].node.childLayoutJson.layout;
- const { locale } = pageContext;
-
- return (
- {tag}
)} - {paths.map(p => ( -
-
- ))}
- {p.summary}
-
-
-
- {p.verb === "post" | p.verb === "patch" ? (
-
-
-
- )}
-
- Parameters
- {p.parameters && (
- | Name | -Example | -
|---|---|
| {parameter.name} {parameter.required && (*)} ({parameter.schema.type}) | -{parameter.schema.example} | -
-
-
-
-
-
-
- Endpoint
-
-
- {p.verb} {p.fullPath}
-
-
-
- ) : ( null ) }
-
- {p.verb === "get" | p.verb === "delete" | p.verb === "put" ? (
-
-
- Attributes
- {IterateJSON(JSON.parse(p.exampleDef))}
-
-
-
-
-
-
-
- Request Body Example
-
-
-
-
-
- {p.example}
-
-
-
-
- ) : ( null ) }
-
-
- {p.verb !== "delete" ? (
- Returns
- {p.childrenOpenApiSpecResponse[0].description}
-
-
- ) : ( null )}
-
-
-
-
-
- Response
-
-
-
-
-
- {p.childrenOpenApiSpecResponse[0].response}
-
-
-
-
- {language.backtohome}
-
-
- {language.notFound}
-
-
- Haystack Benchmarking - Version:
-
- Loading Chart
}
- data={mapDataReader[selectedVersion]}
- options={{
- colors: ['#22BA99', '#63C7CA', '#49B0E4', '#FBB14B'],
- subTitle: subTitelReader[selectedVersion],
- bars: barsReader[selectedVersion],
- legend: "bottom"
- }}
- />
-
-
- {showRetrieverDesc && (
-
- )}
-
- Haystack Benchmarking - Version:
-
- }
- >
- {arrayVersions.map((version) => (
-
- ))}
-
-
-
-
-
-
- {showReaderDesc && (
-
- )}
-
- 
-
- Join the Haystack Community on Slack
-Learn more about what people are building with Haystack, ask questions, share knowledge, track events, meet collaborators.
-
-
-
-
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb)
-
-Question Answering can be used in a variety of use cases. A very common one: Using it to navigate through complex knowledge bases or long documents ("search setting").
-
-A "knowledge base" could for example be your website, an internal wiki or a collection of financial reports.
-In this tutorial we will work on a slightly different domain: "Game of Thrones".
-
-Let's see how we can use a bunch of Wikipedia articles to answer a variety of questions about the
-marvellous seven kingdoms...
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-Haystack finds answers to queries within the documents stored in a `DocumentStore`. The current implementations of `DocumentStore` include `ElasticsearchDocumentStore`, `FAISSDocumentStore`, `SQLDocumentStore`, and `InMemoryDocumentStore`.
-
-**Here:** We recommended Elasticsearch as it comes preloaded with features like [full-text queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html), [BM25 retrieval](https://www.elastic.co/elasticon/conf/2016/sf/improved-text-scoring-with-bm25), and [vector storage for text embeddings](https://www.elastic.co/guide/en/elasticsearch/reference/7.6/dense-vector.html).
-
-**Alternatives:** If you are unable to setup an Elasticsearch instance, then follow the [Tutorial 3](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb) for using SQL/InMemory document stores.
-
-**Hint**: This tutorial creates a new document store instance with Wikipedia articles on Game of Thrones. However, you can configure Haystack to work with your existing document stores.
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker via the Haystack utility function
-from haystack.utils import launch_es
-
-launch_es()
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-# Connect to Elasticsearch
-
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles about Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first fetch some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is:
-# {
-# 'text': "
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.generator.transformers import Seq2SeqGenerator
-```
-
-### Document Store
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-The default flavour of FAISSDocumentStore is "Flat" but can also be set to "HNSW" for
-faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.
-For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-
-
-```python
-from haystack.document_store.faiss import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(vector_dim=128, faiss_index_factory_str="Flat")
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever and Reader/Generator
-
-#### Retriever
-
-**Here:** We use a `RetribertRetriever` and we invoke `update_embeddings` to index the embeddings of documents in the `FAISSDocumentStore`
-
-
-
-
-```python
-from haystack.retriever.dense import EmbeddingRetriever
-
-retriever = EmbeddingRetriever(document_store=document_store,
- embedding_model="yjernite/retribert-base-uncased",
- model_format="retribert")
-
-document_store.update_embeddings(retriever)
-```
-
-Before we blindly use the `RetribertRetriever` let's empirically test it to make sure a simple search indeed finds the relevant documents.
-
-
-```python
-from haystack.utils import print_answers, print_documents
-from haystack.pipeline import DocumentSearchPipeline
-
-p_retrieval = DocumentSearchPipeline(retriever)
-res = p_retrieval.run(
- query="Tell me something about Arya Stark?",
- top_k_retriever=5
-)
-print_documents(res, max_text_len=512)
-
-```
-
-#### Reader/Generator
-
-Similar to previous Tutorials we now initalize our reader/generator.
-
-Here we use a `Seq2SeqGenerator` with the *yjernite/bart_eli5* model (see: https://huggingface.co/yjernite/bart_eli5)
-
-
-
-
-```python
-generator = Seq2SeqGenerator(model_name_or_path="yjernite/bart_eli5")
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `GenerativeQAPipeline` that combines a retriever and a reader/generator to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import GenerativeQAPipeline
-pipe = GenerativeQAPipeline(generator, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-pipe.run(query="Why did Arya Stark's character get portrayed in a television adaptation?", top_k_retriever=1)
-```
-
-
-```python
-pipe.run(query="What kind of character does Arya Stark play?", top_k_retriever=1)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/2.md b/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/2.md
deleted file mode 100644
index ec5349afb..000000000
--- a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/2.md
+++ /dev/null
@@ -1,108 +0,0 @@
----
-title: "Tutorial 2"
-metaTitle: "Fine-tuning a model on your own data"
-metaDescription: ""
-slug: "/docs/tutorial2"
-date: "2020-09-03"
-id: "tutorial2md"
----
-
-# Fine-tuning a Model on Your Own Data
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial2_Finetune_a_model_on_your_data.ipynb)
-
-For many use cases it is sufficient to just use one of the existing public models that were trained on SQuAD or other public QA datasets (e.g. Natural Questions).
-However, if you have domain-specific questions, fine-tuning your model on custom examples will very likely boost your performance.
-While this varies by domain, we saw that ~ 2000 examples can easily increase performance by +5-20%.
-
-This tutorial shows you how to fine-tune a pretrained model on your own dataset.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.reader.farm import FARMReader
-```
-
-
-## Create Training Data
-
-There are two ways to generate training data
-
-1. **Annotation**: You can use the [annotation tool](https://github.com/deepset-ai/haystack#labeling-tool) to label your data, i.e. highlighting answers to your questions in a document. The tool supports structuring your workflow with organizations, projects, and users. The labels can be exported in SQuAD format that is compatible for training with Haystack.
-
-
-
-2. **Feedback**: For production systems, you can collect training data from direct user feedback via Haystack's [REST API interface](https://github.com/deepset-ai/haystack#rest-api). This includes a customizable user feedback API for providing feedback on the answer returned by the API. The API provides a feedback export endpoint to obtain the feedback data for fine-tuning your model further.
-
-
-## Fine-tune your model
-
-Once you have collected training data, you can fine-tune your base models.
-We initialize a reader as a base model and fine-tune it on our own custom dataset (should be in SQuAD-like format).
-We recommend using a base model that was trained on SQuAD or a similar QA dataset before to benefit from Transfer Learning effects.
-
-**Recommendation**: Run training on a GPU.
-If you are using Colab: Enable this in the menu "Runtime" > "Change Runtime type" > Select "GPU" in dropdown.
-Then change the `use_gpu` arguments below to `True`
-
-
-```python
-reader = FARMReader(model_name_or_path="distilbert-base-uncased-distilled-squad", use_gpu=True)
-data_dir = "data/squad20"
-# data_dir = "PATH/TO_YOUR/TRAIN_DATA"
-reader.train(data_dir=data_dir, train_filename="dev-v2.0.json", use_gpu=True, n_epochs=1, save_dir="my_model")
-```
-
-
-```python
-# Saving the model happens automatically at the end of training into the `save_dir` you specified
-# However, you could also save a reader manually again via:
-reader.save(directory="my_model")
-```
-
-
-```python
-# If you want to load it at a later point, just do:
-new_reader = FARMReader(model_name_or_path="my_model")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/3.md b/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/3.md
deleted file mode 100644
index 09f83ace6..000000000
--- a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/3.md
+++ /dev/null
@@ -1,202 +0,0 @@
----
-title: "Tutorial 3"
-metaTitle: "Build a QA System Without Elasticsearch"
-metaDescription: ""
-slug: "/docs/tutorial3"
-date: "2020-09-03"
-id: "tutorial3md"
----
-
-# Build a QA System Without Elasticsearch
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb)
-
-Haystack provides alternatives to Elasticsearch for developing quick prototypes.
-
-You can use an `InMemoryDocumentStore` or a `SQLDocumentStore`(with SQLite) as the document store.
-
-If you are interested in more feature-rich Elasticsearch, then please refer to the Tutorial 1.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-
-
-```python
-# In-Memory Document Store
-from haystack.document_store.memory import InMemoryDocumentStore
-document_store = InMemoryDocumentStore()
-```
-
-
-```python
-# SQLite Document Store
-# from haystack.document_store.sql import SQLDocumentStore
-# document_store = SQLDocumentStore(url="sqlite:///qa.db")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles on Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first get some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# convert files to dicts containing documents that can be indexed to our datastore
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is: {"name": "
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-from haystack.retriever.dense import EmbeddingRetriever
-from haystack.utils import print_answers
-import pandas as pd
-import requests
-
-```
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker via the Haystack utility function
-from haystack.utils import launch_es
-
-launch_es()
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-
-```
-
-### Init the DocumentStore
-In contrast to Tutorial 1 (extractive QA), we:
-
-* specify the name of our `text_field` in Elasticsearch that we want to return as an answer
-* specify the name of our `embedding_field` in Elasticsearch where we'll store the embedding of our question and that is used later for calculating our similarity to the incoming user question
-* set `excluded_meta_data=["question_emb"]` so that we don't return the huge embedding vectors in our search results
-
-
-```python
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="",
- index="document",
- embedding_field="question_emb",
- embedding_dim=768,
- excluded_meta_data=["question_emb"])
-```
-
-### Create a Retriever using embeddings
-Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones).
-We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings.
-
-
-```python
-retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert", use_gpu=True)
-```
-
-### Prepare & Index FAQ data
-We create a pandas dataframe containing some FAQ data (i.e curated pairs of question + answer) and index those in elasticsearch.
-Here: We download some question-answer pairs related to COVID-19
-
-
-```python
-# Download
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/faqs/faq_covidbert.csv")
-open('small_faq_covid.csv', 'wb').write(temp.content)
-
-# Get dataframe with columns "question", "answer" and some custom metadata
-df = pd.read_csv("small_faq_covid.csv")
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-df["question"] = df["question"].apply(lambda x: x.strip())
-print(df.head())
-
-# Get embeddings for our questions from the FAQs
-questions = list(df["question"].values)
-df["question_emb"] = retriever.embed_queries(texts=questions)
-df = df.rename(columns={"question": "text"})
-
-# Convert Dataframe to list of dicts and index them in our DocumentStore
-docs_to_index = df.to_dict(orient="records")
-document_store.write_documents(docs_to_index)
-```
-
-### Ask questions
-Initialize a Pipeline (this time without a reader) and ask questions
-
-
-```python
-from haystack.pipeline import FAQPipeline
-pipe = FAQPipeline(retriever=retriever)
-```
-
-
-```python
-prediction = pipe.run(query="How is the virus spreading?", top_k_retriever=10)
-print_answers(prediction, details="all")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/5.md b/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/5.md
deleted file mode 100644
index b54d5591c..000000000
--- a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/5.md
+++ /dev/null
@@ -1,260 +0,0 @@
----
-title: "Tutorial 5"
-metaTitle: "Evaluation of a QA System"
-metaDescription: ""
-slug: "/docs/tutorial5"
-date: "2020-09-03"
-id: "tutorial5md"
----
-
-# Evaluation of a QA System
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial5_Evaluation.ipynb)
-
-To be able to make a statement about the performance of a question-answering system, it is important to evalute it. Furthermore, evaluation allows to determine which parts of the system can be improved.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-## Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-from farm.utils import initialize_device_settings
-
-device, n_gpu = initialize_device_settings(use_cuda=True)
-```
-
-
-```python
-from haystack.preprocessor.utils import fetch_archive_from_http
-
-# Download evaluation data, which is a subset of Natural Questions development set containing 50 documents
-doc_dir = "../data/nq"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/nq_dev_subset_v2.json.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-
-```python
-# make sure these indices do not collide with existing ones, the indices will be wiped clean before data is inserted
-doc_index = "tutorial5_docs"
-label_index = "tutorial5_labels"
-```
-
-
-```python
-# Connect to Elasticsearch
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-# Connect to Elasticsearch
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document",
- create_index=False, embedding_field="emb",
- embedding_dim=768, excluded_meta_data=["emb"])
-```
-
-
-```python
-from haystack.preprocessor import PreProcessor
-
-# Add evaluation data to Elasticsearch Document Store
-# We first delete the custom tutorial indices to not have duplicate elements
-# and also split our documents into shorter passages using the PreProcessor
-preprocessor = PreProcessor(
- split_length=500,
- split_overlap=0,
- split_respect_sentence_boundary=False,
- clean_empty_lines=False,
- clean_whitespace=False
-)
-document_store.delete_all_documents(index=doc_index)
-document_store.delete_all_documents(index=label_index)
-document_store.add_eval_data(
- filename="../data/nq/nq_dev_subset_v2.json",
- doc_index=doc_index,
- label_index=label_index,
- preprocessor=preprocessor
-)
-
-# Let's prepare the labels that we need for the retriever and the reader
-labels = document_store.get_all_labels_aggregated(index=label_index)
-```
-
-## Initialize components of QA-System
-
-
-```python
-# Initialize Retriever
-from haystack.retriever.sparse import ElasticsearchRetriever
-retriever = ElasticsearchRetriever(document_store=document_store)
-# Alternative: Evaluate DensePassageRetriever
-# Note, that DPR works best when you index short passages < 512 tokens as only those tokens will be used for the embedding.
-# Here, for nq_dev_subset_v2.json we have avg. num of tokens = 5220(!).
-# DPR still outperforms Elastic's BM25 by a small margin here.
-# from haystack.retriever.dense import DensePassageRetriever
-# retriever = DensePassageRetriever(document_store=document_store,
-# query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
-# passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
-# use_gpu=True,
-# embed_title=True,
-# max_seq_len=256,
-# batch_size=16,
-# remove_sep_tok_from_untitled_passages=True)
-#document_store.update_embeddings(retriever, index=doc_index)
-```
-
-
-```python
-# Initialize Reader
-from haystack.reader.farm import FARMReader
-
-reader = FARMReader("deepset/roberta-base-squad2", top_k=4, return_no_answer=True)
-
-```
-
-
-```python
-from haystack.eval import EvalAnswers, EvalDocuments
-
-# Here we initialize the nodes that perform evaluation
-eval_retriever = EvalDocuments()
-eval_reader = EvalAnswers()
-```
-
-## Evaluation of Retriever
-Here we evaluate only the retriever, based on whether the gold_label document is retrieved.
-
-
-```python
-## Evaluate Retriever on its own
-retriever_eval_results = retriever.eval(top_k=20, label_index=label_index, doc_index=doc_index)
-## Retriever Recall is the proportion of questions for which the correct document containing the answer is
-## among the correct documents
-print("Retriever Recall:", retriever_eval_results["recall"])
-## Retriever Mean Avg Precision rewards retrievers that give relevant documents a higher rank
-print("Retriever Mean Avg Precision:", retriever_eval_results["map"])
-```
-
-## Evaluation of Reader
-Here we evaluate only the reader in a closed domain fashion i.e. the reader is given one query
-and one document and metrics are calculated on whether the right position in this text is selected by
-the model as the answer span (i.e. SQuAD style)
-
-
-```python
-# Evaluate Reader on its own
-reader_eval_results = reader.eval(document_store=document_store, device=device, label_index=label_index, doc_index=doc_index)
-# Evaluation of Reader can also be done directly on a SQuAD-formatted file without passing the data to Elasticsearch
-#reader_eval_results = reader.eval_on_file("../data/nq", "nq_dev_subset_v2.json", device=device)
-
-## Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer
-print("Reader Top-N-Accuracy:", reader_eval_results["top_n_accuracy"])
-## Reader Exact Match is the proportion of questions where the predicted answer is exactly the same as the correct answer
-print("Reader Exact Match:", reader_eval_results["EM"])
-## Reader F1-Score is the average overlap between the predicted answers and the correct answers
-print("Reader F1-Score:", reader_eval_results["f1"])
-```
-
-## Evaluation of Retriever and Reader (Open Domain)
-Here we evaluate retriever and reader in open domain fashion i.e. a document is considered
-correctly retrieved if it contains the answer string within it. The reader is evaluated based purely on the
-predicted string, regardless of which document this came from and the position of the extracted span.
-
-
-```python
-from haystack import Pipeline
-
-# Here is the pipeline definition
-p = Pipeline()
-p.add_node(component=retriever, name="ESRetriever", inputs=["Query"])
-p.add_node(component=eval_retriever, name="EvalRetriever", inputs=["ESRetriever"])
-p.add_node(component=reader, name="QAReader", inputs=["EvalRetriever"])
-p.add_node(component=eval_reader, name="EvalReader", inputs=["QAReader"])
-results = []
-```
-
-
-```python
-# This is how to run the pipeline
-for l in labels:
- res = p.run(
- query=l.question,
- top_k_retriever=10,
- labels=l,
- top_k_reader=10,
- index=doc_index,
- )
- results.append(res)
-```
-
-
-```python
-# When we have run evaluation using the pipeline, we can print the results
-n_queries = len(labels)
-eval_retriever.print()
-print()
-retriever.print_time()
-print()
-eval_reader.print(mode="reader")
-print()
-reader.print_time()
-print()
-eval_reader.print(mode="pipeline")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/6.md b/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/6.md
deleted file mode 100644
index 2e2767b24..000000000
--- a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/6.md
+++ /dev/null
@@ -1,240 +0,0 @@
----
-title: "Tutorial 6"
-metaTitle: "Better retrieval via Dense Passage Retrieval"
-metaDescription: ""
-slug: "/docs/tutorial6"
-date: "2020-09-03"
-id: "tutorial6md"
----
-
-# Better Retrieval via "Dense Passage Retrieval"
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb)
-
-### Importance of Retrievers
-
-The Retriever has a huge impact on the performance of our overall search pipeline.
-
-
-### Different types of Retrievers
-#### Sparse
-Family of algorithms based on counting the occurrences of words (bag-of-words) resulting in very sparse vectors with length = vocab size.
-
-**Examples**: BM25, TF-IDF
-
-**Pros**: Simple, fast, well explainable
-
-**Cons**: Relies on exact keyword matches between query and text
-
-
-#### Dense
-These retrievers use neural network models to create "dense" embedding vectors. Within this family there are two different approaches:
-
-a) Single encoder: Use a **single model** to embed both query and passage.
-b) Dual-encoder: Use **two models**, one to embed the query and one to embed the passage
-
-Recent work suggests that dual encoders work better, likely because they can deal better with the different nature of query and passage (length, style, syntax ...).
-
-**Examples**: REALM, DPR, Sentence-Transformers
-
-**Pros**: Captures semantinc similarity instead of "word matches" (e.g. synonyms, related topics ...)
-
-**Cons**: Computationally more heavy, initial training of model
-
-
-### "Dense Passage Retrieval"
-
-In this Tutorial, we want to highlight one "Dense Dual-Encoder" called Dense Passage Retriever.
-It was introdoced by Karpukhin et al. (2020, https://arxiv.org/abs/2004.04906.
-
-Original Abstract:
-
-_"Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks."_
-
-Paper: https://arxiv.org/abs/2004.04906
-Original Code: https://fburl.com/qa-dpr
-
-
-*Use this* [link](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb) *to open the notebook in Google Colab.*
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-### Document Store
-
-#### Option 1: FAISS
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-The default flavour of FAISSDocumentStore is "Flat" but can also be set to "HNSW" for
-faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.
-For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-
-
-```python
-from haystack.document_store import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
-```
-
-#### Option 2: Milvus
-
-Milvus is an open source database library that is also optimized for vector similarity searches like FAISS.
-Like FAISS it has both a "Flat" and "HNSW" mode but it outperforms FAISS when it comes to dynamic data management.
-It does require a little more setup, however, as it is run through Docker and requires the setup of some config files.
-See [their docs](https://milvus.io/docs/v1.0.0/milvus_docker-cpu.md) for more details.
-
-
-```python
-from haystack.utils import launch_milvus
-from haystack.document_store import MilvusDocumentStore
-
-launch_milvus()
-document_store = MilvusDocumentStore()
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever, Reader & Pipeline
-
-#### Retriever
-
-**Here:** We use a `DensePassageRetriever`
-
-**Alternatives:**
-
-- The `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-
-
-```python
-from haystack.retriever.dense import DensePassageRetriever
-retriever = DensePassageRetriever(document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- max_seq_len_query=64,
- max_seq_len_passage=256,
- batch_size=16,
- use_gpu=True,
- embed_title=True,
- use_fast_tokenizers=True)
-# Important:
-# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all
-# previously indexed documents and update their embedding representation.
-# While this can be a time consuming operation (depending on corpus size), it only needs to be done once.
-# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.
-document_store.update_embeddings(retriever)
-```
-
-#### Reader
-
-Similar to previous Tutorials we now initalize our reader.
-
-Here we use a FARMReader with the *deepset/roberta-base-squad2* model (see: https://huggingface.co/deepset/roberta-base-squad2)
-
-
-
-##### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import ExtractiveQAPipeline
-pipe = ExtractiveQAPipeline(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = pipe.run(query="Who created the Dothraki vocabulary?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/7.md b/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/7.md
deleted file mode 100644
index 3e472df19..000000000
--- a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/7.md
+++ /dev/null
@@ -1,217 +0,0 @@
----
-title: "Tutorial 7"
-metaTitle: "Generative QA with RAG"
-metaDescription: ""
-slug: "/docs/tutorial7"
-date: "2020-11-12"
-id: "tutorial7md"
----
-
-# Generative QA with "Retrieval-Augmented Generation"
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial7_RAG_Generator.ipynb)
-
-While extractive QA highlights the span of text that answers a query,
-generative QA can return a novel text answer that it has composed.
-In this tutorial, you will learn how to set up a generative system using the
-[RAG model](https://arxiv.org/abs/2005.11401) which conditions the
-answer generator on a set of retrieved documents.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-Here are the packages and imports that we'll need:
-
-
-```python
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from typing import List
-import requests
-import pandas as pd
-from haystack import Document
-from haystack.document_store.faiss import FAISSDocumentStore
-from haystack.generator.transformers import RAGenerator
-from haystack.retriever.dense import DensePassageRetriever
-```
-
-Let's download a csv containing some sample text and preprocess the data.
-
-
-
-```python
-# Download sample
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/haystack/master/tutorials/small_generator_dataset.csv")
-open('small_generator_dataset.csv', 'wb').write(temp.content)
-
-# Create dataframe with columns "title" and "text"
-df = pd.read_csv("small_generator_dataset.csv", sep=',')
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-
-print(df.head())
-```
-
-We can cast our data into Haystack Document objects.
-Alternatively, we can also just use dictionaries with "text" and "meta" fields
-
-
-```python
-# Use data to initialize Document objects
-titles = list(df["title"].values)
-texts = list(df["text"].values)
-documents: List[Document] = []
-for title, text in zip(titles, texts):
- documents.append(
- Document(
- text=text,
- meta={
- "name": title or ""
- }
- )
- )
-```
-
-Here we initialize the FAISSDocumentStore, DensePassageRetriever and RAGenerator.
-FAISS is chosen here since it is optimized vector storage.
-
-
-```python
-# Initialize FAISS document store.
-# Set `return_embedding` to `True`, so generator doesn't have to perform re-embedding
-document_store = FAISSDocumentStore(
- faiss_index_factory_str="Flat",
- return_embedding=True
-)
-
-# Initialize DPR Retriever to encode documents, encode question and query documents
-retriever = DensePassageRetriever(
- document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- use_gpu=True,
- embed_title=True,
-)
-
-# Initialize RAG Generator
-generator = RAGenerator(
- model_name_or_path="facebook/rag-token-nq",
- use_gpu=True,
- top_k=1,
- max_length=200,
- min_length=2,
- embed_title=True,
- num_beams=2,
-)
-```
-
-We write documents to the DocumentStore, first by deleting any remaining documents then calling `write_documents()`.
-The `update_embeddings()` method uses the retriever to create an embedding for each document.
-
-
-
-```python
-# Delete existing documents in documents store
-document_store.delete_all_documents()
-
-# Write documents to document store
-document_store.write_documents(documents)
-
-# Add documents embeddings to index
-document_store.update_embeddings(
- retriever=retriever
-)
-```
-
-Here are our questions:
-
-
-```python
-QUESTIONS = [
- "who got the first nobel prize in physics",
- "when is the next deadpool movie being released",
- "which mode is used for short wave broadcast service",
- "who is the owner of reading football club",
- "when is the next scandal episode coming out",
- "when is the last time the philadelphia won the superbowl",
- "what is the most current adobe flash player version",
- "how many episodes are there in dragon ball z",
- "what is the first step in the evolution of the eye",
- "where is gall bladder situated in human body",
- "what is the main mineral in lithium batteries",
- "who is the president of usa right now",
- "where do the greasers live in the outsiders",
- "panda is a national animal of which country",
- "what is the name of manchester united stadium",
-]
-```
-
-Now let's run our system!
-The retriever will pick out a small subset of documents that it finds relevant.
-These are used to condition the generator as it generates the answer.
-What it should return then are novel text spans that form and answer to your question!
-
-
-```python
-# Now generate an answer for each question
-for question in QUESTIONS:
- # Retrieve related documents from retriever
- retriever_results = retriever.retrieve(
- query=question
- )
-
- # Now generate answer from question and retrieved documents
- predicted_result = generator.predict(
- query=question,
- documents=retriever_results,
- top_k=1
- )
-
- # Print you answer
- answers = predicted_result["answers"]
- print(f'Generated answer is \'{answers[0]["answer"]}\' for the question = \'{question}\'')
-```
-
-
-```python
-# Or alternatively use the Pipeline class
-from haystack.pipeline import GenerativeQAPipeline
-
-pipe = GenerativeQAPipeline(generator=generator, retriever=retriever)
-for question in QUESTIONS:
- res = pipe.run(query=question, top_k_generator=1, top_k_retriever=5)
- print(res)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/8.md b/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/8.md
deleted file mode 100644
index a53c3a030..000000000
--- a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/8.md
+++ /dev/null
@@ -1,214 +0,0 @@
----
-title: "Tutorial 8"
-metaTitle: "Preprocessing"
-metaDescription: ""
-slug: "/docs/tutorial8"
-date: "2021-01-08"
-id: "tutorial8md"
----
-
-# Preprocessing
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial8_Preprocessing.ipynb)
-
-Haystack includes a suite of tools to extract text from different file types, normalize white space
-and split text into smaller pieces to optimize retrieval.
-These data preprocessing steps can have a big impact on the systems performance and effective handling of data is key to getting the most out of Haystack.
-
-Ultimately, Haystack expects data to be provided as a list documents in the following dictionary format:
-``` python
-docs = [
- {
- 'text': DOCUMENT_TEXT_HERE,
- 'meta': {'name': DOCUMENT_NAME, ...}
- }, ...
-]
-```
-
-This tutorial will show you all the tools that Haystack provides to help you cast your data into this format.
-
-
-```python
-# Let's start by installing Haystack
-
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-!wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.03.tar.gz
-!tar -xvf xpdf-tools-linux-4.03.tar.gz && sudo cp xpdf-tools-linux-4.03/bin64/pdftotext /usr/local/bin
-```
-
-
-```python
-# Here are the imports we need
-
-from haystack.file_converter.txt import TextConverter
-from haystack.file_converter.pdf import PDFToTextConverter
-from haystack.file_converter.docx import DocxToTextConverter
-
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.preprocessor.preprocessor import PreProcessor
-```
-
-
-```python
-# This fetches some sample files to work with
-
-doc_dir = "data/preprocessing_tutorial"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/preprocessing_tutorial.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-## Converters
-
-Haystack's converter classes are designed to help you turn files on your computer into the documents
-that can be processed by the Haystack pipeline.
-There are file converters for txt, pdf, docx files as well as a converter that is powered by Apache Tika.
-
-
-```python
-# Here are some examples of how you would use file converters
-
-converter = TextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_txt = converter.convert(file_path="data/preprocessing_tutorial/classics.txt", meta=None)
-
-converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_pdf = converter.convert(file_path="data/preprocessing_tutorial/bert.pdf", meta=None)
-
-converter = DocxToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_docx = converter.convert(file_path="data/preprocessing_tutorial/heavy_metal.docx", meta=None)
-
-```
-
-
-```python
-# Haystack also has a convenience function that will automatically apply the right converter to each file in a directory.
-
-all_docs = convert_files_to_dicts(dir_path="data/preprocessing_tutorial")
-```
-
-## PreProcessor
-
-The PreProcessor class is designed to help you clean text and split text into sensible units.
-File splitting can have a very significant impact on the system's performance and is absolutely mandatory for Dense Passage Retrieval models.
-In general, we recommend you split the text from your files into small documents of around 100 words for dense retrieval methods
-and no more than 10,000 words for sparse methods.
-Have a look at the [Preprocessing](https://haystack.deepset.ai/docs/latest/preprocessingmd)
-and [Optimization](https://haystack.deepset.ai/docs/latest/optimizationmd) pages on our website for more details.
-
-
-```python
-# This is a default usage of the PreProcessor.
-# Here, it performs cleaning of consecutive whitespaces
-# and splits a single large document into smaller documents.
-# Each document is up to 1000 words long and document breaks cannot fall in the middle of sentences
-# Note how the single document passed into the document gets split into 5 smaller documents
-
-preprocessor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=False,
- split_by="word",
- split_length=100,
- split_respect_sentence_boundary=True
-)
-docs_default = preprocessor.process(doc_txt)
-print(f"n_docs_input: 1\nn_docs_output: {len(docs_default)}")
-```
-
-## Cleaning
-
-- `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
-- `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
-- `clean_header_footer` will remove any long header or footer texts that are repeated on each page
-
-## Splitting
-By default, the PreProcessor will respect sentence boundaries, meaning that documents will not start or end
-midway through a sentence.
-This will help reduce the possibility of answer phrases being split between two documents.
-This feature can be turned off by setting `split_respect_sentence_boundary=False`.
-
-
-```python
-# Not respecting sentence boundary vs respecting sentence boundary
-
-preprocessor_nrsb = PreProcessor(split_respect_sentence_boundary=False)
-docs_nrsb = preprocessor_nrsb.process(doc_txt)
-
-print("RESPECTING SENTENCE BOUNDARY")
-end_text = docs_default[0]["text"][-50:]
-print("End of document: \"..." + end_text + "\"")
-print()
-print("NOT RESPECTING SENTENCE BOUNDARY")
-end_text_nrsb = docs_nrsb[0]["text"][-50:]
-print("End of document: \"..." + end_text_nrsb + "\"")
-```
-
-A commonly used strategy to split long documents, especially in the field of Question Answering,
-is the sliding window approach. If `split_length=10` and `split_overlap=3`, your documents will look like this:
-
-- doc1 = words[0:10]
-- doc2 = words[7:17]
-- doc3 = words[14:24]
-- ...
-
-You can use this strategy by following the code below.
-
-
-```python
-# Sliding window approach
-
-preprocessor_sliding_window = PreProcessor(
- split_overlap=3,
- split_length=10,
- split_respect_sentence_boundary=False
-)
-docs_sliding_window = preprocessor_sliding_window.process(doc_txt)
-
-doc1 = docs_sliding_window[0]["text"][:200]
-doc2 = docs_sliding_window[1]["text"][:100]
-doc3 = docs_sliding_window[2]["text"][:100]
-
-print("Document 1: \"" + doc1 + "...\"")
-print("Document 2: \"" + doc2 + "...\"")
-print("Document 3: \"" + doc3 + "...\"")
-```
-
-## Bringing it all together
-
-
-```python
-all_docs = convert_files_to_dicts(dir_path="data/preprocessing_tutorial")
-preprocessor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=False,
- split_by="word",
- split_length=100,
- split_respect_sentence_boundary=True
-)
-docs = preprocessor.process(all_docs)
-
-print(f"n_files_input: {len(all_docs)}\nn_docs_output: {len(docs)}")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
-
diff --git a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/9.md b/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/9.md
deleted file mode 100644
index 366b7487e..000000000
--- a/src/pages/docs/versions/master/latest/site/en/tutorials/tutorials/9.md
+++ /dev/null
@@ -1,250 +0,0 @@
----
-title: "Tutorial 9"
-metaTitle: "Training a Dense Passage Retrieval model"
-metaDescription: ""
-slug: "/docs/tutorial9"
-date: "2021-01-08"
-id: "tutorial9md"
----
-
-# Training Your Own "Dense Passage Retrieval" Model
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial9_DPR_training.ipynb)
-
-Haystack contains all the tools needed to train your own Dense Passage Retrieval model.
-This tutorial will guide you through the steps required to create a retriever that is specifically tailored to your domain.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-```
-
-
-```python
-# Here are some imports that we'll need
-
-from haystack.retriever.dense import DensePassageRetriever
-from haystack.preprocessor.utils import fetch_archive_from_http
-from haystack.document_store.memory import InMemoryDocumentStore
-```
-
-## Training Data
-
-DPR training performed using Information Retrieval data.
-More specifically, you want to feed in pairs of queries and relevant documents.
-
-To train a model, we will need a dataset that has the same format as the original DPR training data.
-Each data point in the dataset should have the following dictionary structure.
-
-``` python
- {
- "dataset": str,
- "question": str,
- "answers": list of str
- "positive_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- "negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- "hard_negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- }
-```
-
-`positive_ctxs` are context passages which are relevant to the query.
-In some datasets, queries might have more than one positive context
-in which case you can set the `num_positives` parameter to be higher than the default 1.
-Note that `num_positives` needs to be lower or equal to the minimum number of `positive_ctxs` for queries in your data.
-If you have an unequal number of positive contexts per example,
-you might want to generate some soft labels by retrieving similar contexts which contain the answer.
-
-DPR is standardly trained using a method known as in-batch negatives.
-This means that positive contexts for a given query are treated as negative contexts for the other queries in the batch.
-Doing so allows for a high degree of computational efficiency, thus allowing the model to be trained on large amounts of data.
-
-`negative_ctxs` is not actually used in Haystack's DPR training so we recommend you set it to an empty list.
-They were used by the original DPR authors in an experiment to compare it against the in-batch negatives method.
-
-`hard_negative_ctxs` are passages that are not relevant to the query.
-In the original DPR paper, these are fetched using a retriever to find the most relevant passages to the query.
-Passages which contain the answer text are filtered out.
-
-If you'd like to convert your SQuAD format data into something that can train a DPR model,
-check out the utility script at [`haystack/retriever/squad_to_dpr.py`](https://github.com/deepset-ai/haystack/blob/master/haystack/retriever/squad_to_dpr.py)
-
-## Using Question Answering Data
-
-Question Answering datasets can sometimes be used as training data.
-Google's Natural Questions dataset, is sufficiently large
-and contains enough unique passages, that it can be converted into a DPR training set.
-This is done simply by considering answer containing passages as relevant documents to the query.
-
-The SQuAD dataset, however, is not as suited to this use case since its question and answer pairs
-are created on only a very small slice of wikipedia documents.
-
-## Download Original DPR Training Data
-
-WARNING: These files are large! The train set is 7.4GB and the dev set is 800MB
-
-We can download the original DPR training data with the following cell.
-Note that this data is probably only useful if you are trying to train from scratch.
-
-
-```python
-# Download original DPR data
-# WARNING: the train set is 7.4GB and the dev set is 800MB
-
-doc_dir = "data/dpr_training/"
-
-s3_url_train = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-train.json.gz"
-s3_url_dev = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz"
-
-fetch_archive_from_http(s3_url_train, output_dir=doc_dir + "train/")
-fetch_archive_from_http(s3_url_dev, output_dir=doc_dir + "dev/")
-```
-
-## Option 1: Training DPR from Scratch
-
-The default variables that we provide below are chosen to train a DPR model from scratch.
-Here, both passage and query embedding models are initialized using BERT base
-and the model is trained using Google's Natural Questions dataset (in a format specialised for DPR).
-
-If you are working in a language other than English,
-you will want to initialize the passage and query embedding models with a language model that supports your language
-and also provide a dataset in your language.
-
-
-```python
-# Here are the variables to specify our training data, the models that we use to initialize DPR
-# and the directory where we'll be saving the model
-
-doc_dir = "data/dpr_training/"
-
-train_filename = "train/biencoder-nq-train.json"
-dev_filename = "dev/biencoder-nq-dev.json"
-
-query_model = "bert-base-uncased"
-passage_model = "bert-base-uncased"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Option 2: Finetuning DPR
-
-If you have your own domain specific question answering or information retrieval dataset,
-you might instead be interested in finetuning a pretrained DPR model.
-In this case, you would initialize both query and passage models using the original pretrained model.
-You will want to load something like this set of variables instead of the ones above
-
-
-```python
-# Here are the variables you might want to use instead of the set above
-# in order to perform pretraining
-
-doc_dir = "PATH_TO_YOUR_DATA_DIR"
-train_filename = "TRAIN_FILENAME"
-dev_filename = "DEV_FILENAME"
-
-query_model = "facebook/dpr-question_encoder-single-nq-base"
-passage_model = "facebook/dpr-ctx_encoder-single-nq-base"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Initialization
-
-Here we want to initialize our model either with plain language model weights for training from scratch
-or else with pretrained DPR weights for finetuning.
-We follow the [original DPR parameters](https://github.com/facebookresearch/DPR#best-hyperparameter-settings)
-for their max passage length but set max query length to 64 since queries are very rarely longer.
-
-
-```python
-## Initialize DPR model
-
-retriever = DensePassageRetriever(
- document_store=InMemoryDocumentStore(),
- query_embedding_model=query_model,
- passage_embedding_model=passage_model,
- max_seq_len_query=64,
- max_seq_len_passage=256
-)
-```
-
-## Training
-
-Let's start training and save our trained model!
-
-On a V100 GPU, you can fit up to batch size 16 so we set gradient accumulation steps to 8 in order
-to simulate the batch size 128 of the original DPR experiment.
-
-When `embed_title=True`, the document title is prepended to the input text sequence with a `[SEP]` token
-between it and document text.
-
-When training from scratch with the above variables, 1 epoch takes around an hour and we reached the following performance:
-
-```
-loss: 0.046580662854042276
-task_name: text_similarity
-acc: 0.992524064068483
-f1: 0.8804297774366846
-acc_and_f1: 0.9364769207525838
-average_rank: 0.19631619339984652
-report:
- precision recall f1-score support
-
-hard_negative 0.9961 0.9961 0.9961 201887
- positive 0.8804 0.8804 0.8804 6515
-
- accuracy 0.9925 208402
- macro avg 0.9383 0.9383 0.9383 208402
- weighted avg 0.9925 0.9925 0.9925 208402
-
-```
-
-
-```python
-# Start training our model and save it when it is finished
-
-retriever.train(
- data_dir=doc_dir,
- train_filename=train_filename,
- dev_filename=dev_filename,
- test_filename=dev_filename,
- n_epochs=1,
- batch_size=16,
- grad_acc_steps=8,
- save_dir=save_dir,
- evaluate_every=3000,
- embed_title=True,
- num_positives=1,
- num_hard_negatives=1
-)
-```
-
-## Loading
-
-Loading our newly trained model is simple!
-
-
-```python
-reloaded_retriever = DensePassageRetriever.load(load_dir=save_dir, document_store=None)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/annotation.md b/src/pages/docs/versions/master/latest/site/en/usage/usage/annotation.md
deleted file mode 100644
index 6a61fca05..000000000
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/annotation.md
+++ /dev/null
@@ -1,78 +0,0 @@
----
-title: "Annotation Tool"
-metaTitle: "Annotation Tool"
-metaDescription: ""
-slug: "/docs/annotation"
-date: "2020-09-03"
-id: "annotationmd"
----
-
-# Annotation Tool
-
-- Create labels with different techniques: Come up with questions (+ answers) while reading passages (SQuAD style) or have a set of predefined questions and look for answers in the document (~ Natural Questions).
-- Structure your work via organizations, projects, users
-- Upload your documents or import labels from an existing SQuAD-style dataset
-- Export your labels in SQuAD Format
-
-
-
-# Hosted version
- Signup here: [Haystack Annotation Tool](https://annotate.deepset.ai/login)
-
-# Local version (Docker)
-
-1. Configure credentials & database in the [`docker-compose.yml`](https://github.com/deepset-ai/haystack/blob/master/annotation_tool/docker-compose.yml):
-
-The credentials should match in database image and application configuration.
-
- DEFAULT_ADMIN_EMAIL: "example@example.com"
- DEFAULT_ADMIN_PASSWORD: "DEMO-PASSWORD"
-
- PROD_DB_NAME: "databasename"
- PROD_DB_USERNAME: "somesafeuser"
- PROD_DB_PASSWORD: "somesafepassword"
-
-
- POSTGRES_USER: "somesafeuser"
- POSTGRES_PASSWORD: "somesafepassword"
- POSTGRES_DB: "databasename"
-
-
-2. Run docker-compose by executing `docker-compose up`.
-
-
-3. The UI should be available at `localhost:7001`.
-
-# Usage
-The manual (of a slightly earlier version) can be found [here](https://drive.google.com/file/d/1Wv3OIC0Z7ibHIzOm9Xw_r0gjTFmpl-33/view). While it doesn't include all latest features, the basic workflow and tips for label quality are still the same.
-
-# Annotation FAQ
-
-1. What is a good question?
- - A good question is a fact-seeking question that can be answered with an entity (person, organisation, location, etc.) or explanation. A bad question is ambiguous, incomprehensible, dependent on clear false presuppositions, opinion seeking, or not clearly a request for factual information.
- - The question should ask about information present in the text passage given. It should not be answerable only with additional knowledge or your interpretation.
- - Do not copy paste answer text into the question. Good questions do not contain the exact same words as the answer or the context around the answer. The question should be a reformulation with synonyms and in different order as the context of the answer.
- - Questions should be very precise natural questions you would ask when you want information from another person.
-2. How many questions should you ask per text passage?
- - Maximally ask 20 questions per passage
- - Some text passages are not suited for 20 questions. Do not make up very constructed and complicated questions just to fill up the 20 - move on to the next text.
- - Try to ask questions covering the whole passage and focus on questions covering important information. Do not only ask questions about a single sentence in that passage.
-3. What is a good answer span?
- - Always mark whole words. Do not start or end the answer within a word.
- - For short answers: The answer should be as short and as close to a spoken human answer as possible. Do not include punctuation.
- - For long answers: Please mark whole sentences with punctuation. The sentences can also pick up parts of the question, or mark even whole text passages. Mark passages only if they are not too large (e.g. not more than 8-10 sentences).
-4. How do I differentiate long vs short answers?
- - If there is a short answer possible you should always select short answer over long answer.
- - Short precise answers like numbers or a few words are short answers.
- - Long answers include lists of possibilities or multiple sentences are needed to answer the question correctly.
-5. How to handle multiple possible answers to a single question?
- - As of now there is no functionality to mark multiple answers per single question.
- - Workaround: You can add a question with the same text but different answer selection by using the button below the question list (Button reads “custom question”)
-6. What to do with grammatically wrong or incorrectly spelled questions?
- - Include them. When users use the tool and ask questions they will likely contain grammar and spelling errors, too.
- - Exception: The question needs to be understandable without reading and interpretation of the corresponding text passage. If you do not understand the question, please mark the question as “I don’t understand the question”.
-7. What to do with text passages that are not properly converted or contain (in part) information that cannot be labelled (e.g. just lists or garbage text)?
- - Please do not annotate this text
- - You can write down what is missing, or the cause why you cannot label the text + the text number and title.
-8. Which browser to use?
- - Please use the Chrome browser. The tool is not tested for other browsers.
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/document_store.md b/src/pages/docs/versions/master/latest/site/en/usage/usage/document_store.md
deleted file mode 100644
index 85b4af4ec..000000000
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/document_store.md
+++ /dev/null
@@ -1,362 +0,0 @@
----
-title: "Document Store"
-metaTitle: "Document Store"
-metaDescription: ""
-slug: "/docs/documentstore"
-date: "2020-09-03"
-id: "documentstoremd"
----
-
-
-# DocumentStores
-
-You can think of the DocumentStore as a "database" that:
-- stores your texts and meta data
-- provides them to the retriever at query time
-
-There are different DocumentStores in Haystack to fit different use cases and tech stacks.
-
-## Initialisation
-
-Initialising a new DocumentStore within Haystack is straight forward.
-
-
-
-
-
-Each DocumentStore constructor allows for arguments specifying how to connect to existing databases and the names of indexes.
-See API documentation for more info.
-
-## Input Format
-
-DocumentStores expect Documents in dictionary form, like that below.
-They are loaded using the `DocumentStore.write_documents()` method.
-See [Preprocessing](/docs/latest/preprocessingmd) for more information on the cleaning and splitting steps that will help you maximize Haystack's performance.
-
-[//]: # (Add link to preprocessing section)
-
-```python
-from haystack.document_store import ElasticsearchDocumentStore
-
-document_store = ElasticsearchDocumentStore()
-dicts = [
- {
- 'text': DOCUMENT_TEXT_HERE,
- 'meta': {'name': DOCUMENT_NAME, ...}
- }, ...
-]
-document_store.write_documents(dicts)
-```
-
-## Writing Documents (Sparse Retrievers)
-
-Haystack allows for you to write store documents in an optimised fashion so that query times can be kept low.
-For **sparse**, keyword based retrievers such as BM25 and TF-IDF,
-you simply have to call `DocumentStore.write_documents()`.
-The creation of the inverted index which optimises querying speed is handled automatically.
-
-```python
-document_store.write_documents(dicts)
-```
-
-## Writing Documents (Dense Retrievers)
-
-For **dense** neural network based retrievers like Dense Passage Retrieval, or Embedding Retrieval,
-indexing involves computing the Document embeddings which will be compared against the Query embedding.
-
-The storing of the text is handled by `DocumentStore.write_documents()` and the computation of the
-embeddings is started by `DocumentStore.update_embeddings()`.
-
-```python
-document_store.write_documents(dicts)
-document_store.update_embeddings(retriever)
-```
-
-This step is computationally intensive since it will engage the transformer based encoders.
-Having GPU acceleration will significantly speed this up.
-
-
-
-## Choosing the Right Document Store
-
-The Document Stores have different characteristics. You should choose one depending on the maturity of your project, the use case and technical environment:
-
-
-
-
-
-
-
-
-[Install](https://www.elastic.co/guide/en/elasticsearch/reference/current/install-elasticsearch.html)
-Elasticsearch and then [start](https://www.elastic.co/guide/en/elasticsearch/reference/current/starting-elasticsearch.html)
-an instance.
-
-If you have Docker set up, we recommend pulling the Docker image and running it.
-```bash
-docker pull docker.elastic.co/elasticsearch/elasticsearch:7.9.2
-docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.9.2
-```
-
-Note that we also have a utility function `haystack.utils.launch_es` that can start up an Elasticsearch instance.
-
-Next you can initialize the Haystack object that will connect to this instance.
-
-```python
-from haystack.document_store import ElasticsearchDocumentStore
-
-document_store = ElasticsearchDocumentStore()
-```
-
-Note that we also support [OpenSearch](https://opensearch.org/).
-Follow [their documentation](https://opensearch.org/docs/)
-to run it and connect to it using Haystack's `OpenSearchDocumentStore` class.
-
-We further support [AWS Elastic Search Service](https://aws.amazon.com/elasticsearch-service/) with [signed Requests](https://docs.aws.amazon.com/general/latest/gr/signature-version-4.html):
-Use e.g. [aws-requests-auth](https://github.com/davidmuller/aws-requests-auth) to create an auth object and pass it as `aws4auth` to the `ElasticsearchDocumentStore` constructor.
-
-
-
-
-
-
-
-
-
-Follow the [official documentation](https://www.milvus.io/docs/v1.0.0/milvus_docker-cpu.md) to start a Milvus instance via Docker.
-Note that we also have a utility function `haystack.utils.launch_milvus` that can start up a Milvus instance.
-
-You can initialize the Haystack object that will connect to this instance as follows:
-```python
-from haystack.document_store import MilvusDocumentStore
-
-document_store = MilvusDocumentStore()
-```
-
-
-
-
-
-
-
-
-
-The `FAISSDocumentStore` requires no external setup. Start it by simply using this line.
-```python
-from haystack.document_store import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
-```
-
-
-
-
-
-
-
-
-
-The `InMemoryDocumentStore()` requires no external setup. Start it by simply using this line.
-```python
-from haystack.document_store import InMemoryDocumentStore
-
-document_store = InMemoryDocumentStore()
-```
-
-
-
-
-
-
-
-
-
-The `SQLDocumentStore` requires SQLite, PostgresQL or MySQL to be installed and started.
-Note that SQLite already comes packaged with most operating systems.
-
-```python
-from haystack.document_store import SQLDocumentStore
-
-document_store = SQLDocumentStore()
-```
-
-
-
-
-
-
-
-
-
-The `WeaviateDocumentStore` requires a running Weaviate Server.
-You can start a basic instance like this (see the [Weaviate docs](https://www.semi.technology/developers/weaviate/current/) for details):
-```
- docker run -d -p 8080:8080 --env AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED='true' --env PERSISTENCE_DATA_PATH='/var/lib/weaviate' semitechnologies/weaviate:1.6.0
-```
-
-Afterwards, you can use it in Haystack:
-```python
-from haystack.document_store import WeaviateDocumentStore
-
-document_store = WeaviateDocumentStore()
-```
-
-
-
-
-
-
-
-
-
-See the official [OpenSearch documentation](https://opensearch.org/docs/opensearch/install/docker/) on how to install and start an instance.
-
-If you have Docker set up, we recommend pulling the Docker image and running it.
-```bash
-docker pull opensearchproject/opensearch:1.0.0
-docker run -p 9200:9200 -p 9600:9600 -e "discovery.type=single-node" opensearchproject/opensearch:1.0.0
-```
-
-Note that we also have a utility function `haystack.utils.launch_opensearch` that can start up an OpenSearch instance.
-
-Next you can initialize the Haystack object that will connect to this instance.
-
-```python
-from haystack.document_store import OpenSearchDocumentStore
-
-document_store = OpenSearchDocumentStore()
-```
-
-
-
-
-
-
-
-
-
-
-
-
-
-**Pros:**
-- Fast & accurate sparse retrieval with many tuning options
-- Basic support for dense retrieval
-- Production-ready
-- Support also for Open Distro
-
-**Cons:**
-- Slow for dense retrieval with more than ~ 1 Mio documents
-
-
-
-
-
-
-
-
-
-**Pros:**
-- Scalable DocumentStore that excels at handling vectors (hence suited to dense retrieval methods like DPR)
-- Encapsulates multiple ANN libraries (e.g. FAISS and ANNOY) and provides added reliability
-- Runs as a separate service (e.g. a Docker container)
-- Allows dynamic data management
-
-**Cons:**
-- No efficient sparse retrieval
-
-
-
-
-
-
-
-
-
-**Pros:**
-- Fast & accurate dense retrieval
-- Highly scalable due to approximate nearest neighbour algorithms (ANN)
-- Many options to tune dense retrieval via different index types (more info [here](https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index))
-
-**Cons:**
-- No efficient sparse retrieval
-
-
-
-
-
-
-
-
-
-**Pros:**
-- Simple
-- Exists already in many environments
-
-**Cons:**
-- Only compatible with minimal TF-IDF Retriever
-- Bad retrieval performance
-- Not recommended for production
-
-
-
-
-
-
-
-
-
-
-**Pros:**
-- Simple & fast to test
-- No database requirements
-- Supports MySQL, PostgreSQL and SQLite
-
-**Cons:**
-- Not scalable
-- Not persisting your data on disk
-
-
-
-
-
-
-
-
-
-**Pros:**
-- Simple vector search
-- Stores everything in one place: documents, meta data and vectors - so less network overhead when scaling this up
-- Allows combination of vector search and scalar filtering, i.e. you can filter for a certain tag and do dense retrieval on that subset
-
-**Cons:**
-- Less options for ANN algorithms than FAISS or Milvus
-- No BM25 / Tf-idf retrieval
-
-
-
-
-
-
-
-
-
-**Pros:**
-- Fully open source fork of Elasticsearch
-- Has support for Approximate Nearest Neighbours vector search
-
-**Cons:**
-- It's ANN algorithms seem a little less performant that FAISS or Milvus in our benchmarks
-
-
-
-
-#### Our Recommendations
-
-**Restricted environment:** Use the `InMemoryDocumentStore`, if you are just giving Haystack a quick try on a small sample and are working in a restricted environment that complicates running Elasticsearch or other databases
-
-**Allrounder:** Use the `ElasticSearchDocumentStore`, if you want to evaluate the performance of different retrieval options (dense vs. sparse) and are aiming for a smooth transition from PoC to production
-
-**Vector Specialist:** Use the `MilvusDocumentStore`, if you want to focus on dense retrieval and possibly deal with larger datasets
-
-
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/faq.md b/src/pages/docs/versions/master/latest/site/en/usage/usage/faq.md
deleted file mode 100644
index 04bcc48d1..000000000
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/faq.md
+++ /dev/null
@@ -1,90 +0,0 @@
----
-title: "Frequently Asked Questions"
-metaTitle: "Frequently Asked Questions"
-metaDescription: ""
-slug: "/docs/faq"
-date: "2020-09-03"
-id: "faqmd"
----
-
-#Frequently Asked Questions
-
-##Why am I seeing duplicate answers being returned?
-
-The ElasticsearchDocumentStore and MilvusDocumentStore rely on Elasticsearch and Milvus backend services which
-persist after your Python script has finished running.
-If you rerun your script without deleting documents, you could end up with duplicate
-copies of your documents in your database.
-The easiest way to avoid this is to call `DocumentStore.delete_documents()` after initialization
-to ensure that you are working with an empty DocumentStore.
-
-DocumentStores also have a `duplicate_documents` argument in their `__init__()` and `write_documents` methods
-where you can define whether you'd like skip writing duplicates, overwrite existing duplicates or raise an error when there are duplicates.
-
-##How can I make sure that my GPU is being engaged when I use Haystack?
-
-You will want to ensure that a CUDA enabled GPU is being engaged when Haystack is running (you can check by running `nvidia-smi -l` on your command line).
-Components which can be sped up by GPU have a `use_gpu` argument in their constructor which you will want to set to `True`.
-
-##How do I speed up my predictions?
-
-There are many different ways to speed up the performance of your Haystack system.
-
-The Reader is usually the most computationally expensive component in a pipeline
-and you can often speed up your system by using a smaller model, like `deepset/minilm-uncased-squad2` (see [benchmarks](https://huggingface.co/deepset/minilm-uncased-squad2)). This usually comes with a small trade-off in accuracy.
-
-You can reduce the work load on the Reader by instructing the Retriever to pass on less documents.
-This is done by setting the `top_k_retriever` parameter to a lower value.
-
-Making sure that your documents are shorter can also increase the speed of your system. You can split
-your documents into smaller chunks by using the `PreProcessor` (see [tutorial](https://haystack.deepset.ai/docs/latest/tutorial11md)).
-
-For more optimization suggestions, have a look at our [optimization page](https://haystack.deepset.ai/docs/latest/optimizationmd)
-and also our [blogs](https://medium.com/deepset-ai)
-
-##How do I use Haystack for my language?
-
-The components in Haystack, such as the `Retriever` or the `Reader`, are designed in a language agnostic way. However you may
-have to set certain parameters or load models pretrained for your language in order to get good performance out of Haystack.
-See our [languages page](https://haystack.deepset.ai/docs/latest/languagesmd) for more details.
-
-##How can I add metadata to my documents so that I can apply filters?
-
-When providing your documents in the input format (see [here](https://haystack.deepset.ai/docs/latest/documentstoremd#Input-Format))
-you can provide metadata information as a dictionary under the `meta` key. At query time, you can provide a `filters` argument
-(most likely through `Pipelines.run()`) that specifies the accepted values for a certain metadata field
-(for an example of what a `filters` dictionary might look like, please refer to [this example](https://haystack.deepset.ai/docs/latest/apiretrievermd#__init__))
-
-##How can I see predictions during evaluation?
-
-To see predictions during evaluation, you want to initialize the `EvalDocuments` or `EvalAnswers` with `debug=True`.
-This causes their `EvalDocuments.log` or `EvalAnswers.log` to be populated with a record of each prediction made.
-
-##How can I serve my Haystack model?
-
-Haystack models can be wrapped in a REST API. For basic details on how to set this up, please refer to this section
-on our [Github page](https://github.com/deepset-ai/haystack/blob/master/README.md#7-rest-api).
-More comprehensive documentation coming soon!
-
-##How can I interpret the confidence scores being returned by the Reader?
-
-The confidence scores are in the range of 0 and 1 and reflect how confident the model is in each prediction that it makes.
-Having a confidence score is particularly useful in cases where you need Haystack to work with a certain accuracy threshold.
-Many of our users have built systems where predictions below a certain confidence value are routed on to a fallback system.
-
-For more information on model confidence and how to tune it, please refer to [this section](https://haystack.deepset.ai/docs/latest/readermd#Confidence-Scores).
-
-##My documents aren't showing up in my DocumentStore even though I've called `DocumentStore.write_documents()`
-
-When indexing, retrieving or querying for documents from a DocumentStore, you can specify an `index` on which to perform this action.
-This can be specified in almost all methods of `DocumentStore` as well as `Retriever.retrieve()`.
-Ensure that you are performing these operations on the one index!
-Note that this also applies at evaluation where labels are written into their own separate DocumentStore index.
-
-##What is the difference between the FARMReader and the TransformersReader?
-
-In short, the FARMReader using a QA pipeline implementation that comes from our own
-[FARM framework](https://github.com/deepset-ai/FARM) that we can more easily update and also optimize for performance.
-By contrast, the TransformersReader uses a QA pipeline implementation that comes from HuggingFace's [Transformers](https://github.com/huggingface/transformers).
-See [this section](https://haystack.deepset.ai/docs/latest/readermd#Deeper-Dive-FARM-vs-Transformers)
-for a more details about their differences!
\ No newline at end of file
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/generator.md b/src/pages/docs/versions/master/latest/site/en/usage/usage/generator.md
deleted file mode 100644
index fdba2ab18..000000000
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/generator.md
+++ /dev/null
@@ -1,36 +0,0 @@
----
-title: "Generator"
-metaTitle: "Generator"
-metaDescription: ""
-slug: "/docs/generator"
-date: "2020-11-05"
-id: "generatormd"
----
-
-# Generator
-
-While extractive QA highlights the span of text that answers a query,
-generative QA can return a novel text answer that it has composed.
-The best current approaches, such as [Retriever-Augmented Generation](https://arxiv.org/abs/2005.11401) and [LFQA](https://yjernite.github.io/lfqa.html),
-can draw upon both the knowledge it gained during language model pretraining (parametric memory)
-as well as passages provided to it with a retriever (non-parametric memory).
-With the advent of Transformer based retrieval methods such as [Dense Passage Retrieval](https://arxiv.org/abs/2004.04906),
-retriever and generator can be trained concurrently from the one loss signal.
-
-
-
-**Tutorial**
-
-Checkout our tutorial notebooks for a guide on how to build your own generative QA system with RAG ([here](/docs/latest/tutorial7md))
-or with LFQA ([here](/docs/latest/tutorial12md)).
-
-
-
-Pros
-* More appropriately phrased answers
-* Able to synthesize information from different texts
-* Can draw on latent knowledge stored in language model
-
-Cons
-* Not easy to track what piece of information the generator is basing its response off of
-
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/get_started.md b/src/pages/docs/versions/master/latest/site/en/usage/usage/get_started.md
deleted file mode 100644
index 890578095..000000000
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/get_started.md
+++ /dev/null
@@ -1,166 +0,0 @@
----
-title: "Get Started"
-metaTitle: "Get Started"
-metaDescription: ""
-slug: "/docs/get_started"
-date: "2020-09-03"
-id: "get_startedmd"
----
-
-# Get Started
-
-## Installation
-
-
-
-
-
-Note: On Windows add the arg `-f https://download.pytorch.org/whl/torch_stable.html` to install PyTorch correctly
-
-## The Building Blocks of Haystack
-
-Here’s a sample of some Haystack code showing a question answering system using a retriever and a reader.
-For a working code example, check out our [starter tutorial](/docs/latest/tutorial1md).
-
-```python
-# DocumentStore: holds all your data
-document_store = ElasticsearchDocumentStore()
-
-# Clean & load your documents into the DocumentStore
-dicts = convert_files_to_dicts(doc_dir, clean_func=clean_wiki_text)
-document_store.write_documents(dicts)
-
-# Retriever: A Fast and simple algo to indentify the most promising candidate documents
-retriever = ElasticsearchRetriever(document_store)
-
-# Reader: Powerful but slower neural network trained for QA
-model_name = "deepset/roberta-base-squad2"
-reader = FARMReader(model_name)
-
-# Pipeline: Combines all the components
-pipe = ExtractiveQAPipeline(reader, retriever)
-
-# Voilà! Ask a question!
-question = "Who is the father of Sansa Stark?"
-prediction = pipe.run(query=question)
-print_answers(prediction)
-```
-
-## Loading Documents into the DocumentStore
-
-In Haystack, DocumentStores expect Documents in a dictionary format. They are loaded as follows:
-
-```python
-document_store = ElasticsearchDocumentStore()
-dicts = [
- {
- 'text': DOCUMENT_TEXT_HERE,
- 'meta': {'name': DOCUMENT_NAME, ...}
- }, ...
-]
-document_store.write_documents(dicts)
-```
-
-When we talk about Documents in Haystack, we are referring specifically to the individual blocks of text that are being held in the DocumentStore.
-You might want to use all the text in one file as a Document, or split it into multiple Documents.
-This splitting can have a big impact on speed and performance.
-
-
-
-
-
-
-
-The most straightforward way to install Haystack is through pip.
- -```python -$ pip install farm-haystack -``` - -
-- -```python -$ pip install farm-haystack -``` - -
-
-
-
-
-
-If you’d like to run a specific, unreleased version of Haystack, or make edits to the way Haystack runs,
-you’ll want to install it using `git` and `pip --editable`.
-This clones a copy of the repo to a local directory and runs Haystack from there.
- -```python -$ git clone https://github.com/deepset-ai/haystack.git -$ cd haystack -$ pip install --editable . -``` - -By default, this will give you the latest version of the master branch. Use regular git commands to switch between different branches and commits. -
-- -```python -$ git clone https://github.com/deepset-ai/haystack.git -$ cd haystack -$ pip install --editable . -``` - -By default, this will give you the latest version of the master branch. Use regular git commands to switch between different branches and commits. -
-
-**Tip:** If Haystack is running very slowly, you might want to try splitting your text into smaller Documents.
-If you want an improvement to performance, you might want to try concatenating text to make larger Documents.
-See [Optimization](/docs/latest/optimizationmd) for more details.
-
-
-
-
-## Running Search Queries
-
-There are many different flavours of search that can be created using Haystack.
-But to give just one example of what can be achieved, let's look more closely at
-an Open Domain Question Answering (ODQA) Pipeline.
-
-
-**Querying** in an ODQA system involves searching for an answer to a given question within the full document store.
-This process will:
-
-
-* make the Retriever filter for a small set of relevant candidate documents
-
-
-* get the Reader to process this set of candidate documents
-
-
-* return potential answers to the given question
-
-Usually, there are tight time constraints on querying and so it needs to be a lightweight operation.
-When documents are loaded, Haystack will precompute any of the results that might be useful at query time.
-
-In Haystack, querying is performed with a `Pipeline` object which connects the reader to the retriever.
-
-```python
-# Pipeline: Combines all the components
-pipe = ExtractiveQAPipeline(reader, retriever)
-
-# Voilà! Ask a question!
-question = "Who is the father of Sansa Stark?"
-prediction = pipe.run(query=question)
-print_answers(prediction)
-```
-
-When the query is complete, you can expect to see results that look something like this:
-
-```python
-[
- { 'answer': 'Eddard',
- 'context': 's Nymeria after a legendary warrior queen. She travels '
- "with her father, Eddard, to King's Landing when he is made "
- 'Hand of the King. Before she leaves,'
- }, ...
-]
-```
-
-##Custom Search Pipelines
-
-Haystack providers many different building blocks for you to mix and match.
-They include:
-- Readers
-- Retrievers (sparse and dense)
-- DocumentStores
-- Summarizers
-- Generators
-- Translators
-
-These can all be combined in the configuration that you want.
-Have a look at our [Pipelines page](/docs/latest/pipelinesmd) to see what's possible!
\ No newline at end of file
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/intro.md b/src/pages/docs/versions/master/latest/site/en/usage/usage/intro.md
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index 12b405570..000000000
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/intro.md
+++ /dev/null
@@ -1,67 +0,0 @@
----
-title: "What is Haystack"
-metaTitle: "What is Haystack"
-metaDescription: ""
-slug: "/docs/intro"
-date: "2020-09-03"
-id: "intromd"
----
-
-# What is Haystack?
-
-Haystack is an **open-source framework** for building **search systems** that work intelligently over large document collections.
-Recent advances in NLP have enabled the application of question answering, retrieval and summarization to real world settings
-and Haystack is designed to be the bridge between research and industry.
-
-
-* **Latest NLP models**: Utilize all transformer based models (BERT, RoBERTa, MiniLM, DPR ...) and smoothly switch when new ones get published
-
-* **Flexible databases**: Load data into and query from a range of databases such as Elasticsearch, Milvus, FAISS, SQL and more
-
-* **Scalability**: Production-ready deployments that scale to millions of documents
-
-* **End-to-End**: All tooling you need to implement, evaluate, improve and run a search system
-
-* **Domain adaptation**: Fine-tune models to your own domain & improve them continuously via user feedback
-
-## Retriever-Reader
-
-The most common system built with Haystack is the Retriever-Reader Pipeline which is designed to optimize for both
-speed and performance on the task of Open Domain Question Answering.
-In practice, this is a great back bone for creating a search system that can handle detailed full sentence queries.
-
-
-
-
-**Readers**, also known as Open-Domain QA systems in Machine Learning speak,
-are powerful models that do close analysis of documents and perform the core task of question answering.
-The Readers in Haystack are trained from the latest transformer based language models and can be significantly sped up using GPU acceleration.
-However, it is not currently feasible to use the Reader directly on large collection of documents.
-
-
-
-The **Retriever** assists the Reader by acting as a lightweight filter that reduces the number of documents that the Reader has to process.
-It does this by:
-
-
-* Scanning through all documents in the database
-
-
-* Quickly identifying the relevant and dismissing the irrelevant
-
-
-* Passing on only a small candidate set of documents to the Reader
-
-Current methods fall into one of the two categories:
-
-
-* sparse
- * keyword based
- * fast indexing and querying
- * e.g. BM25
-
-
-* dense
- * neural network based
- * computationally heavy indexing but fast querying
- * e.g. Dense Passage Retrieval
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/knowledge_graph.md b/src/pages/docs/versions/master/latest/site/en/usage/usage/knowledge_graph.md
deleted file mode 100644
index 65e8bb6cd..000000000
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/knowledge_graph.md
+++ /dev/null
@@ -1,108 +0,0 @@
----
-title: "Knowledge Graph"
-metaTitle: "Knowledge Graph"
-metaDescription: ""
-slug: "/docs/knowledgegraph"
-date: "2021-04-19"
-id: "knowledgegraphmd"
----
-
-# Question Answering on a Knowledge Graph
-
-Haystack allows loading and querying knowledge graphs. In particular, Haystack can:
-
-* Load an existing knowledge graph given as a .ttl file
-* Execute SPARQL queries on a knowledge graph
-* Execute text queries on the knowledge graph by translating them to SPARQL queries with the help of a pre-trained seq2seq model
-
-Haystack's knowledge graph functionalities are still in a very early stage. Thus, don't expect our [exemplary tutorial](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial10_Knowledge_Graph.py) to work on your custom dataset out-of-the-box.
-Two classes implement the functionalities: GraphDBKnowledgeGraph and Text2SparqlRetriever.
-
-## GraphDBKnowledgeGraph
-
-GraphDBKnowledgeGraph is a triple store similar to Haystack's document stores. Currently, it is the only implementation of the BaseKnowledgeGraph class.
-GraphDBKnowledgeGraph runs on GraphDB. The licensing of GraphDB is rather complicated and it's more than unfortunate that GraphDB cannot be used right away in colab notebooks.
-On your local machine, you can start a GraphDB instance by running:
-
-```docker run -d -p 7200:7200 --name graphdb-instance-tutorial docker-registry.ontotext.com/graphdb-free:9.4.1-adoptopenjdk11```
-
-By default, GraphDBKnowledgeGraph connects to a GraphDB instance running on localhost at port 7200.
-Similar to Haystack's ElasticsearchDocumentStore, the only additional setting needed is an index name.
-(Note that GraphDB internally calls these indices repositories.)
-
-```kg = GraphDBKnowledgeGraph(index="tutorial_10_index")```
-
-Indices can be deleted and created with ```GraphDBKnowledgeGraph.delete_index()``` and ```GraphDBKnowledgeGraph.create_index(config_path)```.
-```config_path``` needs to point to a .ttl file that contains configuration settings (see [GraphDB documentation](https://graphdb.ontotext.com/documentation/free/configuring-a-repository.html#configure-a-repository-programmatically) for details or use the file from our [tutorial](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial10_Knowledge_Graph.py)). It starts with something like:
-
-```
-#
-# Sesame configuration template for a GraphDB Free repository
-#
-@prefix rdfs:
-
-**Note**
-
-This feature will be implemented by [this PR.](https://github.com/deepset-ai/haystack/pull/1160)
-
-
-
-The PreProcessor's sentence tokenization is language specific.
-If you are using the PreProcessor on a language other than English,
-make sure to set the `language` argument when initializing it.
-
-##Retrievers
-
-The sparse retriever methods themselves(BM25, TF-IDF) are language agnostic.
-Their only requirement is that the text be split into words.
-The ElasticsearchDocumentStore relies on an analyzer to impose word boundaries,
-but also to handle punctuation, casing and stop words.
-
-The default analyzer is an English analyzer.
-While it can still work decently for a large range of languages,
-you will want to set it to your language's analyzer for optimal performance.
-In some cases, such as with Thai, the default analyzer is completely incompatible.
-See [this page](https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-lang-analyzer.html)
-for the full list of language specific analyzers.
-
-```python
-from haystack.document_store import ElasticsearchDocumentStore
-
-document_store = ElasticsearchDocumentStore(analyzer="thai")
-```
-
-The models used in dense retrievers are language specific.
-Be sure to check language of the model used in your EmbeddingRetriever.
-The default model that is loaded in the DensePassageRetriever is for English.
-
-We have created a [German DensePassageRetriever model](https://deepset.ai/germanquad) and know other teams who work on further languages.
-If you have a language model and a question answering dataset in your own language, you can also train a DPR model using Haystack!
-Below is a simplified example.
-See [our tutorial](/docs/latest/tutorial9md) and also the [API reference](/docs/latest/apiretrievermd#train) for `DensePassageRetriever.train()` for more details.
-
-```python
-from haystack.retriever import DensePassageRetriever
-
-dense_passage_retriever = DensePassageRetriever(document_store)
-dense_passage_retriever.train(self,
- data_dir: str,
- train_filename: str,
- dev_filename: str = None,
- test_filename: str = None,
- batch_size: int = 16,
- embed_title: bool = True,
- num_hard_negatives: int = 1,
- n_epochs: int = 3)
-```
-
-##Readers
-
-While models are comparatively more performant on English,
-thanks to a wealth of available English training data,
-there are a couple QA models that are directly usable in Haystack.
-
-
-
-
-
-We are the creators of the **German** model and you can find out more about it [here](https://deepset.ai/germanquad)
-
-The **French**, **Italian**, **Spanish**, **Portuguese** and **Chinese** models are monolingual language models trained on versions of the SQuAD dataset in their respective languages
-and their authors report decent results in their model cards
-(e.g. [here](https://huggingface.co/illuin/camembert-base-fquad) and [here](https://huggingface.co/mrm8488/bert-italian-finedtuned-squadv1-it-alfa)).
-There also exist Korean QA models on the model hub but their performance is not reported.
-
-The **zero-shot model** that is shown above is a **multilingual XLM-RoBERTa Large** that is trained on English SQuAD.
-It is clear, from our [evaluations](https://huggingface.co/deepset/xlm-roberta-large-squad2#model_card),
-that the model has been able to transfer some of its English QA capabilities to other languages,
-but still its performance lags behind that of the monolingual models.
-Nonetheless, if there is not yet a monolingual model for your language and it is one of the 100 supported by XLM-RoBERTa,
-this zero-shot model may serve as a decent first baseline.
-
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/optimization.md b/src/pages/docs/versions/master/latest/site/en/usage/usage/optimization.md
deleted file mode 100644
index e6f4b1479..000000000
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/optimization.md
+++ /dev/null
@@ -1,107 +0,0 @@
----
-title: "Optimization"
-metaTitle: "Optimization"
-metaDescription: ""
-slug: "/docs/optimization"
-date: "2020-11-05"
-id: "optimizationmd"
----
-
-# Optimization
-
-## Speeding up Reader
-
-In most pipelines, the Reader will be the most computationally expensive component.
-If this is a step that you would like to speed up, you can opt for a smaller Reader model
-that can process more passages in the same amount of time.
-
-On our [benchmarks page](https://haystack.deepset.ai/bm/benchmarks), you will find a comparison of
-many of the common model architectures. While our default recommendation is RoBERTa,
-MiniLM offers much faster processing for only a minimal drop in accuracy.
-You can find the models that we've trained on [the HuggingFace Model Hub](https://huggingface.co/deepset)
-
-## GPU acceleration
-
-The transformer based models used in Haystack are designed to be run on a GPU enabled machine.
-The design of these models means that they greatly benefit from the parallel processing capabilities of graphics cards.
-If Haystack has successfully detected a graphics card, you should see these lines in your console output.
-
-```
-INFO - farm.utils - Using device: CUDA
-INFO - farm.utils - Number of GPUs: 1
-```
-
-You can track the work load on your CUDA enabled Nvidia GPU by tracking the output of `nvidia-smi -l` on the command line
-while your Haystack program is running.
-
-## Document Length
-
-Document length has a very direct impact on the speed of the Reader
-which is why we recommend using the `PreProcessor` class to clean and split your documents.
-**If you halve the length of your documents, you will halve the workload placed onto your Reader.**
-
-For **sparse retrievers**, very long documents pose a challenge since the signal of the relevant section of text
-can get washed out by the rest of the document.
-To get a good balance between Reader speed and Retriever performance, we splitting documents to a maximum of 500 words.
-If there is no Reader in the pipeline following the Retriever, we recommend that **documents be no longer than 10,000 words**.
-
-**Dense retrievers** are limited in the length of text that they can read in one pass.
-As such, it is important that documents are not longer than the dense retriever's maximum input length.
-By default, Haystack's DensePassageRetriever model has a maximum length of 256 tokens.
-As such, we recommend that documents contain significantly less words.
-We have found decent performance with **documents around 100 words long**.
-
-## Respecting Sentence Boundaries
-
-When splitting documents, it is generally not a good idea to let document boundaries fall in the middle of sentences.
-Doing so means that each document will contain incomplete sentence fragments
-which maybe be hard for both retriever and reader to interpret.
-It is therefore recommended to set `split_respect_sentence_boundary=True` when initializing your `PreProcessor`.
-
-## Choosing the Right top-k Values
-
-The `top-k` parameter in both the `Retriever` and `Reader` determine how many results they return.
-More specifically, `Retriever` `top-k` dictates how many retrieved documents are passed on to the next stage,
-while `Reader` `top-k` determines how many answer candidates to show.
-
-In our experiments, we have found that **`Retriever` `top_k=10`
-gives decent overall performance** and so we have set this as the default in Haystack.
-
-The choice of `Retriever` `top-k` is a trade-off between speed and accuracy,
-especially when there is a `Reader` in the pipeline.
-Setting it higher means passing more documents to the `Reader`,
-thus reducing the chance that the answer-containing passage is missed.
-However, passing more documents to the `Reader` will create a larger workload for the component.
-
-These parameters can easily be tweaked as follows if using a `Finder`:
-``` python
-answers = pipeline.run(query="What did Einstein work on?", top_k_retriever=10, top_k_reader=5)
-```
-or like this if directly calling the `Retriever`:
-``` python
-retrieved_docs = retriever.retrieve(top_k=10)
-```
-
-## Metadata Filtering
-
-Metadata can be attached to the documents which you index into your DocumentStore (see the input data format [here](/docs/latest/retrievermd)).
-At query time, you can apply filters based on this metadata to limit the scope of your search and ensure your answers
-come from a specific slice of your data.
-
-For example, if you have a set of annual reports from various companies,
-you may want to perform a search on just a specific year, or on a small selection of companies.
-This can reduce the work load of the retriever and also ensure that you get more relevant results.
-
-Filters are applied via the `filters` argument of the `Retriever` class. In practice, this argument will probably
-be passed into the `Pipeline.run()` call, which will then route it on to the `Retriever` class
-(see our the Arguments on the [Pipelines page](/docs/latest/pipelinesmd) for an explanation).
-
-```python
-pipeline.run(
- query="Why did the revenue increase?",
- filters={
- "years": ["2019"],
- "companies": ["BMW", "Mercedes"]
- }
-)
-```
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/pipelines.md b/src/pages/docs/versions/master/latest/site/en/usage/usage/pipelines.md
deleted file mode 100644
index 7dfb6bc91..000000000
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/pipelines.md
+++ /dev/null
@@ -1,187 +0,0 @@
----
-title: "Pipelines"
-metaTitle: "Pipelines"
-metaDescription: ""
-slug: "/docs/pipelines"
-date: "2020-11-05"
-id: "pipelinesmd"
----
-
-# Pipelines
-
-### Flexibility powered by DAGs
-In order to build modern search pipelines, you need two things: powerful building blocks and an easy way to stick them together.
-The `Pipeline` class is exactly built for this purpose and enables many search scenarios beyond QA.
-The core idea is that you can build a Directed Acyclic Graph (DAG) where each node is one building block (Reader, Retriever, Generator ...).
-Here's a simple example for a standard Open-Domain QA Pipeline:
-
-```python
-from haystack import Pipeline
-
-p = Pipeline()
-p.add_node(component=retriever, name="ESRetriever1", inputs=["Query"])
-p.add_node(component=reader, name="QAReader", inputs=["ESRetriever1"])
-res = p.run(query="What did Einstein work on?", top_k_retriever=1)
-```
-
-You can **draw the DAG** to better inspect what you are building:
-```python
-p.draw(path="custom_pipe.png")
-```
-
-
-### Arguments
-
-Whatever keyword arguments are passed into the `Pipeline.run()` method will be passed on to each node in the pipeline.
-For example, in the code snippet below, all nodes will receive `query`, `top_k_retriever` and `top_k_reader` as argument,
-even if they don't use those arguments. It is therefore very important when defining custom nodes that their
-keyword argument names do not clash with the other nodes in your pipeline.
-
-```python
-res = pipeline.run(
- query="What did Einstein work on?",
- top_k_retriever=1,
- top_k_reader=5
-)
-```
-
-### YAML File Definitions
-
-For your convenience, there is also the option of defining and loading pipelines in YAML files.
-Having your pipeline available in a YAML is particularly useful when
-you move between experimentation and production environments.
-Just export the YAML from your notebook / IDE and import it into your production environment.
-It also helps with version control of pipelines, allows you to share your pipeline easily with colleagues,
-and simplifies the configuration of pipeline parameters in production.
-
-For example, you can define and save a simple Retriever Reader pipeline by saving the following to a file:
-
-```yaml
-version: '0.7'
-
-components: # define all the building-blocks for Pipeline
-- name: MyReader # custom-name for the component; helpful for visualization & debugging
- type: FARMReader # Haystack Class name for the component
- params:
- no_ans_boost: -10
- model_name_or_path: deepset/roberta-base-squad2
-- name: MyESRetriever
- type: ElasticsearchRetriever
- params:
- document_store: MyDocumentStore # params can reference other components defined in the YAML
- custom_query: null
-- name: MyDocumentStore
- type: ElasticsearchDocumentStore
- params:
- index: haystack_test
-
-pipelines: # multiple Pipelines can be defined using the components from above
-- name: my_query_pipeline # a simple extractive-qa Pipeline
- nodes:
- - name: MyESRetriever
- inputs: [Query]
- - name: MyReader
- inputs: [MyESRetriever]
-```
-
-To load, simply call:
-
-```python
-pipeline.load_from_yaml(Path("sample.yaml"))
-```
-
-For another example YAML config, check out [this file](https://github.com/deepset-ai/haystack/blob/master/rest_api/pipeline/pipelines.yaml).
-
-### Multiple retrievers
-You can now also use multiple Retrievers and join their results:
-```python
-from haystack import Pipeline
-
-p = Pipeline()
-p.add_node(component=es_retriever, name="ESRetriever", inputs=["Query"])
-p.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["Query"])
-p.add_node(component=JoinDocuments(join_mode="concatenate"), name="JoinResults", inputs=["ESRetriever", "DPRRetriever"])
-p.add_node(component=reader, name="QAReader", inputs=["JoinResults"])
-res = p.run(query="What did Einstein work on?", top_k_retriever=1)
-```
-
-
-### Custom nodes
-You can easily build your own custom nodes. Just respect the following requirements:
-
-1. Add a method `run(self, **kwargs)` to your class. `**kwargs` will contain the output from the previous node in your graph.
-2. Do whatever you want within `run()` (e.g. reformatting the query)
-3. Return a tuple that contains your output data (for the next node) and the name of the outgoing edge `output_dict, "output_1`
-4. Add a class attribute `outgoing_edges = 1` that defines the number of output options from your node. You only need a higher number here if you have a decision node (see below).
-
-### Decision nodes
-Or you can add decision nodes where only one "branch" is executed afterwards. This allows, for example, to classify an incoming query and depending on the result routing it to different modules:
-
-```python
- class QueryClassifier():
- outgoing_edges = 2
-
- def run(self, **kwargs):
- if "?" in kwargs["query"]:
- return (kwargs, "output_1")
-
- else:
- return (kwargs, "output_2")
-
- pipe = Pipeline()
- pipe.add_node(component=QueryClassifier(), name="QueryClassifier", inputs=["Query"])
- pipe.add_node(component=es_retriever, name="ESRetriever", inputs=["QueryClassifier.output_1"])
- pipe.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_2"])
- pipe.add_node(component=JoinDocuments(join_mode="concatenate"), name="JoinResults",
- inputs=["ESRetriever", "DPRRetriever"])
- pipe.add_node(component=reader, name="QAReader", inputs=["JoinResults"])
- res = p.run(query="What did Einstein work on?", top_k_retriever=1)
-```
-
-### Evaluation nodes
-
-There are nodes in Haystack that are used to evaluate the performance of readers, retrievers and combine systems.
-To get hands on with this kind of node, have a look at the [evaluation tutorial](/docs/latest/tutorial5md).
-
-### Default Pipelines (replacing the "Finder")
-Last but not least, we added some "Default Pipelines" that allow you to run standard patterns with very few lines of code.
-This is replacing the `Finder` class which was deprecated with Haystack 0.6.0 .
-
-```
-from haystack.pipeline import DocumentSearchPipeline, ExtractiveQAPipeline, Pipeline, JoinDocuments
-
-# Extractive QA
-qa_pipe = ExtractiveQAPipeline(reader=reader, retriever=retriever)
-res = qa_pipe.run(query="When was Kant born?", top_k_retriever=3, top_k_reader=5)
-
-# Document Search
-doc_pipe = DocumentSearchPipeline(retriever=retriever)
-res = doc_pipe.run(query="Physics Einstein", top_k_retriever=1)
-
-# Generative QA
-doc_pipe = GenerativeQAPipeline(generator=rag_generator, retriever=retriever)
-res = doc_pipe.run(query="Physics Einstein", top_k_retriever=1)
-
-# FAQ based QA
-doc_pipe = FAQPipeline(retriever=retriever)
-res = doc_pipe.run(query="How can I change my address?", top_k_retriever=3)
-
-```
-So to migrate your QA system from the deprecated `Finder` to `ExtractiveQAPipeline` you'd need to:
-```
-# 1. Change import
-from haystack.pipeline import ExtractiveQAPipeline
-
-# 2. Replace the Finder
-qa_pipe = ExtractiveQAPipeline(reader=reader, retriever=retriever)
-
-# 3. Replace get_answers() with run()
-res = qa_pipe.run(query="When was Kant born?", top_k_retriever=3, top_k_reader=5)
-
-# 4. Access your results from ["documents"] rather than ["answers"]
-print(res["documents"])
-```
-See also the [Pipelines API documentation](/docs/latest/apipipelinesmd) for more details.
-
-We plan many more features around the new pipelines incl. parallelized execution, distributed execution, dry runs - so stay tuned ...
-
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/preprocessing.md b/src/pages/docs/versions/master/latest/site/en/usage/usage/preprocessing.md
deleted file mode 100644
index 552ae192a..000000000
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/preprocessing.md
+++ /dev/null
@@ -1,144 +0,0 @@
----
-title: "Preprocessing"
-metaTitle: "Preprocessing"
-metaDescription: ""
-slug: "/docs/preprocessing"
-date: "2020-09-03"
-id: "preprocessingmd"
----
-
-# Preprocessing
-
-Haystack includes a suite of tools to:
-
-* extract text from different file types,
-* normalize white space
-* split text into smaller pieces to optimize retrieval
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("deepset/gelectra-large-germanquad")
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("illuin/camembert-base-fquad")
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa")
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("uer/roberta-base-chinese-extractive-qa")
-# or
-reader = FARMReader("wptoux/albert-chinese-large-qa")
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es")
-# or
-reader = FARMReader("mrm8488/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es")
-```
-
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("pierreguillou/bert-base-cased-squad-v1.1-portuguese")
-# or
-reader = FARMReader("pucpr/bioBERTpt-squad-v1.1-portuguese")
-
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("deepset/xlm-roberta-large-squad2")
-```
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("deepset/gelectra-large-germanquad")
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("illuin/camembert-base-fquad")
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa")
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("uer/roberta-base-chinese-extractive-qa")
-# or
-reader = TransformersReader("wptoux/albert-chinese-large-qa")
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es")
-# or
-reader = TransformersReader("mrm8488/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es")
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("pierreguillou/bert-base-cased-squad-v1.1-portuguese")
-# or
-reader = TransformersReader("pucpr/bioBERTpt-squad-v1.1-portuguese")
-
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("deepset/xlm-roberta-large-squad2")
-```
-
-
-
-
-Check out our [preprocessing tutorial](/docs/latest/tutorial8md) if you'd like to start working with code examples already!
-
-
-
-These data preprocessing steps can have a big impact on the systems performance
-and effective handling of data is key to getting the most out of Haystack.
-
-The Document Store expects its inputs to come in the following format.
-The sections below will show you all the tools you'll need to ready your data for storing.
-
-```python
-docs = [
- {
- 'text': DOCUMENT_TEXT_HERE,
- 'meta': {'name': DOCUMENT_NAME, ...}
- }, ...
-]
-```
-
-## File Conversion
-
-There are a range of different file converters in Haystack that
-can extract text from files and cast them into the unified dictionary format shown above.
-Haystack features support for txt, pdf and docx files and there is even a converter that leverages Apache Tika.
-Please refer to [the API docs](/docs/latest/file_convertersmd) to see which converter best suits you.
-
-
-
-
-
-## Web Crawler
-
-In Haystack, you will find a web crawler that will help you scrape text from websites and save it to file.
-See the [API documentation](https://haystack.deepset.ai/docs/latest/apicrawlermd) for more details.
-
-```python
-from haystack.connector import Crawler
-
-crawler = Crawler()
-docs = crawler.crawl(urls=["https://haystack.deepset.ai/docs/latest/get_startedmd"],
- output_dir="crawled_files",
- filter_urls= ["haystack\.deepset\.ai\/docs\/"])
-```
-
-## PreProcessor
-
-While each of the above conversion methods produce documents that are already in the format expected by the Document Store,
-it is recommended that they are further processed in order to ensure optimal Retriever and Reader performance.
-The `PreProcessor` takes one of the documents created by the converter as input,
-performs various cleaning steps and splits them into multiple smaller documents.
-
-For suggestions on how best to split your documents, see [Optimization](/docs/latest/optimizationmd)
-
-```python
-from haystack.preprocessor import PreProcessor
-
-doc = converter.convert(file_path=file, meta=None)
-processor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=True,
- split_by="word",
- split_length=200,
- split_respect_sentence_boundary=True,
- split_overlap=0
-)
-docs = processor.process(doc)
-```
-
-* `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
-* `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
-* `clean_header_footer` will remove any long header or footer texts that are repeated on each page
-* `split_by` determines what unit the document is split by: `'word'`, `'sentence'` or `'passage'`
-* `split_length` sets a maximum number of `'word'`, `'sentence'` or `'passage'` units per output document
-* `split_respect_sentence_boundary` ensures that document boundaries do not fall in the middle of sentences
-* `split_overlap` sets the amount of overlap between two adjacent documents after a split. Setting this to a positive number essentially enables the sliding window approach.
-
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/ranker.md b/src/pages/docs/versions/master/latest/site/en/usage/usage/ranker.md
deleted file mode 100644
index 042f1ae9b..000000000
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/ranker.md
+++ /dev/null
@@ -1,55 +0,0 @@
----
-title: "Ranker"
-metaTitle: "Ranker"
-metaDescription: ""
-slug: "/docs/ranker"
-date: "2020-05-26"
-id: "rankermd"
----
-
-# Ranker
-
-There are pure "semantic document search" use cases that do not need question answering functionality but only document ranking.
-While the [Retriever](/docs/latest/retrievermd) is a perfect fit for document retrieval, we can further improve its results with the Ranker.
-For example, BM25 (sparse retriever) does not take into account semantics of the documents and the query but only their keywords.
-The Ranker can re-rank the results of the retriever step by taking semantics into account.
-Similar to the Reader, it is based on the latest language models.
-Instead of returning answers, it returns documents in re-ranked order.
-
-Without a Ranker and its re-ranking step, the querying process is faster but the query results might be of lower quality.
-If you want to do "semantic document search" instead of a question answering, try first with a Retriever only.
-In case the semantic similarity of the query and the resulting documents is low, add a Ranker.
-
-Note that a Ranker needs to be initialised with a model trained on a text pair classification task.
-You can train the model also with the train() method of the Ranker.
-Alternatively, [this example](https://github.com/deepset-ai/FARM/blob/master/examples/text_pair_classification.py) shows how to train a text pair classification model in FARM.
-
-
-## FARMRanker
-
-### Description
-
-The FARMRanker consists of a Transformer-based model for document re-ranking using the TextPairClassifier of [FARM](https://github.com/deepset-ai/FARM).
-Given a text pair of query and passage, the TextPairClassifier either predicts label "1" if the pair is similar or label "0" if they are dissimilar (accompanied with a probability).
-While the underlying model can vary (BERT, Roberta, DistilBERT, ...), the interface remains the same.
-With a FARMRanker, you can:
-* Directly get predictions (re-ranked version of the supplied list of Document) via predict() if supplying a pre-trained model
-* Take a plain language model (e.g. `bert-base-cased`) and train it for TextPairClassification via train()
-
-### Initialisation
-
-```python
-from haystack.document_store import ElasticsearchDocumentStore
-from haystack.retriever import ElasticsearchRetriever
-from haystack.ranker import FARMRanker
-from haystack import Pipeline
-
-document_store = ElasticsearchDocumentStore()
-...
-retriever = ElasticsearchRetriever(document_store)
-ranker = FARMRanker(model_name_or_path="saved_models/roberta-base-asnq-binary")
-...
-p = Pipeline()
-p.add_node(component=retriever, name="ESRetriever", inputs=["Query"])
-p.add_node(component=ranker, name="Ranker", inputs=["ESRetriever"])
-```
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/reader.md b/src/pages/docs/versions/master/latest/site/en/usage/usage/reader.md
deleted file mode 100644
index 91b3c8c4a..000000000
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/reader.md
+++ /dev/null
@@ -1,386 +0,0 @@
----
-title: "Reader"
-metaTitle: "Reader"
-metaDescription: ""
-slug: "/docs/reader"
-date: "2020-09-03"
-id: "readermd"
----
-
-# Reader
-
-The Reader, also known as Open-Domain QA systems in Machine Learning speak,
-is the core component that enables Haystack to find the answers that you need.
-Haystack’s Readers are:
-
-
-* built on the latest transformer based language models
-
-
-* strong in their grasp of semantics
-
-
-* sensitive to syntactic structure
-
-
-* state-of-the-art in QA tasks like SQuAD and Natural Questions
-
-
-
-
-
-
-
-
-```python
-from haystack.file_converter import PDFToTextConverter
-
-converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=["de","en"])
-doc = converter.convert(file_path=file, meta=None)
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.file_converter import DocxToTextConverter
-
-converter = DocxToTextConverter(remove_numeric_tables=True, valid_languages=["de","en"])
-doc = converter.convert(file_path=file, meta=None)
-```
-
-
-
-
-
-
-
-
-
-
-Haystack also has a `convert_files_to_dicts()` utility function that will convert
-all txt or pdf files in a given folder into this dictionary format.
-
-```python
-from haystack.preprocessor.utils import convert_files_to_dicts
-
-docs = convert_files_to_dicts(dir_path=doc_dir)
-```
-
-
-
-
-
-
-While these models can work on CPU, it is recommended that they are run using GPUs to keep query times low.
-
-## Choosing the Right Model
-
-In Haystack, you can start using pretrained QA models simply by providing its HuggingFace Model Hub name to the Reader.
-The loading of model weights is handled by Haystack,
-and you have the option of using the QA pipeline from deepset FARM or HuggingFace Transformers (see FARM vs Transformers for details).
-
-Currently, there are a lot of different models out there and it can be rather overwhelming trying to pick the one that fits your use case.
-To get you started, we have a few recommendations for you to try out.
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import FARMReader
-
-model = "deepset/roberta-base-squad2"
-reader = FARMReader(model, use_gpu=True)
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import TransformersReader
-
-model = "deepset/roberta-base-squad2"
-reader = TransformersReader(model, use_gpu=1)
-```
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-**An optimised variant of BERT and a great starting point.**
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("deepset/roberta-base-squad2")
-```
-
-* **Pro**: Strong all round model
-
-* **Con**: There are other models that are either faster or more accurate
-
-
-
-
-
-
-
-
-
-**A cleverly distilled model that sacrifices a little accuracy for speed.**
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("deepset/minilm-uncased-squad2")
-```
-
-* **Pro**: Inference speed up to 50% faster than BERT base
-
-* **Con**: Still doesn’t match the best base sized models in accuracy
-
-
-
-
-
-
-
-
-
-**Large, powerful, SotA model.**
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("ahotrod/albert_xxlargev1_squad2_512")
-```
-
-* **Pro**: Better accuracy than any other open source model in QA
-
-* **Con**: The computational power needed make it impractical for most use cases
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-**An optimised variant of BERT and a great starting point.**
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("deepset/roberta-base-squad2")
-```
-
-* **Pro**: Strong all round model
-
-* **Con**: There are other models that are either faster or more accurate
-
-
-
-
-
-
-
-
-
-**A cleverly distilled model that sacrifices a little accuracy for speed.**
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("deepset/minilm-uncased-squad2")
-```
-
-* **Pro**: Inference speed up to 50% faster than BERT base
-
-* **Con**: Still doesn’t match the best base sized models in accuracy
-
-
-
-
-
-
-
-
-
-**Large, powerful, SotA model.**
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("ahotrod/albert_xxlargev1_squad2_512")
-```
-
-* **Pro**: Better accuracy than any other open source model in QA
-
-* **Con**: The computational power needed make it impractical for most use cases
-
-
-
-
-**Recommendations:**
-
-**All-rounder**: In the class of base sized models trained on SQuAD, **RoBERTa** has shown better performance than BERT
-and can be capably handled by any machine equipped with a single NVidia V100 GPU.
-We recommend this as the starting point for anyone wanting to create a performant and computationally reasonable instance of Haystack.
-
-**Built for Speed**: If speed and GPU memory are more of a priority to you than accuracy,
-you should try the MiniLM model.
-It is a smaller model that is trained to mimic larger models through the distillation process,
-and it outperforms the BERT base on SQuAD even though it is about 40% smaller.
-
-
-**State of the Art Accuracy**: For most, **ALBERT XXL** will be too large to feasibly work with.
-But if performance is your sole concern, and you have the computational resources,
-you might like to try ALBERT XXL which has set SoTA performance on SQuAD 2.0.
-
-
-
-
-
-
-## Confidence Scores
-
-When printing the full results of a Reader,
-you will see that each prediction is accompanied
-by a value in the range of 0 to 1 reflecting the model's confidence in that prediction
-
-In the output of `print_answers()`, you will find the model confidence in dictionary key called `confidence`.
-
-```python
-from haystack.utils import print_answers
-
-print_answers(prediction, details="all")
-```
-
-```python
-{
- 'answers': [
- { 'answer': 'Eddard',
- 'context': 's Nymeria after a legendary warrior queen. '
- 'She travels with her father, Eddard, to '
- "King's Landing when he is made Hand of the "
- 'King. Before she leaves,',
- 'confidence': 0.9899835586547852,
- ...
- },
- ]
-}
-```
-
-In order to align this probability score with the model's accuracy, finetuning needs to be performed
-on a specific dataset.
-To this end, the reader has a method `calibrate_confidence_scores(document_store, device, label_index, doc_index, label_origin)`.
-The parameters of this method are the same as for the `eval()` method because the calibration of confidence scores is performed on a dataset that comes with gold labels.
-The calibration calls the `eval()` method internally and therefore needs a DocumentStore containing labeled questions and evaluation documents.
-
-Have a look at this [FARM tutorial](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering_confidence.py)
-to see how to compare calibrated confidence scores with uncalibrated confidence scores within FARM.
-Note that a finetuned confidence score is specific to the domain that it is finetuned on.
-There is no guarantee that this performance can transfer to a new domain.
-
-Having a confidence score is particularly useful in cases where you need Haystack to work with a certain accuracy threshold.
-Many of our users have built systems where predictions below a certain confidence value are routed
-on to a fallback system.
-
-
-
-## Deeper Dive: FARM vs Transformers
-
-Apart from the **model weights**, Haystack Readers contain all the components found in end-to-end open domain QA systems.
-This includes **tokenization**, **embedding computation**, **span prediction** and **candidate aggregation**.
-While the handling of model weights is the same between the FARM and Transformers libraries, their QA pipelines differ in some ways.
-The major points are:
-
-
-* The **TransformersReader** will sometimes predict the same span twice while duplicates are removed in the **FARMReader**
-
-
-* The **FARMReader** currently uses the tokenizers from the HuggingFace Transformers library while the **TransformersReader** uses the tokenizers from the HuggingFace Tokenizers library
-
-
-* Start and end logits are normalized per passage and multiplied in the **TransformersReader** while they are summed and not normalised in the **FARMReader**
-
-If you’re interested in the finer details of these points, have a look at [this](https://github.com/deepset-ai/haystack/issues/248#issuecomment-661977237) GitHub comment.
-
-We see value in maintaining both kinds of Readers since Transformers is a very familiar library to many of Haystack’s users
-but we at deepset can more easily update and optimise the FARM pipeline for speed and performance.
-
-
-Haystack also has a close integration with FARM which means that you can further fine-tune your Readers on labelled data using a FARMReader.
-See our tutorials for an end-to-end example or below for a shortened example.
-
-```python
-from haystack.reader import FARMReader
-
-# Initialise Reader
-model = "deepset/roberta-base-squad2"
-reader = FARMReader(model)
-
-# Perform finetuning
-train_data = "PATH/TO_YOUR/TRAIN_DATA"
-train_filename = "train.json"
-save_dir = "finetuned_model"
-reader.train(train_data, train_filename, save_dir=save_dir)
-
-# Load
-finetuned_reader = FARMReader(save_dir)
-```
-
-## Deeper Dive: From Language Model to Haystack Reader
-
-Language models form the core of most modern NLP systems and that includes the Readers in Haystack.
-They build a general understanding of language when performing training tasks such as Masked Language Modeling or Replaced Token Detection
-on large amounts of text.
-Well trained language models capture the word distribution in one or more languages
-but more importantly, convert input text into a set of word vectors that capture elements of syntax and semantics.
-
-In order to convert a language model into a Reader model, it needs first to be trained on a Question Answering dataset.
-To do so requires the addition of a question answering prediction head on top of the language model.
-The task can be thought of as a token classification task where every input token is assigned a probability of being
-either the start or end token of the correct answer.
-In cases where the answer is not contained within the passage, the prediction head is also expected to return a `no_answer` prediction.
-
-
-Since language models are limited in the number of tokens which they can process in a single forward pass,
-a sliding window mechanism is implemented to handle variable length documents.
-This functions by slicing the document into overlapping passages of (approximately) `max_seq_length`
-that are each offset by `doc_stride` number of tokens.
-These can be set when the Reader is initialized.
-
-
-
-
-
-Predictions are made on each individual passage and the process of aggregation picks the best candidates across all passages.
-If you’d like to learn more about what is happening behind the scenes, have a look at [this](https://medium.com/deepset-ai/modern-question-answering-systems-explained-4d0913744097) article.
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/retriever.md b/src/pages/docs/versions/master/latest/site/en/usage/usage/retriever.md
deleted file mode 100644
index c213390cc..000000000
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/retriever.md
+++ /dev/null
@@ -1,281 +0,0 @@
----
-title: "Retriever"
-metaTitle: "Retriever"
-metaDescription: ""
-slug: "/docs/retriever"
-date: "2020-09-03"
-id: "retrievermd"
----
-
-# Retriever
-
-The Retriever is a lightweight filter that can quickly go through the full document store and pass on a set of candidate documents that are relevant to the query.
-When used in combination with a Reader, it is a tool for sifting out the obvious negative cases, saving the Reader from doing more work than it needs to and speeding up the querying process.
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader(... max_seq_len=384, doc_stride=128 ...)
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader(... max_seq_len=384, doc_stride=128 ...)
-```
-
-
-
-
-**Recommendations**
-
-* BM25 (sparse)
-
-* Dense Passage Retrieval (dense)
-
-
-
-
-
-Note that not all Retrievers can be paired with every DocumentStore.
-Here are the combinations which are supported:
-
-| | Memory | Elasticsearch | SQL | FAISS | Milvus |
-| --- | --- | --- | ---- | ---- | ---- |
-| BM25 | N | Y | N | N | N |
-| TF-IDF | Y | Y | Y | N | N |
-| Embedding | Y | Y | N | Y | Y |
-| DPR | Y | Y | N | Y | Y |
-
-See [Optimization](/docs/latest/optimizationmd) for suggestions on how to choose top-k values.
-
-
-## TF-IDF
-
-### Description
-
-TF-IDF is a commonly used baseline for information retrieval that exploits two key intuitions:
-
-
-* documents that have more lexical overlap with the query are more likely to be relevant
-
-* words that occur in fewer documents are more significant than words that occur in many documents
-
-Given a query, a tf-idf score is computed for each document as follows:
-
-```python
-score = tf * idf
-```
-
-Where:
-
-
-* `tf` is how many times words in the query occur in that document.
-
-
-* `idf` is the inverse of the fraction of documents containing the word.
-
-In practice, both terms are usually log normalised.
-
-### Initialisation
-
-```python
-from haystack.document_store import InMemoryDocumentStore
-from haystack.retriever.sparse import TfidfRetriever
-from haystack.pipeline import ExtractiveQAPipeline
-
-document_store = InMemoryDocumentStore()
-...
-retriever = TfidfRetriever(document_store)
-...
-p = ExtractiveQAPipeline(reader, retriever)
-```
-
-## BM25 (Recommended)
-
-### Description
-
-BM25 is a variant of TF-IDF that we recommend you use if you are looking for a retrieval method that does not need a neural network for indexing.
-It improves upon its predecessor in two main aspects:
-
-
-* It saturates `tf` after a set number of occurrences of the given term in the document
-
-
-* It normalises by document length so that short documents are favoured over long documents if they have the same amount of word overlap with the query
-
-### Initialisation
-
-```python
-from haystack.document_store import ElasticsearchDocumentStore
-from haystack.retriever import ElasticsearchRetriever
-from haystack.pipeline import ExtractiveQAPipeline
-
-document_store = ElasticsearchDocumentStore()
-...
-retriever = ElasticsearchRetriever(document_store)
-...
-p = ExtractiveQAPipeline(reader, retriever)
-```
-
-See [this](https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-and-its-variables) blog post for more details about the algorithm.
-
-
-## Dense Passage Retrieval (Recommended)
-
-### Description
-
-[Dense Passage Retrieval](https://arxiv.org/abs/2004.04906) is a highly performant retrieval method that calculates relevance using dense representations.
-Key features:
-
-
-* One BERT base model to encode documents
-
-
-* One BERT base model to encode queries
-
-
-* Ranking of documents done by dot product similarity between query and document embeddings
-
-
-Indexing using DPR is comparatively expensive in terms of required computation since all documents in the database need to be processed through the transformer.
-The embeddings that are created in this step can be stored in FAISS, a database optimized for vector similarity.
-DPR can also work with the ElasticsearchDocumentStore or the InMemoryDocumentStore.
-
-There are two design decisions that have made DPR particularly performant.
-
-
-* Separate encoders for document and query helps since queries are much shorter than documents
-
-
-* Training with ‘In-batch negatives’ (gold labels are treated as negative examples for other samples in same batch) is highly efficient
-
-In Haystack, you can simply download the pretrained encoders needed to start using DPR.
-If you’d like to learn how to set up a DPR based system, have a look at the [tutorial](/docs/latest/tutorial6md)!
-
-### Initialisation
-
-
-
-**Tip**
-
-When using DPR, it is recommended that you use the dot product similarity function since that is how it is trained.
-To do so, simply provide `similarity="dot_product"` when initializing the DocumentStore
-as is done in the code example below.
-
-
-
-```python
-from haystack.document_store import FAISSDocumentStore
-from haystack.retriever import DensePassageRetriever
-from haystack.pipeline import ExtractiveQAPipeline
-
-document_store = FAISSDocumentStore(similarity="dot_product")
-...
-retriever = DensePassageRetriever(
- document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base"
-)
-...
-finder = ExtractiveQAPipeline(reader, retriever)
-```
-
-
-
-**Training DPR:** Haystack supports training of your own DPR model! Check out the [tutorial](/docs/latest/tutorial9md) to see how this is done!
-
-
-
-
-
-## Embedding Retrieval
-
-### Description
-
-In Haystack, you also have the option of using a single transformer model to encode document and query.
-One style of model that is suited to this kind of retrieval is that of [Sentence Transformers](https://github.com/UKPLab/sentence-transformers).
-These models are trained in Siamese Networks and use triplet loss such that they learn to embed similar sentences near to each other in a shared embedding space.
-
-They are particular suited to cases where your query input is similar in style to that of the documents in your database
-i.e. when you are searching for most similar documents.
-This is not inherently suited to query based search where the length, language and format of the query usually significantly differs from the searched for text.
-
-
-
-**Tip**
-
-When using Sentence Transformer models, we recommend that you use a cosine similarity function.
-To do so, simply provide `similarity="cosine"` when initializing the DocumentStore
-as is done in the code example below.
-
-
-
-### Initialisation
-
-```python
-from haystack.document_store import ElasticsearchDocumentStore
-from haystack.retriever import EmbeddingRetriever
-from haystack.pipeline import ExtractiveQAPipeline
-
-document_store = ElasticsearchDocumentStore(similarity="cosine")
-...
-retriever = EmbeddingRetriever(document_store=document_store,
- embedding_model="deepset/sentence_bert")
-...
-p = ExtractiveQAPipeline(reader, retriever)
-```
-
-## Deeper Dive: Dense vs Sparse
-
-Broadly speaking, retrieval methods can be split into two categories: **dense** and **sparse**.
-
-**Sparse** methods, like TF-IDF and BM25, operate by looking for shared keywords between the document and query.
-They are:
-
-
-* simple but effective
-
-
-* don’t need to be trained
-
-
-* work on any language
-
-More recently, **dense** approaches such as Dense Passage Retrieval (DPR) have shown even better performance than their sparse counter parts.
-These methods embed both document and query into a shared embedding space using deep neural networks
-and the top candidates are the nearest neighbour documents to the query.
-They are:
-
-
-* powerful but computationally more expensive especially during indexing
-
-
-* trained using labelled datasets
-
-
-* language specific
-
-### Qualitative Differences
-
-Between these two types there are also some qualitative differences too.
-For example, sparse methods treat text as a bag-of-words meaning that they **do not take word order and syntax into account**,
-while the latest generation of dense methods use transformer based encoders
-which are designed to be **sensitive** to these factors.
-
-Also dense methods are very capable of building strong semantic representations of text,
-but they **struggle when encountering out-of-vocabulary** words such as new names.
-By contrast, sparse methods don’t need to learn representations of words,
-they only care about whether they are present or absent in the text.
-As such, **they handle out-of-vocabulary words with no problem**.
-
-
-### Indexing
-
-Dense methods perform indexing by processing all the documents through a neural network and storing the resulting vectors.
-This is a much more expensive operation than the creation of the inverted-index in sparse methods
-and will require significant computational power and time.
-
-
-### Terminology
-
-
-
-The terms **dense** and **sparse** refer to the representations that the algorithms build for each document and query.
-**Sparse** methods characterise texts using vectors with one dimension corresponding to each word in the vocabulary.
-Dimensions will be zero if the word is absent and non-zero if it is present.
-Since most documents contain only a small subset of the full vocabulary,
-these vectors are considered sparse since non-zero values are few and far between.
-
-**Dense** methods, by contrast, pass text as input into neural network encoders
-and represent text in a vector of a manually defined size (usually 768).
-Though individual dimensions are not mapped to any corresponding vocabulary or linguistic feature,
-each dimension encodes some information about the text.
-There are rarely 0s in these vectors hence their relative density.
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/summarizer.md b/src/pages/docs/versions/master/latest/site/en/usage/usage/summarizer.md
deleted file mode 100644
index d1aeda7ad..000000000
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/summarizer.md
+++ /dev/null
@@ -1,56 +0,0 @@
----
-title: "Summarizer"
-metaTitle: "Summarizer"
-metaDescription: "Using the Summarizer Node"
-slug: "/docs/summarizer"
-date: "2021-04-16"
-id: "summarizermd"
----
-
-# Summarizer
-
-Retrievers are excellent at returning a set of candidate documents,
-but you might not have the time to read through them all.
-Haystack's Summmarizer is here to help you make sense of the documents at a glance.
-
-There is a full integration with Huggingface Transformers and using any of their summarization
-models is as simple as providing the model name.
-See the up-to-date list of available models [here](https://huggingface.co/models?filter=summarization).
-By default, the Google [Pegasus](https://ai.googleblog.com/2020/06/pegasus-state-of-art-model-for.html) model is loaded.
-
-```python
-from haystack.summarizer import TransformersSummarizer
-from haystack.schema import Document
-
-docs = [Document("PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions.\
- The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by\
- the shutoffs which were expected to last through at least midday tomorrow.")]
-
-summarizer = TransformersSummarizer(model_name_or_path="google/pegasus-xsum")
-summary = summarizer.predict(documents=docs, generate_single_summary=True)
-```
-
-The contents of summary should contain both the summarization and also the original document text.
-
-```python
-[
- {
- "text": "California's largest electricity provider has turned off power to hundreds of thousands of customers.",
- "meta": {
- "context": "PGE stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions."
- },
- ...
- }
-]
-```
-
-The summarizer can also functions as a node in a pipeline.
-
-```python
-from haystack.pipeline import Pipeline
-
-p = Pipeline()
-p.add_node(component=retriever, name="ESRetriever1", inputs=["Query"])
-p.add_node(component=summarizer, name="Summarizer", inputs=["ESRetriever1"])
-res = p.run(query="What did Einstein work on?", top_k_retriever=10)
-```
diff --git a/src/pages/docs/versions/master/latest/site/en/usage/usage/terms.md b/src/pages/docs/versions/master/latest/site/en/usage/usage/terms.md
deleted file mode 100644
index 3509c77d4..000000000
--- a/src/pages/docs/versions/master/latest/site/en/usage/usage/terms.md
+++ /dev/null
@@ -1,65 +0,0 @@
----
-title: "Glossary"
-metaTitle: "Glossary"
-metaDescription: ""
-slug: "/docs/terms"
-date: "2020-09-03"
-id: "termsmd"
----
-
-# Glossary
-
-**BERT** - A popular, transformer based language model which has been improved upon but is still considered a common benchmark.
-
-**Dense** - Vectors that contain many non-zero values are considered dense.
-Retrieval methods can also be called dense if they create dense vector representations of documents.
-
-**Document** - A Document in Haystack refers to the individual pieces of text that are stored in the DocumentStore.
-Multiple Documents might originally come from the one file.
-It is ultimately up to you how to divide up your corpus into Documents.
-
-**Document Store** - The component in Haystack that stores the text documents and their metadata.
-Can have a variety of backends such as Elasticsearch, SQL or FAISS.
-
-**FARM** - An open-source transfer learning [framework](https://github.com/deepset-ai/FARM) by deepset.
-FARM’s question answering models are used in Haystack’s Readers.
-
-**Indexing** - To store data in a database in a way that optimises retrieval time.
-The exact steps involved in indexing depend on what kind of retrieval method is chosen.
-
-**Language Model** - The component in an NLP model that stores general language understanding, but no task specific knowledge.
-
-**Model Hub** - The [repository](https://huggingface.co/models) set up by HuggingFace where trained models can be saved to and loaded from.
-With Haystack, you can directly load and use any question answering model found on the model hub.
-
-**Neural Network** - A machine learning architecture composed of artificial neurons that learn a task when exposed to labelled training data.
-
-**Prediction Head** - The modelling component that adapts the general knowledge of the language model for a specific task.
-In question answering models (and hence in Haystack Readers), this is usually a single layer neural network.
-
-**Querying** - The task of returning relevant documents from a database.
-
-**Question Answering (QA)** - A popular task in the world of NLP where systems have to find answers to questions.
-The term is generally used to refer to extractive question answering,
-where a system has to find the minimal text span in a given document that contains the answer to the question.
-Note however, that it may also refer to abstractive question answering or FAQ matching.
-
-**Reader** - The component in Haystack that does the closest reading of a document to extract
-the exact text which answers a question.
-It is, at its core, a trained Question Answering model.
-
-**Retriever** - A lightweight filter that selects only the most relevant documents for the Reader to further process.
-
-**Semantic Search** - A style of search that relies not on the matching of exact string forms
-but on the similarity of meaning between a query and a piece of text.
-
-**Sparse** - Vectors that are composed primarily of zeros are called sparse.
-Retrieval methods are also considered sparse if they build sparse vector representations of documents.
-
-**SQuAD** - The [Stanford Question Answering Dataset](https://rajpurkar.github.io/SQuAD-explorer/) is the defacto standard QA dataset.
-The documents are paragraphs from Wikipedia and the question / answer pairs are created by human annotators.
-
-**Transformers** - Originally refers to the deep learning architecture that is composed of stacked self-attention layers
-(first conceptualised [here](https://arxiv.org/pdf/1706.03762.pdf)).
-Can also refer to HuggingFace’s [repository](https://github.com/huggingface/transformers)
-which contains implementations of popular model architectures.
diff --git a/src/pages/docs/versions/master/release_docs.sh b/src/pages/docs/versions/master/release_docs.sh
deleted file mode 100755
index 2153de22d..000000000
--- a/src/pages/docs/versions/master/release_docs.sh
+++ /dev/null
@@ -1,19 +0,0 @@
-#!/bin/bash
-
-# Purpose: Release new docs version
-# Input: New version number and name of haystack release branch
-
-mkdir $1
-
-echo $1 >> versions.txt
-echo " " >> versions.txt
-# Remove empty lines
-tr -d '\n' < versions.txt > temp.txt && mv temp.txt versions.txt
-
-npm run update_apidocs_dev $2
-npm run update_tutorialsdocs_dev $2
-npm run update_usagedocs_dev $2
-npm run update_img_dev $2
-npm run update_benchmarks_dev $2
-
-cp -r site/en/menuStructure/ $1site/en/
diff --git a/src/pages/docs/versions/master/site/en/api/api/crawler.md b/src/pages/docs/versions/master/site/en/api/api/crawler.md
deleted file mode 100644
index 33eebda75..000000000
--- a/src/pages/docs/versions/master/site/en/api/api/crawler.md
+++ /dev/null
@@ -1,105 +0,0 @@
----
-title: "Crawler"
-metaTitle: "Crawler"
-metaDescription: ""
-slug: "/docs/crawlermd"
-date: "2020-09-03"
-id: "apicrawlermd"
----
-
-# Crawler
-
-
-# Module: crawler
-
-
-## Class: Crawler
-
-```python
-class Crawler(BaseComponent)
-```
-
-Crawl texts from a website so that we can use them later in Haystack as a corpus for search / question answering etc.
-
-**Example:**
-```python
-| from haystack.connector import Crawler
-|
-| crawler = Crawler()
-| # crawl Haystack docs, i.e. all pages that include haystack.deepset.ai/docs/
-| docs = crawler.crawl(urls=["https://haystack.deepset.ai/docs/latest/get_startedmd"],
-| output_dir="crawled_files",
-| filter_urls= ["haystack\.deepset\.ai\/docs\/"])
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(output_dir: str, urls: Optional[List[str]] = None, crawler_depth: int = 1, filter_urls: Optional[List] = None, overwrite_existing_files=True)
-```
-
-Init object with basic params for crawling (can be overwritten later).
-
-**Arguments**:
-
-- `output_dir`: Path for the directory to store files
-- `urls`: List of http(s) address(es) (can also be supplied later when calling crawl())
-- `crawler_depth`: How many sublinks to follow from the initial list of URLs. Current options:
- 0: Only initial list of urls
- 1: Follow links found on the initial URLs (but no further)
-- `filter_urls`: Optional list of regular expressions that the crawled URLs must comply with.
- All URLs not matching at least one of the regular expressions will be dropped.
-- `overwrite_existing_files`: Whether to overwrite existing files in output_dir with new content
-
-
-#### crawl
-
-```python
- | crawl(output_dir: Union[str, Path, None] = None, urls: Optional[List[str]] = None, crawler_depth: Optional[int] = None, filter_urls: Optional[List] = None, overwrite_existing_files: Optional[bool] = None) -> List[Path]
-```
-
-Craw URL(s), extract the text from the HTML, create a Haystack Document object out of it and save it (one JSON
-file per URL, including text and basic meta data).
-You can optionally specify via `filter_urls` to only crawl URLs that match a certain pattern.
-All parameters are optional here and only meant to overwrite instance attributes at runtime.
-If no parameters are provided to this method, the instance attributes that were passed during __init__ will be used.
-
-**Arguments**:
-
-- `output_dir`: Path for the directory to store files
-- `urls`: List of http addresses or single http address
-- `crawler_depth`: How many sublinks to follow from the initial list of URLs. Current options:
- 0: Only initial list of urls
- 1: Follow links found on the initial URLs (but no further)
-- `filter_urls`: Optional list of regular expressions that the crawled URLs must comply with.
- All URLs not matching at least one of the regular expressions will be dropped.
-- `overwrite_existing_files`: Whether to overwrite existing files in output_dir with new content
-
-**Returns**:
-
-List of paths where the crawled webpages got stored
-
-
-#### run
-
-```python
- | run(output_dir: Union[str, Path, None] = None, urls: Optional[List[str]] = None, crawler_depth: Optional[int] = None, filter_urls: Optional[List] = None, overwrite_existing_files: Optional[bool] = None, **kwargs) -> Tuple[Dict, str]
-```
-
-Method to be executed when the Crawler is used as a Node within a Haystack pipeline.
-
-**Arguments**:
-
-- `output_dir`: Path for the directory to store files
-- `urls`: List of http addresses or single http address
-- `crawler_depth`: How many sublinks to follow from the initial list of URLs. Current options:
- 0: Only initial list of urls
- 1: Follow links found on the initial URLs (but no further)
-- `filter_urls`: Optional list of regular expressions that the crawled URLs must comply with.
- All URLs not matching at least one of the regular expressions will be dropped.
-- `overwrite_existing_files`: Whether to overwrite existing files in output_dir with new content
-
-**Returns**:
-
-Tuple({"paths": List of filepaths, ...}, Name of output edge)
diff --git a/src/pages/docs/versions/master/site/en/api/api/document_store.md b/src/pages/docs/versions/master/site/en/api/api/document_store.md
deleted file mode 100644
index 7b85b5e54..000000000
--- a/src/pages/docs/versions/master/site/en/api/api/document_store.md
+++ /dev/null
@@ -1,1478 +0,0 @@
----
-title: "Document Store"
-metaTitle: "Database"
-metaDescription: ""
-slug: "/docs/apidatabase"
-date: "2020-09-03"
-id: "apidatabasemd"
----
-
-# Document Store
-
-
-# Module: base
-
-
-## Class: BaseDocumentStore
-
-```python
-class BaseDocumentStore(BaseComponent)
-```
-
-Base class for implementing Document Stores.
-
-
-#### write\_documents
-
-```python
- | @abstractmethod
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
- For documents as dictionaries, the format is {"text": "
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb)
-
-Question Answering can be used in a variety of use cases. A very common one: Using it to navigate through complex knowledge bases or long documents ("search setting").
-
-A "knowledge base" could for example be your website, an internal wiki or a collection of financial reports.
-In this tutorial we will work on a slightly different domain: "Game of Thrones".
-
-Let's see how we can use a bunch of Wikipedia articles to answer a variety of questions about the
-marvellous seven kingdoms...
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-Haystack finds answers to queries within the documents stored in a `DocumentStore`. The current implementations of `DocumentStore` include `ElasticsearchDocumentStore`, `FAISSDocumentStore`, `SQLDocumentStore`, and `InMemoryDocumentStore`.
-
-**Here:** We recommended Elasticsearch as it comes preloaded with features like [full-text queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html), [BM25 retrieval](https://www.elastic.co/elasticon/conf/2016/sf/improved-text-scoring-with-bm25), and [vector storage for text embeddings](https://www.elastic.co/guide/en/elasticsearch/reference/7.6/dense-vector.html).
-
-**Alternatives:** If you are unable to setup an Elasticsearch instance, then follow the [Tutorial 3](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb) for using SQL/InMemory document stores.
-
-**Hint**: This tutorial creates a new document store instance with Wikipedia articles on Game of Thrones. However, you can configure Haystack to work with your existing document stores.
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker via the Haystack utility function
-from haystack.utils import launch_es
-
-launch_es()
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-# Connect to Elasticsearch
-
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles about Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first fetch some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is:
-# {
-# 'text': "
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.generator.transformers import Seq2SeqGenerator
-```
-
-### Document Store
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-The default flavour of FAISSDocumentStore is "Flat" but can also be set to "HNSW" for
-faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.
-For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-
-
-```python
-from haystack.document_store.faiss import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(vector_dim=128, faiss_index_factory_str="Flat")
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever and Reader/Generator
-
-#### Retriever
-
-**Here:** We use a `RetribertRetriever` and we invoke `update_embeddings` to index the embeddings of documents in the `FAISSDocumentStore`
-
-
-
-
-```python
-from haystack.retriever.dense import EmbeddingRetriever
-
-retriever = EmbeddingRetriever(document_store=document_store,
- embedding_model="yjernite/retribert-base-uncased",
- model_format="retribert")
-
-document_store.update_embeddings(retriever)
-```
-
-Before we blindly use the `RetribertRetriever` let's empirically test it to make sure a simple search indeed finds the relevant documents.
-
-
-```python
-from haystack.utils import print_answers, print_documents
-from haystack.pipeline import DocumentSearchPipeline
-
-p_retrieval = DocumentSearchPipeline(retriever)
-res = p_retrieval.run(
- query="Tell me something about Arya Stark?",
- top_k_retriever=5
-)
-print_documents(res, max_text_len=512)
-
-```
-
-#### Reader/Generator
-
-Similar to previous Tutorials we now initalize our reader/generator.
-
-Here we use a `Seq2SeqGenerator` with the *yjernite/bart_eli5* model (see: https://huggingface.co/yjernite/bart_eli5)
-
-
-
-
-```python
-generator = Seq2SeqGenerator(model_name_or_path="yjernite/bart_eli5")
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `GenerativeQAPipeline` that combines a retriever and a reader/generator to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import GenerativeQAPipeline
-pipe = GenerativeQAPipeline(generator, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-pipe.run(query="Why did Arya Stark's character get portrayed in a television adaptation?", top_k_retriever=1)
-```
-
-
-```python
-pipe.run(query="What kind of character does Arya Stark play?", top_k_retriever=1)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/site/en/tutorials/tutorials/2.md b/src/pages/docs/versions/master/site/en/tutorials/tutorials/2.md
deleted file mode 100644
index ec5349afb..000000000
--- a/src/pages/docs/versions/master/site/en/tutorials/tutorials/2.md
+++ /dev/null
@@ -1,108 +0,0 @@
----
-title: "Tutorial 2"
-metaTitle: "Fine-tuning a model on your own data"
-metaDescription: ""
-slug: "/docs/tutorial2"
-date: "2020-09-03"
-id: "tutorial2md"
----
-
-# Fine-tuning a Model on Your Own Data
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial2_Finetune_a_model_on_your_data.ipynb)
-
-For many use cases it is sufficient to just use one of the existing public models that were trained on SQuAD or other public QA datasets (e.g. Natural Questions).
-However, if you have domain-specific questions, fine-tuning your model on custom examples will very likely boost your performance.
-While this varies by domain, we saw that ~ 2000 examples can easily increase performance by +5-20%.
-
-This tutorial shows you how to fine-tune a pretrained model on your own dataset.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.reader.farm import FARMReader
-```
-
-
-## Create Training Data
-
-There are two ways to generate training data
-
-1. **Annotation**: You can use the [annotation tool](https://github.com/deepset-ai/haystack#labeling-tool) to label your data, i.e. highlighting answers to your questions in a document. The tool supports structuring your workflow with organizations, projects, and users. The labels can be exported in SQuAD format that is compatible for training with Haystack.
-
-
-
-2. **Feedback**: For production systems, you can collect training data from direct user feedback via Haystack's [REST API interface](https://github.com/deepset-ai/haystack#rest-api). This includes a customizable user feedback API for providing feedback on the answer returned by the API. The API provides a feedback export endpoint to obtain the feedback data for fine-tuning your model further.
-
-
-## Fine-tune your model
-
-Once you have collected training data, you can fine-tune your base models.
-We initialize a reader as a base model and fine-tune it on our own custom dataset (should be in SQuAD-like format).
-We recommend using a base model that was trained on SQuAD or a similar QA dataset before to benefit from Transfer Learning effects.
-
-**Recommendation**: Run training on a GPU.
-If you are using Colab: Enable this in the menu "Runtime" > "Change Runtime type" > Select "GPU" in dropdown.
-Then change the `use_gpu` arguments below to `True`
-
-
-```python
-reader = FARMReader(model_name_or_path="distilbert-base-uncased-distilled-squad", use_gpu=True)
-data_dir = "data/squad20"
-# data_dir = "PATH/TO_YOUR/TRAIN_DATA"
-reader.train(data_dir=data_dir, train_filename="dev-v2.0.json", use_gpu=True, n_epochs=1, save_dir="my_model")
-```
-
-
-```python
-# Saving the model happens automatically at the end of training into the `save_dir` you specified
-# However, you could also save a reader manually again via:
-reader.save(directory="my_model")
-```
-
-
-```python
-# If you want to load it at a later point, just do:
-new_reader = FARMReader(model_name_or_path="my_model")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/site/en/tutorials/tutorials/3.md b/src/pages/docs/versions/master/site/en/tutorials/tutorials/3.md
deleted file mode 100644
index 09f83ace6..000000000
--- a/src/pages/docs/versions/master/site/en/tutorials/tutorials/3.md
+++ /dev/null
@@ -1,202 +0,0 @@
----
-title: "Tutorial 3"
-metaTitle: "Build a QA System Without Elasticsearch"
-metaDescription: ""
-slug: "/docs/tutorial3"
-date: "2020-09-03"
-id: "tutorial3md"
----
-
-# Build a QA System Without Elasticsearch
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb)
-
-Haystack provides alternatives to Elasticsearch for developing quick prototypes.
-
-You can use an `InMemoryDocumentStore` or a `SQLDocumentStore`(with SQLite) as the document store.
-
-If you are interested in more feature-rich Elasticsearch, then please refer to the Tutorial 1.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-
-
-```python
-# In-Memory Document Store
-from haystack.document_store.memory import InMemoryDocumentStore
-document_store = InMemoryDocumentStore()
-```
-
-
-```python
-# SQLite Document Store
-# from haystack.document_store.sql import SQLDocumentStore
-# document_store = SQLDocumentStore(url="sqlite:///qa.db")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles on Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first get some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# convert files to dicts containing documents that can be indexed to our datastore
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is: {"name": "
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-from haystack.retriever.dense import EmbeddingRetriever
-from haystack.utils import print_answers
-import pandas as pd
-import requests
-
-```
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker via the Haystack utility function
-from haystack.utils import launch_es
-
-launch_es()
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-
-```
-
-### Init the DocumentStore
-In contrast to Tutorial 1 (extractive QA), we:
-
-* specify the name of our `text_field` in Elasticsearch that we want to return as an answer
-* specify the name of our `embedding_field` in Elasticsearch where we'll store the embedding of our question and that is used later for calculating our similarity to the incoming user question
-* set `excluded_meta_data=["question_emb"]` so that we don't return the huge embedding vectors in our search results
-
-
-```python
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="",
- index="document",
- embedding_field="question_emb",
- embedding_dim=768,
- excluded_meta_data=["question_emb"])
-```
-
-### Create a Retriever using embeddings
-Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones).
-We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings.
-
-
-```python
-retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert", use_gpu=True)
-```
-
-### Prepare & Index FAQ data
-We create a pandas dataframe containing some FAQ data (i.e curated pairs of question + answer) and index those in elasticsearch.
-Here: We download some question-answer pairs related to COVID-19
-
-
-```python
-# Download
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/faqs/faq_covidbert.csv")
-open('small_faq_covid.csv', 'wb').write(temp.content)
-
-# Get dataframe with columns "question", "answer" and some custom metadata
-df = pd.read_csv("small_faq_covid.csv")
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-df["question"] = df["question"].apply(lambda x: x.strip())
-print(df.head())
-
-# Get embeddings for our questions from the FAQs
-questions = list(df["question"].values)
-df["question_emb"] = retriever.embed_queries(texts=questions)
-df = df.rename(columns={"question": "text"})
-
-# Convert Dataframe to list of dicts and index them in our DocumentStore
-docs_to_index = df.to_dict(orient="records")
-document_store.write_documents(docs_to_index)
-```
-
-### Ask questions
-Initialize a Pipeline (this time without a reader) and ask questions
-
-
-```python
-from haystack.pipeline import FAQPipeline
-pipe = FAQPipeline(retriever=retriever)
-```
-
-
-```python
-prediction = pipe.run(query="How is the virus spreading?", top_k_retriever=10)
-print_answers(prediction, details="all")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/site/en/tutorials/tutorials/5.md b/src/pages/docs/versions/master/site/en/tutorials/tutorials/5.md
deleted file mode 100644
index b54d5591c..000000000
--- a/src/pages/docs/versions/master/site/en/tutorials/tutorials/5.md
+++ /dev/null
@@ -1,260 +0,0 @@
----
-title: "Tutorial 5"
-metaTitle: "Evaluation of a QA System"
-metaDescription: ""
-slug: "/docs/tutorial5"
-date: "2020-09-03"
-id: "tutorial5md"
----
-
-# Evaluation of a QA System
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial5_Evaluation.ipynb)
-
-To be able to make a statement about the performance of a question-answering system, it is important to evalute it. Furthermore, evaluation allows to determine which parts of the system can be improved.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-## Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-from farm.utils import initialize_device_settings
-
-device, n_gpu = initialize_device_settings(use_cuda=True)
-```
-
-
-```python
-from haystack.preprocessor.utils import fetch_archive_from_http
-
-# Download evaluation data, which is a subset of Natural Questions development set containing 50 documents
-doc_dir = "../data/nq"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/nq_dev_subset_v2.json.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-
-```python
-# make sure these indices do not collide with existing ones, the indices will be wiped clean before data is inserted
-doc_index = "tutorial5_docs"
-label_index = "tutorial5_labels"
-```
-
-
-```python
-# Connect to Elasticsearch
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-# Connect to Elasticsearch
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document",
- create_index=False, embedding_field="emb",
- embedding_dim=768, excluded_meta_data=["emb"])
-```
-
-
-```python
-from haystack.preprocessor import PreProcessor
-
-# Add evaluation data to Elasticsearch Document Store
-# We first delete the custom tutorial indices to not have duplicate elements
-# and also split our documents into shorter passages using the PreProcessor
-preprocessor = PreProcessor(
- split_length=500,
- split_overlap=0,
- split_respect_sentence_boundary=False,
- clean_empty_lines=False,
- clean_whitespace=False
-)
-document_store.delete_all_documents(index=doc_index)
-document_store.delete_all_documents(index=label_index)
-document_store.add_eval_data(
- filename="../data/nq/nq_dev_subset_v2.json",
- doc_index=doc_index,
- label_index=label_index,
- preprocessor=preprocessor
-)
-
-# Let's prepare the labels that we need for the retriever and the reader
-labels = document_store.get_all_labels_aggregated(index=label_index)
-```
-
-## Initialize components of QA-System
-
-
-```python
-# Initialize Retriever
-from haystack.retriever.sparse import ElasticsearchRetriever
-retriever = ElasticsearchRetriever(document_store=document_store)
-# Alternative: Evaluate DensePassageRetriever
-# Note, that DPR works best when you index short passages < 512 tokens as only those tokens will be used for the embedding.
-# Here, for nq_dev_subset_v2.json we have avg. num of tokens = 5220(!).
-# DPR still outperforms Elastic's BM25 by a small margin here.
-# from haystack.retriever.dense import DensePassageRetriever
-# retriever = DensePassageRetriever(document_store=document_store,
-# query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
-# passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
-# use_gpu=True,
-# embed_title=True,
-# max_seq_len=256,
-# batch_size=16,
-# remove_sep_tok_from_untitled_passages=True)
-#document_store.update_embeddings(retriever, index=doc_index)
-```
-
-
-```python
-# Initialize Reader
-from haystack.reader.farm import FARMReader
-
-reader = FARMReader("deepset/roberta-base-squad2", top_k=4, return_no_answer=True)
-
-```
-
-
-```python
-from haystack.eval import EvalAnswers, EvalDocuments
-
-# Here we initialize the nodes that perform evaluation
-eval_retriever = EvalDocuments()
-eval_reader = EvalAnswers()
-```
-
-## Evaluation of Retriever
-Here we evaluate only the retriever, based on whether the gold_label document is retrieved.
-
-
-```python
-## Evaluate Retriever on its own
-retriever_eval_results = retriever.eval(top_k=20, label_index=label_index, doc_index=doc_index)
-## Retriever Recall is the proportion of questions for which the correct document containing the answer is
-## among the correct documents
-print("Retriever Recall:", retriever_eval_results["recall"])
-## Retriever Mean Avg Precision rewards retrievers that give relevant documents a higher rank
-print("Retriever Mean Avg Precision:", retriever_eval_results["map"])
-```
-
-## Evaluation of Reader
-Here we evaluate only the reader in a closed domain fashion i.e. the reader is given one query
-and one document and metrics are calculated on whether the right position in this text is selected by
-the model as the answer span (i.e. SQuAD style)
-
-
-```python
-# Evaluate Reader on its own
-reader_eval_results = reader.eval(document_store=document_store, device=device, label_index=label_index, doc_index=doc_index)
-# Evaluation of Reader can also be done directly on a SQuAD-formatted file without passing the data to Elasticsearch
-#reader_eval_results = reader.eval_on_file("../data/nq", "nq_dev_subset_v2.json", device=device)
-
-## Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer
-print("Reader Top-N-Accuracy:", reader_eval_results["top_n_accuracy"])
-## Reader Exact Match is the proportion of questions where the predicted answer is exactly the same as the correct answer
-print("Reader Exact Match:", reader_eval_results["EM"])
-## Reader F1-Score is the average overlap between the predicted answers and the correct answers
-print("Reader F1-Score:", reader_eval_results["f1"])
-```
-
-## Evaluation of Retriever and Reader (Open Domain)
-Here we evaluate retriever and reader in open domain fashion i.e. a document is considered
-correctly retrieved if it contains the answer string within it. The reader is evaluated based purely on the
-predicted string, regardless of which document this came from and the position of the extracted span.
-
-
-```python
-from haystack import Pipeline
-
-# Here is the pipeline definition
-p = Pipeline()
-p.add_node(component=retriever, name="ESRetriever", inputs=["Query"])
-p.add_node(component=eval_retriever, name="EvalRetriever", inputs=["ESRetriever"])
-p.add_node(component=reader, name="QAReader", inputs=["EvalRetriever"])
-p.add_node(component=eval_reader, name="EvalReader", inputs=["QAReader"])
-results = []
-```
-
-
-```python
-# This is how to run the pipeline
-for l in labels:
- res = p.run(
- query=l.question,
- top_k_retriever=10,
- labels=l,
- top_k_reader=10,
- index=doc_index,
- )
- results.append(res)
-```
-
-
-```python
-# When we have run evaluation using the pipeline, we can print the results
-n_queries = len(labels)
-eval_retriever.print()
-print()
-retriever.print_time()
-print()
-eval_reader.print(mode="reader")
-print()
-reader.print_time()
-print()
-eval_reader.print(mode="pipeline")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/site/en/tutorials/tutorials/6.md b/src/pages/docs/versions/master/site/en/tutorials/tutorials/6.md
deleted file mode 100644
index 2e2767b24..000000000
--- a/src/pages/docs/versions/master/site/en/tutorials/tutorials/6.md
+++ /dev/null
@@ -1,240 +0,0 @@
----
-title: "Tutorial 6"
-metaTitle: "Better retrieval via Dense Passage Retrieval"
-metaDescription: ""
-slug: "/docs/tutorial6"
-date: "2020-09-03"
-id: "tutorial6md"
----
-
-# Better Retrieval via "Dense Passage Retrieval"
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb)
-
-### Importance of Retrievers
-
-The Retriever has a huge impact on the performance of our overall search pipeline.
-
-
-### Different types of Retrievers
-#### Sparse
-Family of algorithms based on counting the occurrences of words (bag-of-words) resulting in very sparse vectors with length = vocab size.
-
-**Examples**: BM25, TF-IDF
-
-**Pros**: Simple, fast, well explainable
-
-**Cons**: Relies on exact keyword matches between query and text
-
-
-#### Dense
-These retrievers use neural network models to create "dense" embedding vectors. Within this family there are two different approaches:
-
-a) Single encoder: Use a **single model** to embed both query and passage.
-b) Dual-encoder: Use **two models**, one to embed the query and one to embed the passage
-
-Recent work suggests that dual encoders work better, likely because they can deal better with the different nature of query and passage (length, style, syntax ...).
-
-**Examples**: REALM, DPR, Sentence-Transformers
-
-**Pros**: Captures semantinc similarity instead of "word matches" (e.g. synonyms, related topics ...)
-
-**Cons**: Computationally more heavy, initial training of model
-
-
-### "Dense Passage Retrieval"
-
-In this Tutorial, we want to highlight one "Dense Dual-Encoder" called Dense Passage Retriever.
-It was introdoced by Karpukhin et al. (2020, https://arxiv.org/abs/2004.04906.
-
-Original Abstract:
-
-_"Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks."_
-
-Paper: https://arxiv.org/abs/2004.04906
-Original Code: https://fburl.com/qa-dpr
-
-
-*Use this* [link](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb) *to open the notebook in Google Colab.*
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-### Document Store
-
-#### Option 1: FAISS
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-The default flavour of FAISSDocumentStore is "Flat" but can also be set to "HNSW" for
-faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.
-For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-
-
-```python
-from haystack.document_store import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
-```
-
-#### Option 2: Milvus
-
-Milvus is an open source database library that is also optimized for vector similarity searches like FAISS.
-Like FAISS it has both a "Flat" and "HNSW" mode but it outperforms FAISS when it comes to dynamic data management.
-It does require a little more setup, however, as it is run through Docker and requires the setup of some config files.
-See [their docs](https://milvus.io/docs/v1.0.0/milvus_docker-cpu.md) for more details.
-
-
-```python
-from haystack.utils import launch_milvus
-from haystack.document_store import MilvusDocumentStore
-
-launch_milvus()
-document_store = MilvusDocumentStore()
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever, Reader & Pipeline
-
-#### Retriever
-
-**Here:** We use a `DensePassageRetriever`
-
-**Alternatives:**
-
-- The `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-
-
-```python
-from haystack.retriever.dense import DensePassageRetriever
-retriever = DensePassageRetriever(document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- max_seq_len_query=64,
- max_seq_len_passage=256,
- batch_size=16,
- use_gpu=True,
- embed_title=True,
- use_fast_tokenizers=True)
-# Important:
-# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all
-# previously indexed documents and update their embedding representation.
-# While this can be a time consuming operation (depending on corpus size), it only needs to be done once.
-# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.
-document_store.update_embeddings(retriever)
-```
-
-#### Reader
-
-Similar to previous Tutorials we now initalize our reader.
-
-Here we use a FARMReader with the *deepset/roberta-base-squad2* model (see: https://huggingface.co/deepset/roberta-base-squad2)
-
-
-
-##### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import ExtractiveQAPipeline
-pipe = ExtractiveQAPipeline(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = pipe.run(query="Who created the Dothraki vocabulary?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/site/en/tutorials/tutorials/7.md b/src/pages/docs/versions/master/site/en/tutorials/tutorials/7.md
deleted file mode 100644
index 3e472df19..000000000
--- a/src/pages/docs/versions/master/site/en/tutorials/tutorials/7.md
+++ /dev/null
@@ -1,217 +0,0 @@
----
-title: "Tutorial 7"
-metaTitle: "Generative QA with RAG"
-metaDescription: ""
-slug: "/docs/tutorial7"
-date: "2020-11-12"
-id: "tutorial7md"
----
-
-# Generative QA with "Retrieval-Augmented Generation"
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial7_RAG_Generator.ipynb)
-
-While extractive QA highlights the span of text that answers a query,
-generative QA can return a novel text answer that it has composed.
-In this tutorial, you will learn how to set up a generative system using the
-[RAG model](https://arxiv.org/abs/2005.11401) which conditions the
-answer generator on a set of retrieved documents.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-Here are the packages and imports that we'll need:
-
-
-```python
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from typing import List
-import requests
-import pandas as pd
-from haystack import Document
-from haystack.document_store.faiss import FAISSDocumentStore
-from haystack.generator.transformers import RAGenerator
-from haystack.retriever.dense import DensePassageRetriever
-```
-
-Let's download a csv containing some sample text and preprocess the data.
-
-
-
-```python
-# Download sample
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/haystack/master/tutorials/small_generator_dataset.csv")
-open('small_generator_dataset.csv', 'wb').write(temp.content)
-
-# Create dataframe with columns "title" and "text"
-df = pd.read_csv("small_generator_dataset.csv", sep=',')
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-
-print(df.head())
-```
-
-We can cast our data into Haystack Document objects.
-Alternatively, we can also just use dictionaries with "text" and "meta" fields
-
-
-```python
-# Use data to initialize Document objects
-titles = list(df["title"].values)
-texts = list(df["text"].values)
-documents: List[Document] = []
-for title, text in zip(titles, texts):
- documents.append(
- Document(
- text=text,
- meta={
- "name": title or ""
- }
- )
- )
-```
-
-Here we initialize the FAISSDocumentStore, DensePassageRetriever and RAGenerator.
-FAISS is chosen here since it is optimized vector storage.
-
-
-```python
-# Initialize FAISS document store.
-# Set `return_embedding` to `True`, so generator doesn't have to perform re-embedding
-document_store = FAISSDocumentStore(
- faiss_index_factory_str="Flat",
- return_embedding=True
-)
-
-# Initialize DPR Retriever to encode documents, encode question and query documents
-retriever = DensePassageRetriever(
- document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- use_gpu=True,
- embed_title=True,
-)
-
-# Initialize RAG Generator
-generator = RAGenerator(
- model_name_or_path="facebook/rag-token-nq",
- use_gpu=True,
- top_k=1,
- max_length=200,
- min_length=2,
- embed_title=True,
- num_beams=2,
-)
-```
-
-We write documents to the DocumentStore, first by deleting any remaining documents then calling `write_documents()`.
-The `update_embeddings()` method uses the retriever to create an embedding for each document.
-
-
-
-```python
-# Delete existing documents in documents store
-document_store.delete_all_documents()
-
-# Write documents to document store
-document_store.write_documents(documents)
-
-# Add documents embeddings to index
-document_store.update_embeddings(
- retriever=retriever
-)
-```
-
-Here are our questions:
-
-
-```python
-QUESTIONS = [
- "who got the first nobel prize in physics",
- "when is the next deadpool movie being released",
- "which mode is used for short wave broadcast service",
- "who is the owner of reading football club",
- "when is the next scandal episode coming out",
- "when is the last time the philadelphia won the superbowl",
- "what is the most current adobe flash player version",
- "how many episodes are there in dragon ball z",
- "what is the first step in the evolution of the eye",
- "where is gall bladder situated in human body",
- "what is the main mineral in lithium batteries",
- "who is the president of usa right now",
- "where do the greasers live in the outsiders",
- "panda is a national animal of which country",
- "what is the name of manchester united stadium",
-]
-```
-
-Now let's run our system!
-The retriever will pick out a small subset of documents that it finds relevant.
-These are used to condition the generator as it generates the answer.
-What it should return then are novel text spans that form and answer to your question!
-
-
-```python
-# Now generate an answer for each question
-for question in QUESTIONS:
- # Retrieve related documents from retriever
- retriever_results = retriever.retrieve(
- query=question
- )
-
- # Now generate answer from question and retrieved documents
- predicted_result = generator.predict(
- query=question,
- documents=retriever_results,
- top_k=1
- )
-
- # Print you answer
- answers = predicted_result["answers"]
- print(f'Generated answer is \'{answers[0]["answer"]}\' for the question = \'{question}\'')
-```
-
-
-```python
-# Or alternatively use the Pipeline class
-from haystack.pipeline import GenerativeQAPipeline
-
-pipe = GenerativeQAPipeline(generator=generator, retriever=retriever)
-for question in QUESTIONS:
- res = pipe.run(query=question, top_k_generator=1, top_k_retriever=5)
- print(res)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/site/en/tutorials/tutorials/8.md b/src/pages/docs/versions/master/site/en/tutorials/tutorials/8.md
deleted file mode 100644
index a53c3a030..000000000
--- a/src/pages/docs/versions/master/site/en/tutorials/tutorials/8.md
+++ /dev/null
@@ -1,214 +0,0 @@
----
-title: "Tutorial 8"
-metaTitle: "Preprocessing"
-metaDescription: ""
-slug: "/docs/tutorial8"
-date: "2021-01-08"
-id: "tutorial8md"
----
-
-# Preprocessing
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial8_Preprocessing.ipynb)
-
-Haystack includes a suite of tools to extract text from different file types, normalize white space
-and split text into smaller pieces to optimize retrieval.
-These data preprocessing steps can have a big impact on the systems performance and effective handling of data is key to getting the most out of Haystack.
-
-Ultimately, Haystack expects data to be provided as a list documents in the following dictionary format:
-``` python
-docs = [
- {
- 'text': DOCUMENT_TEXT_HERE,
- 'meta': {'name': DOCUMENT_NAME, ...}
- }, ...
-]
-```
-
-This tutorial will show you all the tools that Haystack provides to help you cast your data into this format.
-
-
-```python
-# Let's start by installing Haystack
-
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-!wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.03.tar.gz
-!tar -xvf xpdf-tools-linux-4.03.tar.gz && sudo cp xpdf-tools-linux-4.03/bin64/pdftotext /usr/local/bin
-```
-
-
-```python
-# Here are the imports we need
-
-from haystack.file_converter.txt import TextConverter
-from haystack.file_converter.pdf import PDFToTextConverter
-from haystack.file_converter.docx import DocxToTextConverter
-
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.preprocessor.preprocessor import PreProcessor
-```
-
-
-```python
-# This fetches some sample files to work with
-
-doc_dir = "data/preprocessing_tutorial"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/preprocessing_tutorial.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-## Converters
-
-Haystack's converter classes are designed to help you turn files on your computer into the documents
-that can be processed by the Haystack pipeline.
-There are file converters for txt, pdf, docx files as well as a converter that is powered by Apache Tika.
-
-
-```python
-# Here are some examples of how you would use file converters
-
-converter = TextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_txt = converter.convert(file_path="data/preprocessing_tutorial/classics.txt", meta=None)
-
-converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_pdf = converter.convert(file_path="data/preprocessing_tutorial/bert.pdf", meta=None)
-
-converter = DocxToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_docx = converter.convert(file_path="data/preprocessing_tutorial/heavy_metal.docx", meta=None)
-
-```
-
-
-```python
-# Haystack also has a convenience function that will automatically apply the right converter to each file in a directory.
-
-all_docs = convert_files_to_dicts(dir_path="data/preprocessing_tutorial")
-```
-
-## PreProcessor
-
-The PreProcessor class is designed to help you clean text and split text into sensible units.
-File splitting can have a very significant impact on the system's performance and is absolutely mandatory for Dense Passage Retrieval models.
-In general, we recommend you split the text from your files into small documents of around 100 words for dense retrieval methods
-and no more than 10,000 words for sparse methods.
-Have a look at the [Preprocessing](https://haystack.deepset.ai/docs/latest/preprocessingmd)
-and [Optimization](https://haystack.deepset.ai/docs/latest/optimizationmd) pages on our website for more details.
-
-
-```python
-# This is a default usage of the PreProcessor.
-# Here, it performs cleaning of consecutive whitespaces
-# and splits a single large document into smaller documents.
-# Each document is up to 1000 words long and document breaks cannot fall in the middle of sentences
-# Note how the single document passed into the document gets split into 5 smaller documents
-
-preprocessor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=False,
- split_by="word",
- split_length=100,
- split_respect_sentence_boundary=True
-)
-docs_default = preprocessor.process(doc_txt)
-print(f"n_docs_input: 1\nn_docs_output: {len(docs_default)}")
-```
-
-## Cleaning
-
-- `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
-- `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
-- `clean_header_footer` will remove any long header or footer texts that are repeated on each page
-
-## Splitting
-By default, the PreProcessor will respect sentence boundaries, meaning that documents will not start or end
-midway through a sentence.
-This will help reduce the possibility of answer phrases being split between two documents.
-This feature can be turned off by setting `split_respect_sentence_boundary=False`.
-
-
-```python
-# Not respecting sentence boundary vs respecting sentence boundary
-
-preprocessor_nrsb = PreProcessor(split_respect_sentence_boundary=False)
-docs_nrsb = preprocessor_nrsb.process(doc_txt)
-
-print("RESPECTING SENTENCE BOUNDARY")
-end_text = docs_default[0]["text"][-50:]
-print("End of document: \"..." + end_text + "\"")
-print()
-print("NOT RESPECTING SENTENCE BOUNDARY")
-end_text_nrsb = docs_nrsb[0]["text"][-50:]
-print("End of document: \"..." + end_text_nrsb + "\"")
-```
-
-A commonly used strategy to split long documents, especially in the field of Question Answering,
-is the sliding window approach. If `split_length=10` and `split_overlap=3`, your documents will look like this:
-
-- doc1 = words[0:10]
-- doc2 = words[7:17]
-- doc3 = words[14:24]
-- ...
-
-You can use this strategy by following the code below.
-
-
-```python
-# Sliding window approach
-
-preprocessor_sliding_window = PreProcessor(
- split_overlap=3,
- split_length=10,
- split_respect_sentence_boundary=False
-)
-docs_sliding_window = preprocessor_sliding_window.process(doc_txt)
-
-doc1 = docs_sliding_window[0]["text"][:200]
-doc2 = docs_sliding_window[1]["text"][:100]
-doc3 = docs_sliding_window[2]["text"][:100]
-
-print("Document 1: \"" + doc1 + "...\"")
-print("Document 2: \"" + doc2 + "...\"")
-print("Document 3: \"" + doc3 + "...\"")
-```
-
-## Bringing it all together
-
-
-```python
-all_docs = convert_files_to_dicts(dir_path="data/preprocessing_tutorial")
-preprocessor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=False,
- split_by="word",
- split_length=100,
- split_respect_sentence_boundary=True
-)
-docs = preprocessor.process(all_docs)
-
-print(f"n_files_input: {len(all_docs)}\nn_docs_output: {len(docs)}")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
-
diff --git a/src/pages/docs/versions/master/site/en/tutorials/tutorials/9.md b/src/pages/docs/versions/master/site/en/tutorials/tutorials/9.md
deleted file mode 100644
index 366b7487e..000000000
--- a/src/pages/docs/versions/master/site/en/tutorials/tutorials/9.md
+++ /dev/null
@@ -1,250 +0,0 @@
----
-title: "Tutorial 9"
-metaTitle: "Training a Dense Passage Retrieval model"
-metaDescription: ""
-slug: "/docs/tutorial9"
-date: "2021-01-08"
-id: "tutorial9md"
----
-
-# Training Your Own "Dense Passage Retrieval" Model
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial9_DPR_training.ipynb)
-
-Haystack contains all the tools needed to train your own Dense Passage Retrieval model.
-This tutorial will guide you through the steps required to create a retriever that is specifically tailored to your domain.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-```
-
-
-```python
-# Here are some imports that we'll need
-
-from haystack.retriever.dense import DensePassageRetriever
-from haystack.preprocessor.utils import fetch_archive_from_http
-from haystack.document_store.memory import InMemoryDocumentStore
-```
-
-## Training Data
-
-DPR training performed using Information Retrieval data.
-More specifically, you want to feed in pairs of queries and relevant documents.
-
-To train a model, we will need a dataset that has the same format as the original DPR training data.
-Each data point in the dataset should have the following dictionary structure.
-
-``` python
- {
- "dataset": str,
- "question": str,
- "answers": list of str
- "positive_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- "negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- "hard_negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- }
-```
-
-`positive_ctxs` are context passages which are relevant to the query.
-In some datasets, queries might have more than one positive context
-in which case you can set the `num_positives` parameter to be higher than the default 1.
-Note that `num_positives` needs to be lower or equal to the minimum number of `positive_ctxs` for queries in your data.
-If you have an unequal number of positive contexts per example,
-you might want to generate some soft labels by retrieving similar contexts which contain the answer.
-
-DPR is standardly trained using a method known as in-batch negatives.
-This means that positive contexts for a given query are treated as negative contexts for the other queries in the batch.
-Doing so allows for a high degree of computational efficiency, thus allowing the model to be trained on large amounts of data.
-
-`negative_ctxs` is not actually used in Haystack's DPR training so we recommend you set it to an empty list.
-They were used by the original DPR authors in an experiment to compare it against the in-batch negatives method.
-
-`hard_negative_ctxs` are passages that are not relevant to the query.
-In the original DPR paper, these are fetched using a retriever to find the most relevant passages to the query.
-Passages which contain the answer text are filtered out.
-
-If you'd like to convert your SQuAD format data into something that can train a DPR model,
-check out the utility script at [`haystack/retriever/squad_to_dpr.py`](https://github.com/deepset-ai/haystack/blob/master/haystack/retriever/squad_to_dpr.py)
-
-## Using Question Answering Data
-
-Question Answering datasets can sometimes be used as training data.
-Google's Natural Questions dataset, is sufficiently large
-and contains enough unique passages, that it can be converted into a DPR training set.
-This is done simply by considering answer containing passages as relevant documents to the query.
-
-The SQuAD dataset, however, is not as suited to this use case since its question and answer pairs
-are created on only a very small slice of wikipedia documents.
-
-## Download Original DPR Training Data
-
-WARNING: These files are large! The train set is 7.4GB and the dev set is 800MB
-
-We can download the original DPR training data with the following cell.
-Note that this data is probably only useful if you are trying to train from scratch.
-
-
-```python
-# Download original DPR data
-# WARNING: the train set is 7.4GB and the dev set is 800MB
-
-doc_dir = "data/dpr_training/"
-
-s3_url_train = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-train.json.gz"
-s3_url_dev = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz"
-
-fetch_archive_from_http(s3_url_train, output_dir=doc_dir + "train/")
-fetch_archive_from_http(s3_url_dev, output_dir=doc_dir + "dev/")
-```
-
-## Option 1: Training DPR from Scratch
-
-The default variables that we provide below are chosen to train a DPR model from scratch.
-Here, both passage and query embedding models are initialized using BERT base
-and the model is trained using Google's Natural Questions dataset (in a format specialised for DPR).
-
-If you are working in a language other than English,
-you will want to initialize the passage and query embedding models with a language model that supports your language
-and also provide a dataset in your language.
-
-
-```python
-# Here are the variables to specify our training data, the models that we use to initialize DPR
-# and the directory where we'll be saving the model
-
-doc_dir = "data/dpr_training/"
-
-train_filename = "train/biencoder-nq-train.json"
-dev_filename = "dev/biencoder-nq-dev.json"
-
-query_model = "bert-base-uncased"
-passage_model = "bert-base-uncased"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Option 2: Finetuning DPR
-
-If you have your own domain specific question answering or information retrieval dataset,
-you might instead be interested in finetuning a pretrained DPR model.
-In this case, you would initialize both query and passage models using the original pretrained model.
-You will want to load something like this set of variables instead of the ones above
-
-
-```python
-# Here are the variables you might want to use instead of the set above
-# in order to perform pretraining
-
-doc_dir = "PATH_TO_YOUR_DATA_DIR"
-train_filename = "TRAIN_FILENAME"
-dev_filename = "DEV_FILENAME"
-
-query_model = "facebook/dpr-question_encoder-single-nq-base"
-passage_model = "facebook/dpr-ctx_encoder-single-nq-base"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Initialization
-
-Here we want to initialize our model either with plain language model weights for training from scratch
-or else with pretrained DPR weights for finetuning.
-We follow the [original DPR parameters](https://github.com/facebookresearch/DPR#best-hyperparameter-settings)
-for their max passage length but set max query length to 64 since queries are very rarely longer.
-
-
-```python
-## Initialize DPR model
-
-retriever = DensePassageRetriever(
- document_store=InMemoryDocumentStore(),
- query_embedding_model=query_model,
- passage_embedding_model=passage_model,
- max_seq_len_query=64,
- max_seq_len_passage=256
-)
-```
-
-## Training
-
-Let's start training and save our trained model!
-
-On a V100 GPU, you can fit up to batch size 16 so we set gradient accumulation steps to 8 in order
-to simulate the batch size 128 of the original DPR experiment.
-
-When `embed_title=True`, the document title is prepended to the input text sequence with a `[SEP]` token
-between it and document text.
-
-When training from scratch with the above variables, 1 epoch takes around an hour and we reached the following performance:
-
-```
-loss: 0.046580662854042276
-task_name: text_similarity
-acc: 0.992524064068483
-f1: 0.8804297774366846
-acc_and_f1: 0.9364769207525838
-average_rank: 0.19631619339984652
-report:
- precision recall f1-score support
-
-hard_negative 0.9961 0.9961 0.9961 201887
- positive 0.8804 0.8804 0.8804 6515
-
- accuracy 0.9925 208402
- macro avg 0.9383 0.9383 0.9383 208402
- weighted avg 0.9925 0.9925 0.9925 208402
-
-```
-
-
-```python
-# Start training our model and save it when it is finished
-
-retriever.train(
- data_dir=doc_dir,
- train_filename=train_filename,
- dev_filename=dev_filename,
- test_filename=dev_filename,
- n_epochs=1,
- batch_size=16,
- grad_acc_steps=8,
- save_dir=save_dir,
- evaluate_every=3000,
- embed_title=True,
- num_positives=1,
- num_hard_negatives=1
-)
-```
-
-## Loading
-
-Loading our newly trained model is simple!
-
-
-```python
-reloaded_retriever = DensePassageRetriever.load(load_dir=save_dir, document_store=None)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/document_store.md b/src/pages/docs/versions/master/site/en/usage/usage/document_store.md
deleted file mode 100644
index 85b4af4ec..000000000
--- a/src/pages/docs/versions/master/site/en/usage/usage/document_store.md
+++ /dev/null
@@ -1,362 +0,0 @@
----
-title: "Document Store"
-metaTitle: "Document Store"
-metaDescription: ""
-slug: "/docs/documentstore"
-date: "2020-09-03"
-id: "documentstoremd"
----
-
-
-# DocumentStores
-
-You can think of the DocumentStore as a "database" that:
-- stores your texts and meta data
-- provides them to the retriever at query time
-
-There are different DocumentStores in Haystack to fit different use cases and tech stacks.
-
-## Initialisation
-
-Initialising a new DocumentStore within Haystack is straight forward.
-
-
-
-
-
-Each DocumentStore constructor allows for arguments specifying how to connect to existing databases and the names of indexes.
-See API documentation for more info.
-
-## Input Format
-
-DocumentStores expect Documents in dictionary form, like that below.
-They are loaded using the `DocumentStore.write_documents()` method.
-See [Preprocessing](/docs/latest/preprocessingmd) for more information on the cleaning and splitting steps that will help you maximize Haystack's performance.
-
-[//]: # (Add link to preprocessing section)
-
-```python
-from haystack.document_store import ElasticsearchDocumentStore
-
-document_store = ElasticsearchDocumentStore()
-dicts = [
- {
- 'text': DOCUMENT_TEXT_HERE,
- 'meta': {'name': DOCUMENT_NAME, ...}
- }, ...
-]
-document_store.write_documents(dicts)
-```
-
-## Writing Documents (Sparse Retrievers)
-
-Haystack allows for you to write store documents in an optimised fashion so that query times can be kept low.
-For **sparse**, keyword based retrievers such as BM25 and TF-IDF,
-you simply have to call `DocumentStore.write_documents()`.
-The creation of the inverted index which optimises querying speed is handled automatically.
-
-```python
-document_store.write_documents(dicts)
-```
-
-## Writing Documents (Dense Retrievers)
-
-For **dense** neural network based retrievers like Dense Passage Retrieval, or Embedding Retrieval,
-indexing involves computing the Document embeddings which will be compared against the Query embedding.
-
-The storing of the text is handled by `DocumentStore.write_documents()` and the computation of the
-embeddings is started by `DocumentStore.update_embeddings()`.
-
-```python
-document_store.write_documents(dicts)
-document_store.update_embeddings(retriever)
-```
-
-This step is computationally intensive since it will engage the transformer based encoders.
-Having GPU acceleration will significantly speed this up.
-
-
-
-## Choosing the Right Document Store
-
-The Document Stores have different characteristics. You should choose one depending on the maturity of your project, the use case and technical environment:
-
-
-
-
-
-
-
-
-[Install](https://www.elastic.co/guide/en/elasticsearch/reference/current/install-elasticsearch.html)
-Elasticsearch and then [start](https://www.elastic.co/guide/en/elasticsearch/reference/current/starting-elasticsearch.html)
-an instance.
-
-If you have Docker set up, we recommend pulling the Docker image and running it.
-```bash
-docker pull docker.elastic.co/elasticsearch/elasticsearch:7.9.2
-docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.9.2
-```
-
-Note that we also have a utility function `haystack.utils.launch_es` that can start up an Elasticsearch instance.
-
-Next you can initialize the Haystack object that will connect to this instance.
-
-```python
-from haystack.document_store import ElasticsearchDocumentStore
-
-document_store = ElasticsearchDocumentStore()
-```
-
-Note that we also support [OpenSearch](https://opensearch.org/).
-Follow [their documentation](https://opensearch.org/docs/)
-to run it and connect to it using Haystack's `OpenSearchDocumentStore` class.
-
-We further support [AWS Elastic Search Service](https://aws.amazon.com/elasticsearch-service/) with [signed Requests](https://docs.aws.amazon.com/general/latest/gr/signature-version-4.html):
-Use e.g. [aws-requests-auth](https://github.com/davidmuller/aws-requests-auth) to create an auth object and pass it as `aws4auth` to the `ElasticsearchDocumentStore` constructor.
-
-
-
-
-
-
-
-
-
-Follow the [official documentation](https://www.milvus.io/docs/v1.0.0/milvus_docker-cpu.md) to start a Milvus instance via Docker.
-Note that we also have a utility function `haystack.utils.launch_milvus` that can start up a Milvus instance.
-
-You can initialize the Haystack object that will connect to this instance as follows:
-```python
-from haystack.document_store import MilvusDocumentStore
-
-document_store = MilvusDocumentStore()
-```
-
-
-
-
-
-
-
-
-
-The `FAISSDocumentStore` requires no external setup. Start it by simply using this line.
-```python
-from haystack.document_store import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
-```
-
-
-
-
-
-
-
-
-
-The `InMemoryDocumentStore()` requires no external setup. Start it by simply using this line.
-```python
-from haystack.document_store import InMemoryDocumentStore
-
-document_store = InMemoryDocumentStore()
-```
-
-
-
-
-
-
-
-
-
-The `SQLDocumentStore` requires SQLite, PostgresQL or MySQL to be installed and started.
-Note that SQLite already comes packaged with most operating systems.
-
-```python
-from haystack.document_store import SQLDocumentStore
-
-document_store = SQLDocumentStore()
-```
-
-
-
-
-
-
-
-
-
-The `WeaviateDocumentStore` requires a running Weaviate Server.
-You can start a basic instance like this (see the [Weaviate docs](https://www.semi.technology/developers/weaviate/current/) for details):
-```
- docker run -d -p 8080:8080 --env AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED='true' --env PERSISTENCE_DATA_PATH='/var/lib/weaviate' semitechnologies/weaviate:1.6.0
-```
-
-Afterwards, you can use it in Haystack:
-```python
-from haystack.document_store import WeaviateDocumentStore
-
-document_store = WeaviateDocumentStore()
-```
-
-
-
-
-
-
-
-
-
-See the official [OpenSearch documentation](https://opensearch.org/docs/opensearch/install/docker/) on how to install and start an instance.
-
-If you have Docker set up, we recommend pulling the Docker image and running it.
-```bash
-docker pull opensearchproject/opensearch:1.0.0
-docker run -p 9200:9200 -p 9600:9600 -e "discovery.type=single-node" opensearchproject/opensearch:1.0.0
-```
-
-Note that we also have a utility function `haystack.utils.launch_opensearch` that can start up an OpenSearch instance.
-
-Next you can initialize the Haystack object that will connect to this instance.
-
-```python
-from haystack.document_store import OpenSearchDocumentStore
-
-document_store = OpenSearchDocumentStore()
-```
-
-
-
-
-
-
-
-
-
-
-
-
-
-**Pros:**
-- Fast & accurate sparse retrieval with many tuning options
-- Basic support for dense retrieval
-- Production-ready
-- Support also for Open Distro
-
-**Cons:**
-- Slow for dense retrieval with more than ~ 1 Mio documents
-
-
-
-
-
-
-
-
-
-**Pros:**
-- Scalable DocumentStore that excels at handling vectors (hence suited to dense retrieval methods like DPR)
-- Encapsulates multiple ANN libraries (e.g. FAISS and ANNOY) and provides added reliability
-- Runs as a separate service (e.g. a Docker container)
-- Allows dynamic data management
-
-**Cons:**
-- No efficient sparse retrieval
-
-
-
-
-
-
-
-
-
-**Pros:**
-- Fast & accurate dense retrieval
-- Highly scalable due to approximate nearest neighbour algorithms (ANN)
-- Many options to tune dense retrieval via different index types (more info [here](https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index))
-
-**Cons:**
-- No efficient sparse retrieval
-
-
-
-
-
-
-
-
-
-**Pros:**
-- Simple
-- Exists already in many environments
-
-**Cons:**
-- Only compatible with minimal TF-IDF Retriever
-- Bad retrieval performance
-- Not recommended for production
-
-
-
-
-
-
-
-
-
-
-**Pros:**
-- Simple & fast to test
-- No database requirements
-- Supports MySQL, PostgreSQL and SQLite
-
-**Cons:**
-- Not scalable
-- Not persisting your data on disk
-
-
-
-
-
-
-
-
-
-**Pros:**
-- Simple vector search
-- Stores everything in one place: documents, meta data and vectors - so less network overhead when scaling this up
-- Allows combination of vector search and scalar filtering, i.e. you can filter for a certain tag and do dense retrieval on that subset
-
-**Cons:**
-- Less options for ANN algorithms than FAISS or Milvus
-- No BM25 / Tf-idf retrieval
-
-
-
-
-
-
-
-
-
-**Pros:**
-- Fully open source fork of Elasticsearch
-- Has support for Approximate Nearest Neighbours vector search
-
-**Cons:**
-- It's ANN algorithms seem a little less performant that FAISS or Milvus in our benchmarks
-
-
-
-
-#### Our Recommendations
-
-**Restricted environment:** Use the `InMemoryDocumentStore`, if you are just giving Haystack a quick try on a small sample and are working in a restricted environment that complicates running Elasticsearch or other databases
-
-**Allrounder:** Use the `ElasticSearchDocumentStore`, if you want to evaluate the performance of different retrieval options (dense vs. sparse) and are aiming for a smooth transition from PoC to production
-
-**Vector Specialist:** Use the `MilvusDocumentStore`, if you want to focus on dense retrieval and possibly deal with larger datasets
-
-
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/faq.md b/src/pages/docs/versions/master/site/en/usage/usage/faq.md
deleted file mode 100644
index 04bcc48d1..000000000
--- a/src/pages/docs/versions/master/site/en/usage/usage/faq.md
+++ /dev/null
@@ -1,90 +0,0 @@
----
-title: "Frequently Asked Questions"
-metaTitle: "Frequently Asked Questions"
-metaDescription: ""
-slug: "/docs/faq"
-date: "2020-09-03"
-id: "faqmd"
----
-
-#Frequently Asked Questions
-
-##Why am I seeing duplicate answers being returned?
-
-The ElasticsearchDocumentStore and MilvusDocumentStore rely on Elasticsearch and Milvus backend services which
-persist after your Python script has finished running.
-If you rerun your script without deleting documents, you could end up with duplicate
-copies of your documents in your database.
-The easiest way to avoid this is to call `DocumentStore.delete_documents()` after initialization
-to ensure that you are working with an empty DocumentStore.
-
-DocumentStores also have a `duplicate_documents` argument in their `__init__()` and `write_documents` methods
-where you can define whether you'd like skip writing duplicates, overwrite existing duplicates or raise an error when there are duplicates.
-
-##How can I make sure that my GPU is being engaged when I use Haystack?
-
-You will want to ensure that a CUDA enabled GPU is being engaged when Haystack is running (you can check by running `nvidia-smi -l` on your command line).
-Components which can be sped up by GPU have a `use_gpu` argument in their constructor which you will want to set to `True`.
-
-##How do I speed up my predictions?
-
-There are many different ways to speed up the performance of your Haystack system.
-
-The Reader is usually the most computationally expensive component in a pipeline
-and you can often speed up your system by using a smaller model, like `deepset/minilm-uncased-squad2` (see [benchmarks](https://huggingface.co/deepset/minilm-uncased-squad2)). This usually comes with a small trade-off in accuracy.
-
-You can reduce the work load on the Reader by instructing the Retriever to pass on less documents.
-This is done by setting the `top_k_retriever` parameter to a lower value.
-
-Making sure that your documents are shorter can also increase the speed of your system. You can split
-your documents into smaller chunks by using the `PreProcessor` (see [tutorial](https://haystack.deepset.ai/docs/latest/tutorial11md)).
-
-For more optimization suggestions, have a look at our [optimization page](https://haystack.deepset.ai/docs/latest/optimizationmd)
-and also our [blogs](https://medium.com/deepset-ai)
-
-##How do I use Haystack for my language?
-
-The components in Haystack, such as the `Retriever` or the `Reader`, are designed in a language agnostic way. However you may
-have to set certain parameters or load models pretrained for your language in order to get good performance out of Haystack.
-See our [languages page](https://haystack.deepset.ai/docs/latest/languagesmd) for more details.
-
-##How can I add metadata to my documents so that I can apply filters?
-
-When providing your documents in the input format (see [here](https://haystack.deepset.ai/docs/latest/documentstoremd#Input-Format))
-you can provide metadata information as a dictionary under the `meta` key. At query time, you can provide a `filters` argument
-(most likely through `Pipelines.run()`) that specifies the accepted values for a certain metadata field
-(for an example of what a `filters` dictionary might look like, please refer to [this example](https://haystack.deepset.ai/docs/latest/apiretrievermd#__init__))
-
-##How can I see predictions during evaluation?
-
-To see predictions during evaluation, you want to initialize the `EvalDocuments` or `EvalAnswers` with `debug=True`.
-This causes their `EvalDocuments.log` or `EvalAnswers.log` to be populated with a record of each prediction made.
-
-##How can I serve my Haystack model?
-
-Haystack models can be wrapped in a REST API. For basic details on how to set this up, please refer to this section
-on our [Github page](https://github.com/deepset-ai/haystack/blob/master/README.md#7-rest-api).
-More comprehensive documentation coming soon!
-
-##How can I interpret the confidence scores being returned by the Reader?
-
-The confidence scores are in the range of 0 and 1 and reflect how confident the model is in each prediction that it makes.
-Having a confidence score is particularly useful in cases where you need Haystack to work with a certain accuracy threshold.
-Many of our users have built systems where predictions below a certain confidence value are routed on to a fallback system.
-
-For more information on model confidence and how to tune it, please refer to [this section](https://haystack.deepset.ai/docs/latest/readermd#Confidence-Scores).
-
-##My documents aren't showing up in my DocumentStore even though I've called `DocumentStore.write_documents()`
-
-When indexing, retrieving or querying for documents from a DocumentStore, you can specify an `index` on which to perform this action.
-This can be specified in almost all methods of `DocumentStore` as well as `Retriever.retrieve()`.
-Ensure that you are performing these operations on the one index!
-Note that this also applies at evaluation where labels are written into their own separate DocumentStore index.
-
-##What is the difference between the FARMReader and the TransformersReader?
-
-In short, the FARMReader using a QA pipeline implementation that comes from our own
-[FARM framework](https://github.com/deepset-ai/FARM) that we can more easily update and also optimize for performance.
-By contrast, the TransformersReader uses a QA pipeline implementation that comes from HuggingFace's [Transformers](https://github.com/huggingface/transformers).
-See [this section](https://haystack.deepset.ai/docs/latest/readermd#Deeper-Dive-FARM-vs-Transformers)
-for a more details about their differences!
\ No newline at end of file
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/generator.md b/src/pages/docs/versions/master/site/en/usage/usage/generator.md
deleted file mode 100644
index fdba2ab18..000000000
--- a/src/pages/docs/versions/master/site/en/usage/usage/generator.md
+++ /dev/null
@@ -1,36 +0,0 @@
----
-title: "Generator"
-metaTitle: "Generator"
-metaDescription: ""
-slug: "/docs/generator"
-date: "2020-11-05"
-id: "generatormd"
----
-
-# Generator
-
-While extractive QA highlights the span of text that answers a query,
-generative QA can return a novel text answer that it has composed.
-The best current approaches, such as [Retriever-Augmented Generation](https://arxiv.org/abs/2005.11401) and [LFQA](https://yjernite.github.io/lfqa.html),
-can draw upon both the knowledge it gained during language model pretraining (parametric memory)
-as well as passages provided to it with a retriever (non-parametric memory).
-With the advent of Transformer based retrieval methods such as [Dense Passage Retrieval](https://arxiv.org/abs/2004.04906),
-retriever and generator can be trained concurrently from the one loss signal.
-
-
-
-**Tutorial**
-
-Checkout our tutorial notebooks for a guide on how to build your own generative QA system with RAG ([here](/docs/latest/tutorial7md))
-or with LFQA ([here](/docs/latest/tutorial12md)).
-
-
-
-Pros
-* More appropriately phrased answers
-* Able to synthesize information from different texts
-* Can draw on latent knowledge stored in language model
-
-Cons
-* Not easy to track what piece of information the generator is basing its response off of
-
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/get_started.md b/src/pages/docs/versions/master/site/en/usage/usage/get_started.md
deleted file mode 100644
index 890578095..000000000
--- a/src/pages/docs/versions/master/site/en/usage/usage/get_started.md
+++ /dev/null
@@ -1,166 +0,0 @@
----
-title: "Get Started"
-metaTitle: "Get Started"
-metaDescription: ""
-slug: "/docs/get_started"
-date: "2020-09-03"
-id: "get_startedmd"
----
-
-# Get Started
-
-## Installation
-
-
-
-
-
-Note: On Windows add the arg `-f https://download.pytorch.org/whl/torch_stable.html` to install PyTorch correctly
-
-## The Building Blocks of Haystack
-
-Here’s a sample of some Haystack code showing a question answering system using a retriever and a reader.
-For a working code example, check out our [starter tutorial](/docs/latest/tutorial1md).
-
-```python
-# DocumentStore: holds all your data
-document_store = ElasticsearchDocumentStore()
-
-# Clean & load your documents into the DocumentStore
-dicts = convert_files_to_dicts(doc_dir, clean_func=clean_wiki_text)
-document_store.write_documents(dicts)
-
-# Retriever: A Fast and simple algo to indentify the most promising candidate documents
-retriever = ElasticsearchRetriever(document_store)
-
-# Reader: Powerful but slower neural network trained for QA
-model_name = "deepset/roberta-base-squad2"
-reader = FARMReader(model_name)
-
-# Pipeline: Combines all the components
-pipe = ExtractiveQAPipeline(reader, retriever)
-
-# Voilà! Ask a question!
-question = "Who is the father of Sansa Stark?"
-prediction = pipe.run(query=question)
-print_answers(prediction)
-```
-
-## Loading Documents into the DocumentStore
-
-In Haystack, DocumentStores expect Documents in a dictionary format. They are loaded as follows:
-
-```python
-document_store = ElasticsearchDocumentStore()
-dicts = [
- {
- 'text': DOCUMENT_TEXT_HERE,
- 'meta': {'name': DOCUMENT_NAME, ...}
- }, ...
-]
-document_store.write_documents(dicts)
-```
-
-When we talk about Documents in Haystack, we are referring specifically to the individual blocks of text that are being held in the DocumentStore.
-You might want to use all the text in one file as a Document, or split it into multiple Documents.
-This splitting can have a big impact on speed and performance.
-
-
-
-
-
-
-
-The most straightforward way to install Haystack is through pip.
- -```python -$ pip install farm-haystack -``` - -
-- -```python -$ pip install farm-haystack -``` - -
-
-
-
-
-
-If you’d like to run a specific, unreleased version of Haystack, or make edits to the way Haystack runs,
-you’ll want to install it using `git` and `pip --editable`.
-This clones a copy of the repo to a local directory and runs Haystack from there.
- -```python -$ git clone https://github.com/deepset-ai/haystack.git -$ cd haystack -$ pip install --editable . -``` - -By default, this will give you the latest version of the master branch. Use regular git commands to switch between different branches and commits. -
-- -```python -$ git clone https://github.com/deepset-ai/haystack.git -$ cd haystack -$ pip install --editable . -``` - -By default, this will give you the latest version of the master branch. Use regular git commands to switch between different branches and commits. -
-
-**Tip:** If Haystack is running very slowly, you might want to try splitting your text into smaller Documents.
-If you want an improvement to performance, you might want to try concatenating text to make larger Documents.
-See [Optimization](/docs/latest/optimizationmd) for more details.
-
-
-
-
-## Running Search Queries
-
-There are many different flavours of search that can be created using Haystack.
-But to give just one example of what can be achieved, let's look more closely at
-an Open Domain Question Answering (ODQA) Pipeline.
-
-
-**Querying** in an ODQA system involves searching for an answer to a given question within the full document store.
-This process will:
-
-
-* make the Retriever filter for a small set of relevant candidate documents
-
-
-* get the Reader to process this set of candidate documents
-
-
-* return potential answers to the given question
-
-Usually, there are tight time constraints on querying and so it needs to be a lightweight operation.
-When documents are loaded, Haystack will precompute any of the results that might be useful at query time.
-
-In Haystack, querying is performed with a `Pipeline` object which connects the reader to the retriever.
-
-```python
-# Pipeline: Combines all the components
-pipe = ExtractiveQAPipeline(reader, retriever)
-
-# Voilà! Ask a question!
-question = "Who is the father of Sansa Stark?"
-prediction = pipe.run(query=question)
-print_answers(prediction)
-```
-
-When the query is complete, you can expect to see results that look something like this:
-
-```python
-[
- { 'answer': 'Eddard',
- 'context': 's Nymeria after a legendary warrior queen. She travels '
- "with her father, Eddard, to King's Landing when he is made "
- 'Hand of the King. Before she leaves,'
- }, ...
-]
-```
-
-##Custom Search Pipelines
-
-Haystack providers many different building blocks for you to mix and match.
-They include:
-- Readers
-- Retrievers (sparse and dense)
-- DocumentStores
-- Summarizers
-- Generators
-- Translators
-
-These can all be combined in the configuration that you want.
-Have a look at our [Pipelines page](/docs/latest/pipelinesmd) to see what's possible!
\ No newline at end of file
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/knowledge_graph.md b/src/pages/docs/versions/master/site/en/usage/usage/knowledge_graph.md
deleted file mode 100644
index 65e8bb6cd..000000000
--- a/src/pages/docs/versions/master/site/en/usage/usage/knowledge_graph.md
+++ /dev/null
@@ -1,108 +0,0 @@
----
-title: "Knowledge Graph"
-metaTitle: "Knowledge Graph"
-metaDescription: ""
-slug: "/docs/knowledgegraph"
-date: "2021-04-19"
-id: "knowledgegraphmd"
----
-
-# Question Answering on a Knowledge Graph
-
-Haystack allows loading and querying knowledge graphs. In particular, Haystack can:
-
-* Load an existing knowledge graph given as a .ttl file
-* Execute SPARQL queries on a knowledge graph
-* Execute text queries on the knowledge graph by translating them to SPARQL queries with the help of a pre-trained seq2seq model
-
-Haystack's knowledge graph functionalities are still in a very early stage. Thus, don't expect our [exemplary tutorial](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial10_Knowledge_Graph.py) to work on your custom dataset out-of-the-box.
-Two classes implement the functionalities: GraphDBKnowledgeGraph and Text2SparqlRetriever.
-
-## GraphDBKnowledgeGraph
-
-GraphDBKnowledgeGraph is a triple store similar to Haystack's document stores. Currently, it is the only implementation of the BaseKnowledgeGraph class.
-GraphDBKnowledgeGraph runs on GraphDB. The licensing of GraphDB is rather complicated and it's more than unfortunate that GraphDB cannot be used right away in colab notebooks.
-On your local machine, you can start a GraphDB instance by running:
-
-```docker run -d -p 7200:7200 --name graphdb-instance-tutorial docker-registry.ontotext.com/graphdb-free:9.4.1-adoptopenjdk11```
-
-By default, GraphDBKnowledgeGraph connects to a GraphDB instance running on localhost at port 7200.
-Similar to Haystack's ElasticsearchDocumentStore, the only additional setting needed is an index name.
-(Note that GraphDB internally calls these indices repositories.)
-
-```kg = GraphDBKnowledgeGraph(index="tutorial_10_index")```
-
-Indices can be deleted and created with ```GraphDBKnowledgeGraph.delete_index()``` and ```GraphDBKnowledgeGraph.create_index(config_path)```.
-```config_path``` needs to point to a .ttl file that contains configuration settings (see [GraphDB documentation](https://graphdb.ontotext.com/documentation/free/configuring-a-repository.html#configure-a-repository-programmatically) for details or use the file from our [tutorial](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial10_Knowledge_Graph.py)). It starts with something like:
-
-```
-#
-# Sesame configuration template for a GraphDB Free repository
-#
-@prefix rdfs:
-
-**Note**
-
-This feature will be implemented by [this PR.](https://github.com/deepset-ai/haystack/pull/1160)
-
-
-
-The PreProcessor's sentence tokenization is language specific.
-If you are using the PreProcessor on a language other than English,
-make sure to set the `language` argument when initializing it.
-
-##Retrievers
-
-The sparse retriever methods themselves(BM25, TF-IDF) are language agnostic.
-Their only requirement is that the text be split into words.
-The ElasticsearchDocumentStore relies on an analyzer to impose word boundaries,
-but also to handle punctuation, casing and stop words.
-
-The default analyzer is an English analyzer.
-While it can still work decently for a large range of languages,
-you will want to set it to your language's analyzer for optimal performance.
-In some cases, such as with Thai, the default analyzer is completely incompatible.
-See [this page](https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-lang-analyzer.html)
-for the full list of language specific analyzers.
-
-```python
-from haystack.document_store import ElasticsearchDocumentStore
-
-document_store = ElasticsearchDocumentStore(analyzer="thai")
-```
-
-The models used in dense retrievers are language specific.
-Be sure to check language of the model used in your EmbeddingRetriever.
-The default model that is loaded in the DensePassageRetriever is for English.
-
-We have created a [German DensePassageRetriever model](https://deepset.ai/germanquad) and know other teams who work on further languages.
-If you have a language model and a question answering dataset in your own language, you can also train a DPR model using Haystack!
-Below is a simplified example.
-See [our tutorial](/docs/latest/tutorial9md) and also the [API reference](/docs/latest/apiretrievermd#train) for `DensePassageRetriever.train()` for more details.
-
-```python
-from haystack.retriever import DensePassageRetriever
-
-dense_passage_retriever = DensePassageRetriever(document_store)
-dense_passage_retriever.train(self,
- data_dir: str,
- train_filename: str,
- dev_filename: str = None,
- test_filename: str = None,
- batch_size: int = 16,
- embed_title: bool = True,
- num_hard_negatives: int = 1,
- n_epochs: int = 3)
-```
-
-##Readers
-
-While models are comparatively more performant on English,
-thanks to a wealth of available English training data,
-there are a couple QA models that are directly usable in Haystack.
-
-
-
-
-
-We are the creators of the **German** model and you can find out more about it [here](https://deepset.ai/germanquad)
-
-The **French**, **Italian**, **Spanish**, **Portuguese** and **Chinese** models are monolingual language models trained on versions of the SQuAD dataset in their respective languages
-and their authors report decent results in their model cards
-(e.g. [here](https://huggingface.co/illuin/camembert-base-fquad) and [here](https://huggingface.co/mrm8488/bert-italian-finedtuned-squadv1-it-alfa)).
-There also exist Korean QA models on the model hub but their performance is not reported.
-
-The **zero-shot model** that is shown above is a **multilingual XLM-RoBERTa Large** that is trained on English SQuAD.
-It is clear, from our [evaluations](https://huggingface.co/deepset/xlm-roberta-large-squad2#model_card),
-that the model has been able to transfer some of its English QA capabilities to other languages,
-but still its performance lags behind that of the monolingual models.
-Nonetheless, if there is not yet a monolingual model for your language and it is one of the 100 supported by XLM-RoBERTa,
-this zero-shot model may serve as a decent first baseline.
-
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/optimization.md b/src/pages/docs/versions/master/site/en/usage/usage/optimization.md
deleted file mode 100644
index e6f4b1479..000000000
--- a/src/pages/docs/versions/master/site/en/usage/usage/optimization.md
+++ /dev/null
@@ -1,107 +0,0 @@
----
-title: "Optimization"
-metaTitle: "Optimization"
-metaDescription: ""
-slug: "/docs/optimization"
-date: "2020-11-05"
-id: "optimizationmd"
----
-
-# Optimization
-
-## Speeding up Reader
-
-In most pipelines, the Reader will be the most computationally expensive component.
-If this is a step that you would like to speed up, you can opt for a smaller Reader model
-that can process more passages in the same amount of time.
-
-On our [benchmarks page](https://haystack.deepset.ai/bm/benchmarks), you will find a comparison of
-many of the common model architectures. While our default recommendation is RoBERTa,
-MiniLM offers much faster processing for only a minimal drop in accuracy.
-You can find the models that we've trained on [the HuggingFace Model Hub](https://huggingface.co/deepset)
-
-## GPU acceleration
-
-The transformer based models used in Haystack are designed to be run on a GPU enabled machine.
-The design of these models means that they greatly benefit from the parallel processing capabilities of graphics cards.
-If Haystack has successfully detected a graphics card, you should see these lines in your console output.
-
-```
-INFO - farm.utils - Using device: CUDA
-INFO - farm.utils - Number of GPUs: 1
-```
-
-You can track the work load on your CUDA enabled Nvidia GPU by tracking the output of `nvidia-smi -l` on the command line
-while your Haystack program is running.
-
-## Document Length
-
-Document length has a very direct impact on the speed of the Reader
-which is why we recommend using the `PreProcessor` class to clean and split your documents.
-**If you halve the length of your documents, you will halve the workload placed onto your Reader.**
-
-For **sparse retrievers**, very long documents pose a challenge since the signal of the relevant section of text
-can get washed out by the rest of the document.
-To get a good balance between Reader speed and Retriever performance, we splitting documents to a maximum of 500 words.
-If there is no Reader in the pipeline following the Retriever, we recommend that **documents be no longer than 10,000 words**.
-
-**Dense retrievers** are limited in the length of text that they can read in one pass.
-As such, it is important that documents are not longer than the dense retriever's maximum input length.
-By default, Haystack's DensePassageRetriever model has a maximum length of 256 tokens.
-As such, we recommend that documents contain significantly less words.
-We have found decent performance with **documents around 100 words long**.
-
-## Respecting Sentence Boundaries
-
-When splitting documents, it is generally not a good idea to let document boundaries fall in the middle of sentences.
-Doing so means that each document will contain incomplete sentence fragments
-which maybe be hard for both retriever and reader to interpret.
-It is therefore recommended to set `split_respect_sentence_boundary=True` when initializing your `PreProcessor`.
-
-## Choosing the Right top-k Values
-
-The `top-k` parameter in both the `Retriever` and `Reader` determine how many results they return.
-More specifically, `Retriever` `top-k` dictates how many retrieved documents are passed on to the next stage,
-while `Reader` `top-k` determines how many answer candidates to show.
-
-In our experiments, we have found that **`Retriever` `top_k=10`
-gives decent overall performance** and so we have set this as the default in Haystack.
-
-The choice of `Retriever` `top-k` is a trade-off between speed and accuracy,
-especially when there is a `Reader` in the pipeline.
-Setting it higher means passing more documents to the `Reader`,
-thus reducing the chance that the answer-containing passage is missed.
-However, passing more documents to the `Reader` will create a larger workload for the component.
-
-These parameters can easily be tweaked as follows if using a `Finder`:
-``` python
-answers = pipeline.run(query="What did Einstein work on?", top_k_retriever=10, top_k_reader=5)
-```
-or like this if directly calling the `Retriever`:
-``` python
-retrieved_docs = retriever.retrieve(top_k=10)
-```
-
-## Metadata Filtering
-
-Metadata can be attached to the documents which you index into your DocumentStore (see the input data format [here](/docs/latest/retrievermd)).
-At query time, you can apply filters based on this metadata to limit the scope of your search and ensure your answers
-come from a specific slice of your data.
-
-For example, if you have a set of annual reports from various companies,
-you may want to perform a search on just a specific year, or on a small selection of companies.
-This can reduce the work load of the retriever and also ensure that you get more relevant results.
-
-Filters are applied via the `filters` argument of the `Retriever` class. In practice, this argument will probably
-be passed into the `Pipeline.run()` call, which will then route it on to the `Retriever` class
-(see our the Arguments on the [Pipelines page](/docs/latest/pipelinesmd) for an explanation).
-
-```python
-pipeline.run(
- query="Why did the revenue increase?",
- filters={
- "years": ["2019"],
- "companies": ["BMW", "Mercedes"]
- }
-)
-```
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/pipelines.md b/src/pages/docs/versions/master/site/en/usage/usage/pipelines.md
deleted file mode 100644
index 7dfb6bc91..000000000
--- a/src/pages/docs/versions/master/site/en/usage/usage/pipelines.md
+++ /dev/null
@@ -1,187 +0,0 @@
----
-title: "Pipelines"
-metaTitle: "Pipelines"
-metaDescription: ""
-slug: "/docs/pipelines"
-date: "2020-11-05"
-id: "pipelinesmd"
----
-
-# Pipelines
-
-### Flexibility powered by DAGs
-In order to build modern search pipelines, you need two things: powerful building blocks and an easy way to stick them together.
-The `Pipeline` class is exactly built for this purpose and enables many search scenarios beyond QA.
-The core idea is that you can build a Directed Acyclic Graph (DAG) where each node is one building block (Reader, Retriever, Generator ...).
-Here's a simple example for a standard Open-Domain QA Pipeline:
-
-```python
-from haystack import Pipeline
-
-p = Pipeline()
-p.add_node(component=retriever, name="ESRetriever1", inputs=["Query"])
-p.add_node(component=reader, name="QAReader", inputs=["ESRetriever1"])
-res = p.run(query="What did Einstein work on?", top_k_retriever=1)
-```
-
-You can **draw the DAG** to better inspect what you are building:
-```python
-p.draw(path="custom_pipe.png")
-```
-
-
-### Arguments
-
-Whatever keyword arguments are passed into the `Pipeline.run()` method will be passed on to each node in the pipeline.
-For example, in the code snippet below, all nodes will receive `query`, `top_k_retriever` and `top_k_reader` as argument,
-even if they don't use those arguments. It is therefore very important when defining custom nodes that their
-keyword argument names do not clash with the other nodes in your pipeline.
-
-```python
-res = pipeline.run(
- query="What did Einstein work on?",
- top_k_retriever=1,
- top_k_reader=5
-)
-```
-
-### YAML File Definitions
-
-For your convenience, there is also the option of defining and loading pipelines in YAML files.
-Having your pipeline available in a YAML is particularly useful when
-you move between experimentation and production environments.
-Just export the YAML from your notebook / IDE and import it into your production environment.
-It also helps with version control of pipelines, allows you to share your pipeline easily with colleagues,
-and simplifies the configuration of pipeline parameters in production.
-
-For example, you can define and save a simple Retriever Reader pipeline by saving the following to a file:
-
-```yaml
-version: '0.7'
-
-components: # define all the building-blocks for Pipeline
-- name: MyReader # custom-name for the component; helpful for visualization & debugging
- type: FARMReader # Haystack Class name for the component
- params:
- no_ans_boost: -10
- model_name_or_path: deepset/roberta-base-squad2
-- name: MyESRetriever
- type: ElasticsearchRetriever
- params:
- document_store: MyDocumentStore # params can reference other components defined in the YAML
- custom_query: null
-- name: MyDocumentStore
- type: ElasticsearchDocumentStore
- params:
- index: haystack_test
-
-pipelines: # multiple Pipelines can be defined using the components from above
-- name: my_query_pipeline # a simple extractive-qa Pipeline
- nodes:
- - name: MyESRetriever
- inputs: [Query]
- - name: MyReader
- inputs: [MyESRetriever]
-```
-
-To load, simply call:
-
-```python
-pipeline.load_from_yaml(Path("sample.yaml"))
-```
-
-For another example YAML config, check out [this file](https://github.com/deepset-ai/haystack/blob/master/rest_api/pipeline/pipelines.yaml).
-
-### Multiple retrievers
-You can now also use multiple Retrievers and join their results:
-```python
-from haystack import Pipeline
-
-p = Pipeline()
-p.add_node(component=es_retriever, name="ESRetriever", inputs=["Query"])
-p.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["Query"])
-p.add_node(component=JoinDocuments(join_mode="concatenate"), name="JoinResults", inputs=["ESRetriever", "DPRRetriever"])
-p.add_node(component=reader, name="QAReader", inputs=["JoinResults"])
-res = p.run(query="What did Einstein work on?", top_k_retriever=1)
-```
-
-
-### Custom nodes
-You can easily build your own custom nodes. Just respect the following requirements:
-
-1. Add a method `run(self, **kwargs)` to your class. `**kwargs` will contain the output from the previous node in your graph.
-2. Do whatever you want within `run()` (e.g. reformatting the query)
-3. Return a tuple that contains your output data (for the next node) and the name of the outgoing edge `output_dict, "output_1`
-4. Add a class attribute `outgoing_edges = 1` that defines the number of output options from your node. You only need a higher number here if you have a decision node (see below).
-
-### Decision nodes
-Or you can add decision nodes where only one "branch" is executed afterwards. This allows, for example, to classify an incoming query and depending on the result routing it to different modules:
-
-```python
- class QueryClassifier():
- outgoing_edges = 2
-
- def run(self, **kwargs):
- if "?" in kwargs["query"]:
- return (kwargs, "output_1")
-
- else:
- return (kwargs, "output_2")
-
- pipe = Pipeline()
- pipe.add_node(component=QueryClassifier(), name="QueryClassifier", inputs=["Query"])
- pipe.add_node(component=es_retriever, name="ESRetriever", inputs=["QueryClassifier.output_1"])
- pipe.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_2"])
- pipe.add_node(component=JoinDocuments(join_mode="concatenate"), name="JoinResults",
- inputs=["ESRetriever", "DPRRetriever"])
- pipe.add_node(component=reader, name="QAReader", inputs=["JoinResults"])
- res = p.run(query="What did Einstein work on?", top_k_retriever=1)
-```
-
-### Evaluation nodes
-
-There are nodes in Haystack that are used to evaluate the performance of readers, retrievers and combine systems.
-To get hands on with this kind of node, have a look at the [evaluation tutorial](/docs/latest/tutorial5md).
-
-### Default Pipelines (replacing the "Finder")
-Last but not least, we added some "Default Pipelines" that allow you to run standard patterns with very few lines of code.
-This is replacing the `Finder` class which was deprecated with Haystack 0.6.0 .
-
-```
-from haystack.pipeline import DocumentSearchPipeline, ExtractiveQAPipeline, Pipeline, JoinDocuments
-
-# Extractive QA
-qa_pipe = ExtractiveQAPipeline(reader=reader, retriever=retriever)
-res = qa_pipe.run(query="When was Kant born?", top_k_retriever=3, top_k_reader=5)
-
-# Document Search
-doc_pipe = DocumentSearchPipeline(retriever=retriever)
-res = doc_pipe.run(query="Physics Einstein", top_k_retriever=1)
-
-# Generative QA
-doc_pipe = GenerativeQAPipeline(generator=rag_generator, retriever=retriever)
-res = doc_pipe.run(query="Physics Einstein", top_k_retriever=1)
-
-# FAQ based QA
-doc_pipe = FAQPipeline(retriever=retriever)
-res = doc_pipe.run(query="How can I change my address?", top_k_retriever=3)
-
-```
-So to migrate your QA system from the deprecated `Finder` to `ExtractiveQAPipeline` you'd need to:
-```
-# 1. Change import
-from haystack.pipeline import ExtractiveQAPipeline
-
-# 2. Replace the Finder
-qa_pipe = ExtractiveQAPipeline(reader=reader, retriever=retriever)
-
-# 3. Replace get_answers() with run()
-res = qa_pipe.run(query="When was Kant born?", top_k_retriever=3, top_k_reader=5)
-
-# 4. Access your results from ["documents"] rather than ["answers"]
-print(res["documents"])
-```
-See also the [Pipelines API documentation](/docs/latest/apipipelinesmd) for more details.
-
-We plan many more features around the new pipelines incl. parallelized execution, distributed execution, dry runs - so stay tuned ...
-
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/preprocessing.md b/src/pages/docs/versions/master/site/en/usage/usage/preprocessing.md
deleted file mode 100644
index 552ae192a..000000000
--- a/src/pages/docs/versions/master/site/en/usage/usage/preprocessing.md
+++ /dev/null
@@ -1,144 +0,0 @@
----
-title: "Preprocessing"
-metaTitle: "Preprocessing"
-metaDescription: ""
-slug: "/docs/preprocessing"
-date: "2020-09-03"
-id: "preprocessingmd"
----
-
-# Preprocessing
-
-Haystack includes a suite of tools to:
-
-* extract text from different file types,
-* normalize white space
-* split text into smaller pieces to optimize retrieval
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("deepset/gelectra-large-germanquad")
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("illuin/camembert-base-fquad")
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa")
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("uer/roberta-base-chinese-extractive-qa")
-# or
-reader = FARMReader("wptoux/albert-chinese-large-qa")
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es")
-# or
-reader = FARMReader("mrm8488/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es")
-```
-
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("pierreguillou/bert-base-cased-squad-v1.1-portuguese")
-# or
-reader = FARMReader("pucpr/bioBERTpt-squad-v1.1-portuguese")
-
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("deepset/xlm-roberta-large-squad2")
-```
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("deepset/gelectra-large-germanquad")
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("illuin/camembert-base-fquad")
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa")
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("uer/roberta-base-chinese-extractive-qa")
-# or
-reader = TransformersReader("wptoux/albert-chinese-large-qa")
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es")
-# or
-reader = TransformersReader("mrm8488/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es")
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("pierreguillou/bert-base-cased-squad-v1.1-portuguese")
-# or
-reader = TransformersReader("pucpr/bioBERTpt-squad-v1.1-portuguese")
-
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("deepset/xlm-roberta-large-squad2")
-```
-
-
-
-
-Check out our [preprocessing tutorial](/docs/latest/tutorial8md) if you'd like to start working with code examples already!
-
-
-
-These data preprocessing steps can have a big impact on the systems performance
-and effective handling of data is key to getting the most out of Haystack.
-
-The Document Store expects its inputs to come in the following format.
-The sections below will show you all the tools you'll need to ready your data for storing.
-
-```python
-docs = [
- {
- 'text': DOCUMENT_TEXT_HERE,
- 'meta': {'name': DOCUMENT_NAME, ...}
- }, ...
-]
-```
-
-## File Conversion
-
-There are a range of different file converters in Haystack that
-can extract text from files and cast them into the unified dictionary format shown above.
-Haystack features support for txt, pdf and docx files and there is even a converter that leverages Apache Tika.
-Please refer to [the API docs](/docs/latest/file_convertersmd) to see which converter best suits you.
-
-
-
-
-
-## Web Crawler
-
-In Haystack, you will find a web crawler that will help you scrape text from websites and save it to file.
-See the [API documentation](https://haystack.deepset.ai/docs/latest/apicrawlermd) for more details.
-
-```python
-from haystack.connector import Crawler
-
-crawler = Crawler()
-docs = crawler.crawl(urls=["https://haystack.deepset.ai/docs/latest/get_startedmd"],
- output_dir="crawled_files",
- filter_urls= ["haystack\.deepset\.ai\/docs\/"])
-```
-
-## PreProcessor
-
-While each of the above conversion methods produce documents that are already in the format expected by the Document Store,
-it is recommended that they are further processed in order to ensure optimal Retriever and Reader performance.
-The `PreProcessor` takes one of the documents created by the converter as input,
-performs various cleaning steps and splits them into multiple smaller documents.
-
-For suggestions on how best to split your documents, see [Optimization](/docs/latest/optimizationmd)
-
-```python
-from haystack.preprocessor import PreProcessor
-
-doc = converter.convert(file_path=file, meta=None)
-processor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=True,
- split_by="word",
- split_length=200,
- split_respect_sentence_boundary=True,
- split_overlap=0
-)
-docs = processor.process(doc)
-```
-
-* `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
-* `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
-* `clean_header_footer` will remove any long header or footer texts that are repeated on each page
-* `split_by` determines what unit the document is split by: `'word'`, `'sentence'` or `'passage'`
-* `split_length` sets a maximum number of `'word'`, `'sentence'` or `'passage'` units per output document
-* `split_respect_sentence_boundary` ensures that document boundaries do not fall in the middle of sentences
-* `split_overlap` sets the amount of overlap between two adjacent documents after a split. Setting this to a positive number essentially enables the sliding window approach.
-
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/query_classifier.md b/src/pages/docs/versions/master/site/en/usage/usage/query_classifier.md
deleted file mode 100644
index a1741174e..000000000
--- a/src/pages/docs/versions/master/site/en/usage/usage/query_classifier.md
+++ /dev/null
@@ -1,188 +0,0 @@
----
-title: "Query Classifier"
-metaTitle: "Query Classifier"
-metaDescription: ""
-slug: "/docs/query_classifier"
-date: "2021-08-17"
-id: "query_classifiermd"
----
-
-# Query Classifier
-
-Queries come in all shapes and forms. A keyword-based search differs from a question posed in natural language. In Haystack, we can account for these differences by integrating a special node into our QA pipeline: the query classifier.
-
-A query classifier puts each incoming query into one of two predefined classes, and routes it to the appropriate section of the pipeline.
-Haystack comes with classifiers to distinguish between the three most common query types (Keywords, Question, Statement) and allows two different types of models (SKlearn and Transformer).
-
-Using a query classifier can potentially yield the following benefits:
-
-* Getting better search results (e.g. by routing only proper questions to DPR / QA branches and not keyword queries)
-* Less GPU costs (e.g. if 50% of your traffic is only keyword queries you could just use elastic here and save the GPU resources for the other 50% of traffic with semantic queries)
-
-
-### Common Query types
-
-#### 1. Keyword Queries:
-Such queries don't have semantic meaning, merely consist of keywords and the order of words does not matter:
-* arya stark father
-* jon snow country
-* arya stark younger brothers
-
-#### 2. Questions (Interrogative Queries):
-In such queries users ask a question in a complete, "natural" sentence. Regardless of the presence of "?" in the query the goal here is to detect the intent of the user whether any question is asked or not in the query:
-
-* who is the father of arya stark?
-* which country was jon snow filmed in
-* who are the younger brothers of arya stark?
-
-#### 3. Statements (Declarative Queries):
-Such queries consist also of a regular, natural sentence with semantic relations between the words. However, they are rather a statement than a question:
-
-* Arya stark was a daughter of a lord.
-* Show countries that Jon snow was filmed in.
-* List all brothers of Arya.
-
-### Usage standalone: Try a Query Classifier
-To test how a query classifier works before integrating it into a pipeline, you can run it just as an individual component:
-
-```python
-from haystack.pipeline import TransformersQueryClassifier
-
-queries = ["Arya Stark father","Jon Snow UK",
- "who is the father of arya stark?","Which country was jon snow filmed in?"]
-
-question_classifier = TransformersQueryClassifier(model_name_or_path="shahrukhx01/bert-mini-finetune-question-detection")
-# Or Sklearn based:
-
-for query in queries:
- result = question_classifier.run(query=query)
- if result[1] == "output_1":
- category = "question"
- else:
- category = "keywords"
-
- print(f"Query: {query}, raw_output: {result}, class: {category}")
-
-# Returns:
-# Query: Arya Stark father, raw_output: ({'query': 'Arya Stark father'}, 'output_2'), class: keywords
-# Query: Jon Snow UK, raw_output: ({'query': 'Jon Snow UK'}, 'output_2'), class: keywords
-# Query: who is the father of arya stark?, raw_output: ({'query': 'who is the father of arya stark?'}, 'output_1'), class: question
-# Query: Which country was jon snow filmed in?, raw_output: ({'query': 'Which country was jon snow filmed in?'}, 'output_1'), class: question
-
-```
-Note how the node returns two objects: the query (e.g.'Arya Stark father') and the name of the output edge (e.g. "output_2"). This information can be leveraged in a pipeline for routing the query to the next node.
-
-### Usage in a pipeline: Use different retrievers depending on the query type
-
-You can use a Query Classifier within a pipeline as a "decision node". Depending on the output of the classifier other parts of the pipeline will be executed. For example, we can route keyword queries to an ElasticsearchRetriever and semantic queries (questions/statements) to DPR.
-
-
-
-Below, we define a pipeline with a `TransformersQueryClassifier` that routes questions/statements to the node's `output_1` and keyword queries to `output_2`. We leverage this structure in the pipeline by connecting the DPRRetriever to `QueryClassifier.output_1` and the ESRetriever to `QueryClassifier.output_2`.
-
-```python
-from haystack.pipeline import TransformersQueryClassifier, Pipeline
-from haystack.utils import print_answers
-
-query_classifier = TransformersQueryClassifier(model_name_or_path="shahrukhx01/bert-mini-finetune-question-detection")
-
-pipe = Pipeline()
-pipe.add_node(component=query_classifier, name="QueryClassifier", inputs=["Query"])
-pipe.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_1"])
-pipe.add_node(component=es_retriever, name="ESRetriever", inputs=["QueryClassifier.output_2"])
-
-# Pass a question -> run DPR
-res_1 = pipe.run(
- query="Who is the father of Arya Stark?",
- top_k_retriever=10
-)
-
-# Pass keywords -> run the ElasticsearchRetriever
-res_2 = pipe.run(
- query="arya stark father",
- top_k_retriever=10
-)
-
-```
-### Usage in a pipeline: Run QA only on proper questions
-
-If you add QA to an existing search system, it can make sense to only use it for real questions that come in and keep a basic document search with elasticsearch for the remaining keyword queries. You can use a Query Classifier to build such a hybrid pipeline:
-
-```python
-haystack.pipeline import TransformersQueryClassifier, Pipeline
-from haystack.utils import print_answers
-
-query_classifier = TransformersQueryClassifier(model_name_or_path="shahrukhx01/question-vs-statement-classifier")
-
-pipe = Pipeline()
-pipe.add_node(component=query_classifier, name="QueryClassifier", inputs=["Query"])
-pipe.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_1"])
-pipe.add_node(component=es_retriever, name="ESRetriever", inputs=["QueryClassifier.output_2"])
-pipe.add_node(component=reader, name="QAReader", inputs=["DPRRetriever"])
-
-# Pass a question -> run DPR + QA -> return answers
-res_1 = pipe.run(
- query="Who is the father of Arya Stark?",
- top_k_retriever=10
-)
-
-# Pass keywords -> run only ElasticsearchRetriever -> return docs
-res_2 = pipe.run(
- query="arya stark father",
- top_k_retriever=10
-)
-
-```
-
-
-### Which models are available?
-The transformer classifier is more accurate than the SkLearn classifier as it can use the context and order of words. However, it requires more memory and most probably GPU for faster inference. You can mitigate those down sides by choosing a very small transformer model. The default models we trained are using a mini BERT architecture which is only about `50 MB` in size and allows relatively fast inference on CPU.
-
-#### Transformers
-Pass your own `Transformer` binary classification model from file/huggingface or use one of the following pretrained ones hosted on Huggingface:
-1) Keywords vs. Questions/Statements (Default)
-
- ```python
- TransformersQueryClassifier(model_name_or_path="shahrukhx01/bert-mini-finetune-question-detection")
- # output_1 => question/statement
- # output_2 => keyword query
- ```
-
- [Readme](https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier/readme.txt)
-
-
-2) Questions vs. Statements
- ```python
- TransformersQueryClassifier(model_name_or_path="shahrukhx01/question-vs-statement-classifier")
- # output_1 => question
- # output_2 => statement
- ```
-
- [Readme](https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier_statements/readme.txt)
-
-
-#### Sklearn
-Pass your own `Sklearn` binary classification model or use one of the following pretrained Gradient boosting models:
-
-1) Keywords vs. Questions/Statements (Default)
-
- ```python
- SklearnQueryClassifier(query_classifier = "https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier/model.pickle",
- query_vectorizer = "https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier/vectorizer.pickle")
-
- # output_1 => question/statement
- # output_2 => keyword query
- ```
- [Readme](https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier/readme.txt)
-
-
-2) Questions vs. Statements
-
- ```python
- SklearnQueryClassifier(query_classifier = "https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier_statements/model.pickle",
- query_vectorizer = "https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier_statements/vectorizer.pickle")
-
- output_1 => question
- output_2 => statement
- ```
- [Readme](https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier_statements/readme.txt)
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/ranker.md b/src/pages/docs/versions/master/site/en/usage/usage/ranker.md
deleted file mode 100644
index 042f1ae9b..000000000
--- a/src/pages/docs/versions/master/site/en/usage/usage/ranker.md
+++ /dev/null
@@ -1,55 +0,0 @@
----
-title: "Ranker"
-metaTitle: "Ranker"
-metaDescription: ""
-slug: "/docs/ranker"
-date: "2020-05-26"
-id: "rankermd"
----
-
-# Ranker
-
-There are pure "semantic document search" use cases that do not need question answering functionality but only document ranking.
-While the [Retriever](/docs/latest/retrievermd) is a perfect fit for document retrieval, we can further improve its results with the Ranker.
-For example, BM25 (sparse retriever) does not take into account semantics of the documents and the query but only their keywords.
-The Ranker can re-rank the results of the retriever step by taking semantics into account.
-Similar to the Reader, it is based on the latest language models.
-Instead of returning answers, it returns documents in re-ranked order.
-
-Without a Ranker and its re-ranking step, the querying process is faster but the query results might be of lower quality.
-If you want to do "semantic document search" instead of a question answering, try first with a Retriever only.
-In case the semantic similarity of the query and the resulting documents is low, add a Ranker.
-
-Note that a Ranker needs to be initialised with a model trained on a text pair classification task.
-You can train the model also with the train() method of the Ranker.
-Alternatively, [this example](https://github.com/deepset-ai/FARM/blob/master/examples/text_pair_classification.py) shows how to train a text pair classification model in FARM.
-
-
-## FARMRanker
-
-### Description
-
-The FARMRanker consists of a Transformer-based model for document re-ranking using the TextPairClassifier of [FARM](https://github.com/deepset-ai/FARM).
-Given a text pair of query and passage, the TextPairClassifier either predicts label "1" if the pair is similar or label "0" if they are dissimilar (accompanied with a probability).
-While the underlying model can vary (BERT, Roberta, DistilBERT, ...), the interface remains the same.
-With a FARMRanker, you can:
-* Directly get predictions (re-ranked version of the supplied list of Document) via predict() if supplying a pre-trained model
-* Take a plain language model (e.g. `bert-base-cased`) and train it for TextPairClassification via train()
-
-### Initialisation
-
-```python
-from haystack.document_store import ElasticsearchDocumentStore
-from haystack.retriever import ElasticsearchRetriever
-from haystack.ranker import FARMRanker
-from haystack import Pipeline
-
-document_store = ElasticsearchDocumentStore()
-...
-retriever = ElasticsearchRetriever(document_store)
-ranker = FARMRanker(model_name_or_path="saved_models/roberta-base-asnq-binary")
-...
-p = Pipeline()
-p.add_node(component=retriever, name="ESRetriever", inputs=["Query"])
-p.add_node(component=ranker, name="Ranker", inputs=["ESRetriever"])
-```
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/reader.md b/src/pages/docs/versions/master/site/en/usage/usage/reader.md
deleted file mode 100644
index 91b3c8c4a..000000000
--- a/src/pages/docs/versions/master/site/en/usage/usage/reader.md
+++ /dev/null
@@ -1,386 +0,0 @@
----
-title: "Reader"
-metaTitle: "Reader"
-metaDescription: ""
-slug: "/docs/reader"
-date: "2020-09-03"
-id: "readermd"
----
-
-# Reader
-
-The Reader, also known as Open-Domain QA systems in Machine Learning speak,
-is the core component that enables Haystack to find the answers that you need.
-Haystack’s Readers are:
-
-
-* built on the latest transformer based language models
-
-
-* strong in their grasp of semantics
-
-
-* sensitive to syntactic structure
-
-
-* state-of-the-art in QA tasks like SQuAD and Natural Questions
-
-
-
-
-
-
-
-
-```python
-from haystack.file_converter import PDFToTextConverter
-
-converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=["de","en"])
-doc = converter.convert(file_path=file, meta=None)
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.file_converter import DocxToTextConverter
-
-converter = DocxToTextConverter(remove_numeric_tables=True, valid_languages=["de","en"])
-doc = converter.convert(file_path=file, meta=None)
-```
-
-
-
-
-
-
-
-
-
-
-Haystack also has a `convert_files_to_dicts()` utility function that will convert
-all txt or pdf files in a given folder into this dictionary format.
-
-```python
-from haystack.preprocessor.utils import convert_files_to_dicts
-
-docs = convert_files_to_dicts(dir_path=doc_dir)
-```
-
-
-
-
-
-
-While these models can work on CPU, it is recommended that they are run using GPUs to keep query times low.
-
-## Choosing the Right Model
-
-In Haystack, you can start using pretrained QA models simply by providing its HuggingFace Model Hub name to the Reader.
-The loading of model weights is handled by Haystack,
-and you have the option of using the QA pipeline from deepset FARM or HuggingFace Transformers (see FARM vs Transformers for details).
-
-Currently, there are a lot of different models out there and it can be rather overwhelming trying to pick the one that fits your use case.
-To get you started, we have a few recommendations for you to try out.
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import FARMReader
-
-model = "deepset/roberta-base-squad2"
-reader = FARMReader(model, use_gpu=True)
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import TransformersReader
-
-model = "deepset/roberta-base-squad2"
-reader = TransformersReader(model, use_gpu=1)
-```
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-**An optimised variant of BERT and a great starting point.**
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("deepset/roberta-base-squad2")
-```
-
-* **Pro**: Strong all round model
-
-* **Con**: There are other models that are either faster or more accurate
-
-
-
-
-
-
-
-
-
-**A cleverly distilled model that sacrifices a little accuracy for speed.**
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("deepset/minilm-uncased-squad2")
-```
-
-* **Pro**: Inference speed up to 50% faster than BERT base
-
-* **Con**: Still doesn’t match the best base sized models in accuracy
-
-
-
-
-
-
-
-
-
-**Large, powerful, SotA model.**
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader("ahotrod/albert_xxlargev1_squad2_512")
-```
-
-* **Pro**: Better accuracy than any other open source model in QA
-
-* **Con**: The computational power needed make it impractical for most use cases
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-**An optimised variant of BERT and a great starting point.**
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("deepset/roberta-base-squad2")
-```
-
-* **Pro**: Strong all round model
-
-* **Con**: There are other models that are either faster or more accurate
-
-
-
-
-
-
-
-
-
-**A cleverly distilled model that sacrifices a little accuracy for speed.**
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("deepset/minilm-uncased-squad2")
-```
-
-* **Pro**: Inference speed up to 50% faster than BERT base
-
-* **Con**: Still doesn’t match the best base sized models in accuracy
-
-
-
-
-
-
-
-
-
-**Large, powerful, SotA model.**
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader("ahotrod/albert_xxlargev1_squad2_512")
-```
-
-* **Pro**: Better accuracy than any other open source model in QA
-
-* **Con**: The computational power needed make it impractical for most use cases
-
-
-
-
-**Recommendations:**
-
-**All-rounder**: In the class of base sized models trained on SQuAD, **RoBERTa** has shown better performance than BERT
-and can be capably handled by any machine equipped with a single NVidia V100 GPU.
-We recommend this as the starting point for anyone wanting to create a performant and computationally reasonable instance of Haystack.
-
-**Built for Speed**: If speed and GPU memory are more of a priority to you than accuracy,
-you should try the MiniLM model.
-It is a smaller model that is trained to mimic larger models through the distillation process,
-and it outperforms the BERT base on SQuAD even though it is about 40% smaller.
-
-
-**State of the Art Accuracy**: For most, **ALBERT XXL** will be too large to feasibly work with.
-But if performance is your sole concern, and you have the computational resources,
-you might like to try ALBERT XXL which has set SoTA performance on SQuAD 2.0.
-
-
-
-
-
-
-## Confidence Scores
-
-When printing the full results of a Reader,
-you will see that each prediction is accompanied
-by a value in the range of 0 to 1 reflecting the model's confidence in that prediction
-
-In the output of `print_answers()`, you will find the model confidence in dictionary key called `confidence`.
-
-```python
-from haystack.utils import print_answers
-
-print_answers(prediction, details="all")
-```
-
-```python
-{
- 'answers': [
- { 'answer': 'Eddard',
- 'context': 's Nymeria after a legendary warrior queen. '
- 'She travels with her father, Eddard, to '
- "King's Landing when he is made Hand of the "
- 'King. Before she leaves,',
- 'confidence': 0.9899835586547852,
- ...
- },
- ]
-}
-```
-
-In order to align this probability score with the model's accuracy, finetuning needs to be performed
-on a specific dataset.
-To this end, the reader has a method `calibrate_confidence_scores(document_store, device, label_index, doc_index, label_origin)`.
-The parameters of this method are the same as for the `eval()` method because the calibration of confidence scores is performed on a dataset that comes with gold labels.
-The calibration calls the `eval()` method internally and therefore needs a DocumentStore containing labeled questions and evaluation documents.
-
-Have a look at this [FARM tutorial](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering_confidence.py)
-to see how to compare calibrated confidence scores with uncalibrated confidence scores within FARM.
-Note that a finetuned confidence score is specific to the domain that it is finetuned on.
-There is no guarantee that this performance can transfer to a new domain.
-
-Having a confidence score is particularly useful in cases where you need Haystack to work with a certain accuracy threshold.
-Many of our users have built systems where predictions below a certain confidence value are routed
-on to a fallback system.
-
-
-
-## Deeper Dive: FARM vs Transformers
-
-Apart from the **model weights**, Haystack Readers contain all the components found in end-to-end open domain QA systems.
-This includes **tokenization**, **embedding computation**, **span prediction** and **candidate aggregation**.
-While the handling of model weights is the same between the FARM and Transformers libraries, their QA pipelines differ in some ways.
-The major points are:
-
-
-* The **TransformersReader** will sometimes predict the same span twice while duplicates are removed in the **FARMReader**
-
-
-* The **FARMReader** currently uses the tokenizers from the HuggingFace Transformers library while the **TransformersReader** uses the tokenizers from the HuggingFace Tokenizers library
-
-
-* Start and end logits are normalized per passage and multiplied in the **TransformersReader** while they are summed and not normalised in the **FARMReader**
-
-If you’re interested in the finer details of these points, have a look at [this](https://github.com/deepset-ai/haystack/issues/248#issuecomment-661977237) GitHub comment.
-
-We see value in maintaining both kinds of Readers since Transformers is a very familiar library to many of Haystack’s users
-but we at deepset can more easily update and optimise the FARM pipeline for speed and performance.
-
-
-Haystack also has a close integration with FARM which means that you can further fine-tune your Readers on labelled data using a FARMReader.
-See our tutorials for an end-to-end example or below for a shortened example.
-
-```python
-from haystack.reader import FARMReader
-
-# Initialise Reader
-model = "deepset/roberta-base-squad2"
-reader = FARMReader(model)
-
-# Perform finetuning
-train_data = "PATH/TO_YOUR/TRAIN_DATA"
-train_filename = "train.json"
-save_dir = "finetuned_model"
-reader.train(train_data, train_filename, save_dir=save_dir)
-
-# Load
-finetuned_reader = FARMReader(save_dir)
-```
-
-## Deeper Dive: From Language Model to Haystack Reader
-
-Language models form the core of most modern NLP systems and that includes the Readers in Haystack.
-They build a general understanding of language when performing training tasks such as Masked Language Modeling or Replaced Token Detection
-on large amounts of text.
-Well trained language models capture the word distribution in one or more languages
-but more importantly, convert input text into a set of word vectors that capture elements of syntax and semantics.
-
-In order to convert a language model into a Reader model, it needs first to be trained on a Question Answering dataset.
-To do so requires the addition of a question answering prediction head on top of the language model.
-The task can be thought of as a token classification task where every input token is assigned a probability of being
-either the start or end token of the correct answer.
-In cases where the answer is not contained within the passage, the prediction head is also expected to return a `no_answer` prediction.
-
-
-Since language models are limited in the number of tokens which they can process in a single forward pass,
-a sliding window mechanism is implemented to handle variable length documents.
-This functions by slicing the document into overlapping passages of (approximately) `max_seq_length`
-that are each offset by `doc_stride` number of tokens.
-These can be set when the Reader is initialized.
-
-
-
-
-
-Predictions are made on each individual passage and the process of aggregation picks the best candidates across all passages.
-If you’d like to learn more about what is happening behind the scenes, have a look at [this](https://medium.com/deepset-ai/modern-question-answering-systems-explained-4d0913744097) article.
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/retriever.md b/src/pages/docs/versions/master/site/en/usage/usage/retriever.md
deleted file mode 100644
index c213390cc..000000000
--- a/src/pages/docs/versions/master/site/en/usage/usage/retriever.md
+++ /dev/null
@@ -1,281 +0,0 @@
----
-title: "Retriever"
-metaTitle: "Retriever"
-metaDescription: ""
-slug: "/docs/retriever"
-date: "2020-09-03"
-id: "retrievermd"
----
-
-# Retriever
-
-The Retriever is a lightweight filter that can quickly go through the full document store and pass on a set of candidate documents that are relevant to the query.
-When used in combination with a Reader, it is a tool for sifting out the obvious negative cases, saving the Reader from doing more work than it needs to and speeding up the querying process.
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import FARMReader
-
-reader = FARMReader(... max_seq_len=384, doc_stride=128 ...)
-```
-
-
-
-
-
-
-
-
-
-```python
-from haystack.reader import TransformersReader
-
-reader = TransformersReader(... max_seq_len=384, doc_stride=128 ...)
-```
-
-
-
-
-**Recommendations**
-
-* BM25 (sparse)
-
-* Dense Passage Retrieval (dense)
-
-
-
-
-
-Note that not all Retrievers can be paired with every DocumentStore.
-Here are the combinations which are supported:
-
-| | Memory | Elasticsearch | SQL | FAISS | Milvus |
-| --- | --- | --- | ---- | ---- | ---- |
-| BM25 | N | Y | N | N | N |
-| TF-IDF | Y | Y | Y | N | N |
-| Embedding | Y | Y | N | Y | Y |
-| DPR | Y | Y | N | Y | Y |
-
-See [Optimization](/docs/latest/optimizationmd) for suggestions on how to choose top-k values.
-
-
-## TF-IDF
-
-### Description
-
-TF-IDF is a commonly used baseline for information retrieval that exploits two key intuitions:
-
-
-* documents that have more lexical overlap with the query are more likely to be relevant
-
-* words that occur in fewer documents are more significant than words that occur in many documents
-
-Given a query, a tf-idf score is computed for each document as follows:
-
-```python
-score = tf * idf
-```
-
-Where:
-
-
-* `tf` is how many times words in the query occur in that document.
-
-
-* `idf` is the inverse of the fraction of documents containing the word.
-
-In practice, both terms are usually log normalised.
-
-### Initialisation
-
-```python
-from haystack.document_store import InMemoryDocumentStore
-from haystack.retriever.sparse import TfidfRetriever
-from haystack.pipeline import ExtractiveQAPipeline
-
-document_store = InMemoryDocumentStore()
-...
-retriever = TfidfRetriever(document_store)
-...
-p = ExtractiveQAPipeline(reader, retriever)
-```
-
-## BM25 (Recommended)
-
-### Description
-
-BM25 is a variant of TF-IDF that we recommend you use if you are looking for a retrieval method that does not need a neural network for indexing.
-It improves upon its predecessor in two main aspects:
-
-
-* It saturates `tf` after a set number of occurrences of the given term in the document
-
-
-* It normalises by document length so that short documents are favoured over long documents if they have the same amount of word overlap with the query
-
-### Initialisation
-
-```python
-from haystack.document_store import ElasticsearchDocumentStore
-from haystack.retriever import ElasticsearchRetriever
-from haystack.pipeline import ExtractiveQAPipeline
-
-document_store = ElasticsearchDocumentStore()
-...
-retriever = ElasticsearchRetriever(document_store)
-...
-p = ExtractiveQAPipeline(reader, retriever)
-```
-
-See [this](https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-and-its-variables) blog post for more details about the algorithm.
-
-
-## Dense Passage Retrieval (Recommended)
-
-### Description
-
-[Dense Passage Retrieval](https://arxiv.org/abs/2004.04906) is a highly performant retrieval method that calculates relevance using dense representations.
-Key features:
-
-
-* One BERT base model to encode documents
-
-
-* One BERT base model to encode queries
-
-
-* Ranking of documents done by dot product similarity between query and document embeddings
-
-
-Indexing using DPR is comparatively expensive in terms of required computation since all documents in the database need to be processed through the transformer.
-The embeddings that are created in this step can be stored in FAISS, a database optimized for vector similarity.
-DPR can also work with the ElasticsearchDocumentStore or the InMemoryDocumentStore.
-
-There are two design decisions that have made DPR particularly performant.
-
-
-* Separate encoders for document and query helps since queries are much shorter than documents
-
-
-* Training with ‘In-batch negatives’ (gold labels are treated as negative examples for other samples in same batch) is highly efficient
-
-In Haystack, you can simply download the pretrained encoders needed to start using DPR.
-If you’d like to learn how to set up a DPR based system, have a look at the [tutorial](/docs/latest/tutorial6md)!
-
-### Initialisation
-
-
-
-**Tip**
-
-When using DPR, it is recommended that you use the dot product similarity function since that is how it is trained.
-To do so, simply provide `similarity="dot_product"` when initializing the DocumentStore
-as is done in the code example below.
-
-
-
-```python
-from haystack.document_store import FAISSDocumentStore
-from haystack.retriever import DensePassageRetriever
-from haystack.pipeline import ExtractiveQAPipeline
-
-document_store = FAISSDocumentStore(similarity="dot_product")
-...
-retriever = DensePassageRetriever(
- document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base"
-)
-...
-finder = ExtractiveQAPipeline(reader, retriever)
-```
-
-
-
-**Training DPR:** Haystack supports training of your own DPR model! Check out the [tutorial](/docs/latest/tutorial9md) to see how this is done!
-
-
-
-
-
-## Embedding Retrieval
-
-### Description
-
-In Haystack, you also have the option of using a single transformer model to encode document and query.
-One style of model that is suited to this kind of retrieval is that of [Sentence Transformers](https://github.com/UKPLab/sentence-transformers).
-These models are trained in Siamese Networks and use triplet loss such that they learn to embed similar sentences near to each other in a shared embedding space.
-
-They are particular suited to cases where your query input is similar in style to that of the documents in your database
-i.e. when you are searching for most similar documents.
-This is not inherently suited to query based search where the length, language and format of the query usually significantly differs from the searched for text.
-
-
-
-**Tip**
-
-When using Sentence Transformer models, we recommend that you use a cosine similarity function.
-To do so, simply provide `similarity="cosine"` when initializing the DocumentStore
-as is done in the code example below.
-
-
-
-### Initialisation
-
-```python
-from haystack.document_store import ElasticsearchDocumentStore
-from haystack.retriever import EmbeddingRetriever
-from haystack.pipeline import ExtractiveQAPipeline
-
-document_store = ElasticsearchDocumentStore(similarity="cosine")
-...
-retriever = EmbeddingRetriever(document_store=document_store,
- embedding_model="deepset/sentence_bert")
-...
-p = ExtractiveQAPipeline(reader, retriever)
-```
-
-## Deeper Dive: Dense vs Sparse
-
-Broadly speaking, retrieval methods can be split into two categories: **dense** and **sparse**.
-
-**Sparse** methods, like TF-IDF and BM25, operate by looking for shared keywords between the document and query.
-They are:
-
-
-* simple but effective
-
-
-* don’t need to be trained
-
-
-* work on any language
-
-More recently, **dense** approaches such as Dense Passage Retrieval (DPR) have shown even better performance than their sparse counter parts.
-These methods embed both document and query into a shared embedding space using deep neural networks
-and the top candidates are the nearest neighbour documents to the query.
-They are:
-
-
-* powerful but computationally more expensive especially during indexing
-
-
-* trained using labelled datasets
-
-
-* language specific
-
-### Qualitative Differences
-
-Between these two types there are also some qualitative differences too.
-For example, sparse methods treat text as a bag-of-words meaning that they **do not take word order and syntax into account**,
-while the latest generation of dense methods use transformer based encoders
-which are designed to be **sensitive** to these factors.
-
-Also dense methods are very capable of building strong semantic representations of text,
-but they **struggle when encountering out-of-vocabulary** words such as new names.
-By contrast, sparse methods don’t need to learn representations of words,
-they only care about whether they are present or absent in the text.
-As such, **they handle out-of-vocabulary words with no problem**.
-
-
-### Indexing
-
-Dense methods perform indexing by processing all the documents through a neural network and storing the resulting vectors.
-This is a much more expensive operation than the creation of the inverted-index in sparse methods
-and will require significant computational power and time.
-
-
-### Terminology
-
-
-
-The terms **dense** and **sparse** refer to the representations that the algorithms build for each document and query.
-**Sparse** methods characterise texts using vectors with one dimension corresponding to each word in the vocabulary.
-Dimensions will be zero if the word is absent and non-zero if it is present.
-Since most documents contain only a small subset of the full vocabulary,
-these vectors are considered sparse since non-zero values are few and far between.
-
-**Dense** methods, by contrast, pass text as input into neural network encoders
-and represent text in a vector of a manually defined size (usually 768).
-Though individual dimensions are not mapped to any corresponding vocabulary or linguistic feature,
-each dimension encodes some information about the text.
-There are rarely 0s in these vectors hence their relative density.
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/terms.md b/src/pages/docs/versions/master/site/en/usage/usage/terms.md
deleted file mode 100644
index 3509c77d4..000000000
--- a/src/pages/docs/versions/master/site/en/usage/usage/terms.md
+++ /dev/null
@@ -1,65 +0,0 @@
----
-title: "Glossary"
-metaTitle: "Glossary"
-metaDescription: ""
-slug: "/docs/terms"
-date: "2020-09-03"
-id: "termsmd"
----
-
-# Glossary
-
-**BERT** - A popular, transformer based language model which has been improved upon but is still considered a common benchmark.
-
-**Dense** - Vectors that contain many non-zero values are considered dense.
-Retrieval methods can also be called dense if they create dense vector representations of documents.
-
-**Document** - A Document in Haystack refers to the individual pieces of text that are stored in the DocumentStore.
-Multiple Documents might originally come from the one file.
-It is ultimately up to you how to divide up your corpus into Documents.
-
-**Document Store** - The component in Haystack that stores the text documents and their metadata.
-Can have a variety of backends such as Elasticsearch, SQL or FAISS.
-
-**FARM** - An open-source transfer learning [framework](https://github.com/deepset-ai/FARM) by deepset.
-FARM’s question answering models are used in Haystack’s Readers.
-
-**Indexing** - To store data in a database in a way that optimises retrieval time.
-The exact steps involved in indexing depend on what kind of retrieval method is chosen.
-
-**Language Model** - The component in an NLP model that stores general language understanding, but no task specific knowledge.
-
-**Model Hub** - The [repository](https://huggingface.co/models) set up by HuggingFace where trained models can be saved to and loaded from.
-With Haystack, you can directly load and use any question answering model found on the model hub.
-
-**Neural Network** - A machine learning architecture composed of artificial neurons that learn a task when exposed to labelled training data.
-
-**Prediction Head** - The modelling component that adapts the general knowledge of the language model for a specific task.
-In question answering models (and hence in Haystack Readers), this is usually a single layer neural network.
-
-**Querying** - The task of returning relevant documents from a database.
-
-**Question Answering (QA)** - A popular task in the world of NLP where systems have to find answers to questions.
-The term is generally used to refer to extractive question answering,
-where a system has to find the minimal text span in a given document that contains the answer to the question.
-Note however, that it may also refer to abstractive question answering or FAQ matching.
-
-**Reader** - The component in Haystack that does the closest reading of a document to extract
-the exact text which answers a question.
-It is, at its core, a trained Question Answering model.
-
-**Retriever** - A lightweight filter that selects only the most relevant documents for the Reader to further process.
-
-**Semantic Search** - A style of search that relies not on the matching of exact string forms
-but on the similarity of meaning between a query and a piece of text.
-
-**Sparse** - Vectors that are composed primarily of zeros are called sparse.
-Retrieval methods are also considered sparse if they build sparse vector representations of documents.
-
-**SQuAD** - The [Stanford Question Answering Dataset](https://rajpurkar.github.io/SQuAD-explorer/) is the defacto standard QA dataset.
-The documents are paragraphs from Wikipedia and the question / answer pairs are created by human annotators.
-
-**Transformers** - Originally refers to the deep learning architecture that is composed of stacked self-attention layers
-(first conceptualised [here](https://arxiv.org/pdf/1706.03762.pdf)).
-Can also refer to HuggingFace’s [repository](https://github.com/huggingface/transformers)
-which contains implementations of popular model architectures.
diff --git a/src/pages/docs/versions/master/site/en/usage/usage/translator.md b/src/pages/docs/versions/master/site/en/usage/usage/translator.md
deleted file mode 100644
index dbdc2ccf4..000000000
--- a/src/pages/docs/versions/master/site/en/usage/usage/translator.md
+++ /dev/null
@@ -1,61 +0,0 @@
----
-title: "Translator"
-metaTitle: "Translator"
-metaDescription: ""
-slug: "/docs/translator"
-date: "2021-02-10"
-id: "translatormd"
----
-
-# Translator
-
-Texts come in different languages. This is not different for search and there are plenty of options to deal with it.
-One of them is actually to translate the incoming query, the documents or the search results.
-
-Let's imagine you have an English corpus of technical docs, but the mother tongue of many of your users is French.
-You can use a Translator node in your pipeline to
-1. Translate the incoming query from French to English
-2. Search in your English corpus for the right document / answer
-3. Translate the results back from English to French
-
-
-
-**Example (Stand-alone Translator)**
-
-You can use the Translator component directly to translate your query or document(s):
-```python
-from haystack.schema import Document
-from haystack.translator import TransformersTranslator
-
-DOCS = [
- Document(
- text="""Heinz von Foerster was an Austrian American scientist
- combining physics and philosophy, and widely attributed
- as the originator of Second-order cybernetics."""
- )
- ]
-translator = TransformersTranslator(model_name_or_path="Helsinki-NLP/opus-mt-en-fr")
-res = translator.translate(documents=DOCS, query=None)
-```
-
-**Example (Wrapping another Pipeline)**
-
-You can also wrap one of your existing pipelines and "add" the translation nodes at the beginning and at the end of your pipeline.
-For example, lets translate the incoming query to from French to English, then do our document retrieval and then translate the results back from English to French:
-
-```python
-from haystack.pipeline import TranslationWrapperPipeline, DocumentSearchPipeline
-from haystack.translator import TransformersTranslator
-
-pipeline = DocumentSearchPipeline(retriever=my_dpr_retriever)
-
-in_translator = TransformersTranslator(model_name_or_path="Helsinki-NLP/opus-mt-fr-en")
-out_translator = TransformersTranslator(model_name_or_path="Helsinki-NLP/opus-mt-en-fr")
-
-pipeline_with_translation = TranslationWrapperPipeline(input_translator=in_translator,
- output_translator=out_translator,
- pipeline=pipeline)
-```
-
-
-
diff --git a/src/pages/docs/versions/master/update_apidocs.sh b/src/pages/docs/versions/master/update_apidocs.sh
deleted file mode 100755
index 56253d1ba..000000000
--- a/src/pages/docs/versions/master/update_apidocs.sh
+++ /dev/null
@@ -1,111 +0,0 @@
-#!/bin/bash
-
-# Used branch
-echo "Haystack branch: $1"
-
-# Master data
-versionstxt=$(cat src/pages/docs/versions/master/versions.txt)
-versions=("" $versionstxt)
-
-for i in "${versions[@]}"
-do
- rm ./src/pages/docs/versions/master/${i}site/en/api/api/*
-done
-
-if [ ! -d "./src/pages/docs/versions/master/${i}site/en/api/api/" ]; then
- mkdir -p ./src/pages/docs/versions/master/${i}site/en/api/api
-fi
-
-for i in "${versions[@]}"
-do
- j=$i
- if [ "$i" = "latest/" ]; then
- j=''
- fi
-
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/api/api/document_store.md -O ./src/pages/docs/versions/master/${i}site/en/api/api/document_store.md
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/api/api/file_converter.md -O ./src/pages/docs/versions/master/${i}site/en/api/api/file_converter.md
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/api/api/preprocessor.md -O ./src/pages/docs/versions/master/${i}site/en/api/api/preprocessor.md
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/api/api/reader.md -O ./src/pages/docs/versions/master/${i}site/en/api/api/reader.md
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/api/api/retriever.md -O ./src/pages/docs/versions/master/${i}site/en/api/api/retriever.md
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/api/api/generator.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/api/api/generator.md -O ./src/pages/docs/versions/master/${i}site/en/api/api/generator.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/api/api/pipelines.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/api/api/pipelines.md -O ./src/pages/docs/versions/master/${i}site/en/api/api/pipelines.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/api/api/translator.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/api/api/translator.md -O ./src/pages/docs/versions/master/${i}site/en/api/api/translator.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/api/api/summarizer.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/api/api/summarizer.md -O ./src/pages/docs/versions/master/${i}site/en/api/api/summarizer.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/api/api/crawler.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/api/api/crawler.md -O ./src/pages/docs/versions/master/${i}site/en/api/api/crawler.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/api/api/evaluation.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/api/api/evaluation.md -O ./src/pages/docs/versions/master/${i}site/en/api/api/evaluation.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/api/api/graph_retriever.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/api/api/graph_retriever.md -O ./src/pages/docs/versions/master/${i}site/en/api/api/graph_retriever.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/api/api/knowledge_graph.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/api/api/knowledge_graph.md -O ./src/pages/docs/versions/master/${i}site/en/api/api/knowledge_graph.md
- fi
-
- echo -e "---\ntitle: \"Document Store\"\nmetaTitle: \"Database\"\nmetaDescription: \"\"\nslug: \"/docs/apidatabase\"\ndate: \"2020-09-03\"\nid: \"apidatabasemd\"\n---\n\n# Document Store\n\n$(cat ./src/pages/docs/versions/master/${i}site/en/api/api/document_store.md)" > ./src/pages/docs/versions/master/${i}site/en/api/api/document_store.md
- sed -i 's/# Module /# Module: /g' ./src/pages/docs/versions/master/${i}site/en/api/api/document_store.md
- sed -i 's/## \(.*\) Objects/## Class: \1/g' ./src/pages/docs/versions/master/${i}site/en/api/api/document_store.md
- echo -e "---\ntitle: \"File Converters\"\nmetaTitle: \"File Converters\"\nmetaDescription: \"\"\nslug: \"/docs/file_converters\"\ndate: \"2020-09-03\"\nid: \"file_convertersmd\"\n---\n\n# File Converters\n\n$(cat ./src/pages/docs/versions/master/${i}site/en/api/api/file_converter.md)" > ./src/pages/docs/versions/master/${i}site/en/api/api/file_converter.md
- sed -i 's/# Module /# Module: /g' ./src/pages/docs/versions/master/${i}site/en/api/api/file_converter.md
- sed -i 's/## \(.*\) Objects/## Class: \1/g' ./src/pages/docs/versions/master/${i}site/en/api/api/file_converter.md
- echo -e "---\ntitle: \"Preprocessor\"\nmetaTitle: \"Preprocessor\"\nmetaDescription: \"\"\nslug: \"/docs/apiindexing\"\ndate: \"2020-09-03\"\nid: \"apiindexingmd\"\n---\n\n# Preprocessor\n\n$(cat ./src/pages/docs/versions/master/${i}site/en/api/api/preprocessor.md)" > ./src/pages/docs/versions/master/${i}site/en/api/api/preprocessor.md
- sed -i 's/# Module /# Module: /g' ./src/pages/docs/versions/master/${i}site/en/api/api/preprocessor.md
- sed -i 's/## \(.*\) Objects/## Class: \1/g' ./src/pages/docs/versions/master/${i}site/en/api/api/preprocessor.md
- echo -e "---\ntitle: \"Reader\"\nmetaTitle: \"Reader\"\nmetaDescription: \"\"\nslug: \"/docs/apireadermd\"\ndate: \"2020-09-03\"\nid: \"apireadermd\"\n---\n\n# Reader\n\n$(cat ./src/pages/docs/versions/master/${i}site/en/api/api/reader.md)" > ./src/pages/docs/versions/master/${i}site/en/api/api/reader.md
- sed -i 's/# Module /# Module: /g' ./src/pages/docs/versions/master/${i}site/en/api/api/reader.md
- sed -i 's/## \(.*\) Objects/## Class: \1/g' ./src/pages/docs/versions/master/${i}site/en/api/api/reader.md
- echo -e "---\ntitle: \"Retriever\"\nmetaTitle: \"Retriever\"\nmetaDescription: \"\"\nslug: \"/docs/apiretrievermd\"\ndate: \"2020-09-03\"\nid: \"apiretrievermd\"\n---\n\n# Retriever\n\n$(cat ./src/pages/docs/versions/master/${i}site/en/api/api/retriever.md)" > ./src/pages/docs/versions/master/${i}site/en/api/api/retriever.md
- sed -i 's/# Module /# Module: /g' ./src/pages/docs/versions/master/${i}site/en/api/api/retriever.md
- sed -i 's/## \(.*\) Objects/## Class: \1/g' ./src/pages/docs/versions/master/${i}site/en/api/api/retriever.md
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/api/api/generator.md" ]]; then
- echo -e "---\ntitle: \"Generator\"\nmetaTitle: \"Generator\"\nmetaDescription: \"\"\nslug: \"/docs/apigeneratormd\"\ndate: \"2020-09-03\"\nid: \"apigeneratormd\"\n---\n\n# Generator\n\n$(cat ./src/pages/docs/versions/master/${i}site/en/api/api/generator.md)" > ./src/pages/docs/versions/master/${i}site/en/api/api/generator.md
- sed -i 's/# Module /# Module: /g' ./src/pages/docs/versions/master/${i}site/en/api/api/generator.md
- sed -i 's/## \(.*\) Objects/## Class: \1/g' ./src/pages/docs/versions/master/${i}site/en/api/api/generator.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/api/api/pipelines.md" ]]; then
- echo -e "---\ntitle: \"Pipelines\"\nmetaTitle: \"Pipelines\"\nmetaDescription: \"\"\nslug: \"/docs/apipipelinesmd\"\ndate: \"2020-09-03\"\nid: \"apipipelinesmd\"\n---\n\n# Pipelines\n\n$(cat ./src/pages/docs/versions/master/${i}site/en/api/api/pipelines.md)" > ./src/pages/docs/versions/master/${i}site/en/api/api/pipelines.md
- sed -i 's/# Module /# Module: /g' ./src/pages/docs/versions/master/${i}site/en/api/api/pipelines.md
- sed -i 's/## \(.*\) Objects/## Class: \1/g' ./src/pages/docs/versions/master/${i}site/en/api/api/pipelines.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/api/api/translator.md" ]]; then
- echo -e "---\ntitle: \"Translator\"\nmetaTitle: \"Translator\"\nmetaDescription: \"\"\nslug: \"/docs/translatormd\"\ndate: \"2020-09-03\"\nid: \"apitranslatormd\"\n---\n\n# Translator\n\n$(cat ./src/pages/docs/versions/master/${i}site/en/api/api/translator.md)" > ./src/pages/docs/versions/master/${i}site/en/api/api/translator.md
- sed -i 's/# Module /# Module: /g' ./src/pages/docs/versions/master/${i}site/en/api/api/translator.md
- sed -i 's/## \(.*\) Objects/## Class: \1/g' ./src/pages/docs/versions/master/${i}site/en/api/api/translator.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/api/api/summarizer.md" ]]; then
- echo -e "---\ntitle: \"Summarizer\"\nmetaTitle: \"Summarizer\"\nmetaDescription: \"\"\nslug: \"/docs/summarizermd\"\ndate: \"2020-09-03\"\nid: \"apisummarizermd\"\n---\n\n# Summarizer\n\n$(cat ./src/pages/docs/versions/master/${i}site/en/api/api/summarizer.md)" > ./src/pages/docs/versions/master/${i}site/en/api/api/summarizer.md
- sed -i 's/# Module /# Module: /g' ./src/pages/docs/versions/master/${i}site/en/api/api/summarizer.md
- sed -i 's/## \(.*\) Objects/## Class: \1/g' ./src/pages/docs/versions/master/${i}site/en/api/api/summarizer.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/api/api/crawler.md" ]]; then
- echo -e "---\ntitle: \"Crawler\"\nmetaTitle: \"Crawler\"\nmetaDescription: \"\"\nslug: \"/docs/crawlermd\"\ndate: \"2020-09-03\"\nid: \"apicrawlermd\"\n---\n\n# Crawler\n\n$(cat ./src/pages/docs/versions/master/${i}site/en/api/api/crawler.md)" > ./src/pages/docs/versions/master/${i}site/en/api/api/crawler.md
- sed -i 's/# Module /# Module: /g' ./src/pages/docs/versions/master/${i}site/en/api/api/crawler.md
- sed -i 's/## \(.*\) Objects/## Class: \1/g' ./src/pages/docs/versions/master/${i}site/en/api/api/crawler.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/api/api/evaluation.md" ]]; then
- echo -e "---\ntitle: \"Evaluation\"\nmetaTitle: \"Evaluation\"\nmetaDescription: \"\"\nslug: \"/docs/evaluationmd\"\ndate: \"2020-09-03\"\nid: \"apievaluationmd\"\n---\n\n# Evaluation\n\n$(cat ./src/pages/docs/versions/master/${i}site/en/api/api/evaluation.md)" > ./src/pages/docs/versions/master/${i}site/en/api/api/evaluation.md
- sed -i 's/# Module /# Module: /g' ./src/pages/docs/versions/master/${i}site/en/api/api/evaluation.md
- sed -i 's/## \(.*\) Objects/## Class: \1/g' ./src/pages/docs/versions/master/${i}site/en/api/api/evaluation.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/api/api/graph_retriever.md" ]]; then
- echo -e "---\ntitle: \"Graph Retriever\"\nmetaTitle: \"Graph Retriever\"\nmetaDescription: \"\"\nslug: \"/docs/graph_retrievermd\"\ndate: \"2020-09-03\"\nid: \"apigraphretrievermd\"\n---\n\n# Graph Retriever\n\n$(cat ./src/pages/docs/versions/master/${i}site/en/api/api/graph_retriever.md)" > ./src/pages/docs/versions/master/${i}site/en/api/api/graph_retriever.md
- sed -i 's/# Module /# Module: /g' ./src/pages/docs/versions/master/${i}site/en/api/api/graph_retriever.md
- sed -i 's/## \(.*\) Objects/## Class: \1/g' ./src/pages/docs/versions/master/${i}site/en/api/api/graph_retriever.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/api/api/knowledge_graph.md" ]]; then
- echo -e "---\ntitle: \"Knowledge Graph\"\nmetaTitle: \"Knowledge Graph\"\nmetaDescription: \"\"\nslug: \"/docs/knowledge_graphmd\"\ndate: \"2020-09-03\"\nid: \"apiknowledgegraphmd\"\n---\n\n# Knowledge Graph\n\n$(cat ./src/pages/docs/versions/master/${i}site/en/api/api/knowledge_graph.md)" > ./src/pages/docs/versions/master/${i}site/en/api/api/knowledge_graph.md
- sed -i 's/# Module /# Module: /g' ./src/pages/docs/versions/master/${i}site/en/api/api/knowledge_graph.md
- sed -i 's/## \(.*\) Objects/## Class: \1/g' ./src/pages/docs/versions/master/${i}site/en/api/api/knowledge_graph.md
- fi
-done
\ No newline at end of file
diff --git a/src/pages/docs/versions/master/update_img.sh b/src/pages/docs/versions/master/update_img.sh
deleted file mode 100755
index 6b2d604f8..000000000
--- a/src/pages/docs/versions/master/update_img.sh
+++ /dev/null
@@ -1,48 +0,0 @@
-#!/bin/bash
-
-# Used branch
-echo "Haystack branch: $1"
-
-# Master data
-versionstxt=$(cat src/pages/docs/versions/master/versions.txt)
-versions=("" $versionstxt)
-
-for i in "${versions[@]}"
-do
- rm ./src/pages/docs/versions/master/${i}site/en/img/*
-done
-
-if [ ! -d "./src/pages/docs/versions/master/${i}site/en/img/" ]; then
- mkdir -p ./src/pages/docs/versions/master/${i}site/en/img
-fi
-
-for i in "${versions[@]}"
-do
-
- j=$i
- if [ "$i" = "latest/" ]; then
- j=''
- fi
-
- wget https://github.com/deepset-ai/haystack/raw/${1}/docs/${j}_src/img/annotation_tool.png -O ./src/pages/docs/versions/master/${i}site/en/img/annotation_tool.png
- if [[ `wget -S --spider https://github.com/deepset-ai/haystack/raw/${1}/docs/${j}_src/img/demo.png 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://github.com/deepset-ai/haystack/raw/${1}/docs/${j}_src/img/demo.png -O ./src/pages/docs/versions/master/${i}site/en/img/demo.png
- fi
- wget https://github.com/deepset-ai/haystack/raw/${1}/docs/${j}_src/img/code_snippet_usage.png -O ./src/pages/docs/versions/master/${i}site/en/img/code_snippet_usage.png
- wget https://github.com/deepset-ai/haystack/raw/${1}/docs/${j}_src/img/colab_gpu_runtime.jpg -O ./src/pages/docs/versions/master/${i}site/en/img/colab_gpu_runtime.jpg
- wget https://github.com/deepset-ai/haystack/raw/${1}/docs/${j}_src/img/haystack_logo_blue_banner.png -O ./src/pages/docs/versions/master/${i}site/en/img/haystack_logo_blue_banner.png
- wget https://github.com/deepset-ai/haystack/raw/${1}/docs/${j}_src/img/haystack_logo_blue_banner_social_media.png -O ./src/pages/docs/versions/master/${i}site/en/img/haystack_logo_blue_banner_social_media.png
- wget https://github.com/deepset-ai/haystack/raw/${1}/docs/${j}_src/img/logo.png -O ./src/pages/docs/versions/master/${i}site/en/img/logo.png
- wget https://github.com/deepset-ai/haystack/raw/${1}/docs/${j}_src/img/retriever_reader.png -O ./src/pages/docs/versions/master/${i}site/en/img/retriever_reader.png
- wget https://github.com/deepset-ai/haystack/raw/${1}/docs/${j}_src/img/search.png -O ./src/pages/docs/versions/master/${i}site/en/img/search.png
- wget https://github.com/deepset-ai/haystack/raw/${1}/docs/${j}_src/img/sketched_concepts_white.png -O ./src/pages/docs/versions/master/${i}site/en/img/sketched_concepts_white.png
- wget https://github.com/deepset-ai/haystack/raw/${1}/docs/${j}_src/img/zenhub_board.png -O ./src/pages/docs/versions/master/${i}site/en/img/zenhub_board.png
- wget https://github.com/deepset-ai/haystack/raw/${1}/docs/${j}_src/img/zenhub_issue.png -O ./src/pages/docs/versions/master/${i}site/en/img/zenhub_issue.png
- wget https://github.com/deepset-ai/haystack/raw/${1}/docs/${j}_src/img/zenhub_roadmap.png -O ./src/pages/docs/versions/master/${i}site/en/img/zenhub_roadmap.png
- if [[ `wget -S --spider https://github.com/deepset-ai/haystack/raw/${1}/docs/${j}_src/img/colab_gpu_runtime.jpg 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://github.com/deepset-ai/haystack/raw/${1}/docs/${j}_src/img/colab_gpu_runtime.jpg -O ./src/pages/docs/versions/master/${i}site/en/img/colab_gpu_runtime.jpg
- fi
-done
-
-
-
diff --git a/src/pages/docs/versions/master/update_tutorialsdocs.sh b/src/pages/docs/versions/master/update_tutorialsdocs.sh
deleted file mode 100755
index 9f7d0d79d..000000000
--- a/src/pages/docs/versions/master/update_tutorialsdocs.sh
+++ /dev/null
@@ -1,90 +0,0 @@
-#!/bin/bash
-
-# Used branch
-echo "Haystack branch: $1"
-
-# Used branch
-echo "Haystack branch: $1"
-
-# Master data
-versionstxt=$(cat src/pages/docs/versions/master/versions.txt)
-versions=("" $versionstxt)
-
-for i in "${versions[@]}"
-do
- rm ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/*
-done
-
-if [ ! -d "./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/" ]; then
- mkdir -p ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials
-fi
-
-for i in "${versions[@]}"
-do
- j=$i
- if [ "$i" = "latest/" ]; then
- j=''
- fi
-
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/tutorials/tutorials/1.md -O ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/1.md
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/tutorials/tutorials/2.md -O ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/2.md
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/tutorials/tutorials/3.md -O ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/3.md
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/tutorials/tutorials/4.md -O ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/4.md
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/tutorials/tutorials/5.md -O ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/5.md
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/tutorials/tutorials/6.md -O ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/6.md
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/tutorials/tutorials/7.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/tutorials/tutorials/7.md -O ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/7.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/tutorials/tutorials/8.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/tutorials/tutorials/8.md -O ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/8.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/tutorials/tutorials/9.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/tutorials/tutorials/9.md -O ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/9.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/tutorials/tutorials/10.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/tutorials/tutorials/10.md -O ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/10.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/tutorials/tutorials/11.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/tutorials/tutorials/11.md -O ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/11.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/tutorials/tutorials/12.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/tutorials/tutorials/12.md -O ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/12.md
- fi
-
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/1.md
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/2.md
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/3.md
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/4.md
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/5.md
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/6.md
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/7.md" ]]; then
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/7.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/8.md" ]]; then
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/8.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/9.md" ]]; then
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/9.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/10.md" ]]; then
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/10.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/11.md" ]]; then
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/11.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/12.md" ]]; then
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/tutorials/tutorials/12.md
- fi
-done
\ No newline at end of file
diff --git a/src/pages/docs/versions/master/update_usagedocs.sh b/src/pages/docs/versions/master/update_usagedocs.sh
deleted file mode 100755
index 55f80d429..000000000
--- a/src/pages/docs/versions/master/update_usagedocs.sh
+++ /dev/null
@@ -1,157 +0,0 @@
-#!/bin/bash
-
-# Used branch
-echo "Haystack branch: $1"
-
-# Master data
-versionstxt=$(cat src/pages/docs/versions/master/versions.txt)
-versions=("" $versionstxt)
-
-for i in "${versions[@]}"
-do
- rm ./src/pages/docs/versions/master/${i}site/en/usage/usage/*
-done
-
-if [ ! -d "./src/pages/docs/versions/master/${i}site/en/usage/usage/" ]; then
- mkdir -p ./src/pages/docs/versions/master/${i}site/en/usage/usage
-fi
-
-for i in "${versions[@]}"
-do
-
- j=$i
- if [ "$i" = "latest/" ]; then
- j=''
- fi
-
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/roadmap.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/roadmap.md
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/database.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/database.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/database.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/document_store.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/document_store.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/document_store.md
- fi
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/domain_adaptation.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/domain_adaptation.md
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/intro.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/intro.md
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/languages.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/languages.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/languages.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/preprocessing.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/preprocessing.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/preprocessing.md
- fi
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/reader.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/reader.md
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/retriever.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/retriever.md
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/terms.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/terms.md
- #wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/tutorials.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/tutorials.md
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/get_started.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/get_started.md
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/annotation.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/annotation.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/annotation.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/generator.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/generator.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/generator.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/optimization.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/optimization.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/optimization.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/use_cases.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/use_cases.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/use_cases.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/pipelines.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/pipelines.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/pipelines.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/translator.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/translator.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/translator.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/summarizer.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/summarizer.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/summarizer.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/knowledge_graph.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/knowledge_graph.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/knowledge_graph.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/ranker.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/ranker.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/ranker.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/faq.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/faq.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/faq.md
- fi
- if [[ `wget -S --spider https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/query_classifier.md 2>&1 | grep 'HTTP/1.1 200 OK'` ]]; then
- wget https://raw.githubusercontent.com/deepset-ai/haystack/${1}/docs/${j}_src/usage/usage/query_classifier.md -O ./src/pages/docs/versions/master/${i}site/en/usage/usage/query_classifier.md
- fi
-
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/roadmap.md
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/usage/usage/database.md" ]]; then
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/database.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/usage/usage/document_store.md" ]]; then
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/document_store.md
- fi
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/domain_adaptation.md
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/intro.md
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/usage/usage/languages.md" ]]; then
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/languages.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/usage/usage/preprocessing.md" ]]; then
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/preprocessing.md
- fi
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/reader.md
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/retriever.md
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/terms.md
- #sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/tutorials.md
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/get_started.md
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/usage/usage/annotation.md" ]]; then
- echo -e "---\ntitle: \"Annotation Tool\"\nmetaTitle: \"Annotation Tool\"\nmetaDescription: \"\"\nslug: \"/docs/annotation\"\ndate: \"2020-09-03\"\nid: \"annotationmd\"\n---\n\n$(cat ./src/pages/docs/versions/master/${i}site/en/usage/usage/annotation.md )" > ./src/pages/docs/versions/master/${i}site/en/usage/usage/annotation.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/usage/usage/generator.md" ]]; then
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/generator.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/usage/usage/optimization.md" ]]; then
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/optimization.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/usage/usage/use_cases.md" ]]; then
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/use_cases.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/usage/usage/pipelines.md" ]]; then
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/pipelines.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/usage/usage/summarizer.md" ]]; then
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/summarizer.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/usage/usage/translator.md" ]]; then
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/translator.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/usage/usage/knowledge_graph.md" ]]; then
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/knowledge_graph.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/usage/usage/ranker.md" ]]; then
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/ranker.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/usage/usage/faq.md" ]]; then
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/faq.md
- fi
- if [[ -f "./src/pages/docs/versions/master/${i}site/en/usage/usage/query_classifier.md" ]]; then
- sed -i 's//---/' ./src/pages/docs/versions/master/${i}site/en/usage/usage/query_classifier.md
- fi
-done
-
diff --git a/src/pages/docs/versions/master/v0.4.0/site/en/api/api/document_store.md b/src/pages/docs/versions/master/v0.4.0/site/en/api/api/document_store.md
deleted file mode 100644
index 28082e499..000000000
--- a/src/pages/docs/versions/master/v0.4.0/site/en/api/api/document_store.md
+++ /dev/null
@@ -1,423 +0,0 @@
----
-title: "Document Store"
-metaTitle: "Database"
-metaDescription: ""
-slug: "/docs/apidatabase"
-date: "2020-09-03"
-id: "apidatabasemd"
----
-
-# Document Store
-
-
-# elasticsearch
-
-
-## ElasticsearchDocumentStore
-
-```python
-class ElasticsearchDocumentStore(BaseDocumentStore)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(host: str = "localhost", port: int = 9200, username: str = "", password: str = "", index: str = "document", label_index: str = "label", search_fields: Union[str, list] = "text", text_field: str = "text", name_field: str = "name", embedding_field: str = "embedding", embedding_dim: int = 768, custom_mapping: Optional[dict] = None, excluded_meta_data: Optional[list] = None, faq_question_field: Optional[str] = None, scheme: str = "http", ca_certs: bool = False, verify_certs: bool = True, create_index: bool = True, update_existing_documents: bool = False, refresh_type: str = "wait_for")
-```
-
-A DocumentStore using Elasticsearch to store and query the documents for our search.
-
-* Keeps all the logic to store and query documents from Elastic, incl. mapping of fields, adding filters or boosts to your queries, and storing embeddings
-* You can either use an existing Elasticsearch index or create a new one via haystack
-* Retrievers operate on top of this DocumentStore to find the relevant documents for a query
-
-**Arguments**:
-
-- `host`: url of elasticsearch
-- `port`: port of elasticsearch
-- `username`: username
-- `password`: password
-- `index`: Name of index in elasticsearch to use. If not existing yet, we will create one.
-- `search_fields`: Name of fields used by ElasticsearchRetriever to find matches in the docs to our incoming query (using elastic's multi_match query), e.g. ["title", "full_text"]
-- `text_field`: Name of field that might contain the answer and will therefore be passed to the Reader Model (e.g. "full_text").
-If no Reader is used (e.g. in FAQ-Style QA) the plain content of this field will just be returned.
-- `name_field`: Name of field that contains the title of the the doc
-- `embedding_field`: Name of field containing an embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `embedding_dim`: Dimensionality of embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `custom_mapping`: If you want to use your own custom mapping for creating a new index in Elasticsearch, you can supply it here as a dictionary.
-- `excluded_meta_data`: Name of fields in Elasticsearch that should not be returned (e.g. [field_one, field_two]).
-Helpful if you have fields with long, irrelevant content that you don't want to display in results (e.g. embedding vectors).
-- `scheme`: 'https' or 'http', protocol used to connect to your elasticsearch instance
-- `ca_certs`: Root certificates for SSL
-- `verify_certs`: Whether to be strict about ca certificates
-- `create_index`: Whether to try creating a new index (If the index of that name is already existing, we will just continue in any case)
-- `update_existing_documents`: Whether to update any existing documents with the same ID when adding
-documents. When set as True, any document with an existing ID gets updated.
-If set to False, an error is raised if the document ID of the document being
-added already exists.
-- `refresh_type`: Type of ES refresh used to control when changes made by a request (e.g. bulk) are made visible to search.
-Values:
-- 'wait_for' => continue only after changes are visible (slow, but safe)
-- 'false' => continue directly (fast, but sometimes unintuitive behaviour when docs are not immediately available after ingestion)
-More info at https://www.elastic.co/guide/en/elasticsearch/reference/6.8/docs-refresh.html
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
-```
-
-Indexes documents for later queries in Elasticsearch.
-
-When using explicit document IDs, any existing document with the same ID gets updated.
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
-For documents as dictionaries, the format is {"text": "
-
-EXECUTABLE VERSION: [*colab*](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb)
-
-Question Answering can be used in a variety of use cases. A very common one: Using it to navigate through complex knowledge bases or long documents ("search setting").
-
-A "knowledge base" could for example be your website, an internal wiki or a collection of financial reports.
-In this tutorial we will work on a slightly different domain: "Game of Thrones".
-
-Let's see how we can use a bunch of Wikipedia articles to answer a variety of questions about the
-marvellous seven kingdoms...
-
-
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack and install the version of torch that works with the colab GPUs
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install torch==1.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-Haystack finds answers to queries within the documents stored in a `DocumentStore`. The current implementations of `DocumentStore` include `ElasticsearchDocumentStore`, `FAISSDocumentStore`, `SQLDocumentStore`, and `InMemoryDocumentStore`.
-
-**Here:** We recommended Elasticsearch as it comes preloaded with features like [full-text queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html), [BM25 retrieval](https://www.elastic.co/elasticon/conf/2016/sf/improved-text-scoring-with-bm25), and [vector storage for text embeddings](https://www.elastic.co/guide/en/elasticsearch/reference/7.6/dense-vector.html).
-
-**Alternatives:** If you are unable to setup an Elasticsearch instance, then follow the [Tutorial 3](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb) for using SQL/InMemory document stores.
-
-**Hint**: This tutorial creates a new document store instance with Wikipedia articles on Game of Thrones. However, you can configure Haystack to work with your existing document stores.
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker
-#! docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.6.2
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.6.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.6.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.6.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.6.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-# Connect to Elasticsearch
-
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles about Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first fetch some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is:
-# {
-# 'text': "
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack and install the version of torch that works with the colab GPUs
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install torch==1.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-### Document Store
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-
-
-```python
-from haystack.document_store.faiss import FAISSDocumentStore
-
-document_store = FAISSDocumentStore()
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever, Reader, & Finder
-
-#### Retriever
-
-**Here:** We use a `DensePassageRetriever`
-
-**Alternatives:**
-
-- The `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-
-
-```python
-from haystack.retriever.dense import DensePassageRetriever
-retriever = DensePassageRetriever(document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- use_gpu=True,
- embed_title=True,
- max_seq_len=256,
- batch_size=16,
- remove_sep_tok_from_untitled_passages=True)
-# Important:
-# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all
-# previously indexed documents and update their embedding representation.
-# While this can be a time consuming operation (depending on corpus size), it only needs to be done once.
-# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.
-document_store.update_embeddings(retriever)
-```
-
-#### Reader
-
-Similar to previous Tutorials we now initalize our reader.
-
-Here we use a FARMReader with the *deepset/roberta-base-squad2* model (see: https://huggingface.co/deepset/roberta-base-squad2)
-
-
-
-##### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-#### Finder
-
-The Finder sticks together reader and retriever in a pipeline to answer our actual questions.
-
-
-```python
-finder = Finder(reader, retriever)
-```
-
-### Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = finder.get_answers(question="Who created the Dothraki vocabulary?", top_k_retriever=10, top_k_reader=5)
-
-#prediction = finder.get_answers(question="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-#prediction = finder.get_answers(question="Who is the sister of Sansa?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
-
-
-```python
-
-```
diff --git a/src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/reader.md b/src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/reader.md
deleted file mode 100644
index 1e013f35a..000000000
--- a/src/pages/docs/versions/master/v0.4.0/site/en/usage/usage/reader.md
+++ /dev/null
@@ -1,439 +0,0 @@
----
-title: "Reader"
-metaTitle: "Reader"
-metaDescription: ""
-slug: "/docs/reader"
-date: "2020-09-03"
-id: "readermd"
----
-
-# Reader
-
-The Reader, also known as Open-Domain QA systems in Machine Learning speak,
-is the core component that enables Haystack to find the answers that you need.
-Haystack’s Readers are:
-
-
-* built on the latest transformer based language models
-
-
-* strong in their grasp of semantics
-
-
-* sensitive to syntactic structure
-
-
-* state-of-the-art in QA tasks like SQuAD and Natural Questions
-
-
-
-
-
-While these models can work on CPU, it is recommended that they are run using GPUs to keep query times low.
-
-## Choosing the Right Model
-
-In Haystack, you can start using pretrained QA models simply by providing its HuggingFace Model Hub name to the Reader.
-The loading of model weights is handled by Haystack,
-and you have the option of using the QA pipeline from deepset FARM or HuggingFace Transformers (see FARM vs Transformers for details).
-
-Currently, there are a lot of different models out there and it can be rather overwhelming trying to pick the one that fits your use case.
-To get you started, we have a few recommendations for you to try out.
-
-
-
-
-
-
-
-
-```python
-model = "deepset/roberta-base-squad2"
-reader = FARMReader(model, use_gpu=True)
-finder = Finder(reader, retriever)
-```
-
-
-
-
-
-
-
-
-
-```python
-model = "deepset/roberta-base-squad2"
-reader = TransformersReader(model, use_gpu=1)
-finder = Finder(reader, retriever)
-```
-
-
-
-
-
-
-**All-rounder**: In the class of base sized models trained on SQuAD, **RoBERTa** has shown better performance than BERT
-and can be capably handled by any machine equipped with a single NVidia V100 GPU.
-We recommend this as the starting point for anyone wanting to create a performant and computationally reasonable instance of Haystack.
-
-**Built for Speed**: If speed and GPU memory are more of a priority to you than accuracy,
-you should try the MiniLM model.
-It is a smaller model that is trained to mimic larger models through the distillation process,
-and it outperforms the BERT base on SQuAD even though it is about 40% smaller.
-
-
-**State of the Art Accuracy**: For most, **ALBERT XXL** will be too large to feasibly work with.
-But if performance is your sole concern, and you have the computational resources,
-you might like to try ALBERT XXL which has set SoTA performance on SQuAD 2.0.
-
-
-## Languages other than English
-
-Haystack is also very well suited to open-domain QA on languages other than English.
-While models are comparatively more performant on English,
-thanks to a wealth of available English training data,
-there are a couple QA models that are directly usable in Haystack.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-**An optimised variant of BERT and a great starting point.**
-
-```python
-reader = FARMReader("deepset/roberta-base-squad2")
-```
-
-* **Pro**: Strong all round model
-
-* **Con**: There are other models that are either faster or more accurate
-
-
-
-
-
-
-
-
-
-**A cleverly distilled model that sacrifices a little accuracy for speed.**
-
-```python
-reader = FARMReader("deepset/minilm-uncased-squad2")
-```
-
-* **Pro**: Inference speed up to 50% faster than BERT base
-
-* **Con**: Still doesn’t match the best base sized models in accuracy
-
-
-
-
-
-
-
-
-
-**Large, powerful, SotA model.**
-
-```python
-reader = FARMReader("ahotrod/albert_xxlargev1_squad2_512")
-```
-
-* **Pro**: Better accuracy than any other open source model in QA
-
-* **Con**: The computational power needed make it impractical for most use cases
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-**An optimised variant of BERT and a great starting point.**
-
-```python
-reader = TransformersReader("deepset/roberta-base-squad2")
-```
-
-* **Pro**: Strong all round model
-
-* **Con**: There are other models that are either faster or more accurate
-
-
-
-
-
-
-
-
-
-**A cleverly distilled model that sacrifices a little accuracy for speed.**
-
-```python
-reader = TransformersReader("deepset/minilm-uncased-squad2")
-```
-
-* **Pro**: Inference speed up to 50% faster than BERT base
-
-* **Con**: Still doesn’t match the best base sized models in accuracy
-
-
-
-
-
-
-
-
-
-**Large, powerful, SotA model.**
-
-```python
-reader = TransformersReader("ahotrod/albert_xxlargev1_squad2_512")
-```
-
-* **Pro**: Better accuracy than any other open source model in QA
-
-* **Con**: The computational power needed make it impractical for most use cases
-
-
-
-
-
-
-The **French** and **Italian models** are both monolingual language models trained on French and Italian versions of the SQuAD dataset
-and their authors report decent results in their model cards
-[here](https://huggingface.co/illuin/camembert-base-fquad) and [here](https://huggingface.co/mrm8488/bert-italian-finedtuned-squadv1-it-alfa) .
-Note that there is also a [large variant](https://huggingface.co/illuin/camembert-large-fquad) of the French model available on the model hub.
-There also exist Korean QA models on the model hub but their performance is not reported.
-
-The **zero-shot model** that is shown above is a **multilingual XLM-RoBERTa Large** that is trained on English SQuAD.
-It is clear, from our [evaluations](https://huggingface.co/deepset/xlm-roberta-large-squad2#model_card),
-that the model has been able to transfer some of its English QA capabilities to other languages,
-but still its performance lags behind that of the monolingual models.
-Nonetheless, if there is not yet a monolingual model for your language and it is one of the 100 supported by XLM-RoBERTa,
-this zero-shot model may serve as a decent first baseline.
-
-When using a Reader of any language, it’s important to ensure that the Retriever is also compatible.
-While sparse methods like BM25 and TF-IDF are language agnostic,
-dense method like Dense Passage Retrieval are trained for a particular language.
-
-
-## Deeper Dive: FARM vs Transformers
-
-Apart from the **model weights**, Haystack Readers contain all the components found in end-to-end open domain QA systems.
-This includes **tokenization**, **embedding computation**, **span prediction** and **candidate aggregation**.
-While the handling of model weights is the same between the FARM and Transformers libraries, their QA pipelines differ in some ways.
-The major points are:
-
-
-* The **TransformersReader** will sometimes predict the same span twice while duplicates are removed in the **FARMReader**
-
-
-* The **FARMReader** currently uses the tokenizers from the HuggingFace Transformers library while the **TransformersReader** uses the tokenizers from the HuggingFace Tokenizers library
-
-
-* Start and end logits are normalized per passage and multiplied in the **TransformersReader** while they are summed and not normalised in the **FARMReader**
-
-If you’re interested in the finer details of these points, have a look at [this](https://github.com/deepset-ai/haystack/issues/248#issuecomment-661977237) GitHub comment.
-
-We see value in maintaining both kinds of Readers since Transformers is a very familiar library to many of Haystack’s users
-but we at deepset can more easily update and optimise the FARM pipeline for speed and performance.
-
-
-Haystack also has a close integration with FARM which means that you can further fine-tune your Readers on labelled data using a FARMReader.
-See our tutorials for an end-to-end example or below for a shortened example.
-
-```python
-# Initialise Reader
-model = "deepset/roberta-base-squad2"
-reader = FARMReader(model)
-
-# Perform finetuning
-train_data = "PATH/TO_YOUR/TRAIN_DATA"
-train_filename = "train.json"
-save_dir = "finetuned_model"
-reader.train(train_data, train_filename, save_dir=save_dir)
-
-# Load
-finetuned_reader = FARMReader(save_dir)
-```
-
-## Deeper Dive: From Language Model to Haystack Reader
-
-Language models form the core of most modern NLP systems and that includes the Readers in Haystack.
-They build a general understanding of language when performing training tasks such as Masked Language Modeling or Replaced Token Detection
-on large amounts of text.
-Well trained language models capture the word distribution in one or more languages
-but more importantly, convert input text into a set of word vectors that capture elements of syntax and semantics.
-
-In order to convert a language model into a Reader model, it needs first to be trained on a Question Answering dataset.
-To do so requires the addition of a question answering prediction head on top of the language model.
-The task can be thought of as a token classification task where every input token is assigned a probability of being
-either the start or end token of the correct answer.
-In cases where the answer is not contained within the passage, the prediction head is also expected to return a `no_answer` prediction.
-
-
-Since language models are limited in the number of tokens which they can process in a single forward pass,
-a sliding window mechanism is implemented to handle variable length documents.
-This functions by slicing the document into overlapping passages of (approximately) `max_seq_length`
-that are each offset by `doc_stride` number of tokens.
-These can be set when the Reader is initialized.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-```python
-reader = FARMReader("illuin/camembert-base-fquad")
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = FARMReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa")
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = FARMReader("deepset/xlm-roberta-large-squad2")
-```
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-```python
-reader = TransformersReader("illuin/camembert-base-fquad")
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = TransformersReader("mrm8488/bert-italian-finedtuned-squadv1-it-alfa")
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = TransformersReader("deepset/xlm-roberta-large-squad2")
-```
-
-
-
-
-
-
-Predictions are made on each individual passage and the process of aggregation picks the best candidates across all passages.
-If you’d like to learn more about what is happening behind the scenes, have a look at [this](https://medium.com/deepset-ai/modern-question-answering-systems-explained-4d0913744097) article.
-
-
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/api/api/document_store.md b/src/pages/docs/versions/master/v0.5.0/site/en/api/api/document_store.md
deleted file mode 100644
index 20e3f35de..000000000
--- a/src/pages/docs/versions/master/v0.5.0/site/en/api/api/document_store.md
+++ /dev/null
@@ -1,534 +0,0 @@
----
-title: "Document Store"
-metaTitle: "Database"
-metaDescription: ""
-slug: "/docs/apidatabase"
-date: "2020-09-03"
-id: "apidatabasemd"
----
-
-# Document Store
-
-
-# memory
-
-
-## InMemoryDocumentStore
-
-```python
-class InMemoryDocumentStore(BaseDocumentStore)
-```
-
-In-memory document store
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
-For documents as dictionaries, the format is {"text": "
-
-
-
-
-
-
-```python
-reader = FARMReader(... max_seq_len=384, doc_stride=128 ...)
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = TransformersReader(... max_seq_len=384, doc_stride=128 ...
-```
-
-
-
-
-EXECUTABLE VERSION: [*colab*](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb)
-
-Question Answering can be used in a variety of use cases. A very common one: Using it to navigate through complex knowledge bases or long documents ("search setting").
-
-A "knowledge base" could for example be your website, an internal wiki or a collection of financial reports.
-In this tutorial we will work on a slightly different domain: "Game of Thrones".
-
-Let's see how we can use a bunch of Wikipedia articles to answer a variety of questions about the
-marvellous seven kingdoms...
-
-
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack and install the version of torch that works with the colab GPUs
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install torch==1.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-Haystack finds answers to queries within the documents stored in a `DocumentStore`. The current implementations of `DocumentStore` include `ElasticsearchDocumentStore`, `FAISSDocumentStore`, `SQLDocumentStore`, and `InMemoryDocumentStore`.
-
-**Here:** We recommended Elasticsearch as it comes preloaded with features like [full-text queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html), [BM25 retrieval](https://www.elastic.co/elasticon/conf/2016/sf/improved-text-scoring-with-bm25), and [vector storage for text embeddings](https://www.elastic.co/guide/en/elasticsearch/reference/7.6/dense-vector.html).
-
-**Alternatives:** If you are unable to setup an Elasticsearch instance, then follow the [Tutorial 3](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb) for using SQL/InMemory document stores.
-
-**Hint**: This tutorial creates a new document store instance with Wikipedia articles on Game of Thrones. However, you can configure Haystack to work with your existing document stores.
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker
-#! docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.6.2
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.6.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.6.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.6.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.6.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-# Connect to Elasticsearch
-
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles about Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first fetch some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is:
-# {
-# 'text': "
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack and install the version of torch that works with the colab GPUs
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install torch==1.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-### Document Store
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-
-
-```python
-from haystack.document_store.faiss import FAISSDocumentStore
-
-document_store = FAISSDocumentStore()
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever, Reader, & Finder
-
-#### Retriever
-
-**Here:** We use a `DensePassageRetriever`
-
-**Alternatives:**
-
-- The `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-
-
-```python
-from haystack.retriever.dense import DensePassageRetriever
-retriever = DensePassageRetriever(document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- use_gpu=True,
- embed_title=True,
- max_seq_len=256,
- batch_size=16,
- remove_sep_tok_from_untitled_passages=True)
-# Important:
-# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all
-# previously indexed documents and update their embedding representation.
-# While this can be a time consuming operation (depending on corpus size), it only needs to be done once.
-# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.
-document_store.update_embeddings(retriever)
-```
-
-#### Reader
-
-Similar to previous Tutorials we now initalize our reader.
-
-Here we use a FARMReader with the *deepset/roberta-base-squad2* model (see: https://huggingface.co/deepset/roberta-base-squad2)
-
-
-
-##### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-#### Finder
-
-The Finder sticks together reader and retriever in a pipeline to answer our actual questions.
-
-
-```python
-finder = Finder(reader, retriever)
-```
-
-### Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = finder.get_answers(question="Who created the Dothraki vocabulary?", top_k_retriever=10, top_k_reader=5)
-
-#prediction = finder.get_answers(question="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-#prediction = finder.get_answers(question="Who is the sister of Sansa?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
-
-
-```python
-
-```
diff --git a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/reader.md b/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/reader.md
deleted file mode 100644
index fb6812e4f..000000000
--- a/src/pages/docs/versions/master/v0.5.0/site/en/usage/usage/reader.md
+++ /dev/null
@@ -1,316 +0,0 @@
----
-title: "Reader"
-metaTitle: "Reader"
-metaDescription: ""
-slug: "/docs/reader"
-date: "2020-09-03"
-id: "readermd"
----
-
-# Reader
-
-The Reader, also known as Open-Domain QA systems in Machine Learning speak,
-is the core component that enables Haystack to find the answers that you need.
-Haystack’s Readers are:
-
-
-* built on the latest transformer based language models
-
-
-* strong in their grasp of semantics
-
-
-* sensitive to syntactic structure
-
-
-* state-of-the-art in QA tasks like SQuAD and Natural Questions
-
-
-
-
-
-While these models can work on CPU, it is recommended that they are run using GPUs to keep query times low.
-
-## Choosing the Right Model
-
-In Haystack, you can start using pretrained QA models simply by providing its HuggingFace Model Hub name to the Reader.
-The loading of model weights is handled by Haystack,
-and you have the option of using the QA pipeline from deepset FARM or HuggingFace Transformers (see FARM vs Transformers for details).
-
-Currently, there are a lot of different models out there and it can be rather overwhelming trying to pick the one that fits your use case.
-To get you started, we have a few recommendations for you to try out.
-
-
-
-
-
-
-
-
-```python
-model = "deepset/roberta-base-squad2"
-reader = FARMReader(model, use_gpu=True)
-finder = Finder(reader, retriever)
-```
-
-
-
-
-
-
-
-
-
-```python
-model = "deepset/roberta-base-squad2"
-reader = TransformersReader(model, use_gpu=1)
-finder = Finder(reader, retriever)
-```
-
-
-
-
-
-
-**All-rounder**: In the class of base sized models trained on SQuAD, **RoBERTa** has shown better performance than BERT
-and can be capably handled by any machine equipped with a single NVidia V100 GPU.
-We recommend this as the starting point for anyone wanting to create a performant and computationally reasonable instance of Haystack.
-
-**Built for Speed**: If speed and GPU memory are more of a priority to you than accuracy,
-you should try the MiniLM model.
-It is a smaller model that is trained to mimic larger models through the distillation process,
-and it outperforms the BERT base on SQuAD even though it is about 40% smaller.
-
-
-**State of the Art Accuracy**: For most, **ALBERT XXL** will be too large to feasibly work with.
-But if performance is your sole concern, and you have the computational resources,
-you might like to try ALBERT XXL which has set SoTA performance on SQuAD 2.0.
-
-
-
-
-## Deeper Dive: FARM vs Transformers
-
-Apart from the **model weights**, Haystack Readers contain all the components found in end-to-end open domain QA systems.
-This includes **tokenization**, **embedding computation**, **span prediction** and **candidate aggregation**.
-While the handling of model weights is the same between the FARM and Transformers libraries, their QA pipelines differ in some ways.
-The major points are:
-
-
-* The **TransformersReader** will sometimes predict the same span twice while duplicates are removed in the **FARMReader**
-
-
-* The **FARMReader** currently uses the tokenizers from the HuggingFace Transformers library while the **TransformersReader** uses the tokenizers from the HuggingFace Tokenizers library
-
-
-* Start and end logits are normalized per passage and multiplied in the **TransformersReader** while they are summed and not normalised in the **FARMReader**
-
-If you’re interested in the finer details of these points, have a look at [this](https://github.com/deepset-ai/haystack/issues/248#issuecomment-661977237) GitHub comment.
-
-We see value in maintaining both kinds of Readers since Transformers is a very familiar library to many of Haystack’s users
-but we at deepset can more easily update and optimise the FARM pipeline for speed and performance.
-
-
-Haystack also has a close integration with FARM which means that you can further fine-tune your Readers on labelled data using a FARMReader.
-See our tutorials for an end-to-end example or below for a shortened example.
-
-```python
-# Initialise Reader
-model = "deepset/roberta-base-squad2"
-reader = FARMReader(model)
-
-# Perform finetuning
-train_data = "PATH/TO_YOUR/TRAIN_DATA"
-train_filename = "train.json"
-save_dir = "finetuned_model"
-reader.train(train_data, train_filename, save_dir=save_dir)
-
-# Load
-finetuned_reader = FARMReader(save_dir)
-```
-
-## Deeper Dive: From Language Model to Haystack Reader
-
-Language models form the core of most modern NLP systems and that includes the Readers in Haystack.
-They build a general understanding of language when performing training tasks such as Masked Language Modeling or Replaced Token Detection
-on large amounts of text.
-Well trained language models capture the word distribution in one or more languages
-but more importantly, convert input text into a set of word vectors that capture elements of syntax and semantics.
-
-In order to convert a language model into a Reader model, it needs first to be trained on a Question Answering dataset.
-To do so requires the addition of a question answering prediction head on top of the language model.
-The task can be thought of as a token classification task where every input token is assigned a probability of being
-either the start or end token of the correct answer.
-In cases where the answer is not contained within the passage, the prediction head is also expected to return a `no_answer` prediction.
-
-
-Since language models are limited in the number of tokens which they can process in a single forward pass,
-a sliding window mechanism is implemented to handle variable length documents.
-This functions by slicing the document into overlapping passages of (approximately) `max_seq_length`
-that are each offset by `doc_stride` number of tokens.
-These can be set when the Reader is initialized.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-**An optimised variant of BERT and a great starting point.**
-
-```python
-reader = FARMReader("deepset/roberta-base-squad2")
-```
-
-* **Pro**: Strong all round model
-
-* **Con**: There are other models that are either faster or more accurate
-
-
-
-
-
-
-
-
-
-**A cleverly distilled model that sacrifices a little accuracy for speed.**
-
-```python
-reader = FARMReader("deepset/minilm-uncased-squad2")
-```
-
-* **Pro**: Inference speed up to 50% faster than BERT base
-
-* **Con**: Still doesn’t match the best base sized models in accuracy
-
-
-
-
-
-
-
-
-
-**Large, powerful, SotA model.**
-
-```python
-reader = FARMReader("ahotrod/albert_xxlargev1_squad2_512")
-```
-
-* **Pro**: Better accuracy than any other open source model in QA
-
-* **Con**: The computational power needed make it impractical for most use cases
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-**An optimised variant of BERT and a great starting point.**
-
-```python
-reader = TransformersReader("deepset/roberta-base-squad2")
-```
-
-* **Pro**: Strong all round model
-
-* **Con**: There are other models that are either faster or more accurate
-
-
-
-
-
-
-
-
-
-**A cleverly distilled model that sacrifices a little accuracy for speed.**
-
-```python
-reader = TransformersReader("deepset/minilm-uncased-squad2")
-```
-
-* **Pro**: Inference speed up to 50% faster than BERT base
-
-* **Con**: Still doesn’t match the best base sized models in accuracy
-
-
-
-
-
-
-
-
-
-**Large, powerful, SotA model.**
-
-```python
-reader = TransformersReader("ahotrod/albert_xxlargev1_squad2_512")
-```
-
-* **Pro**: Better accuracy than any other open source model in QA
-
-* **Con**: The computational power needed make it impractical for most use cases
-
-
-
-
-
-
-Predictions are made on each individual passage and the process of aggregation picks the best candidates across all passages.
-If you’d like to learn more about what is happening behind the scenes, have a look at [this](https://medium.com/deepset-ai/modern-question-answering-systems-explained-4d0913744097) article.
-
-
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/api/api/document_store.md b/src/pages/docs/versions/master/v0.6.0/site/en/api/api/document_store.md
deleted file mode 100644
index 64ba9bdaf..000000000
--- a/src/pages/docs/versions/master/v0.6.0/site/en/api/api/document_store.md
+++ /dev/null
@@ -1,950 +0,0 @@
----
-title: "Document Store"
-metaTitle: "Database"
-metaDescription: ""
-slug: "/docs/apidatabase"
-date: "2020-09-03"
-id: "apidatabasemd"
----
-
-# Document Store
-
-
-# Module: elasticsearch
-
-
-## Class: ElasticsearchDocumentStore
-
-```python
-class ElasticsearchDocumentStore(BaseDocumentStore)
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(host: str = "localhost", port: int = 9200, username: str = "", password: str = "", index: str = "document", label_index: str = "label", search_fields: Union[str, list] = "text", text_field: str = "text", name_field: str = "name", embedding_field: str = "embedding", embedding_dim: int = 768, custom_mapping: Optional[dict] = None, excluded_meta_data: Optional[list] = None, faq_question_field: Optional[str] = None, analyzer: str = "standard", scheme: str = "http", ca_certs: bool = False, verify_certs: bool = True, create_index: bool = True, update_existing_documents: bool = False, refresh_type: str = "wait_for", similarity="dot_product", timeout=30, return_embedding: bool = False)
-```
-
-A DocumentStore using Elasticsearch to store and query the documents for our search.
-
-* Keeps all the logic to store and query documents from Elastic, incl. mapping of fields, adding filters or boosts to your queries, and storing embeddings
-* You can either use an existing Elasticsearch index or create a new one via haystack
-* Retrievers operate on top of this DocumentStore to find the relevant documents for a query
-
-**Arguments**:
-
-- `host`: url of elasticsearch
-- `port`: port of elasticsearch
-- `username`: username
-- `password`: password
-- `index`: Name of index in elasticsearch to use. If not existing yet, we will create one.
-- `search_fields`: Name of fields used by ElasticsearchRetriever to find matches in the docs to our incoming query (using elastic's multi_match query), e.g. ["title", "full_text"]
-- `text_field`: Name of field that might contain the answer and will therefore be passed to the Reader Model (e.g. "full_text").
-If no Reader is used (e.g. in FAQ-Style QA) the plain content of this field will just be returned.
-- `name_field`: Name of field that contains the title of the the doc
-- `embedding_field`: Name of field containing an embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `embedding_dim`: Dimensionality of embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
-- `custom_mapping`: If you want to use your own custom mapping for creating a new index in Elasticsearch, you can supply it here as a dictionary.
-- `analyzer`: Specify the default analyzer from one of the built-ins when creating a new Elasticsearch Index.
-Elasticsearch also has built-in analyzers for different languages (e.g. impacting tokenization). More info at:
-https://www.elastic.co/guide/en/elasticsearch/reference/7.9/analysis-analyzers.html
-- `excluded_meta_data`: Name of fields in Elasticsearch that should not be returned (e.g. [field_one, field_two]).
-Helpful if you have fields with long, irrelevant content that you don't want to display in results (e.g. embedding vectors).
-- `scheme`: 'https' or 'http', protocol used to connect to your elasticsearch instance
-- `ca_certs`: Root certificates for SSL
-- `verify_certs`: Whether to be strict about ca certificates
-- `create_index`: Whether to try creating a new index (If the index of that name is already existing, we will just continue in any case)
-- `update_existing_documents`: Whether to update any existing documents with the same ID when adding
-documents. When set as True, any document with an existing ID gets updated.
-If set to False, an error is raised if the document ID of the document being
-added already exists.
-- `refresh_type`: Type of ES refresh used to control when changes made by a request (e.g. bulk) are made visible to search.
-If set to 'wait_for', continue only after changes are visible (slow, but safe).
-If set to 'false', continue directly (fast, but sometimes unintuitive behaviour when docs are not immediately available after ingestion).
-More info at https://www.elastic.co/guide/en/elasticsearch/reference/6.8/docs-refresh.html
-- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
-more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
-- `timeout`: Number of seconds after which an ElasticSearch request times out.
-- `return_embedding`: To return document embedding
-
-
-#### get\_document\_by\_id
-
-```python
- | get_document_by_id(id: str, index=None) -> Optional[Document]
-```
-
-Fetch a document by specifying its text id string
-
-
-#### get\_documents\_by\_id
-
-```python
- | get_documents_by_id(ids: List[str], index=None) -> List[Document]
-```
-
-Fetch documents by specifying a list of text id strings
-
-
-#### write\_documents
-
-```python
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
-```
-
-Indexes documents for later queries in Elasticsearch.
-
-Behaviour if a document with the same ID already exists in ElasticSearch:
-a) (Default) Throw Elastic's standard error message for duplicate IDs.
-b) If `self.update_existing_documents=True` for DocumentStore: Overwrite existing documents.
-(This is only relevant if you pass your own ID when initializing a `Document`.
-If don't set custom IDs for your Documents or just pass a list of dictionaries here,
-they will automatically get UUIDs assigned. See the `Document` class for details)
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
-For documents as dictionaries, the format is {"text": "
-
-
-
-
-
-
-```python
-reader = FARMReader(... max_seq_len=384, doc_stride=128 ...)
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = TransformersReader(... max_seq_len=384, doc_stride=128 ...
-```
-
-
-
-
-EXECUTABLE VERSION: [*colab*](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb)
-
-Question Answering can be used in a variety of use cases. A very common one: Using it to navigate through complex knowledge bases or long documents ("search setting").
-
-A "knowledge base" could for example be your website, an internal wiki or a collection of financial reports.
-In this tutorial we will work on a slightly different domain: "Game of Thrones".
-
-Let's see how we can use a bunch of Wikipedia articles to answer a variety of questions about the
-marvellous seven kingdoms...
-
-
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-Haystack finds answers to queries within the documents stored in a `DocumentStore`. The current implementations of `DocumentStore` include `ElasticsearchDocumentStore`, `FAISSDocumentStore`, `SQLDocumentStore`, and `InMemoryDocumentStore`.
-
-**Here:** We recommended Elasticsearch as it comes preloaded with features like [full-text queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html), [BM25 retrieval](https://www.elastic.co/elasticon/conf/2016/sf/improved-text-scoring-with-bm25), and [vector storage for text embeddings](https://www.elastic.co/guide/en/elasticsearch/reference/7.6/dense-vector.html).
-
-**Alternatives:** If you are unable to setup an Elasticsearch instance, then follow the [Tutorial 3](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb) for using SQL/InMemory document stores.
-
-**Hint**: This tutorial creates a new document store instance with Wikipedia articles on Game of Thrones. However, you can configure Haystack to work with your existing document stores.
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker
-#! docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.6.2
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.6.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.6.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.6.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.6.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-# Connect to Elasticsearch
-
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles about Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first fetch some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is:
-# {
-# 'text': "
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-### Document Store
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-The default flavour of FAISSDocumentStore is "Flat" but can also be set to "HNSW" for
-faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.
-For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-
-
-```python
-from haystack.document_store.faiss import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever, Reader, & Finder
-
-#### Retriever
-
-**Here:** We use a `DensePassageRetriever`
-
-**Alternatives:**
-
-- The `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-
-
-```python
-from haystack.retriever.dense import DensePassageRetriever
-retriever = DensePassageRetriever(document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- max_seq_len_query=64,
- max_seq_len_passage=256,
- batch_size=16,
- use_gpu=True,
- embed_title=True,
- use_fast_tokenizers=True)
-# Important:
-# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all
-# previously indexed documents and update their embedding representation.
-# While this can be a time consuming operation (depending on corpus size), it only needs to be done once.
-# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.
-document_store.update_embeddings(retriever)
-```
-
-#### Reader
-
-Similar to previous Tutorials we now initalize our reader.
-
-Here we use a FARMReader with the *deepset/roberta-base-squad2* model (see: https://huggingface.co/deepset/roberta-base-squad2)
-
-
-
-##### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-#### Finder
-
-The Finder sticks together reader and retriever in a pipeline to answer our actual questions.
-
-
-```python
-finder = Finder(reader, retriever)
-```
-
-### Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = finder.get_answers(question="Who created the Dothraki vocabulary?", top_k_retriever=10, top_k_reader=5)
-
-#prediction = finder.get_answers(question="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-#prediction = finder.get_answers(question="Who is the sister of Sansa?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
-
-
-```python
-
-```
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/7.md b/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/7.md
deleted file mode 100644
index c9c4abf21..000000000
--- a/src/pages/docs/versions/master/v0.6.0/site/en/tutorials/tutorials/7.md
+++ /dev/null
@@ -1,174 +0,0 @@
----
-title: "Tutorial 7"
-metaTitle: "Generative QA"
-metaDescription: ""
-slug: "/docs/tutorial7"
-date: "2020-11-12"
-id: "tutorial7md"
----
-
-# Generative QA with "Retrieval-Augmented Generation"
-
-While extractive QA highlights the span of text that answers a query,
-generative QA can return a novel text answer that it has composed.
-In this tutorial, you will learn how to set up a generative system using the
-[RAG model](https://arxiv.org/abs/2005.11401) which conditions the
-answer generator on a set of retrieved documents.
-
-Here are the packages and imports that we'll need:
-
-
-```python
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-from typing import List
-import requests
-import pandas as pd
-from haystack import Document
-from haystack.document_store.faiss import FAISSDocumentStore
-from haystack.generator.transformers import RAGenerator
-from haystack.retriever.dense import DensePassageRetriever
-```
-
-Let's download a csv containing some sample text and preprocess the data.
-
-
-
-```python
-# Download sample
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/haystack/master/tutorials/small_generator_dataset.csv")
-open('small_generator_dataset.csv', 'wb').write(temp.content)
-
-# Create dataframe with columns "title" and "text"
-df = pd.read_csv("small_generator_dataset.csv", sep=',')
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-
-print(df.head())
-```
-
-We can cast our data into Haystack Document objects.
-Alternatively, we can also just use dictionaries with "text" and "meta" fields
-
-
-```python
-# Use data to initialize Document objects
-titles = list(df["title"].values)
-texts = list(df["text"].values)
-documents: List[Document] = []
-for title, text in zip(titles, texts):
- documents.append(
- Document(
- text=text,
- meta={
- "name": title or ""
- }
- )
- )
-```
-
-Here we initialize the FAISSDocumentStore, DensePassageRetriever and RAGenerator.
-FAISS is chosen here since it is optimized vector storage.
-
-
-```python
-# Initialize FAISS document store.
-# Set `return_embedding` to `True`, so generator doesn't have to perform re-embedding
-document_store = FAISSDocumentStore(
- faiss_index_factory_str="Flat",
- return_embedding=True
-)
-
-# Initialize DPR Retriever to encode documents, encode question and query documents
-retriever = DensePassageRetriever(
- document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- use_gpu=False,
- embed_title=True,
-)
-
-# Initialize RAG Generator
-generator = RAGenerator(
- model_name_or_path="facebook/rag-token-nq",
- use_gpu=False,
- top_k_answers=1,
- max_length=200,
- min_length=2,
- embed_title=True,
- num_beams=2,
-)
-```
-
-We write documents to the DocumentStore, first by deleting any remaining documents then calling `write_documents()`.
-The `update_embeddings()` method uses the retriever to create an embedding for each document.
-
-
-
-```python
-# Delete existing documents in documents store
-document_store.delete_all_documents()
-
-# Write documents to document store
-document_store.write_documents(documents)
-
-# Add documents embeddings to index
-document_store.update_embeddings(
- retriever=retriever
-)
-```
-
-Here are our questions:
-
-
-```python
-QUESTIONS = [
- "who got the first nobel prize in physics",
- "when is the next deadpool movie being released",
- "which mode is used for short wave broadcast service",
- "who is the owner of reading football club",
- "when is the next scandal episode coming out",
- "when is the last time the philadelphia won the superbowl",
- "what is the most current adobe flash player version",
- "how many episodes are there in dragon ball z",
- "what is the first step in the evolution of the eye",
- "where is gall bladder situated in human body",
- "what is the main mineral in lithium batteries",
- "who is the president of usa right now",
- "where do the greasers live in the outsiders",
- "panda is a national animal of which country",
- "what is the name of manchester united stadium",
-]
-```
-
-Now let's run our system!
-The retriever will pick out a small subset of documents that it finds relevant.
-These are used to condition the generator as it generates the answer.
-What it should return then are novel text spans that form and answer to your question!
-
-
-```python
-# Now generate an answer for each question
-for question in QUESTIONS:
- # Retrieve related documents from retriever
- retriever_results = retriever.retrieve(
- query=question
- )
-
- # Now generate answer from question and retrieved documents
- predicted_result = generator.predict(
- query=question,
- documents=retriever_results,
- top_k=1
- )
-
- # Print you answer
- answers = predicted_result["answers"]
- print(f'Generated answer is \'{answers[0]["answer"]}\' for the question = \'{question}\'')
-```
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/preprocessing.md b/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/preprocessing.md
deleted file mode 100644
index 6924f3d39..000000000
--- a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/preprocessing.md
+++ /dev/null
@@ -1,111 +0,0 @@
----
-title: "Preprocessing"
-metaTitle: "Preprocessing"
-metaDescription: ""
-slug: "/docs/preprocessing"
-date: "2020-09-03"
-id: "preprocessingmd"
----
-
-# Preprocessing
-
-Haystack includes a suite of tools to:
-
-* extract text from different file types,
-* normalize white space
-* split text into smaller pieces to optimize retrieval
-
-These data preprocessing steps can have a big impact on the systems performance
-and effective handling of data is key to getting the most out of Haystack.
-
-The Document Store expects its inputs to come in the following format.
-The sections below will show you all the tools you'll need to ready your data for storing.
-
-```python
-docs = [
- {
- 'text': DOCUMENT_TEXT_HERE,
- 'meta': {'name': DOCUMENT_NAME, ...}
- }, ...
-]
-```
-
-## File Conversion
-
-There are a range of different file converters in Haystack that
-can extract text from files and cast them into the unified dictionary format shown above.
-Haystack features support for txt, pdf and docx files and there is even a converter that leverages Apache Tika.
-Please refer to [the API docs](/docs/latest/file_convertersmd) to see which converter best suits you.
-
-
-
-
-
-## PreProcessor
-
-While each of the above conversion methods produce documents that are already in the format expected by the Document Store,
-it is recommended that they are further processed in order to ensure optimal Retriever and Reader performance.
-The `PreProcessor` takes one of the documents created by the converter as input,
-performs various cleaning steps and splits them into multiple smaller documents.
-
-For suggestions on how best to split your documents, see [Optimization](/docs/latest/optimizationmd)
-
-```python
-doc = converter.convert(file_path=file, meta=None)
-processor = PreProcessor(clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=True,
- split_by="word",
- split_length=200,
- split_respect_sentence_boundary=True)
-docs = processor.process(d)
-```
-
-* `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
-* `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
-* `clean_header_footer` will remove any long header or footer texts that are repeated on each page
-* `split_by` determines what unit the document is split by: `'word'`, `'sentence'` or `'passage'`
-* `split_length` sets a maximum number of `'word'`, `'sentence'` or `'passage'` units per output document
-* `split_respect_sentence_boundary` ensures that document boundaries do not fall in the middle of sentences
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/reader.md b/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/reader.md
deleted file mode 100644
index 42dec94b5..000000000
--- a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/reader.md
+++ /dev/null
@@ -1,328 +0,0 @@
----
-title: "Reader"
-metaTitle: "Reader"
-metaDescription: ""
-slug: "/docs/reader"
-date: "2020-09-03"
-id: "readermd"
----
-
-# Reader
-
-The Reader, also known as Open-Domain QA systems in Machine Learning speak,
-is the core component that enables Haystack to find the answers that you need.
-Haystack’s Readers are:
-
-
-* built on the latest transformer based language models
-
-
-* strong in their grasp of semantics
-
-
-* sensitive to syntactic structure
-
-
-* state-of-the-art in QA tasks like SQuAD and Natural Questions
-
-
-
-
-
-
-
-
-```python
-converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=["de","en"])
-doc = converter.convert(file_path=file, meta=None)
-```
-
-
-
-
-
-
-
-
-
-```python
-converter = DocxToTextConverter(remove_numeric_tables=True, valid_languages=["de","en"])
-doc = converter.convert(file_path=file, meta=None)
-```
-
-
-
-
-
-
-
-
-
-
-Haystack also has a `convert_files_to_dicts()` utility function that will convert
-all txt or pdf files in a given folder into this dictionary format.
-
-```python
-docs = convert_files_to_dicts(dir_path=doc_dir)
-```
-
-
-
-
-
-
-
-
-
-
-
-
-
-```python
-model = "deepset/roberta-base-squad2"
-reader = FARMReader(model, use_gpu=True)
-finder = Finder(reader, retriever)
-```
-
-
-
-
-
-
-
-
-
-```python
-model = "deepset/roberta-base-squad2"
-reader = TransformersReader(model, use_gpu=1)
-finder = Finder(reader, retriever)
-```
-
-
-
-
-**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
-
-
-
-While these models can work on CPU, it is recommended that they are run using GPUs to keep query times low.
-
-## Choosing the Right Model
-
-In Haystack, you can start using pretrained QA models simply by providing its HuggingFace Model Hub name to the Reader.
-The loading of model weights is handled by Haystack,
-and you have the option of using the QA pipeline from deepset FARM or HuggingFace Transformers (see FARM vs Transformers for details).
-
-Currently, there are a lot of different models out there and it can be rather overwhelming trying to pick the one that fits your use case.
-To get you started, we have a few recommendations for you to try out.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-**An optimised variant of BERT and a great starting point.**
-
-```python
-reader = FARMReader("deepset/roberta-base-squad2")
-```
-
-* **Pro**: Strong all round model
-
-* **Con**: There are other models that are either faster or more accurate
-
-
-
-
-
-
-
-
-
-**A cleverly distilled model that sacrifices a little accuracy for speed.**
-
-```python
-reader = FARMReader("deepset/minilm-uncased-squad2")
-```
-
-* **Pro**: Inference speed up to 50% faster than BERT base
-
-* **Con**: Still doesn’t match the best base sized models in accuracy
-
-
-
-
-
-
-
-
-
-**Large, powerful, SotA model.**
-
-```python
-reader = FARMReader("ahotrod/albert_xxlargev1_squad2_512")
-```
-
-* **Pro**: Better accuracy than any other open source model in QA
-
-* **Con**: The computational power needed make it impractical for most use cases
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-**An optimised variant of BERT and a great starting point.**
-
-```python
-reader = TransformersReader("deepset/roberta-base-squad2")
-```
-
-* **Pro**: Strong all round model
-
-* **Con**: There are other models that are either faster or more accurate
-
-
-
-
-
-
-
-
-
-**A cleverly distilled model that sacrifices a little accuracy for speed.**
-
-```python
-reader = TransformersReader("deepset/minilm-uncased-squad2")
-```
-
-* **Pro**: Inference speed up to 50% faster than BERT base
-
-* **Con**: Still doesn’t match the best base sized models in accuracy
-
-
-
-
-
-
-
-
-
-**Large, powerful, SotA model.**
-
-```python
-reader = TransformersReader("ahotrod/albert_xxlargev1_squad2_512")
-```
-
-* **Pro**: Better accuracy than any other open source model in QA
-
-* **Con**: The computational power needed make it impractical for most use cases
-
-
-
-
-**Recommendations:**
-
-**All-rounder**: In the class of base sized models trained on SQuAD, **RoBERTa** has shown better performance than BERT
-and can be capably handled by any machine equipped with a single NVidia V100 GPU.
-We recommend this as the starting point for anyone wanting to create a performant and computationally reasonable instance of Haystack.
-
-**Built for Speed**: If speed and GPU memory are more of a priority to you than accuracy,
-you should try the MiniLM model.
-It is a smaller model that is trained to mimic larger models through the distillation process,
-and it outperforms the BERT base on SQuAD even though it is about 40% smaller.
-
-
-**State of the Art Accuracy**: For most, **ALBERT XXL** will be too large to feasibly work with.
-But if performance is your sole concern, and you have the computational resources,
-you might like to try ALBERT XXL which has set SoTA performance on SQuAD 2.0.
-
-
-
-
-
-
-## Deeper Dive: FARM vs Transformers
-
-Apart from the **model weights**, Haystack Readers contain all the components found in end-to-end open domain QA systems.
-This includes **tokenization**, **embedding computation**, **span prediction** and **candidate aggregation**.
-While the handling of model weights is the same between the FARM and Transformers libraries, their QA pipelines differ in some ways.
-The major points are:
-
-
-* The **TransformersReader** will sometimes predict the same span twice while duplicates are removed in the **FARMReader**
-
-
-* The **FARMReader** currently uses the tokenizers from the HuggingFace Transformers library while the **TransformersReader** uses the tokenizers from the HuggingFace Tokenizers library
-
-
-* Start and end logits are normalized per passage and multiplied in the **TransformersReader** while they are summed and not normalised in the **FARMReader**
-
-If you’re interested in the finer details of these points, have a look at [this](https://github.com/deepset-ai/haystack/issues/248#issuecomment-661977237) GitHub comment.
-
-We see value in maintaining both kinds of Readers since Transformers is a very familiar library to many of Haystack’s users
-but we at deepset can more easily update and optimise the FARM pipeline for speed and performance.
-
-
-Haystack also has a close integration with FARM which means that you can further fine-tune your Readers on labelled data using a FARMReader.
-See our tutorials for an end-to-end example or below for a shortened example.
-
-```python
-# Initialise Reader
-model = "deepset/roberta-base-squad2"
-reader = FARMReader(model)
-
-# Perform finetuning
-train_data = "PATH/TO_YOUR/TRAIN_DATA"
-train_filename = "train.json"
-save_dir = "finetuned_model"
-reader.train(train_data, train_filename, save_dir=save_dir)
-
-# Load
-finetuned_reader = FARMReader(save_dir)
-```
-
-## Deeper Dive: From Language Model to Haystack Reader
-
-Language models form the core of most modern NLP systems and that includes the Readers in Haystack.
-They build a general understanding of language when performing training tasks such as Masked Language Modeling or Replaced Token Detection
-on large amounts of text.
-Well trained language models capture the word distribution in one or more languages
-but more importantly, convert input text into a set of word vectors that capture elements of syntax and semantics.
-
-In order to convert a language model into a Reader model, it needs first to be trained on a Question Answering dataset.
-To do so requires the addition of a question answering prediction head on top of the language model.
-The task can be thought of as a token classification task where every input token is assigned a probability of being
-either the start or end token of the correct answer.
-In cases where the answer is not contained within the passage, the prediction head is also expected to return a `no_answer` prediction.
-
-
-Since language models are limited in the number of tokens which they can process in a single forward pass,
-a sliding window mechanism is implemented to handle variable length documents.
-This functions by slicing the document into overlapping passages of (approximately) `max_seq_length`
-that are each offset by `doc_stride` number of tokens.
-These can be set when the Reader is initialized.
-
-
-
-
-
-Predictions are made on each individual passage and the process of aggregation picks the best candidates across all passages.
-If you’d like to learn more about what is happening behind the scenes, have a look at [this](https://medium.com/deepset-ai/modern-question-answering-systems-explained-4d0913744097) article.
-
-
diff --git a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/roadmap.md b/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/roadmap.md
deleted file mode 100644
index bf28c14e5..000000000
--- a/src/pages/docs/versions/master/v0.6.0/site/en/usage/usage/roadmap.md
+++ /dev/null
@@ -1,45 +0,0 @@
----
-title: "Roadmap"
-metaTitle: "Roadmap and Zenhub Access"
-metaDescription: "How to access our open roadmap"
-slug: "/docs/roadmap"
-date: "2020-09-24"
-id: "roadmapmd"
----
-
-# Open roadmap
-
-We believe open-source is more than open source code. It's a lot about people, collaboration, transparency and trust.
-Therefore, we decided to be as open as possible with our roadmap and sprint planning.
-In fact, you can see all of it in real-time on GitHub.
-We hope this helps to clarify the direction of the open-source project and inspires discussions in the community.
-
-## How to access it
-
-### Add Zenhub Plugin
-We decided for Zenhub, as it allows a close integration with GitHub and real-time sharing of roadmaps and sprints.
-Once you have installed the browser plugin below, you will see additional tabs and infos on the Haystack GitHub page.
-
-Zenhub Plugin: https://www.zenhub.com/extension
-
-### Roadmap
-_Zenhub Tab -> Roadmap (left menu)_
-
-Here you can find our most recent roadmap with the **high-level projects** that are planned for the upcoming quarters.
-We update it regularly and refine the projects as they come closer in time.
-
-
-
-### Board
-_Zenhub Tab -> Board (left menu)_
-
-If you are interested in the **operational tasks** and their status, you can find our agile board here.
-
-
-### Additional issue details & Releases
-_Right panel in regular Github issues_
-
-With Zenhub you can also see some additional tags in every GitHub issue.
-For those of you who wonder about the next release date: We aim for releases every ~ 4 weeks and will tag the issues that will need to be finished before a bit in advance.
-
-
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/api/api/document_store.md b/src/pages/docs/versions/master/v0.7.0/site/en/api/api/document_store.md
deleted file mode 100644
index 929a1a660..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/api/api/document_store.md
+++ /dev/null
@@ -1,956 +0,0 @@
----
-title: "Document Store"
-metaTitle: "Database"
-metaDescription: ""
-slug: "/docs/apidatabase"
-date: "2020-09-03"
-id: "apidatabasemd"
----
-
-# Document Store
-
-
-# Module: base
-
-
-## Class: BaseDocumentStore
-
-```python
-class BaseDocumentStore(ABC)
-```
-
-Base class for implementing Document Stores.
-
-
-#### write\_documents
-
-```python
- | @abstractmethod
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
-For documents as dictionaries, the format is {"text": "
-
-
-
-
-
-
-```python
-reader = FARMReader(... max_seq_len=384, doc_stride=128 ...)
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = TransformersReader(... max_seq_len=384, doc_stride=128 ...
-```
-
-
-
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb)
-
-Question Answering can be used in a variety of use cases. A very common one: Using it to navigate through complex knowledge bases or long documents ("search setting").
-
-A "knowledge base" could for example be your website, an internal wiki or a collection of financial reports.
-In this tutorial we will work on a slightly different domain: "Game of Thrones".
-
-Let's see how we can use a bunch of Wikipedia articles to answer a variety of questions about the
-marvellous seven kingdoms...
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-Haystack finds answers to queries within the documents stored in a `DocumentStore`. The current implementations of `DocumentStore` include `ElasticsearchDocumentStore`, `FAISSDocumentStore`, `SQLDocumentStore`, and `InMemoryDocumentStore`.
-
-**Here:** We recommended Elasticsearch as it comes preloaded with features like [full-text queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html), [BM25 retrieval](https://www.elastic.co/elasticon/conf/2016/sf/improved-text-scoring-with-bm25), and [vector storage for text embeddings](https://www.elastic.co/guide/en/elasticsearch/reference/7.6/dense-vector.html).
-
-**Alternatives:** If you are unable to setup an Elasticsearch instance, then follow the [Tutorial 3](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb) for using SQL/InMemory document stores.
-
-**Hint**: This tutorial creates a new document store instance with Wikipedia articles on Game of Thrones. However, you can configure Haystack to work with your existing document stores.
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker
-#! docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.6.2
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.6.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.6.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.6.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.6.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-# Connect to Elasticsearch
-
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles about Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first fetch some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is:
-# {
-# 'text': "
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-from haystack.reader.farm import FARMReader
-```
-
-
-## Create Training Data
-
-There are two ways to generate training data
-
-1. **Annotation**: You can use the [annotation tool](https://github.com/deepset-ai/haystack#labeling-tool) to label your data, i.e. highlighting answers to your questions in a document. The tool supports structuring your workflow with organizations, projects, and users. The labels can be exported in SQuAD format that is compatible for training with Haystack.
-
-
-
-2. **Feedback**: For production systems, you can collect training data from direct user feedback via Haystack's [REST API interface](https://github.com/deepset-ai/haystack#rest-api). This includes a customizable user feedback API for providing feedback on the answer returned by the API. The API provides a feedback export endpoint to obtain the feedback data for fine-tuning your model further.
-
-
-## Fine-tune your model
-
-Once you have collected training data, you can fine-tune your base models.
-We initialize a reader as a base model and fine-tune it on our own custom dataset (should be in SQuAD-like format).
-We recommend using a base model that was trained on SQuAD or a similar QA dataset before to benefit from Transfer Learning effects.
-
-**Recommendation**: Run training on a GPU.
-If you are using Colab: Enable this in the menu "Runtime" > "Change Runtime type" > Select "GPU" in dropdown.
-Then change the `use_gpu` arguments below to `True`
-
-
-```python
-reader = FARMReader(model_name_or_path="distilbert-base-uncased-distilled-squad", use_gpu=True)
-train_data = "data/squad20"
-# train_data = "PATH/TO_YOUR/TRAIN_DATA"
-reader.train(data_dir=train_data, train_filename="dev-v2.0.json", use_gpu=True, n_epochs=1, save_dir="my_model")
-```
-
-
-```python
-# Saving the model happens automatically at the end of training into the `save_dir` you specified
-# However, you could also save a reader manually again via:
-reader.save(directory="my_model")
-```
-
-
-```python
-# If you want to load it at a later point, just do:
-new_reader = FARMReader(model_name_or_path="my_model")
-```
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/3.md b/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/3.md
deleted file mode 100644
index 1740bcf2b..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/3.md
+++ /dev/null
@@ -1,183 +0,0 @@
----
-title: "Tutorial 3"
-metaTitle: "Build a QA System Without Elasticsearch"
-metaDescription: ""
-slug: "/docs/tutorial3"
-date: "2020-09-03"
-id: "tutorial3md"
----
-
-# Build a QA System Without Elasticsearch
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb)
-
-Haystack provides alternatives to Elasticsearch for developing quick prototypes.
-
-You can use an `InMemoryDocumentStore` or a `SQLDocumentStore`(with SQLite) as the document store.
-
-If you are interested in more feature-rich Elasticsearch, then please refer to the Tutorial 1.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-
-
-```python
-# In-Memory Document Store
-from haystack.document_store.memory import InMemoryDocumentStore
-document_store = InMemoryDocumentStore()
-```
-
-
-```python
-# SQLite Document Store
-# from haystack.document_store.sql import SQLDocumentStore
-# document_store = SQLDocumentStore(url="sqlite:///qa.db")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles on Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first get some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# convert files to dicts containing documents that can be indexed to our datastore
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is: {"name": "
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-from haystack import Finder
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-from haystack.retriever.dense import EmbeddingRetriever
-from haystack.utils import print_answers
-import pandas as pd
-import requests
-
-```
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker
-# ! docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.9.2
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-
-```
-
-### Init the DocumentStore
-In contrast to Tutorial 1 (extractive QA), we:
-
-* specify the name of our `text_field` in Elasticsearch that we want to return as an answer
-* specify the name of our `embedding_field` in Elasticsearch where we'll store the embedding of our question and that is used later for calculating our similarity to the incoming user question
-* set `excluded_meta_data=["question_emb"]` so that we don't return the huge embedding vectors in our search results
-
-
-```python
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="",
- index="document",
- embedding_field="question_emb",
- embedding_dim=768,
- excluded_meta_data=["question_emb"])
-```
-
-### Create a Retriever using embeddings
-Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones).
-We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings.
-
-
-```python
-retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert", use_gpu=True)
-```
-
-### Prepare & Index FAQ data
-We create a pandas dataframe containing some FAQ data (i.e curated pairs of question + answer) and index those in elasticsearch.
-Here: We download some question-answer pairs related to COVID-19
-
-
-```python
-# Download
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/faqs/faq_covidbert.csv")
-open('small_faq_covid.csv', 'wb').write(temp.content)
-
-# Get dataframe with columns "question", "answer" and some custom metadata
-df = pd.read_csv("small_faq_covid.csv")
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-df["question"] = df["question"].apply(lambda x: x.strip())
-print(df.head())
-
-# Get embeddings for our questions from the FAQs
-questions = list(df["question"].values)
-df["question_emb"] = retriever.embed_queries(texts=questions)
-df = df.rename(columns={"answer": "text"})
-
-# Convert Dataframe to list of dicts and index them in our DocumentStore
-docs_to_index = df.to_dict(orient="records")
-document_store.write_documents(docs_to_index)
-```
-
-### Ask questions
-Initialize a Finder (this time without a reader) and ask questions
-
-
-```python
-finder = Finder(reader=None, retriever=retriever)
-prediction = finder.get_answers_via_similar_questions(question="How is the virus spreading?", top_k_retriever=10)
-print_answers(prediction, details="all")
-```
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/5.md b/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/5.md
deleted file mode 100644
index 0f3ed45fe..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/5.md
+++ /dev/null
@@ -1,188 +0,0 @@
----
-title: "Tutorial 5"
-metaTitle: "Evaluation of a QA System"
-metaDescription: ""
-slug: "/docs/tutorial5"
-date: "2020-09-03"
-id: "tutorial5md"
----
-
-# Evaluation of a QA System
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial5_Evaluation.ipynb)
-
-To be able to make a statement about the performance of a question-answering system, it is important to evalute it. Furthermore, evaluation allows to determine which parts of the system can be improved.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-## Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-from farm.utils import initialize_device_settings
-
-device, n_gpu = initialize_device_settings(use_cuda=True)
-```
-
-
-```python
-from haystack.preprocessor.utils import fetch_archive_from_http
-
-# Download evaluation data, which is a subset of Natural Questions development set containing 50 documents
-doc_dir = "../data/nq"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/nq_dev_subset_v2.json.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-
-```python
-# make sure these indices do not collide with existing ones, the indices will be wiped clean before data is inserted
-doc_index = "tutorial5_docs"
-label_index = "tutorial5_labels"
-```
-
-
-```python
-# Connect to Elasticsearch
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-# Connect to Elasticsearch
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document",
- create_index=False, embedding_field="emb",
- embedding_dim=768, excluded_meta_data=["emb"])
-```
-
-
-```python
-# Add evaluation data to Elasticsearch Document Store
-# We first delete the custom tutorial indices to not have duplicate elements
-document_store.delete_all_documents(index=doc_index)
-document_store.delete_all_documents(index=label_index)
-document_store.add_eval_data(filename="../data/nq/nq_dev_subset_v2.json", doc_index=doc_index, label_index=label_index)
-```
-
-## Initialize components of QA-System
-
-
-```python
-# Initialize Retriever
-from haystack.retriever.sparse import ElasticsearchRetriever
-retriever = ElasticsearchRetriever(document_store=document_store)
-# Alternative: Evaluate DensePassageRetriever
-# Note, that DPR works best when you index short passages < 512 tokens as only those tokens will be used for the embedding.
-# Here, for nq_dev_subset_v2.json we have avg. num of tokens = 5220(!).
-# DPR still outperforms Elastic's BM25 by a small margin here.
-# from haystack.retriever.dense import DensePassageRetriever
-# retriever = DensePassageRetriever(document_store=document_store,
-# query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
-# passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
-# use_gpu=True,
-# embed_title=True,
-# max_seq_len=256,
-# batch_size=16,
-# remove_sep_tok_from_untitled_passages=True)
-#document_store.update_embeddings(retriever, index=doc_index)
-```
-
-
-```python
-# Initialize Reader
-from haystack.reader.farm import FARMReader
-
-reader = FARMReader("deepset/roberta-base-squad2", top_k_per_candidate=4)
-```
-
-
-```python
-# Initialize Finder which sticks together Reader and Retriever
-from haystack.finder import Finder
-
-finder = Finder(reader, retriever)
-```
-
-## Evaluation of Retriever
-
-
-```python
-## Evaluate Retriever on its own
-retriever_eval_results = retriever.eval(top_k=20, label_index=label_index, doc_index=doc_index)
-## Retriever Recall is the proportion of questions for which the correct document containing the answer is
-## among the correct documents
-print("Retriever Recall:", retriever_eval_results["recall"])
-## Retriever Mean Avg Precision rewards retrievers that give relevant documents a higher rank
-print("Retriever Mean Avg Precision:", retriever_eval_results["map"])
-```
-
-## Evaluation of Reader
-
-
-```python
-# Evaluate Reader on its own
-reader_eval_results = reader.eval(document_store=document_store, device=device, label_index=label_index, doc_index=doc_index)
-# Evaluation of Reader can also be done directly on a SQuAD-formatted file without passing the data to Elasticsearch
-#reader_eval_results = reader.eval_on_file("../data/nq", "nq_dev_subset_v2.json", device=device)
-
-## Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer
-print("Reader Top-N-Accuracy:", reader_eval_results["top_n_accuracy"])
-## Reader Exact Match is the proportion of questions where the predicted answer is exactly the same as the correct answer
-print("Reader Exact Match:", reader_eval_results["EM"])
-## Reader F1-Score is the average overlap between the predicted answers and the correct answers
-print("Reader F1-Score:", reader_eval_results["f1"])
-```
-
-## Evaluation of Finder
-
-
-```python
-# Evaluate combination of Reader and Retriever through Finder
-# Evaluate combination of Reader and Retriever through Finder
-finder_eval_results = finder.eval(top_k_retriever=1, top_k_reader=10, label_index=label_index, doc_index=doc_index)
-finder.print_eval_results(finder_eval_results)
-```
-
-
-```python
-
-```
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/6.md b/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/6.md
deleted file mode 100644
index 147ed7c10..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/6.md
+++ /dev/null
@@ -1,211 +0,0 @@
----
-title: "Tutorial 6"
-metaTitle: "Better retrieval via Dense Passage Retrieval"
-metaDescription: ""
-slug: "/docs/tutorial6"
-date: "2020-09-03"
-id: "tutorial6md"
----
-
-# Better Retrieval via "Dense Passage Retrieval"
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb)
-
-### Importance of Retrievers
-
-The Retriever has a huge impact on the performance of our overall search pipeline.
-
-
-### Different types of Retrievers
-#### Sparse
-Family of algorithms based on counting the occurrences of words (bag-of-words) resulting in very sparse vectors with length = vocab size.
-
-**Examples**: BM25, TF-IDF
-
-**Pros**: Simple, fast, well explainable
-
-**Cons**: Relies on exact keyword matches between query and text
-
-
-#### Dense
-These retrievers use neural network models to create "dense" embedding vectors. Within this family there are two different approaches:
-
-a) Single encoder: Use a **single model** to embed both query and passage.
-b) Dual-encoder: Use **two models**, one to embed the query and one to embed the passage
-
-Recent work suggests that dual encoders work better, likely because they can deal better with the different nature of query and passage (length, style, syntax ...).
-
-**Examples**: REALM, DPR, Sentence-Transformers
-
-**Pros**: Captures semantinc similarity instead of "word matches" (e.g. synonyms, related topics ...)
-
-**Cons**: Computationally more heavy, initial training of model
-
-
-### "Dense Passage Retrieval"
-
-In this Tutorial, we want to highlight one "Dense Dual-Encoder" called Dense Passage Retriever.
-It was introdoced by Karpukhin et al. (2020, https://arxiv.org/abs/2004.04906.
-
-Original Abstract:
-
-_"Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks."_
-
-Paper: https://arxiv.org/abs/2004.04906
-Original Code: https://fburl.com/qa-dpr
-
-
-*Use this* [link](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb) *to open the notebook in Google Colab.*
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-### Document Store
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-The default flavour of FAISSDocumentStore is "Flat" but can also be set to "HNSW" for
-faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.
-For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-
-
-```python
-from haystack.document_store.faiss import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever, Reader, & Finder
-
-#### Retriever
-
-**Here:** We use a `DensePassageRetriever`
-
-**Alternatives:**
-
-- The `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-
-
-```python
-from haystack.retriever.dense import DensePassageRetriever
-retriever = DensePassageRetriever(document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- max_seq_len_query=64,
- max_seq_len_passage=256,
- batch_size=16,
- use_gpu=True,
- embed_title=True,
- use_fast_tokenizers=True)
-# Important:
-# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all
-# previously indexed documents and update their embedding representation.
-# While this can be a time consuming operation (depending on corpus size), it only needs to be done once.
-# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.
-document_store.update_embeddings(retriever)
-```
-
-#### Reader
-
-Similar to previous Tutorials we now initalize our reader.
-
-Here we use a FARMReader with the *deepset/roberta-base-squad2* model (see: https://huggingface.co/deepset/roberta-base-squad2)
-
-
-
-##### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-#### Finder
-
-The Finder sticks together reader and retriever in a pipeline to answer our actual questions.
-
-
-```python
-finder = Finder(reader, retriever)
-```
-
-### Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = finder.get_answers(question="Who created the Dothraki vocabulary?", top_k_retriever=10, top_k_reader=5)
-
-#prediction = finder.get_answers(question="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
-#prediction = finder.get_answers(question="Who is the sister of Sansa?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
-
-
-```python
-
-```
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/7.md b/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/7.md
deleted file mode 100644
index 5ae360451..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/7.md
+++ /dev/null
@@ -1,190 +0,0 @@
----
-title: "Tutorial 7"
-metaTitle: "Generative QA"
-metaDescription: ""
-slug: "/docs/tutorial7"
-date: "2020-11-12"
-id: "tutorial7md"
----
-
-# Generative QA with "Retrieval-Augmented Generation"
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial7_RAG_Generator.ipynb)
-
-While extractive QA highlights the span of text that answers a query,
-generative QA can return a novel text answer that it has composed.
-In this tutorial, you will learn how to set up a generative system using the
-[RAG model](https://arxiv.org/abs/2005.11401) which conditions the
-answer generator on a set of retrieved documents.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-Here are the packages and imports that we'll need:
-
-
-```python
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-```python
-from typing import List
-import requests
-import pandas as pd
-from haystack import Document
-from haystack.document_store.faiss import FAISSDocumentStore
-from haystack.generator.transformers import RAGenerator
-from haystack.retriever.dense import DensePassageRetriever
-```
-
-Let's download a csv containing some sample text and preprocess the data.
-
-
-
-```python
-# Download sample
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/haystack/master/tutorials/small_generator_dataset.csv")
-open('small_generator_dataset.csv', 'wb').write(temp.content)
-
-# Create dataframe with columns "title" and "text"
-df = pd.read_csv("small_generator_dataset.csv", sep=',')
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-
-print(df.head())
-```
-
-We can cast our data into Haystack Document objects.
-Alternatively, we can also just use dictionaries with "text" and "meta" fields
-
-
-```python
-# Use data to initialize Document objects
-titles = list(df["title"].values)
-texts = list(df["text"].values)
-documents: List[Document] = []
-for title, text in zip(titles, texts):
- documents.append(
- Document(
- text=text,
- meta={
- "name": title or ""
- }
- )
- )
-```
-
-Here we initialize the FAISSDocumentStore, DensePassageRetriever and RAGenerator.
-FAISS is chosen here since it is optimized vector storage.
-
-
-```python
-# Initialize FAISS document store.
-# Set `return_embedding` to `True`, so generator doesn't have to perform re-embedding
-document_store = FAISSDocumentStore(
- faiss_index_factory_str="Flat",
- return_embedding=True
-)
-
-# Initialize DPR Retriever to encode documents, encode question and query documents
-retriever = DensePassageRetriever(
- document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- use_gpu=True,
- embed_title=True,
-)
-
-# Initialize RAG Generator
-generator = RAGenerator(
- model_name_or_path="facebook/rag-token-nq",
- use_gpu=True,
- top_k_answers=1,
- max_length=200,
- min_length=2,
- embed_title=True,
- num_beams=2,
-)
-```
-
-We write documents to the DocumentStore, first by deleting any remaining documents then calling `write_documents()`.
-The `update_embeddings()` method uses the retriever to create an embedding for each document.
-
-
-
-```python
-# Delete existing documents in documents store
-document_store.delete_all_documents()
-
-# Write documents to document store
-document_store.write_documents(documents)
-
-# Add documents embeddings to index
-document_store.update_embeddings(
- retriever=retriever
-)
-```
-
-Here are our questions:
-
-
-```python
-QUESTIONS = [
- "who got the first nobel prize in physics",
- "when is the next deadpool movie being released",
- "which mode is used for short wave broadcast service",
- "who is the owner of reading football club",
- "when is the next scandal episode coming out",
- "when is the last time the philadelphia won the superbowl",
- "what is the most current adobe flash player version",
- "how many episodes are there in dragon ball z",
- "what is the first step in the evolution of the eye",
- "where is gall bladder situated in human body",
- "what is the main mineral in lithium batteries",
- "who is the president of usa right now",
- "where do the greasers live in the outsiders",
- "panda is a national animal of which country",
- "what is the name of manchester united stadium",
-]
-```
-
-Now let's run our system!
-The retriever will pick out a small subset of documents that it finds relevant.
-These are used to condition the generator as it generates the answer.
-What it should return then are novel text spans that form and answer to your question!
-
-
-```python
-# Now generate an answer for each question
-for question in QUESTIONS:
- # Retrieve related documents from retriever
- retriever_results = retriever.retrieve(
- query=question
- )
-
- # Now generate answer from question and retrieved documents
- predicted_result = generator.predict(
- query=question,
- documents=retriever_results,
- top_k=1
- )
-
- # Print you answer
- answers = predicted_result["answers"]
- print(f'Generated answer is \'{answers[0]["answer"]}\' for the question = \'{question}\'')
-```
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/8.md b/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/8.md
deleted file mode 100644
index 678b6c621..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/8.md
+++ /dev/null
@@ -1,202 +0,0 @@
----
-title: "Tutorial 8"
-metaTitle: "Preprocessing"
-metaDescription: ""
-slug: "/docs/tutorial8"
-date: "2021-01-08"
-id: "tutorial8md"
----
-
-# Preprocessing
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial9_DPR_training.ipynb)
-
-Haystack includes a suite of tools to extract text from different file types, normalize white space
-and split text into smaller pieces to optimize retrieval.
-These data preprocessing steps can have a big impact on the systems performance and effective handling of data is key to getting the most out of Haystack.
-
-Ultimately, Haystack expects data to be provided as a list documents in the following dictionary format:
-``` python
-docs = [
- {
- 'text': DOCUMENT_TEXT_HERE,
- 'meta': {'name': DOCUMENT_NAME, ...}
- }, ...
-]
-```
-
-This tutorial will show you all the tools that Haystack provides to help you cast your data into this format.
-
-
-```python
-# Let's start by installing Haystack
-
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-!wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.02.tar.gz
-!tar -xvf xpdf-tools-linux-4.02.tar.gz && sudo cp xpdf-tools-linux-4.02/bin64/pdftotext /usr/local/bin
-```
-
-
-```python
-# Here are the imports we need
-
-from haystack.file_converter.txt import TextConverter
-from haystack.file_converter.pdf import PDFToTextConverter
-from haystack.file_converter.docx import DocxToTextConverter
-
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.preprocessor.preprocessor import PreProcessor
-```
-
-
-```python
-# This fetches some sample files to work with
-
-doc_dir = "data/preprocessing_tutorial"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/preprocessing_tutorial.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-## Converters
-
-Haystack's converter classes are designed to help you turn files on your computer into the documents
-that can be processed by the Haystack pipeline.
-There are file converters for txt, pdf, docx files as well as a converter that is powered by Apache Tika.
-
-
-```python
-# Here are some examples of how you would use file converters
-
-converter = TextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_txt = converter.convert(file_path="data/preprocessing_tutorial/classics.txt", meta=None)
-
-converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_pdf = converter.convert(file_path="data/preprocessing_tutorial/bert.pdf", meta=None)
-
-converter = DocxToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_docx = converter.convert(file_path="data/preprocessing_tutorial/heavy_metal.docx", meta=None)
-
-```
-
-
-```python
-# Haystack also has a convenience function that will automatically apply the right converter to each file in a directory.
-
-all_docs = convert_files_to_dicts(dir_path="data/preprocessing_tutorial")
-```
-
-## PreProcessor
-
-The PreProcessor class is designed to help you clean text and split text into sensible units.
-File splitting can have a very significant impact on the system's performance and is absolutely mandatory for Dense Passage Retrieval models.
-In general, we recommend you split the text from your files into small documents of around 100 words for dense retrieval methods
-and no more than 10,000 words for sparse methods.
-Have a look at the [Preprocessing](https://haystack.deepset.ai/docs/latest/preprocessingmd)
-and [Optimization](https://haystack.deepset.ai/docs/latest/optimizationmd) pages on our website for more details.
-
-
-```python
-# This is a default usage of the PreProcessor.
-# Here, it performs cleaning of consecutive whitespaces
-# and splits a single large document into smaller documents.
-# Each document is up to 1000 words long and document breaks cannot fall in the middle of sentences
-# Note how the single document passed into the document gets split into 5 smaller documents
-
-preprocessor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=False,
- split_by="word",
- split_length=100,
- split_respect_sentence_boundary=True
-)
-docs_default = preprocessor.process(doc_txt)
-print(f"n_docs_input: 1\nn_docs_output: {len(docs_default)}")
-```
-
-## Cleaning
-
-- `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
-- `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
-- `clean_header_footer` will remove any long header or footer texts that are repeated on each page
-
-## Splitting
-By default, the PreProcessor will respect sentence boundaries, meaning that documents will not start or end
-midway through a sentence.
-This will help reduce the possibility of answer phrases being split between two documents.
-This feature can be turned off by setting `split_respect_sentence_boundary=False`.
-
-
-```python
-# Not respecting sentence boundary vs respecting sentence boundary
-
-preprocessor_nrsb = PreProcessor(split_respect_sentence_boundary=False)
-docs_nrsb = preprocessor_nrsb.process(doc_txt)
-
-print("RESPECTING SENTENCE BOUNDARY")
-end_text = docs_default[0]["text"][-50:]
-print("End of document: \"..." + end_text + "\"")
-print()
-print("NOT RESPECTING SENTENCE BOUNDARY")
-end_text_nrsb = docs_nrsb[0]["text"][-50:]
-print("End of document: \"..." + end_text_nrsb + "\"")
-```
-
-A commonly used strategy to split long documents, especially in the field of Question Answering,
-is the sliding window approach. If `split_length=10` and `split_overlap=3`, your documents will look like this:
-
-- doc1 = words[0:10]
-- doc2 = words[7:17]
-- doc3 = words[14:24]
-- ...
-
-You can use this strategy by following the code below.
-
-
-```python
-# Sliding window approach
-
-preprocessor_sliding_window = PreProcessor(
- split_overlap=3,
- split_length=10,
- split_respect_sentence_boundary=False
-)
-docs_sliding_window = preprocessor_sliding_window.process(doc_txt)
-
-doc1 = docs_sliding_window[0]["text"][:200]
-doc2 = docs_sliding_window[1]["text"][:100]
-doc3 = docs_sliding_window[2]["text"][:100]
-
-print("Document 1: \"" + doc1 + "...\"")
-print("Document 2: \"" + doc2 + "...\"")
-print("Document 3: \"" + doc3 + "...\"")
-```
-
-## Bringing it all together
-
-
-```python
-all_docs = convert_files_to_dicts(dir_path="data/preprocessing_tutorial")
-preprocessor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=False,
- split_by="word",
- split_length=100,
- split_respect_sentence_boundary=True
-)
-nested_docs = [preprocessor.process(d) for d in all_docs]
-docs = [d for x in nested_docs for d in x]
-
-print(f"n_files_input: {len(all_docs)}\nn_docs_output: {len(docs)}")
-```
-
-
-```python
-
-```
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/9.md b/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/9.md
deleted file mode 100644
index 4dd76c175..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/tutorials/tutorials/9.md
+++ /dev/null
@@ -1,211 +0,0 @@
----
-title: "Tutorial 9"
-metaTitle: "Training a Dense Passage Retrieval model"
-metaDescription: ""
-slug: "/docs/tutorial9"
-date: "2021-01-08"
-id: "tutorial9md"
----
-
-# Training Your Own "Dense Passage Retrieval" Model
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial9_DPR_training.ipynb)
-
-Haystack contains all the tools needed to train your own Dense Passage Retrieval model.
-This tutorial will guide you through the steps required to create a retriever that is specifically tailored to your domain.
-
-
-
-```python
-# Here are some imports that we'll need
-
-from haystack.retriever.dense import DensePassageRetriever
-from haystack.preprocessor.utils import fetch_archive_from_http
-```
-
-## Training Data
-
-DPR training performed using Information Retrieval data.
-More specifically, you want to feed in pairs of queries and relevant documents.
-
-To train a model, we will need a dataset that has the same format as the original DPR training data.
-Each data point in the dataset should have the following dictionary structure.
-
-``` python
- {
- "dataset": str,
- "question": str,
- "answers": list of str
- "positive_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- "negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- "hard_negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- }
-```
-
-`positive_ctxs` are context passages which are relevant to the query.
-In some datasets, queries might have more than one positive context
-in which case you can set the `num_positives` parameter to be higher than the default 1.
-Note that `num_positives` needs to be lower or equal to the minimum number of `positive_ctxs` for queries in your data.
-If you have an unequal number of positive contexts per example,
-you might want to generate some soft labels retrieving similar contexts which contain the answer.
-
-DPR is standardly trained using a method known as in-batch negatives.
-This means that positive contexts for given query are treated as negative contexts for the other queries in the batch.
-Doing so allows for a high degree of computational efficiency, thus allowing the model to be trained on large amounts of data.
-
-`negative_ctxs` is not actually used in Haystack's DPR training so we recommend you set it to an empty list.
-They were used by the original DPR authors in an experiment to compare it against the in-batch negatives method.
-
-`hard_negative_ctxs` are passages that are not relevant to the query.
-In the original DPR paper, these are fetched using a retriever to find the most similar passages to the positive passage.
-Passages which contain the answer text are filtered out.
-
-We are [currently working](https://github.com/deepset-ai/haystack/issues/705) on a script that will convert SQuAD format data into a DPR dataset!
-
-## Using Question Answering Data
-
-Question Answering datasets can sometimes be used as training data.
-Google's Natural Questions dataset, is sufficiently large
-and contains enough unique passages, that it can be converted into a DPR training set.
-This is done simply by considering answer containing passages as relevant documents to the query.
-
-The SQuAD dataset, however, is not as suited to this use case since its question and answer pairs
-are created on only a very small slice of wikipedia documents.
-
-## Download Original DPR Training Data
-
-WARNING: These files are large! The train set is 7.4GB and the dev set is 800MB
-
-We can download the original DPR training data with the following cell.
-Note that this data is probably only useful if you are trying to train from scratch.
-
-
-```python
-# Download original DPR data
-# WARNING: the train set is 7.4GB and the dev set is 800MB
-
-doc_dir = "data/dpr_training/"
-
-s3_url_train = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-train.json.gz"
-s3_url_dev = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz"
-
-fetch_archive_from_http(s3_url_train, output_dir=doc_dir + "train/")
-fetch_archive_from_http(s3_url_dev, output_dir=doc_dir + "dev/")
-```
-
-## Option 1: Training DPR from Scratch
-
-The default variables that we provide below are chosen to train a DPR model from scratch.
-Here, both passage and query embedding models are initialized using BERT base
-and the model is trained using Google's Natural Questions dataset (in a format specialised for DPR).
-
-If you are working in a language other than English,
-you will want to initialize the passage and query embedding models with a language model that supports your language
-and also provide a dataset in your language.
-
-
-```python
-# Here are the variables to specify our training data, the models that we use to initialize DPR
-# and the directory where we'll be saving the model
-
-doc_dir = "data/dpr_training/"
-
-train_filename = "train/biencoder-nq-train.json"
-dev_filename = "dev/biencoder-nq-dev.json"
-
-query_model = "bert-base-uncased"
-passage_model = "bert-base-uncased"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Option 2: Finetuning DPR
-
-If you have your own domain specific question answering or information retrieval dataset,
-you might instead be interested in finetuning a pretrained DPR model.
-In this case, you would initialize both query and passage models using the original pretrained model.
-You will want to load something like this set of variables instead of the ones above
-
-
-```python
-# Here are the variables you might want to use instead of the set above
-# in order to perform pretraining
-
-doc_dir = "PATH_TO_YOUR_DATA_DIR"
-train_filename = "TRAIN_FILENAME"
-dev_filename = "DEV_FILENAME"
-
-query_model = "facebook/dpr-question_encoder-single-nq-base"
-passage_model = "facebook/dpr-ctx_encoder-single-nq-base"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Initialization
-
-Here we want to initialize our model either with plain language model weights for training from scratch
-or else with pretrained DPR weights for finetuning.
-We follow the [original DPR parameters](https://github.com/facebookresearch/DPR#best-hyperparameter-settings)
-for their max passage length but set max query length to 64 since queries are very rarely longer.
-
-
-```python
-## Initialize DPR model
-
-retriever = DensePassageRetriever(
- document_store=None,
- query_embedding_model=query_model,
- passage_embedding_model=passage_model,
- max_seq_len_query=64,
- max_seq_len_passage=256
-)
-```
-
-## Training
-
-Let's start training and save our trained model!
-
-On a V100 GPU, you can fit up to batch size 4 so we set gradient accumulation steps to 4 in order
-to simulate the batch size 16 of the original DPR experiment.
-
-When `embed_title=True`, the document title is prepended to the input text sequence with a `[SEP]` token
-between it and document text.
-
-When training from scratch with the above variables, 1 epoch takes around an hour and we reached the following performance:
-
-```
-loss: 0.09334952129693501
-acc: 0.984035000191887
-f1: 0.936147352264006
-acc_and_f1: 0.9600911762279465
-average_rank: 0.07075978511128166
-```
-
-
-```python
-# Start training our model and save it when it is finished
-
-retriever.train(
- data_dir=doc_dir,
- train_filename=dev_filename,
- dev_filename=dev_filename,
- test_filename=dev_filename,
- n_epochs=1,
- batch_size=4,
- grad_acc_steps=4,
- save_dir=save_dir,
- evaluate_every=3000,
- embed_title=True,
- num_positives=1,
- num_hard_negatives=1
-)
-```
-
-## Loading
-
-Loading our newly trained model is simple!
-
-
-```python
-reloaded_retriever = DensePassageRetriever.load(load_dir=save_dir, document_store=None)
-```
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/preprocessing.md b/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/preprocessing.md
deleted file mode 100644
index 943bee6a3..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/preprocessing.md
+++ /dev/null
@@ -1,116 +0,0 @@
----
-title: "Preprocessing"
-metaTitle: "Preprocessing"
-metaDescription: ""
-slug: "/docs/preprocessing"
-date: "2020-09-03"
-id: "preprocessingmd"
----
-
-# Preprocessing
-
-Haystack includes a suite of tools to:
-
-* extract text from different file types,
-* normalize white space
-* split text into smaller pieces to optimize retrieval
-
-These data preprocessing steps can have a big impact on the systems performance
-and effective handling of data is key to getting the most out of Haystack.
-
-The Document Store expects its inputs to come in the following format.
-The sections below will show you all the tools you'll need to ready your data for storing.
-
-```python
-docs = [
- {
- 'text': DOCUMENT_TEXT_HERE,
- 'meta': {'name': DOCUMENT_NAME, ...}
- }, ...
-]
-```
-
-## File Conversion
-
-There are a range of different file converters in Haystack that
-can extract text from files and cast them into the unified dictionary format shown above.
-Haystack features support for txt, pdf and docx files and there is even a converter that leverages Apache Tika.
-Please refer to [the API docs](/docs/latest/file_convertersmd) to see which converter best suits you.
-
-
-
-
-
-## PreProcessor
-
-While each of the above conversion methods produce documents that are already in the format expected by the Document Store,
-it is recommended that they are further processed in order to ensure optimal Retriever and Reader performance.
-The `PreProcessor` takes one of the documents created by the converter as input,
-performs various cleaning steps and splits them into multiple smaller documents.
-
-For suggestions on how best to split your documents, see [Optimization](/docs/latest/optimizationmd)
-
-```python
-doc = converter.convert(file_path=file, meta=None)
-processor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=True,
- split_by="word",
- split_length=200,
- split_respect_sentence_boundary=True,
- split_overlap=0
-)
-docs = processor.process(d)
-```
-
-* `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
-* `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
-* `clean_header_footer` will remove any long header or footer texts that are repeated on each page
-* `split_by` determines what unit the document is split by: `'word'`, `'sentence'` or `'passage'`
-* `split_length` sets a maximum number of `'word'`, `'sentence'` or `'passage'` units per output document
-* `split_respect_sentence_boundary` ensures that document boundaries do not fall in the middle of sentences
-* `split_overlap` sets the amount of overlap between two adjacent documents after a split. Setting this to a positive number essentially enables the sliding window approach.
-
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/reader.md b/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/reader.md
deleted file mode 100644
index 42dec94b5..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/reader.md
+++ /dev/null
@@ -1,328 +0,0 @@
----
-title: "Reader"
-metaTitle: "Reader"
-metaDescription: ""
-slug: "/docs/reader"
-date: "2020-09-03"
-id: "readermd"
----
-
-# Reader
-
-The Reader, also known as Open-Domain QA systems in Machine Learning speak,
-is the core component that enables Haystack to find the answers that you need.
-Haystack’s Readers are:
-
-
-* built on the latest transformer based language models
-
-
-* strong in their grasp of semantics
-
-
-* sensitive to syntactic structure
-
-
-* state-of-the-art in QA tasks like SQuAD and Natural Questions
-
-
-
-
-
-
-
-
-```python
-converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=["de","en"])
-doc = converter.convert(file_path=file, meta=None)
-```
-
-
-
-
-
-
-
-
-
-```python
-converter = DocxToTextConverter(remove_numeric_tables=True, valid_languages=["de","en"])
-doc = converter.convert(file_path=file, meta=None)
-```
-
-
-
-
-
-
-
-
-
-
-Haystack also has a `convert_files_to_dicts()` utility function that will convert
-all txt or pdf files in a given folder into this dictionary format.
-
-```python
-docs = convert_files_to_dicts(dir_path=doc_dir)
-```
-
-
-
-
-
-
-
-
-
-
-
-
-
-```python
-model = "deepset/roberta-base-squad2"
-reader = FARMReader(model, use_gpu=True)
-finder = Finder(reader, retriever)
-```
-
-
-
-
-
-
-
-
-
-```python
-model = "deepset/roberta-base-squad2"
-reader = TransformersReader(model, use_gpu=1)
-finder = Finder(reader, retriever)
-```
-
-
-
-
-**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
-
-
-
-While these models can work on CPU, it is recommended that they are run using GPUs to keep query times low.
-
-## Choosing the Right Model
-
-In Haystack, you can start using pretrained QA models simply by providing its HuggingFace Model Hub name to the Reader.
-The loading of model weights is handled by Haystack,
-and you have the option of using the QA pipeline from deepset FARM or HuggingFace Transformers (see FARM vs Transformers for details).
-
-Currently, there are a lot of different models out there and it can be rather overwhelming trying to pick the one that fits your use case.
-To get you started, we have a few recommendations for you to try out.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-**An optimised variant of BERT and a great starting point.**
-
-```python
-reader = FARMReader("deepset/roberta-base-squad2")
-```
-
-* **Pro**: Strong all round model
-
-* **Con**: There are other models that are either faster or more accurate
-
-
-
-
-
-
-
-
-
-**A cleverly distilled model that sacrifices a little accuracy for speed.**
-
-```python
-reader = FARMReader("deepset/minilm-uncased-squad2")
-```
-
-* **Pro**: Inference speed up to 50% faster than BERT base
-
-* **Con**: Still doesn’t match the best base sized models in accuracy
-
-
-
-
-
-
-
-
-
-**Large, powerful, SotA model.**
-
-```python
-reader = FARMReader("ahotrod/albert_xxlargev1_squad2_512")
-```
-
-* **Pro**: Better accuracy than any other open source model in QA
-
-* **Con**: The computational power needed make it impractical for most use cases
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-**An optimised variant of BERT and a great starting point.**
-
-```python
-reader = TransformersReader("deepset/roberta-base-squad2")
-```
-
-* **Pro**: Strong all round model
-
-* **Con**: There are other models that are either faster or more accurate
-
-
-
-
-
-
-
-
-
-**A cleverly distilled model that sacrifices a little accuracy for speed.**
-
-```python
-reader = TransformersReader("deepset/minilm-uncased-squad2")
-```
-
-* **Pro**: Inference speed up to 50% faster than BERT base
-
-* **Con**: Still doesn’t match the best base sized models in accuracy
-
-
-
-
-
-
-
-
-
-**Large, powerful, SotA model.**
-
-```python
-reader = TransformersReader("ahotrod/albert_xxlargev1_squad2_512")
-```
-
-* **Pro**: Better accuracy than any other open source model in QA
-
-* **Con**: The computational power needed make it impractical for most use cases
-
-
-
-
-**Recommendations:**
-
-**All-rounder**: In the class of base sized models trained on SQuAD, **RoBERTa** has shown better performance than BERT
-and can be capably handled by any machine equipped with a single NVidia V100 GPU.
-We recommend this as the starting point for anyone wanting to create a performant and computationally reasonable instance of Haystack.
-
-**Built for Speed**: If speed and GPU memory are more of a priority to you than accuracy,
-you should try the MiniLM model.
-It is a smaller model that is trained to mimic larger models through the distillation process,
-and it outperforms the BERT base on SQuAD even though it is about 40% smaller.
-
-
-**State of the Art Accuracy**: For most, **ALBERT XXL** will be too large to feasibly work with.
-But if performance is your sole concern, and you have the computational resources,
-you might like to try ALBERT XXL which has set SoTA performance on SQuAD 2.0.
-
-
-
-
-
-
-## Deeper Dive: FARM vs Transformers
-
-Apart from the **model weights**, Haystack Readers contain all the components found in end-to-end open domain QA systems.
-This includes **tokenization**, **embedding computation**, **span prediction** and **candidate aggregation**.
-While the handling of model weights is the same between the FARM and Transformers libraries, their QA pipelines differ in some ways.
-The major points are:
-
-
-* The **TransformersReader** will sometimes predict the same span twice while duplicates are removed in the **FARMReader**
-
-
-* The **FARMReader** currently uses the tokenizers from the HuggingFace Transformers library while the **TransformersReader** uses the tokenizers from the HuggingFace Tokenizers library
-
-
-* Start and end logits are normalized per passage and multiplied in the **TransformersReader** while they are summed and not normalised in the **FARMReader**
-
-If you’re interested in the finer details of these points, have a look at [this](https://github.com/deepset-ai/haystack/issues/248#issuecomment-661977237) GitHub comment.
-
-We see value in maintaining both kinds of Readers since Transformers is a very familiar library to many of Haystack’s users
-but we at deepset can more easily update and optimise the FARM pipeline for speed and performance.
-
-
-Haystack also has a close integration with FARM which means that you can further fine-tune your Readers on labelled data using a FARMReader.
-See our tutorials for an end-to-end example or below for a shortened example.
-
-```python
-# Initialise Reader
-model = "deepset/roberta-base-squad2"
-reader = FARMReader(model)
-
-# Perform finetuning
-train_data = "PATH/TO_YOUR/TRAIN_DATA"
-train_filename = "train.json"
-save_dir = "finetuned_model"
-reader.train(train_data, train_filename, save_dir=save_dir)
-
-# Load
-finetuned_reader = FARMReader(save_dir)
-```
-
-## Deeper Dive: From Language Model to Haystack Reader
-
-Language models form the core of most modern NLP systems and that includes the Readers in Haystack.
-They build a general understanding of language when performing training tasks such as Masked Language Modeling or Replaced Token Detection
-on large amounts of text.
-Well trained language models capture the word distribution in one or more languages
-but more importantly, convert input text into a set of word vectors that capture elements of syntax and semantics.
-
-In order to convert a language model into a Reader model, it needs first to be trained on a Question Answering dataset.
-To do so requires the addition of a question answering prediction head on top of the language model.
-The task can be thought of as a token classification task where every input token is assigned a probability of being
-either the start or end token of the correct answer.
-In cases where the answer is not contained within the passage, the prediction head is also expected to return a `no_answer` prediction.
-
-
-Since language models are limited in the number of tokens which they can process in a single forward pass,
-a sliding window mechanism is implemented to handle variable length documents.
-This functions by slicing the document into overlapping passages of (approximately) `max_seq_length`
-that are each offset by `doc_stride` number of tokens.
-These can be set when the Reader is initialized.
-
-
-
-
-
-Predictions are made on each individual passage and the process of aggregation picks the best candidates across all passages.
-If you’d like to learn more about what is happening behind the scenes, have a look at [this](https://medium.com/deepset-ai/modern-question-answering-systems-explained-4d0913744097) article.
-
-
diff --git a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/roadmap.md b/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/roadmap.md
deleted file mode 100644
index bf28c14e5..000000000
--- a/src/pages/docs/versions/master/v0.7.0/site/en/usage/usage/roadmap.md
+++ /dev/null
@@ -1,45 +0,0 @@
----
-title: "Roadmap"
-metaTitle: "Roadmap and Zenhub Access"
-metaDescription: "How to access our open roadmap"
-slug: "/docs/roadmap"
-date: "2020-09-24"
-id: "roadmapmd"
----
-
-# Open roadmap
-
-We believe open-source is more than open source code. It's a lot about people, collaboration, transparency and trust.
-Therefore, we decided to be as open as possible with our roadmap and sprint planning.
-In fact, you can see all of it in real-time on GitHub.
-We hope this helps to clarify the direction of the open-source project and inspires discussions in the community.
-
-## How to access it
-
-### Add Zenhub Plugin
-We decided for Zenhub, as it allows a close integration with GitHub and real-time sharing of roadmaps and sprints.
-Once you have installed the browser plugin below, you will see additional tabs and infos on the Haystack GitHub page.
-
-Zenhub Plugin: https://www.zenhub.com/extension
-
-### Roadmap
-_Zenhub Tab -> Roadmap (left menu)_
-
-Here you can find our most recent roadmap with the **high-level projects** that are planned for the upcoming quarters.
-We update it regularly and refine the projects as they come closer in time.
-
-
-
-### Board
-_Zenhub Tab -> Board (left menu)_
-
-If you are interested in the **operational tasks** and their status, you can find our agile board here.
-
-
-### Additional issue details & Releases
-_Right panel in regular Github issues_
-
-With Zenhub you can also see some additional tags in every GitHub issue.
-For those of you who wonder about the next release date: We aim for releases every ~ 4 weeks and will tag the issues that will need to be finished before a bit in advance.
-
-
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/api/api/document_store.md b/src/pages/docs/versions/master/v0.8.0/site/en/api/api/document_store.md
deleted file mode 100644
index 689272cea..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/api/api/document_store.md
+++ /dev/null
@@ -1,1270 +0,0 @@
----
-title: "Document Store"
-metaTitle: "Database"
-metaDescription: ""
-slug: "/docs/apidatabase"
-date: "2020-09-03"
-id: "apidatabasemd"
----
-
-# Document Store
-
-
-# Module: base
-
-
-## Class: BaseDocumentStore
-
-```python
-class BaseDocumentStore(BaseComponent)
-```
-
-Base class for implementing Document Stores.
-
-
-#### write\_documents
-
-```python
- | @abstractmethod
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
- For documents as dictionaries, the format is {"text": "
-
-
-
-
-
-
-```python
-reader = FARMReader(... max_seq_len=384, doc_stride=128 ...)
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = TransformersReader(... max_seq_len=384, doc_stride=128 ...
-```
-
-
-
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb)
-
-Question Answering can be used in a variety of use cases. A very common one: Using it to navigate through complex knowledge bases or long documents ("search setting").
-
-A "knowledge base" could for example be your website, an internal wiki or a collection of financial reports.
-In this tutorial we will work on a slightly different domain: "Game of Thrones".
-
-Let's see how we can use a bunch of Wikipedia articles to answer a variety of questions about the
-marvellous seven kingdoms...
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-Haystack finds answers to queries within the documents stored in a `DocumentStore`. The current implementations of `DocumentStore` include `ElasticsearchDocumentStore`, `FAISSDocumentStore`, `SQLDocumentStore`, and `InMemoryDocumentStore`.
-
-**Here:** We recommended Elasticsearch as it comes preloaded with features like [full-text queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html), [BM25 retrieval](https://www.elastic.co/elasticon/conf/2016/sf/improved-text-scoring-with-bm25), and [vector storage for text embeddings](https://www.elastic.co/guide/en/elasticsearch/reference/7.6/dense-vector.html).
-
-**Alternatives:** If you are unable to setup an Elasticsearch instance, then follow the [Tutorial 3](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb) for using SQL/InMemory document stores.
-
-**Hint**: This tutorial creates a new document store instance with Wikipedia articles on Game of Thrones. However, you can configure Haystack to work with your existing document stores.
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker
-#! docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.9.2
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-# Connect to Elasticsearch
-
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles about Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first fetch some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is:
-# {
-# 'text': "
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-```
-
-
-```python
-from haystack.reader.farm import FARMReader
-```
-
-
-## Create Training Data
-
-There are two ways to generate training data
-
-1. **Annotation**: You can use the [annotation tool](https://github.com/deepset-ai/haystack#labeling-tool) to label your data, i.e. highlighting answers to your questions in a document. The tool supports structuring your workflow with organizations, projects, and users. The labels can be exported in SQuAD format that is compatible for training with Haystack.
-
-
-
-2. **Feedback**: For production systems, you can collect training data from direct user feedback via Haystack's [REST API interface](https://github.com/deepset-ai/haystack#rest-api). This includes a customizable user feedback API for providing feedback on the answer returned by the API. The API provides a feedback export endpoint to obtain the feedback data for fine-tuning your model further.
-
-
-## Fine-tune your model
-
-Once you have collected training data, you can fine-tune your base models.
-We initialize a reader as a base model and fine-tune it on our own custom dataset (should be in SQuAD-like format).
-We recommend using a base model that was trained on SQuAD or a similar QA dataset before to benefit from Transfer Learning effects.
-
-**Recommendation**: Run training on a GPU.
-If you are using Colab: Enable this in the menu "Runtime" > "Change Runtime type" > Select "GPU" in dropdown.
-Then change the `use_gpu` arguments below to `True`
-
-
-```python
-reader = FARMReader(model_name_or_path="distilbert-base-uncased-distilled-squad", use_gpu=True)
-train_data = "data/squad20"
-# train_data = "PATH/TO_YOUR/TRAIN_DATA"
-reader.train(data_dir=train_data, train_filename="dev-v2.0.json", use_gpu=True, n_epochs=1, save_dir="my_model")
-```
-
-
-```python
-# Saving the model happens automatically at the end of training into the `save_dir` you specified
-# However, you could also save a reader manually again via:
-reader.save(directory="my_model")
-```
-
-
-```python
-# If you want to load it at a later point, just do:
-new_reader = FARMReader(model_name_or_path="my_model")
-```
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/3.md b/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/3.md
deleted file mode 100644
index ee79c1215..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/3.md
+++ /dev/null
@@ -1,185 +0,0 @@
----
-title: "Tutorial 3"
-metaTitle: "Build a QA System Without Elasticsearch"
-metaDescription: ""
-slug: "/docs/tutorial3"
-date: "2020-09-03"
-id: "tutorial3md"
----
-
-# Build a QA System Without Elasticsearch
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb)
-
-Haystack provides alternatives to Elasticsearch for developing quick prototypes.
-
-You can use an `InMemoryDocumentStore` or a `SQLDocumentStore`(with SQLite) as the document store.
-
-If you are interested in more feature-rich Elasticsearch, then please refer to the Tutorial 1.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-
-
-```python
-# In-Memory Document Store
-from haystack.document_store.memory import InMemoryDocumentStore
-document_store = InMemoryDocumentStore()
-```
-
-
-```python
-# SQLite Document Store
-# from haystack.document_store.sql import SQLDocumentStore
-# document_store = SQLDocumentStore(url="sqlite:///qa.db")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles on Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first get some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# convert files to dicts containing documents that can be indexed to our datastore
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is: {"name": "
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-```
-
-
-```python
-from haystack import Finder
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-from haystack.retriever.dense import EmbeddingRetriever
-from haystack.utils import print_answers
-import pandas as pd
-import requests
-
-```
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker
-# ! docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.9.2
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-
-```
-
-### Init the DocumentStore
-In contrast to Tutorial 1 (extractive QA), we:
-
-* specify the name of our `text_field` in Elasticsearch that we want to return as an answer
-* specify the name of our `embedding_field` in Elasticsearch where we'll store the embedding of our question and that is used later for calculating our similarity to the incoming user question
-* set `excluded_meta_data=["question_emb"]` so that we don't return the huge embedding vectors in our search results
-
-
-```python
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="",
- index="document",
- embedding_field="question_emb",
- embedding_dim=768,
- excluded_meta_data=["question_emb"])
-```
-
-### Create a Retriever using embeddings
-Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones).
-We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings.
-
-
-```python
-retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert", use_gpu=True)
-```
-
-### Prepare & Index FAQ data
-We create a pandas dataframe containing some FAQ data (i.e curated pairs of question + answer) and index those in elasticsearch.
-Here: We download some question-answer pairs related to COVID-19
-
-
-```python
-# Download
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/faqs/faq_covidbert.csv")
-open('small_faq_covid.csv', 'wb').write(temp.content)
-
-# Get dataframe with columns "question", "answer" and some custom metadata
-df = pd.read_csv("small_faq_covid.csv")
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-df["question"] = df["question"].apply(lambda x: x.strip())
-print(df.head())
-
-# Get embeddings for our questions from the FAQs
-questions = list(df["question"].values)
-df["question_emb"] = retriever.embed_queries(texts=questions)
-df = df.rename(columns={"question": "text"})
-
-# Convert Dataframe to list of dicts and index them in our DocumentStore
-docs_to_index = df.to_dict(orient="records")
-document_store.write_documents(docs_to_index)
-```
-
-### Ask questions
-Initialize a Pipeline (this time without a reader) and ask questions
-
-
-```python
-from haystack.pipeline import FAQPipeline
-pipe = FAQPipeline(retriever=retriever)
-```
-
-
-```python
-prediction = pipe.run(query="How is the virus spreading?", top_k_retriever=10)
-print_answers(prediction, details="all")
-
-
-```
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/5.md b/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/5.md
deleted file mode 100644
index 236f205f3..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/5.md
+++ /dev/null
@@ -1,248 +0,0 @@
----
-title: "Tutorial 5"
-metaTitle: "Evaluation of a QA System"
-metaDescription: ""
-slug: "/docs/tutorial5"
-date: "2020-09-03"
-id: "tutorial5md"
----
-
-# Evaluation of a QA System
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial5_Evaluation.ipynb)
-
-To be able to make a statement about the performance of a question-answering system, it is important to evalute it. Furthermore, evaluation allows to determine which parts of the system can be improved.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-## Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-from farm.utils import initialize_device_settings
-
-device, n_gpu = initialize_device_settings(use_cuda=True)
-```
-
-
-```python
-from haystack.preprocessor.utils import fetch_archive_from_http
-
-# Download evaluation data, which is a subset of Natural Questions development set containing 50 documents
-doc_dir = "../data/nq"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/nq_dev_subset_v2.json.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-
-```python
-# make sure these indices do not collide with existing ones, the indices will be wiped clean before data is inserted
-doc_index = "tutorial5_docs"
-label_index = "tutorial5_labels"
-```
-
-
-```python
-# Connect to Elasticsearch
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-# Connect to Elasticsearch
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document",
- create_index=False, embedding_field="emb",
- embedding_dim=768, excluded_meta_data=["emb"])
-```
-
-
-```python
-from haystack.preprocessor import PreProcessor
-
-# Add evaluation data to Elasticsearch Document Store
-# We first delete the custom tutorial indices to not have duplicate elements
-# and also split our documents into shorter passages using the PreProcessor
-preprocessor = PreProcessor(
- split_length=500,
- split_overlap=0,
- split_respect_sentence_boundary=False,
- clean_empty_lines=False,
- clean_whitespace=False
-)
-document_store.delete_all_documents(index=doc_index)
-document_store.delete_all_documents(index=label_index)
-document_store.add_eval_data(
- filename="../data/nq/nq_dev_subset_v2.json",
- doc_index=doc_index,
- label_index=label_index,
- preprocessor=preprocessor
-)
-
-# Let's prepare the labels that we need for the retriever and the reader
-labels = document_store.get_all_labels_aggregated(index=label_index)
-q_to_l_dict = {
- l.question: {
- "retriever": l,
- "reader": l
- } for l in labels
-}
-```
-
-## Initialize components of QA-System
-
-
-```python
-# Initialize Retriever
-from haystack.retriever.sparse import ElasticsearchRetriever
-retriever = ElasticsearchRetriever(document_store=document_store)
-# Alternative: Evaluate DensePassageRetriever
-# Note, that DPR works best when you index short passages < 512 tokens as only those tokens will be used for the embedding.
-# Here, for nq_dev_subset_v2.json we have avg. num of tokens = 5220(!).
-# DPR still outperforms Elastic's BM25 by a small margin here.
-# from haystack.retriever.dense import DensePassageRetriever
-# retriever = DensePassageRetriever(document_store=document_store,
-# query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
-# passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
-# use_gpu=True,
-# embed_title=True,
-# max_seq_len=256,
-# batch_size=16,
-# remove_sep_tok_from_untitled_passages=True)
-#document_store.update_embeddings(retriever, index=doc_index)
-```
-
-
-```python
-# Initialize Reader
-from haystack.reader.farm import FARMReader
-
-reader = FARMReader("deepset/roberta-base-squad2", top_k_per_candidate=4, return_no_answer=True)
-
-```
-
-```python
-from haystack.eval import EvalAnswers, EvalDocuments
-
-# Here we initialize the nodes that perform evaluation
-eval_retriever = EvalRetriever()
-eval_reader = EvalReader()
-```
-
-## Evaluation of Retriever
-Here we evaluate only the retriever, based on whether the gold_label document is retrieved.
-
-
-```python
-## Evaluate Retriever on its own
-retriever_eval_results = retriever.eval(top_k=20, label_index=label_index, doc_index=doc_index)
-## Retriever Recall is the proportion of questions for which the correct document containing the answer is
-## among the correct documents
-print("Retriever Recall:", retriever_eval_results["recall"])
-## Retriever Mean Avg Precision rewards retrievers that give relevant documents a higher rank
-print("Retriever Mean Avg Precision:", retriever_eval_results["map"])
-```
-
-## Evaluation of Reader
-Here we evaluate only the reader in a closed domain fashion i.e. the reader is given one query
-and one document and metrics are calculated on whether the right position in this text is selected by
-the model as the answer span (i.e. SQuAD style)
-
-
-```python
-# Evaluate Reader on its own
-reader_eval_results = reader.eval(document_store=document_store, device=device, label_index=label_index, doc_index=doc_index)
-# Evaluation of Reader can also be done directly on a SQuAD-formatted file without passing the data to Elasticsearch
-#reader_eval_results = reader.eval_on_file("../data/nq", "nq_dev_subset_v2.json", device=device)
-
-## Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer
-print("Reader Top-N-Accuracy:", reader_eval_results["top_n_accuracy"])
-## Reader Exact Match is the proportion of questions where the predicted answer is exactly the same as the correct answer
-print("Reader Exact Match:", reader_eval_results["EM"])
-## Reader F1-Score is the average overlap between the predicted answers and the correct answers
-print("Reader F1-Score:", reader_eval_results["f1"])
-```
-
-## Evaluation of Retriever and Reader (Open Domain)
-Here we evaluate retriever and reader in open domain fashion i.e. a document is considered
-correctly retrieved if it contains the answer string within it. The reader is evaluated based purely on the
-predicted string, regardless of which document this came from and the position of the extracted span.
-
-
-```python
-from haystack import Pipeline
-
-# Here is the pipeline definition
-p = Pipeline()
-p.add_node(component=retriever, name="ESRetriever", inputs=["Query"])
-p.add_node(component=eval_retriever, name="EvalDocuments", inputs=["ESRetriever"])
-p.add_node(component=reader, name="QAReader", inputs=["EvalDocuments"])
-p.add_node(component=eval_reader, name="EvalAnswers", inputs=["QAReader"])
-results = []
-```
-
-
-```python
-# This is how to run the pipeline
-for q, l in q_to_l_dict.items():
- res = p.run(
- query=q,
- top_k_retriever=10,
- labels=l,
- top_k_reader=10,
- index=doc_index,
- )
- results.append(res)
-```
-
-
-```python
-# When we have run evaluation using the pipeline, we can print the results
-n_queries = len(labels)
-eval_retriever.print()
-print()
-retriever.print_time()
-print()
-eval_reader.print(mode="reader")
-print()
-reader.print_time()
-print()
-eval_reader.print(mode="pipeline")
-
-```
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/6.md b/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/6.md
deleted file mode 100644
index 8385df887..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/6.md
+++ /dev/null
@@ -1,207 +0,0 @@
----
-title: "Tutorial 6"
-metaTitle: "Better retrieval via Dense Passage Retrieval"
-metaDescription: ""
-slug: "/docs/tutorial6"
-date: "2020-09-03"
-id: "tutorial6md"
----
-
-# Better Retrieval via "Dense Passage Retrieval"
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb)
-
-### Importance of Retrievers
-
-The Retriever has a huge impact on the performance of our overall search pipeline.
-
-
-### Different types of Retrievers
-#### Sparse
-Family of algorithms based on counting the occurrences of words (bag-of-words) resulting in very sparse vectors with length = vocab size.
-
-**Examples**: BM25, TF-IDF
-
-**Pros**: Simple, fast, well explainable
-
-**Cons**: Relies on exact keyword matches between query and text
-
-
-#### Dense
-These retrievers use neural network models to create "dense" embedding vectors. Within this family there are two different approaches:
-
-a) Single encoder: Use a **single model** to embed both query and passage.
-b) Dual-encoder: Use **two models**, one to embed the query and one to embed the passage
-
-Recent work suggests that dual encoders work better, likely because they can deal better with the different nature of query and passage (length, style, syntax ...).
-
-**Examples**: REALM, DPR, Sentence-Transformers
-
-**Pros**: Captures semantinc similarity instead of "word matches" (e.g. synonyms, related topics ...)
-
-**Cons**: Computationally more heavy, initial training of model
-
-
-### "Dense Passage Retrieval"
-
-In this Tutorial, we want to highlight one "Dense Dual-Encoder" called Dense Passage Retriever.
-It was introdoced by Karpukhin et al. (2020, https://arxiv.org/abs/2004.04906.
-
-Original Abstract:
-
-_"Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks."_
-
-Paper: https://arxiv.org/abs/2004.04906
-Original Code: https://fburl.com/qa-dpr
-
-
-*Use this* [link](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb) *to open the notebook in Google Colab.*
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-### Document Store
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-The default flavour of FAISSDocumentStore is "Flat" but can also be set to "HNSW" for
-faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.
-For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-
-
-```python
-from haystack.document_store.faiss import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever, Reader, & Finder
-
-#### Retriever
-
-**Here:** We use a `DensePassageRetriever`
-
-**Alternatives:**
-
-- The `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-
-
-```python
-from haystack.retriever.dense import DensePassageRetriever
-retriever = DensePassageRetriever(document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- max_seq_len_query=64,
- max_seq_len_passage=256,
- batch_size=16,
- use_gpu=True,
- embed_title=True,
- use_fast_tokenizers=True)
-# Important:
-# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all
-# previously indexed documents and update their embedding representation.
-# While this can be a time consuming operation (depending on corpus size), it only needs to be done once.
-# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.
-document_store.update_embeddings(retriever)
-```
-
-#### Reader
-
-Similar to previous Tutorials we now initalize our reader.
-
-Here we use a FARMReader with the *deepset/roberta-base-squad2* model (see: https://huggingface.co/deepset/roberta-base-squad2)
-
-
-
-##### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import ExtractiveQAPipeline
-pipe = ExtractiveQAPipeline(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = pipe.run(query="Who created the Dothraki vocabulary?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-
-
-```
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/7.md b/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/7.md
deleted file mode 100644
index fc73caf6b..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/7.md
+++ /dev/null
@@ -1,199 +0,0 @@
----
-title: "Tutorial 7"
-metaTitle: "Generative QA"
-metaDescription: ""
-slug: "/docs/tutorial7"
-date: "2020-11-12"
-id: "tutorial7md"
----
-
-# Generative QA with "Retrieval-Augmented Generation"
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial7_RAG_Generator.ipynb)
-
-While extractive QA highlights the span of text that answers a query,
-generative QA can return a novel text answer that it has composed.
-In this tutorial, you will learn how to set up a generative system using the
-[RAG model](https://arxiv.org/abs/2005.11401) which conditions the
-answer generator on a set of retrieved documents.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-Here are the packages and imports that we'll need:
-
-
-```python
-!pip install git+https://github.com/deepset-ai/haystack.git
-!pip install urllib3==1.25.4
-```
-
-
-```python
-from typing import List
-import requests
-import pandas as pd
-from haystack import Document
-from haystack.document_store.faiss import FAISSDocumentStore
-from haystack.generator.transformers import RAGenerator
-from haystack.retriever.dense import DensePassageRetriever
-```
-
-Let's download a csv containing some sample text and preprocess the data.
-
-
-
-```python
-# Download sample
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/haystack/master/tutorials/small_generator_dataset.csv")
-open('small_generator_dataset.csv', 'wb').write(temp.content)
-
-# Create dataframe with columns "title" and "text"
-df = pd.read_csv("small_generator_dataset.csv", sep=',')
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-
-print(df.head())
-```
-
-We can cast our data into Haystack Document objects.
-Alternatively, we can also just use dictionaries with "text" and "meta" fields
-
-
-```python
-# Use data to initialize Document objects
-titles = list(df["title"].values)
-texts = list(df["text"].values)
-documents: List[Document] = []
-for title, text in zip(titles, texts):
- documents.append(
- Document(
- text=text,
- meta={
- "name": title or ""
- }
- )
- )
-```
-
-Here we initialize the FAISSDocumentStore, DensePassageRetriever and RAGenerator.
-FAISS is chosen here since it is optimized vector storage.
-
-
-```python
-# Initialize FAISS document store.
-# Set `return_embedding` to `True`, so generator doesn't have to perform re-embedding
-document_store = FAISSDocumentStore(
- faiss_index_factory_str="Flat",
- return_embedding=True
-)
-
-# Initialize DPR Retriever to encode documents, encode question and query documents
-retriever = DensePassageRetriever(
- document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- use_gpu=True,
- embed_title=True,
-)
-
-# Initialize RAG Generator
-generator = RAGenerator(
- model_name_or_path="facebook/rag-token-nq",
- use_gpu=True,
- top_k=1,
- max_length=200,
- min_length=2,
- embed_title=True,
- num_beams=2,
-)
-```
-
-We write documents to the DocumentStore, first by deleting any remaining documents then calling `write_documents()`.
-The `update_embeddings()` method uses the retriever to create an embedding for each document.
-
-
-
-```python
-# Delete existing documents in documents store
-document_store.delete_all_documents()
-
-# Write documents to document store
-document_store.write_documents(documents)
-
-# Add documents embeddings to index
-document_store.update_embeddings(
- retriever=retriever
-)
-```
-
-Here are our questions:
-
-
-```python
-QUESTIONS = [
- "who got the first nobel prize in physics",
- "when is the next deadpool movie being released",
- "which mode is used for short wave broadcast service",
- "who is the owner of reading football club",
- "when is the next scandal episode coming out",
- "when is the last time the philadelphia won the superbowl",
- "what is the most current adobe flash player version",
- "how many episodes are there in dragon ball z",
- "what is the first step in the evolution of the eye",
- "where is gall bladder situated in human body",
- "what is the main mineral in lithium batteries",
- "who is the president of usa right now",
- "where do the greasers live in the outsiders",
- "panda is a national animal of which country",
- "what is the name of manchester united stadium",
-]
-```
-
-Now let's run our system!
-The retriever will pick out a small subset of documents that it finds relevant.
-These are used to condition the generator as it generates the answer.
-What it should return then are novel text spans that form and answer to your question!
-
-
-```python
-# Now generate an answer for each question
-for question in QUESTIONS:
- # Retrieve related documents from retriever
- retriever_results = retriever.retrieve(
- query=question
- )
-
- # Now generate answer from question and retrieved documents
- predicted_result = generator.predict(
- query=question,
- documents=retriever_results,
- top_k=1
- )
-
- # Print you answer
- answers = predicted_result["answers"]
- print(f'Generated answer is \'{answers[0]["answer"]}\' for the question = \'{question}\'')
-```
-
-
-```python
-# Or alternatively use the Pipeline class
-from haystack.pipeline import GenerativeQAPipeline
-
-pipe = GenerativeQAPipeline(generator=generator, retriever=retriever)
-for question in QUESTIONS:
- res = pipe.run(query=question, top_k_generator=1, top_k_retriever=5)
- print(res)
-```
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/8.md b/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/8.md
deleted file mode 100644
index b2ee61786..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/8.md
+++ /dev/null
@@ -1,201 +0,0 @@
----
-title: "Tutorial 8"
-metaTitle: "Preprocessing"
-metaDescription: ""
-slug: "/docs/tutorial8"
-date: "2021-01-08"
-id: "tutorial8md"
----
-
-# Preprocessing
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial8_Preprocessing.ipynb)
-
-Haystack includes a suite of tools to extract text from different file types, normalize white space
-and split text into smaller pieces to optimize retrieval.
-These data preprocessing steps can have a big impact on the systems performance and effective handling of data is key to getting the most out of Haystack.
-
-Ultimately, Haystack expects data to be provided as a list documents in the following dictionary format:
-``` python
-docs = [
- {
- 'text': DOCUMENT_TEXT_HERE,
- 'meta': {'name': DOCUMENT_NAME, ...}
- }, ...
-]
-```
-
-This tutorial will show you all the tools that Haystack provides to help you cast your data into this format.
-
-
-```python
-# Let's start by installing Haystack
-
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-!wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.03.tar.gz
-!tar -xvf xpdf-tools-linux-4.03.tar.gz && sudo cp xpdf-tools-linux-4.03/bin64/pdftotext /usr/local/bin
-```
-
-
-```python
-# Here are the imports we need
-
-from haystack.file_converter.txt import TextConverter
-from haystack.file_converter.pdf import PDFToTextConverter
-from haystack.file_converter.docx import DocxToTextConverter
-
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.preprocessor.preprocessor import PreProcessor
-```
-
-
-```python
-# This fetches some sample files to work with
-
-doc_dir = "data/preprocessing_tutorial"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/preprocessing_tutorial.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-## Converters
-
-Haystack's converter classes are designed to help you turn files on your computer into the documents
-that can be processed by the Haystack pipeline.
-There are file converters for txt, pdf, docx files as well as a converter that is powered by Apache Tika.
-
-
-```python
-# Here are some examples of how you would use file converters
-
-converter = TextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_txt = converter.convert(file_path="data/preprocessing_tutorial/classics.txt", meta=None)
-
-converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_pdf = converter.convert(file_path="data/preprocessing_tutorial/bert.pdf", meta=None)
-
-converter = DocxToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_docx = converter.convert(file_path="data/preprocessing_tutorial/heavy_metal.docx", meta=None)
-
-```
-
-
-```python
-# Haystack also has a convenience function that will automatically apply the right converter to each file in a directory.
-
-all_docs = convert_files_to_dicts(dir_path="data/preprocessing_tutorial")
-```
-
-## PreProcessor
-
-The PreProcessor class is designed to help you clean text and split text into sensible units.
-File splitting can have a very significant impact on the system's performance and is absolutely mandatory for Dense Passage Retrieval models.
-In general, we recommend you split the text from your files into small documents of around 100 words for dense retrieval methods
-and no more than 10,000 words for sparse methods.
-Have a look at the [Preprocessing](https://haystack.deepset.ai/docs/latest/preprocessingmd)
-and [Optimization](https://haystack.deepset.ai/docs/latest/optimizationmd) pages on our website for more details.
-
-
-```python
-# This is a default usage of the PreProcessor.
-# Here, it performs cleaning of consecutive whitespaces
-# and splits a single large document into smaller documents.
-# Each document is up to 1000 words long and document breaks cannot fall in the middle of sentences
-# Note how the single document passed into the document gets split into 5 smaller documents
-
-preprocessor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=False,
- split_by="word",
- split_length=100,
- split_respect_sentence_boundary=True
-)
-docs_default = preprocessor.process(doc_txt)
-print(f"n_docs_input: 1\nn_docs_output: {len(docs_default)}")
-```
-
-## Cleaning
-
-- `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
-- `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
-- `clean_header_footer` will remove any long header or footer texts that are repeated on each page
-
-## Splitting
-By default, the PreProcessor will respect sentence boundaries, meaning that documents will not start or end
-midway through a sentence.
-This will help reduce the possibility of answer phrases being split between two documents.
-This feature can be turned off by setting `split_respect_sentence_boundary=False`.
-
-
-```python
-# Not respecting sentence boundary vs respecting sentence boundary
-
-preprocessor_nrsb = PreProcessor(split_respect_sentence_boundary=False)
-docs_nrsb = preprocessor_nrsb.process(doc_txt)
-
-print("RESPECTING SENTENCE BOUNDARY")
-end_text = docs_default[0]["text"][-50:]
-print("End of document: \"..." + end_text + "\"")
-print()
-print("NOT RESPECTING SENTENCE BOUNDARY")
-end_text_nrsb = docs_nrsb[0]["text"][-50:]
-print("End of document: \"..." + end_text_nrsb + "\"")
-```
-
-A commonly used strategy to split long documents, especially in the field of Question Answering,
-is the sliding window approach. If `split_length=10` and `split_overlap=3`, your documents will look like this:
-
-- doc1 = words[0:10]
-- doc2 = words[7:17]
-- doc3 = words[14:24]
-- ...
-
-You can use this strategy by following the code below.
-
-
-```python
-# Sliding window approach
-
-preprocessor_sliding_window = PreProcessor(
- split_overlap=3,
- split_length=10,
- split_respect_sentence_boundary=False
-)
-docs_sliding_window = preprocessor_sliding_window.process(doc_txt)
-
-doc1 = docs_sliding_window[0]["text"][:200]
-doc2 = docs_sliding_window[1]["text"][:100]
-doc3 = docs_sliding_window[2]["text"][:100]
-
-print("Document 1: \"" + doc1 + "...\"")
-print("Document 2: \"" + doc2 + "...\"")
-print("Document 3: \"" + doc3 + "...\"")
-```
-
-## Bringing it all together
-
-
-```python
-all_docs = convert_files_to_dicts(dir_path="data/preprocessing_tutorial")
-preprocessor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=False,
- split_by="word",
- split_length=100,
- split_respect_sentence_boundary=True
-)
-nested_docs = [preprocessor.process(d) for d in all_docs]
-docs = [d for x in nested_docs for d in x]
-
-print(f"n_files_input: {len(all_docs)}\nn_docs_output: {len(docs)}")
-```
-
-
-```python
-
-```
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/9.md b/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/9.md
deleted file mode 100644
index 193abe908..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/tutorials/tutorials/9.md
+++ /dev/null
@@ -1,231 +0,0 @@
----
-title: "Tutorial 9"
-metaTitle: "Training a Dense Passage Retrieval model"
-metaDescription: ""
-slug: "/docs/tutorial9"
-date: "2021-01-08"
-id: "tutorial9md"
----
-
-# Training Your Own "Dense Passage Retrieval" Model
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial9_DPR_training.ipynb)
-
-Haystack contains all the tools needed to train your own Dense Passage Retrieval model.
-This tutorial will guide you through the steps required to create a retriever that is specifically tailored to your domain.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-```
-
-
-```python
-# Here are some imports that we'll need
-
-from haystack.retriever.dense import DensePassageRetriever
-from haystack.preprocessor.utils import fetch_archive_from_http
-from haystack.document_store.memory import InMemoryDocumentStore
-```
-
-## Training Data
-
-DPR training performed using Information Retrieval data.
-More specifically, you want to feed in pairs of queries and relevant documents.
-
-To train a model, we will need a dataset that has the same format as the original DPR training data.
-Each data point in the dataset should have the following dictionary structure.
-
-``` python
- {
- "dataset": str,
- "question": str,
- "answers": list of str
- "positive_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- "negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- "hard_negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- }
-```
-
-`positive_ctxs` are context passages which are relevant to the query.
-In some datasets, queries might have more than one positive context
-in which case you can set the `num_positives` parameter to be higher than the default 1.
-Note that `num_positives` needs to be lower or equal to the minimum number of `positive_ctxs` for queries in your data.
-If you have an unequal number of positive contexts per example,
-you might want to generate some soft labels by retrieving similar contexts which contain the answer.
-
-DPR is standardly trained using a method known as in-batch negatives.
-This means that positive contexts for a given query are treated as negative contexts for the other queries in the batch.
-Doing so allows for a high degree of computational efficiency, thus allowing the model to be trained on large amounts of data.
-
-`negative_ctxs` is not actually used in Haystack's DPR training so we recommend you set it to an empty list.
-They were used by the original DPR authors in an experiment to compare it against the in-batch negatives method.
-
-`hard_negative_ctxs` are passages that are not relevant to the query.
-In the original DPR paper, these are fetched using a retriever to find the most relevant passages to the query.
-Passages which contain the answer text are filtered out.
-
-We are [currently working](https://github.com/deepset-ai/haystack/issues/705) on a script that will convert SQuAD format data into a DPR dataset!
-
-## Using Question Answering Data
-
-Question Answering datasets can sometimes be used as training data.
-Google's Natural Questions dataset, is sufficiently large
-and contains enough unique passages, that it can be converted into a DPR training set.
-This is done simply by considering answer containing passages as relevant documents to the query.
-
-The SQuAD dataset, however, is not as suited to this use case since its question and answer pairs
-are created on only a very small slice of wikipedia documents.
-
-## Download Original DPR Training Data
-
-WARNING: These files are large! The train set is 7.4GB and the dev set is 800MB
-
-We can download the original DPR training data with the following cell.
-Note that this data is probably only useful if you are trying to train from scratch.
-
-
-```python
-# Download original DPR data
-# WARNING: the train set is 7.4GB and the dev set is 800MB
-
-doc_dir = "data/dpr_training/"
-
-s3_url_train = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-train.json.gz"
-s3_url_dev = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz"
-
-fetch_archive_from_http(s3_url_train, output_dir=doc_dir + "train/")
-fetch_archive_from_http(s3_url_dev, output_dir=doc_dir + "dev/")
-```
-
-## Option 1: Training DPR from Scratch
-
-The default variables that we provide below are chosen to train a DPR model from scratch.
-Here, both passage and query embedding models are initialized using BERT base
-and the model is trained using Google's Natural Questions dataset (in a format specialised for DPR).
-
-If you are working in a language other than English,
-you will want to initialize the passage and query embedding models with a language model that supports your language
-and also provide a dataset in your language.
-
-
-```python
-# Here are the variables to specify our training data, the models that we use to initialize DPR
-# and the directory where we'll be saving the model
-
-doc_dir = "data/dpr_training/"
-
-train_filename = "train/biencoder-nq-train.json"
-dev_filename = "dev/biencoder-nq-dev.json"
-
-query_model = "bert-base-uncased"
-passage_model = "bert-base-uncased"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Option 2: Finetuning DPR
-
-If you have your own domain specific question answering or information retrieval dataset,
-you might instead be interested in finetuning a pretrained DPR model.
-In this case, you would initialize both query and passage models using the original pretrained model.
-You will want to load something like this set of variables instead of the ones above
-
-
-```python
-# Here are the variables you might want to use instead of the set above
-# in order to perform pretraining
-
-doc_dir = "PATH_TO_YOUR_DATA_DIR"
-train_filename = "TRAIN_FILENAME"
-dev_filename = "DEV_FILENAME"
-
-query_model = "facebook/dpr-question_encoder-single-nq-base"
-passage_model = "facebook/dpr-ctx_encoder-single-nq-base"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Initialization
-
-Here we want to initialize our model either with plain language model weights for training from scratch
-or else with pretrained DPR weights for finetuning.
-We follow the [original DPR parameters](https://github.com/facebookresearch/DPR#best-hyperparameter-settings)
-for their max passage length but set max query length to 64 since queries are very rarely longer.
-
-
-```python
-## Initialize DPR model
-
-retriever = DensePassageRetriever(
- document_store=InMemoryDocumentStore(),
- query_embedding_model=query_model,
- passage_embedding_model=passage_model,
- max_seq_len_query=64,
- max_seq_len_passage=256
-)
-```
-
-## Training
-
-Let's start training and save our trained model!
-
-On a V100 GPU, you can fit up to batch size 16 so we set gradient accumulation steps to 8 in order
-to simulate the batch size 128 of the original DPR experiment.
-
-When `embed_title=True`, the document title is prepended to the input text sequence with a `[SEP]` token
-between it and document text.
-
-When training from scratch with the above variables, 1 epoch takes around an hour and we reached the following performance:
-
-```
-loss: 0.046580662854042276
-task_name: text_similarity
-acc: 0.992524064068483
-f1: 0.8804297774366846
-acc_and_f1: 0.9364769207525838
-average_rank: 0.19631619339984652
-report:
- precision recall f1-score support
-
-hard_negative 0.9961 0.9961 0.9961 201887
- positive 0.8804 0.8804 0.8804 6515
-
- accuracy 0.9925 208402
- macro avg 0.9383 0.9383 0.9383 208402
- weighted avg 0.9925 0.9925 0.9925 208402
-
-```
-
-
-```python
-# Start training our model and save it when it is finished
-
-retriever.train(
- data_dir=doc_dir,
- train_filename=train_filename,
- dev_filename=dev_filename,
- test_filename=dev_filename,
- n_epochs=1,
- batch_size=16,
- grad_acc_steps=8,
- save_dir=save_dir,
- evaluate_every=3000,
- embed_title=True,
- num_positives=1,
- num_hard_negatives=1
-)
-```
-
-## Loading
-
-Loading our newly trained model is simple!
-
-
-```python
-reloaded_retriever = DensePassageRetriever.load(load_dir=save_dir, document_store=None)
-```
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/domain_adaptation.md b/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/domain_adaptation.md
deleted file mode 100644
index 9bd415e61..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/domain_adaptation.md
+++ /dev/null
@@ -1,157 +0,0 @@
----
-title: "Domain Adaptation"
-metaTitle: "Domain Adaptation"
-metaDescription: ""
-slug: "/docs/domain_adaptation"
-date: "2020-09-03"
-id: "domain_adaptationmd"
----
-
-# Domain Adaptation
-
-## Generalisation
-
-In our experience, language models trained on SQuAD show very strong general question answering capabilities.
-Though SQuAD is composed entirely of Wikipedia articles, these models are flexible enough to deal with many different styles of text.
-
-Before trying to adapt these models to your domain, we’d recommend trying one of the off the shelf models.
-We’ve found that these models are often flexible enough for a wide range of use cases.
-
-
-
-**Intuition**
-
-Most people probably don’t know what an HP Valve is.
-But you don’t always need to know what a HP Valve is to answer “What is connected to a HP Valve?”
-The answer might be there in plain language.
-In the same way, many QA models have a good enough grasp of language to answer questions about concepts in an unseen domain.
-
-
-
-
-## Finetuning
-
-Any model that can be loaded into Haystack can also be finetuned within Haystack.
-Simply provide the domain specific dataset and call `Reader.train()` on an initialised model.
-
-```
-reader.train(data_dir=train_data,
- train_filename="dev-v2.0.json",
- n_epochs=1,
- save_dir="my_model")
-```
-
-At the end of training, the finetuned model will be saved in the specified `save_dir` and can be loaded as a `Reader`.
-
-
-
-**Recommendation**
-
-See Tutorial 2 for a runnable example of this process.
-If you’re interested in measuring how much your model has improved,
-please also check out Tutorial 5 which walks through the steps needed to perform evaluation.
-
-
-
-## Generating Labels
-
-Using our [Haystack Annotate tool](https://annotate.deepset.ai/login) (Beta),
-you can easily create a labelled dataset using your own documents featuring your own question/ answer pairs.
-
-
-
-
-
-Features include:
-
-
-* Structured workspaces via organisations, projects and users
-
-
-* Easy upload of your own documents and labels in a variety of formats (txt, pdf, SQuAD style)
-
-
-* Export of labels to be used directly in Haystack
-
-Annotate also supports two different workflows:
-
-
-* Think up questions and answers while reading passages (SQuAD style)
-
-
-* Have a set of predefined questions and look for answers in the document (~ Natural Questions style)
-
-## User Feedback
-
-A simpler and faster process to finetune models to your domain is to utilise user feedback.
-Dedicated annotation work can be costly and time consuming
-but user feedback is an efficient alternative since it allows for labels to be generated by users, for users,
-all while the system is already in production.
-We, for example, have used a simple thumbs up / down system in our demos to allow
-users to give feedback.
-
-
-
-Through the Rest API, users can annotate each Haystack result as being either:
-
-* Correct
-* Correct document but wrong text
-* Wrong document and wrong text
-
-To get started, follow these steps:
-
-* Start up the REST API
- * The simplest way to do this is to call `docker-compose up` from the root directory of the Haystack repository
- * Alternatively, run `gunicorn rest_api.application:app -b 0.0.0.0:8000 -k uvicorn.workers.UvicornWorker -t 300`
-* Make a POST request to the `doc-qa-feedback` endpoint with the following payload:
-```
-{
- "question": "string",
- "is_correct_answer": true,
- "document_id": "string",
- "model_id": 0,
- "is_correct_document": true,
- "answer": "string",
- "offset_start_in_doc": 0
-}
-```
-To fill out all these values, you can use the response from an executed search request on the `doc-qa` endpoint.
-
-* Export your labels in SQuAD format by making a GET request to the `export-doc-qa-feedback` endpoint
-``` python
-# SQUAD format
-{
- "data": [
- {
- "title": "Normans",
- "paragraphs": [
- {
- "context": "The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the...",
- "qas": [
- {
- "question": "In what country is Normandy located?",
- "id": "56ddde6b9a695914005b9628",
- "answers": [
- {
- "text": "France",
- "answer_start": 159
- }
- ]
- }
- ]
- }
- ]
- }
- ]
-}
-```
-
-User feedback labelling also works with FAQ style QA.
-Refer to the Swagger API documentation (http://127.0.0.1:8000/docs) for more details.
-
-This labelled data can then be used to
-augment the training data and enable `Reader` finetuning.
-Alternatively, they can also be used to form an evaluation set to
-measure the performance of trained models,
-or monitor how well the model is doing in a live environment.
-
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/preprocessing.md b/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/preprocessing.md
deleted file mode 100644
index 943bee6a3..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/preprocessing.md
+++ /dev/null
@@ -1,116 +0,0 @@
----
-title: "Preprocessing"
-metaTitle: "Preprocessing"
-metaDescription: ""
-slug: "/docs/preprocessing"
-date: "2020-09-03"
-id: "preprocessingmd"
----
-
-# Preprocessing
-
-Haystack includes a suite of tools to:
-
-* extract text from different file types,
-* normalize white space
-* split text into smaller pieces to optimize retrieval
-
-These data preprocessing steps can have a big impact on the systems performance
-and effective handling of data is key to getting the most out of Haystack.
-
-The Document Store expects its inputs to come in the following format.
-The sections below will show you all the tools you'll need to ready your data for storing.
-
-```python
-docs = [
- {
- 'text': DOCUMENT_TEXT_HERE,
- 'meta': {'name': DOCUMENT_NAME, ...}
- }, ...
-]
-```
-
-## File Conversion
-
-There are a range of different file converters in Haystack that
-can extract text from files and cast them into the unified dictionary format shown above.
-Haystack features support for txt, pdf and docx files and there is even a converter that leverages Apache Tika.
-Please refer to [the API docs](/docs/latest/file_convertersmd) to see which converter best suits you.
-
-
-
-
-
-## PreProcessor
-
-While each of the above conversion methods produce documents that are already in the format expected by the Document Store,
-it is recommended that they are further processed in order to ensure optimal Retriever and Reader performance.
-The `PreProcessor` takes one of the documents created by the converter as input,
-performs various cleaning steps and splits them into multiple smaller documents.
-
-For suggestions on how best to split your documents, see [Optimization](/docs/latest/optimizationmd)
-
-```python
-doc = converter.convert(file_path=file, meta=None)
-processor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=True,
- split_by="word",
- split_length=200,
- split_respect_sentence_boundary=True,
- split_overlap=0
-)
-docs = processor.process(d)
-```
-
-* `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
-* `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
-* `clean_header_footer` will remove any long header or footer texts that are repeated on each page
-* `split_by` determines what unit the document is split by: `'word'`, `'sentence'` or `'passage'`
-* `split_length` sets a maximum number of `'word'`, `'sentence'` or `'passage'` units per output document
-* `split_respect_sentence_boundary` ensures that document boundaries do not fall in the middle of sentences
-* `split_overlap` sets the amount of overlap between two adjacent documents after a split. Setting this to a positive number essentially enables the sliding window approach.
-
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/reader.md b/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/reader.md
deleted file mode 100644
index 42dec94b5..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/reader.md
+++ /dev/null
@@ -1,328 +0,0 @@
----
-title: "Reader"
-metaTitle: "Reader"
-metaDescription: ""
-slug: "/docs/reader"
-date: "2020-09-03"
-id: "readermd"
----
-
-# Reader
-
-The Reader, also known as Open-Domain QA systems in Machine Learning speak,
-is the core component that enables Haystack to find the answers that you need.
-Haystack’s Readers are:
-
-
-* built on the latest transformer based language models
-
-
-* strong in their grasp of semantics
-
-
-* sensitive to syntactic structure
-
-
-* state-of-the-art in QA tasks like SQuAD and Natural Questions
-
-
-
-
-
-
-
-
-```python
-converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=["de","en"])
-doc = converter.convert(file_path=file, meta=None)
-```
-
-
-
-
-
-
-
-
-
-```python
-converter = DocxToTextConverter(remove_numeric_tables=True, valid_languages=["de","en"])
-doc = converter.convert(file_path=file, meta=None)
-```
-
-
-
-
-
-
-
-
-
-
-Haystack also has a `convert_files_to_dicts()` utility function that will convert
-all txt or pdf files in a given folder into this dictionary format.
-
-```python
-docs = convert_files_to_dicts(dir_path=doc_dir)
-```
-
-
-
-
-
-
-
-
-
-
-
-
-
-```python
-model = "deepset/roberta-base-squad2"
-reader = FARMReader(model, use_gpu=True)
-finder = Finder(reader, retriever)
-```
-
-
-
-
-
-
-
-
-
-```python
-model = "deepset/roberta-base-squad2"
-reader = TransformersReader(model, use_gpu=1)
-finder = Finder(reader, retriever)
-```
-
-
-
-
-**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
-
-
-
-While these models can work on CPU, it is recommended that they are run using GPUs to keep query times low.
-
-## Choosing the Right Model
-
-In Haystack, you can start using pretrained QA models simply by providing its HuggingFace Model Hub name to the Reader.
-The loading of model weights is handled by Haystack,
-and you have the option of using the QA pipeline from deepset FARM or HuggingFace Transformers (see FARM vs Transformers for details).
-
-Currently, there are a lot of different models out there and it can be rather overwhelming trying to pick the one that fits your use case.
-To get you started, we have a few recommendations for you to try out.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-**An optimised variant of BERT and a great starting point.**
-
-```python
-reader = FARMReader("deepset/roberta-base-squad2")
-```
-
-* **Pro**: Strong all round model
-
-* **Con**: There are other models that are either faster or more accurate
-
-
-
-
-
-
-
-
-
-**A cleverly distilled model that sacrifices a little accuracy for speed.**
-
-```python
-reader = FARMReader("deepset/minilm-uncased-squad2")
-```
-
-* **Pro**: Inference speed up to 50% faster than BERT base
-
-* **Con**: Still doesn’t match the best base sized models in accuracy
-
-
-
-
-
-
-
-
-
-**Large, powerful, SotA model.**
-
-```python
-reader = FARMReader("ahotrod/albert_xxlargev1_squad2_512")
-```
-
-* **Pro**: Better accuracy than any other open source model in QA
-
-* **Con**: The computational power needed make it impractical for most use cases
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-**An optimised variant of BERT and a great starting point.**
-
-```python
-reader = TransformersReader("deepset/roberta-base-squad2")
-```
-
-* **Pro**: Strong all round model
-
-* **Con**: There are other models that are either faster or more accurate
-
-
-
-
-
-
-
-
-
-**A cleverly distilled model that sacrifices a little accuracy for speed.**
-
-```python
-reader = TransformersReader("deepset/minilm-uncased-squad2")
-```
-
-* **Pro**: Inference speed up to 50% faster than BERT base
-
-* **Con**: Still doesn’t match the best base sized models in accuracy
-
-
-
-
-
-
-
-
-
-**Large, powerful, SotA model.**
-
-```python
-reader = TransformersReader("ahotrod/albert_xxlargev1_squad2_512")
-```
-
-* **Pro**: Better accuracy than any other open source model in QA
-
-* **Con**: The computational power needed make it impractical for most use cases
-
-
-
-
-**Recommendations:**
-
-**All-rounder**: In the class of base sized models trained on SQuAD, **RoBERTa** has shown better performance than BERT
-and can be capably handled by any machine equipped with a single NVidia V100 GPU.
-We recommend this as the starting point for anyone wanting to create a performant and computationally reasonable instance of Haystack.
-
-**Built for Speed**: If speed and GPU memory are more of a priority to you than accuracy,
-you should try the MiniLM model.
-It is a smaller model that is trained to mimic larger models through the distillation process,
-and it outperforms the BERT base on SQuAD even though it is about 40% smaller.
-
-
-**State of the Art Accuracy**: For most, **ALBERT XXL** will be too large to feasibly work with.
-But if performance is your sole concern, and you have the computational resources,
-you might like to try ALBERT XXL which has set SoTA performance on SQuAD 2.0.
-
-
-
-
-
-
-## Deeper Dive: FARM vs Transformers
-
-Apart from the **model weights**, Haystack Readers contain all the components found in end-to-end open domain QA systems.
-This includes **tokenization**, **embedding computation**, **span prediction** and **candidate aggregation**.
-While the handling of model weights is the same between the FARM and Transformers libraries, their QA pipelines differ in some ways.
-The major points are:
-
-
-* The **TransformersReader** will sometimes predict the same span twice while duplicates are removed in the **FARMReader**
-
-
-* The **FARMReader** currently uses the tokenizers from the HuggingFace Transformers library while the **TransformersReader** uses the tokenizers from the HuggingFace Tokenizers library
-
-
-* Start and end logits are normalized per passage and multiplied in the **TransformersReader** while they are summed and not normalised in the **FARMReader**
-
-If you’re interested in the finer details of these points, have a look at [this](https://github.com/deepset-ai/haystack/issues/248#issuecomment-661977237) GitHub comment.
-
-We see value in maintaining both kinds of Readers since Transformers is a very familiar library to many of Haystack’s users
-but we at deepset can more easily update and optimise the FARM pipeline for speed and performance.
-
-
-Haystack also has a close integration with FARM which means that you can further fine-tune your Readers on labelled data using a FARMReader.
-See our tutorials for an end-to-end example or below for a shortened example.
-
-```python
-# Initialise Reader
-model = "deepset/roberta-base-squad2"
-reader = FARMReader(model)
-
-# Perform finetuning
-train_data = "PATH/TO_YOUR/TRAIN_DATA"
-train_filename = "train.json"
-save_dir = "finetuned_model"
-reader.train(train_data, train_filename, save_dir=save_dir)
-
-# Load
-finetuned_reader = FARMReader(save_dir)
-```
-
-## Deeper Dive: From Language Model to Haystack Reader
-
-Language models form the core of most modern NLP systems and that includes the Readers in Haystack.
-They build a general understanding of language when performing training tasks such as Masked Language Modeling or Replaced Token Detection
-on large amounts of text.
-Well trained language models capture the word distribution in one or more languages
-but more importantly, convert input text into a set of word vectors that capture elements of syntax and semantics.
-
-In order to convert a language model into a Reader model, it needs first to be trained on a Question Answering dataset.
-To do so requires the addition of a question answering prediction head on top of the language model.
-The task can be thought of as a token classification task where every input token is assigned a probability of being
-either the start or end token of the correct answer.
-In cases where the answer is not contained within the passage, the prediction head is also expected to return a `no_answer` prediction.
-
-
-Since language models are limited in the number of tokens which they can process in a single forward pass,
-a sliding window mechanism is implemented to handle variable length documents.
-This functions by slicing the document into overlapping passages of (approximately) `max_seq_length`
-that are each offset by `doc_stride` number of tokens.
-These can be set when the Reader is initialized.
-
-
-
-
-
-Predictions are made on each individual passage and the process of aggregation picks the best candidates across all passages.
-If you’d like to learn more about what is happening behind the scenes, have a look at [this](https://medium.com/deepset-ai/modern-question-answering-systems-explained-4d0913744097) article.
-
-
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/roadmap.md b/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/roadmap.md
deleted file mode 100644
index bf28c14e5..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/roadmap.md
+++ /dev/null
@@ -1,45 +0,0 @@
----
-title: "Roadmap"
-metaTitle: "Roadmap and Zenhub Access"
-metaDescription: "How to access our open roadmap"
-slug: "/docs/roadmap"
-date: "2020-09-24"
-id: "roadmapmd"
----
-
-# Open roadmap
-
-We believe open-source is more than open source code. It's a lot about people, collaboration, transparency and trust.
-Therefore, we decided to be as open as possible with our roadmap and sprint planning.
-In fact, you can see all of it in real-time on GitHub.
-We hope this helps to clarify the direction of the open-source project and inspires discussions in the community.
-
-## How to access it
-
-### Add Zenhub Plugin
-We decided for Zenhub, as it allows a close integration with GitHub and real-time sharing of roadmaps and sprints.
-Once you have installed the browser plugin below, you will see additional tabs and infos on the Haystack GitHub page.
-
-Zenhub Plugin: https://www.zenhub.com/extension
-
-### Roadmap
-_Zenhub Tab -> Roadmap (left menu)_
-
-Here you can find our most recent roadmap with the **high-level projects** that are planned for the upcoming quarters.
-We update it regularly and refine the projects as they come closer in time.
-
-
-
-### Board
-_Zenhub Tab -> Board (left menu)_
-
-If you are interested in the **operational tasks** and their status, you can find our agile board here.
-
-
-### Additional issue details & Releases
-_Right panel in regular Github issues_
-
-With Zenhub you can also see some additional tags in every GitHub issue.
-For those of you who wonder about the next release date: We aim for releases every ~ 4 weeks and will tag the issues that will need to be finished before a bit in advance.
-
-
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/translator.md b/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/translator.md
deleted file mode 100644
index 8b1f0c5f6..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/translator.md
+++ /dev/null
@@ -1,58 +0,0 @@
----
-title: "Translator"
-metaTitle: "Translator"
-metaDescription: ""
-slug: "/docs/translator"
-date: "2021-02-10"
-id: "translatormd"
----
-
-# Translator
-
-Texts come in different languages. This is not different for search and there are plenty of options to deal with it.
-One of them is actually to translate the incoming query, the documents or the search results.
-
-Let's imagine you have an English corpus of technical docs, but the mother tongue of many of your users is French.
-You can use a Translator node in your pipeline to
-1. Translate the incoming query from French to English
-2. Search in your English corpus for the right document / answer
-3. Translate the results back from English to French
-
-
-
-
-
-
-
-
-```python
-reader = FARMReader(... max_seq_len=384, doc_stride=128 ...)
-```
-
-
-
-
-
-
-
-
-
-```python
-reader = TransformersReader(... max_seq_len=384, doc_stride=128 ...
-```
-
-
-
-
-**Example (Stand-alone Translator)**
-
-You can use the Translator component directly to translate your query or document(s):
-```python
-DOCS = [
- Document(
- text="""Heinz von Foerster was an Austrian American scientist
- combining physics and philosophy, and widely attributed
- as the originator of Second-order cybernetics."""
- )
- ]
-translator = TransformersTranslator(model_name_or_path="Helsinki-NLP/opus-mt-en-fr")
-res = translator.translate(documents=DOCS, query=None)
-```
-
-**Example (Wrapping another Pipeline)**
-
-You can also wrap one of your existing pipelines and "add" the translation nodes at the beginning and at the end of your pipeline.
-For example, lets translate the incoming query to from French to English, then do our document retrieval and then translate the results back from English to French:
-
-```python
-from haystack.pipeline import TranslationWrapperPipeline, DocumentSearchPipeline
-from haystack.translator import TransformersTranslator
-
-pipeline = DocumentSearchPipeline(retriever=my_dpr_retriever)
-
-in_translator = TransformersTranslator(model_name_or_path="Helsinki-NLP/opus-mt-fr-en")
-out_translator = TransformersTranslator(model_name_or_path="Helsinki-NLP/opus-mt-en-fr")
-
-pipeline_with_translation = TranslationWrapperPipeline(input_translator=in_translator,
- output_translator=out_translator,
- pipeline=pipeline)
-```
-
-
-
diff --git a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/use_cases.md b/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/use_cases.md
deleted file mode 100644
index 76b420410..000000000
--- a/src/pages/docs/versions/master/v0.8.0/site/en/usage/usage/use_cases.md
+++ /dev/null
@@ -1,69 +0,0 @@
----
-title: "Use Cases"
-metaTitle: "Use Cases"
-metaDescription: ""
-slug: "/docs/use_cases"
-date: "2020-11-16"
-id: "use_casesmd"
----
-
-
-# Use cases
-
-## Semantic Search System
-
-Take the leap from using keyword search on your own documents to semantic search with Haystack.
-
-
-* Store your documents in the database of your choice (Elasticsearch, SQL, in memory, FAISS)
-
-
-* Perform question driven queries.
-
-Expect to see results that highlight the very sentence that contains the answer to your question.
-Thanks to the power of Transformer based language models, results are chosen based on compatibility in meaning
-rather than lexical overlap.
-
-
-
-
-
-## Information Extractor
-
-Automate the extraction of relevant information from a set of documents that pertain to the same topics but for different entities.
-
-Haystack can:
-
-
-* Apply a set of standard questions to each document in a store
-
-
-* Return a NO_ANSWER if a given document does not contain the answer to a question
-
-Say you have the financial reports for different companies over different years.
-You can gather a set of standard questions which are applicable to each financial report,
-like *what is the revenue forecast for 2020?* or *what are the main sources of income?*.
-Haystack will try to find an answer for each question within each document!
-
-We’ve seen this style of application be particularly effective in the sphere of finance and patent law
-but we see a lot of potential in using this to gain a better overview of academic papers and internal business documents.
-
-
-## FAQ Style Question Answering
-
-Leverage existing FAQ documents and semantic similarity search to answer new incoming questions.
-The workflow is as follows:
-
-
-* Store a set of FAQ documents in Haystack
-
-
-* The user presents a new question
-
-
-* Haystack will find the closest match to the new question in the FAQ documents
-
-
-* The user will be presented with the most similar Question Answer pair
-
-Haystack’s flexibility allows you to give new users more dynamic access to your existing documentation.
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/api/api/crawler.md b/src/pages/docs/versions/master/v0.9.0/site/en/api/api/crawler.md
deleted file mode 100644
index 33eebda75..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/api/api/crawler.md
+++ /dev/null
@@ -1,105 +0,0 @@
----
-title: "Crawler"
-metaTitle: "Crawler"
-metaDescription: ""
-slug: "/docs/crawlermd"
-date: "2020-09-03"
-id: "apicrawlermd"
----
-
-# Crawler
-
-
-# Module: crawler
-
-
-## Class: Crawler
-
-```python
-class Crawler(BaseComponent)
-```
-
-Crawl texts from a website so that we can use them later in Haystack as a corpus for search / question answering etc.
-
-**Example:**
-```python
-| from haystack.connector import Crawler
-|
-| crawler = Crawler()
-| # crawl Haystack docs, i.e. all pages that include haystack.deepset.ai/docs/
-| docs = crawler.crawl(urls=["https://haystack.deepset.ai/docs/latest/get_startedmd"],
-| output_dir="crawled_files",
-| filter_urls= ["haystack\.deepset\.ai\/docs\/"])
-```
-
-
-#### \_\_init\_\_
-
-```python
- | __init__(output_dir: str, urls: Optional[List[str]] = None, crawler_depth: int = 1, filter_urls: Optional[List] = None, overwrite_existing_files=True)
-```
-
-Init object with basic params for crawling (can be overwritten later).
-
-**Arguments**:
-
-- `output_dir`: Path for the directory to store files
-- `urls`: List of http(s) address(es) (can also be supplied later when calling crawl())
-- `crawler_depth`: How many sublinks to follow from the initial list of URLs. Current options:
- 0: Only initial list of urls
- 1: Follow links found on the initial URLs (but no further)
-- `filter_urls`: Optional list of regular expressions that the crawled URLs must comply with.
- All URLs not matching at least one of the regular expressions will be dropped.
-- `overwrite_existing_files`: Whether to overwrite existing files in output_dir with new content
-
-
-#### crawl
-
-```python
- | crawl(output_dir: Union[str, Path, None] = None, urls: Optional[List[str]] = None, crawler_depth: Optional[int] = None, filter_urls: Optional[List] = None, overwrite_existing_files: Optional[bool] = None) -> List[Path]
-```
-
-Craw URL(s), extract the text from the HTML, create a Haystack Document object out of it and save it (one JSON
-file per URL, including text and basic meta data).
-You can optionally specify via `filter_urls` to only crawl URLs that match a certain pattern.
-All parameters are optional here and only meant to overwrite instance attributes at runtime.
-If no parameters are provided to this method, the instance attributes that were passed during __init__ will be used.
-
-**Arguments**:
-
-- `output_dir`: Path for the directory to store files
-- `urls`: List of http addresses or single http address
-- `crawler_depth`: How many sublinks to follow from the initial list of URLs. Current options:
- 0: Only initial list of urls
- 1: Follow links found on the initial URLs (but no further)
-- `filter_urls`: Optional list of regular expressions that the crawled URLs must comply with.
- All URLs not matching at least one of the regular expressions will be dropped.
-- `overwrite_existing_files`: Whether to overwrite existing files in output_dir with new content
-
-**Returns**:
-
-List of paths where the crawled webpages got stored
-
-
-#### run
-
-```python
- | run(output_dir: Union[str, Path, None] = None, urls: Optional[List[str]] = None, crawler_depth: Optional[int] = None, filter_urls: Optional[List] = None, overwrite_existing_files: Optional[bool] = None, **kwargs) -> Tuple[Dict, str]
-```
-
-Method to be executed when the Crawler is used as a Node within a Haystack pipeline.
-
-**Arguments**:
-
-- `output_dir`: Path for the directory to store files
-- `urls`: List of http addresses or single http address
-- `crawler_depth`: How many sublinks to follow from the initial list of URLs. Current options:
- 0: Only initial list of urls
- 1: Follow links found on the initial URLs (but no further)
-- `filter_urls`: Optional list of regular expressions that the crawled URLs must comply with.
- All URLs not matching at least one of the regular expressions will be dropped.
-- `overwrite_existing_files`: Whether to overwrite existing files in output_dir with new content
-
-**Returns**:
-
-Tuple({"paths": List of filepaths, ...}, Name of output edge)
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/api/api/document_store.md b/src/pages/docs/versions/master/v0.9.0/site/en/api/api/document_store.md
deleted file mode 100644
index 7736755e9..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/api/api/document_store.md
+++ /dev/null
@@ -1,1729 +0,0 @@
----
-title: "Document Store"
-metaTitle: "Database"
-metaDescription: ""
-slug: "/docs/apidatabase"
-date: "2020-09-03"
-id: "apidatabasemd"
----
-
-# Document Store
-
-
-# Module: base
-
-
-## Class: BaseDocumentStore
-
-```python
-class BaseDocumentStore(BaseComponent)
-```
-
-Base class for implementing Document Stores.
-
-
-#### write\_documents
-
-```python
- | @abstractmethod
- | write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000, duplicate_documents: Optional[str] = None)
-```
-
-Indexes documents for later queries.
-
-**Arguments**:
-
-- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
- For documents as dictionaries, the format is {"text": "
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb)
-
-Question Answering can be used in a variety of use cases. A very common one: Using it to navigate through complex knowledge bases or long documents ("search setting").
-
-A "knowledge base" could for example be your website, an internal wiki or a collection of financial reports.
-In this tutorial we will work on a slightly different domain: "Game of Thrones".
-
-Let's see how we can use a bunch of Wikipedia articles to answer a variety of questions about the
-marvellous seven kingdoms...
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-Haystack finds answers to queries within the documents stored in a `DocumentStore`. The current implementations of `DocumentStore` include `ElasticsearchDocumentStore`, `FAISSDocumentStore`, `SQLDocumentStore`, and `InMemoryDocumentStore`.
-
-**Here:** We recommended Elasticsearch as it comes preloaded with features like [full-text queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html), [BM25 retrieval](https://www.elastic.co/elasticon/conf/2016/sf/improved-text-scoring-with-bm25), and [vector storage for text embeddings](https://www.elastic.co/guide/en/elasticsearch/reference/7.6/dense-vector.html).
-
-**Alternatives:** If you are unable to setup an Elasticsearch instance, then follow the [Tutorial 3](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb) for using SQL/InMemory document stores.
-
-**Hint**: This tutorial creates a new document store instance with Wikipedia articles on Game of Thrones. However, you can configure Haystack to work with your existing document stores.
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker via the Haystack utility function
-from haystack.utils import launch_es
-
-launch_es()
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-# Connect to Elasticsearch
-
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles about Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first fetch some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is:
-# {
-# 'text': "
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest master of Haystack
-!pip install git+https://github.com/deepset-ai/haystack.git
-```
-
-
-```python
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.generator.transformers import Seq2SeqGenerator
-```
-
-### Document Store
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-The default flavour of FAISSDocumentStore is "Flat" but can also be set to "HNSW" for
-faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.
-For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-
-
-```python
-from haystack.document_store.faiss import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(vector_dim=128, faiss_index_factory_str="Flat")
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever and Reader/Generator
-
-#### Retriever
-
-**Here:** We use a `RetribertRetriever` and we invoke `update_embeddings` to index the embeddings of documents in the `FAISSDocumentStore`
-
-
-
-
-```python
-from haystack.retriever.dense import EmbeddingRetriever
-
-retriever = EmbeddingRetriever(document_store=document_store,
- embedding_model="yjernite/retribert-base-uncased",
- model_format="retribert")
-
-document_store.update_embeddings(retriever)
-```
-
-Before we blindly use the `RetribertRetriever` let's empirically test it to make sure a simple search indeed finds the relevant documents.
-
-
-```python
-from haystack.utils import print_answers, print_documents
-from haystack.pipeline import DocumentSearchPipeline
-
-p_retrieval = DocumentSearchPipeline(retriever)
-res = p_retrieval.run(
- query="Tell me something about Arya Stark?",
- top_k_retriever=5
-)
-print_documents(res, max_text_len=512)
-
-```
-
-#### Reader/Generator
-
-Similar to previous Tutorials we now initalize our reader/generator.
-
-Here we use a `Seq2SeqGenerator` with the *yjernite/bart_eli5* model (see: https://huggingface.co/yjernite/bart_eli5)
-
-
-
-
-```python
-generator = Seq2SeqGenerator(model_name_or_path="yjernite/bart_eli5")
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `GenerativeQAPipeline` that combines a retriever and a reader/generator to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import GenerativeQAPipeline
-pipe = GenerativeQAPipeline(generator, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-pipe.run(query="Why did Arya Stark's character get portrayed in a television adaptation?", top_k_retriever=1)
-```
-
-
-```python
-pipe.run(query="What kind of character does Arya Stark play?", top_k_retriever=1)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/2.md b/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/2.md
deleted file mode 100644
index d9eba3738..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/2.md
+++ /dev/null
@@ -1,108 +0,0 @@
----
-title: "Tutorial 2"
-metaTitle: "Fine-tuning a model on your own data"
-metaDescription: ""
-slug: "/docs/tutorial2"
-date: "2020-09-03"
-id: "tutorial2md"
----
-
-# Fine-tuning a Model on Your Own Data
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial2_Finetune_a_model_on_your_data.ipynb)
-
-For many use cases it is sufficient to just use one of the existing public models that were trained on SQuAD or other public QA datasets (e.g. Natural Questions).
-However, if you have domain-specific questions, fine-tuning your model on custom examples will very likely boost your performance.
-While this varies by domain, we saw that ~ 2000 examples can easily increase performance by +5-20%.
-
-This tutorial shows you how to fine-tune a pretrained model on your own dataset.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack.reader.farm import FARMReader
-```
-
-
-## Create Training Data
-
-There are two ways to generate training data
-
-1. **Annotation**: You can use the [annotation tool](https://github.com/deepset-ai/haystack#labeling-tool) to label your data, i.e. highlighting answers to your questions in a document. The tool supports structuring your workflow with organizations, projects, and users. The labels can be exported in SQuAD format that is compatible for training with Haystack.
-
-
-
-2. **Feedback**: For production systems, you can collect training data from direct user feedback via Haystack's [REST API interface](https://github.com/deepset-ai/haystack#rest-api). This includes a customizable user feedback API for providing feedback on the answer returned by the API. The API provides a feedback export endpoint to obtain the feedback data for fine-tuning your model further.
-
-
-## Fine-tune your model
-
-Once you have collected training data, you can fine-tune your base models.
-We initialize a reader as a base model and fine-tune it on our own custom dataset (should be in SQuAD-like format).
-We recommend using a base model that was trained on SQuAD or a similar QA dataset before to benefit from Transfer Learning effects.
-
-**Recommendation**: Run training on a GPU.
-If you are using Colab: Enable this in the menu "Runtime" > "Change Runtime type" > Select "GPU" in dropdown.
-Then change the `use_gpu` arguments below to `True`
-
-
-```python
-reader = FARMReader(model_name_or_path="distilbert-base-uncased-distilled-squad", use_gpu=True)
-train_data = "data/squad20"
-# train_data = "PATH/TO_YOUR/TRAIN_DATA"
-reader.train(data_dir=train_data, train_filename="dev-v2.0.json", use_gpu=True, n_epochs=1, save_dir="my_model")
-```
-
-
-```python
-# Saving the model happens automatically at the end of training into the `save_dir` you specified
-# However, you could also save a reader manually again via:
-reader.save(directory="my_model")
-```
-
-
-```python
-# If you want to load it at a later point, just do:
-new_reader = FARMReader(model_name_or_path="my_model")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/3.md b/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/3.md
deleted file mode 100644
index 180d5eabd..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/3.md
+++ /dev/null
@@ -1,203 +0,0 @@
----
-title: "Tutorial 3"
-metaTitle: "Build a QA System Without Elasticsearch"
-metaDescription: ""
-slug: "/docs/tutorial3"
-date: "2020-09-03"
-id: "tutorial3md"
----
-
-# Build a QA System Without Elasticsearch
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.ipynb)
-
-Haystack provides alternatives to Elasticsearch for developing quick prototypes.
-
-You can use an `InMemoryDocumentStore` or a `SQLDocumentStore`(with SQLite) as the document store.
-
-If you are interested in more feature-rich Elasticsearch, then please refer to the Tutorial 1.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-## Document Store
-
-
-
-```python
-# In-Memory Document Store
-from haystack.document_store.memory import InMemoryDocumentStore
-document_store = InMemoryDocumentStore()
-```
-
-
-```python
-# SQLite Document Store
-# from haystack.document_store.sql import SQLDocumentStore
-# document_store = SQLDocumentStore(url="sqlite:///qa.db")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles on Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-# Let's first get some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# convert files to dicts containing documents that can be indexed to our datastore
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is: {"name": "
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack import Finder
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-from haystack.retriever.dense import EmbeddingRetriever
-from haystack.utils import print_answers
-import pandas as pd
-import requests
-
-```
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker via the Haystack utility function
-from haystack.utils import launch_es
-
-launch_es()
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-
-```
-
-### Init the DocumentStore
-In contrast to Tutorial 1 (extractive QA), we:
-
-* specify the name of our `text_field` in Elasticsearch that we want to return as an answer
-* specify the name of our `embedding_field` in Elasticsearch where we'll store the embedding of our question and that is used later for calculating our similarity to the incoming user question
-* set `excluded_meta_data=["question_emb"]` so that we don't return the huge embedding vectors in our search results
-
-
-```python
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="",
- index="document",
- embedding_field="question_emb",
- embedding_dim=768,
- excluded_meta_data=["question_emb"])
-```
-
-### Create a Retriever using embeddings
-Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones).
-We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings.
-
-
-```python
-retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert", use_gpu=True)
-```
-
-### Prepare & Index FAQ data
-We create a pandas dataframe containing some FAQ data (i.e curated pairs of question + answer) and index those in elasticsearch.
-Here: We download some question-answer pairs related to COVID-19
-
-
-```python
-# Download
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/faqs/faq_covidbert.csv")
-open('small_faq_covid.csv', 'wb').write(temp.content)
-
-# Get dataframe with columns "question", "answer" and some custom metadata
-df = pd.read_csv("small_faq_covid.csv")
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-df["question"] = df["question"].apply(lambda x: x.strip())
-print(df.head())
-
-# Get embeddings for our questions from the FAQs
-questions = list(df["question"].values)
-df["question_emb"] = retriever.embed_queries(texts=questions)
-df = df.rename(columns={"question": "text"})
-
-# Convert Dataframe to list of dicts and index them in our DocumentStore
-docs_to_index = df.to_dict(orient="records")
-document_store.write_documents(docs_to_index)
-```
-
-### Ask questions
-Initialize a Pipeline (this time without a reader) and ask questions
-
-
-```python
-from haystack.pipeline import FAQPipeline
-pipe = FAQPipeline(retriever=retriever)
-```
-
-
-```python
-prediction = pipe.run(query="How is the virus spreading?", top_k_retriever=10)
-print_answers(prediction, details="all")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/5.md b/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/5.md
deleted file mode 100644
index 9ce00b1b3..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/5.md
+++ /dev/null
@@ -1,260 +0,0 @@
----
-title: "Tutorial 5"
-metaTitle: "Evaluation of a QA System"
-metaDescription: ""
-slug: "/docs/tutorial5"
-date: "2020-09-03"
-id: "tutorial5md"
----
-
-# Evaluation of a QA System
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial5_Evaluation.ipynb)
-
-To be able to make a statement about the performance of a question-answering system, it is important to evalute it. Furthermore, evaluation allows to determine which parts of the system can be improved.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-## Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
- stdout=PIPE, stderr=STDOUT,
- preexec_fn=lambda: os.setuid(1) # as daemon
- )
-# wait until ES has started
-! sleep 30
-```
-
-
-```python
-from farm.utils import initialize_device_settings
-
-device, n_gpu = initialize_device_settings(use_cuda=True)
-```
-
-
-```python
-from haystack.preprocessor.utils import fetch_archive_from_http
-
-# Download evaluation data, which is a subset of Natural Questions development set containing 50 documents
-doc_dir = "../data/nq"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/nq_dev_subset_v2.json.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-
-```python
-# make sure these indices do not collide with existing ones, the indices will be wiped clean before data is inserted
-doc_index = "tutorial5_docs"
-label_index = "tutorial5_labels"
-```
-
-
-```python
-# Connect to Elasticsearch
-from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
-
-# Connect to Elasticsearch
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document",
- create_index=False, embedding_field="emb",
- embedding_dim=768, excluded_meta_data=["emb"])
-```
-
-
-```python
-from haystack.preprocessor import PreProcessor
-
-# Add evaluation data to Elasticsearch Document Store
-# We first delete the custom tutorial indices to not have duplicate elements
-# and also split our documents into shorter passages using the PreProcessor
-preprocessor = PreProcessor(
- split_length=500,
- split_overlap=0,
- split_respect_sentence_boundary=False,
- clean_empty_lines=False,
- clean_whitespace=False
-)
-document_store.delete_all_documents(index=doc_index)
-document_store.delete_all_documents(index=label_index)
-document_store.add_eval_data(
- filename="../data/nq/nq_dev_subset_v2.json",
- doc_index=doc_index,
- label_index=label_index,
- preprocessor=preprocessor
-)
-
-# Let's prepare the labels that we need for the retriever and the reader
-labels = document_store.get_all_labels_aggregated(index=label_index)
-```
-
-## Initialize components of QA-System
-
-
-```python
-# Initialize Retriever
-from haystack.retriever.sparse import ElasticsearchRetriever
-retriever = ElasticsearchRetriever(document_store=document_store)
-# Alternative: Evaluate DensePassageRetriever
-# Note, that DPR works best when you index short passages < 512 tokens as only those tokens will be used for the embedding.
-# Here, for nq_dev_subset_v2.json we have avg. num of tokens = 5220(!).
-# DPR still outperforms Elastic's BM25 by a small margin here.
-# from haystack.retriever.dense import DensePassageRetriever
-# retriever = DensePassageRetriever(document_store=document_store,
-# query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
-# passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
-# use_gpu=True,
-# embed_title=True,
-# max_seq_len=256,
-# batch_size=16,
-# remove_sep_tok_from_untitled_passages=True)
-#document_store.update_embeddings(retriever, index=doc_index)
-```
-
-
-```python
-# Initialize Reader
-from haystack.reader.farm import FARMReader
-
-reader = FARMReader("deepset/roberta-base-squad2", top_k_per_candidate=4, return_no_answer=True)
-
-```
-
-
-```python
-from haystack.eval import EvalAnswers, EvalDocuments
-
-# Here we initialize the nodes that perform evaluation
-eval_retriever = EvalDocuments()
-eval_reader = EvalAnswers()
-```
-
-## Evaluation of Retriever
-Here we evaluate only the retriever, based on whether the gold_label document is retrieved.
-
-
-```python
-## Evaluate Retriever on its own
-retriever_eval_results = retriever.eval(top_k=20, label_index=label_index, doc_index=doc_index)
-## Retriever Recall is the proportion of questions for which the correct document containing the answer is
-## among the correct documents
-print("Retriever Recall:", retriever_eval_results["recall"])
-## Retriever Mean Avg Precision rewards retrievers that give relevant documents a higher rank
-print("Retriever Mean Avg Precision:", retriever_eval_results["map"])
-```
-
-## Evaluation of Reader
-Here we evaluate only the reader in a closed domain fashion i.e. the reader is given one query
-and one document and metrics are calculated on whether the right position in this text is selected by
-the model as the answer span (i.e. SQuAD style)
-
-
-```python
-# Evaluate Reader on its own
-reader_eval_results = reader.eval(document_store=document_store, device=device, label_index=label_index, doc_index=doc_index)
-# Evaluation of Reader can also be done directly on a SQuAD-formatted file without passing the data to Elasticsearch
-#reader_eval_results = reader.eval_on_file("../data/nq", "nq_dev_subset_v2.json", device=device)
-
-## Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer
-print("Reader Top-N-Accuracy:", reader_eval_results["top_n_accuracy"])
-## Reader Exact Match is the proportion of questions where the predicted answer is exactly the same as the correct answer
-print("Reader Exact Match:", reader_eval_results["EM"])
-## Reader F1-Score is the average overlap between the predicted answers and the correct answers
-print("Reader F1-Score:", reader_eval_results["f1"])
-```
-
-## Evaluation of Retriever and Reader (Open Domain)
-Here we evaluate retriever and reader in open domain fashion i.e. a document is considered
-correctly retrieved if it contains the answer string within it. The reader is evaluated based purely on the
-predicted string, regardless of which document this came from and the position of the extracted span.
-
-
-```python
-from haystack import Pipeline
-
-# Here is the pipeline definition
-p = Pipeline()
-p.add_node(component=retriever, name="ESRetriever", inputs=["Query"])
-p.add_node(component=eval_retriever, name="EvalRetriever", inputs=["ESRetriever"])
-p.add_node(component=reader, name="QAReader", inputs=["EvalRetriever"])
-p.add_node(component=eval_reader, name="EvalReader", inputs=["QAReader"])
-results = []
-```
-
-
-```python
-# This is how to run the pipeline
-for l in labels:
- res = p.run(
- query=l.question,
- top_k_retriever=10,
- labels=l,
- top_k_reader=10,
- index=doc_index,
- )
- results.append(res)
-```
-
-
-```python
-# When we have run evaluation using the pipeline, we can print the results
-n_queries = len(labels)
-eval_retriever.print()
-print()
-retriever.print_time()
-print()
-eval_reader.print(mode="reader")
-print()
-reader.print_time()
-print()
-eval_reader.print(mode="pipeline")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/6.md b/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/6.md
deleted file mode 100644
index 4f47974eb..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/6.md
+++ /dev/null
@@ -1,241 +0,0 @@
----
-title: "Tutorial 6"
-metaTitle: "Better retrieval via Dense Passage Retrieval"
-metaDescription: ""
-slug: "/docs/tutorial6"
-date: "2020-09-03"
-id: "tutorial6md"
----
-
-# Better Retrieval via "Dense Passage Retrieval"
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb)
-
-### Importance of Retrievers
-
-The Retriever has a huge impact on the performance of our overall search pipeline.
-
-
-### Different types of Retrievers
-#### Sparse
-Family of algorithms based on counting the occurrences of words (bag-of-words) resulting in very sparse vectors with length = vocab size.
-
-**Examples**: BM25, TF-IDF
-
-**Pros**: Simple, fast, well explainable
-
-**Cons**: Relies on exact keyword matches between query and text
-
-
-#### Dense
-These retrievers use neural network models to create "dense" embedding vectors. Within this family there are two different approaches:
-
-a) Single encoder: Use a **single model** to embed both query and passage.
-b) Dual-encoder: Use **two models**, one to embed the query and one to embed the passage
-
-Recent work suggests that dual encoders work better, likely because they can deal better with the different nature of query and passage (length, style, syntax ...).
-
-**Examples**: REALM, DPR, Sentence-Transformers
-
-**Pros**: Captures semantinc similarity instead of "word matches" (e.g. synonyms, related topics ...)
-
-**Cons**: Computationally more heavy, initial training of model
-
-
-### "Dense Passage Retrieval"
-
-In this Tutorial, we want to highlight one "Dense Dual-Encoder" called Dense Passage Retriever.
-It was introdoced by Karpukhin et al. (2020, https://arxiv.org/abs/2004.04906.
-
-Original Abstract:
-
-_"Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks."_
-
-Paper: https://arxiv.org/abs/2004.04906
-Original Code: https://fburl.com/qa-dpr
-
-
-*Use this* [link](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb) *to open the notebook in Google Colab.*
-
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from haystack import Finder
-from haystack.preprocessor.cleaning import clean_wiki_text
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.reader.farm import FARMReader
-from haystack.reader.transformers import TransformersReader
-from haystack.utils import print_answers
-```
-
-### Document Store
-
-#### Option 1: FAISS
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-The default flavour of FAISSDocumentStore is "Flat" but can also be set to "HNSW" for
-faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.
-For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-
-
-```python
-from haystack.document_store import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
-```
-
-#### Option 2: Milvus
-
-Milvus is an open source database library that is also optimized for vector similarity searches like FAISS.
-Like FAISS it has both a "Flat" and "HNSW" mode but it outperforms FAISS when it comes to dynamic data management.
-It does require a little more setup, however, as it is run through Docker and requires the setup of some config files.
-See [their docs](https://milvus.io/docs/v1.0.0/milvus_docker-cpu.md) for more details.
-
-
-```python
-from haystack.utils import launch_milvus
-from haystack.document_store import MilvusDocumentStore
-
-launch_milvus()
-document_store = MilvusDocumentStore()
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-# Let's first get some files that we want to use
-doc_dir = "data/article_txt_got"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(dicts)
-```
-
-### Initalize Retriever, Reader, & Finder
-
-#### Retriever
-
-**Here:** We use a `DensePassageRetriever`
-
-**Alternatives:**
-
-- The `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-
-
-```python
-from haystack.retriever.dense import DensePassageRetriever
-retriever = DensePassageRetriever(document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- max_seq_len_query=64,
- max_seq_len_passage=256,
- batch_size=16,
- use_gpu=True,
- embed_title=True,
- use_fast_tokenizers=True)
-# Important:
-# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all
-# previously indexed documents and update their embedding representation.
-# While this can be a time consuming operation (depending on corpus size), it only needs to be done once.
-# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.
-document_store.update_embeddings(retriever)
-```
-
-#### Reader
-
-Similar to previous Tutorials we now initalize our reader.
-
-Here we use a FARMReader with the *deepset/roberta-base-squad2* model (see: https://huggingface.co/deepset/roberta-base-squad2)
-
-
-
-##### FARMReader
-
-
-```python
-# Load a local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipeline import ExtractiveQAPipeline
-pipe = ExtractiveQAPipeline(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = pipe.run(query="Who created the Dothraki vocabulary?", top_k_retriever=10, top_k_reader=5)
-```
-
-
-```python
-print_answers(prediction, details="minimal")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/7.md b/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/7.md
deleted file mode 100644
index 3e472df19..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/7.md
+++ /dev/null
@@ -1,217 +0,0 @@
----
-title: "Tutorial 7"
-metaTitle: "Generative QA with RAG"
-metaDescription: ""
-slug: "/docs/tutorial7"
-date: "2020-11-12"
-id: "tutorial7md"
----
-
-# Generative QA with "Retrieval-Augmented Generation"
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial7_RAG_Generator.ipynb)
-
-While extractive QA highlights the span of text that answers a query,
-generative QA can return a novel text answer that it has composed.
-In this tutorial, you will learn how to set up a generative system using the
-[RAG model](https://arxiv.org/abs/2005.11401) which conditions the
-answer generator on a set of retrieved documents.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-Here are the packages and imports that we'll need:
-
-
-```python
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-
-```
-
-
-```python
-from typing import List
-import requests
-import pandas as pd
-from haystack import Document
-from haystack.document_store.faiss import FAISSDocumentStore
-from haystack.generator.transformers import RAGenerator
-from haystack.retriever.dense import DensePassageRetriever
-```
-
-Let's download a csv containing some sample text and preprocess the data.
-
-
-
-```python
-# Download sample
-temp = requests.get("https://raw.githubusercontent.com/deepset-ai/haystack/master/tutorials/small_generator_dataset.csv")
-open('small_generator_dataset.csv', 'wb').write(temp.content)
-
-# Create dataframe with columns "title" and "text"
-df = pd.read_csv("small_generator_dataset.csv", sep=',')
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-
-print(df.head())
-```
-
-We can cast our data into Haystack Document objects.
-Alternatively, we can also just use dictionaries with "text" and "meta" fields
-
-
-```python
-# Use data to initialize Document objects
-titles = list(df["title"].values)
-texts = list(df["text"].values)
-documents: List[Document] = []
-for title, text in zip(titles, texts):
- documents.append(
- Document(
- text=text,
- meta={
- "name": title or ""
- }
- )
- )
-```
-
-Here we initialize the FAISSDocumentStore, DensePassageRetriever and RAGenerator.
-FAISS is chosen here since it is optimized vector storage.
-
-
-```python
-# Initialize FAISS document store.
-# Set `return_embedding` to `True`, so generator doesn't have to perform re-embedding
-document_store = FAISSDocumentStore(
- faiss_index_factory_str="Flat",
- return_embedding=True
-)
-
-# Initialize DPR Retriever to encode documents, encode question and query documents
-retriever = DensePassageRetriever(
- document_store=document_store,
- query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
- passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
- use_gpu=True,
- embed_title=True,
-)
-
-# Initialize RAG Generator
-generator = RAGenerator(
- model_name_or_path="facebook/rag-token-nq",
- use_gpu=True,
- top_k=1,
- max_length=200,
- min_length=2,
- embed_title=True,
- num_beams=2,
-)
-```
-
-We write documents to the DocumentStore, first by deleting any remaining documents then calling `write_documents()`.
-The `update_embeddings()` method uses the retriever to create an embedding for each document.
-
-
-
-```python
-# Delete existing documents in documents store
-document_store.delete_all_documents()
-
-# Write documents to document store
-document_store.write_documents(documents)
-
-# Add documents embeddings to index
-document_store.update_embeddings(
- retriever=retriever
-)
-```
-
-Here are our questions:
-
-
-```python
-QUESTIONS = [
- "who got the first nobel prize in physics",
- "when is the next deadpool movie being released",
- "which mode is used for short wave broadcast service",
- "who is the owner of reading football club",
- "when is the next scandal episode coming out",
- "when is the last time the philadelphia won the superbowl",
- "what is the most current adobe flash player version",
- "how many episodes are there in dragon ball z",
- "what is the first step in the evolution of the eye",
- "where is gall bladder situated in human body",
- "what is the main mineral in lithium batteries",
- "who is the president of usa right now",
- "where do the greasers live in the outsiders",
- "panda is a national animal of which country",
- "what is the name of manchester united stadium",
-]
-```
-
-Now let's run our system!
-The retriever will pick out a small subset of documents that it finds relevant.
-These are used to condition the generator as it generates the answer.
-What it should return then are novel text spans that form and answer to your question!
-
-
-```python
-# Now generate an answer for each question
-for question in QUESTIONS:
- # Retrieve related documents from retriever
- retriever_results = retriever.retrieve(
- query=question
- )
-
- # Now generate answer from question and retrieved documents
- predicted_result = generator.predict(
- query=question,
- documents=retriever_results,
- top_k=1
- )
-
- # Print you answer
- answers = predicted_result["answers"]
- print(f'Generated answer is \'{answers[0]["answer"]}\' for the question = \'{question}\'')
-```
-
-
-```python
-# Or alternatively use the Pipeline class
-from haystack.pipeline import GenerativeQAPipeline
-
-pipe = GenerativeQAPipeline(generator=generator, retriever=retriever)
-for question in QUESTIONS:
- res = pipe.run(query=question, top_k_generator=1, top_k_retriever=5)
- print(res)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/8.md b/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/8.md
deleted file mode 100644
index f630573d2..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/8.md
+++ /dev/null
@@ -1,215 +0,0 @@
----
-title: "Tutorial 8"
-metaTitle: "Preprocessing"
-metaDescription: ""
-slug: "/docs/tutorial8"
-date: "2021-01-08"
-id: "tutorial8md"
----
-
-# Preprocessing
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial8_Preprocessing.ipynb)
-
-Haystack includes a suite of tools to extract text from different file types, normalize white space
-and split text into smaller pieces to optimize retrieval.
-These data preprocessing steps can have a big impact on the systems performance and effective handling of data is key to getting the most out of Haystack.
-
-Ultimately, Haystack expects data to be provided as a list documents in the following dictionary format:
-``` python
-docs = [
- {
- 'text': DOCUMENT_TEXT_HERE,
- 'meta': {'name': DOCUMENT_NAME, ...}
- }, ...
-]
-```
-
-This tutorial will show you all the tools that Haystack provides to help you cast your data into this format.
-
-
-```python
-# Let's start by installing Haystack
-
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-!wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.03.tar.gz
-!tar -xvf xpdf-tools-linux-4.03.tar.gz && sudo cp xpdf-tools-linux-4.03/bin64/pdftotext /usr/local/bin
-```
-
-
-```python
-# Here are the imports we need
-
-from haystack.file_converter.txt import TextConverter
-from haystack.file_converter.pdf import PDFToTextConverter
-from haystack.file_converter.docx import DocxToTextConverter
-
-from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
-from haystack.preprocessor.preprocessor import PreProcessor
-```
-
-
-```python
-# This fetches some sample files to work with
-
-doc_dir = "data/preprocessing_tutorial"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/preprocessing_tutorial.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-## Converters
-
-Haystack's converter classes are designed to help you turn files on your computer into the documents
-that can be processed by the Haystack pipeline.
-There are file converters for txt, pdf, docx files as well as a converter that is powered by Apache Tika.
-
-
-```python
-# Here are some examples of how you would use file converters
-
-converter = TextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_txt = converter.convert(file_path="data/preprocessing_tutorial/classics.txt", meta=None)
-
-converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_pdf = converter.convert(file_path="data/preprocessing_tutorial/bert.pdf", meta=None)
-
-converter = DocxToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_docx = converter.convert(file_path="data/preprocessing_tutorial/heavy_metal.docx", meta=None)
-
-```
-
-
-```python
-# Haystack also has a convenience function that will automatically apply the right converter to each file in a directory.
-
-all_docs = convert_files_to_dicts(dir_path="data/preprocessing_tutorial")
-```
-
-## PreProcessor
-
-The PreProcessor class is designed to help you clean text and split text into sensible units.
-File splitting can have a very significant impact on the system's performance and is absolutely mandatory for Dense Passage Retrieval models.
-In general, we recommend you split the text from your files into small documents of around 100 words for dense retrieval methods
-and no more than 10,000 words for sparse methods.
-Have a look at the [Preprocessing](https://haystack.deepset.ai/docs/latest/preprocessingmd)
-and [Optimization](https://haystack.deepset.ai/docs/latest/optimizationmd) pages on our website for more details.
-
-
-```python
-# This is a default usage of the PreProcessor.
-# Here, it performs cleaning of consecutive whitespaces
-# and splits a single large document into smaller documents.
-# Each document is up to 1000 words long and document breaks cannot fall in the middle of sentences
-# Note how the single document passed into the document gets split into 5 smaller documents
-
-preprocessor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=False,
- split_by="word",
- split_length=100,
- split_respect_sentence_boundary=True
-)
-docs_default = preprocessor.process(doc_txt)
-print(f"n_docs_input: 1\nn_docs_output: {len(docs_default)}")
-```
-
-## Cleaning
-
-- `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
-- `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
-- `clean_header_footer` will remove any long header or footer texts that are repeated on each page
-
-## Splitting
-By default, the PreProcessor will respect sentence boundaries, meaning that documents will not start or end
-midway through a sentence.
-This will help reduce the possibility of answer phrases being split between two documents.
-This feature can be turned off by setting `split_respect_sentence_boundary=False`.
-
-
-```python
-# Not respecting sentence boundary vs respecting sentence boundary
-
-preprocessor_nrsb = PreProcessor(split_respect_sentence_boundary=False)
-docs_nrsb = preprocessor_nrsb.process(doc_txt)
-
-print("RESPECTING SENTENCE BOUNDARY")
-end_text = docs_default[0]["text"][-50:]
-print("End of document: \"..." + end_text + "\"")
-print()
-print("NOT RESPECTING SENTENCE BOUNDARY")
-end_text_nrsb = docs_nrsb[0]["text"][-50:]
-print("End of document: \"..." + end_text_nrsb + "\"")
-```
-
-A commonly used strategy to split long documents, especially in the field of Question Answering,
-is the sliding window approach. If `split_length=10` and `split_overlap=3`, your documents will look like this:
-
-- doc1 = words[0:10]
-- doc2 = words[7:17]
-- doc3 = words[14:24]
-- ...
-
-You can use this strategy by following the code below.
-
-
-```python
-# Sliding window approach
-
-preprocessor_sliding_window = PreProcessor(
- split_overlap=3,
- split_length=10,
- split_respect_sentence_boundary=False
-)
-docs_sliding_window = preprocessor_sliding_window.process(doc_txt)
-
-doc1 = docs_sliding_window[0]["text"][:200]
-doc2 = docs_sliding_window[1]["text"][:100]
-doc3 = docs_sliding_window[2]["text"][:100]
-
-print("Document 1: \"" + doc1 + "...\"")
-print("Document 2: \"" + doc2 + "...\"")
-print("Document 3: \"" + doc3 + "...\"")
-```
-
-## Bringing it all together
-
-
-```python
-all_docs = convert_files_to_dicts(dir_path="data/preprocessing_tutorial")
-preprocessor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=False,
- split_by="word",
- split_length=100,
- split_respect_sentence_boundary=True
-)
-nested_docs = [preprocessor.process(d) for d in all_docs]
-docs = [d for x in nested_docs for d in x]
-
-print(f"n_files_input: {len(all_docs)}\nn_docs_output: {len(docs)}")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
-
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/9.md b/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/9.md
deleted file mode 100644
index 366b7487e..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/tutorials/tutorials/9.md
+++ /dev/null
@@ -1,250 +0,0 @@
----
-title: "Tutorial 9"
-metaTitle: "Training a Dense Passage Retrieval model"
-metaDescription: ""
-slug: "/docs/tutorial9"
-date: "2021-01-08"
-id: "tutorial9md"
----
-
-# Training Your Own "Dense Passage Retrieval" Model
-
-[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial9_DPR_training.ipynb)
-
-Haystack contains all the tools needed to train your own Dense Passage Retrieval model.
-This tutorial will guide you through the steps required to create a retriever that is specifically tailored to your domain.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest master of Haystack
-!pip install grpcio-tools==1.34.1
-!pip install git+https://github.com/deepset-ai/haystack.git
-```
-
-
-```python
-# Here are some imports that we'll need
-
-from haystack.retriever.dense import DensePassageRetriever
-from haystack.preprocessor.utils import fetch_archive_from_http
-from haystack.document_store.memory import InMemoryDocumentStore
-```
-
-## Training Data
-
-DPR training performed using Information Retrieval data.
-More specifically, you want to feed in pairs of queries and relevant documents.
-
-To train a model, we will need a dataset that has the same format as the original DPR training data.
-Each data point in the dataset should have the following dictionary structure.
-
-``` python
- {
- "dataset": str,
- "question": str,
- "answers": list of str
- "positive_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- "negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- "hard_negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
- }
-```
-
-`positive_ctxs` are context passages which are relevant to the query.
-In some datasets, queries might have more than one positive context
-in which case you can set the `num_positives` parameter to be higher than the default 1.
-Note that `num_positives` needs to be lower or equal to the minimum number of `positive_ctxs` for queries in your data.
-If you have an unequal number of positive contexts per example,
-you might want to generate some soft labels by retrieving similar contexts which contain the answer.
-
-DPR is standardly trained using a method known as in-batch negatives.
-This means that positive contexts for a given query are treated as negative contexts for the other queries in the batch.
-Doing so allows for a high degree of computational efficiency, thus allowing the model to be trained on large amounts of data.
-
-`negative_ctxs` is not actually used in Haystack's DPR training so we recommend you set it to an empty list.
-They were used by the original DPR authors in an experiment to compare it against the in-batch negatives method.
-
-`hard_negative_ctxs` are passages that are not relevant to the query.
-In the original DPR paper, these are fetched using a retriever to find the most relevant passages to the query.
-Passages which contain the answer text are filtered out.
-
-If you'd like to convert your SQuAD format data into something that can train a DPR model,
-check out the utility script at [`haystack/retriever/squad_to_dpr.py`](https://github.com/deepset-ai/haystack/blob/master/haystack/retriever/squad_to_dpr.py)
-
-## Using Question Answering Data
-
-Question Answering datasets can sometimes be used as training data.
-Google's Natural Questions dataset, is sufficiently large
-and contains enough unique passages, that it can be converted into a DPR training set.
-This is done simply by considering answer containing passages as relevant documents to the query.
-
-The SQuAD dataset, however, is not as suited to this use case since its question and answer pairs
-are created on only a very small slice of wikipedia documents.
-
-## Download Original DPR Training Data
-
-WARNING: These files are large! The train set is 7.4GB and the dev set is 800MB
-
-We can download the original DPR training data with the following cell.
-Note that this data is probably only useful if you are trying to train from scratch.
-
-
-```python
-# Download original DPR data
-# WARNING: the train set is 7.4GB and the dev set is 800MB
-
-doc_dir = "data/dpr_training/"
-
-s3_url_train = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-train.json.gz"
-s3_url_dev = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz"
-
-fetch_archive_from_http(s3_url_train, output_dir=doc_dir + "train/")
-fetch_archive_from_http(s3_url_dev, output_dir=doc_dir + "dev/")
-```
-
-## Option 1: Training DPR from Scratch
-
-The default variables that we provide below are chosen to train a DPR model from scratch.
-Here, both passage and query embedding models are initialized using BERT base
-and the model is trained using Google's Natural Questions dataset (in a format specialised for DPR).
-
-If you are working in a language other than English,
-you will want to initialize the passage and query embedding models with a language model that supports your language
-and also provide a dataset in your language.
-
-
-```python
-# Here are the variables to specify our training data, the models that we use to initialize DPR
-# and the directory where we'll be saving the model
-
-doc_dir = "data/dpr_training/"
-
-train_filename = "train/biencoder-nq-train.json"
-dev_filename = "dev/biencoder-nq-dev.json"
-
-query_model = "bert-base-uncased"
-passage_model = "bert-base-uncased"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Option 2: Finetuning DPR
-
-If you have your own domain specific question answering or information retrieval dataset,
-you might instead be interested in finetuning a pretrained DPR model.
-In this case, you would initialize both query and passage models using the original pretrained model.
-You will want to load something like this set of variables instead of the ones above
-
-
-```python
-# Here are the variables you might want to use instead of the set above
-# in order to perform pretraining
-
-doc_dir = "PATH_TO_YOUR_DATA_DIR"
-train_filename = "TRAIN_FILENAME"
-dev_filename = "DEV_FILENAME"
-
-query_model = "facebook/dpr-question_encoder-single-nq-base"
-passage_model = "facebook/dpr-ctx_encoder-single-nq-base"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Initialization
-
-Here we want to initialize our model either with plain language model weights for training from scratch
-or else with pretrained DPR weights for finetuning.
-We follow the [original DPR parameters](https://github.com/facebookresearch/DPR#best-hyperparameter-settings)
-for their max passage length but set max query length to 64 since queries are very rarely longer.
-
-
-```python
-## Initialize DPR model
-
-retriever = DensePassageRetriever(
- document_store=InMemoryDocumentStore(),
- query_embedding_model=query_model,
- passage_embedding_model=passage_model,
- max_seq_len_query=64,
- max_seq_len_passage=256
-)
-```
-
-## Training
-
-Let's start training and save our trained model!
-
-On a V100 GPU, you can fit up to batch size 16 so we set gradient accumulation steps to 8 in order
-to simulate the batch size 128 of the original DPR experiment.
-
-When `embed_title=True`, the document title is prepended to the input text sequence with a `[SEP]` token
-between it and document text.
-
-When training from scratch with the above variables, 1 epoch takes around an hour and we reached the following performance:
-
-```
-loss: 0.046580662854042276
-task_name: text_similarity
-acc: 0.992524064068483
-f1: 0.8804297774366846
-acc_and_f1: 0.9364769207525838
-average_rank: 0.19631619339984652
-report:
- precision recall f1-score support
-
-hard_negative 0.9961 0.9961 0.9961 201887
- positive 0.8804 0.8804 0.8804 6515
-
- accuracy 0.9925 208402
- macro avg 0.9383 0.9383 0.9383 208402
- weighted avg 0.9925 0.9925 0.9925 208402
-
-```
-
-
-```python
-# Start training our model and save it when it is finished
-
-retriever.train(
- data_dir=doc_dir,
- train_filename=train_filename,
- dev_filename=dev_filename,
- test_filename=dev_filename,
- n_epochs=1,
- batch_size=16,
- grad_acc_steps=8,
- save_dir=save_dir,
- evaluate_every=3000,
- embed_title=True,
- num_positives=1,
- num_hard_negatives=1
-)
-```
-
-## Loading
-
-Loading our newly trained model is simple!
-
-
-```python
-reloaded_retriever = DensePassageRetriever.load(load_dir=save_dir, document_store=None)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://apply.workable.com/deepset/)
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/annotation.md b/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/annotation.md
deleted file mode 100644
index ed29d18c8..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/annotation.md
+++ /dev/null
@@ -1,47 +0,0 @@
----
-title: "Annotation Tool"
-metaTitle: "Annotation Tool"
-metaDescription: ""
-slug: "/docs/annotation"
-date: "2020-09-03"
-id: "annotationmd"
----
-
-# Annotation Tool
-
-- Create labels with different techniques: Come up with questions (+ answers) while reading passages (SQuAD style) or have a set of predefined questions and look for answers in the document (~ Natural Questions).
-- Structure your work via organizations, projects, users
-- Upload your documents or import labels from an existing SQuAD-style dataset
-- Export your labels in SQuAD Format
-
-
-
-# Hosted version
- Signup here: [Haystack Annotation Tool](https://annotate.deepset.ai/login)
-
-# Local version (Docker)
-
-1. Configure credentials & database in the [`docker-compose.yml`](https://github.com/deepset-ai/haystack/blob/master/annotation_tool/docker-compose.yml):
-
-The credentials should match in database image and application configuration.
-
- DEFAULT_ADMIN_EMAIL: "example@example.com"
- DEFAULT_ADMIN_PASSWORD: "DEMO-PASSWORD"
-
- PROD_DB_NAME: "databasename"
- PROD_DB_USERNAME: "somesafeuser"
- PROD_DB_PASSWORD: "somesafepassword"
-
-
- POSTGRES_USER: "somesafeuser"
- POSTGRES_PASSWORD: "somesafepassword"
- POSTGRES_DB: "databasename"
-
-
-2. Run docker-compose by executing `docker-compose up`.
-
-
-3. The UI should be available at `localhost:7001`.
-
-# Usage
-The manual (of a slightly earlier version) can be found [here](https://drive.google.com/file/d/1Wv3OIC0Z7ibHIzOm9Xw_r0gjTFmpl-33/view). While it doesn't include all latest features, the basic workflow and tips for label quality are still the same.
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/domain_adaptation.md b/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/domain_adaptation.md
deleted file mode 100644
index 9bd415e61..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/domain_adaptation.md
+++ /dev/null
@@ -1,157 +0,0 @@
----
-title: "Domain Adaptation"
-metaTitle: "Domain Adaptation"
-metaDescription: ""
-slug: "/docs/domain_adaptation"
-date: "2020-09-03"
-id: "domain_adaptationmd"
----
-
-# Domain Adaptation
-
-## Generalisation
-
-In our experience, language models trained on SQuAD show very strong general question answering capabilities.
-Though SQuAD is composed entirely of Wikipedia articles, these models are flexible enough to deal with many different styles of text.
-
-Before trying to adapt these models to your domain, we’d recommend trying one of the off the shelf models.
-We’ve found that these models are often flexible enough for a wide range of use cases.
-
-
-
-**Intuition**
-
-Most people probably don’t know what an HP Valve is.
-But you don’t always need to know what a HP Valve is to answer “What is connected to a HP Valve?”
-The answer might be there in plain language.
-In the same way, many QA models have a good enough grasp of language to answer questions about concepts in an unseen domain.
-
-
-
-
-## Finetuning
-
-Any model that can be loaded into Haystack can also be finetuned within Haystack.
-Simply provide the domain specific dataset and call `Reader.train()` on an initialised model.
-
-```
-reader.train(data_dir=train_data,
- train_filename="dev-v2.0.json",
- n_epochs=1,
- save_dir="my_model")
-```
-
-At the end of training, the finetuned model will be saved in the specified `save_dir` and can be loaded as a `Reader`.
-
-
-
-**Recommendation**
-
-See Tutorial 2 for a runnable example of this process.
-If you’re interested in measuring how much your model has improved,
-please also check out Tutorial 5 which walks through the steps needed to perform evaluation.
-
-
-
-## Generating Labels
-
-Using our [Haystack Annotate tool](https://annotate.deepset.ai/login) (Beta),
-you can easily create a labelled dataset using your own documents featuring your own question/ answer pairs.
-
-
-
-
-
-Features include:
-
-
-* Structured workspaces via organisations, projects and users
-
-
-* Easy upload of your own documents and labels in a variety of formats (txt, pdf, SQuAD style)
-
-
-* Export of labels to be used directly in Haystack
-
-Annotate also supports two different workflows:
-
-
-* Think up questions and answers while reading passages (SQuAD style)
-
-
-* Have a set of predefined questions and look for answers in the document (~ Natural Questions style)
-
-## User Feedback
-
-A simpler and faster process to finetune models to your domain is to utilise user feedback.
-Dedicated annotation work can be costly and time consuming
-but user feedback is an efficient alternative since it allows for labels to be generated by users, for users,
-all while the system is already in production.
-We, for example, have used a simple thumbs up / down system in our demos to allow
-users to give feedback.
-
-
-
-Through the Rest API, users can annotate each Haystack result as being either:
-
-* Correct
-* Correct document but wrong text
-* Wrong document and wrong text
-
-To get started, follow these steps:
-
-* Start up the REST API
- * The simplest way to do this is to call `docker-compose up` from the root directory of the Haystack repository
- * Alternatively, run `gunicorn rest_api.application:app -b 0.0.0.0:8000 -k uvicorn.workers.UvicornWorker -t 300`
-* Make a POST request to the `doc-qa-feedback` endpoint with the following payload:
-```
-{
- "question": "string",
- "is_correct_answer": true,
- "document_id": "string",
- "model_id": 0,
- "is_correct_document": true,
- "answer": "string",
- "offset_start_in_doc": 0
-}
-```
-To fill out all these values, you can use the response from an executed search request on the `doc-qa` endpoint.
-
-* Export your labels in SQuAD format by making a GET request to the `export-doc-qa-feedback` endpoint
-``` python
-# SQUAD format
-{
- "data": [
- {
- "title": "Normans",
- "paragraphs": [
- {
- "context": "The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the...",
- "qas": [
- {
- "question": "In what country is Normandy located?",
- "id": "56ddde6b9a695914005b9628",
- "answers": [
- {
- "text": "France",
- "answer_start": 159
- }
- ]
- }
- ]
- }
- ]
- }
- ]
-}
-```
-
-User feedback labelling also works with FAQ style QA.
-Refer to the Swagger API documentation (http://127.0.0.1:8000/docs) for more details.
-
-This labelled data can then be used to
-augment the training data and enable `Reader` finetuning.
-Alternatively, they can also be used to form an evaluation set to
-measure the performance of trained models,
-or monitor how well the model is doing in a live environment.
-
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/intro.md b/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/intro.md
deleted file mode 100644
index 12b405570..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/intro.md
+++ /dev/null
@@ -1,67 +0,0 @@
----
-title: "What is Haystack"
-metaTitle: "What is Haystack"
-metaDescription: ""
-slug: "/docs/intro"
-date: "2020-09-03"
-id: "intromd"
----
-
-# What is Haystack?
-
-Haystack is an **open-source framework** for building **search systems** that work intelligently over large document collections.
-Recent advances in NLP have enabled the application of question answering, retrieval and summarization to real world settings
-and Haystack is designed to be the bridge between research and industry.
-
-
-* **Latest NLP models**: Utilize all transformer based models (BERT, RoBERTa, MiniLM, DPR ...) and smoothly switch when new ones get published
-
-* **Flexible databases**: Load data into and query from a range of databases such as Elasticsearch, Milvus, FAISS, SQL and more
-
-* **Scalability**: Production-ready deployments that scale to millions of documents
-
-* **End-to-End**: All tooling you need to implement, evaluate, improve and run a search system
-
-* **Domain adaptation**: Fine-tune models to your own domain & improve them continuously via user feedback
-
-## Retriever-Reader
-
-The most common system built with Haystack is the Retriever-Reader Pipeline which is designed to optimize for both
-speed and performance on the task of Open Domain Question Answering.
-In practice, this is a great back bone for creating a search system that can handle detailed full sentence queries.
-
-
-
-
-**Readers**, also known as Open-Domain QA systems in Machine Learning speak,
-are powerful models that do close analysis of documents and perform the core task of question answering.
-The Readers in Haystack are trained from the latest transformer based language models and can be significantly sped up using GPU acceleration.
-However, it is not currently feasible to use the Reader directly on large collection of documents.
-
-
-
-The **Retriever** assists the Reader by acting as a lightweight filter that reduces the number of documents that the Reader has to process.
-It does this by:
-
-
-* Scanning through all documents in the database
-
-
-* Quickly identifying the relevant and dismissing the irrelevant
-
-
-* Passing on only a small candidate set of documents to the Reader
-
-Current methods fall into one of the two categories:
-
-
-* sparse
- * keyword based
- * fast indexing and querying
- * e.g. BM25
-
-
-* dense
- * neural network based
- * computationally heavy indexing but fast querying
- * e.g. Dense Passage Retrieval
diff --git a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/languages.md b/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/languages.md
deleted file mode 100644
index 14f551cab..000000000
--- a/src/pages/docs/versions/master/v0.9.0/site/en/usage/usage/languages.md
+++ /dev/null
@@ -1,333 +0,0 @@
----
-title: "Languages Other Than English"
-metaTitle: "Languages Other Than English"
-metaDescription: ""
-slug: "/docs/languages"
-date: "2020-11-05"
-id: "languagesmd"
----
-
-# Languages Other Than English
-
-Haystack is well suited to open-domain QA on languages other than English.
-While our defaults are tuned for English,
-you will find some tips and tricks here for using Haystack in your language.
-
-##Preprocessor
-
-
-
-**Note**
-
-This feature will be implemented by [this PR.](https://github.com/deepset-ai/haystack/pull/1160)
-
-
-
-The PreProcessor's sentence tokenization is language specific.
-If you are using the PreProcessor on a language other than English,
-make sure to set the `language` argument when initializing it.
-
-##Retrievers
-
-The sparse retriever methods themselves(BM25, TF-IDF) are language agnostic.
-Their only requirement is that the text be split into words.
-The ElasticsearchDocumentStore relies on an analyzer to impose word boundaries,
-but also to handle punctuation, casing and stop words.
-
-The default analyzer is an English analyzer.
-While it can still work decently for a large range of languages,
-you will want to set it to your language's analyzer for optimal performance.
-In some cases, such as with Thai, the default analyzer is completely incompatible.
-See [this page](https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-lang-analyzer.html)
-for the full list of language specific analyzers.
-
-```python
-from haystack.document_store import ElasticsearchDocumentStore
-
-document_store = ElasticsearchDocumentStore(analyzer="thai")
-```
-
-The models used in dense retrievers are language specific.
-Be sure to check language of the model used in your EmbeddingRetriever.
-The default model that is loaded in the DensePassageRetriever is for English.
-
-We have created a [German DensePassageRetriever model](https://deepset.ai/germanquad) and know other teams who work on further languages.
-If you have a language model and a question answering dataset in your own language, you can also train a DPR model using Haystack!
-Below is a simplified example.
-See [our tutorial](/docs/v0.9.0/tutorial9md) and also the [API reference](/docs/v0.9.0/apiretrievermd#train) for `DensePassageRetriever.train()` for more details.
-
-```python
-from haystack.retriever import DensePassageRetriever
-
-dense_passage_retriever = DensePassageRetriever(document_store)
-dense_passage_retriever.train(self,
- data_dir: str,
- train_filename: str,
- dev_filename: str = None,
- test_filename: str = None,
- batch_size: int = 16,
- embed_title: bool = True,
- num_hard_negatives: int = 1,
- n_epochs: int = 3)
-```
-
-##Readers
-
-While models are comparatively more performant on English,
-thanks to a wealth of available English training data,
-there are a couple QA models that are directly usable in Haystack.
-
-
-
-
-