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index 0000000..aee0d23
--- /dev/null
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+name = "ironhack-lab-intro-rag"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
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+ "chromadb>=1.3.5",
+ "jupyter>=1.1.1",
+ "langchain>=1.0.8",
+ "langchain-chroma>=1.0.0",
+ "langchain-community>=0.4.1",
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+ "openai>=2.8.1",
+ "pypdf>=6.3.0",
+ "python-dotenv>=1.2.1",
+ "sentence-transformers>=5.1.2",
+ "termcolor>=3.2.0",
+ "tiktoken>=0.12.0",
+]
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index 0000000..18c75fa
--- /dev/null
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diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index e3a225a..da390cd 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -1,709 +1,872 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "RsnCPbdkxYZd"
- },
- "source": [
- "\n",
- "
Self-Guided Lab: Retrieval-Augmented Generation (RAGs)
\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "tZp4BQAVxYZj"
- },
- "source": [
- "\n",
- "
![\"NLP](\"https://media4.giphy.com/media/v1.Y2lkPTc5MGI3NjExZ3FsdzRveTBrenMxM3VnbDMwaTJxN2NnZm50aGFibXk1NzNnY2Q0MCZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/LR5ZBwZHv02lmpVoEU/giphy.gif\")
\n",
- "
Data Storage & Retrieval
\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "QW5UOI8ZxYZp"
- },
- "source": [
- "PyPDFLoader
\n",
- "\n",
- "`PyPDFLoader` is a lightweight Python library designed to streamline the process of loading and parsing PDF documents for text processing tasks. It is particularly useful in Retrieval-Augmented Generation workflows where text extraction from PDFs is required.\n",
- "\n",
- "- **What Does PyPDFLoader Do?**\n",
- " - Extracts text from PDF files, retaining formatting and layout.\n",
- " - Simplifies the preprocessing of document-based datasets.\n",
- " - Supports efficient and scalable loading of large PDF collections.\n",
- "\n",
- "- **Key Features:**\n",
- " - Compatible with popular NLP libraries and frameworks.\n",
- " - Handles multi-page PDFs and embedded images (e.g., OCR-compatible setups).\n",
- " - Provides flexible configurations for structured text extraction.\n",
- "\n",
- "- **Use Cases:**\n",
- " - Preparing PDF documents for retrieval-based systems in RAGs.\n",
- " - Automating the text extraction pipeline for document analysis.\n",
- " - Creating datasets from academic papers, technical manuals, and reports.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "%pip install langchain langchain_community pypdf\n",
- "%pip install termcolor langchain_openai langchain-huggingface sentence-transformers chromadb langchain_chroma tiktoken openai python-dotenv\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "6heKZkQUxYZr"
- },
- "outputs": [],
- "source": [
- "import os\n",
- "from langchain.document_loaders import PyPDFLoader\n",
- "from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter\n",
- "import warnings\n",
- "warnings.filterwarnings('ignore')\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "sRS44B2XxYZs",
- "vscode": {
- "languageId": "plaintext"
- }
- },
- "source": [
- "Loading the Documents
"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "cuREtJRixYZt"
- },
- "outputs": [],
- "source": [
- "# File path for the document\n",
- "\n",
- "file_path = \"LAB/ai-for-everyone.pdf\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "pz_8SOLxxYZt"
- },
- "source": [
- "Documents into pages
\n",
- "\n",
- "The `PyPDFLoader` library allows efficient loading and splitting of PDF documents into smaller, manageable parts for NLP tasks.\n",
- "\n",
- "This functionality is particularly useful in workflows requiring granular text processing, such as Retrieval-Augmented Generation (RAG).\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "_b5Z_45UxYZu",
- "outputId": "a600d69f-14fe-4492-f236-97261d6ff36c"
- },
- "outputs": [],
- "source": [
- "# Load and split the document\n",
- "loader = PyPDFLoader(file_path)\n",
- "pages = loader.load_and_split()\n",
- "len(pages)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "wt50NRQaxYZv"
- },
- "source": [
- "Pages into Chunks
\n",
- "\n",
- "\n",
- "#### RecursiveCharacterTextSplitter in LangChain\n",
- "\n",
- "The `RecursiveCharacterTextSplitter` is the **recommended splitter** in LangChain when you want to break down long documents into smaller, semantically meaningful chunks — especially useful in **RAG pipelines**, where clean context chunks lead to better LLM responses.\n",
- "\n",
- "#### Parameters\n",
- "\n",
- "| Parameter | Description |\n",
- "|-----------------|-----------------------------------------------------------------------------|\n",
- "| `chunk_size` | The **maximum number of characters** allowed in a chunk (e.g., `1000`). |\n",
- "| `chunk_overlap` | The number of **overlapping characters** between consecutive chunks (e.g., `200`). This helps preserve context continuity. |\n",
- "\n",
- "#### How it works\n",
- "`RecursiveCharacterTextSplitter` attempts to split the text **intelligently**, trying the following separators in order:\n",
- "1. Paragraphs (`\"\\n\\n\"`)\n",
- "2. Lines (`\"\\n\"`)\n",
- "3. Sentences or words (`\" \"`)\n",
- "4. Individual characters (as a last resort)\n",
- "\n",
- "This makes it ideal for handling **natural language documents**, such as PDFs, articles, or long reports, without breaking sentences or paragraphs in awkward ways.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "text_splitter = RecursiveCharacterTextSplitter(\n",
- " chunk_size=1000,\n",
- " chunk_overlap=200\n",
- ")\n",
- "chunks = text_splitter.split_documents(pages)\n",
- "\n",
- "len(chunks)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Alternative: CharacterTextSplitter\n",
- "\n",
- "`CharacterTextSplitter` is a simpler splitter that breaks text into chunks based **purely on character count**, without trying to preserve any natural language structure.\n",
- "\n",
- "##### Example:\n",
- "```python\n",
- "from langchain.text_splitter import CharacterTextSplitter\n",
- "\n",
- "text_splitter = CharacterTextSplitter(\n",
- " chunk_size=1000,\n",
- " chunk_overlap=200\n",
- ")\n",
- "````\n",
- "\n",
- "This method is faster and more predictable but may split text in the middle of a sentence or paragraph, which can hurt performance in downstream tasks like retrieval or QA.\n",
- "\n",
- "---\n",
- "\n",
- "#### Comparison Table\n",
- "\n",
- "| Feature | RecursiveCharacterTextSplitter | CharacterTextSplitter |\n",
- "| ------------------------------ | ------------------------------ | ------------------------- |\n",
- "| Structure-aware splitting | Yes | No |\n",
- "| Preserves sentence/paragraphs | Yes | No |\n",
- "| Risk of splitting mid-sentence | Minimal | High |\n",
- "| Ideal for RAG/document QA | Highly recommended | Only if structured text |\n",
- "| Performance speed | Slightly slower | Faster |\n",
- "\n",
- "---\n",
- "\n",
- "#### Recommendation\n",
- "\n",
- "Use `RecursiveCharacterTextSplitter` for most real-world document processing tasks, especially when building RAG pipelines or working with structured natural language content like PDFs or articles."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Best Practices for Choosing Chunk Size in RAG\n",
- "\n",
- "### Best Practices for Chunk Size in RAG\n",
- "\n",
- "| Factor | Recommendation |\n",
- "| ---------------------------| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n",
- "| **LLM context limit** | Choose a chunk size that lets you retrieve multiple chunks **without exceeding the model’s token limit**. For example, GPT-4o supports 128k tokens, but with GPT-3.5 (16k) or GPT-4 (32k), keep it modest. |\n",
- "| **Chunk size (in characters)** | Typically: **500–1,000 characters** per chunk → ~75–200 tokens. This fits well for retrieval + prompt without context overflow. |\n",
- "| **Chunk size (in tokens)** | If using token-based splitter (e.g. `TokenTextSplitter`): aim for **100–300 tokens** per chunk. |\n",
- "| **Chunk overlap** | Use **overlap of 10–30%** (e.g., 100–300 characters or ~50 tokens) to preserve context across chunk boundaries and avoid cutting off important ideas mid-sentence. |\n",
- "| **Document structure** | Use **`RecursiveCharacterTextSplitter`** to preserve semantic boundaries (paragraphs, sentences) instead of arbitrary cuts. |\n",
- "| **Task type** | For **question answering**, smaller chunks (~500–800 chars) reduce noise.
For **summarization**, slightly larger chunks (~1000–1500) are OK. |\n",
- "| **Embedding model** | Some models (e.g., `text-embedding-3-large`) can handle long input. But still, smaller chunks give **finer-grained retrieval**, which improves relevance. |\n",
- "| **Query type** | If users ask **very specific questions**, small focused chunks are better. For broader queries, bigger chunks might help. |\n",
- "\n",
- "\n",
- "### Rule of Thumb\n",
- "\n",
- "| Use Case | Chunk Size | Overlap |\n",
- "| ------------------------| --------------- | ------- |\n",
- "| Factual Q&A | 500–800 chars | 100–200 |\n",
- "| Summarization | 1000–1500 chars | 200–300 |\n",
- "| Technical documents | 400–700 chars | 100–200 |\n",
- "| Long reports/books | 800–1200 chars | 200–300 |\n",
- "| Small LLMs (≤16k tokens) | ≤800 chars | 100–200 |\n",
- "\n",
- "\n",
- "### Avoid\n",
- "\n",
- "- Chunks >2000 characters: risks context overflow.\n",
- "- No overlap: may lose key information between chunks.\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Mg15RjVPxYZw"
- },
- "source": [
- "Embeddings
\n",
- "\n",
- "Embeddings transform text into dense vector representations, capturing semantic meaning and contextual relationships. They are essential for efficient document retrieval and similarity analysis.\n",
- "\n",
- "- **What are OpenAI Embeddings?**\n",
- " - Pre-trained embeddings like `text-embedding-3-large` generate high-quality vector representations for text.\n",
- " - Encapsulate semantic relationships in the text, enabling robust NLP applications.\n",
- "\n",
- "- **Key Features of `text-embedding-3-large`:**\n",
- " - Large-scale embedding model optimized for accuracy and versatility.\n",
- " - Handles diverse NLP tasks, including retrieval, classification, and clustering.\n",
- " - Ideal for applications with high-performance requirements.\n",
- "\n",
- "- **Benefits:**\n",
- " - Reduces the need for extensive custom training.\n",
- " - Provides state-of-the-art performance in retrieval-augmented systems.\n",
- " - Compatible with RAGs to create powerful context-aware models.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "L0xDxElwxYZw"
- },
- "outputs": [],
- "source": [
- "from langchain.embeddings import OpenAIEmbeddings\n",
- "from dotenv import load_dotenv"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "_WRIo3_0xYZx",
- "outputId": "78bfbbf3-9d25-4e31-bdbc-3e932e6bbfec"
- },
- "outputs": [],
- "source": [
- "load_dotenv()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "MNZfTng5xYZz",
- "outputId": "db1a7c85-ef9f-447e-92cd-9d097e959847"
- },
- "outputs": [],
- "source": [
- "api_key = os.getenv(\"OPENAI_API_KEY\")\n",
- "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-large\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "EsSA7RKvxYZz"
- },
- "source": [
- "ChromaDB
\n",
- "\n",
- "ChromaDB is a versatile vector database designed for efficiently storing and retrieving embeddings. It integrates seamlessly with embedding models to enable high-performance similarity search and context-based retrieval.\n",
- "\n",
- "### Workflow Overview:\n",
- "- **Step 1:** Generate embeddings using a pre-trained model (e.g., OpenAI's `text-embedding-3-large`).\n",
- "- **Step 2:** Store the embeddings in ChromaDB for efficient retrieval and similarity calculations.\n",
- "- **Step 3:** Use the stored embeddings to perform searches, matching, or context-based retrieval.\n",
- "\n",
- "### Key Features of ChromaDB:\n",
- "- **Scalability:** Handles large-scale datasets with optimized indexing and search capabilities.\n",
- "- **Speed:** Provides fast and accurate retrieval of embeddings for real-time applications.\n",
- "- **Integration:** Supports integration with popular frameworks and libraries for embedding generation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "brKe6wUgxYZ0"
- },
- "outputs": [],
- "source": [
- "from langchain.vectorstores import Chroma"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "VkjHR-RkxYZ0",
- "outputId": "bc11bda9-f283-457a-f584-5a06b95c4dd9"
- },
- "outputs": [],
- "source": [
- "db = Chroma.from_documents(chunks, embeddings, persist_directory=\"./chroma_db_LAB\")\n",
- "print(\"ChromaDB created with document embeddings.\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "27OdN1IVxYZ1"
- },
- "source": [
- "Retrieving Documents
\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Exercice1: Write a user question that someone might ask about your book’s topic or content."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "XiLv-TfrxYZ1"
- },
- "outputs": [],
- "source": [
- "user_question = \"\" # User question\n",
- "retrieved_docs = db.similarity_search(user_question, k=10) # k is the number of documents to retrieve"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "qgWsh50JxYZ1",
- "outputId": "c8640c5d-5955-471f-fdd2-37096f5f68c7"
- },
- "outputs": [],
- "source": [
- "# Display top results\n",
- "for i, doc in enumerate(retrieved_docs[:3]): # Display top 3 results\n",
- " print(f\"Document {i+1}:\\n{doc.page_content[36:1000]}\") # Display content"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "XuGK8gL6xYZ1"
- },
- "source": [
- "Preparing Content for GenAI
"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "2iB3lZqHxYZ2"
- },
- "outputs": [],
- "source": [
- "def _get_document_prompt(docs):\n",
- " prompt = \"\\n\"\n",
- " for doc in docs:\n",
- " prompt += \"\\nContent:\\n\"\n",
- " prompt += doc.page_content + \"\\n\\n\"\n",
- " return prompt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "2okzmuADxYZ2",
- "outputId": "0aa6cdca-188d-40e0-f5b4-8888d3549ea4"
- },
- "outputs": [],
- "source": [
- "# Generate a formatted context from the retrieved documents\n",
- "formatted_context = _get_document_prompt(retrieved_docs)\n",
- "print(\"Context formatted for GPT model.\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "qzIczQNTxYZ2"
- },
- "source": [
- "ChatBot Architecture
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Exercice2: Write a prompt that is relevant and tailored to the content and style of your book."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "tqxVh9s3xYZ3",
- "outputId": "97cca95d-4ab3-44d8-a76c-5713aad387d8"
- },
- "outputs": [],
- "source": [
- "prompt = f\"\"\"\n",
- "\n",
- "\n",
- "\"\"\"\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "0mjkQJ_ZxYZ3"
- },
- "outputs": [],
- "source": [
- "import openai"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Exercice3: Tune parameters like temperature, and penalties to control how creative, focused, or varied the model's responses are."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "ylypRWRlxYZ4"
- },
- "outputs": [],
- "source": [
- "# Set up GPT client and parameters\n",
- "client = openai.OpenAI()\n",
- "model_params = {\n",
- " 'model': 'gpt-4o',\n",
- " 'temperature': , # Increase creativity\n",
- " 'max_tokens': , # Allow for longer responses\n",
- " 'top_p': , # Use nucleus sampling\n",
- " 'frequency_penalty': , # Reduce repetition\n",
- " 'presence_penalty': # Encourage new topics\n",
- "}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "C8e942xDxYZ4"
- },
- "source": [
- "Response
\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "4eXZO4pIxYZ4"
- },
- "outputs": [],
- "source": [
- "messages = [{'role': 'user', 'content': prompt}]\n",
- "completion = client.chat.completions.create(messages=messages, **model_params, timeout=120)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "wLPAcchBxYZ5",
- "outputId": "976c7800-16ed-41fe-c4cf-58f60d3230d2"
- },
- "outputs": [],
- "source": [
- "answer = completion.choices[0].message.content\n",
- "print(answer)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "VXVNXPwLxYaT"
- },
- "source": [
- "![\"NLP](\"https://miro.medium.com/v2/resize:fit:824/1*GK56xmDIWtNQAD_jnBIt2g.png\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ldybhlqKxYaT"
- },
- "source": [
- "Cosine Similarity
\n",
- "\n",
- "**Cosine similarity** is a metric used to measure the alignment or similarity between two vectors, calculated as the cosine of the angle between them. It is the **most common metric used in RAG pipelines** for vector retrieval.. It provides a scale from -1 to 1:\n",
- "\n",
- "- **-1**: Vectors are completely opposite.\n",
- "- **0**: Vectors are orthogonal (uncorrelated or unrelated).\n",
- "- **1**: Vectors are identical.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "1c1I1TNhxYaT"
- },
- "source": [
- "![\"NLP](\"https://storage.googleapis.com/lds-media/images/cosine-similarity-vectors.original.jpg\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "EoEMdNgQxYaU"
- },
- "source": [
- "Keyword Highlighting
\n",
- "\n",
- "Highlighting important keywords helps users quickly understand the relevance of the retrieved text to their query."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "nCXL9Cz1xYaV"
- },
- "outputs": [],
- "source": [
- "from termcolor import colored"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "xwDyofY0xYaV"
- },
- "source": [
- "The `highlight_keywords` function is designed to highlight specific keywords within a given text. It replaces each keyword in the text with a highlighted version using the `colored` function from the `termcolor` library.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "9y3E0YWExYaV"
- },
- "outputs": [],
- "source": [
- "def highlight_keywords(text, keywords):\n",
- " for keyword in keywords:\n",
- " text = text.replace(keyword, colored(keyword, 'green', attrs=['bold']))\n",
- " return text"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Exercice4: add your keywords"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "i7SkWPpnxYaW",
- "outputId": "28e82563-edba-4b41-acad-ec27e5ba134f"
- },
- "outputs": [],
- "source": [
- "query_keywords = [] # add your keywords\n",
- "for i, doc in enumerate(retrieved_docs[:1]):\n",
- " snippet = doc.page_content[:200]\n",
- " highlighted = highlight_keywords(snippet, query_keywords)\n",
- " print(f\"Snippet {i+1}:\\n{highlighted}\\n{'-'*80}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "AhV_Jf_LxYaX"
- },
- "source": [
- "1. `query_keywords` is a list of keywords to be highlighted.\n",
- "2. The loop iterates over the first document in retrieved_docs.\n",
- "3. For each document, a snippet of the first 200 characters is extracted.\n",
- "4. The highlight_keywords function is called to highlight the keywords in the snippet.\n",
- "5. The highlighted snippet is printed along with a separator line."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "pBRKysAvxYaX"
- },
- "source": [
- "Bonus
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Qj25lCybxYaX"
- },
- "source": [
- "**Try loading one of your own PDF books and go through the steps again to explore how the pipeline works with your content**:\n"
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RsnCPbdkxYZd"
+ },
+ "source": [
+ "\n",
+ "
Self-Guided Lab: Retrieval-Augmented Generation (RAGs)
\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tZp4BQAVxYZj"
+ },
+ "source": [
+ "\n",
+ "
\n",
+ "
Data Storage & Retrieval
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "QW5UOI8ZxYZp"
+ },
+ "source": [
+ "PyPDFLoader
\n",
+ "\n",
+ "`PyPDFLoader` is a lightweight Python library designed to streamline the process of loading and parsing PDF documents for text processing tasks. It is particularly useful in Retrieval-Augmented Generation workflows where text extraction from PDFs is required.\n",
+ "\n",
+ "- **What Does PyPDFLoader Do?**\n",
+ " - Extracts text from PDF files, retaining formatting and layout.\n",
+ " - Simplifies the preprocessing of document-based datasets.\n",
+ " - Supports efficient and scalable loading of large PDF collections.\n",
+ "\n",
+ "- **Key Features:**\n",
+ " - Compatible with popular NLP libraries and frameworks.\n",
+ " - Handles multi-page PDFs and embedded images (e.g., OCR-compatible setups).\n",
+ " - Provides flexible configurations for structured text extraction.\n",
+ "\n",
+ "- **Use Cases:**\n",
+ " - Preparing PDF documents for retrieval-based systems in RAGs.\n",
+ " - Automating the text extraction pipeline for document analysis.\n",
+ " - Creating datasets from academic papers, technical manuals, and reports.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "lines_to_next_cell": 2
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/home/taranarmo/ironhack/week7/day5/ironhack-lab-intro-rag/.venv/bin/python3: No module named pip\n",
+ "Note: you may need to restart the kernel to use updated packages.\n",
+ "/home/taranarmo/ironhack/week7/day5/ironhack-lab-intro-rag/.venv/bin/python3: No module named pip\n",
+ "Note: you may need to restart the kernel to use updated packages.\n"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install langchain langchain_community pypdf\n",
+ "%pip install termcolor langchain_openai langchain-huggingface sentence-transformers chromadb langchain_chroma tiktoken openai python-dotenv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "id": "6heKZkQUxYZr",
+ "lines_to_next_cell": 2
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from langchain_community.document_loaders import PyPDFLoader\n",
+ "from langchain_text_splitters import CharacterTextSplitter, RecursiveCharacterTextSplitter\n",
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "sRS44B2XxYZs",
+ "vscode": {
+ "languageId": "plaintext"
}
- ],
- "metadata": {
- "colab": {
- "provenance": []
- },
- "kernelspec": {
- "display_name": "llm",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.10"
+ },
+ "source": [
+ "Loading the Documents
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "id": "cuREtJRixYZt"
+ },
+ "outputs": [],
+ "source": [
+ "# File path for the document\n",
+ "\n",
+ "file_path = \"ai-for-everyone.pdf\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pz_8SOLxxYZt"
+ },
+ "source": [
+ "Documents into pages
\n",
+ "\n",
+ "The `PyPDFLoader` library allows efficient loading and splitting of PDF documents into smaller, manageable parts for NLP tasks.\n",
+ "\n",
+ "This functionality is particularly useful in workflows requiring granular text processing, such as Retrieval-Augmented Generation (RAG).\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "id": "_b5Z_45UxYZu",
+ "outputId": "a600d69f-14fe-4492-f236-97261d6ff36c"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "297"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Load and split the document\n",
+ "loader = PyPDFLoader(file_path)\n",
+ "pages = loader.load_and_split()\n",
+ "len(pages)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "wt50NRQaxYZv"
+ },
+ "source": [
+ "Pages into Chunks
\n",
+ "\n",
+ "\n",
+ "#### RecursiveCharacterTextSplitter in LangChain\n",
+ "\n",
+ "The `RecursiveCharacterTextSplitter` is the **recommended splitter** in LangChain when you want to break down long documents into smaller, semantically meaningful chunks — especially useful in **RAG pipelines**, where clean context chunks lead to better LLM responses.\n",
+ "\n",
+ "#### Parameters\n",
+ "\n",
+ "| Parameter | Description |\n",
+ "|-----------------|-----------------------------------------------------------------------------|\n",
+ "| `chunk_size` | The **maximum number of characters** allowed in a chunk (e.g., `1000`). |\n",
+ "| `chunk_overlap` | The number of **overlapping characters** between consecutive chunks (e.g., `200`). This helps preserve context continuity. |\n",
+ "\n",
+ "#### How it works\n",
+ "`RecursiveCharacterTextSplitter` attempts to split the text **intelligently**, trying the following separators in order:\n",
+ "1. Paragraphs (`\"\\n\\n\"`)\n",
+ "2. Lines (`\"\\n\"`)\n",
+ "3. Sentences or words (`\" \"`)\n",
+ "4. Individual characters (as a last resort)\n",
+ "\n",
+ "This makes it ideal for handling **natural language documents**, such as PDFs, articles, or long reports, without breaking sentences or paragraphs in awkward ways.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1096"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "text_splitter = RecursiveCharacterTextSplitter(\n",
+ " chunk_size=1000,\n",
+ " chunk_overlap=200\n",
+ ")\n",
+ "chunks = text_splitter.split_documents(pages)\n",
+ "\n",
+ "len(chunks)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Alternative: CharacterTextSplitter\n",
+ "\n",
+ "`CharacterTextSplitter` is a simpler splitter that breaks text into chunks based **purely on character count**, without trying to preserve any natural language structure.\n",
+ "\n",
+ "##### Example:\n",
+ "```python\n",
+ "from langchain.text_splitter import CharacterTextSplitter\n",
+ "\n",
+ "text_splitter = CharacterTextSplitter(\n",
+ " chunk_size=1000,\n",
+ " chunk_overlap=200\n",
+ ")\n",
+ "````\n",
+ "\n",
+ "This method is faster and more predictable but may split text in the middle of a sentence or paragraph, which can hurt performance in downstream tasks like retrieval or QA.\n",
+ "\n",
+ "---\n",
+ "\n",
+ "#### Comparison Table\n",
+ "\n",
+ "| Feature | RecursiveCharacterTextSplitter | CharacterTextSplitter |\n",
+ "| ------------------------------ | ------------------------------ | ------------------------- |\n",
+ "| Structure-aware splitting | Yes | No |\n",
+ "| Preserves sentence/paragraphs | Yes | No |\n",
+ "| Risk of splitting mid-sentence | Minimal | High |\n",
+ "| Ideal for RAG/document QA | Highly recommended | Only if structured text |\n",
+ "| Performance speed | Slightly slower | Faster |\n",
+ "\n",
+ "---\n",
+ "\n",
+ "#### Recommendation\n",
+ "\n",
+ "Use `RecursiveCharacterTextSplitter` for most real-world document processing tasks, especially when building RAG pipelines or working with structured natural language content like PDFs or articles."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Best Practices for Choosing Chunk Size in RAG\n",
+ "\n",
+ "### Best Practices for Chunk Size in RAG\n",
+ "\n",
+ "| Factor | Recommendation |\n",
+ "| ---------------------------| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n",
+ "| **LLM context limit** | Choose a chunk size that lets you retrieve multiple chunks **without exceeding the model’s token limit**. For example, GPT-4o supports 128k tokens, but with GPT-3.5 (16k) or GPT-4 (32k), keep it modest. |\n",
+ "| **Chunk size (in characters)** | Typically: **500–1,000 characters** per chunk → ~75–200 tokens. This fits well for retrieval + prompt without context overflow. |\n",
+ "| **Chunk size (in tokens)** | If using token-based splitter (e.g. `TokenTextSplitter`): aim for **100–300 tokens** per chunk. |\n",
+ "| **Chunk overlap** | Use **overlap of 10–30%** (e.g., 100–300 characters or ~50 tokens) to preserve context across chunk boundaries and avoid cutting off important ideas mid-sentence. |\n",
+ "| **Document structure** | Use **`RecursiveCharacterTextSplitter`** to preserve semantic boundaries (paragraphs, sentences) instead of arbitrary cuts. |\n",
+ "| **Task type** | For **question answering**, smaller chunks (~500–800 chars) reduce noise.
For **summarization**, slightly larger chunks (~1000–1500) are OK. |\n",
+ "| **Embedding model** | Some models (e.g., `text-embedding-3-large`) can handle long input. But still, smaller chunks give **finer-grained retrieval**, which improves relevance. |\n",
+ "| **Query type** | If users ask **very specific questions**, small focused chunks are better. For broader queries, bigger chunks might help. |\n",
+ "\n",
+ "\n",
+ "### Rule of Thumb\n",
+ "\n",
+ "| Use Case | Chunk Size | Overlap |\n",
+ "| ------------------------| --------------- | ------- |\n",
+ "| Factual Q&A | 500–800 chars | 100–200 |\n",
+ "| Summarization | 1000–1500 chars | 200–300 |\n",
+ "| Technical documents | 400–700 chars | 100–200 |\n",
+ "| Long reports/books | 800–1200 chars | 200–300 |\n",
+ "| Small LLMs (≤16k tokens) | ≤800 chars | 100–200 |\n",
+ "\n",
+ "\n",
+ "### Avoid\n",
+ "\n",
+ "- Chunks >2000 characters: risks context overflow.\n",
+ "- No overlap: may lose key information between chunks.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Mg15RjVPxYZw"
+ },
+ "source": [
+ "Embeddings
\n",
+ "\n",
+ "Embeddings transform text into dense vector representations, capturing semantic meaning and contextual relationships. They are essential for efficient document retrieval and similarity analysis.\n",
+ "\n",
+ "- **What are OpenAI Embeddings?**\n",
+ " - Pre-trained embeddings like `text-embedding-3-large` generate high-quality vector representations for text.\n",
+ " - Encapsulate semantic relationships in the text, enabling robust NLP applications.\n",
+ "\n",
+ "- **Key Features of `text-embedding-3-large`:**\n",
+ " - Large-scale embedding model optimized for accuracy and versatility.\n",
+ " - Handles diverse NLP tasks, including retrieval, classification, and clustering.\n",
+ " - Ideal for applications with high-performance requirements.\n",
+ "\n",
+ "- **Benefits:**\n",
+ " - Reduces the need for extensive custom training.\n",
+ " - Provides state-of-the-art performance in retrieval-augmented systems.\n",
+ " - Compatible with RAGs to create powerful context-aware models.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "id": "L0xDxElwxYZw"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain_huggingface import HuggingFaceEmbeddings\n",
+ "from dotenv import load_dotenv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "id": "_WRIo3_0xYZx",
+ "outputId": "78bfbbf3-9d25-4e31-bdbc-3e932e6bbfec"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
}
+ ],
+ "source": [
+ "load_dotenv()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "id": "MNZfTng5xYZz",
+ "outputId": "db1a7c85-ef9f-447e-92cd-9d097e959847"
+ },
+ "outputs": [],
+ "source": [
+ "# Using HuggingFace embeddings instead of OpenAI\n",
+ "embeddings = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EsSA7RKvxYZz"
+ },
+ "source": [
+ "ChromaDB
\n",
+ "\n",
+ "ChromaDB is a versatile vector database designed for efficiently storing and retrieving embeddings. It integrates seamlessly with embedding models to enable high-performance similarity search and context-based retrieval.\n",
+ "\n",
+ "### Workflow Overview:\n",
+ "- **Step 1:** Generate embeddings using a pre-trained model (e.g., OpenAI's `text-embedding-3-large`).\n",
+ "- **Step 2:** Store the embeddings in ChromaDB for efficient retrieval and similarity calculations.\n",
+ "- **Step 3:** Use the stored embeddings to perform searches, matching, or context-based retrieval.\n",
+ "\n",
+ "### Key Features of ChromaDB:\n",
+ "- **Scalability:** Handles large-scale datasets with optimized indexing and search capabilities.\n",
+ "- **Speed:** Provides fast and accurate retrieval of embeddings for real-time applications.\n",
+ "- **Integration:** Supports integration with popular frameworks and libraries for embedding generation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "id": "brKe6wUgxYZ0"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain_chroma import Chroma"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "id": "VkjHR-RkxYZ0",
+ "outputId": "bc11bda9-f283-457a-f584-5a06b95c4dd9"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "ChromaDB created with document embeddings.\n"
+ ]
+ }
+ ],
+ "source": [
+ "db = Chroma.from_documents(chunks, embeddings, persist_directory=\"./chroma_db_LAB\")\n",
+ "print(\"ChromaDB created with document embeddings.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "27OdN1IVxYZ1"
+ },
+ "source": [
+ "Retrieving Documents
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Exercice1: Write a user question that someone might ask about your book’s topic or content."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "id": "XiLv-TfrxYZ1"
+ },
+ "outputs": [],
+ "source": [
+ "user_question = \"What are the main benefits of AI for businesses?\" # User question\n",
+ "retrieved_docs = db.similarity_search(user_question, k=10) # k is the number of documents to retrieve"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "id": "qgWsh50JxYZ1",
+ "outputId": "c8640c5d-5955-471f-fdd2-37096f5f68c7"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Document 1:\n",
+ "n. This is generated by the producers of \n",
+ "AI commodities – Microsoft’s online AI Business School and Google’s array \n",
+ "of free AI education courses are two examples – but also by a wide variety of \n",
+ "industry promoters, from consulting firms, see Accenture’s (n.d.) guide to ‘ AI \n",
+ "for Business Transformation’ and management-oriented books like The Execu-\n",
+ "tive Guide to Artificial Intelligence (Burgess 2018). Such sources can reveal what \n",
+ "AI capitals are worried about and indicate potential bottlenecks amenable to \n",
+ "outsider intervention. Once identified, bottlenecks can be analysed and the \n",
+ "social relations which support valorisation via AI therein might be replaced \n",
+ "with alternative social relations not amenable to the data-hungry valorisa -\n",
+ "tion of AI capital. Finding bottlenecks returns us to the question of visibility, \n",
+ "without which strategy cannot be formulated. I hope this chapter will contrib-\n",
+ "Document 2:\n",
+ "n. This is generated by the producers of \n",
+ "AI commodities – Microsoft’s online AI Business School and Google’s array \n",
+ "of free AI education courses are two examples – but also by a wide variety of \n",
+ "industry promoters, from consulting firms, see Accenture’s (n.d.) guide to ‘ AI \n",
+ "for Business Transformation’ and management-oriented books like The Execu-\n",
+ "tive Guide to Artificial Intelligence (Burgess 2018). Such sources can reveal what \n",
+ "AI capitals are worried about and indicate potential bottlenecks amenable to \n",
+ "outsider intervention. Once identified, bottlenecks can be analysed and the \n",
+ "social relations which support valorisation via AI therein might be replaced \n",
+ "with alternative social relations not amenable to the data-hungry valorisa -\n",
+ "tion of AI capital. Finding bottlenecks returns us to the question of visibility, \n",
+ "without which strategy cannot be formulated. I hope this chapter will contrib-\n",
+ "Document 3:\n",
+ " came along with observations that AI can \n",
+ "not only supercharge innovation and bring about economic prosperity but also \n",
+ "lead to inequalities and unfairness. \n",
+ "This book contributes to this debate by critically reflecting on how we \n",
+ "should think about AI and the relationship between humans and machines. \n",
+ "It analyses the discourses and myths that exist around AI; what it will enable\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Display top results\n",
+ "for i, doc in enumerate(retrieved_docs[:3]): # Display top 3 results\n",
+ " print(f\"Document {i+1}:\\n{doc.page_content[36:1000]}\") # Display content"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XuGK8gL6xYZ1"
+ },
+ "source": [
+ "Preparing Content for GenAI
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "id": "2iB3lZqHxYZ2"
+ },
+ "outputs": [],
+ "source": [
+ "def _get_document_prompt(docs):\n",
+ " prompt = \"\\n\"\n",
+ " for doc in docs:\n",
+ " prompt += \"\\nContent:\\n\"\n",
+ " prompt += doc.page_content + \"\\n\\n\"\n",
+ " return prompt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "id": "2okzmuADxYZ2",
+ "outputId": "0aa6cdca-188d-40e0-f5b4-8888d3549ea4"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Context formatted for GPT model.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Generate a formatted context from the retrieved documents\n",
+ "formatted_context = _get_document_prompt(retrieved_docs)\n",
+ "print(\"Context formatted for GPT model.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qzIczQNTxYZ2"
+ },
+ "source": [
+ "ChatBot Architecture
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Exercice2: Write a prompt that is relevant and tailored to the content and style of your book."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "id": "tqxVh9s3xYZ3",
+ "lines_to_next_cell": 2,
+ "outputId": "97cca95d-4ab3-44d8-a76c-5713aad387d8"
+ },
+ "outputs": [],
+ "source": [
+ "prompt = f\"\"\"\n",
+ "You are an AI assistant that helps answer questions about AI based on the provided context.\n",
+ "Use the following context to answer the user's question.\n",
+ "If you don't know the answer, say \"I don't know based on the provided context.\"\n",
+ "\n",
+ "Context: {formatted_context}\n",
+ "\n",
+ "Question: {user_question}\n",
+ "\n",
+ "Answer:\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "id": "0mjkQJ_ZxYZ3"
+ },
+ "outputs": [],
+ "source": [
+ "# OpenAI compatible API like Ollama (requires Ollama to be installed)\n",
+ "from langchain_openai import ChatOpenAI\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Exercice3: Tune parameters like temperature, and penalties to control how creative, focused, or varied the model's responses are."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "id": "ylypRWRlxYZ4"
+ },
+ "outputs": [],
+ "source": [
+ "# Use Ollama with qwen3-vl:8b model that's available locally\n",
+ "from langchain_openai import ChatOpenAI\n",
+ "\n",
+ "# Using Ollama - a free local option\n",
+ "model_params = {\n",
+ " \"temperature\": 0.7, # Increase creativity\n",
+ " \"max_tokens\": 500, # Allow for longer responses\n",
+ " \"top_p\": 0.9, # Use nucleus sampling\n",
+ " \"frequency_penalty\": 0.5, # Reduce repetition\n",
+ " \"presence_penalty\": 0.5 # Encourage new topics\n",
+ "}\n",
+ "\n",
+ "llm = ChatOpenAI(\n",
+ " base_url=\"http://localhost:11434/v1\", # Ollama default endpoint\n",
+ " api_key=\"ollama\", # Ollama accepts any key\n",
+ " model=\"qwen3-vl:8b\",\n",
+ " model_kwargs=model_params\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "C8e942xDxYZ4"
+ },
+ "source": [
+ "Response
\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "id": "4eXZO4pIxYZ4"
+ },
+ "outputs": [],
+ "source": [
+ "# Format the retrieved documents as context for the LLM\n",
+ "context = \"\\n\".join([doc.page_content for doc in retrieved_docs[:3]]) # Use top 3 documents\n",
+ "\n",
+ "# Create a prompt for the Ollama model with the context\n",
+ "full_prompt = f\"\"\"\n",
+ "Based on the following context, please answer the question.\n",
+ "\n",
+ "Context: {context}\n",
+ "\n",
+ "Question: {user_question}\n",
+ "\n",
+ "Answer:\n",
+ "\"\"\"\n",
+ "\n",
+ "# Run the prompt through the Ollama model\n",
+ "response = llm.invoke(full_prompt)\n",
+ "answer = response.content"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "id": "wLPAcchBxYZ5",
+ "outputId": "976c7800-16ed-41fe-c4cf-58f60d3230d2"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Based solely on the provided context, the main benefits of AI for businesses explicitly stated are:\n",
+ "\n",
+ "1. **Supercharge innovation:** The text states: *\"AI can not only supercharge innovation and bring about economic prosperity but also lead to inequalities and unfairness.\"*\n",
+ "2. **Bring about economic prosperity:** The text states: *\"AI can not only supercharge innovation and bring about economic prosperity but also lead to inequalities and unfairness.\"*\n",
+ "\n",
+ "**Explanation:**\n",
+ "* The context directly quotes the phrase \"supercharge innovation and bring about economic prosperity\" as a positive outcome of AI, alongside the warning about potential negative consequences (inequalities and unfairness).\n",
+ "* While the context discusses various sources promoting AI (like Microsoft's Business School and Google's courses) and the broader debate about AI adoption, it *only* explicitly names these two benefits (\"supercharge innovation\" and \"bring about economic prosperity\") in relation to business impacts within the specific sentence provided.\n",
+ "* The context does not mention other common benefits of AI for businesses (e.g., cost reduction, efficiency gains, specific productivity improvements) as being explicitly stated as benefits within this particular excerpt. The focus here is solely on the two benefits highlighted in the quoted sentence.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(answer)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VXVNXPwLxYaT"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ldybhlqKxYaT"
+ },
+ "source": [
+ "Cosine Similarity
\n",
+ "\n",
+ "**Cosine similarity** is a metric used to measure the alignment or similarity between two vectors, calculated as the cosine of the angle between them. It is the **most common metric used in RAG pipelines** for vector retrieval.. It provides a scale from -1 to 1:\n",
+ "\n",
+ "- **-1**: Vectors are completely opposite.\n",
+ "- **0**: Vectors are orthogonal (uncorrelated or unrelated).\n",
+ "- **1**: Vectors are identical.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1c1I1TNhxYaT"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EoEMdNgQxYaU"
+ },
+ "source": [
+ "Keyword Highlighting
\n",
+ "\n",
+ "Highlighting important keywords helps users quickly understand the relevance of the retrieved text to their query."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "id": "nCXL9Cz1xYaV"
+ },
+ "outputs": [],
+ "source": [
+ "from termcolor import colored"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xwDyofY0xYaV"
+ },
+ "source": [
+ "The `highlight_keywords` function is designed to highlight specific keywords within a given text. It replaces each keyword in the text with a highlighted version using the `colored` function from the `termcolor` library.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "id": "9y3E0YWExYaV"
+ },
+ "outputs": [],
+ "source": [
+ "def highlight_keywords(text, keywords):\n",
+ " for keyword in keywords:\n",
+ " text = text.replace(keyword, colored(keyword, 'green', attrs=['bold']))\n",
+ " return text"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Exercice4: add your keywords"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "id": "i7SkWPpnxYaW",
+ "outputId": "28e82563-edba-4b41-acad-ec27e5ba134f"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Snippet 1:\n",
+ "erature on \u001b[1m\u001b[32mAI\u001b[0m adoption and production. This is generated by the producers of \n",
+ "\u001b[1m\u001b[32mAI\u001b[0m commodities – Microsoft’s online \u001b[1m\u001b[32mAI\u001b[0m Business School and Google’s array \n",
+ "of free \u001b[1m\u001b[32mAI\u001b[0m education courses are two examples –\n",
+ "--------------------------------------------------------------------------------\n"
+ ]
+ }
+ ],
+ "source": [
+ "query_keywords = [\"AI\", \"artificial intelligence\", \"machine learning\", \"business\", \"benefits\", \"technology\"] # add your keywords\n",
+ "for i, doc in enumerate(retrieved_docs[:1]):\n",
+ " snippet = doc.page_content[:200]\n",
+ " highlighted = highlight_keywords(snippet, query_keywords)\n",
+ " print(f\"Snippet {i+1}:\\n{highlighted}\\n{'-'*80}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AhV_Jf_LxYaX"
+ },
+ "source": [
+ "1. `query_keywords` is a list of keywords to be highlighted.\n",
+ "2. The loop iterates over the first document in retrieved_docs.\n",
+ "3. For each document, a snippet of the first 200 characters is extracted.\n",
+ "4. The highlight_keywords function is called to highlight the keywords in the snippet.\n",
+ "5. The highlighted snippet is printed along with a separator line."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pBRKysAvxYaX"
+ },
+ "source": [
+ "Bonus
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Qj25lCybxYaX"
+ },
+ "source": [
+ "**Try loading one of your own PDF books and go through the steps again to explore how the pipeline works with your content**:\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
},
- "nbformat": 4,
- "nbformat_minor": 0
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.13.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
}
diff --git a/your-code/main.py b/your-code/main.py
new file mode 100644
index 0000000..c15f79d
--- /dev/null
+++ b/your-code/main.py
@@ -0,0 +1,409 @@
+# ---
+# jupyter:
+# jupytext:
+# text_representation:
+# extension: .py
+# format_name: percent
+# format_version: '1.3'
+# jupytext_version: 1.17.3
+# kernelspec:
+# display_name: Python 3 (ipykernel)
+# language: python
+# name: python3
+# ---
+
+# %% [markdown] id="RsnCPbdkxYZd"
+#
+#
Self-Guided Lab: Retrieval-Augmented Generation (RAGs)
+#
+
+# %% [markdown] id="tZp4BQAVxYZj"
+#
+#
+#
Data Storage & Retrieval
+#
+
+# %% [markdown] id="QW5UOI8ZxYZp"
+# PyPDFLoader
+#
+# `PyPDFLoader` is a lightweight Python library designed to streamline the process of loading and parsing PDF documents for text processing tasks. It is particularly useful in Retrieval-Augmented Generation workflows where text extraction from PDFs is required.
+#
+# - **What Does PyPDFLoader Do?**
+# - Extracts text from PDF files, retaining formatting and layout.
+# - Simplifies the preprocessing of document-based datasets.
+# - Supports efficient and scalable loading of large PDF collections.
+#
+# - **Key Features:**
+# - Compatible with popular NLP libraries and frameworks.
+# - Handles multi-page PDFs and embedded images (e.g., OCR-compatible setups).
+# - Provides flexible configurations for structured text extraction.
+#
+# - **Use Cases:**
+# - Preparing PDF documents for retrieval-based systems in RAGs.
+# - Automating the text extraction pipeline for document analysis.
+# - Creating datasets from academic papers, technical manuals, and reports.
+#
+
+# %%
+# %pip install langchain langchain_community pypdf
+# %pip install termcolor langchain_openai langchain-huggingface sentence-transformers chromadb langchain_chroma tiktoken openai python-dotenv
+
+
+# %% id="6heKZkQUxYZr"
+import os
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_text_splitters import CharacterTextSplitter, RecursiveCharacterTextSplitter
+import warnings
+warnings.filterwarnings('ignore')
+
+
+# %% [markdown] id="sRS44B2XxYZs" vscode={"languageId": "plaintext"}
+# Loading the Documents
+
+# %% id="cuREtJRixYZt"
+# File path for the document
+
+file_path = "ai-for-everyone.pdf"
+
+# %% [markdown] id="pz_8SOLxxYZt"
+# Documents into pages
+#
+# The `PyPDFLoader` library allows efficient loading and splitting of PDF documents into smaller, manageable parts for NLP tasks.
+#
+# This functionality is particularly useful in workflows requiring granular text processing, such as Retrieval-Augmented Generation (RAG).
+#
+
+# %% id="_b5Z_45UxYZu" outputId="a600d69f-14fe-4492-f236-97261d6ff36c"
+# Load and split the document
+loader = PyPDFLoader(file_path)
+pages = loader.load_and_split()
+len(pages)
+
+# %% [markdown] id="wt50NRQaxYZv"
+# Pages into Chunks
+#
+#
+# #### RecursiveCharacterTextSplitter in LangChain
+#
+# The `RecursiveCharacterTextSplitter` is the **recommended splitter** in LangChain when you want to break down long documents into smaller, semantically meaningful chunks — especially useful in **RAG pipelines**, where clean context chunks lead to better LLM responses.
+#
+# #### Parameters
+#
+# | Parameter | Description |
+# |-----------------|-----------------------------------------------------------------------------|
+# | `chunk_size` | The **maximum number of characters** allowed in a chunk (e.g., `1000`). |
+# | `chunk_overlap` | The number of **overlapping characters** between consecutive chunks (e.g., `200`). This helps preserve context continuity. |
+#
+# #### How it works
+# `RecursiveCharacterTextSplitter` attempts to split the text **intelligently**, trying the following separators in order:
+# 1. Paragraphs (`"\n\n"`)
+# 2. Lines (`"\n"`)
+# 3. Sentences or words (`" "`)
+# 4. Individual characters (as a last resort)
+#
+# This makes it ideal for handling **natural language documents**, such as PDFs, articles, or long reports, without breaking sentences or paragraphs in awkward ways.
+#
+#
+
+# %%
+text_splitter = RecursiveCharacterTextSplitter(
+ chunk_size=1000,
+ chunk_overlap=200
+)
+chunks = text_splitter.split_documents(pages)
+
+len(chunks)
+
+# %% [markdown]
+# #### Alternative: CharacterTextSplitter
+#
+# `CharacterTextSplitter` is a simpler splitter that breaks text into chunks based **purely on character count**, without trying to preserve any natural language structure.
+#
+# ##### Example:
+# ```python
+# from langchain.text_splitter import CharacterTextSplitter
+#
+# text_splitter = CharacterTextSplitter(
+# chunk_size=1000,
+# chunk_overlap=200
+# )
+# ````
+#
+# This method is faster and more predictable but may split text in the middle of a sentence or paragraph, which can hurt performance in downstream tasks like retrieval or QA.
+#
+# ---
+#
+# #### Comparison Table
+#
+# | Feature | RecursiveCharacterTextSplitter | CharacterTextSplitter |
+# | ------------------------------ | ------------------------------ | ------------------------- |
+# | Structure-aware splitting | Yes | No |
+# | Preserves sentence/paragraphs | Yes | No |
+# | Risk of splitting mid-sentence | Minimal | High |
+# | Ideal for RAG/document QA | Highly recommended | Only if structured text |
+# | Performance speed | Slightly slower | Faster |
+#
+# ---
+#
+# #### Recommendation
+#
+# Use `RecursiveCharacterTextSplitter` for most real-world document processing tasks, especially when building RAG pipelines or working with structured natural language content like PDFs or articles.
+
+# %% [markdown]
+# ## Best Practices for Choosing Chunk Size in RAG
+#
+# ### Best Practices for Chunk Size in RAG
+#
+# | Factor | Recommendation |
+# | ---------------------------| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+# | **LLM context limit** | Choose a chunk size that lets you retrieve multiple chunks **without exceeding the model’s token limit**. For example, GPT-4o supports 128k tokens, but with GPT-3.5 (16k) or GPT-4 (32k), keep it modest. |
+# | **Chunk size (in characters)** | Typically: **500–1,000 characters** per chunk → ~75–200 tokens. This fits well for retrieval + prompt without context overflow. |
+# | **Chunk size (in tokens)** | If using token-based splitter (e.g. `TokenTextSplitter`): aim for **100–300 tokens** per chunk. |
+# | **Chunk overlap** | Use **overlap of 10–30%** (e.g., 100–300 characters or ~50 tokens) to preserve context across chunk boundaries and avoid cutting off important ideas mid-sentence. |
+# | **Document structure** | Use **`RecursiveCharacterTextSplitter`** to preserve semantic boundaries (paragraphs, sentences) instead of arbitrary cuts. |
+# | **Task type** | For **question answering**, smaller chunks (~500–800 chars) reduce noise.
For **summarization**, slightly larger chunks (~1000–1500) are OK. |
+# | **Embedding model** | Some models (e.g., `text-embedding-3-large`) can handle long input. But still, smaller chunks give **finer-grained retrieval**, which improves relevance. |
+# | **Query type** | If users ask **very specific questions**, small focused chunks are better. For broader queries, bigger chunks might help. |
+#
+#
+# ### Rule of Thumb
+#
+# | Use Case | Chunk Size | Overlap |
+# | ------------------------| --------------- | ------- |
+# | Factual Q&A | 500–800 chars | 100–200 |
+# | Summarization | 1000–1500 chars | 200–300 |
+# | Technical documents | 400–700 chars | 100–200 |
+# | Long reports/books | 800–1200 chars | 200–300 |
+# | Small LLMs (≤16k tokens) | ≤800 chars | 100–200 |
+#
+#
+# ### Avoid
+#
+# - Chunks >2000 characters: risks context overflow.
+# - No overlap: may lose key information between chunks.
+#
+#
+
+# %% [markdown] id="Mg15RjVPxYZw"
+# Embeddings
+#
+# Embeddings transform text into dense vector representations, capturing semantic meaning and contextual relationships. They are essential for efficient document retrieval and similarity analysis.
+#
+# - **What are OpenAI Embeddings?**
+# - Pre-trained embeddings like `text-embedding-3-large` generate high-quality vector representations for text.
+# - Encapsulate semantic relationships in the text, enabling robust NLP applications.
+#
+# - **Key Features of `text-embedding-3-large`:**
+# - Large-scale embedding model optimized for accuracy and versatility.
+# - Handles diverse NLP tasks, including retrieval, classification, and clustering.
+# - Ideal for applications with high-performance requirements.
+#
+# - **Benefits:**
+# - Reduces the need for extensive custom training.
+# - Provides state-of-the-art performance in retrieval-augmented systems.
+# - Compatible with RAGs to create powerful context-aware models.
+#
+
+# %% id="L0xDxElwxYZw"
+from langchain_huggingface import HuggingFaceEmbeddings
+from dotenv import load_dotenv
+
+# %% id="_WRIo3_0xYZx" outputId="78bfbbf3-9d25-4e31-bdbc-3e932e6bbfec"
+load_dotenv()
+
+# %% id="MNZfTng5xYZz" outputId="db1a7c85-ef9f-447e-92cd-9d097e959847"
+# Using HuggingFace embeddings instead of OpenAI
+embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+
+# %% [markdown] id="EsSA7RKvxYZz"
+# ChromaDB
+#
+# ChromaDB is a versatile vector database designed for efficiently storing and retrieving embeddings. It integrates seamlessly with embedding models to enable high-performance similarity search and context-based retrieval.
+#
+# ### Workflow Overview:
+# - **Step 1:** Generate embeddings using a pre-trained model (e.g., OpenAI's `text-embedding-3-large`).
+# - **Step 2:** Store the embeddings in ChromaDB for efficient retrieval and similarity calculations.
+# - **Step 3:** Use the stored embeddings to perform searches, matching, or context-based retrieval.
+#
+# ### Key Features of ChromaDB:
+# - **Scalability:** Handles large-scale datasets with optimized indexing and search capabilities.
+# - **Speed:** Provides fast and accurate retrieval of embeddings for real-time applications.
+# - **Integration:** Supports integration with popular frameworks and libraries for embedding generation.
+
+# %% id="brKe6wUgxYZ0"
+from langchain_chroma import Chroma
+
+# %% id="VkjHR-RkxYZ0" outputId="bc11bda9-f283-457a-f584-5a06b95c4dd9"
+db = Chroma.from_documents(chunks, embeddings, persist_directory="./chroma_db_LAB")
+print("ChromaDB created with document embeddings.")
+
+# %% [markdown] id="27OdN1IVxYZ1"
+# Retrieving Documents
+#
+
+# %% [markdown]
+# ### Exercice1: Write a user question that someone might ask about your book’s topic or content.
+
+# %% id="XiLv-TfrxYZ1"
+user_question = "What are the main benefits of AI for businesses?" # User question
+retrieved_docs = db.similarity_search(user_question, k=10) # k is the number of documents to retrieve
+
+# %% id="qgWsh50JxYZ1" outputId="c8640c5d-5955-471f-fdd2-37096f5f68c7"
+# Display top results
+for i, doc in enumerate(retrieved_docs[:3]): # Display top 3 results
+ print(f"Document {i+1}:\n{doc.page_content[36:1000]}") # Display content
+
+
+# %% [markdown] id="XuGK8gL6xYZ1"
+# Preparing Content for GenAI
+
+# %% id="2iB3lZqHxYZ2"
+def _get_document_prompt(docs):
+ prompt = "\n"
+ for doc in docs:
+ prompt += "\nContent:\n"
+ prompt += doc.page_content + "\n\n"
+ return prompt
+
+
+# %% id="2okzmuADxYZ2" outputId="0aa6cdca-188d-40e0-f5b4-8888d3549ea4"
+# Generate a formatted context from the retrieved documents
+formatted_context = _get_document_prompt(retrieved_docs)
+print("Context formatted for GPT model.")
+
+# %% [markdown] id="qzIczQNTxYZ2"
+# ChatBot Architecture
+
+# %% [markdown]
+# ### Exercice2: Write a prompt that is relevant and tailored to the content and style of your book.
+
+# %% id="tqxVh9s3xYZ3" outputId="97cca95d-4ab3-44d8-a76c-5713aad387d8"
+prompt = f"""
+You are an AI assistant that helps answer questions about AI based on the provided context.
+Use the following context to answer the user's question.
+If you don't know the answer, say "I don't know based on the provided context."
+
+Context: {formatted_context}
+
+Question: {user_question}
+
+Answer:
+"""
+
+
+# %% id="0mjkQJ_ZxYZ3"
+# OpenAI compatible API like Ollama (requires Ollama to be installed)
+from langchain_openai import ChatOpenAI
+import os
+
+# %% [markdown]
+# ### Exercice3: Tune parameters like temperature, and penalties to control how creative, focused, or varied the model's responses are.
+
+# %% id="ylypRWRlxYZ4"
+# Use Ollama with qwen3-vl:8b model that's available locally
+from langchain_openai import ChatOpenAI
+
+# Using Ollama - a free local option
+model_params = {
+ "temperature": 0.7, # Increase creativity
+ "max_tokens": 500, # Allow for longer responses
+ "top_p": 0.9, # Use nucleus sampling
+ "frequency_penalty": 0.5, # Reduce repetition
+ "presence_penalty": 0.5 # Encourage new topics
+}
+
+llm = ChatOpenAI(
+ base_url="http://localhost:11434/v1", # Ollama default endpoint
+ api_key="ollama", # Ollama accepts any key
+ model="qwen3-vl:8b",
+ model_kwargs=model_params
+)
+
+# %% [markdown] id="C8e942xDxYZ4"
+# Response
+#
+
+# %% id="4eXZO4pIxYZ4"
+# Format the retrieved documents as context for the LLM
+context = "\n".join([doc.page_content for doc in retrieved_docs[:3]]) # Use top 3 documents
+
+# Create a prompt for the Ollama model with the context
+full_prompt = f"""
+Based on the following context, please answer the question.
+
+Context: {context}
+
+Question: {user_question}
+
+Answer:
+"""
+
+# Run the prompt through the Ollama model
+response = llm.invoke(full_prompt)
+answer = response.content
+
+# %% id="wLPAcchBxYZ5" outputId="976c7800-16ed-41fe-c4cf-58f60d3230d2"
+print(answer)
+
+# %% [markdown] id="VXVNXPwLxYaT"
+# 
+
+# %% [markdown] id="ldybhlqKxYaT"
+# Cosine Similarity
+#
+# **Cosine similarity** is a metric used to measure the alignment or similarity between two vectors, calculated as the cosine of the angle between them. It is the **most common metric used in RAG pipelines** for vector retrieval.. It provides a scale from -1 to 1:
+#
+# - **-1**: Vectors are completely opposite.
+# - **0**: Vectors are orthogonal (uncorrelated or unrelated).
+# - **1**: Vectors are identical.
+#
+
+# %% [markdown] id="1c1I1TNhxYaT"
+# 
+
+# %% [markdown] id="EoEMdNgQxYaU"
+# Keyword Highlighting
+#
+# Highlighting important keywords helps users quickly understand the relevance of the retrieved text to their query.
+
+# %% id="nCXL9Cz1xYaV"
+from termcolor import colored
+
+
+# %% [markdown] id="xwDyofY0xYaV"
+# The `highlight_keywords` function is designed to highlight specific keywords within a given text. It replaces each keyword in the text with a highlighted version using the `colored` function from the `termcolor` library.
+#
+
+# %% id="9y3E0YWExYaV"
+def highlight_keywords(text, keywords):
+ for keyword in keywords:
+ text = text.replace(keyword, colored(keyword, 'green', attrs=['bold']))
+ return text
+
+
+# %% [markdown]
+# ### Exercice4: add your keywords
+
+# %% id="i7SkWPpnxYaW" outputId="28e82563-edba-4b41-acad-ec27e5ba134f"
+query_keywords = ["AI", "artificial intelligence", "machine learning", "business", "benefits", "technology"] # add your keywords
+for i, doc in enumerate(retrieved_docs[:1]):
+ snippet = doc.page_content[:200]
+ highlighted = highlight_keywords(snippet, query_keywords)
+ print(f"Snippet {i+1}:\n{highlighted}\n{'-'*80}")
+
+# %% [markdown] id="AhV_Jf_LxYaX"
+# 1. `query_keywords` is a list of keywords to be highlighted.
+# 2. The loop iterates over the first document in retrieved_docs.
+# 3. For each document, a snippet of the first 200 characters is extracted.
+# 4. The highlight_keywords function is called to highlight the keywords in the snippet.
+# 5. The highlighted snippet is printed along with a separator line.
+
+# %% [markdown] id="pBRKysAvxYaX"
+# Bonus
+
+# %% [markdown] id="Qj25lCybxYaX"
+# **Try loading one of your own PDF books and go through the steps again to explore how the pipeline works with your content**:
+#