9 add notebook demo

demo/notebook.ipynb

@@ -0,0 +1,239 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Structured Q&A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Source code: https://github.com/mozilla-ai/structured-q-a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Docs: https://mozilla-ai.github.io/structured-q-a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## GPU Check"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, you'll need to enable GPUs for the notebook:\n",
+    "\n",
+    "- Navigate to `Edit`→`Notebook Settings`\n",
+    "- Select T4 GPU from the Hardware Accelerator section\n",
+    "- Click `Save` and accept.\n",
+    "\n",
+    "Next, we'll confirm that we can connect to the GPU:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "\n",
+    "if not torch.cuda.is_available():\n",
+    "    raise RuntimeError(\"GPU not available\")\n",
+    "else:\n",
+    "    print(\"GPU is available!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --quiet https://github.com/abetlen/llama-cpp-python/releases/download/v0.3.4-cu122/llama_cpp_python-0.3.4-cp311-cp311-linux_x86_64.whl\n",
+    "%pip install --quiet structured-qa"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Uploading data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files\n",
+    "\n",
+    "uploaded = files.upload()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Converting document to a directory of sections"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "from structured_qa.preprocessing import document_to_sections_dir\n",
+    "\n",
+    "input_file = list(uploaded.keys())[0]\n",
+    "sections_dir = f\"output/{Path(input_file).stem}\"\n",
+    "section_names = document_to_sections_dir(input_file, sections_dir)\n",
+    "section_names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from structured_qa.model_loaders import load_llama_cpp_model\n",
+    "\n",
+    "model = load_llama_cpp_model(\n",
+    "    \"bartowski/Qwen2.5-3B-Instruct-GGUF/Qwen2.5-3B-Instruct-f16.gguf\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Find, Retrieve, and Answer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "FIND_PROMPT = \"\"\"\n",
+    "You are given two pieces of information:\n",
+    "1. A user question.\n",
+    "2. A list of valid section names.\n",
+    "\n",
+    "Your task is to:\n",
+    "- Identify exactly one `section_name` from the provided list that seems related to the user question.\n",
+    "- Return the `section_name` exactly as it appears in the list.\n",
+    "- Do NOT return any additional text, explanation, or formatting.\n",
+    "- Do NOT combine multiple section names into a single response.\n",
+    "\n",
+    "Here is the list of valid `section_names`:\n",
+    "\n",
+    "```\n",
+    "{SECTIONS}\n",
+    "```\n",
+    "\n",
+    "Now, based on the input question, return the single most relevant `section_name` from the list.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ANSWER_PROMPT = \"\"\"\n",
+    "You are a rigorous assistant answering questions.\n",
+    "You only answer based on the current information available.\n",
+    "\n",
+    "The current information available is:\n",
+    "\n",
+    "```\n",
+    "{CURRENT_INFO}\n",
+    "```\n",
+    "\n",
+    "If the current information available is not enough to answer the question,\n",
+    "you must return the following message and nothing else:\n",
+    "\n",
+    "```\n",
+    "I need more info.\n",
+    "```\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "QUESTION = \"What optimizer was used to train the model?\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from structured_qa.workflow import find_retrieve_answer\n",
+    "\n",
+    "find_retrieve_answer(\n",
+    "    question=QUESTION,\n",
+    "    model=model,\n",
+    "    sections_dir=sections_dir,\n",
+    "    find_prompt=FIND_PROMPT,\n",
+    "    answer_prompt=ANSWER_PROMPT,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/step-by-step-guide.md

@@ -1,9 +1,5 @@
 # **Step-by-Step Guide: How the Structured-Q-A Blueprint Works**
 
-Transforming static documents into engaging podcast episodes involves an integration of pre-processing, LLM-powered transcript generation, and text-to-speech generation. Here's how it all works under the hood:
-
----
-
 ## **Overview**
 
 This system has the following core stages:

mkdocs.yml

@@ -1,4 +1,4 @@
-site_name: Structured Q&A
+site_name: Blueprints Docs
 repo_url: https://github.com/mozilla-ai/structured-q-a
 repo_name: structured-q-a