Retrieval-Augmented Generation (RAG) Chatbot

This repository contains a Jupyter Notebook that demonstrates how to build a simple Retrieval-Augmented Generation (RAG) chatbot using LangChain, OpenAI, and ChromaDB. The notebook guides you through the entire process, from loading a document to generating answers based on its content.

Overview

Retrieval-Augmented Generation (RAG) is a technique that enhances the accuracy and reliability of Large Language Models (LLMs) by grounding them in external knowledge bases. Instead of relying solely on its pre-trained knowledge, the model first retrieves relevant information from a specified document source and then uses that information to generate a response.

This notebook implements the following workflow:

1. Load Document: Ingest a PDF document using LangChain's PyPDFLoader.
2. Split & Chunk: Break the document into smaller, semantically meaningful chunks using RecursiveCharacterTextSplitter.
3. Embed: Convert the text chunks into numerical vector representations (embeddings) using OpenAI's text-embedding-3-large model.
4. Store: Store these embeddings in a ChromaDB vector store for efficient retrieval.
5. Retrieve: Given a user question, perform a similarity search in the vector store to find the most relevant chunks.
6. Generate: Pass the original question and the retrieved context to an LLM (like GPT-4o) to generate a final, context-aware answer.

Technologies Used

• Python 3.10+
• LangChain: A framework for developing applications powered by language models.
• OpenAI: For generating text embeddings and the final chat response.
• ChromaDB: An open-source vector database for storing and querying embeddings.
• PyPDF: A library to read and extract text from PDF files.
• Jupyter Notebook: For interactive development and demonstration.

Getting Started

Follow these steps to set up and run the project on your local machine.

Prerequisites

• An OpenAI API Key.

Installation

1. Clone the repository:

   git clone [https://github.com/your-username/your-repo-name.git](https://github.com/your-username/your-repo-name.git)
   cd your-repo-name

2. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

   (You can create a requirements.txt file with the content from the first code cell in the notebook, or simply run the pip install commands from the notebook.)

4. Set up your environment variables:

   • Create a file named .env in the root of the project directory.
   • Add your OpenAI API key to this file:

     OPENAI_API_KEY="your-openai-api-key-here"
     

5. Add your data source:

   • Place the PDF file you want to use as the knowledge base into the appropriate directory.
   • Update the file_path variable in the notebook to point to your PDF file. For example:

     file_path = "path/to/your/document.pdf"

Running the Notebook

1. Start Jupyter Notebook:

   jupyter notebook

2. Open the LAB_GenAI_RAG_Chatbot.ipynb file.
3. Run the cells sequentially from top to bottom.

exercice

1. Exercice1: Write a user question that someone might ask about your book’s topic or content.
2. Exercice2: Write a prompt that is relevant and tailored to the content and style of your book.
3. Exercice3: Tune parameters like temperature, and penalties to control how creative, focused, or varied the model's responses are.
4. Exercice4: add your keywords