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RAG Chatbot Prototype

This README document provides an overview of the RAG (Retrieval-Augmented Generation) Chatbot Prototype. The chatbot utilizes language models and vector database searches to generate contextually relevant responses based on input queries.

Features

  • Interaction with a chatbot through a web-based interface.
  • Use of OpenAI's GPT-4 model for natural language understanding and response generation.
  • Contextual answers based on a similarity search within the provided PDF documents.
  • Integration of Gradio for creating a user-friendly web interface.
  • Ability to add more documents to the vector database to enhance retrieval capability.

Requirements

To run this chatbot, you will need the following:

  • Python 3.8 or higher
  • An OpenAI API key (GPT-4 access)

Setup

Install Dependencies

First, ensure that you have all required packages installed by running:

pip install gradio langchain langchain-community langchain-openai dotenv

Environment Variables

Create a .env file in the root directory of your project and add your OpenAI API key:

OPENAI_API_KEY=your_openai_api_key_here

Usage

To use the chatbot, follow these steps:

  1. Place your PDF files in the ./pdf/ directory.

  2. Run the script with:

    python app.py

  3. The chatbot interface will be served on a local web server, and optionally, a public link is created if share=True is passed to demo.launch().

How It Works

The system performs the following operations:

  • Loads environment variables using dotenv.
  • Splits text from PDF documents into chunks using CharacterTextSplitter.
  • Creates vector embeddings for the text chunks with OpenAIEmbeddings and stores them in a chroma vector database (Chroma).
  • When a query is received, it performs a similarity search against the vector database.
  • A question-answering chain (StuffDocumentsChain) uses the retrieved documents to generate a response based on the context and the current user query.
  • The response is then sent back to the Gradio web interface.

Clearing Chat

A button is provided in the web interface to clear the chat history when needed.

Development Notes

  • Ensure that the create_vectardb function correctly initializes the vectorstore before attempting to add documents.
  • Confirm that the load_qa_chain function's signature accords with the library's expected parameters.
  • Debug mode is enabled in the Gradio interface launch for easier debugging during development.

License

This project is licensed under the MIT License - see the LICENSE file for details.