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.