RAG Chat Application

This project implements a Retrieval-Augmented Generation (RAG) system that integrates document processing, vector embeddings, and OpenAI's language models to provide context-aware responses to user queries.

Application Screenshot

Features

• Document Processing: Upload and process PDF and TXT files
• Semantic Search: Find relevant information using embeddings-based similarity
• Conversation History: Maintain context across multiple user interactions
• Query Enhancement: Improve search results with automatic query expansion
• Multi-Source Retrieval: Get information from diverse documents
• Interactive Interface: User-friendly Gradio web interface

Setup Instructions

Prerequisites

• OpenAI API Key

Environment Setup

1. Clone the repository:

   git clone https://github.com/jigarthummar/rag-chat.git
   cd rag-chat
   

2. Create a virtual environment:

   python -m venv .venv
   

3. Activate the virtual environment:

   source .venv/bin/activate  # On Linux/Mac
   .venv\Scripts\activate     # On Windows
   

4. Install requirements:

   pip install -r requirements.txt
   

5. Set up your .env file with your OpenAI API key:

   OPENAI_API_KEY=your_api_key_here
   

Running the Application

Start the Gradio web interface:

python gradio_app.py

The application will be available at http://127.0.0.1:7860