RAG Chatbot with ChromaDB and LLM Integration

This project is a Retrieval-Augmented Generation (RAG) Chatbot that combines ChromaDB for document retrieval and a large language model (LLM) for answer generation. The chatbot provides intelligent responses based on information from provided PDFs, making it useful for complex document-driven Q&A.

Features

• Context-Aware Responses: Retrieves the closest text match from multiple documents.
• Document Search: Searches across multiple PDFs to retrieve relevant content.
• Efficient Processing: Uses Redis for caching and RabbitMQ for task management.

Tech Stack

• Backend: Python, Flask, ChromaDB, Sentence Transformers, OpenAI API
• Frontend: HTML, JavaScript
• Message Queue & Cache: RabbitMQ, Redis

Setup

Clone the repository and install dependencies:

git clone https://github.com/patilkalpesh/RAG-Chatbot-with-ChromaDB-and-LLM-Integration.git

cd RAG-Chatbot-with-ChromaDB-and-LLM-Integration

pip install -r requirements.txt

Add your OpenAI API key:

export OPENAI_API_KEY='your_api_key'

RabbitMQ:

docker run -it --rm --name rabbitmq -p 5672:5672 -p 15672:15672 rabbitmq:4.0-management

Redis:

redis-server

Run Backend:

cd backend

python embedding_manager.py

python worker.py

python main.py