Simple RAG Chatbot

A modern, responsive chatbot application that uses Retrieval-Augmented Generation (RAG) to provide accurate, context-aware responses based on your PDF documents. Built with Google's Gemini AI, ChromaDB, and a React + Flask stack.

Features

• 📄 PDF Document Processing: Upload and process PDF documents for contextual information
• 🔍 Smart Retrieval: Uses ChromaDB vector store for efficient semantic search
• 🤖 Advanced AI Responses: Powered by Google's Gemini AI model
• 💬 Real-time Chat Interface: Modern React-based UI with streaming responses
• 📚 Source Citations: Automatic citation of sources with page numbers
• 🎯 Context-Aware: Responses are based on your document content

Architecture

Backend Components

• Flask Server: REST API handling document processing and chat endpoints
• Document Processor: Manages PDF processing and vector storage
• Gemini AI: Google's latest LLM for generating responses
• ChromaDB: Vector database for efficient semantic search
• LangChain: Framework for connecting various components

Frontend Components

• React + TypeScript: Modern frontend framework
• Real-time Updates: Server-Sent Events for streaming responses
• Responsive Design: Modern UI that works on all devices

Setup Instructions

Prerequisites

• Python 3.8+
• Node.js 16+
• Google Cloud API key with Gemini API access