Welcome to RAG Chat
RAG Chat is an intelligent document question-answering system that combines the power of Retrieval-Augmented Generation (RAG) with OpenAI’s GPT models. Upload your PDF documents and interact with them through natural language questions.Quick Start
Get up and running with RAG Chat in minutes
Installation Guide
Complete setup instructions and dependencies
Core Concepts
Learn how RAG technology works
API Reference
Explore the function API
Key Features
Document Upload
Upload and process PDF documents for intelligent question answering
Multiple GPT Models
Choose from GPT-3.5, GPT-4, GPT-4 Turbo, and GPT-4o models
Persistent Vector Store
ChromaDB-powered embeddings store that persists across sessions
Chat Interface
Interactive Streamlit interface with conversation history
Smart Chunking
Intelligent document splitting with configurable overlap
Context-Aware
Responses that leverage full chat history for better context
How It Works
RAG Chat combines several technologies to provide accurate, context-aware answers:- Document Processing - PDF documents are loaded and split into manageable chunks using LangChain’s text splitters
- Embedding Generation - Document chunks are converted to vector embeddings using OpenAI’s embedding models
- Vector Storage - Embeddings are stored in ChromaDB for efficient similarity search
- Question Answering - When you ask a question, relevant document chunks are retrieved and sent to GPT models for answer generation
- Interactive Chat - The Streamlit interface provides a seamless chat experience with conversation history
Technology Stack
RAG Chat is built on modern AI and data processing tools:- LangChain - Framework for building LLM applications
- ChromaDB - Vector database for embedding storage and retrieval
- OpenAI API - GPT models and embedding generation
- Streamlit - Interactive web interface
- PyPDF - PDF document processing
Next Steps
Install Dependencies
Follow the installation guide to set up your environment