RAG System with Advanced Python Concepts

A production-ready Retrieval-Augmented Generation (RAG) system designed for sales teams to implement Go-To-Market (GTM) strategies. This system demonstrates advanced Python programming concepts including async programming, performance optimization, AI pipeline architecture, and production-ready patterns.

Features

• Document Processing: Async document upload and processing with chunking
• Persona-based Querying: Context-aware responses based on user personas
• Vector Storage: Efficient document embedding and similarity search
• Production Ready: Comprehensive observability, error handling, and scaling patterns
• Advanced Python: Demonstrates asyncio, context managers, decorators, type hints, and memory optimization

Architecture

The system follows microservices principles with:

• FastAPI for high-performance async API
• LangChain for RAG orchestration
• Vector databases for embedding storage
• Redis for caching and rate limiting
• Comprehensive observability stack