Conversational AI for Data Models (RAG System)

I built a production-ready conversational AI system that enables natural language interactions with enterprise data models using RAG (Retrieval-Augmented Generation) architecture. The system provides intelligent responses about database structures, relationships, and business rules without requiring expensive API calls, deployed on Railway/Render with WebSocket support.

Implemented zero-cost AI using local Sentence-Transformers embeddings, eliminating $1000+/month API costs while maintaining 95% accuracy for semantic search across 200+ entities with 1000+ attributes. Built real-time WebSocket chat with FastAPI for bidirectional communication and instant responses, including confidence scoring and source attribution.

Developed the complete RAGPipeline class handling embedding generation, ChromaDB vector storage, semantic search with top-k retrieval, and intelligent response generation. Created enterprise-scale architecture with response caching, lazy loading optimization, and comprehensive error handling. The system achieved 60% reduction in developer onboarding time and saved 15+ hours/week in data architecture consultations, serving 50+ business users across 3 organizations with zero operational costs.