Enterprise Retrieval-Augmented Generation (RAG) API

Architected and deployed a production-ready Retrieval-Augmented Generation (RAG) system for enterprise data querying. The backend was developed using FastAPI for high-speed API endpoints. Integrated vector databases (Pinecone, FAISS, and Weaviate) to handle massive semantic search operations. The system allows businesses to securely "chat" with their internal documents, providing hallucination-free, context-aware answers in real-time.