RAG Assistant
Document Q&A with Retrieval-Augmented Generation
Built a production-style RAG backend where users upload documents (PDF, DOCX, TXT, MD) and ask questions with context-grounded answers. Features hybrid search (vector + keyword) using Supabase pgvector, optional cross-encoder reranking, conversation memory, and LangChain-powered LLM answer generation with source references.