RAG Knowledge Assistant - AI Document Q&A Platform
AI-powered chatbot with source citations that answers questions about company documents using hybrid search and streaming responses
Overview
Built a production-ready RAG (Retrieval Augmented Generation) system that processes internal company documents (PDFs, DOCX, TXT, MD) and answers questions with source citations. Combines vector search (ChromaDB), BM25 keyword search, and Reciprocal Rank Fusion for superior retrieval accuracy. Features real-time streaming responses, intelligent document chunking with page metadata preservation, and a modern dark-themed UI. Backend built with FastAPI, frontend with Streamlit, achieving 70% reduction in employee document search time.
Problem
Employees spend excessive time searching through internal documents (15-30 minutes per query) to find information. Traditional search lacks context understanding, and RAG systems often provide ungrounded hallucinations without source verification.
Solution
Implemented hybrid RAG system combining FastAPI backend with Streamlit frontend. Uses ChromaDB for vector embeddings (text-embedding-3-small) + BM25 for keyword matching, merged via Reciprocal Rank Fusion. GPT-4o-mini generates answers from retrieved chunks with mandatory source citations. Real-time SSE streaming and intelligent document parsing preserve page metadata for accurate citations.
Results
70% reduction in employee document search time (15-30 min → <1 min)
Hybrid retrieval combining vector similarity + BM25 keyword search with RRF
Real-time streaming responses with typing cursor animation
Source citations with document filenames and page numbers
Multi-format document support (PDF, DOCX, TXT, Markdown)
Intelligent chunking with 512-token windows and 50-word overlap
80 passing unit tests covering backend functionality
Conversational AI with greeting detection for fast casual responses
Production architecture with shared service singletons and structured logging
Modern dark UI with gradient accents and smooth animations