RAG-Powered Document Intelligence System

Built an advanced Retrieval-Augmented Generation (RAG) system that transforms PDF documents into intelligent knowledge bases. Engineered a dual-index architecture combining FAISS (Facebook AI Similarity Search) vector search with BM25 keyword retrieval for superior context precision.

Key achievements:

• Implemented hybrid retrieval leveraging SentenceTransformers and semantic search
• Developed chunking strategies optimized for LLM context windows (Flan-T5)
• Created end-to-end pipeline from PDF processing to AI-powered Q&A

This system demonstrates cutting-edge RAG implementation for enterprise document intelligence, showcasing expertise in vector databases, LLM integration, and large-scale text processing.