A production-grade Retrieval-Augmented Generation (RAG) chatbot with multi-query retrieval, evidence grounding, and voice interaction.
Designed and implemented an end-to-end Retrieval-Augmented Generation (RAG) system that enables grounded, evidence-backed question answering over unstructured documents. Built a multi-stage pipeline including document ingestion, chunking, embedding generation, and semantic retrieval using ChromaDB. Integrated a multi-query retrieval strategy leveraging chat history with deduplication and dynamic ranking of chunks. Developed a FastAPI backend with a modular architecture and added real-time voice interaction using local Whisper for speech-to-text.
