I Built a RAG System That Listens to Live BBC News and Answers Questions About "What Happened 10 Minutes Ago"

Rakshit Aralimatti developed a real-time Retrieval-Augmented Generation (RAG) system that processes live audio streams, specifically from the BBC World Service, to answer questions about recent events, overcoming traditional RAG limitations with static documents. The system captures, transcribes, and indexes audio in real-time, embedding the data with temporal metadata to enable time-sensitive queries. It uses NVIDIA Riva for transcription, NeMo Retriever for embedding, and ChromaDB for indexing, allowing users to query past audio segments using natural language and receive answers with precise timestamps. This architecture is versatile and can be applied to various domains such as defense, emergency response, and corporate compliance to monitor and analyze time-framed audio communications. The breakthrough lies in the combination of semantic similarity and temporal filtering, with a two-stage retrieval process enhancing accuracy, demonstrating that temporal RAG is both feasible and practical for real-world applications.