This article demonstrates how to build an intelligent Q&A system for video and audio content using LLMs and RAG on Amazon SageMaker with multilingual support.

• Convert video/audio to text using Amazon Transcribe or OpenAI Whisper with timestamp preservation
• Organize and chunk transcribed text semantically using sentence embeddings for better retrieval
• Deploy embedding models and LLMs (Falcon-40B) on SageMaker endpoints for inference
• Build RAG solution using LangChain with FAISS vector store for semantic search
• Create Streamlit chatbot accepting text and audio files with conversation memory
• Retrieve relevant video clips with timestamps when answering questions from knowledge base
• Support multilingual transcription and translation into single language for consistency

The solution enables businesses to efficiently search and extract insights from large video/audio libraries without manual metadata tagging, improving content discovery and employee productivity.