An Easy Introduction to Multimodal Retrieval-Augmented Generation for Video and Audio

Building a multimodal retrieval augmented generation (RAG) system for video and audio involves capturing and indexing information across different modalities like text, images, audio, and video. There are three primary approaches: using a common embedding space, building parallel retrieval pipelines, or grounding information in a common modality like text. When dealing with videos, it’s crucial to manage computational costs, extract meaningful information from frames, and preserve actions across frames. The process includes audio and video ingestion, blending information from both, setting up a retriever, and generating answers using a large language model.