Building a multimodal retrieval-augmented generation (RAG) system is challenging. The difficulty comes from capturing and indexing information from across multiple modalities, including text, images, tables, audio, video, and more. In NVIDIA previous post, An Easy Introduction to Multimodal Retrieval-Augmented Generation, authors discussed how to tackle text and images. This post extends this conversation to audio and videos. Specifically, they explore how to build a multimodal RAG pipeline to search information in videos.
Read more on An Easy Introduction to Multimodal Retrieval-Augmented Generation for Video and Audio | NVIDIA Technical Blog.
