VideoRAG

Why we included this project

Searching inside a video is still far more awkward than searching inside a document, and VideoRAG targets exactly that gap. It is a retrieval-augmented generation framework that indexes both the visual and audio content of a video, so an LLM can pull up the relevant moments and answer questions grounded in what actually happens on screen rather than guessing. That makes it a natural fit for anyone with large collections of recordings, from meeting archives to lecture libraries to raw footage, because the long-context handling processes hours of video without cutting it into forgettable snippets. The repo pairs the research framework with a desktop app (Vimo), which is a convenient way to try the approach interactively before building it into your own stack. There is also a published benchmark with more than a hundred hours of labeled content, giving you a concrete yardstick if you want to evaluate retrieval quality on your own use case.