video1.0<iframe src="https://www.loom.com/embed/adc1e9a402644b0f9ffeb3c7754e141a" frameborder="0" width="1662" height="1246" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12461662Loomhttps://www.loom.com12461662https://cdn.loom.com/sessions/thumbnails/adc1e9a402644b0f9ffeb3c7754e141a-0faeed3af7ee9d7b.gif556.965This Loom demonstrates a semantic video search engine that finds specific moments in a large video library using natural language queries. It addresses the problem of time-consuming clip searching for video editors by indexing about 200 copyright-free stock videos downloaded from Pexabay and Pexels. The project uses OpenAI’s CLIP to embed images and text into a vector space, then FAISS to retrieve the closest matching frames sampled from videos every 2 or 3 seconds. Viewers see example queries like “a red car driving at night” and “a person opens a laptop,” along with an ingest option to add new videos to the library.