Video embeddings
Video embeddings are lists of numbers (vectors) that an AI model produces to represent the meaning of a video or a segment of it, including its visuals, speech and text. Videos with similar content end up with similar vectors, so a computer can compare them by measuring the distance between them. Embeddings are typically stored in a vector database for fast lookup.
Why it matters
Embeddings are the foundation for search by meaning, recommendations, duplicate detection and video RAG. The embedding approach a team picks affects result quality, storage cost and how easily the library can be updated. Switching models later usually means re-embedding the whole library.
How it works
- A video is split into segments, such as fixed time windows or detected scenes.
- An embedding model processes the frames, audio or transcript text of each segment.
- Each segment becomes a vector, often hundreds or thousands of numbers long.
- Vectors go into a vector database, where similar content can be found by distance.
What are video embeddings used for?
Video embeddings are used to compare videos by meaning. Common uses are semantic search, "more like this" recommendations, near-duplicate detection, grouping a library by topic and retrieving context for AI assistants. For example, a training platform can embed every lesson so a question about expense reports surfaces the right clip even when the title says reimbursement.
What is the difference between video embeddings and video metadata?
Metadata is human-readable labels, such as titles, tags and categories. Embeddings are numeric vectors that capture meaning in a form only software can compare. Metadata is easy to filter and audit, while embeddings catch similarity that labels miss. Most search systems store both and use metadata filters to narrow results before comparing embeddings.
Where it's used
SaaS products and video platforms use video embeddings to power search by meaning, recommendations and "find similar" features. Generative AI video companies use them to organize training data and remove near-duplicates.
By the numbers
- OpenAI's CLIP model learned a shared embedding space for images and text from 400 million image and text pairs collected from the internet. (Learning Transferable Visual Models From Natural Language Supervision (arXiv))
