An embedding turns content into a point in a high-dimensional space where distance means similarity. Embeddings power semantic search, recommendation, re-identification, and retrieval for RAG; you compare two items by measuring how close their vectors are.
The book · Volume 6 of 7
AI for Video: A Complete Guide to Intelligence in Video Products: From Models and Inference to Computer Vision, Speech, Search, and Generative Video
An embedding is how a video product compares things by meaning, and the choice of embedding model decides what search can find. This volume covers embeddings in depth: vector representations for frames, faces and speech, search and RAG over a video archive, re-identification, and the model artifacts behind them. Written by Nikolay Sapunov, CEO at Fora Soft.
Building search or recommendations on embeddings from your video content?
Share your content and the queries users run, and we will propose an embedding model, an index design and a storage and query cost estimate. 250+ video products shipped since 2005.
