A vector database indexes high-dimensional embeddings so it can return the most similar items to a query in milliseconds, even across millions of entries. It is the practical backbone of semantic search, recommendation, and retrieval for RAG.
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
A vector database is the retrieval engine under video search, and choices about chunking and embeddings matter more than the brand of database. Volume 6 covers the vector database in detail within search and RAG over a video archive: embeddings for frames and transcripts, indexing, retrieval quality and the lifecycle of a search feature. Written by Nikolay Sapunov, CEO at Fora Soft.
Picking a vector database for semantic search over your video library?
Tell us your archive size and query patterns, and we will recommend a vector database, an embedding scheme and an indexing cost estimate. 250+ video products shipped since 2005.
