AI
Embedding
An embedding is a dense vector of floating-point numbers that represents the semantic content of an input, positioned such that similar meanings occupy nearby points in the vector space.
Because similarity becomes distance, embeddings make semantic search, clustering, recommendation and deduplication tractable with straightforward mathematics.
Embeddings from different models are not comparable, so changing embedding model requires re-indexing the entire corpus.