Vector Embedding

Also known as: dense vector

A vector embedding is a dense, fixed-length numerical representation of a piece of content — text, image, audio, or code — in a high-dimensional space. Embedding models learn to place semantically similar inputs near each other so that distance in the vector space correlates with conceptual similarity. Embeddings are the foundational artifact on which all vector retrieval is built: without them, similarity-based search is impossible.