Embedding

An embedding maps an object such as a word, sentence, document, image, or user item into a vector of numbers. Useful embedding spaces place objects with related meanings or functions near one another according to a chosen similarity measure.

Embedding models are trained so that useful relationships are reflected in vector geometry. Applications often compare vectors with cosine similarity, dot product, or distance metrics. Sentence and document embeddings are widely used to index large text collections.

Embeddings power semantic search, clustering, recommendation, duplicate detection, and many RAG pipelines. Their quality depends on the training objective and the domain in which they are used.

See also Vector Search, Tokenization, and Retrieval-Augmented Generation (RAG).