Embeddings translate words, sentences, or entire documents into high-dimensional vectors that encode their semantic meaning. This allows content to be compared and found by meaning rather than exact keywords. They are the foundation of semantic search, recommendation systems, and RAG architectures. Embeddings are stored in specialized vector databases that allow fast similarity searches.
Embedding
An embedding is the representation of text as a list of numbers (a vector) that captures its meaning. Similar content lies close together in vector space, which enables semantic search.