A dense vector representation of text so that similar meaning tends to land near similar vectors—used for search, clustering, and RAG retrieval.
An embedding model maps text to a fixed-length vector. Distance (for example cosine) approximates semantic similarity for that model’s training distribution.
In engineering workflows
Embeddings power semantic search and many RAG pipelines, often stored in a vector database. Quality depends on domain fit and freshness of the embedded content.