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Numeric vectors that capture the meaning of text (or images) so that similar things end up close together in space.
An embedding model turns a chunk of text into a fixed-length list of numbers where semantic similarity becomes geometric closeness — 'dog' and 'puppy' land near each other, 'dog' and 'tax form' don't. You compute them once, store them in a vector DB, and compare new queries by distance. They're the foundation of semantic search, RAG retrieval, clustering, and dedup — anywhere you need 'find things that mean the same' rather than 'find this exact string'.
Plainly
Think of Embeddings as roads and power for the app city. Numeric vectors that capture the meaning of text (or images) so that similar things end up close together in space.
In practice
Use it when local behavior needs to become a reachable, reliable deployed service. In practice, define the owner, input, output, and failure mode before you rely on it.