Embeddings
An embedding is a numeric vector that represents the meaning of a piece of text or image, positioned so that similar meanings sit close together — the mechanism behind semantic search and retrieval.
An embedding model turns text into a list of numbers — a vector — placed so that semantically similar texts are near each other in that space. This lets a system find relevant material by meaning rather than exact keywords: embed the query, embed the documents, and return the nearest vectors. Embeddings are the engine under retrieval-augmented generation and long-term agent memory, and the quality of the embedding model largely sets the ceiling on retrieval quality.
Key points
- Text or an image becomes a vector where similar meanings are close together.
- Enables semantic search — matching by meaning, not exact keywords.
- They power retrieval-augmented generation and long-term memory.
- Embedding quality largely caps how good retrieval can be.
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