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Glossary

What is embedding?

An embedding is a numeric vector that represents the meaning of text, code, or an image, so similar items sit close together in vector space and can be compared mathematically.

An embedding is produced by an embedding model that maps an input — a sentence, a document, a snippet of code — to a fixed-length vector of numbers. Inputs with similar meaning land near each other, which turns 'is this similar?' into a fast distance calculation. Embeddings are the foundation of semantic search, recommendations, clustering, and retrieval-augmented generation.

Open-source embedding models trade off dimensionality, speed, language coverage, and domain fit. The right choice depends on your data: a general model works for prose, while code or a specialised domain often benefits from a model trained for it.

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▌ embedding — FAQ

What is embedding?
An embedding is a numeric vector that represents the meaning of text, code, or an image, so similar items sit close together in vector space and can be compared mathematically. An embedding is produced by an embedding model that maps an input — a sentence, a document, a snippet of code — to a fixed-length vector of numbers. Inputs with similar meaning land near each other, which turns 'is this similar?' into a fast distance calculation. Embeddings are the foundation of semantic search, recommendations, clustering, and retrieval-augmented generation.