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AI & Machine Learning · Foundational

Embedding

A dense numeric vector representing the meaning of text, images or other data so that similar items are close together.

Definition

Embedding models map inputs into a high-dimensional vector space, typically hundreds to a few thousand dimensions. Distances between vectors reflect semantic similarity, enabling semantic search, clustering, recommendations and RAG retrieval.

Example

The sentences 'reset my password' and 'I forgot my login' have embeddings close together.

Exam tip

Embeddings power semantic search; the same embedding model must be used for documents and queries.

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