AI Glossary Term

What does “Embedding Model” mean?

Definition

A model specialized for converting data into vectors used for semantic search, clustering, and retrieval.

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How Embedding Model works

Diagram for Embedding Model, steps in order: Text, image, or audio; then Embedding model; then Vector of numbers; then Used for search, clustering, retrieval.Text, image, or audioEmbedding modelVector of numbersUsed for search, clustering, retrieval

Example

A company runs every product description through an embedding model once and stores the vectors. A shopper's search is embedded with the same model, and the nearest product vectors are returned.

Why it matters

The embedding model determines how well similarity search works. Choosing one that fits your language and domain matters.

Common questions

Embedding model vs chat model?

An embedding model outputs vectors for comparison; a chat model generates text.

Can I mix models?

Not for comparison. Vectors from different embedding models are not interchangeable.

How do I choose one?

Test candidates on your own queries and documents, and check language support and cost.

Related terms

EmbeddingA numeric vector representation that captures semantic meaning of text, images, or other data.Feature ExtractionConverting raw data into informative features that a model can use.Hybrid SearchA retrieval approach that combines keyword (lexical) search with vector (semantic) search for better recall and precision.Knowledge GraphA graph structure of entities and relationships used for reasoning or retrieval.RAG (Retrieval-Augmented Generation)A method that retrieves external knowledge and feeds it into generation at inference time.Semantic SearchSearch that matches meaning rather than exact keyword overlap, often using embeddings.

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See also

Distilled ModelEval HarnessDifferential PrivacyFeature StoreData ProvenanceGroundednessData LineageGuarded Decoding