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What is Embedding Models

Definition

Embedding models are machine-learning models trained to convert input such as text, images, or code into dense vector embeddings that capture semantic meaning, providing the representations that power similarity search, retrieval, clustering, and other downstream AI tasks.

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Convert text, images, or code into meaning-rich vectors
Provide the representations behind semantic search and RAG
Enable similarity comparison, clustering, and classification
Offer reusable encodings across many downstream tasks

Real World Example

A team uses an embedding model to turn every support article into a vector, storing them in a vector database so user questions can be matched to the most semantically relevant article through similarity search.

FAQs

What do embedding models produce?

Dense vectors that represent the semantic meaning of inputs like text or images so similar items have similar vectors.

How are embedding models used?

To power semantic search, retrieval for RAG, clustering, recommendation, and similarity comparison.

How do you choose an embedding model?

Based on the data type, language, quality on relevant benchmarks, vector size, and cost for your use case.