Embedding Model
A neural network that converts text (or images, audio) into dense numeric vectors capturing meaning.
An embedding model maps content into a high-dimensional vector space where semantically similar items sit close together. It's the foundation of vector search, RAG, recommendation and semantic clustering.
In 2026, dominant text embedding models include OpenAI's text-embedding-3-large, Cohere embed-v4, Google Gecko, and open-source BGE and E5 variants. Multimodal embeddings (CLIP, SigLIP, EVA) handle image+text; audio embeddings (CLAP) handle audio+text.
Choice matters: dimension size affects cost and speed; language coverage matters for non-English; task-specific vs general-purpose can shift accuracy 10+ points. Benchmark on your workload.
Key points
- Maps content to dense vectors capturing meaning
- Foundation of vector search, RAG and recommendations
- Dimension size: 384 to 3072 typical; affects storage and speed
- Language coverage varies dramatically — non-English needs multilingual models
Common use cases
Frequently asked
Related terms
A numerical vector that captures the semantic meaning of a piece of content.
A database optimized for storing and searching high-dimensional embeddings by similarity.
A pattern that grounds LLM answers in your private documents by retrieving relevant snippets at query time.
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