Glossary · Definition
Embeddings
Embeddings are dense numerical vectors that represent the meaning of text (or images, audio) in a way that semantic similarity = vector closeness. They're the foundation of RAG, semantic search, recommendation, and clustering.
Definition
Embeddings are dense numerical vectors that represent the meaning of text (or images, audio) in a way that semantic similarity = vector closeness. They're the foundation of RAG, semantic search, recommendation, and clustering.
What it means
An embedding model takes input (usually text) and outputs a vector of typically 512-3,072 floats. Two pieces of text with similar meaning produce vectors close together (by cosine distance). OpenAI text-embedding-3-large produces 3,072-dim vectors and dominates US production deployments. Voyage 3 and Cohere embed-v4 are competitive; BGE-M3 is the leading open-weight option.
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Why it matters
Embeddings are how RAG, semantic search, and most personalization systems actually work under the hood. Embedding quality directly determines RAG retrieval quality. The model + dimension choice has cost implications (storage cost scales with dimension; inference cost scales with model size).
Related free tools
Frequently asked questions
Best embedding model in 2026?
For English: OpenAI text-embedding-3-large (highest MTEB) or Voyage 3 large. For self-host: BGE-M3 (multilingual + free). For cost: text-embedding-3-small at $0.02/1M tokens.
How do I use them?
Embed your documents into a vector DB. At query time, embed the query and retrieve the top-k most-similar documents. Pass to LLM as context (RAG).
Related terms
- DefinitionRAG (Retrieval-Augmented Generation)RAG (Retrieval Augmented Generation) augments an LLM with documents retrieved at query time — typically from a vector database. The LLM grounds its answer in the retrieved text instead of relying purely on training data.
- DefinitionVector databaseA vector database stores high-dimensional embedding vectors and supports fast nearest-neighbor search (find vectors similar to a query). Foundation of RAG, semantic search, and recommendation systems.