What Are Embeddings?
Embeddings are numerical vector representations of text, images, or other data that capture meaning, so similar items sit close together in vector space — the foundation of semantic search and RAG.
Embeddings are numerical vector representations of data — text, images, or audio — that capture meaning, so that items with similar meaning have similar vectors and sit close together in “vector space.”
Why they matter
Embeddings are what make semantic search possible: instead of matching exact keywords, a system can find content that means the same thing. They’re the foundation of retrieval-augmented generation, recommendation systems, and clustering.
How they’re used
Text is converted to embeddings by an embedding model and stored in a vector database. A query is embedded the same way, and the system retrieves the nearest vectors — the most semantically relevant content.
Related
See RAG development and vector databases.
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