Glossary · AI & Development
Embeddings
Numerical vector representations of text (or other content) that capture semantic meaning, enabling similarity search and retrieval.
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An embedding is a fixed size array of floating point numbers produced by a model when it encodes a piece of text, image or other content. Semantically similar content produces similar (close in vector space) embeddings. This property is what makes similarity search work. Given a query, you embed it, then find the closest stored embeddings in a vector database and retrieve their associated content. Embedding models include OpenAI's text-embedding series, Cohere's Embed models, and open source options like Nomic Embed. Embeddings are the input layer for retrieval-augmented generation and semantic search.
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