Definition: Embeddings are numerical vectors that represent the meaning of text, images or other data, placed so that similar items sit close together, which lets software compare things by meaning rather than exact words.
An embedding model reads an input, such as a sentence or a product description, and outputs a fixed-length list of numbers. Inputs with related meaning produce vectors that are near each other, measured with a similarity score such as cosine similarity. The vectors are usually stored in a vector database or a database extension so they can be searched quickly.
Embeddings are the building block behind semantic search, recommendations, clustering, deduplication and the retrieval step in RAG. The quality of the embedding model and how documents are split into chunks strongly affect what a system can find.
Teams choose an embedding model, decide how to chunk and label their data, build a pipeline that keeps vectors in sync as source documents change, and test retrieval quality with real queries before connecting it to a language model.
Ryz vector database engineers and NLP engineers design embedding pipelines in your cloud, working on your team.
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