Embedding

An embedding is a dense numerical vector that represents a piece of content (text, image, audio) in a way that semantically similar items end up close together in the vector space.

What is Embedding?

Embeddings are produced by neural networks trained for similarity. A sentence like 'reset my password' will have a vector very close to 'change my login credentials' even though no words overlap. Embeddings power semantic search, clustering, deduplication, recommendation, and the retrieval step of RAG pipelines. Quality and dimensionality vary by model. Embeddings of personal data are themselves personal data under GDPR.

How does Embedding apply to enterprise AI?

Enterprises store document embeddings in a vector database to enable semantic retrieval over policy libraries, knowledge bases, ticket archives, and contract repositories.