This article provides a high-level, educational overview of pgvector rather than detailed implementation guidance.

pgvector adds a new data type to Postgres called vector. It allows embeddings, numerical representations of text, images or other content to be stored alongside relational data without requiring a separate system. These embeddings are typically generated by machine learning models that convert content such as text or images into numerical form.

At a high level, the process is simple. Embeddings are stored in the database. When a query is received, a query embedding is generated from the input, and pgvector returns the records whose vectors are most similar, or closest in meaning to that query. Instead of matching keywords, results are retrieved based on meaning.

pgvector includes several features that make vector search practical within Postgres.