While pgvector enables powerful semantic search, it doesn’t automatically keep embeddings in sync when your data changes, requiring manual updates. The pgai Vectorizer automatically keeps PostgreSQL vector embeddings in sync by generating and updating them whenever your data changes, removing the need for manual regeneration with pgvector.
It runs in the background using a worker that processes changes via queues, triggers, and embedding APIs. This makes it easy to build real-time semantic search in PostgreSQL using pgvector and TigerData’s pgai tools. Learn everything you need to know in this guide.
It’s no secret that PostgreSQL now stores vector embeddings