pgvector is a PostgreSQL extension that enables vector embedding storage and semantic search within existing databases without requiring separate vector systems.
- •Uses similarity metrics (L2, cosine similarity, inner product) to find semantically similar data
- •Supports two indexing types: HNSW for fast queries but high memory use, and IVFFlat for memory efficiency with slower queries
- •Powers AI features including semantic search, RAG, recommendations, image similarity and anomaly detection within Postgres
- •Handles millions to tens of millions of vectors; pgvectorscale extends scalability for larger workloads
- •Best for ~100M vector applications; dedicated vector databases suit massive multi-tenant workloads
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