RAG & Data

PostgreSQL / pgvector

Postgres plus pgvector gives AI apps a familiar SQL database with embedding storage and similarity search.

Best for

Teams that want to keep RAG data, metadata, and application records in Postgres.

Integration profile

Workflow: RAG & DataLocal support: trueSetup: IntermediateOpen source

Model support

Stores embeddings from local or hosted embedding models; generation happens through application code.

How to use this integration

Embedding search
RAG retrieval
Metadata filtering
AI app storage

Where it fits

PostgreSQL / pgvector fits in the rag & data layer of an AI stack. Use it when you need model outputs connected to real workflows, and evaluate it against your privacy needs, deployment model, team habits, and operational complexity.

Related models

Multilingual E5 Large
Qwen3 Embedding

Source and docs

pgvector GitHub