The RAG infrastructure stack is often over-engineered: a separate vector database, a separate document store, a separate metadata store. PostgreSQL with pgvector gives you all three in one system: vector storage and search, document storage with full-text search, and structured metadata queries. This simplifies your architecture, reduces operational overhead, and leverages a database your team already knows.
Why PostgreSQL for RAG
PostgreSQL with pgvector provides: vector similarity search (cosine, L2, inner product), full-text search (for keyword-based retrieval), structured queries (filter by metadata — date, category, access level), ACID transactions (consistent writes), replication (high availability), and row-level security (access-controlled retrieval). You get a unified data layer instead of maintaining three separate systems. The operational benefit: one database to monitor, back up, and scale — not three.
The Schema Design
The RAG schema in PostgreSQL: a documents table (document metadata — title, source, created_at, access_level), a chunks table (chunk content, embedding vector(1536), document_id foreign key, chunk_index), and appropriate indexes. Use HNSW indexes on the embedding column for fast approximate nearest neighbour search. Use GIN indexes on the content column for full-text search. The combination enables hybrid search (semantic + keyword) in a single database.
Pro Tip
pgvector HNSW indexes achieve sub-50ms retrieval latency for millions of vectors. This is production-ready performance without a separate vector database.
Hybrid Search in PostgreSQL
Implement hybrid search with a single query: combine pgvector's vector similarity search with PostgreSQL's full-text search using a UNION ALL or weighted combination. The query retrieves semantically similar chunks AND keyword-matching chunks, then ranks the combined results. This catches both: semantic matches (intent-based retrieval) and exact matches (specific terms, error codes, product names). The query runs in a single database round-trip — no need to query two separate systems.
Conclusion
PostgreSQL with pgvector is a production-ready RAG database that simplifies your architecture. You get vector search, full-text search, structured queries, and access control in one system. For most RAG deployments, PostgreSQL is sufficient — and significantly simpler than a separate vector database.
Key Takeaways
- PostgreSQL + pgvector: vector search, full-text search, structured queries, access control in one system
- HNSW indexes achieve sub-50ms retrieval for millions of vectors — production-ready performance
- Hybrid search (semantic + keyword) in a single query — no separate systems needed
- One database to monitor, back up, and scale — not three
- For most RAG deployments, PostgreSQL is sufficient and simpler than a dedicated vector database