PostgreSQL + pgvector
By adding pgvector to the PostgreSQL ecosystem, semantic search and Retrieval-Augmented Generation (RAG) scenarios become manageable in the SQL pattern.
Architecture
- Embeddings table, text/document metadata
- KNN/IVFFLAT indexes and filterable search
- Multi-tenant with authorization and row-level security
Performance Tips
- Dimension and distance metric selection
- Caching and paging strategies
- Batch insert and periodic reindex
When Should It Be Preferred?
- When you want to add semantic search without breaking the existing SQL engine
- In enterprise environments where compliance and auditability are a priority
Conclusion: With pgvector, Postgres offers a practical and manageable platform for RAG.
