github.com/andre-carbajal/sql-ai-database-solutions

Databases and AI used to live in separate worlds. Your PostgreSQL instance handled structured queries, and your ML pipeline ran somewhere else entirely. That separation is rapidly disappearing.

In 2025, your SQL database can store embeddings, answer natural language questions, power semantic search, and serve as the memory layer for autonomous AI agents — all without adding a separate vector database to your stack.

This article walks through four real-world patterns with working code:

pgvector — storing and querying embeddings in PostgreSQL