pgsemantic adds zero-config semantic search to Postgres

This is an effective approach to Postgres AI tooling that manages embedding logic internally instead of requiring external vector stores. The UI-driven column scoring simplifies setup, while trigger-backed synchronization and a background worker maintain embeddings efficiently. Local and cloud model options provide a practical privacy ladder, and MCP support offers a smart wedge for agent workflows. However, the tool's effectiveness remains dependent on descriptive text and sensible database hygiene.