April 2026
Intermediate to advanced
580 pages
14h 12m
English
Content preview from AI-Ready PostgreSQL 18
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20
PostgreSQL and MCP: The Blueprint for a Robust AI Assistant
If Chapter 19, Production-Ready AI Embedding Pipeline Patterns was the engine room, pipelines, queues, retries, and freshness, then this chapter is the cockpit. So far, we've taught PostgreSQL how to store meaning (pgvector), maintain meaning (embedding pipelines), and retrieve meaning (semantic search and recommendations). That's a powerful foundation, but it still leaves a gap between capability and experience. Users don't want to "run a vector query." They want to ask things like the following:
- Find something like a tailored men's jacket under $500.
- Show me polo shirts in different colors, but only what's currently priced.
- What are the closest alternatives to this product?
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