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  • Book Overview & Buying AI-Ready PostgreSQL 18
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AI-Ready PostgreSQL 18

AI-Ready PostgreSQL 18

By : Vibhor Kumar, Marc Linster
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AI-Ready PostgreSQL 18

AI-Ready PostgreSQL 18

By: Vibhor Kumar, Marc Linster

Overview of this book

In today’s data-first world, businesses need applications that blend transactions, analytics, and AI to power real-time insights at scale. Mastering PostgreSQL 18 for AI-Powered Enterprise Apps is your essential guide to building intelligent, high-performance systems with the latest features of PostgreSQL 18. Through hands-on examples and expert guidance, you’ll learn to design architectures that unite OLTP and OLAP, embed AI directly into apps, and optimize for speed, scalability, and reliability. Discover how to apply cutting-edge PostgreSQL tools for real-time decisions, predictive analytics, and automation. Go beyond basics with advanced strategies trusted by industry leaders. Whether you’re building data-rich applications, internal analytics platforms, or AI-driven services, this book equips you with the patterns and insights to deliver enterprise-grade innovation. Ideal for developers, architects, and tech leads driving digital transformation, this book empowers you to lead the future of intelligent applications. Harness the power of PostgreSQL 18—and unlock the full potential of your data.
Table of Contents (28 chapters)
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1
Part 1: Introducing PostgreSQL and Setting the Stage
5
Part 2: Creating Transactional Applications
11
Part 3: Creating Analytical Applications
18
Part 4: Using PostgreSQL as an AI Platform
27
Index

Summary

This chapter turned embedding generation into a production pipeline rather than a one-off API call. Building on Chapter 18, Step-by-step Approach to Integrating LLMs with PostgreSQL to Create Complete AI Applications, we shifted the core question from "Can we create embeddings?" to "How do we operationalize embeddings without breaking OLTP?"—with clear answers around freshness, failure handling, and scale.

We introduced the two-speeds-of-truth model: operational truth (product.*) must be fast and exact, while semantic truth (embeddings.*) must be reliable and recoverable—but should never hold transactions hostage. From that foundation, we made the key production decision: triggers should enqueue, not embed. Instead of calling the LLM (OpenAI in our example) directly from triggers, we implemented a durable embedding job queue (embeddings.embedding_job), a single-enqueue helper, and lightweight triggers for category, brand, product, and...

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AI-Ready PostgreSQL 18
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