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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
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27
Index

Part 4

Using PostgreSQL as an AI Platform

In the fourth part of the book, you'll learn about AI-specific requirements and how they can be addressed with PostgreSQL. We'll show you how to handle natural language as input and output, and how to combine SQL data with semantic data using vectors and embeddings. pgvector is PostgreSQL's preferred tool for representing semantic information, and we'll teach you how to use it to enable semantic search, build recommendation engines, and extend simple reasoning models into multi-modal tools that support advanced Retrieval Augmented Generation (RAG) capabilities.

Separate chapters focus on integrating with Large Language Models to generate AI embeddings, building robust pipeline architectures, and leveraging the Model Context Protocol (MCP) to create reliable, auditable, and secure AI applications.

This part of the book includes the following chapters:

  • Chapter 15: Database Requirements for Artificial...
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