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Book synopsis

Building AI applications with PostgreSQL can feel complicated once embeddings, vector search, RAG pipelines, indexing, security, and production performance enter the picture. PostgreSQL AI Engineering turns those moving parts into a clear, practical path you can follow step by step.

You do not need prior experience with pgvector, vector indexing, hybrid retrieval, or production RAG systems. The book introduces these topics progressively through practical implementation. You should, however, be comfortable with basic SQL, PostgreSQL, and application development.

Rather than teaching isolated commands, this book shows how PostgreSQL can become a dependable foundation for real AI applications. A recurring multi-tenant knowledge assistant ties the chapters together so you can see how data modeling, retrieval, security, evaluation, and operations connect in a production system.

Key Features

• Practical PostgreSQL and pgvector implementation

• Exact and approximate vector search with HNSW and IVFFlat

• Production RAG ingestion, retrieval, and context pipelines

• PostgreSQL full-text and hybrid search

• Metadata-aware and permission-aware retrieval

• Query optimization with EXPLAIN and EXPLAIN ANALYZE

• Multitenancy, authorization, and Row-Level Security

• Retrieval evaluation, observability, scaling, backup, and recovery

• Practical exercises, schemas, checklists, and quick-reference material

What You Will Learn

• Design PostgreSQL-centered AI architectures

• Model documents, chunks, metadata, provenance, and versioned embeddings

• Build semantic search with pgvector

• Choose and tune vector indexes based on measurable requirements

• Combine lexical and vector retrieval for stronger search quality

• Build reliable production RAG pipelines

• Integrate retrieval with APIs, workers, workflows, and transactions

• Secure AI data with tenant isolation and authorization controls

• Monitor retrieval quality, latency, database health, and failures

• Recognize when PostgreSQL is the right fit-and when specialized retrieval infrastructure is justified

Who This Book Is For

Backend developers, PostgreSQL users, AI application engineers, data engineers, platform engineers, and technical practitioners who understand basic SQL and application development but are new to pgvector, embeddings, semantic search, RAG, hybrid retrieval, or production AI architecture.

Table of Contents

1. PostgreSQL as a Production AI Data Platform

2. Modeling Documents, Metadata, Embeddings, and AI State

3. Engineering Vector Search with pgvector

4. Metadata-Aware Retrieval and Query Performance

5. Full-Text, Hybrid Retrieval, and Ranking

6. Building Production RAG with PostgreSQL

7. Integrating PostgreSQL into Production AI Applications

8. Multitenancy, Authorization, and AI Data Security

9. Scaling and Operating PostgreSQL AI Systems

If you are ready to move beyond simple vector-search demos and understand how PostgreSQL, pgvector, RAG, and production engineering fit together, this book gives you a practical roadmap to build, evaluate, secure, scale, and operate reliable AI retrieval systems.

Book details

• Full title: PostgreSQL AI Engineering
• Subtitle: A Practical Guide to Production RAG, Vector Search, and AI Applications with pgvector
• Author: Adrian Solvek
• Language: English
• Binding: Paperback
• Number of pages: 290
• EAN: 9798172128455
• ID: 53783743
• Publisher: Independently published
• Weight: 508 g
• Dimensions: 254 × 178 × 15 mm
• Date of publishing: 05. September 2026

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