Vector Database Systems Engineering (Paperback)

Zhao Colton

ISBN 13: 9798275866773

Published by Independently Published, 2025

About this Item

Paperback. Vector Database Systems Engineering is an advanced, practical guide to building intelligent, scalable, and high-performance vector-native architectures for modern AI applications, this book explores the core principles, engineering patterns, and production methodologies behind vector search, embedding management, and semantic retrieval.You'll learn how to evaluate and implement vector databases, optimize similarity search, design hybrid indexing structures, and build large-scale retrieval-augmented generation (RAG) pipelines for real-world applications. From embeddings lifecycle management to distributed storage, from latency optimization to multi-model retrieval routing, this book explains how to construct robust AI data systems that support search, reasoning, and generative intelligence.Packed with detailed architectural breakdowns, hands-on examples, performance benchmarks, and infrastructure blueprints, this guide empowers you to make informed decisions about vector database technologies, system capacity planning, replication strategies, and long-term AI data governance. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9798275866773

Bibliographic Details

• Title: Vector Database Systems Engineering (Paperback)
• Publisher: Independently Published
• Publication Date: 2025
• Language: English
• ISBN 13: 9798275866773
• Binding: Paperback
• Condition: new

About this title

Synopsis

Vector Database Systems Engineering is an advanced, practical guide to building intelligent, scalable, and high-performance vector-native architectures for modern AI applications, this book explores the core principles, engineering patterns, and production methodologies behind vector search, embedding management, and semantic retrieval.
You’ll learn how to evaluate and implement vector databases, optimize similarity search, design hybrid indexing structures, and build large-scale retrieval-augmented generation (RAG) pipelines for real-world applications. From embeddings lifecycle management to distributed storage, from latency optimization to multi-model retrieval routing, this book explains how to construct robust AI data systems that support search, reasoning, and generative intelligence.
Packed with detailed architectural breakdowns, hands-on examples, performance benchmarks, and infrastructure blueprints, this guide empowers you to make informed decisions about vector database technologies, system capacity planning, replication strategies, and long-term AI data governance.