menu
{ "item_title" : "Vector Databases for AI Applications", "item_author" : [" Edwards Hulett "], "item_description" : "Vector Databases for AI Applications: Build Semantic Search, RAG Systems, and High-Performance Embedding Pipelines for Intelligent Applications Modern AI applications are no longer built on keywords alone-they rely on meaning, context, and intelligent retrieval. Vector Databases for AI Applications shows you how to design and build systems that understand data at a deeper level, enabling smarter search, more accurate recommendations, and powerful AI-driven experiences. This book takes you beyond theory and into real implementation. You will learn how embeddings transform text, images, and data into vectors that machines can understand, and how vector databases make it possible to store, index, and query this information efficiently at scale. From there, you will build practical systems that power semantic search, similarity matching, and real-time intelligent applications. A major focus of the book is Retrieval-Augmented Generation (RAG)-one of the most important techniques in modern AI. You will learn how to connect large language models with external knowledge sources, improving accuracy, reducing hallucinations, and delivering context-aware responses. Step by step, you will design and implement RAG pipelines that are fast, scalable, and production-ready. As you progress, you will explore how to build high-performance embedding pipelines, optimize retrieval strategies, and design architectures that handle large datasets and high query volumes. The book also covers performance tuning, indexing strategies, and system design patterns that ensure your applications remain efficient as they grow. Throughout the book, you will work with real-world scenarios such as building intelligent search engines, recommendation systems, knowledge assistants, and AI-powered business tools. Each concept is explained clearly and backed by practical examples that show how these systems are built and deployed in real environments. Whether you are a software developer, machine learning engineer, or technical architect, this book equips you with the knowledge and skills to build scalable, intelligent applications powered by vector databases and modern AI techniques. If you want to move beyond basic AI integrations and start building systems that truly understand and retrieve information intelligently, this book will guide you every step of the way. Start building smarter AI applications today and unlock the full power of semantic search, RAG, and vector-based intelligence.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/9/79/825/725/9798257253034_b.jpg", "price_data" : { "retail_price" : "32.00", "online_price" : "32.00", "our_price" : "32.00", "club_price" : "32.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Vector Databases for AI Applications|Edwards Hulett

Vector Databases for AI Applications : Build Semantic Search, RAG Systems, and High-Performance Embedding Pipelines for Intelligent Applications

local_shippingShip to Me
In Stock.
FREE Shipping for Club Members help

Overview

Vector Databases for AI Applications: Build Semantic Search, RAG Systems, and High-Performance Embedding Pipelines for Intelligent Applications

Modern AI applications are no longer built on keywords alone-they rely on meaning, context, and intelligent retrieval. Vector Databases for AI Applications shows you how to design and build systems that understand data at a deeper level, enabling smarter search, more accurate recommendations, and powerful AI-driven experiences.

This book takes you beyond theory and into real implementation. You will learn how embeddings transform text, images, and data into vectors that machines can understand, and how vector databases make it possible to store, index, and query this information efficiently at scale. From there, you will build practical systems that power semantic search, similarity matching, and real-time intelligent applications.

A major focus of the book is Retrieval-Augmented Generation (RAG)-one of the most important techniques in modern AI. You will learn how to connect large language models with external knowledge sources, improving accuracy, reducing hallucinations, and delivering context-aware responses. Step by step, you will design and implement RAG pipelines that are fast, scalable, and production-ready.

As you progress, you will explore how to build high-performance embedding pipelines, optimize retrieval strategies, and design architectures that handle large datasets and high query volumes. The book also covers performance tuning, indexing strategies, and system design patterns that ensure your applications remain efficient as they grow.

Throughout the book, you will work with real-world scenarios such as building intelligent search engines, recommendation systems, knowledge assistants, and AI-powered business tools. Each concept is explained clearly and backed by practical examples that show how these systems are built and deployed in real environments.

Whether you are a software developer, machine learning engineer, or technical architect, this book equips you with the knowledge and skills to build scalable, intelligent applications powered by vector databases and modern AI techniques.

If you want to move beyond basic AI integrations and start building systems that truly understand and retrieve information intelligently, this book will guide you every step of the way.

Start building smarter AI applications today and unlock the full power of semantic search, RAG, and vector-based intelligence.

This item is Non-Returnable

Details

  • ISBN-13: 9798257253034
  • ISBN-10: 9798257253034
  • Publisher: Independently Published
  • Publish Date: April 2026
  • Dimensions: 9.61 x 6.69 x 1.12 inches
  • Shipping Weight: 1.92 pounds
  • Page Count: 554

Related Categories

You May Also Like...

    1

BAM Customer Reviews