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Welcome to the world of vector search! By opening this book, you’ve taken the first step toward mastering an area of technology that powers modern search experiences. Vector search leverages advanced machine learning techniques to deliver relevant, precise, and context-aware results across various domains. Whether it’s driving the search bar on a website, personalizing content recommendations, or enabling cutting-edge anomaly detection, vector search redefines how we access and interact with information. As user expectations for seamless and intuitive experiences grow, traditional keyword-based search systems often fall short of the mark. Users demand accurate results even when their queries are imprecise, support for contextual understanding, and the ability to surface hidden insights within vast datasets. Vector search meets these challenges by embedding data into dense mathematical representations, enabling powerful similarity calculations that uncover deeper relationships and meaning. This book is your guide to understanding and implementing vector search systems. We’ll explore the core concepts, tools, and techniques that underpin vector search and provide you with the knowledge and skills to build your search applications. By the end of this book, you’ll harness the potential of vector search and create intelligent, efficient search systems that deliver value to your users. This book is written for developers who want to build smarter, more effective search experiences. If you’re a back-end engineer looking to go beyond traditional search techniques or a full-stack developer curious about integrating AI into your applications, you’ll find the concepts and code examples in this book approachable and actionable. You’ll need some basic familiarity with JavaScript and Node.js, but you don’t need a background in machine learning or search theory. Every concept is introduced clearly, explained in plain language, and backed by working code. By the time you’re done, you’ll have both a solid understanding of vector search and the skills to apply it in your own projects. This book provides a hands-on approach to vector search, blending theoretical explanations with real-world coding examples. You’ll learn how to implement vector search step by step, from generating embeddings to optimizing performance. We’ll cover key concepts such as vector indexing, query handling, and hybrid search techniques along the way. Each chapter builds on the last, but if you’re already familiar with specific topics, feel free to jump to the sections that interest you most; for example, if you’re already knowledgeable on the foundations of vector search, you may want to skip Chapter 2, Understanding Vector Search, on page 11. Throughout the book, you’ll work on a project based on the RealWorld example app, a full-stack blogging platform that mimics the features of Medium. You’ll enhance this familiar app with vector-powered functionality, step by step. By embedding content, enabling similarity search, and layering in hybrid techniques, you’ll transform a conventional CRUD app into a modern, intelligent search experience. This project grounds each concept in practical, real-world implementation and serves as a running example you can adapt and expand in your own work. This book is structured in two parts. Part 1, Foundations of Vector Search, includes Chapters 1 through 3 and focuses on the core ideas: what vector search is, how it works, and the building blocks such as embeddings and similarity. Part 2, Building a Vector Search Service, spans Chapters 4 through 11 and walks you through designing, implementing, and optimizing a fully functional search system using real code and real data. Wondering where to begin? You’ll get your feet wet with a quick hands-on vector search project in the first chapter. The second chapter will introduce you to the fundamentals of vector search, including how it differs from traditional search methods and a high-level overview of commonly used techniques. If you’re already familiar with the basics, you can skip ahead to Chapter 3, Generating Vector Embeddings, on page 15, to dive into how vector embeddings work and why they’re essential. Feeling ready to build? Head straight to Chapter 4, Building the Foundation for Vector Search, on page 25, to set up your environment and start coding your own vector search system from the ground up.