Description

Stop relying on outdated search methods. Deploy vector search and deliver smarter, more intuitive search experiences that keep users engaged. This comprehensive guide takes a deep dive into the world of vector search, offering a hands-on approach for developers looking to bring AI-powered utility and precision into their projects. This book demystifies the core concepts of vector search, making them accessible and practical—and you don’t need a background in math to learn vector search techniques!

Revolutionize search with AI-powered techniques that go beyond simple keyword matching. Implement vector search to build applications that understand intent, meaning, and similarity. Generate embeddings, construct efficient vector indexes, and power recommendation systems, personalized search, and content discovery tools. Master practical techniques to integrate vector search into JavaScript applications with real-world examples and step-by-step tutorials.

Cut through the complexity and apply AI-driven search strategies to create better user experiences. Use vector search to return more relevant results, surface hidden insights, and handle ambiguous queries with greater precision. Build scalable, high-performance search systems that enhance products across industries, from e-commerce and media to finance and healthcare.

Contents and Extracts

Foreword

Introduction

• Foundations of Vector Search
  • Getting Started with Vector Search
    • Setting Up the Project
    • Scraping the Titles
    • Embedding the Titles
    • Comparing to Your Title Idea
    • Putting It All Together
    • Key Takeaways
  • Understanding Vector Search
    • What Do Vectors Have to Do with Similarity?
    • Unpacking the Concept of Embeddings excerpt
    • Understanding Similarity
    • Key Takeaways
  • Generating Vector Embeddings
    • Breaking Down an Individual Embedding
    • Generating Embeddings with the OpenAI API
    • Key Takeaways
• Building a Vector Search Service
  • Building the Foundation for Vector Search
    • Scaffolding Your Node.js Project
    • Choosing a Vector Database
    • Installing Dependencies
    • Key Takeaways
  • Structuring the Back End for Vector Search
    • Introduction to the Back-End Architecture
    • Creating Data Models for the Platform
    • Key Takeaways
  • Building the Vector Embedding Generation Service
    • Structuring the Embedding Service
    • Storing Embeddings in the Database
    • Creating Vector Embeddings
    • Key Takeaways
  • Creating a Vector Search Service
    • Generating Embeddings for User Queries
    • Performing Similarity Searches
    • Key Takeaways
  • Creating a Vector Search Index
    • Understanding Search Indexes
    • Creating a Vector Search Index
    • Integrating the Vector Search Index
    • Key Takeaways
  • Incorporating Vector Search Functionality
    • Understanding the Current API Service Layer
    • Designing the Search API Endpoint
    • Implementing the Search API Endpoint
    • Managing Rate Limiting and Security Considerations
    • Key Takeaways
  • Optimizing Search Results
    • Weighted Ranking
    • Understanding Stop Words
    • Optimizing Results with a Hybrid Search System
    • Key Takeaways
  • Practical Applications and Next Steps
    • Exploring Upcoming New Developments
    • Taking a Look at Real-World Use Cases