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Nitin Borwankar Vector Databases A Practical Introduction

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9 7 8 1 0 9 8 1 7 7 5 9 1 5 6 9 9 9 ISBN: 978-1-098-17759-1 US $69.99 CAN $87.99 DATA Nitin Borwankar is a seasoned data scientist and database professional, known for his contributions to data education and open source machine learning tools over a career spanning three decades. He is a frequent conference speaker, offering a pragmatic approach to AI and large language models. The AI revolution is here, and at its core lies a game-changing technology that most developers haven’t fully explored: vector databases. From powering semantic search to enabling large language models (LLMs) and generative AI, vector databases are reshaping how we build applications with unstructured data like text, images, and audio. But how do you go from curious to capable with this vital technology? That’s where this book comes in. In this hands-on guide, author Nitin Borwankar takes you through the why, what, and how of vector databases, starting with the basic theory behind vector embeddings and progressing to building applications with real-world tools. You’ll learn about Word2vec, how to convert open source SQL databases like SQLite3 and PostgreSQL into vector databases, and how to integrate them into retrieval-augmented generation (RAG) applications. Whether you’re a Python developer, data engineer, or ML practitioner, this book gives you the foundation to leverage vector databases confidently in your AI projects. • Understand the connection between vector databases, embeddings, and LLMs • Learn practical approaches for transforming SQL databases into vector databases • Build RAG applications for both personal and enterprise use • Apply vector databases to solve real-world AI challenges • Learn how to use vector databases with LLMs to build applications Vector Databases “Vector Databases by Nitin Borwankar demystifies the technology powering modern AI—from embeddings to semantic search and RAG systems. If you’re building AI applications today, this is a book you need to understand and apply these concepts in practice.” Tom Taulli Author of AI-Assisted Programming

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Nitin Borwankar Vector Databases A Practical Introduction

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978-1-098-17759-1 [LSI] Vector Databases by Nitin Borwankar Copyright © 2026 Nitin Borwankar. All rights reserved. Published by O’Reilly Media, Inc., 141 Stony Circle, Suite 195, Santa Rosa, CA 95401. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com. Acquisitions Editor: Andy Kwan Development Editor: Sara Hunter Production Editor: Beth Kelly Copyeditor: Audrey Doyle Proofreader: Krsta Technology Solutions Indexer: WordCo Indexing Services, Inc. Cover Designer: Susan Thompson Cover Illustrator: José Marzan Jr. Interior Designer: David Futato Interior Illustrator: Kate Dullea April 2026: First Edition Revision History for the First Edition 2026-04-06: First Release See http://oreilly.com/catalog/errata.csp?isbn=9781098177591 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Vector Databases, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the author and do not represent the publisher’s views. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.

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Table of Contents Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi 1. Introduction to Vector Databases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Why Do You Need Vector Databases? 1 A New Data Type: Vector 2 Similarity Search 3 What’s Different About the Vector Type? 6 Where Do You Use Vector Databases? 8 SQL Versus Vector Databases 9 The Foundation of Business Math: Accounting Arithmetic 9 Vector Representation in a Relational Database Management System 10 The Need for Vector-Specific Capabilities 11 NoSQL Versus Vector Databases 11 NoSQL Databases and Vector Storage 11 Limitations of Vector Extensions in NoSQL Databases 12 When to Choose NoSQL with Vector Extensions 12 Hybrid Approaches: Combining Structured and Vector Data 13 The Need for Both Vector Data and Metadata 13 Limitations of Pure Vector Storage 13 Hybrid Database Architecture 14 Example of a Hybrid Query 14 Benefits of the Hybrid Approach 15 Conclusion 15 2. Embeddings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Understanding Vector Embeddings: Why We Need Them 17 Word2Vec: The Breakthrough That Changed Everything 19 Doc2Vec: From Words to Documents 20 iii

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From Embeddings to Modern Language Models: The Transformer Connection 23 Encoder-Only Transformers (BERT and Its Variants) 24 Decoder-Only Transformers (GPT Family) 24 Encoder-Decoder Transformers (T5, BART) 25 Embedding Models: The Specialized Vector Generators 26 Distinction from Traditional Models 26 Role in Modern LLM Applications 27 Practical Applications and Use Cases 28 Simple RAG Pipeline 28 The sentence-transformers Library: The Swiss Army Knife of Text Embeddings 31 Best Practices for Using SentenceTransformers: A Detailed Guide 35 The Embedding Layer: The Gateway to Zero-Shot Learning 40 Anatomy of Transformer Embeddings 40 Connection to Zero-Shot Learning 42 Key Characteristics That Enable Zero-Shot Learning 43 Limitations and Considerations 45 Latest Developments and Trends 46 Vector Arithmetic with Word2Vec: A Hands-On Guide 46 Step 1: Setup and Installation 46 Step 2: Load Pretrained Word2Vec Model 46 Step 3: Implement Vector Arithmetic Functions 47 Step 4: Classic King–Queen Analogy 48 Step 5: More Interesting Analogies 49 Step 6: Interactive Exploration Tool 49 Final Words on Vector Arithmetic 50 Conclusion 51 3. Similarity Search with FAISS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Foundations 53 Vector Representations 55 Distance Metrics 56 Selection Heuristics 58 FAISS Indexes 58 Flat Indexes (Brute Force) 58 IVF-Based Indexes 59 LSH-Based Indexes 60 HNSW-Based Indexes 61 Other Specialized Indexes 61 Composite and Transformative Indexes 62 Choosing the Right Index 62 iv | Table of Contents

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Quantization 65 SQ 65 PQ 67 The ANN Problem 71 The Problem 72 Avoid Computational Cost 72 Key ANN Techniques in FAISS 73 Choosing an Index in FAISS 75 Code Example 75 Understanding HNSW Indexes 76 What Is HNSW? 77 How HNSW Works 78 Key Parameters Explained 79 Practical Example: Building a Similarity Search System 80 Performance Characteristics 81 Best Practices 82 FAISS Architecture and Components 83 Foundation 83 Core Concepts 85 Key Components 85 Common Workflow 87 Illustrative Example 87 Key Takeaways 88 Further Exploration 88 Conclusion 89 4. Semantic Search with SQLite3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 Understanding the SQLite Vector Similarity Search Extension 91 Core Capabilities 92 Architecture Overview 93 Limitations 93 Setting Up the Development Environment 94 Installing Dependencies 94 Verifying the Installation 95 Operational Pragmas 96 Designing the Database Schema 96 Schema Requirements 96 Table Definitions 96 Schema Design Decisions 98 Connecting to Reddit with the Python Reddit API Wrapper 99 Creating Reddit API Credentials 99 PRAW Client Implementation 99 Table of Contents | v

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Usage Example 102 Content Extraction and Preprocessing 102 Text Cleaning Pipeline 102 Quality Filtering 105 Generating and Storing Embeddings 105 Embedding Generator 106 Database Storage 108 Batch Processing Pipeline 112 Building the Vector Index 113 Understanding VSS Indexing 113 Index Management 114 Implementing Semantic Search 117 Search Result Container 117 Search Engine 117 Putting It All Together 123 Workflow Example 124 Example Output 126 Extension: Incremental Indexing 127 Conclusion 129 5. Building an ArXiv Paper Search System with PostgreSQL pgvector. . . . . . . . . . . . . . . . 131 The Challenge of Searching Scientific Literature 131 Why ArXiv Makes an Ideal Data Source 131 Real-World Use Cases 132 Technology Stack Rationale 132 Architecture Overview 133 System Components 133 Data Flow 134 Design Philosophy 135 Environment Setup and Dependencies 136 PostgreSQL and pgvector Installation 136 Python Environment Configuration 137 Directory Structure and Configuration 137 Verification and Testing 138 Database Design for Scientific Papers 139 Schema Design Principles 139 Core Tables Structure 140 Vector Storage Strategy 144 Indexing Strategy 144 ArXiv Integration and PDF Management 145 ArXiv API Client Implementation 146 PDF Download Pipeline 147 vi | Table of Contents

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Batch Processing System 148 PDF Text Extraction and Processing 150 PDF Extraction Challenges 150 Intelligent Text Chunking 152 Embedding Generation and Storage 153 Embedding Model Strategy 154 Batch Processing Pipeline 155 Similarity Search Implementation 156 Interactive Application and UI 158 Docker Packaging for Local Deployment 160 Container Architecture 161 Docker Compose Configuration 161 Database Initialization Scripts 163 Development Workflow 164 Cloud-Ready Design 164 Basic Performance Tuning 165 Index Configuration 165 Query Performance 165 Resource Management 165 Next Steps 165 Current Limitations 165 Enhancement Ideas 166 What We Did 166 System Achievements 166 Technical Skills Gained 167 Practical Research Tool 167 Foundation for Advanced Systems 167 Future Potential 167 Conclusion 167 6. Building a Retrieval-Augmented Generation System with SQLite VSS and Ollama. . . 169 System Architecture Overview 170 Database Foundation with Vector Support 171 Setting Up the Vector-Enabled Database 171 Schema Design for RAG 172 Creating Search Indexes 173 Text Processing and Embedding Generation 174 Embedding Model Management 174 Intelligent Text Chunking 175 Storing Content with Embeddings 176 Hybrid Search Implementation 177 Hybrid Search Algorithm 177 Table of Contents | vii

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Semantic Search Component 178 Keyword Search Component 179 Score Fusion and Ranking 180 LLM Integration with Ollama 181 Ollama API Client 181 Health Check Function 182 The RAG Pipeline 182 Context Formatting 182 Question-Answering Pipeline 183 Demonstration and Testing 185 Sample Data Loading 185 Main Demonstration Function 186 Interactive Q&A Interface 187 Quick Testing Utility 188 Next Steps: Extending the System 188 Missing Reddit Data Features 188 Performance Optimizations 190 Production Considerations 190 Advanced RAG Patterns 191 Conclusion 191 7. Building a Scientific RAG System with PostgreSQL and pgvector. . . . . . . . . . . . . . . . . . 193 System Goals and Capabilities 194 Architecture Overview 194 Database Foundation with pgvector 196 Database Configuration and Setup 197 Schema Design for Scientific Papers 197 High-Performance Vector Indexes 199 Embedding Generation Strategy 199 ArXiv Integration and PDF Processing 200 Paper Discovery with ArXiv API 200 Intelligent PDF Text Extraction 201 Advanced Text Chunking 203 Storage Pipeline with Embeddings 204 Multilevel Semantic Search 206 Abstract-Level Search 206 Section-Level Search 207 The RAG Pipeline: Deep Dive 208 Local LLM Integration with Ollama 209 Health Check and Model Discovery 210 Intelligent Context Retrieval 210 Scientific Prompt Engineering 211 viii | Table of Contents

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Complete RAG Execution Pipeline 212 Demonstration and Interactive Interface 213 Main Demonstration Flow 213 Search Demonstrations 214 RAG Demonstration 215 Interactive Search Interface 216 Entry Point with Mode Selection 217 Technical Note on HNSW 217 How to Evaluate Your Results 219 Next Steps: Extending the Scientific RAG System 220 Conclusion 223 8. Building a Complete Conversation Search and RAG System. . . . . . . . . . . . . . . . . . . . . . 225 System Goals and Capabilities 226 System Architecture Overview 227 What We’ll Build Together 229 Database Foundation for Conversation Storage 230 Designing the Conversation Schema 230 Three-Table Architecture for Optimal Performance 231 High-Performance Vector Indexing 232 Conversation Import and Data Processing Pipeline 233 Robust JSON Import with Error Handling 233 Atomic Transaction Processing 234 Timestamp Handling and Data Validation 234 Error Recovery and Logging 235 Efficient Embedding Generation and Batch Processing 236 Singleton Pattern for Model Management 236 Incremental Processing Strategy 237 Batch Processing for Optimal Performance 237 Database Insertion with Conflict Handling 238 Contextual Search with Conversational Understanding 239 Semantic Similarity Search 239 Multitable Joins for Rich Context 240 Result Formatting and Structure 240 Conversation Context Retrieval 241 Context Window Calculation 241 RAG Integration for Conversation History 242 Structured Context Management 242 Local LLM Integration with Ollama 243 Health Monitoring and Model Discovery 243 Context Retrieval and Assembly 244 Conversational Prompt Engineering 244 Table of Contents | ix

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Complete RAG Pipeline with Performance Monitoring 245 Complete Web API with FastAPI 247 FastAPI Application Structure 247 Request Models with Validation 247 Search Endpoint Implementation 248 RAG Question-Answering Endpoint 248 System Statistics and Monitoring 248 Server Startup and Configuration 249 Demonstration and Sample Data 250 Realistic Sample Data Generation 250 Multitopic Sample Coverage 251 Sample Data Processing Pipeline 252 Comprehensive System Demonstration 252 Progressive Feature Demonstration 253 RAG Demonstration with Conditional Execution 254 Production Import Functionality 254 Application Entry Points 255 Conclusion: A Complete Personal Knowledge System 255 9. Vector Query Language. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Core Concepts 258 Data Model 258 Basic Syntax Structure 259 Vector Operations 260 Similarity Search 260 Hybrid Search 261 Range Search 261 Batch Operations 261 Vector Functions and Aggregations 262 Vector Functions 262 Vector Aggregations 263 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 x | Table of Contents

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Preface To repeat a well-worn phrase, this is the book I wish I had when I started exploring vector databases. I was looking for a single source of basic theory and practical appli‐ cations of vector databases, but I found none. Existing vector database resources are fragmented across vendor documentation, blog posts, YouTube videos, and GitHub code. I wanted practical working examples rather than theoretical coverage. I also have a personal preference for proceeding systemati‐ cally from basics to simple working systems to help organize data locally—especially private data. Thus began my journey to pull together information from multiple sources and mul‐ tiple directions into a coherent whole. And this book is what emerged. It is meant to be useful to the intermediate developer, from the theoretically minded to the “just show me the code” type. There’s something for everyone. The book follows a clear pedagogical arc: concepts → tools → applications → future directions. Each chapter builds on previous ones while remaining practical with working code examples. The progression moves from understanding vector databases to building working applications that are meant for personal use, hands-on interac‐ tion, and extension by developers who want to build personal data management sys‐ tems. It culminates in a forward-looking proposal for industry standardization of a Vector Query Language modeled after SQL. The somewhat opinionated part of this book’s approach is a focus on simple hybrid vector relational databases—created by adding vector extension plug-ins to the well- known open source databases SQLite3 and PostgreSQL. The reason for this is that these databases are well known among database developers. Simply adding a data type of “vector” opens up a whole new world of AI data applications. This makes onboarding as low-effort as possible. xi

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It can’t be emphasized enough that these applications are for hands-on learning and not for web-scale production deployment. Modify them as you see fit; make them your own. And now let’s dive into the contents. What’s in This Book Chapters 1 and 2 establish foundational concepts—vector databases, embeddings, and semantic search fundamentals: Chapter 1, “Introduction to Vector Databases” • Introduces vector databases as foundational technology for AI applications, explaining why unstructured data (text, images, audio, video) requires semantic search rather than keyword matching • Covers the vector data type, similarity search, and key operations (cosine similar‐ ity, nearest-neighbor search, vector arithmetic) • Contrasts vector databases with SQL and NoSQL databases, highlighting hybrid architectures that combine structured metadata with vector similarity search • Establishes the conceptual foundation for understanding how embeddings pre‐ serve semantic meaning in vector space Chapter 2, “Embeddings” • Takes a deep dive into the history, evolution, and current usage of vector embed‐ dings with code examples for language model applications • Explains how embeddings bridge the gap between unstructured data and machine-processable representations, mapping raw data into vectors that capture semantic relationships • Covers the mathematical foundations and how proximity in vector space reflects semantic similarity • Provides the conceptual framework needed for practical applications later in the book Chapters 3 and 4 discuss practical implementations with Facebook AI Similarity Search (FAISS) and SQLite, showing how to build personal-scale search systems: Chapter 3, “Similarity Search with FAISS” • Explores FAISS as both a production similarity search engine and a flexible tool‐ kit for building custom vector databases xii | Preface

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• Is designed for developers interested in the internals of vector search and those wanting to extend capabilities of systems such as sqlite-vss • Covers the mathematical foundations and implementation details of efficient similarity search at scale • Focuses on CPU-based implementations for maximum accessibility Chapter 4, “Semantic Search with SQLite3” • Builds a personal knowledge management system using semantic search over Reddit content, demonstrating meaning-based search versus keyword matching • Introduces sqlite-vss, a SQLite extension that wraps FAISS and integrates vector search into the relational database world • Combines semantic retrieval with relational metadata filtering in a single SQL workflow • Uses an “overfetch-then-filter” pattern to handle the fact that metadata filters aren’t pushed down into FAISS search Chapters 5 through 8 progress through increasingly sophisticated applications— ArXiv paper search, local retrieval-augmented generation (RAG) systems, scientific RAG, and personal conversation search—moving from public to private data: Chapter 5, “Building an ArXiv Paper Search System with PostgreSQL pgvector” • Constructs a scientific literature search system addressing the challenge of discover‐ ing relevant research across millions of papers • Demonstrates how vector embeddings capture semantic relationships that keyword search misses (e.g., connecting “neural network optimization” with “gradient descent improvements”) • Leverages PostgreSQL with the pgvector extension for enterprise-scale applications • Shows how to handle technical terminology, structured academic content, and meta‐ data in a unified search system Chapter 6, “Building a Retrieval-Augmented Generation System with SQLite VSS and Ollama” • Assembles a complete RAG system combining vector search with local large lan‐ guage model (LLM) capabilities • Builds an entirely local, private system running on a single desktop using SQLite Vec‐ tor Similarity Search (SQLite VSS) and Ollama • Addresses the fundamental LLM limitation: knowledge frozen at training time without access to private or recent information • Creates a question-answering system that grounds LLM responses in actual retrieved data to reduce hallucinations Preface | xiii

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Chapter 7, “Building a Scientific RAG System with PostgreSQL and pgvector” • Develops a RAG system specifically designed for scientific literature with unique challenges: technical terminology, structured content, citation networks, and evi‐ dence quality • Enables semantic discovery, cross-paper synthesis, and contextual understanding across ArXiv’s 15,000+ monthly publications • Uses PostgreSQL with pgvector for scientific knowledge retrieval • Demonstrates handling domain-specific language and structured academic conventions Chapter 8, “Building a Complete Conversation Search and RAG System” • Moves from searching public data to building a “second brain” for personal chat his‐ tory with AI assistants • Addresses unique characteristics of conversational data: contextual dependencies, conversational flow across exchanges, personal language patterns, and privacy requirements • Handles the messiness of real conversational data where ideas develop across multi‐ ple message exchanges • Creates a system for retrieving valuable insights, solutions, and learned knowledge from thousands of personal AI conversations Chapter 9 proposes an experimental Vector Query Language (VQL) to standardize the fragmented vector database landscape: Chapter 9, “Vector Query Language” • Presents a prototype SQL-inspired query language for vector databases, designed to start community discussion and consensus building • Addresses the needs of three communities: application developers seeking data‐ base abstractions, database maintainers needing cross-vendor tooling, and AI/ machine learning researchers requiring efficient data manipulation • Defines a data model (collections, tables, vectors, embeddings, metadata) and query syntax combining SQL familiarity with vector operations • Covers similarity search, hybrid search, range search, batch operations, vector functions, and aggregations • Proposes a path toward standardization across the fragmented vector database landscape xiv | Preface

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Who This Book Is For This book’s target audience comprises: • Enterprise software developers building their first AI applications • Data scientists implementing semantic search • Machine learning engineers working on RAG systems • Backend developers adding vector capabilities to existing systems Additionally, it is geared toward people who learn by doing, so there are also practical applications focused on individual use. Prerequisites include Python programming proficiency, a basic understanding of databases and SQL, and familiarity with running LLMs locally for the RAG chapters. Readers should also be comfortable with simple mathematical concepts. The book is for those who want to learn how to build small- to medium-scale seman‐ tic search systems, implement RAG applications with local LLMs, optimize vector similarity search, choose the right indexes, and integrate vector search with relational databases. The book focuses on Python implementations rather than other languages, covers CPU-based solutions rather than GPU optimization, and excludes cloud-managed vector database services in favor of self-hosted solutions. The scope of the applica‐ tions and the theory is to cover enough to get started and do something useful with personal data as a first step. This is not a textbook on how to build web-scale produc‐ tion vector database applications. However, the lessons in the chapters can serve as a foundation and a springboard to take the next leap up in scope and complexity. How to Use This Book For the reader who is coming from a purely relational database background, reading the first two chapters is a must. Chapter 3 is more detailed on the innards of FAISS, but you can just use the code examples to dive right in and use it. Chapters 4 through 8 use SQLite3 and Postgres to build semantic search and RAG applications using local data and local LLMs. Chapter 8 is especially topical, as it cre‐ ates a semantic search over your LLM conversations—in this case, exported from Claude. Finally, those interested in exploring similarities with SQL can dive into Chapter 9, which is a speculative design of a Vector Query Language to enable a unified, vendor- independent, client access metaphor for vector databases. Preface | xv

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Software, Environment, and Resource Requirements With the following resources, the example applications will run on standard devel‐ oper laptops: • Python version 3.11+ • Key dependencies: PostgreSQL, SQLite3, FAISS, Ollama • OS: Linux, macOS, or WSL on Windows • Minimum 16 GB RAM; preferably 24 GB For a developer machine that can run PostgreSQL and an 8B LLM: • Storage requirements: 512 GB to 1 TB Conventions Used in This Book The following typographical conventions are used in this book: Italic Indicates new terms, URLs, email addresses, filenames, and file extensions. Constant width Used for program listings, as well as within paragraphs to refer to program ele‐ ments such as variable or function names, databases, data types, environment variables, statements, and keywords. Constant width bold Shows commands or other text that should be typed literally by the user. Constant width italic Shows text that should be replaced with user-supplied values or by values deter‐ mined by context. This element signifies a tip or suggestion. This element signifies a general note. xvi | Preface

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This element indicates a warning or caution. Using Code Examples Supplemental material (code examples, exercises, etc.) is available for download at https://github.com/nborwankar/VectorDatabaseBook. If you have a technical question or a problem using the code examples, please send email to support@oreilly.com. This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission. We appreciate, but generally do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “Vector Databases by Nitin Borwankar (O’Reilly). Copyright 2026 Nitin Borwankar, 978-1-098-17759-1.” If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at permissions@oreilly.com. O’Reilly Online Learning For more than 40 years, O’Reilly Media has provided technol‐ ogy and business training, knowledge, and insight to help companies succeed. Our unique network of experts and innovators share their knowledge and expertise through books, articles, and our online learning platform. O’Reilly’s online learning platform gives you on-demand access to live training courses, in-depth learning paths, interactive coding environments, and a vast collection of text and video from O’Reilly and 200+ other publishers. For more information, visit https://oreilly.com. Preface | xvii

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How to Contact Us Please address comments and questions concerning this book to the publisher: O’Reilly Media, Inc. 141 Stony Circle, Suite 195 Santa Rosa, CA 95401 800-889-8969 (in the United States or Canada) 707-827-7019 (international or local) 707-829-0104 (fax) support@oreilly.com https://oreilly.com/about/contact.html We have a web page for this book, where we list errata and any additional informa‐ tion. You can access this page at https://oreil.ly/vector-databases. For news and information about our books and courses, visit https://oreilly.com. Find us on LinkedIn: https://linkedin.com/company/oreilly-media Watch us on YouTube: https://youtube.com/oreillymedia Acknowledgments I want to thank all the technical reviewers, especially Sunil Sawant. Because of their efforts, the book is a much better product. Sara Hunter, my development editor, deserves praise for her patience during the yo- yo delivery cadence of the chapters. I would also like to thank the rest of the O’Reilly editorial team—both the early release and final production teams—for putting up with the inexperience of a first-time author. My wife, Garima, and daughter, Vanita, had to tolerate my minimal participation in family events during all of last year. I would like to thank them deeply for their sup‐ port and understanding during the writing of “the book.” It’s finally done, guys! Of course, none of this would be possible without the open source developer com‐ munities of PostgreSQL, SQLite, and the vector extensions, whose work enabled the example code. xviii | Preface