About This Book
AI models are only as good as the context they can retrieve. Without the right data at the right moment, even the most powerful models fail. You might even say that search and retrieval is the most important layer of the AI stack.
Written by Nicholas Knize, the creator of AWS OpenSearch and Founder of Lucenia, this book explores the full lifecycle of search systems from indexing and query execution to sharding, vector search, hybrid retrieval, and real-world AI integration.
What makes this book unique is its systems-first approach. Rather than explaining how to operate existing tools, it teaches you how to build the tools themselves.
With this book, you will:
- Architect search and retrieval systems that enable scalable, performant, and secure AI inference
- Navigate the trade-offs between indexing and retrieval models
- Apply proven patterns to build fault-tolerant, efficient search infrastructure
- Support hybrid and AI-native workloads with structured, unstructured, and vector data
- Optimize performance, storage, and resilience across varied deployment topologies and constraints
Who This Book Is For
What You Will Learn
A comprehensive journey from fundamentals to production-scale systems
You will understand:
The Foundation of Contextual AI
Why search and retrieval form the foundation of contextual AI and how they enable performant, secure, and scalable AI inference.
Modern Search Architecture
How modern search engines are architected from core data structures to distributed execution.
Key Principles & Characteristics
The key principles and characteristics of effective search and information retrieval systems that power contextual AI within organizations.
Indexing & Retrieval Tradeoffs
The tradeoffs between different indexing and retrieval models, including inverted indexes, vector graphs, and hybrid pipelines.
You will be able to:
Design Scalable Systems
Identify and apply the design patterns required for building scalable, efficient, and resilient search systems from local environments to global deployments.
Integrate Hybrid Retrieval
Integrate structured, unstructured, and vector-based retrieval methods to support hybrid and AI-native applications.
Optimize Performance
Diagnose and optimize search performance, storage footprint, and system resilience across a variety of deployment topologies and resource constraints.
Powered by Lucenia
All examples in this book use Lucenia, the open-source scalable search AI platform. Lucenia provides a production-ready environment for implementing the concepts covered in each chapter.
From basic indexing operations to complex distributed vector search, you'll gain practical experience with real-world tools and techniques.
Become a Technical Reviewer
We're looking for experienced engineers, researchers, and practitioners to provide feedback on early drafts. As a reviewer, you'll get early access to chapters and influence the final content.