"OpenSearch Neural Search: Hybrid Retrieval, Reranking, and Operations"
Modern search systems are no longer won by keywords alone. This book is written for experienced search engineers, platform architects, and relevance practitioners who need to design high-quality retrieval systems in OpenSearch that combine semantic understanding with production-grade control. Rather than offering a lightweight introduction, it gives advanced readers a clear architectural and operational framework for building neural search systems that perform under real traffic, changing data, and strict latency constraints.
Across the book, readers move from the foundations of OpenSearch neural search into the hard decisions that shape real-world relevance: model provisioning, embedding generation, vector index design, neural query execution, search pipelines, hybrid lexical-semantic retrieval, score and rank fusion, and multi-stage reranking. It also shows how to evaluate these systems rigorously, explain ranking behavior, tune candidate generation and rerank depth, and manage the trade-offs between recall, precision, latency, and cost.
A major strength of the book is its operational focus. It treats search pipelines as a control plane, addresses version-specific feature milestones, and connects relevance design directly to deployment strategy, observability, capacity planning, failure handling, and safe rollout practices. Readers should already be comfortable with OpenSearch fundamentals and modern search concepts; in return, they gain a deep, implementation-aware guide to building and operating
