Overview

Modern AI systems no longer fail because of weak models-they fail because of poor retrieval, fragile pipelines, and unoperated infrastructure. Vector Databases in Practice is a hands-on, infra-first guide to building, operating, and validating production-grade RAG and AI search systems using Qdrant, Milvus, and open-source tooling. This is not a theory book. It is a builder's playbook for engineers who need systems that scale, recover, and perform under real workloads. From the first chapter, you work directly with real datasets, deterministic ingestion pipelines, hybrid retrieval strategies, benchmarking harnesses, and operational guardrails. You deploy both Qdrant and Milvus, tune indexing and filtering for performance, measure recall and latency with evidence, and learn how to make data-driven deployment decisions instead of guessing. Unlike most vector database books that stop at ""how search works,"" this book goes all the way to production readiness. You implement versioned embeddings, idempotent ingestion, multi-tenant layouts, backup and restore drills, upgrade rehearsals, observability dashboards, and acceptance gates that catch regressions before users do. The capstone project brings everything together: you ship a full end-to-end RAG + AI search platform with dual backends (Qdrant and Milvus), a hardened FastAPI service, hybrid retrieval and reranking, load testing, restore validation, and an ops-ready runbook. By the end, you don't just ""know"" vector databases-you can operate them with confidence. This book is written for: Backend and platform engineers building AI search or RAG systems DevOps and infrastructure engineers supporting AI workloads Builders running homelab, on-prem, or cloud-native vector platforms Teams who need reproducibility, evidence, and operational safety-not demos If you want a 2026-ready, production-oriented guide that treats vector databases as critical infrastructure, not experiments, this book was written for you. You will finish this book with: A repeatable vector database deployment workflow Proven hybrid retrieval and reranking patterns A benchmarking framework to compare engines fairly Backup, restore, and upgrade confidence A complete, real-world AI search system you can extend and trust This is how vector databases are built in practice.

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