[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvODEzOTE","title","Mastering Qdrant for RAG Applications","state","published","contactAuthorMethod","email","aboutText","

Retrieval-Augmented Generation has become the dominant architecture for production AI applications, and vector databases form its critical retrieval layer. This book takes you from the fundamentals of embeddings and similarity search through to advanced distributed deployments of Qdrant, the high-performance open-source vector database written in Rust. You will learn every facet of Qdrant's feature set: collections, points, payloads, HNSW indexing, quantization, hybrid search with dense and sparse vectors, metadata filtering, faceting, sharding, replication, security, monitoring, and scaling. Through detailed code examples in Python, JavaScript, Go, Java, C#, and Rust, you will build complete RAG pipelines integrated with LangChain, LlamaIndex, Haystack, and a wide range of embedding providers. This is not a tutorial collection; it is a comprehensive reference that explains the why behind every feature, the trade-offs between every configuration choice, and the production engineering practices that separate experimental prototypes from systems serving millions of queries per day.

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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