Jina AI Search Stack : Embeddings, Rerankers, and Hybrid Retrieval End¿to¿End
Language: English
Published by Nobletrex Press, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 41.00
Quantity: 2 available
Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - 'Jina AI Search Stack: Embeddings, Rerankers, and Hybrid Retrieval End¿töEnd'Modern search systems are no longer built from a single retriever or a single model decision. They are layered, evidence-driven pipelines that must balance recall, precision, latency, cost, and operational resilience. This book is written for experienced engineers, ML practitioners, search specialists, and technical architects who want to design serious retrieval systems with Jina's evolving stack rather than assemble isolated components without a unifying architecture.Across the book, readers move from the foundations of multi-stage retrieval to dense embeddings, lexical search, hybrid fusion, and reranking as a precision layer. It then extends into advanced territory: late interaction retrieval, multimodal search, long-document chunking, rigorous evaluation, ablation design, and production deployment. The emphasis is not just on what each technique does, but on when it wins, where it fails, how it interacts with neighboring stages, and how to make sound model, indexing, and systems decisions under real constraints.The treatment is practical but advanced, assuming familiarity with modern IR, vector search, and machine learning concepts. Rather than offering a shallow tour, the book provides a system-level view of Jina's search capabilities, including version-aware design choices and operational trade-offs, so readers can build, tune, and evolve retrieval platforms with confidence.…
Seller Inventory # 9798896653370
- Title
- Jina AI Search Stack : Embeddings, Rerankers, and Hybrid Retrieval End¿to¿End
- Author
- Trex Team
- Publisher
- Nobletrex Press
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798896653370
- Item weight
- 396 grams
- Dimensions
- 229x152x15 mm
"Jina AI Search Stack: Embeddings, Rerankers, and Hybrid Retrieval End‑to‑End"
Modern search systems are no longer built from a single retriever or a single model decision. They are layered, evidence-driven pipelines that must balance recall, precision, latency, cost, and operational resilience. This book is written for experienced engineers, ML practitioners, search specialists, and technical architects who want to design serious retrieval systems with Jina’s evolving stack rather than assemble isolated components without a unifying architecture.
Across the book, readers move from the foundations of multi-stage retrieval to dense embeddings, lexical search, hybrid fusion, and reranking as a precision layer. It then extends into advanced territory: late interaction retrieval, multimodal search, long-document chunking, rigorous evaluation, ablation design, and production deployment. The emphasis is not just on what each technique does, but on when it wins, where it fails, how it interacts with neighboring stages, and how to make sound model, indexing, and systems decisions under real constraints.
The treatment is practical but advanced, assuming familiarity with modern IR, vector search, and machine learning concepts. Rather than offering a shallow tour, the book provides a system-level view of Jina’s search capabilities, including version-aware design choices and operational trade-offs, so readers can build, tune, and evolve retrieval platforms with confidence.
"Synopsis" may belong to another edition of this title.
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