Retrieval-augmented generation in production with Haystack

• Verfasst von: Vivek, Skanda [VerfasserIn]
• Titel: Retrieval-augmented generation in production with Haystack
• Titelzusatz: building trustworthy, scalable, reliable, and secure AI systems
• Verf.angabe: Skanda Vivek
• Ausgabe: [First edition].
• Verlagsort: Sebastopol, CA
• Verlag: O'Reilly Media, Inc.
• Jahr: 2025
• Umfang: 1 online resource (132 pages)
• Illustrationen: illustrations
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Génération automatique de texte
• Sach-SW: Intelligence artificielle ; Logiciels
• Sach-SW: Traitement automatique des langues naturelles
• K10plus-PPN: 1924462044
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4718964458
• Lokale URL Inst.: Zum Volltext

Abstract

In today's rapidly changing AI technology environment, software engineers often struggle to build real-world applications with large language models (LLM). The benefits of incorporating open source LLMs into existing workflows is often offset by the need to create custom components. That's where Haystack comes in. This open source framework is a collection of the most useful tools, integrations, and infrastructure building blocks to help you design and build scalable, API-driven LLM backends. With Haystack, it's easy to build extractive or generative QA, Google-like semantic search to query large-scale textual data, or a reliable and secure ChatGPT-like experience on top of technical documentation. This guide serves as a collection of useful retrieval-augmented generation (RAG) mental models and offers ML engineers, AI engineers, and backend engineers a practical blueprint for the LLM software development lifecycle.