Learning LangChain

• Verfasst von: Oshin, Mayo [VerfasserIn]
• Verfasst von: Campos, Nuno [VerfasserIn]
• Titel: Learning LangChain
• Titelzusatz: building AI and LLM applications with LangChain and LangGraph
• Verf.angabe: Mayo Oshin and Nuno Campos
• Verlagsort: Sebastopol, CA
• Verlag: O'Reilly Media, Inc.
• Jahr: 2025
• Umfang: 1 online resource (xxv, 267 pages)
• Illustrationen: illustrations (some color)
• Fussnoten: Includes bibliographical references and index. - Description based on online resource; title from digital title page (viewed on March 14, 2025)
• ISBN: 978-1-0981-6725-7
• ISBN: 1-0981-6725-2
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Sach-SW: Intelligence artificielle
• Sach-SW: Programmation (Informatique)
• Sach-SW: Interfaces de programmation d'applications
• Sach-SW: Python (Langage de programmation)
• Sach-SW: artificial intelligence
• Sach-SW: computer programming
• Sach-SW: APIs (interfaces)
• K10plus-PPN: 1921170980
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4696549291
• Lokale URL Inst.: Zum Volltext

Abstract

If you're looking to build production-ready AI applications that can reason and retrieve external data for context-awareness, you'll need to master LangChain--a popular development framework and platform for building, running, and managing agentic applications. LangChain is used by several leading companies, including Zapier, Replit, Databricks, and many more. This guide is an indispensable resource for developers who understand Python or JavaScript but are beginners eager to harness the power of AI. Authors Mayo Oshin and Nuno Campos demystify the use of LangChain through practical insights and in-depth tutorials. Starting with basic concepts, this book shows you step-by-step how to build a production-ready AI agent that uses your data. Harness the power of retrieval-augmented generation (RAG) to enhance the accuracy of LLMs using external up-to-date data Develop and deploy AI applications that interact intelligently and contextually with users Make use of the powerful agent architecture with LangGraph Integrate and manage third-party APIs and tools to extend the functionality of your AI applications Monitor, test, and evaluate your AI applications to improve performance Understand the foundations of LLM app development and how they can be used with LangChain.