Generative AI with LangChain: Build production-ready LLM applications and advanced agents using Python LangChain and LangGraph
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Generative AI with LangChain: Build production-ready LLM applications and advanced agents using Python LangChain and LangGraph
Bridge the gap between prototype and production with robust LangGraph agent architectures.
Generative AI with LangChain: Build production-ready LLM applications and advanced agents using Python LangChain and LangGraph
物品 #: 168241566

Generative AI with LangChain: Build production-ready LLM applications and advanced agents using Python LangChain and LangGraph

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物品 #: 168241566

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Shop Generative AI with LangChain: Build production-ready LLM applications and advanced agents using Python LangChain and LangGraph online at a best price in 香港. 1837022011
  • Go beyond foundational LangChain documentation with detailed coverage of LangGraph interfaces, design patterns for building AI agents, and scalable architectures used in production—ideal for Python developers building GenAI applicationsKey FeaturesBridge the gap between prototype and production with robust LangGraph agent architecturesApply enterprise-grade practices for testing, observability, and monitoringBuild specialized agents for software development and data analysisPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThis second edition tackles the biggest challenge facing companies in AI today: moving from prototypes to production. Fully updated to reflect the latest developments in the LangChain ecosystem, it captures how modern AI systems are developed, deployed, and scaled in enterprise environments. This edition places a strong focus on multi-agent architectures, robust LangGraph workflows, and advanced retrieval-augmented generation (RAG) pipelines.You'll explore design patterns for building agentic systems, with practical implementations of multi-agent setups for complex tasks. The book guides you through reasoning techniques such as Tree-of -Thoughts, structured generation, and agent handoffs—complete with error handling examples. Expanded chapters on testing, evaluation, and deployment address the demands of modern LLM applications, showing you how to design secure, compliant AI systems with built-in safeguards and responsible development principles. This edition also expands RAG coverage with guidance on hybrid search, re-ranking, and fact-checking pipelines to enhance output accuracy.Whether you're extending existing workflows or architecting multi-agent systems from scratch, this book provides the technical depth and practical instruction needed to design LLM applications ready for success in production environments.What you will learnDesign and implement multi-agent systems using LangGraphImplement testing strategies that identify issues before deploymentDeploy observability and monitoring solutions for production environmentsBuild agentic RAG systems with re-ranking capabilitiesArchitect scalable, production-ready AI agents using LangGraph and MCPWork with the latest LLMs and providers like Google Gemini, Anthropic, Mistral, DeepSeek, and OpenAI's o3-miniDesign secure, compliant AI systems aligned with modern ethical practicesWho this book is forThis book is for developers, researchers, and anyone looking to learn more about LangChain and LangGraph. With a strong emphasis on enterprise deployment patterns, it’s especially valuable for teams implementing LLM solutions at scale. While the first edition focused on individual developers, this updated edition expands its reach to support engineering teams and decision-makers working on enterprise-scale LLM strategies. A basic understanding of Python is required, and familiarity with machine learning will help you get the most out of this book.Table of ContentsThe Rise of Generative AI: From Language Models to AgentsFirst Steps with LangChainBuilding Workflows with LangGraphBuilding Intelligent RAG Systems with LangChainBuilding Intelligent AgentsAdvanced Applications and Multi-Agent SystemsSoftware Development and Data Analysis AgentsEvaluation and TestingObservability and Production DeploymentThe Future of LLM Applications
Publisher Packt Publishing
Publication date 23 May 2025
Edition 2.
Language English
Print length 476 pages
ISBN-10 1837022011
ISBN-13 978-1837022014
Dimensions 19.05 x 2.77 x 23.5 cm

產品敘述

Generative AI with LangChain: Build production-ready LLM applications and advanced agents using Python LangChain and LangGraph

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English edition Ben Auffarth Computer Science Editorial Review

Generative AI With LangChain: Build Production-ready LLM Applications And Advanced Agents Using Python LangChain And LangGraph is an essential resource for developers looking to delve into the world of AI applications. Published by Packt Publishing, this 476-page book, set for release on 23 May 2025, thoroughly explores how to leverage Python LangChain and LangGraph for building advanced AI solutions. Readers can expect to learn techniques for creating production-ready applications that utilize large language models and advanced agents effectively. As such, it serves as an excellent guide for both novice and experienced developers looking to enhance their skills in AI application development.

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優點

  • In-depth exploration of AI application development
  • Focus on Python LangChain and LangGraph
  • Suitable for both beginners and advanced users
  • Comprehensive 476-page guide
  • Future-proof knowledge with upcoming release

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  • Future publication date may mean delayed access to content

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特色和優勢

  • Ideal for Python developers focused on building GenAI applications.
  • Covers transition from prototypes to production for AI systems.
  • Details enterprise-grade practices for testing, observability, and monitoring.
  • Explores design patterns for multi-agent systems and complex tasks.
  • Includes practical implementations and updated information on modern AI practices.
  • Guides development of secure and compliant AI systems tailored for enterprise environments.

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