Envío gratis siempre a todo México.
Haz clic para hacer zoom
Not Available
Leonid Kuligin
Generative AI with LangChain
Cargando comentarios…
Comparte
$985
$886
Envío gratis siempre
Comentarios
Cargando el resumen…
Cargando comentarios…
product
7331564Generative AI with LangChainhttps://www.gandhi.com.mx/generative-ai-with-langchain-9781837022007/phttps://gandhi.vtexassets.com/arquivos/ids/6910427/image.jpg?v=638884342247870000886985MXNPackt PublishingInStock/Ebooks/6969082Generative AI with LangChain886985https://www.gandhi.com.mx/generative-ai-with-langchain-9781837022007/phttps://gandhi.vtexassets.com/arquivos/ids/6910427/image.jpg?v=638884342247870000InStockMXN99999DIEbook20259781837022007_W3siaWQiOiJiMjQ0OWM4ZC1iNWU4LTQ2ZGYtYjQ3Mi00NTZlZDNlZjY4ZjYiLCJsaXN0UHJpY2UiOjk4NSwiZGlzY291bnQiOjk5LCJzZWxsaW5nUHJpY2UiOjg4NiwiaW5jbHVkZXNUYXgiOnRydWUsInByaWNlVHlwZSI6Ildob2xlc2FsZSIsImN1cnJlbmN5IjoiTVhOIiwiZnJvbSI6IjIwMjUtMDUtMjNUMDA6MDA6MDBaIiwicmVnaW9uIjoiTVgiLCJpc1ByZW9yZGVyIjpmYWxzZX1d9781837022007_<p><b>Beyond foundational LangChain documentation and LangGraph interfaces, learn enterprise patterns, key design pattern to build AI agents, battle-tested strategies, and proven architectures used in production. Ideal for Python developers building generative AI at scale. </b></p><h2>Key Features</h2><ul><li>Get to grips with building AI agents with LangGraph</li><li>Learn about enterprise-grade testing, observability, and LLM evaluation frameworks</li><li>Cover RAG implementation with cutting-edge retrieval strategies and new reliability techniques</li><li>Purchase of the print or Kindle book includes a free PDF eBook</li></ul><h2>Book Description</h2>This revised edition builds a foundation in agentic AI, LLM fundamentals, LangChain, & LangGraph for developers at all levels. Fully updated to cover the latest in LangChain and production LLM applications, it captures the evolving ecosystem and enterprise deployment landscape. New coverage includes multi-agent architectures, LangGraph interfaces, robust RAG techniques with hybrid search, re-rankers, and advanced fact-checking mechanisms, plus enterprise-grade testing frameworks. It provides coverage of key design patterns behind agentic systems, practical implementations of multi-agent systems for complex tasks. Explore cutting-edge agent strategies such as Tree of Thought, multi-agent orchestration, detailed error handling, and structured output generation. Coverage dedicated to evaluation, testing, and production deployment reflect the maturing LLM application landscape. Design secure, compliant AI systems with built-in production safeguards, responsible development practices, and a perspective on future research directions.The enhanced RAG coverage features techniques like hybrid search, re-rankers, and fact-checking mechanisms. Whether upgrading existing LLM applications or building new enterprise-scale solutions, by the end of the book, you will have updated knowledge on the practical patterns needed for production success<h2>What you will learn</h2><ul><li>Design and implement refined multi-agent systems using LangGraph</li><li>Enterprise-grade testing and evaluation frameworks for LLM applications</li><li>Deploy production-ready observability and monitoring solutions</li><li>Build RAG systems with hybrid search and re-ranking capabilities</li><li>Implement agents for software development and data analysis</li><li>Work with latest LLMs and providers Google Gemini, Anthropic and Mistral, DeepSeek, and OpenAI o3-mini</li><li>Optimize cost and performance across different deployment types</li><li>Design secure, compliant AI systems with current best practices</li></ul><h2>Who this book is for</h2><p>The book is for developers, researchers, and anyone interested in learning more about LangChain and LangGraph, wanting to build production-ready LLM applications. This book emphasizes on enterprise deployment patterns, making it especially valuable for teams implementing LLM solutions at scale. While the first edition focused on individual developers, this version also caters to engineering teams and decision-makers implementing enterprise-wide LLM strategies. Basic knowledge of Python is a prerequisite, while prior exposure to machine learning will help you follow along more easily.</p>...(*_*)9781837022007_<p><b>Go beyond foundational LangChain documentation with detailed coverage of LangGraph interfaces, design patterns for building AI agents, and scalable architectures used in productionideal for Python developers building GenAI applications</b></p><h2>Key Features</h2><ul><li>Bridge the gap between prototype and production with robust LangGraph agent architectures</li><li>Apply enterprise-grade practices for testing, observability, and monitoring</li><li>Build specialized agents for software development and data analysis</li><li>Purchase of the print or Kindle book includes a free PDF eBook</li></ul><h2>Book Description</h2>This 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. Youll 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 handoffscomplete 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 youre 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.<h2>What you will learn</h2><ul><li>Design and implement multi-agent systems using LangGraph</li><li>Implement testing strategies that identify issues before deployment</li><li>Deploy observability and monitoring solutions for production environments</li><li>Build agentic RAG systems with re-ranking capabilities</li><li>Architect scalable, production-ready AI agents using LangGraph and MCP</li><li>Work with the latest LLMs and providers like Google Gemini, Anthropic, Mistral, DeepSeek, and OpenAIs o3-mini</li><li>Design secure, compliant AI systems aligned with modern ethical practices</li></ul><h2>Who this book is for</h2><p>This book is for developers, researchers, and anyone looking to learn more about LangChain and LangGraph. With a strong emphasis on enterprise deployment patterns, its 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.</p>...9781837022007_Packt Publishinglibro_electonico_9781837022007_9781837022007Leonid KuliginInglésMéxico2025-05-23T00:00:00+00:00https://getbook.kobo.com/koboid-prod-public/packt-epub-e37a6bf7-c5b6-4ec5-98b2-1540c9bc6da7.epub2025-05-23T00:00:00+00:00Packt Publishing