Mastering Langchain and Langgraph: Build Rag and Agentic AI Applications with Llms, McP, and Langchain Agents
暫譯: 精通 LangChain 與 LangGraph：使用 LLM、MCP 與 LangChain Agents 建構 RAG 與代理式 AI 應用程式

Kulshreshtha, Ankur

Mastering Langchain and Langgraph: Build Rag and Agentic AI Applications with Llms, McP, and Langchain Agents
  • 出版商: Apress
  • 出版日期: 2026-10-08
  • 售價: $2,130
  • 貴賓價: 9.5 折 $2,023
  • 語言: 英文
  • 頁數: 741
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9798868829451
  • ISBN-13: 9798868829451
  • 相關分類: LangChain、Large language model

    • 海外代購書籍(需單獨結帳)

商品描述

Mastering LangChain and LangGraph is a comprehensive, hands-on guide for developers, data scientists, and AI practitioners looking to build robust, production-ready applications using large language models, retrieval-augmented generation (RAG), and agentic systems.

The book begins by establishing a clear foundation, introducing the LLM ecosystem and core concepts such as RAG and AI agents, before guiding readers into the LangChain framework and its practical abstractions. Readers will explore essential building blocks including chat models, prompt templates, and structured output generation, followed by in-depth coverage of document loaders, text splitters, embeddings, vector stores, and retrievers--key components for creating scalable, knowledge-grounded AI systems. As the book progresses, it introduces LangGraph, enabling readers to design stateful, multi-step, and resilient agent workflows with fine-grained control over execution. Advanced chapters dive into tools and the Model Context Protocol (MCP), checkpointing, memory management, and middleware design, providing the infrastructure needed to manage complexity in real-world applications. Topics such as human-in-the-loop workflows, time travel, and streaming demonstrate how to build systems that are transparent, debuggable, and interactive. The book concludes with a focused exploration of LangChain agents, tying together tools, memory, and control flow into cohesive agentic architectures.

Blending conceptual clarity with practical implementation guidance, this book equips readers with the skills to design, build, and scale modern AI applications that go beyond simple prompts--delivering intelligent, reliable, and extensible systems ready for production use.

    What you will learn: Understand the LLM ecosystem, including Retrieval-Augmented Generation (RAG) and agent-based AI systems. Build scalable, knowledge-grounded applications using LangChain components like prompts, embeddings, vector stores, and retrievers. Design structured, stateful, and multi-step workflows with LangGraph for reliable agent execution. Implement tools, memory, checkpointing, and middleware to manage complexity in real-world AI applications. Create production-ready agentic systems with human-in-the-loop, streaming, and debugging capabilities.
Who this book is for:

This book is for software developers, data scientists, and AI practitioners who want to move beyond basic prompt engineering and build production-ready AI applications using large language models. It is ideal for engineers working with LangChain who want a deeper, structured understanding of its components and how they fit together in real-world systems.

商品描述(中文翻譯)

精通 LangChain 與 LangGraph 是一本全面且實作導向的指南，專為希望運用大型語言模型（large language models）、檢索增強生成（Retrieval-Augmented Generation，RAG）與代理式系統（agentic systems），建構健壯且可投入正式環境的應用程式之開發人員、資料科學家與 AI 實務工作者所撰寫。

本書首先建立清晰的基礎，介紹 LLM 生態系統，以及 RAG 與 AI agents 等核心概念，接著引導讀者深入 LangChain framework 及其實用的抽象概念。讀者將探索 chat models、prompt templates 與 structured output generation 等重要建構元件，接著深入了解 document loaders、text splitters、embeddings、vector stores 與 retrievers——這些都是建立可擴充、以知識為基礎之 AI 系統的關鍵元件。

隨著內容逐步深入，本書將介紹 LangGraph，讓讀者能夠設計具備狀態、多步驟且具韌性的代理工作流程，並對執行過程進行細緻控制。進階章節將深入探討 tools、Model Context Protocol（MCP）、checkpointing、memory management 與 middleware design，提供在真實世界應用程式中管理複雜度所需的基礎架構。human-in-the-loop workflows、time travel 與 streaming 等主題，則示範如何建構透明、易於除錯且具互動性的系統。本書最後將聚焦於 LangChain agents，整合 tools、memory 與 control flow，建立完整一致的代理式架構。

本書兼具清晰的概念說明與實務實作指引，協助讀者掌握設計、建構與擴展現代 AI 應用程式所需的技能，超越單純提示詞的使用方式，打造可投入正式環境、具備智慧、可靠性與可擴充性的系統。

你將學會：

• 了解 LLM 生態系統，包括檢索增強生成（Retrieval-Augmented Generation，RAG）與以代理為基礎的 AI 系統。
• 運用 LangChain 的 prompts、embeddings、vector stores 與 retrievers 等元件，建構可擴充且以知識為基礎的應用程式。
• 使用 LangGraph 設計結構化、具狀態且多步驟的工作流程，以實現可靠的代理執行。
• 實作 tools、memory、checkpointing 與 middleware，以管理真實世界 AI 應用程式中的複雜度。
• 建立具備 human-in-the-loop、streaming 與除錯能力，可投入正式環境的代理式系統。

本書適合哪些讀者：

本書適合希望超越基礎提示工程，並運用大型語言模型建構可投入正式環境之 AI 應用程式的軟體開發人員、資料科學家與 AI 實務工作者。對於正在使用 LangChain，並希望更深入且有系統地了解其元件，以及這些元件如何在真實世界系統中彼此整合的工程師而言，本書尤其適合。

作者簡介

Ankur Kulshreshtha is a Data Architect at Infosys with 15 years of experience in Data Engineering, Machine Learning, and Generative AI. He has worked with leading Telecom and Media clients such as British Telecom, AT&T, DirecTV, Cisco, and Singtel. Ankur is an expert in designing enterprise solutions for AI and data-driven projects and is a member of Infosys' GenAI COE, driving R&D for tailored Generative AI solutions. Ankur holds an M.Tech in Software Systems with a specialization in Data from BITS Pilani. With skills in AWS, Azure, TensorFlow, LangChain, and various database technologies, he combines technical proficiency with a passion for sharing knowledge through his writing and consulting work.

作者簡介(中文翻譯)

Ankur Kulshreshtha 是 Infosys 的資料架構師，擁有 15 年資料工程、Machine Learning 與 Generative AI 經驗。他曾與 British Telecom、AT&T、DirecTV、Cisco 和 Singtel 等電信與媒體業的領導企業客戶合作。Ankur 擅長為 AI 與資料驅動專案設計企業級解決方案，並且是 Infosys GenAI COE 的成員，負責推動客製化 Generative AI 解決方案的研發工作。

Ankur 持有 BITS Pilani 的軟體系統工程碩士（M.Tech）學位，專攻資料領域。他具備 AWS、Azure、TensorFlow、LangChain 及各種資料庫技術的專業技能，並熱衷於透過寫作與顧問工作分享知識。

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