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所在平台: Udemy |
课程主页: https://www.udemy.com/course/langgraph-for-beginners/
课程评论：没有评论
课程名称：初学者的LangGraph：简单步骤中的代理工作流程 课程概述：准备好超越简单的LLM应用程序，使用LangGraph构建强大的、有状态和代理的工作流程吗？在这个适合初学者的课程中，您将掌握LangGraph，这是一种基于LangChain构建的开源库，旨在使用图形结构来协调多智能体应用程序。无论您是构建智能代理、动态RAG管道，还是现实世界的企业解决方案，本课程都将为您提供稳固的基础。 您将学习到的内容： - 了解LangGraph及其在生成AI生态系统中的位置 - 使用状态机构建您的第一个LangGraph工作流程 - 使用Pydantic模型验证和结构化状态 - 利用异步和流式编程构建响应式应用程序 - 基于LLM输出实现条件路由 - 理解reducer及其如何管理状态转移 - 精通LangGraph内置的ToolNode工具调用 - 学习检查点的概念，应用短期（内存）和长期（SQLite、Redis）内存存储 - 使用工具和检索器构建代理RAG工作流程 - 实施人机交互循环工作流程，使用中断和恢复机制 - 使用子图模块化复杂图形 - 在实际的医院保险索赔管理用例中应用所有内容 - 使用LangSmith添加跟踪和可观察性 - 探索关键的代理设计模式，以扩展您的应用程序 适合人群： - 希望构建生产级代理应用程序的AI开发人员 - 希望提高图形化协调能力的LangChain用户 - 对工具使用、内存和状态控制感兴趣的后端工程师 - 从事现实世界用例中的LLM工作流程的任何人 先决条件： - 基础Python知识 - 对LangChain的基本熟悉度 通过本课程的学习，您将能够： - 自信地构建、扩展和调试LangGraph工作流程 - 将LLM、工具、内存和人类反馈整合到您的应用程序中 - 在索赔处理、客户支持和文档分析等现实商业用例中应用LangGraph 准备好掌握LangGraph，提升您的LLM应用程序到一个新高度吗？现在就注册，轻松构建智能、互动的代理系统！
Are you ready to go beyond simple LLM apps and build powerful, stateful, and agentic workflows using LangGraph?In this beginner-friendly course, you'll master LangGraph, an open-source library built on top of LangChain, designed for orchestrating multi-agent applications using a graph-based architecture. Whether you're building intelligent agents, dynamic RAG pipelines, or real-world enterprise solutions, this course gives you the solid foundation you need.What You'll Learn• What LangGraph is and how it fits into the GenAI ecosystem• Build your first LangGraph workflow using a state machine• Validate and structure your state using Pydantic models• Use async and streaming to build responsive applications• Implement conditional routing based on LLM output• Understand reducers and how they manage state transitions• Master tool calling with LangGraph's built-in ToolNode• Learn about checkpointers, and apply both short-term (in-memory) and long-term (SQLite, Redis) memory storage• Build Agentic RAG workflows using tools and retrievers• Implement Human-in-the-Loop workflows using Interrupt and resume• Modularize complex graphs using subgraphs• Apply everything in a real-time Hospital Insurance Claim Management use case• Add tracing and observability using LangSmith• Explore essential agentic design patterns to scale your applicationsWho This Course Is For• AI developers looking to build production-grade agentic apps• LangChain users who want to level up to graph-based orchestration• Backend engineers interested in tool use, memory, and state control• Anyone working on LLM workflows in real-world use casesPrerequisites• Basic Python knowledge• Some familiarity with LangChainBy the end of this course, you'll be able to:• Confidently build, scale, and debug LangGraph workflows• Integrate LLMs, tools, memory, and human feedback into your apps• Apply LangGraph in real-world business use cases like claim processing, customer support, and document analysisReady to master LangGraph and take your LLM applications to the next level?Enroll now and start building intelligent, interactive agentic systems with ease!
