Course Code: llmautogen
Duration: 21 hours
Prerequisites:
  • 理解Python编程
  • 熟悉大型语言模型和提示工程
  • 具备API和自动化工作流程的经验

目标受众

  • AI工程师
  • ML开发者
  • 自动化架构师
Overview:

使用AutoGen构建LLM代理系统是一门实作课程,专注于利用Microsoft的AutoGen框架开发多代理系统,适用于大型语言模型(LLMs)。

这项由讲师主导的培训(线上或线下)针对中级AI与自动化专业人士,旨在帮助他们设计、实现并协调使用AutoGen与Python及LLMs的多代理系统。

培训结束后,参与者将能够:

  • 使用AutoGen框架设计多代理架构。
  • 配置代理角色、能力与协调行为。
  • 使用函数调用与记忆处理进行代理互动。
  • 构建并测试基于Python的LLM代理工作流程,适用于实际案例。

课程形式

  • 互动式讲座与讨论。
  • 大量练习与实践。
  • 在实作实验室环境中进行动手操作。

课程定制选项

  • 如需为本课程定制培训,请联系我们安排。
Course Outline:

LLM Agent Systems 简介

  • LLM agents 和多代理架构概念
  • AutoGen 框架和生态系统概述
  • 代理角色:用户代理、助理、函数调用者等

安装与配置 AutoGen

  • 设置 Python 环境和依赖项
  • AutoGen 配置文件基础
  • 连接 LLM 提供商(OpenAI、Azure、本地模型)

代理设计与角色分配

  • 了解代理类型和对话模式
  • 定义代理目标、提示和指令
  • 基于角色的任务委派和控制流程

函数调用与工具集成

  • 注册供代理使用的函数
  • 自主和协作函数执行
  • 将外部 API 和 Python 脚本连接到代理

对话 Management 与记忆

  • 会话跟踪和持久记忆
  • 代理间消息传递和令牌处理
  • 管理对话上下文和历史记录

端到端代理工作流程

  • 构建多步骤协作任务(例如文档分析、代码审查)
  • 模拟用户代理对话和决策链
  • 调试和优化代理性能

Use Case 与部署

  • 内部自动化代理:研究、报告、脚本
  • 外部机器人:聊天助理、语音集成
  • 在生产环境中打包和部署代理系统

总结与下一步

Sites Published:

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