- 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
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。
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
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