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# 人机协同：Deep Agents 模式与追问
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本示例演示了 **Deep Agents** 多智能体模式与 **追问** 人机协同模式的结合。
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它展示了如何构建一个深度推理智能体系统，当用户需求不明确时主动提出澄清问题，确保分析结果准确且个性化。
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## 工作原理
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1. **Deep Agent 架构**：系统使用一个深度智能体来协调专业化的子智能体：
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- **ResearchAgent**：搜索市场、技术和金融信息
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- **AnalysisAgent**：执行趋势、对比和统计分析
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- **FollowUpTool**：提出澄清问题以收集缺失信息
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2. **追问工具**：`FollowUpTool` 在以下情况触发中断向用户提问：
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- 分析需求不明确
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- 需要特定参数（时间段、行业、风险承受能力）
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- 智能体需要确认用户偏好
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3. **工作流程**：
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- 用户请求分析（例如，"分析市场趋势并提供投资建议"）
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- 深度智能体识别模糊性 → 使用 `FollowUpTool` → **中断**
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- 用户回答澄清问题 → 恢复
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- 智能体使用明确的需求进行研究和分析
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- 生成最终综合报告
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4. **指令驱动**：深度智能体被明确指示在任何分析之前首先使用 `FollowUpTool`，以确保收集所有必要信息。
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## 实际示例
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以下是追问流程的示例：
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```
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========================================
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User Query: Analyze the market trends and provide investment recommendations.
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========================================
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name: DataAnalysisAgent
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path: [{DataAnalysisAgent}]
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tool name: FollowUpTool
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arguments: {"questions":["What specific market sectors are you interested in?","What time period should the analysis cover?","What is your risk tolerance?"]}
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========================================
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CLARIFICATION NEEDED
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========================================
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The agent needs more information to proceed:
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1. What specific market sectors are you interested in (e.g., technology, finance, healthcare)?
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2. What time period should the analysis cover (e.g., last quarter, year-to-date)?
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3. What type of analysis do you need (e.g., trend, comparison, statistical)?
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4. What is your risk tolerance for investment recommendations (e.g., conservative, moderate, aggressive)?
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----------------------------------------
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Answer for Q1 (What specific market sectors...): technology and finance
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Answer for Q2 (What time period...): last quarter
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Answer for Q3 (What type of analysis...): trend analysis
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Answer for Q4 (What is your risk tolerance...): moderate
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========================================
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Resuming with your answers...
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========================================
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name: DataAnalysisAgent
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path: [{DataAnalysisAgent}]
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action: transfer to ResearchAgent
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name: ResearchAgent
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path: [{DataAnalysisAgent} {ResearchAgent}]
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tool name: search
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arguments: {"query":"technology market trends Q3 2025","category":"technology"}
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name: ResearchAgent
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path: [{DataAnalysisAgent} {ResearchAgent}]
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tool response: {"results":[{"title":"AI Industry Report 2025",...}]}
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name: DataAnalysisAgent
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path: [{DataAnalysisAgent}]
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action: transfer to AnalysisAgent
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name: AnalysisAgent
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path: [{DataAnalysisAgent} {AnalysisAgent}]
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tool name: analyze
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arguments: {"data":"...","analysis_type":"trend"}
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name: DataAnalysisAgent
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path: [{DataAnalysisAgent}]
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answer: Based on your preferences for technology and finance sectors with moderate risk tolerance...
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```
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此跟踪记录展示了：
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- **主动澄清**：智能体在开始分析前提出问题
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- **结构化问题**：在单次中断中收集多个问题
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- **用户回答**：收集所有答案并用于指导分析
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- **定向分析**：研究和分析根据用户的具体需求进行定制
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## 如何配置环境变量
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在运行示例之前，您需要设置 LLM API 所需的环境变量。您有两个选项：
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### 选项 1: OpenAI 兼容配置
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```bash
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export OPENAI_API_KEY="{your api key}"
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export OPENAI_BASE_URL="{your model base url}"
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# 仅在使用 Azure 类 LLM 提供商时配置此项
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export OPENAI_BY_AZURE=true
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# 'gpt-4o' 只是一个示例，请配置您的 LLM 提供商提供的实际模型名称
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export OPENAI_MODEL="gpt-4o-2024-05-13"
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```
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### 选项 2: ARK 配置
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```bash
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export MODEL_TYPE="ark"
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export ARK_API_KEY="{your ark api key}"
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export ARK_MODEL="{your ark model name}"
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```
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或者，您可以在项目根目录创建一个 `.env` 文件来设置这些变量。
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## 如何运行
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确保您已设置好环境变量（例如，LLM API 密钥）。然后，在 `eino-examples` 仓库的根目录下运行以下命令：
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```sh
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go run ./adk/human-in-the-loop/7_deep-agents
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```
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您将看到深度智能体询问关于您分析需求的澄清问题，在您提供答案后，它将进行定制化的市场分析。
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## 工作流程图
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```mermaid
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graph TD
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A[用户请求] --> B{深度智能体};
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B --> C[识别模糊性];
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C --> D[FollowUpTool];
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D --> E[中断：提问];
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E --> F{用户回答};
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F --> G[恢复并携带答案];
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G --> H[委托给 ResearchAgent];
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H --> I[搜索市场数据];
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I --> J[返回研究结果];
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J --> B;
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B --> K[委托给 AnalysisAgent];
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K --> L[分析数据];
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L --> M[返回分析结果];
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M --> B;
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B --> N[生成最终报告];
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N --> O[最终响应];
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```
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## 与其他模式的主要区别
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| 方面 | 审批 | 审阅编辑 | 追问 |
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|------|------|----------|------|
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| **触发条件** | 敏感操作 | 预订/修改 | 模糊需求 |
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| **用户操作** | 是/否 | 批准/编辑/拒绝 | 回答问题 |
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| **目的** | 授权 | 参数验证 | 信息收集 |
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| **时机** | 执行前 | 执行前 | 规划前 |
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