feat: 添加智能专家生成、决策场景模板和用户反馈功能
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# Python 字节码缓存
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__pycache__/
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*.py[cod]
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*$py.class
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# 项目特定的存储/缓存文件夹
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.storage/
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# 环境变量文件（通常包含敏感信息）
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.env
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@ -1,6 +1,6 @@
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{
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"provider": "DeepSeek",
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"api_key": "sk-ca812c913baa474182f6d4e83e078302",
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"base_url": "https://api.deepseek.com",
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"provider": "AIHubMix",
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"api_key": "sk-yd8Tik0nFW5emKYcBdFc433b7c8b4dC182848f76819bBe73",
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"base_url": "https://aihubmix.com/v1",
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"language": "Chinese"
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}
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109
README.md
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README.md
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# Multi-Agent Council & Debate Workshop (V4)
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# 🍎 智能决策工作坊 (Multi-Agent Council V4)
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一个极简而强大的多智能体（Multi-Agent）决策辅助系统。
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**V4 版本**将传统的 "线性研究" 进化为 **"多模型智囊团 (Council V4)"**，支持多轮对话讨论、动态专家组建、以及多 API 平台接入。
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AI驱动的多智能体决策分析系统 - 基于多模型智囊团
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## ✨ 核心功能 (V4 Update)
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## ✨ 核心功能
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### 1. 🧪 Multi-Model Council V4 (多模型智囊团)
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摒弃了单一的"规划-执行"模式，现在的系统是一个真正的**圆桌会议**：
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* **多轮对话讨论**: 专家不再是各自为战，而是像真实会议一样进行多轮（Round-Robin）对话，互相批判、补充观点。
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* **动态专家组建**: 你可以自定义 **2-5 位** 不同的专家（如 CEO, CTO, 法务）。
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* **自定义模型分配**: 为每个专家指定最擅长的模型（例如：让 DeepSeek-Coder 担任技术专家，让 GPT-4o 担任产品专家）。
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* **最终决策合成**: 讨论结束后，最后一位专家（Synthesizer）会综合全场观点，生成最终决策方案，并绘制 **Mermaid 路线图**。
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### 🧪 Multi-Model Council V4 (智囊团模式)
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- **多轮对话讨论**: 专家像真实会议一样进行多轮对话，互相批判、补充观点
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- **动态专家组建**: 自定义 2-5 位专家，为每位指定最擅长的模型
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- **🪄 智能专家生成**: AI 根据主题自动推荐最合适的专家角色
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- **最终决策合成**: 最后一位专家综合全场观点，生成方案并绘制 Mermaid 路线图
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### 2. 🎭 Debate Workshop (辩论工作坊)
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经典的辩论模式，让 AI 扮演不同立场的角色（如正方、反方、评审），通过激烈的辩论帮助你厘清复杂决策的利弊。
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### 🎯 内置决策场景
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系统预置 4 大典型决策场景，每个场景都配置了专业的典型问题：
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### 3. 🌐 Multi-Provider Support (多平台支持)
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不再局限于单一平台，系统原生支持多种 API 源，随心切换：
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* **DeepSeek Official**: 直接连接 `api.deepseek.com`
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* **SiliconFlow (硅基流动)**: 连接 `api.siliconflow.cn`
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* **AIHubMix**: 聚合平台
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* **OpenAI / Custom**: 支持标准 OpenAI 接口或本地 vLLM/Ollama
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| 场景 | 描述 |
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|------|------|
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| 🚀 新产品发布评审 | 评估产品可行性、市场潜力和实施计划 |
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| 💰 投资审批决策 | 分析投资项目的 ROI、风险和战略价值 |
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| 🤝 合作伙伴评估 | 评估合作伙伴的匹配度和合作价值 |
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| 📦 供应商评估 | 对比分析供应商的综合能力 |
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### 🎭 Debate Workshop (辩论工作坊)
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让 AI 扮演不同立场角色，通过辩论帮助厘清复杂决策的利弊
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### 💬 用户反馈
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内置用户反馈系统，收集功能建议和使用体验
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### 🌐 多平台支持
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- **DeepSeek**: V3, R1, Coder
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- **OpenAI**: GPT-4o, GPT-4o-mini
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- **Anthropic**: Claude 3.5 Sonnet
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- **Google**: Gemini 1.5/2.0
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- **SiliconFlow / AIHubMix / Deepseek**
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---
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## 🛠️ 安装
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```bash
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# 1. 克隆项目
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# 克隆项目
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git clone https://github.com/HomoDeusss/multi-agent.git
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cd multi-agent
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# 2. 安装依赖
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pip install -r requirements.txt
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# 初始化 uv 项目（如首次使用）
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uv init
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# 安装依赖
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uv add streamlit openai anthropic python-dotenv
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# 或者同步现有依赖
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uv sync
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```
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## 🚀 快速开始
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### 1. 启动应用
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```bash
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streamlit run app.py
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uv run streamlit run app.py
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```
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### 2. 配置 API (V4 新特性)
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无需手动修改 `.env` 文件（可选），直接在 Web 界面侧边栏配置：
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1. 在侧边栏选择 **"API Provider"** (例如 `DeepSeek` 或 `SiliconFlow`)。
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2. 输入对应的 **API Key**。
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3. 系统会自动配置好 Base URL。
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### 使用步骤
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### 3. 使用 Council V4 模式
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1. 选择 **"Deep Research" (现已升级为 Council V4)**。
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2. **设定专家**: 选择专家人数（例如 3 人），并为每位专家命名并指定模型。
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* *Tip: 建议最后一位专家选一个逻辑能力强的模型（如 Claude 3.5 Sonnet）作为决策者。*
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3. **设定轮数**: 选择讨论轮数（建议 2-3 轮）。
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4. 输入议题，点击开始。观察专家们如何互相对话！
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### 4. 使用 Debate 模式
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1. 切换到 **"Debate Workshop"**。
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2. 输入议题（如“是否应该全职做独立开发？”）。
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3. 选择参与辩论的角色。
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4. 点击开始，观看唇枪舌战。
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1. **配置 API**: 在侧边栏选择 Provider 并输入 API Key
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2. **选择场景**: 点击预置的决策场景或自定义主题
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3. **生成专家**: 点击 "🪄 根据主题自动生成专家" 或手动配置
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4. **开始决策**: 观察专家们如何互相对话，生成综合方案
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---
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## 🤖 支持的模型 (V4 Expanded)
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## 📁 项目结构
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系统内置了最新的模型配置，支持在界面直接选择：
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* **DeepSeek**: V3 (`deepseek-chat`), R1 (`deepseek-reasoner`), Coder V2
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* **OpenAI**: GPT-4o, GPT-4o-mini
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* **Anthropic**: Claude 3.5 Sonnet, Claude 3 Opus
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* **Google**: Gemini 1.5 Pro/Flash
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* **Meta/Alibaba**: Llama 3.3, Qwen 2.5
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```
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multi_agent_workshop/
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├── app.py # Streamlit 主应用
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├── config.py # 配置文件
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├── agents/ # Agent 定义
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│ ├── agent_profiles.py # 预设角色配置
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│ ├── base_agent.py # 基础 Agent 类
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│ └── research_agent.py # 研究型 Agent
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├── orchestrator/ # 编排器
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│ ├── debate_manager.py # 辩论管理
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│ └── research_manager.py # 智囊团管理
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├── utils/
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│ ├── llm_client.py # LLM 客户端封装
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│ ├── storage.py # 存储管理
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│ └── auto_agent_generator.py # 智能专家生成
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└── report/ # 报告生成
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```
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## 📝 License
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[MIT License](LICENSE)
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app.py
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app.py
@ -17,6 +17,7 @@ from report import ReportGenerator
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from report import ReportGenerator
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from utils import LLMClient
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from utils.storage import StorageManager
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from utils.auto_agent_generator import generate_experts_for_topic
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import config
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# ==================== 页面配置 ====================
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@ -30,37 +31,146 @@ st.set_page_config(
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# ==================== 样式 ====================
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st.markdown("""
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<style>
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/* 蓝紫色渐变主题 - 模仿参考UI */
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.stApp {
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background: linear-gradient(180deg, #E8EEFF 0%, #F5F7FF 100%);
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}
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/* 标题渐变 - 蓝紫色 */
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.stApp h1 {
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background: linear-gradient(135deg, #4A5CDB 0%, #667eea 50%, #764ba2 100%);
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-webkit-background-clip: text;
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-webkit-text-fill-color: transparent;
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background-clip: text;
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font-weight: 700;
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}
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.stApp h2, .stApp h3 {
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background: linear-gradient(90deg, #4A5CDB 0%, #667eea 100%);
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-webkit-background-clip: text;
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-webkit-text-fill-color: transparent;
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background-clip: text;
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font-weight: 600;
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}
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/* 正文保持深色可读性 */
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.stApp .stMarkdown p, .stApp .stMarkdown li {
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color: #333;
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}
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/* 主卡片样式 */
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.main-card {
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background: white;
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border-radius: 1rem;
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padding: 2rem;
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box-shadow: 0 4px 20px rgba(74, 92, 219, 0.1);
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margin: 1rem 0;
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border: 1px solid rgba(74, 92, 219, 0.1);
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}
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/* 场景卡片 */
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.scenario-card {
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background: white;
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border-radius: 0.75rem;
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padding: 1.5rem;
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margin: 0.5rem 0;
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border-left: 4px solid #4A5CDB;
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box-shadow: 0 2px 10px rgba(0,0,0,0.05);
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}
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.scenario-card h4 {
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color: #4A5CDB;
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margin-bottom: 0.5rem;
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font-weight: 600;
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}
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.scenario-card p {
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color: #666;
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font-size: 0.9rem;
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}
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/* 典型问题列表 */
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.typical-questions {
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background: #F8F9FF;
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border-radius: 0.5rem;
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padding: 1rem;
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margin-top: 0.5rem;
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}
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.typical-questions strong {
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color: #4A5CDB;
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}
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/* 状态指示器 */
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.status-indicator {
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display: inline-flex;
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align-items: center;
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gap: 0.5rem;
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background: #E8FFE8;
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padding: 0.5rem 1rem;
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border-radius: 0.5rem;
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border: 1px solid #4CAF50;
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}
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.status-dot {
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width: 10px;
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height: 10px;
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background: #4CAF50;
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border-radius: 50%;
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animation: pulse 2s infinite;
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}
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@keyframes pulse {
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0%, 100% { opacity: 1; }
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50% { opacity: 0.5; }
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}
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/* 原有样式保留 */
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.agent-card {
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padding: 1rem;
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border-radius: 0.5rem;
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margin-bottom: 0.5rem;
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border-left: 4px solid #4A90A4;
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background-color: #f8f9fa;
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border-left: 4px solid #4A5CDB;
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background-color: #F8F9FF;
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}
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.speech-bubble {
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background-color: #f0f2f6;
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background-color: #F8F9FF;
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padding: 1rem;
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border-radius: 0.5rem;
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margin: 0.5rem 0;
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}
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.round-header {
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background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
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background: linear-gradient(90deg, #4A5CDB 0%, #667eea 50%, #764ba2 100%);
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color: white;
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padding: 0.5rem 1rem;
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border-radius: 0.5rem;
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margin: 1rem 0;
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}
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.custom-agent-form {
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background-color: #e8f4f8;
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background-color: #F8F9FF;
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padding: 1rem;
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border-radius: 0.5rem;
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margin: 0.5rem 0;
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}
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.research-step {
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border-left: 3px solid #FF4B4B;
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border-left: 3px solid #4A5CDB;
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padding-left: 10px;
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margin-bottom: 10px;
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}
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/* 按钮样式增强 */
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.stButton > button {
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border-radius: 0.5rem;
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font-weight: 500;
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}
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/* 分隔线 */
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hr {
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border: none;
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height: 1px;
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background: linear-gradient(90deg, transparent, #4A5CDB, transparent);
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margin: 1.5rem 0;
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}
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</style>
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""", unsafe_allow_html=True)
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st.session_state.research_output = "" # Final report
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if "research_steps_output" not in st.session_state:
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st.session_state.research_steps_output = [] # List of step results
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if "generated_experts" not in st.session_state:
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st.session_state.generated_experts = None # Auto-generated expert configs
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# ==================== 侧边栏：配置 ====================
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save_current_config()
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if not api_key:
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st.warning("请配置 API Key 以继续")
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st.warning("⚠️ 请配置 API Key 以启用 AI 功能 (仍可查看历史档案)")
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# Output Language Selection
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lang_options = config.SUPPORTED_LANGUAGES
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# 模式选择
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mode = st.radio(
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"📊 选择模式",
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["Council V4 (Deep Research)", "Debate Workshop", "📜 History Archives"],
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index=0 if st.session_state.mode == "Deep Research" else (1 if st.session_state.mode == "Debate Workshop" else 2)
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["Council V4 (Deep Research)", "Debate Workshop", "📜 History Archives", "💬 用户反馈"],
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index=0 if st.session_state.mode == "Deep Research" else (1 if st.session_state.mode == "Debate Workshop" else (2 if st.session_state.mode == "History Archives" else 3))
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)
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# Map selection back to internal mode string
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@ -260,8 +372,10 @@ with st.sidebar:
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st.session_state.mode = "Deep Research"
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elif mode == "Debate Workshop":
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st.session_state.mode = "Debate Workshop"
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else:
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elif mode == "📜 History Archives":
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st.session_state.mode = "History Archives"
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else:
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st.session_state.mode = "Feedback"
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st.divider()
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@ -341,18 +455,167 @@ if st.session_state.get("bg_image_data_url"):
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# ==================== 主界面逻辑 ====================
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if st.session_state.mode == "Deep Research":
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st.title("🧪 Multi-Model Council V4")
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st.markdown("*多模型智囊团：自定义 N 个专家进行多轮对话讨论，最后由最后一位专家决策*")
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# ==================== 主标题区域 ====================
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st.markdown("""
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<div style="text-align: center; padding: 1rem 0;">
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<h1 style="font-size: 2.5rem;">🍎 智能决策工作坊</h1>
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<p style="color: #666; font-size: 1.1rem;">AI驱动的多智能体决策分析系统 - 基于多模型智囊团</p>
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</div>
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""", unsafe_allow_html=True)
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# 状态指示器和语言选择
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col_status, col_lang = st.columns([2, 1])
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with col_status:
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if api_key:
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st.markdown("""
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<div class="status-indicator">
|
||||
<div class="status-dot"></div>
|
||||
<span style="color: #4CAF50;">✓ 已连接到服务器</span>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
else:
|
||||
st.warning("⚠️ 请在侧边栏配置 API Key")
|
||||
|
||||
with col_lang:
|
||||
st.markdown(f"**语言/Language:** {output_language}")
|
||||
|
||||
st.divider()
|
||||
|
||||
# ==================== 开始决策按钮 ====================
|
||||
st.markdown("""
|
||||
<div class="main-card" style="text-align: center;">
|
||||
<h3>🚀 开始决策</h3>
|
||||
<p style="color: #666;">选择场景或自定义主题，开始多专家协作分析</p>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
st.divider()
|
||||
|
||||
# ==================== 支持的决策场景 ====================
|
||||
st.markdown("""
|
||||
<div class="main-card">
|
||||
<h2>📋 支持的决策场景</h2>
|
||||
<p style="color: #666; margin-bottom: 1.5rem;">系统支持以下决策场景，每个场景都配置了专业的AI专家团队</p>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# Decision scenario templates with typical questions
|
||||
DECISION_SCENARIOS = {
|
||||
"🚀 新产品发布评审": {
|
||||
"topic": "新产品发布评审：评估产品功能完备性、市场准备度、发布时机和潜在风险",
|
||||
"description": "评估新产品概念的可行性、市场潜力和实施计划",
|
||||
"example": "我们计划在下个季度发布AI助手功能，需要评估技术准备度、市场时机和竞争态势",
|
||||
"questions": [
|
||||
"这个产品的核心价值主张是什么？",
|
||||
"目标用户群体是谁？需求是否真实存在？",
|
||||
"技术实现难度如何？团队是否具备能力？",
|
||||
"竞争对手有类似产品吗？我们的差异化在哪？"
|
||||
]
|
||||
},
|
||||
"💰 投资审批决策": {
|
||||
"topic": "投资审批决策：评估投资项目的财务回报、战略价值、风险因素和执行可行性",
|
||||
"description": "分析投资项目的ROI、风险和战略价值",
|
||||
"example": "公司考虑投资1000万用于数据中台建设，需要评估ROI、技术风险和业务价值",
|
||||
"questions": [
|
||||
"预期投资回报率(ROI)是多少？",
|
||||
"投资回收期需要多长时间？",
|
||||
"主要风险因素有哪些？如何缓解？",
|
||||
"是否有更优的替代方案？"
|
||||
]
|
||||
},
|
||||
"🤝 合作伙伴评估": {
|
||||
"topic": "合作伙伴评估：分析潜在合作方的能力、信誉、战略协同和合作风险",
|
||||
"description": "评估潜在合作伙伴的匹配度和合作价值",
|
||||
"example": "评估与XX公司建立战略合作的可行性，包括技术互补性、市场协同和风险",
|
||||
"questions": [
|
||||
"合作方的核心能力是什么？",
|
||||
"双方资源如何互补？",
|
||||
"合作的战略协同效应有多大？",
|
||||
"合作失败的风险和退出机制是什么？"
|
||||
]
|
||||
},
|
||||
"📦 供应商评估": {
|
||||
"topic": "供应商评估：评估供应商的质量、成本、交付能力、稳定性和合作风险",
|
||||
"description": "对比分析供应商的综合能力",
|
||||
"example": "评估更换核心零部件供应商的利弊，包括成本对比、质量风险和切换成本",
|
||||
"questions": [
|
||||
"供应商的质量控制体系如何？",
|
||||
"价格竞争力与行业均值对比？",
|
||||
"交付能力和响应速度如何？",
|
||||
"供应商的财务稳定性如何？"
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
# Display scenario cards with typical questions
|
||||
for scenario_name, scenario_data in DECISION_SCENARIOS.items():
|
||||
st.markdown(f"""
|
||||
<div class="scenario-card">
|
||||
<h4>{scenario_name}</h4>
|
||||
<p>{scenario_data['description']}</p>
|
||||
<div class="typical-questions">
|
||||
<strong>典型问题：</strong>
|
||||
<ul style="margin: 0.5rem 0; padding-left: 1.5rem; color: #555;">
|
||||
{''.join([f'<li>{q}</li>' for q in scenario_data['questions']])}
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
if st.button(f"使用此场景", key=f"use_{scenario_name}", use_container_width=True):
|
||||
st.session_state.selected_scenario = scenario_data
|
||||
st.session_state.prefill_topic = scenario_data['topic']
|
||||
st.rerun()
|
||||
|
||||
st.divider()
|
||||
|
||||
# Get prefilled topic if available
|
||||
prefill_topic = st.session_state.get("prefill_topic", "")
|
||||
if st.session_state.get("selected_scenario"):
|
||||
prefill_topic = prefill_topic or st.session_state.selected_scenario.get("topic", "")
|
||||
|
||||
col1, col2 = st.columns([3, 1])
|
||||
with col1:
|
||||
research_topic = st.text_area("研究/决策主题", placeholder="请输入你想深入研究或决策的主题...", height=100)
|
||||
research_topic = st.text_area("研究/决策主题", value=prefill_topic, placeholder="请输入你想深入研究或决策的主题...", height=100)
|
||||
with col2:
|
||||
max_rounds = st.number_input("讨论轮数", min_value=1, max_value=5, value=2, help="专家们进行对话的轮数")
|
||||
|
||||
# Expert Configuration
|
||||
st.subheader("👥 专家配置")
|
||||
num_experts = st.number_input("专家数量", min_value=2, max_value=5, value=3)
|
||||
|
||||
# Auto-generate experts row
|
||||
col_num, col_auto = st.columns([2, 3])
|
||||
with col_num:
|
||||
num_experts = st.number_input("专家数量", min_value=2, max_value=5, value=3)
|
||||
with col_auto:
|
||||
st.write("") # Spacing
|
||||
auto_gen_btn = st.button(
|
||||
"🪄 根据主题自动生成专家",
|
||||
disabled=(not research_topic or not api_key),
|
||||
help="AI 将根据您的主题自动推荐合适的专家角色"
|
||||
)
|
||||
|
||||
# Handle auto-generation
|
||||
if auto_gen_btn and research_topic and api_key:
|
||||
with st.spinner("🤖 AI 正在分析主题并生成专家配置..."):
|
||||
try:
|
||||
temp_client = LLMClient(
|
||||
provider=provider_id,
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
model="gpt-4o-mini" # Use fast model for generation
|
||||
)
|
||||
generated = generate_experts_for_topic(
|
||||
topic=research_topic,
|
||||
num_experts=num_experts,
|
||||
llm_client=temp_client,
|
||||
language=output_language
|
||||
)
|
||||
st.session_state.generated_experts = generated
|
||||
st.success(f"✅ 已生成 {len(generated)} 位专家配置！")
|
||||
st.rerun()
|
||||
except Exception as e:
|
||||
st.error(f"生成失败: {e}")
|
||||
|
||||
experts_config = []
|
||||
cols = st.columns(num_experts)
|
||||
@ -360,11 +623,20 @@ if st.session_state.mode == "Deep Research":
|
||||
for i in range(num_experts):
|
||||
with cols[i]:
|
||||
default_model_key = list(AVAILABLE_MODELS.keys())[i % len(AVAILABLE_MODELS)]
|
||||
st.markdown(f"**Expert {i+1}**")
|
||||
# Default names
|
||||
default_name = f"Expert {i+1}"
|
||||
if i == num_experts - 1:
|
||||
default_name = f"Expert {i+1} (Synthesizer)"
|
||||
|
||||
# Use generated expert name if available
|
||||
if st.session_state.generated_experts and i < len(st.session_state.generated_experts):
|
||||
gen_expert = st.session_state.generated_experts[i]
|
||||
default_name = gen_expert.get("name", f"Expert {i+1}")
|
||||
perspective = gen_expert.get("perspective", "")
|
||||
st.markdown(f"**{default_name}**")
|
||||
if perspective:
|
||||
st.caption(f"_{perspective}_")
|
||||
else:
|
||||
default_name = f"Expert {i+1}"
|
||||
if i == num_experts - 1:
|
||||
default_name = f"Expert {i+1} (Synthesizer)"
|
||||
st.markdown(f"**Expert {i+1}**")
|
||||
|
||||
expert_name = st.text_input(f"名称 #{i+1}", value=default_name, key=f"expert_name_{i}")
|
||||
expert_model = st.selectbox(f"模型 #{i+1}", options=list(AVAILABLE_MODELS.keys()), index=list(AVAILABLE_MODELS.keys()).index(default_model_key), key=f"expert_model_{i}")
|
||||
@ -376,12 +648,58 @@ if st.session_state.mode == "Deep Research":
|
||||
|
||||
research_context = st.text_area("补充背景 (可选)", placeholder="任何额外的背景信息...", height=80)
|
||||
|
||||
start_research_btn = st.button("🚀 开始多模型协作", type="primary", disabled=not research_topic)
|
||||
start_research_btn = st.button("🚀 开始多模型协作", type="primary", disabled=(not research_topic or not api_key))
|
||||
if not api_key:
|
||||
st.info("💡 请先在侧边栏配置 API Key 才能开始任务")
|
||||
|
||||
|
||||
# ==================== 恢复会话逻辑 (Resume Logic) ====================
|
||||
# Try to load cached session
|
||||
cached_session = st.session_state.storage.load_session_state("council_cache")
|
||||
|
||||
# If we have a cached session, and we are NOT currently running one (research_started is False)
|
||||
if cached_session and not st.session_state.research_started:
|
||||
st.info(f"🔍 检测到上次未完成的会话: {cached_session.get('topic', 'Unknown Topic')}")
|
||||
col_res1, col_res2 = st.columns([1, 4])
|
||||
with col_res1:
|
||||
if st.button("🔄 恢复会话", type="primary"):
|
||||
# Restore state
|
||||
st.session_state.research_started = True
|
||||
st.session_state.research_output = "" # Usually empty if unfinished
|
||||
st.session_state.research_steps_output = cached_session.get("steps_output", [])
|
||||
|
||||
# Restore inputs if possible (tricky with widgets, but we can set defaults or just rely on cache for display)
|
||||
# For simplicity, we restore the viewing state. Continuing generation is harder without rebuilding the exact generator state.
|
||||
# Currently, "Resume" means "Restore View". To continue adding to it would require skipping done steps in manager.
|
||||
|
||||
st.rerun()
|
||||
with col_res2:
|
||||
if st.button("🗑️ 放弃", type="secondary"):
|
||||
st.session_state.storage.clear_session_state("council_cache")
|
||||
st.rerun()
|
||||
|
||||
# ==================== 历史渲染区域 (Always visible if started) ====================
|
||||
if st.session_state.research_started and st.session_state.research_steps_output and not start_research_btn:
|
||||
st.subheader("🗣️ 智囊团讨论历史")
|
||||
for step in st.session_state.research_steps_output:
|
||||
step_name = step.get('step', 'Unknown')
|
||||
content = step.get('output', '')
|
||||
role_type = "assistant"
|
||||
|
||||
with st.chat_message(role_type, avatar="🤖"):
|
||||
st.markdown(f"**{step_name}**")
|
||||
st.markdown(content)
|
||||
st.divider()
|
||||
|
||||
# ==================== 执行区域 (Triggered by Button) ====================
|
||||
if start_research_btn and research_topic:
|
||||
st.session_state.research_started = True
|
||||
st.session_state.research_output = ""
|
||||
st.session_state.research_steps_output = []
|
||||
|
||||
# Clear any old cache when starting fresh
|
||||
st.session_state.storage.clear_session_state("council_cache")
|
||||
|
||||
# 使用全局页面背景（若已上传）
|
||||
research_bg_path = st.session_state.get("bg_image_path")
|
||||
if st.session_state.get("bg_image_data_url"):
|
||||
@ -404,9 +722,7 @@ if st.session_state.mode == "Deep Research":
|
||||
)
|
||||
manager.create_agents(config_obj)
|
||||
|
||||
st.divider()
|
||||
st.subheader("🗣️ 智囊团讨论中...")
|
||||
|
||||
chat_container = st.container()
|
||||
|
||||
try:
|
||||
@ -418,10 +734,11 @@ if st.session_state.mode == "Deep Research":
|
||||
|
||||
# Create a chat message block
|
||||
with chat_container:
|
||||
st.markdown(f"#### {current_step_name}")
|
||||
st.caption(f"🤖 {current_agent} ({current_model})")
|
||||
message_placeholder = st.empty()
|
||||
current_content = ""
|
||||
with st.chat_message("assistant", avatar="🤖"):
|
||||
st.markdown(f"**{current_step_name}**")
|
||||
st.caption(f"({current_model})")
|
||||
message_placeholder = st.empty()
|
||||
current_content = ""
|
||||
|
||||
elif event["type"] == "content":
|
||||
current_content += event["content"]
|
||||
@ -433,7 +750,18 @@ if st.session_state.mode == "Deep Research":
|
||||
"step": current_step_name,
|
||||
"output": event["output"]
|
||||
})
|
||||
st.divider() # Separator between turns
|
||||
|
||||
# === AUTO-SAVE CACHE ===
|
||||
# Save current progress to session cache
|
||||
cache_data = {
|
||||
"topic": research_topic,
|
||||
"context": research_context,
|
||||
"steps_output": st.session_state.research_steps_output,
|
||||
"experts_config": experts_config,
|
||||
"max_rounds": max_rounds
|
||||
}
|
||||
st.session_state.storage.save_session_state("council_cache", cache_data)
|
||||
# =======================
|
||||
|
||||
# The last step output is the final plan
|
||||
if st.session_state.research_steps_output:
|
||||
@ -456,6 +784,10 @@ if st.session_state.mode == "Deep Research":
|
||||
content=final_plan,
|
||||
metadata=metadata
|
||||
)
|
||||
|
||||
# Clear session cache as we finished successfully
|
||||
st.session_state.storage.clear_session_state("council_cache")
|
||||
|
||||
st.toast("✅ 记录已保存到历史档案")
|
||||
|
||||
except Exception as e:
|
||||
@ -624,6 +956,8 @@ elif st.session_state.mode == "Debate Workshop":
|
||||
type="primary",
|
||||
use_container_width=True
|
||||
)
|
||||
if not api_key:
|
||||
st.caption("🔒 需配置 API Key")
|
||||
|
||||
with col_btn2:
|
||||
reset_btn = st.button(
|
||||
@ -863,6 +1197,117 @@ elif st.session_state.mode == "History Archives":
|
||||
file_name=f"{record['type']}_{record['id']}.md"
|
||||
)
|
||||
|
||||
# ==================== 用户反馈页面 ====================
|
||||
elif st.session_state.mode == "Feedback":
|
||||
st.title("💬 用户反馈")
|
||||
st.markdown("*您的反馈帮助我们不断改进产品*")
|
||||
|
||||
# Feedback form
|
||||
st.subheader("📝 提交反馈")
|
||||
|
||||
feedback_type = st.selectbox(
|
||||
"反馈类型",
|
||||
["功能建议", "Bug 报告", "使用体验", "其他"],
|
||||
help="选择您要反馈的类型"
|
||||
)
|
||||
|
||||
# Rating
|
||||
st.markdown("**整体满意度**")
|
||||
rating = st.slider("", 1, 5, 4, format="%d ⭐")
|
||||
rating_labels = {1: "😞 非常不满意", 2: "😕 不满意", 3: "😐 一般", 4: "😊 满意", 5: "🤩 非常满意"}
|
||||
st.caption(rating_labels.get(rating, ""))
|
||||
|
||||
# Feedback content
|
||||
feedback_content = st.text_area(
|
||||
"详细描述",
|
||||
placeholder="请描述您的反馈内容...\n\n例如：\n- 您遇到了什么问题？\n- 您希望增加什么功能？\n- 您对哪些方面有改进建议？",
|
||||
height=200
|
||||
)
|
||||
|
||||
# Feature requests for Council V4
|
||||
st.subheader("🎯 功能需求调研")
|
||||
st.markdown("您最希望看到哪些新功能？（可多选）")
|
||||
|
||||
feature_options = {
|
||||
"more_scenarios": "📋 更多决策场景模板",
|
||||
"export_pdf": "📄 导出 PDF 报告",
|
||||
"voice_input": "🎤 语音输入支持",
|
||||
"realtime_collab": "👥 多人实时协作",
|
||||
"custom_prompts": "✏️ 自定义专家 Prompt",
|
||||
"api_access": "🔌 API 接口支持",
|
||||
"mobile_app": "📱 移动端应用"
|
||||
}
|
||||
|
||||
selected_features = []
|
||||
cols = st.columns(3)
|
||||
for idx, (key, label) in enumerate(feature_options.items()):
|
||||
with cols[idx % 3]:
|
||||
if st.checkbox(label, key=f"feature_{key}"):
|
||||
selected_features.append(key)
|
||||
|
||||
# Contact info (optional)
|
||||
st.subheader("📧 联系方式（可选）")
|
||||
contact_email = st.text_input("邮箱", placeholder="your@email.com")
|
||||
|
||||
# Submit button
|
||||
st.divider()
|
||||
if st.button("📤 提交反馈", type="primary", use_container_width=True):
|
||||
if feedback_content.strip():
|
||||
# Save feedback
|
||||
feedback_data = {
|
||||
"type": feedback_type,
|
||||
"rating": rating,
|
||||
"content": feedback_content,
|
||||
"features": selected_features,
|
||||
"email": contact_email,
|
||||
"timestamp": st.session_state.storage._get_timestamp() if hasattr(st.session_state.storage, '_get_timestamp') else ""
|
||||
}
|
||||
|
||||
# Save to storage
|
||||
try:
|
||||
import json
|
||||
import os
|
||||
feedback_dir = os.path.join(st.session_state.storage.base_dir, "feedback")
|
||||
os.makedirs(feedback_dir, exist_ok=True)
|
||||
|
||||
from datetime import datetime
|
||||
filename = f"feedback_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
|
||||
filepath = os.path.join(feedback_dir, filename)
|
||||
|
||||
with open(filepath, 'w', encoding='utf-8') as f:
|
||||
json.dump(feedback_data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
st.success("🎉 感谢您的反馈！我们会认真阅读并持续改进产品。")
|
||||
st.balloons()
|
||||
except Exception as e:
|
||||
st.error(f"保存反馈时出错: {e}")
|
||||
else:
|
||||
st.warning("请填写反馈内容")
|
||||
|
||||
# Show previous feedback summary
|
||||
st.divider()
|
||||
with st.expander("📊 我的反馈历史"):
|
||||
try:
|
||||
import os
|
||||
import json
|
||||
feedback_dir = os.path.join(st.session_state.storage.base_dir, "feedback")
|
||||
if os.path.exists(feedback_dir):
|
||||
files = sorted(os.listdir(feedback_dir), reverse=True)[:5]
|
||||
if files:
|
||||
for f in files:
|
||||
filepath = os.path.join(feedback_dir, f)
|
||||
with open(filepath, 'r', encoding='utf-8') as file:
|
||||
data = json.load(file)
|
||||
st.markdown(f"**{data.get('timestamp', 'Unknown')}** | {data.get('type', '')} | {'⭐' * data.get('rating', 0)}")
|
||||
st.caption(data.get('content', '')[:100] + "...")
|
||||
st.divider()
|
||||
else:
|
||||
st.info("暂无反馈记录")
|
||||
else:
|
||||
st.info("暂无反馈记录")
|
||||
except Exception:
|
||||
st.info("暂无反馈记录")
|
||||
|
||||
# ==================== 底部信息 ====================
|
||||
st.divider()
|
||||
col_footer1, col_footer2, col_footer3 = st.columns(3)
|
||||
|
||||
@ -96,6 +96,9 @@ MAX_AGENTS = 6 # 最大参与 Agent 数量
|
||||
# 支持的输出语言
|
||||
SUPPORTED_LANGUAGES = ["Chinese", "English", "Japanese", "Spanish", "French", "German"]
|
||||
|
||||
# 生成配置
|
||||
MAX_OUTPUT_TOKENS = 300 # 限制单次回复长度，保持精简
|
||||
|
||||
# 研究模式模型角色配置
|
||||
RESEARCH_MODEL_ROLES = {
|
||||
"expert_a": {
|
||||
|
||||
Binary file not shown.
Binary file not shown.
108
utils/auto_agent_generator.py
Normal file
108
utils/auto_agent_generator.py
Normal file
@ -0,0 +1,108 @@
|
||||
"""
|
||||
Auto Agent Generator - 根据主题自动生成专家配置
|
||||
Uses LLM to analyze the topic and suggest appropriate expert agents.
|
||||
"""
|
||||
import json
|
||||
import re
|
||||
from typing import List, Dict
|
||||
from utils.llm_client import LLMClient
|
||||
|
||||
|
||||
EXPERT_GENERATION_PROMPT = """You are an expert team composition advisor. Given a research/decision topic, you need to suggest the most appropriate team of experts to analyze it.
|
||||
|
||||
Instructions:
|
||||
1. Analyze the topic carefully to understand its domain and key aspects
|
||||
2. Generate {num_experts} distinct expert roles that would provide the most valuable perspectives
|
||||
3. Each expert should have a unique focus area relevant to the topic
|
||||
4. The LAST expert should always be a "Synthesizer" role who can integrate all perspectives
|
||||
|
||||
Output Format (MUST be valid JSON array):
|
||||
[
|
||||
{{"name": "Expert Name", "perspective": "Brief description of their viewpoint", "focus": "Key areas they analyze"}},
|
||||
...
|
||||
]
|
||||
|
||||
Examples of good expert names based on topic:
|
||||
- For "Should we launch an e-commerce platform?": "市场渠道分析师", "电商运营专家", "供应链顾问", "数字化转型综合师"
|
||||
- For "Career transition to AI field": "职业发展顾问", "AI行业专家", "技能评估分析师", "综合规划师"
|
||||
|
||||
IMPORTANT:
|
||||
- Use {language} for all names and descriptions
|
||||
- Make names specific to the topic, not generic like "Expert 1"
|
||||
- The last expert MUST be a synthesizer/integrator type
|
||||
|
||||
Topic: {topic}
|
||||
|
||||
Generate exactly {num_experts} experts as a JSON array:"""
|
||||
|
||||
|
||||
def generate_experts_for_topic(
|
||||
topic: str,
|
||||
num_experts: int,
|
||||
llm_client: LLMClient,
|
||||
language: str = "Chinese"
|
||||
) -> List[Dict[str, str]]:
|
||||
"""
|
||||
Use LLM to generate appropriate expert configurations based on the topic.
|
||||
|
||||
Args:
|
||||
topic: The research/decision topic
|
||||
num_experts: Number of experts to generate (2-5)
|
||||
llm_client: LLM client instance for API calls
|
||||
language: Output language (Chinese/English)
|
||||
|
||||
Returns:
|
||||
List of expert dicts: [{"name": "...", "perspective": "...", "focus": "..."}, ...]
|
||||
"""
|
||||
if not topic.strip():
|
||||
return []
|
||||
|
||||
prompt = EXPERT_GENERATION_PROMPT.format(
|
||||
topic=topic,
|
||||
num_experts=num_experts,
|
||||
language=language
|
||||
)
|
||||
|
||||
try:
|
||||
response = llm_client.chat(
|
||||
system_prompt="You are a helpful assistant that generates JSON output only. No markdown, no explanation.",
|
||||
user_prompt=prompt,
|
||||
max_tokens=800
|
||||
)
|
||||
|
||||
# Extract JSON from response (handle potential markdown wrapping)
|
||||
json_match = re.search(r'\[[\s\S]*\]', response)
|
||||
if json_match:
|
||||
experts = json.loads(json_match.group())
|
||||
# Validate structure
|
||||
if isinstance(experts, list) and len(experts) >= 1:
|
||||
validated = []
|
||||
for exp in experts[:num_experts]:
|
||||
if isinstance(exp, dict) and "name" in exp:
|
||||
validated.append({
|
||||
"name": exp.get("name", "Expert"),
|
||||
"perspective": exp.get("perspective", ""),
|
||||
"focus": exp.get("focus", "")
|
||||
})
|
||||
return validated
|
||||
except (json.JSONDecodeError, Exception) as e:
|
||||
print(f"[AutoAgentGenerator] Error parsing LLM response: {e}")
|
||||
|
||||
# Fallback: return generic experts
|
||||
fallback = []
|
||||
for i in range(num_experts):
|
||||
if i == num_experts - 1:
|
||||
fallback.append({"name": f"综合分析师", "perspective": "整合视角", "focus": "综合决策"})
|
||||
else:
|
||||
fallback.append({"name": f"专家 {i+1}", "perspective": "分析视角", "focus": "专业分析"})
|
||||
return fallback
|
||||
|
||||
|
||||
def get_default_model_for_expert(expert_index: int, total_experts: int, available_models: list) -> str:
|
||||
"""
|
||||
Assign a default model to an expert based on their position.
|
||||
Spreads experts across available models for diversity.
|
||||
"""
|
||||
if not available_models:
|
||||
return "gpt-4o"
|
||||
return available_models[expert_index % len(available_models)]
|
||||
@ -5,6 +5,8 @@ from typing import Generator
|
||||
import os
|
||||
|
||||
|
||||
import config
|
||||
|
||||
class LLMClient:
|
||||
"""LLM API 统一客户端"""
|
||||
|
||||
@ -62,7 +64,7 @@ class LLMClient:
|
||||
self,
|
||||
system_prompt: str,
|
||||
user_prompt: str,
|
||||
max_tokens: int = 1024
|
||||
max_tokens: int = config.MAX_OUTPUT_TOKENS
|
||||
) -> Generator[str, None, None]:
|
||||
"""
|
||||
流式对话
|
||||
|
||||
@ -150,3 +150,35 @@ class StorageManager:
|
||||
return json.load(f)
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
# ==================== Session Cache (Resume Functionality) ====================
|
||||
def save_session_state(self, key: str, data: Dict[str, Any]):
|
||||
"""Save temporary session state for recovery"""
|
||||
try:
|
||||
# We use a dedicated cache file per key
|
||||
cache_file = self.root_dir / f"{key}_cache.json"
|
||||
data["_timestamp"] = int(time.time())
|
||||
with open(cache_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(data, f, indent=2, ensure_ascii=False)
|
||||
except Exception as e:
|
||||
print(f"Error saving session cache: {e}")
|
||||
|
||||
def load_session_state(self, key: str) -> Dict[str, Any]:
|
||||
"""Load temporary session state"""
|
||||
cache_file = self.root_dir / f"{key}_cache.json"
|
||||
if not cache_file.exists():
|
||||
return None
|
||||
try:
|
||||
with open(cache_file, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
def clear_session_state(self, key: str):
|
||||
"""Clear temporary session state"""
|
||||
cache_file = self.root_dir / f"{key}_cache.json"
|
||||
if cache_file.exists():
|
||||
try:
|
||||
os.remove(cache_file)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
Loading…
Reference in New Issue
Block a user