feat: 添加智能专家生成、决策场景模板和用户反馈功能

.gitignore

@@ -0,0 +1,10 @@
+# Python 字节码缓存
+__pycache__/
+*.py[cod]
+*$py.class
+
+# 项目特定的存储/缓存文件夹
+.storage/
+
+# 环境变量文件（通常包含敏感信息）
+.env

.storage/config.json

@@ -1,6 +1,6 @@
 {
-  "provider": "DeepSeek",
+  "provider": "AIHubMix",
-  "api_key": "sk-ca812c913baa474182f6d4e83e078302",
+  "api_key": "sk-yd8Tik0nFW5emKYcBdFc433b7c8b4dC182848f76819bBe73",
-  "base_url": "https://api.deepseek.com",
+  "base_url": "https://aihubmix.com/v1",
   "language": "Chinese"
 }

README.md

@@ -1,76 +1,91 @@
-# Multi-Agent Council & Debate Workshop (V4)
+# 🍎 智能决策工作坊 (Multi-Agent Council V4)
 
-一个极简而强大的多智能体（Multi-Agent）决策辅助系统。
+AI驱动的多智能体决策分析系统 - 基于多模型智囊团
-**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。
 
-## ✨ 核心功能 (V4 Update)
+## ✨ 核心功能
 
-### 1. 🧪 Multi-Model Council V4 (多模型智囊团)
+### 🧪 Multi-Model Council V4 (智囊团模式)
-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**：
+- **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
-*   **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。
+- **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
-*   **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）。
+- **🪄 智能专家生成**: AI 根据主题自动推荐最合适的专家角色
-*   **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）。
+- **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
-*   **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**。
 
-### 2. 🎭 Debate Workshop (辩论工作坊)
+### 🎯 内置决策场景
-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。
+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：
 
-### 3. 🌐 Multi-Provider Support (多平台支持)
+| 场景 | 描述 |
-不再局限于单一平台，系统原生支持多种 API 源，随心切换：
+|------|------|
-*   **DeepSeek Official**: 直接连接 `api.deepseek.com`
+| 🚀 新产品发布评审 | 评估产品可行性、市场潜力和实施计划 |
-*   **SiliconFlow (硅基流动)**: 连接 `api.siliconflow.cn`
+| 💰 投资审批决策 | 分析投资项目的 ROI、风险和战略价值 |
-*   **AIHubMix**: 聚合平台
+| 🤝 合作伙伴评估 | 评估合作伙伴的匹配度和合作价值 |
-*   **OpenAI / Custom**: 支持标准 OpenAI 接口或本地 vLLM/Ollama
+| 📦 供应商评估 | 对比分析供应商的综合能力 |
+
+### 🎭 Debate Workshop (辩论工作坊)
+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
+
+### 💬 用户反馈
+内置用户反馈系统，收集功能建议和使用体验
+
+### 🌐 多平台支持
+- **DeepSeek**: V3, R1, Coder
+- **OpenAI**: GPT-4o, GPT-4o-mini
+- **Anthropic**: Claude 3.5 Sonnet
+- **Google**: Gemini 1.5/2.0
+- **SiliconFlow / AIHubMix / Deepseek**
 
 ---
 
 ## 🛠️ 安装
 
 ```bash
-# 1. 克隆项目
+# 克隆项目
 git clone https://github.com/HomoDeusss/multi-agent.git
 cd multi-agent
 
-# 2. 安装依赖
+# 初始化 uv 项目（如首次使用）
-pip install -r requirements.txt
+uv init
+
+# 安装依赖
+uv add streamlit openai anthropic python-dotenv
+
+# 或者同步现有依赖
+uv sync
 ```
 
 ## 🚀 快速开始
 
-### 1. 启动应用
 ```bash
-streamlit run app.py
+uv run streamlit run app.py
 ```
 
-### 2. 配置 API (V4 新特性)
+### 使用步骤
-无需手动修改 `.env` 文件（可选），直接在 Web 界面侧边栏配置：
-1.  在侧边栏选择 **"API Provider"** (例如 `DeepSeek` 或 `SiliconFlow`)。
-2.  输入对应的 **API Key**。
-3.  系统会自动配置好 Base URL。
 
-### 3. 使用 Council V4 模式
+1. **配置 API**: 在侧边栏选择 Provider 并输入 API Key
-1.  选择 **"Deep Research" (现已升级为 Council V4)**。
+2. **选择场景**: 点击预置的决策场景或自定义主题
-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。
+3. **生成专家**: 点击 "🪄 根据主题自动生成专家" 或手动配置
-    *   *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。*
+4. **开始决策**: 观察专家们如何互相对话，生成综合方案
-3.  **设定轮数**: 选择讨论轮数（建议 2-3 轮）。
-4.  输入议题，点击开始。观察专家们如何互相对话！
-
-### 4. 使用 Debate 模式
-1.  切换到 **"Debate Workshop"**。
-2.  输入议题（如“是否应该全职做独立开发？”）。
-3.  选择参与辩论的角色。
-4.  点击开始，观看唇枪舌战。
 
 ---
 
-## 🤖 支持的模型 (V4 Expanded)
+## 📁 项目结构
 
-系统内置了最新的模型配置，支持在界面直接选择：
+```
-*   **DeepSeek**: V3 (`deepseek-chat`), R1 (`deepseek-reasoner`), Coder V2
+multi_agent_workshop/
-*   **OpenAI**: GPT-4o, GPT-4o-mini
+├── app.py                 # Streamlit 主应用
-*   **Anthropic**: Claude 3.5 Sonnet, Claude 3 Opus
+├── config.py              # 配置文件
-*   **Google**: Gemini 1.5 Pro/Flash
+├── agents/                # Agent 定义
-*   **Meta/Alibaba**: Llama 3.3, Qwen 2.5
+│   ├── agent_profiles.py  # 预设角色配置
+│   ├── base_agent.py      # 基础 Agent 类
+│   └── research_agent.py  # 研究型 Agent
+├── orchestrator/          # 编排器
+│   ├── debate_manager.py  # 辩论管理
+│   └── research_manager.py # 智囊团管理
+├── utils/
+│   ├── llm_client.py      # LLM 客户端封装
+│   ├── storage.py         # 存储管理
+│   └── auto_agent_generator.py # 智能专家生成
+└── report/                # 报告生成
+```
 
 ## 📝 License
 [MIT License](LICENSE)

app.py

@@ -17,6 +17,7 @@ from report import ReportGenerator
 from report import ReportGenerator
 from utils import LLMClient
 from utils.storage import StorageManager
+from utils.auto_agent_generator import generate_experts_for_topic
 import config
 
 # ==================== 页面配置 ====================
@@ -30,37 +31,146 @@ st.set_page_config(
 # ==================== 样式 ====================
 st.markdown("""
 <style>
+    /* 蓝紫色渐变主题 - 模仿参考UI */
+    .stApp {
+        background: linear-gradient(180deg, #E8EEFF 0%, #F5F7FF 100%);
+    }
+    
+    /* 标题渐变 - 蓝紫色 */
+    .stApp h1 {
+        background: linear-gradient(135deg, #4A5CDB 0%, #667eea 50%, #764ba2 100%);
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent;
+        background-clip: text;
+        font-weight: 700;
+    }
+    
+    .stApp h2, .stApp h3 {
+        background: linear-gradient(90deg, #4A5CDB 0%, #667eea 100%);
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent;
+        background-clip: text;
+        font-weight: 600;
+    }
+    
+    /* 正文保持深色可读性 */
+    .stApp .stMarkdown p, .stApp .stMarkdown li {
+        color: #333;
+    }
+    
+    /* 主卡片样式 */
+    .main-card {
+        background: white;
+        border-radius: 1rem;
+        padding: 2rem;
+        box-shadow: 0 4px 20px rgba(74, 92, 219, 0.1);
+        margin: 1rem 0;
+        border: 1px solid rgba(74, 92, 219, 0.1);
+    }
+    
+    /* 场景卡片 */
+    .scenario-card {
+        background: white;
+        border-radius: 0.75rem;
+        padding: 1.5rem;
+        margin: 0.5rem 0;
+        border-left: 4px solid #4A5CDB;
+        box-shadow: 0 2px 10px rgba(0,0,0,0.05);
+    }
+    
+    .scenario-card h4 {
+        color: #4A5CDB;
+        margin-bottom: 0.5rem;
+        font-weight: 600;
+    }
+    
+    .scenario-card p {
+        color: #666;
+        font-size: 0.9rem;
+    }
+    
+    /* 典型问题列表 */
+    .typical-questions {
+        background: #F8F9FF;
+        border-radius: 0.5rem;
+        padding: 1rem;
+        margin-top: 0.5rem;
+    }
+    
+    .typical-questions strong {
+        color: #4A5CDB;
+    }
+    
+    /* 状态指示器 */
+    .status-indicator {
+        display: inline-flex;
+        align-items: center;
+        gap: 0.5rem;
+        background: #E8FFE8;
+        padding: 0.5rem 1rem;
+        border-radius: 0.5rem;
+        border: 1px solid #4CAF50;
+    }
+    
+    .status-dot {
+        width: 10px;
+        height: 10px;
+        background: #4CAF50;
+        border-radius: 50%;
+        animation: pulse 2s infinite;
+    }
+    
+    @keyframes pulse {
+        0%, 100% { opacity: 1; }
+        50% { opacity: 0.5; }
+    }
+    
+    /* 原有样式保留 */
     .agent-card {
         padding: 1rem;
         border-radius: 0.5rem;
         margin-bottom: 0.5rem;
-        border-left: 4px solid #4A90A4;
+        border-left: 4px solid #4A5CDB;
-        background-color: #f8f9fa;
+        background-color: #F8F9FF;
     }
     .speech-bubble {
-        background-color: #f0f2f6;
+        background-color: #F8F9FF;
         padding: 1rem;
         border-radius: 0.5rem;
         margin: 0.5rem 0;
     }
     .round-header {
-        background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
+        background: linear-gradient(90deg, #4A5CDB 0%, #667eea 50%, #764ba2 100%);
         color: white;
         padding: 0.5rem 1rem;
         border-radius: 0.5rem;
         margin: 1rem 0;
     }
     .custom-agent-form {
-        background-color: #e8f4f8;
+        background-color: #F8F9FF;
         padding: 1rem;
         border-radius: 0.5rem;
         margin: 0.5rem 0;
     }
     .research-step {
-        border-left: 3px solid #FF4B4B;
+        border-left: 3px solid #4A5CDB;
         padding-left: 10px;
         margin-bottom: 10px;
     }
+    
+    /* 按钮样式增强 */
+    .stButton > button {
+        border-radius: 0.5rem;
+        font-weight: 500;
+    }
+    
+    /* 分隔线 */
+    hr {
+        border: none;
+        height: 1px;
+        background: linear-gradient(90deg, transparent, #4A5CDB, transparent);
+        margin: 1.5rem 0;
+    }
 </style>
 """, unsafe_allow_html=True)
 
@@ -122,6 +232,8 @@ if "research_output" not in st.session_state:
     st.session_state.research_output = "" # Final report
 if "research_steps_output" not in st.session_state:
     st.session_state.research_steps_output = [] # List of step results
+if "generated_experts" not in st.session_state:
+    st.session_state.generated_experts = None  # Auto-generated expert configs
 
 
 # ==================== 侧边栏：配置 ====================
@@ -210,7 +322,7 @@ with st.sidebar:
              save_current_config()
 
         if not api_key:
-            st.warning("请配置 API Key 以继续")
+            st.warning("⚠️ 请配置 API Key 以启用 AI 功能 (仍可查看历史档案)")
             
     # Output Language Selection
     lang_options = config.SUPPORTED_LANGUAGES
@@ -251,8 +363,8 @@ with st.sidebar:
     # 模式选择
     mode = st.radio(
         "📊 选择模式",
-        ["Council V4 (Deep Research)", "Debate Workshop", "📜 History Archives"],
+        ["Council V4 (Deep Research)", "Debate Workshop", "📜 History Archives", "💬 用户反馈"],
-        index=0 if st.session_state.mode == "Deep Research" else (1 if st.session_state.mode == "Debate Workshop" else 2)
+        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))
     )
     
     # Map selection back to internal mode string
@@ -260,8 +372,10 @@ with st.sidebar:
         st.session_state.mode = "Deep Research"
     elif mode == "Debate Workshop":
         st.session_state.mode = "Debate Workshop"
-    else:
+    elif mode == "📜 History Archives":
         st.session_state.mode = "History Archives"
+    else:
+        st.session_state.mode = "Feedback"
     
     st.divider()
 
@@ -341,18 +455,167 @@ if st.session_state.get("bg_image_data_url"):
 # ==================== 主界面逻辑 ====================
 
 if st.session_state.mode == "Deep Research":
-    st.title("🧪 Multi-Model Council V4")
+    # ==================== 主标题区域 ====================
-    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*")
+    st.markdown("""
+    <div style="text-align: center; padding: 1rem 0;">
+        <h1 style="font-size: 2.5rem;">🍎 智能决策工作坊</h1>
+        <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>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    # 状态指示器和语言选择
+    col_status, col_lang = st.columns([2, 1])
+    with col_status:
+        if api_key:
+            st.markdown("""
+            <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")
+    
+    # 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
+            # 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:
@@ -418,8 +734,9 @@ if st.session_state.mode == "Deep Research":
                     
                     # Create a chat message block
                     with chat_container:
-                        st.markdown(f"#### {current_step_name}")
+                        with st.chat_message("assistant", avatar="🤖"):
-                        st.caption(f"🤖 {current_agent} ({current_model})")
+                            st.markdown(f"**{current_step_name}**")
+                            st.caption(f"({current_model})")
                             message_placeholder = st.empty()
                             current_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)

config.py

@@ -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": {

utils/auto_agent_generator.py

@@ -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)]

utils/llm_client.py

@@ -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

utils/storage.py

@@ -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