Improve forum communication mechanism between agents.

@@ -131,7 +131,12 @@ class LogMonitor:
             "已更新段落",
             "已更新段落",
             "正在生成",
             "正在生成",
             "开始处理",
             "开始处理",
-            "处理完成"
+            "处理完成",
+            "已读取HOST发言",
+            "读取HOST发言失败",
+            "未找到HOST发言",
+            "调试输出",
+            "信息记录"
         ]
         ]
         
         
         for pattern in exclude_patterns:
         for pattern in exclude_patterns:

@@ -18,6 +18,17 @@ from ..utils.text_processing import (
     format_search_results_for_prompt
     format_search_results_for_prompt
 )
 )
 
 
+# 导入论坛读取工具
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+try:
+    from utils.forum_reader import get_latest_host_speech, format_host_speech_for_prompt
+    FORUM_READER_AVAILABLE = True
+except ImportError:
+    FORUM_READER_AVAILABLE = False
+    print("警告: 无法导入forum_reader模块，将跳过HOST发言读取功能")
+
 
 
 class FirstSummaryNode(StateMutationNode):
 class FirstSummaryNode(StateMutationNode):
     """根据搜索结果生成段落首次总结的节点"""
     """根据搜索结果生成段落首次总结的节点"""
@@ -62,9 +73,28 @@ class FirstSummaryNode(StateMutationNode):
             
             
             # 准备输入数据
             # 准备输入数据
             if isinstance(input_data, str):
             if isinstance(input_data, str):
-                message = input_data
+                data = json.loads(input_data)
             else:
             else:
-                message = json.dumps(input_data, ensure_ascii=False)
+                data = input_data.copy() if isinstance(input_data, dict) else input_data
+            
+            # 读取最新的HOST发言（如果可用）
+            if FORUM_READER_AVAILABLE:
+                try:
+                    host_speech = get_latest_host_speech()
+                    if host_speech:
+                        # 将HOST发言添加到输入数据中
+                        data['host_speech'] = host_speech
+                        self.log_info(f"已读取HOST发言，长度: {len(host_speech)}字符")
+                except Exception as e:
+                    self.log_info(f"读取HOST发言失败: {str(e)}")
+            
+            # 转换为JSON字符串
+            message = json.dumps(data, ensure_ascii=False)
+            
+            # 如果有HOST发言，添加到消息前面作为参考
+            if FORUM_READER_AVAILABLE and 'host_speech' in data and data['host_speech']:
+                formatted_host = format_host_speech_for_prompt(data['host_speech'])
+                message = formatted_host + "\n" + message
             
             
             self.log_info("正在生成首次段落总结")
             self.log_info("正在生成首次段落总结")
             
             
@@ -208,9 +238,28 @@ class ReflectionSummaryNode(StateMutationNode):
             
             
             # 准备输入数据
             # 准备输入数据
             if isinstance(input_data, str):
             if isinstance(input_data, str):
-                message = input_data
+                data = json.loads(input_data)
             else:
             else:
-                message = json.dumps(input_data, ensure_ascii=False)
+                data = input_data.copy() if isinstance(input_data, dict) else input_data
+            
+            # 读取最新的HOST发言（如果可用）
+            if FORUM_READER_AVAILABLE:
+                try:
+                    host_speech = get_latest_host_speech()
+                    if host_speech:
+                        # 将HOST发言添加到输入数据中
+                        data['host_speech'] = host_speech
+                        self.log_info(f"已读取HOST发言，长度: {len(host_speech)}字符")
+                except Exception as e:
+                    self.log_info(f"读取HOST发言失败: {str(e)}")
+            
+            # 转换为JSON字符串
+            message = json.dumps(data, ensure_ascii=False)
+            
+            # 如果有HOST发言，添加到消息前面作为参考
+            if FORUM_READER_AVAILABLE and 'host_speech' in data and data['host_speech']:
+                formatted_host = format_host_speech_for_prompt(data['host_speech'])
+                message = formatted_host + "\n" + message
             
             
             self.log_info("正在生成反思总结")
             self.log_info("正在生成反思总结")
             
             

@@ -18,6 +18,17 @@ from ..utils.text_processing import (
     format_search_results_for_prompt
     format_search_results_for_prompt
 )
 )
 
 
+# 导入论坛读取工具
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+try:
+    from utils.forum_reader import get_latest_host_speech, format_host_speech_for_prompt
+    FORUM_READER_AVAILABLE = True
+except ImportError:
+    FORUM_READER_AVAILABLE = False
+    print("警告: 无法导入forum_reader模块，将跳过HOST发言读取功能")
+
 
 
 class FirstSummaryNode(StateMutationNode):
 class FirstSummaryNode(StateMutationNode):
     """根据搜索结果生成段落首次总结的节点"""
     """根据搜索结果生成段落首次总结的节点"""
@@ -62,9 +73,28 @@ class FirstSummaryNode(StateMutationNode):
             
             
             # 准备输入数据
             # 准备输入数据
             if isinstance(input_data, str):
             if isinstance(input_data, str):
-                message = input_data
+                data = json.loads(input_data)
             else:
             else:
-                message = json.dumps(input_data, ensure_ascii=False)
+                data = input_data.copy() if isinstance(input_data, dict) else input_data
+            
+            # 读取最新的HOST发言（如果可用）
+            if FORUM_READER_AVAILABLE:
+                try:
+                    host_speech = get_latest_host_speech()
+                    if host_speech:
+                        # 将HOST发言添加到输入数据中
+                        data['host_speech'] = host_speech
+                        self.log_info(f"已读取HOST发言，长度: {len(host_speech)}字符")
+                except Exception as e:
+                    self.log_info(f"读取HOST发言失败: {str(e)}")
+            
+            # 转换为JSON字符串
+            message = json.dumps(data, ensure_ascii=False)
+            
+            # 如果有HOST发言，添加到消息前面作为参考
+            if FORUM_READER_AVAILABLE and 'host_speech' in data and data['host_speech']:
+                formatted_host = format_host_speech_for_prompt(data['host_speech'])
+                message = formatted_host + "\n" + message
             
             
             self.log_info("正在生成首次段落总结")
             self.log_info("正在生成首次段落总结")
             
             
@@ -212,9 +242,28 @@ class ReflectionSummaryNode(StateMutationNode):
             
             
             # 准备输入数据
             # 准备输入数据
             if isinstance(input_data, str):
             if isinstance(input_data, str):
-                message = input_data
+                data = json.loads(input_data)
             else:
             else:
-                message = json.dumps(input_data, ensure_ascii=False)
+                data = input_data.copy() if isinstance(input_data, dict) else input_data
+            
+            # 读取最新的HOST发言（如果可用）
+            if FORUM_READER_AVAILABLE:
+                try:
+                    host_speech = get_latest_host_speech()
+                    if host_speech:
+                        # 将HOST发言添加到输入数据中
+                        data['host_speech'] = host_speech
+                        self.log_info(f"已读取HOST发言，长度: {len(host_speech)}字符")
+                except Exception as e:
+                    self.log_info(f"读取HOST发言失败: {str(e)}")
+            
+            # 转换为JSON字符串
+            message = json.dumps(data, ensure_ascii=False)
+            
+            # 如果有HOST发言，添加到消息前面作为参考
+            if FORUM_READER_AVAILABLE and 'host_speech' in data and data['host_speech']:
+                formatted_host = format_host_speech_for_prompt(data['host_speech'])
+                message = formatted_host + "\n" + message
             
             
             self.log_info("正在生成反思总结")
             self.log_info("正在生成反思总结")
             
             

@@ -18,6 +18,17 @@ from ..utils.text_processing import (
     format_search_results_for_prompt
     format_search_results_for_prompt
 )
 )
 
 
+# 导入论坛读取工具
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+try:
+    from utils.forum_reader import get_latest_host_speech, format_host_speech_for_prompt
+    FORUM_READER_AVAILABLE = True
+except ImportError:
+    FORUM_READER_AVAILABLE = False
+    print("警告: 无法导入forum_reader模块，将跳过HOST发言读取功能")
+
 
 
 class FirstSummaryNode(StateMutationNode):
 class FirstSummaryNode(StateMutationNode):
     """根据搜索结果生成段落首次总结的节点"""
     """根据搜索结果生成段落首次总结的节点"""
@@ -62,9 +73,28 @@ class FirstSummaryNode(StateMutationNode):
             
             
             # 准备输入数据
             # 准备输入数据
             if isinstance(input_data, str):
             if isinstance(input_data, str):
-                message = input_data
+                data = json.loads(input_data)
             else:
             else:
-                message = json.dumps(input_data, ensure_ascii=False)
+                data = input_data.copy() if isinstance(input_data, dict) else input_data
+            
+            # 读取最新的HOST发言（如果可用）
+            if FORUM_READER_AVAILABLE:
+                try:
+                    host_speech = get_latest_host_speech()
+                    if host_speech:
+                        # 将HOST发言添加到输入数据中
+                        data['host_speech'] = host_speech
+                        self.log_info(f"已读取HOST发言，长度: {len(host_speech)}字符")
+                except Exception as e:
+                    self.log_info(f"读取HOST发言失败: {str(e)}")
+            
+            # 转换为JSON字符串
+            message = json.dumps(data, ensure_ascii=False)
+            
+            # 如果有HOST发言，添加到消息前面作为参考
+            if FORUM_READER_AVAILABLE and 'host_speech' in data and data['host_speech']:
+                formatted_host = format_host_speech_for_prompt(data['host_speech'])
+                message = formatted_host + "\n" + message
             
             
             self.log_info("正在生成首次段落总结")
             self.log_info("正在生成首次段落总结")
             
             
@@ -212,9 +242,28 @@ class ReflectionSummaryNode(StateMutationNode):
             
             
             # 准备输入数据
             # 准备输入数据
             if isinstance(input_data, str):
             if isinstance(input_data, str):
-                message = input_data
+                data = json.loads(input_data)
             else:
             else:
-                message = json.dumps(input_data, ensure_ascii=False)
+                data = input_data.copy() if isinstance(input_data, dict) else input_data
+            
+            # 读取最新的HOST发言（如果可用）
+            if FORUM_READER_AVAILABLE:
+                try:
+                    host_speech = get_latest_host_speech()
+                    if host_speech:
+                        # 将HOST发言添加到输入数据中
+                        data['host_speech'] = host_speech
+                        self.log_info(f"已读取HOST发言，长度: {len(host_speech)}字符")
+                except Exception as e:
+                    self.log_info(f"读取HOST发言失败: {str(e)}")
+            
+            # 转换为JSON字符串
+            message = json.dumps(data, ensure_ascii=False)
+            
+            # 如果有HOST发言，添加到消息前面作为参考
+            if FORUM_READER_AVAILABLE and 'host_speech' in data and data['host_speech']:
+                formatted_host = format_host_speech_for_prompt(data['host_speech'])
+                message = formatted_host + "\n" + message
             
             
             self.log_info("正在生成反思总结")
             self.log_info("正在生成反思总结")
             
             

@@ -58,16 +58,18 @@ Say goodbye to traditional data dashboards. In "WeiYu", everything starts with a
 
 
 ### A Complete Analysis Workflow
 ### A Complete Analysis Workflow
 
 
-| Step | Phase Name | Main Operations | Participating Components |
-|------|------------|-----------------|-------------------------|
-| 1 | User Query | Flask main application receives the query | Flask Main Application |
-| 2 | Parallel Launch | Three Agents start working simultaneously | Query Agent, Media Agent, Insight Agent |
-| 3 | Preliminary Analysis | Each Agent uses dedicated tools for overview search | Each Agent + Dedicated Toolsets |
-| 4 | Strategy Formulation | Develop segmented research strategies based on preliminary results | Internal Decision Modules of Each Agent |
-| 5 | In-depth Research | Multi-round search and reflection mechanisms calling respective tools | Each Agent + Reflection Mechanisms |
-| 6 | Forum Collaboration | ForumEngine accepts key findings from each Agent and facilitates Agent communication | ForumEngine + All Agents |
-| 7 | Result Integration | Report Agent collects all analysis results and forum content | Report Agent |
-| 8 | Report Generation | Dynamically select templates and styles, generate final reports through multiple rounds | Report Agent + Template Engine |
+| Step | Phase Name | Main Operations | Participating Components | Cycle Nature |
+|------|------------|-----------------|-------------------------|--------------|
+| 1 | User Query | Flask main application receives the query | Flask Main Application | - |
+| 2 | Parallel Launch | Three Agents start working simultaneously | Query Agent, Media Agent, Insight Agent | - |
+| 3 | Preliminary Analysis | Each Agent uses dedicated tools for overview search | Each Agent + Dedicated Toolsets | - |
+| 4 | Strategy Formulation | Develop segmented research strategies based on preliminary results | Internal Decision Modules of Each Agent | - |
+| 5-N | **Iterative Phase** | **Forum Collaboration + In-depth Research** | **ForumEngine + All Agents** | **Multi-round cycles** |
+| 5.1 | In-depth Research | Each Agent conducts specialized search guided by forum host | Each Agent + Reflection Mechanisms + Forum Guidance | Each cycle |
+| 5.2 | Forum Collaboration | ForumEngine monitors Agent communications and generates host summaries | ForumEngine + LLM Host | Each cycle |
+| 5.3 | Communication Integration | Each Agent adjusts research directions based on discussions | Each Agent + forum_reader tool | Each cycle |
+| N+1 | Result Integration | Report Agent collects all analysis results and forum content | Report Agent | - |
+| N+2 | Report Generation | Dynamically select templates and styles, generate final reports through multiple rounds | Report Agent + Template Engine | - |
 
 
 ### Project Code Structure Tree
 ### Project Code Structure Tree
 
 
@@ -161,6 +163,8 @@ Weibo_PublicOpinion_AnalysisSystem/
 ├── logs/                          # Runtime log directory
 ├── logs/                          # Runtime log directory
 ├── final_reports/                 # Final generated HTML report files
 ├── final_reports/                 # Final generated HTML report files
 ├── utils/                         # Common utility functions
 ├── utils/                         # Common utility functions
+│   ├── forum_reader.py            # Agent forum communication
+│   └── retry_helper.py            # Network request retry mechanism tool
 ├── app.py                         # Flask main application entry
 ├── app.py                         # Flask main application entry
 ├── config.py                      # Global configuration file
 ├── config.py                      # Global configuration file
 └── requirements.txt               # Python dependency list
 └── requirements.txt               # Python dependency list

@@ -58,16 +58,18 @@
 
 
 ### 一次完整分析流程
 ### 一次完整分析流程
 
 
-| 步骤 | 阶段名称 | 主要操作 | 参与组件 |
-|------|----------|----------|----------|
-| 1 | 用户提问 | Flask主应用接收查询 | Flask主应用 |
-| 2 | 并行启动 | 三个Agent同时开始工作 | Query Agent、Media Agent、Insight Agent |
-| 3 | 初步分析 | 各Agent使用专属工具进行概览搜索 | 各Agent + 专属工具集 |
-| 4 | 策略制定 | 基于初步结果制定分块研究策略 | 各Agent内部决策模块 |
-| 5 | 深度研究 | 多轮搜索与反思机制调用各自工具 | 各Agent + 反思机制 |
-| 6 | 论坛协作 | ForumEngine接受各Agent关键发现并促进Agent交流 | ForumEngine + 所有Agent |
-| 7 | 结果整合 | Report Agent收集所有分析结果和论坛内容 | Report Agent |
-| 8 | 报告生成 | 动态选择模板和样式，多轮生成最终报告 | Report Agent + 模板引擎 |
+| 步骤 | 阶段名称 | 主要操作 | 参与组件 | 循环特性 |
+|------|----------|----------|----------|----------|
+| 1 | 用户提问 | Flask主应用接收查询 | Flask主应用 | - |
+| 2 | 并行启动 | 三个Agent同时开始工作 | Query Agent、Media Agent、Insight Agent | - |
+| 3 | 初步分析 | 各Agent使用专属工具进行概览搜索 | 各Agent + 专属工具集 | - |
+| 4 | 策略制定 | 基于初步结果制定分块研究策略 | 各Agent内部决策模块 | - |
+| 5-N | **循环阶段** | **论坛协作 + 深度研究** | **ForumEngine + 所有Agent** | **多轮循环** |
+| 5.1 | 深度研究 | 各Agent基于论坛主持人引导进行专项搜索 | 各Agent + 反思机制 + 论坛引导 | 每轮循环 |
+| 5.2 | 论坛协作 | ForumEngine监控Agent发言并生成主持人总结 | ForumEngine + LLM主持人 | 每轮循环 |
+| 5.3 | 交流融合 | 各Agent根据讨论调整研究方向 | 各Agent + forum_reader工具 | 每轮循环 |
+| N+1 | 结果整合 | Report Agent收集所有分析结果和论坛内容 | Report Agent | - |
+| N+2 | 报告生成 | 动态选择模板和样式，多轮生成最终报告 | Report Agent + 模板引擎 | - |
 
 
 ### 项目代码结构树
 ### 项目代码结构树
 
 
@@ -161,6 +163,8 @@ Weibo_PublicOpinion_AnalysisSystem/
 ├── logs/                          # 运行日志目录
 ├── logs/                          # 运行日志目录
 ├── final_reports/                 # 最终生成的HTML报告文件
 ├── final_reports/                 # 最终生成的HTML报告文件
 ├── utils/                         # 通用工具函数
 ├── utils/                         # 通用工具函数
+│   ├── forum_reader.py            # Agent间论坛通信
+│   └── retry_helper.py            # 网络请求重试机制工具
 ├── app.py                         # Flask主应用入口
 ├── app.py                         # Flask主应用入口
 ├── config.py                      # 全局配置文件
 ├── config.py                      # 全局配置文件
 └── requirements.txt               # Python依赖包清单
 └── requirements.txt               # Python依赖包清单

@@ -26,7 +26,7 @@ TAVILY_API_KEY = "your_tavily_api_key"
 KIMI_API_KEY = "your_kimi_api_key"
 KIMI_API_KEY = "your_kimi_api_key"
 
 
 # Gemini API Key (via OpenAI format proxy)
 # Gemini API Key (via OpenAI format proxy)
-# 申请地址https://api.chataiapi.com/
+# 这里我用了一个中转api来接入Gemini，申请地址https://api.chataiapi.com/，你也可以使用其他
 GEMINI_API_KEY = "your_gemini_api_key"
 GEMINI_API_KEY = "your_gemini_api_key"
 
 
 # Bocha Search API Key
 # Bocha Search API Key

+"""
+Forum日志读取工具
+用于读取forum.log中的最新HOST发言
+"""
+
+import re
+from pathlib import Path
+from typing import Optional, List, Dict
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+def get_latest_host_speech(log_dir: str = "logs") -> Optional[str]:
+    """
+    获取forum.log中最新的HOST发言
+    
+    Args:
+        log_dir: 日志目录路径
+        
+    Returns:
+        最新的HOST发言内容，如果没有则返回None
+    """
+    try:
+        forum_log_path = Path(log_dir) / "forum.log"
+        
+        if not forum_log_path.exists():
+            logger.debug("forum.log文件不存在")
+            return None
+            
+        with open(forum_log_path, 'r', encoding='utf-8', errors='ignore') as f:
+            lines = f.readlines()
+        
+        # 从后往前查找最新的HOST发言
+        host_speech = None
+        for line in reversed(lines):
+            # 匹配格式: [时间] [HOST] 内容
+            match = re.match(r'\[(\d{2}:\d{2}:\d{2})\]\s*\[HOST\]\s*(.+)', line)
+            if match:
+                _, content = match.groups()
+                # 处理转义的换行符，还原为实际换行
+                host_speech = content.replace('\\n', '\n').strip()
+                break
+        
+        if host_speech:
+            logger.info(f"找到最新的HOST发言，长度: {len(host_speech)}字符")
+        else:
+            logger.debug("未找到HOST发言")
+            
+        return host_speech
+        
+    except Exception as e:
+        logger.error(f"读取forum.log失败: {str(e)}")
+        return None
+
+
+def get_all_host_speeches(log_dir: str = "logs") -> List[Dict[str, str]]:
+    """
+    获取forum.log中所有的HOST发言
+    
+    Args:
+        log_dir: 日志目录路径
+        
+    Returns:
+        包含所有HOST发言的列表，每个元素是包含timestamp和content的字典
+    """
+    try:
+        forum_log_path = Path(log_dir) / "forum.log"
+        
+        if not forum_log_path.exists():
+            logger.debug("forum.log文件不存在")
+            return []
+            
+        with open(forum_log_path, 'r', encoding='utf-8', errors='ignore') as f:
+            lines = f.readlines()
+        
+        host_speeches = []
+        for line in lines:
+            # 匹配格式: [时间] [HOST] 内容
+            match = re.match(r'\[(\d{2}:\d{2}:\d{2})\]\s*\[HOST\]\s*(.+)', line)
+            if match:
+                timestamp, content = match.groups()
+                # 处理转义的换行符
+                content = content.replace('\\n', '\n').strip()
+                host_speeches.append({
+                    'timestamp': timestamp,
+                    'content': content
+                })
+        
+        logger.info(f"找到{len(host_speeches)}条HOST发言")
+        return host_speeches
+        
+    except Exception as e:
+        logger.error(f"读取forum.log失败: {str(e)}")
+        return []
+
+
+def get_recent_agent_speeches(log_dir: str = "logs", limit: int = 5) -> List[Dict[str, str]]:
+    """
+    获取forum.log中最近的Agent发言（不包括HOST）
+    
+    Args:
+        log_dir: 日志目录路径
+        limit: 返回的最大发言数量
+        
+    Returns:
+        包含最近Agent发言的列表
+    """
+    try:
+        forum_log_path = Path(log_dir) / "forum.log"
+        
+        if not forum_log_path.exists():
+            return []
+            
+        with open(forum_log_path, 'r', encoding='utf-8', errors='ignore') as f:
+            lines = f.readlines()
+        
+        agent_speeches = []
+        for line in reversed(lines):  # 从后往前读取
+            # 匹配格式: [时间] [AGENT_NAME] 内容
+            match = re.match(r'\[(\d{2}:\d{2}:\d{2})\]\s*\[(INSIGHT|MEDIA|QUERY)\]\s*(.+)', line)
+            if match:
+                timestamp, agent, content = match.groups()
+                # 处理转义的换行符
+                content = content.replace('\\n', '\n').strip()
+                agent_speeches.append({
+                    'timestamp': timestamp,
+                    'agent': agent,
+                    'content': content
+                })
+                if len(agent_speeches) >= limit:
+                    break
+        
+        agent_speeches.reverse()  # 恢复时间顺序
+        return agent_speeches
+        
+    except Exception as e:
+        logger.error(f"读取forum.log失败: {str(e)}")
+        return []
+
+
+def format_host_speech_for_prompt(host_speech: str) -> str:
+    """
+    格式化HOST发言，用于添加到prompt中
+    
+    Args:
+        host_speech: HOST发言内容
+        
+    Returns:
+        格式化后的内容
+    """
+    if not host_speech:
+        return ""
+    
+    return f"""
+### 论坛主持人最新总结
+以下是论坛主持人对各Agent讨论的最新总结和引导，请参考其中的观点和建议：
+
+{host_speech}
+
+---
+"""