The single-multi-agent framework has been initially completed.

@@ -168,7 +168,8 @@ def start_streamlit_app(app_name, script_path, port):
             '--server.port', str(port),
             '--server.port', str(port),
             '--server.headless', 'true',
             '--server.headless', 'true',
             '--browser.gatherUsageStats', 'false',
             '--browser.gatherUsageStats', 'false',
-            '--logger.level', 'debug',  # 增加日志详细程度
+            # '--logger.level', 'debug',  # 增加日志详细程度
+            '--logger.level', 'info',
             '--server.enableCORS', 'false'
             '--server.enableCORS', 'false'
         ]
         ]
         
         

-"""
-Streamlit Web界面
-为DInsight Agent提供友好的Web界面
-"""
-
-import os
-import sys
-import streamlit as st
-from datetime import datetime
-import json
-
-# 添加src目录到Python路径
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
-
-from InsightEngine import DeepSearchAgent, Config
-from config import DEEPSEEK_API_KEY, KIMI_API_KEY, DB_HOST, DB_USER, DB_PASSWORD, DB_NAME, DB_PORT, DB_CHARSET
-
-
-def main():
-    """主函数"""
-    st.set_page_config(
-        page_title="Insight Agent",
-        page_icon="",
-        layout="wide"
-    )
-
-    st.title("Insight Engine")
-    st.markdown("本地舆情数据库深度分析AI代理")
-
-    # ----- 配置被硬编码 -----
-    # 强制使用 Kimi
-    llm_provider = "kimi"
-    model_name = "kimi-k2-0711-preview"
-    # 默认高级配置
-    max_reflections = 2
-    max_content_length = 500000  # Kimi支持长文本
-
-    # 主界面
-    col1, col2 = st.columns([2, 1])
-
-    with col1:
-        st.header("研究查询")
-        query = st.text_area(
-            "请输入您要研究的问题",
-            placeholder="例如：2025年人工智能发展趋势",
-            height=100
-        )
-
-    with col2:
-        st.header("状态信息")
-        if 'agent' in st.session_state and hasattr(st.session_state.agent, 'state'):
-            progress = st.session_state.agent.get_progress_summary()
-            st.metric("总段落数", progress['total_paragraphs'])
-            st.metric("已完成", progress['completed_paragraphs'])
-            st.progress(progress['progress_percentage'] / 100)
-        else:
-            st.info("尚未开始研究")
-
-    # 执行按钮
-    col1_btn, col2_btn, col3_btn = st.columns([1, 1, 1])
-    with col2_btn:
-        start_research = st.button("开始研究", type="primary", use_container_width=True)
-
-    # 验证配置
-    if start_research:
-        if not query.strip():
-            st.error("请输入研究查询")
-            return
-
-        # 由于强制使用Kimi，只检查KIMI_API_KEY
-        if not KIMI_API_KEY:
-            st.error("请在您的配置文件(config.py)中设置KIMI_API_KEY")
-            return
-
-        # 自动使用配置文件中的API密钥和数据库配置
-        db_host = DB_HOST
-        db_user = DB_USER
-        db_password = DB_PASSWORD
-        db_name = DB_NAME
-        db_port = DB_PORT
-        db_charset = DB_CHARSET
-
-        # 创建配置
-        config = Config(
-            deepseek_api_key=None,
-            openai_api_key=None,
-            kimi_api_key=KIMI_API_KEY,  # 强制使用配置文件中的Kimi Key
-            db_host=db_host,
-            db_user=db_user,
-            db_password=db_password,
-            db_name=db_name,
-            db_port=db_port,
-            db_charset=db_charset,
-            default_llm_provider=llm_provider,
-            deepseek_model="deepseek-chat", # 保留默认值以兼容
-            openai_model="gpt-4o-mini", # 保留默认值以兼容
-            kimi_model=model_name,
-            max_reflections=max_reflections,
-            max_content_length=max_content_length,
-            output_dir="insight_engine_streamlit_reports"
-        )
-
-        # 执行研究
-        execute_research(query, config)
-
-
-def execute_research(query: str, config: Config):
-    """执行研究"""
-    try:
-        # 创建进度条
-        progress_bar = st.progress(0)
-        status_text = st.empty()
-
-        # 初始化Agent
-        status_text.text("正在初始化Agent...")
-        agent = DeepSearchAgent(config)
-        st.session_state.agent = agent
-
-        progress_bar.progress(10)
-
-        # 生成报告结构
-        status_text.text("正在生成报告结构...")
-        agent._generate_report_structure(query)
-        progress_bar.progress(20)
-
-        # 处理段落
-        total_paragraphs = len(agent.state.paragraphs)
-        for i in range(total_paragraphs):
-            status_text.text(f"正在处理段落 {i + 1}/{total_paragraphs}: {agent.state.paragraphs[i].title}")
-
-            # 初始搜索和总结
-            agent._initial_search_and_summary(i)
-            progress_value = 20 + (i + 0.5) / total_paragraphs * 60
-            progress_bar.progress(int(progress_value))
-
-            # 反思循环
-            agent._reflection_loop(i)
-            agent.state.paragraphs[i].research.mark_completed()
-
-            progress_value = 20 + (i + 1) / total_paragraphs * 60
-            progress_bar.progress(int(progress_value))
-
-        # 生成最终报告
-        status_text.text("正在生成最终报告...")
-        final_report = agent._generate_final_report()
-        progress_bar.progress(90)
-
-        # 保存报告
-        status_text.text("正在保存报告...")
-        agent._save_report(final_report)
-        progress_bar.progress(100)
-
-        status_text.text("研究完成！")
-
-        # 显示结果
-        display_results(agent, final_report)
-
-    except Exception as e:
-        st.error(f"研究过程中发生错误: {str(e)}")
-
-
-def display_results(agent: DeepSearchAgent, final_report: str):
-    """显示研究结果"""
-    st.header("研究结果")
-
-    # 结果标签页（已移除下载选项）
-    tab1, tab2 = st.tabs(["最终报告", "详细信息"])
-
-    with tab1:
-        st.markdown(final_report)
-
-    with tab2:
-        # 段落详情
-        st.subheader("段落详情")
-        for i, paragraph in enumerate(agent.state.paragraphs):
-            with st.expander(f"段落 {i + 1}: {paragraph.title}"):
-                st.write("**预期内容:**", paragraph.content)
-                st.write("**最终内容:**", paragraph.research.latest_summary[:300] + "..."
-                if len(paragraph.research.latest_summary) > 300
-                else paragraph.research.latest_summary)
-                st.write("**搜索次数:**", paragraph.research.get_search_count())
-                st.write("**反思次数:**", paragraph.research.reflection_iteration)
-
-        # 搜索历史
-        st.subheader("搜索历史")
-        all_searches = []
-        for paragraph in agent.state.paragraphs:
-            all_searches.extend(paragraph.research.search_history)
-
-        if all_searches:
-            for i, search in enumerate(all_searches):
-                with st.expander(f"搜索 {i + 1}: {search.query}"):
-                    st.write("**URL:**", search.url)
-                    st.write("**标题:**", search.title)
-                    st.write("**内容预览:**",
-                             search.content[:200] + "..." if len(search.content) > 200 else search.content)
-                    if search.score:
-                        st.write("**相关度评分:**", search.score)
-
-
-if __name__ == "__main__":
-    main()

-"""
-Streamlit Web界面
-为DInsight Agent提供友好的Web界面
-"""
-
-import os
-import sys
-import streamlit as st
-from datetime import datetime
-import json
-
-# 添加src目录到Python路径
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
-
-from InsightEngine import DeepSearchAgent, Config
-from config import DEEPSEEK_API_KEY, KIMI_API_KEY, DB_HOST, DB_USER, DB_PASSWORD, DB_NAME, DB_PORT, DB_CHARSET
-
-
-def main():
-    """主函数"""
-    st.set_page_config(
-        page_title="Insight Agent",
-        page_icon="",
-        layout="wide"
-    )
-
-    st.title("Insight Engine")
-    st.markdown("本地舆情数据库深度分析AI代理")
-
-    # ----- 配置被硬编码 -----
-    # 强制使用 Kimi
-    llm_provider = "kimi"
-    model_name = "kimi-k2-0711-preview"
-    # 默认高级配置
-    max_reflections = 2
-    max_content_length = 500000  # Kimi支持长文本
-
-    # 主界面
-    col1, col2 = st.columns([2, 1])
-
-    with col1:
-        st.header("研究查询")
-        query = st.text_area(
-            "请输入您要研究的问题",
-            placeholder="例如：2025年人工智能发展趋势",
-            height=100
-        )
-
-    with col2:
-        st.header("状态信息")
-        if 'agent' in st.session_state and hasattr(st.session_state.agent, 'state'):
-            progress = st.session_state.agent.get_progress_summary()
-            st.metric("总段落数", progress['total_paragraphs'])
-            st.metric("已完成", progress['completed_paragraphs'])
-            st.progress(progress['progress_percentage'] / 100)
-        else:
-            st.info("尚未开始研究")
-
-    # 执行按钮
-    col1_btn, col2_btn, col3_btn = st.columns([1, 1, 1])
-    with col2_btn:
-        start_research = st.button("开始研究", type="primary", use_container_width=True)
-
-    # 验证配置
-    if start_research:
-        if not query.strip():
-            st.error("请输入研究查询")
-            return
-
-        # 由于强制使用Kimi，只检查KIMI_API_KEY
-        if not KIMI_API_KEY:
-            st.error("请在您的配置文件(config.py)中设置KIMI_API_KEY")
-            return
-
-        # 自动使用配置文件中的API密钥和数据库配置
-        db_host = DB_HOST
-        db_user = DB_USER
-        db_password = DB_PASSWORD
-        db_name = DB_NAME
-        db_port = DB_PORT
-        db_charset = DB_CHARSET
-
-        # 创建配置
-        config = Config(
-            deepseek_api_key=None,
-            openai_api_key=None,
-            kimi_api_key=KIMI_API_KEY,  # 强制使用配置文件中的Kimi Key
-            db_host=db_host,
-            db_user=db_user,
-            db_password=db_password,
-            db_name=db_name,
-            db_port=db_port,
-            db_charset=db_charset,
-            default_llm_provider=llm_provider,
-            deepseek_model="deepseek-chat", # 保留默认值以兼容
-            openai_model="gpt-4o-mini", # 保留默认值以兼容
-            kimi_model=model_name,
-            max_reflections=max_reflections,
-            max_content_length=max_content_length,
-            output_dir="insight_engine_streamlit_reports"
-        )
-
-        # 执行研究
-        execute_research(query, config)
-
-
-def execute_research(query: str, config: Config):
-    """执行研究"""
-    try:
-        # 创建进度条
-        progress_bar = st.progress(0)
-        status_text = st.empty()
-
-        # 初始化Agent
-        status_text.text("正在初始化Agent...")
-        agent = DeepSearchAgent(config)
-        st.session_state.agent = agent
-
-        progress_bar.progress(10)
-
-        # 生成报告结构
-        status_text.text("正在生成报告结构...")
-        agent._generate_report_structure(query)
-        progress_bar.progress(20)
-
-        # 处理段落
-        total_paragraphs = len(agent.state.paragraphs)
-        for i in range(total_paragraphs):
-            status_text.text(f"正在处理段落 {i + 1}/{total_paragraphs}: {agent.state.paragraphs[i].title}")
-
-            # 初始搜索和总结
-            agent._initial_search_and_summary(i)
-            progress_value = 20 + (i + 0.5) / total_paragraphs * 60
-            progress_bar.progress(int(progress_value))
-
-            # 反思循环
-            agent._reflection_loop(i)
-            agent.state.paragraphs[i].research.mark_completed()
-
-            progress_value = 20 + (i + 1) / total_paragraphs * 60
-            progress_bar.progress(int(progress_value))
-
-        # 生成最终报告
-        status_text.text("正在生成最终报告...")
-        final_report = agent._generate_final_report()
-        progress_bar.progress(90)
-
-        # 保存报告
-        status_text.text("正在保存报告...")
-        agent._save_report(final_report)
-        progress_bar.progress(100)
-
-        status_text.text("研究完成！")
-
-        # 显示结果
-        display_results(agent, final_report)
-
-    except Exception as e:
-        st.error(f"研究过程中发生错误: {str(e)}")
-
-
-def display_results(agent: DeepSearchAgent, final_report: str):
-    """显示研究结果"""
-    st.header("研究结果")
-
-    # 结果标签页（已移除下载选项）
-    tab1, tab2 = st.tabs(["最终报告", "详细信息"])
-
-    with tab1:
-        st.markdown(final_report)
-
-    with tab2:
-        # 段落详情
-        st.subheader("段落详情")
-        for i, paragraph in enumerate(agent.state.paragraphs):
-            with st.expander(f"段落 {i + 1}: {paragraph.title}"):
-                st.write("**预期内容:**", paragraph.content)
-                st.write("**最终内容:**", paragraph.research.latest_summary[:300] + "..."
-                if len(paragraph.research.latest_summary) > 300
-                else paragraph.research.latest_summary)
-                st.write("**搜索次数:**", paragraph.research.get_search_count())
-                st.write("**反思次数:**", paragraph.research.reflection_iteration)
-
-        # 搜索历史
-        st.subheader("搜索历史")
-        all_searches = []
-        for paragraph in agent.state.paragraphs:
-            all_searches.extend(paragraph.research.search_history)
-
-        if all_searches:
-            for i, search in enumerate(all_searches):
-                with st.expander(f"搜索 {i + 1}: {search.query}"):
-                    st.write("**URL:**", search.url)
-                    st.write("**标题:**", search.title)
-                    st.write("**内容预览:**",
-                             search.content[:200] + "..." if len(search.content) > 200 else search.content)
-                    if search.score:
-                        st.write("**相关度评分:**", search.score)
-
-
-if __name__ == "__main__":
-    main()

@@ -22,10 +22,11 @@
 
 
         .container {
         .container {
             max-width: 100vw;
             max-width: 100vw;
-            min-height: 100vh;
+            height: 100vh; /* 固定高度为视口高度 */
             display: flex;
             display: flex;
             flex-direction: column;
             flex-direction: column;
             border: 2px solid #000000;
             border: 2px solid #000000;
+            overflow: hidden; /* 防止整体滚动 */
         }
         }
 
 
         /* 搜索框区域 */
         /* 搜索框区域 */
@@ -117,6 +118,7 @@
             flex-direction: column;
             flex-direction: column;
             background-color: #ffffff;
             background-color: #ffffff;
             min-height: 0; /* 允许子元素缩小 */
             min-height: 0; /* 允许子元素缩小 */
+            overflow: hidden; /* 防止内容溢出 */
         }
         }
 
 
         /* 应用切换按钮 */
         /* 应用切换按钮 */
@@ -178,10 +180,10 @@
             font-family: 'Courier New', monospace;
             font-family: 'Courier New', monospace;
             font-size: 12px;
             font-size: 12px;
             overflow-y: auto;
             overflow-y: auto;
+            overflow-x: hidden;
             white-space: pre-wrap;
             white-space: pre-wrap;
             word-break: break-all;
             word-break: break-all;
             min-height: 0; /* 允许内容缩小 */
             min-height: 0; /* 允许内容缩小 */
-            max-height: 100%; /* 限制最大高度 */
         }
         }
 
 
         .console-line {
         .console-line {