戒酒的李白
236 lines
8.0 KiB
Python
236 lines
8.0 KiB
Python
"""
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Streamlit Web界面
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为Deep Search Agent提供友好的Web界面
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"""
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import os
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import sys
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import streamlit as st
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from datetime import datetime
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import json
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# 添加src目录到Python路径
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), '.'))
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from src import DeepSearchAgent, Config
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from config import DEEPSEEK_API_KEY, TAVILY_API_KEY
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def main():
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"""主函数"""
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st.set_page_config(
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page_title="Deep Search Agent",
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page_icon="🔍",
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layout="wide"
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)
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st.title("Deep Search Agent")
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st.markdown("基于DeepSeek的无框架深度搜索AI代理")
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# 侧边栏配置
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with st.sidebar:
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st.header("配置")
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# 高级配置
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st.subheader("高级配置")
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max_reflections = st.slider("反思次数", 1, 5, 2)
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max_content_length = st.number_input("最大内容长度", 1000, 50000, 20000)
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# 模型选择
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llm_provider = st.selectbox("LLM提供商", ["deepseek", "openai"])
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if llm_provider == "deepseek":
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model_name = st.selectbox("DeepSeek模型", ["deepseek-chat"])
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else:
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model_name = st.selectbox("OpenAI模型", ["gpt-4o-mini", "gpt-4o"])
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openai_key = st.text_input("OpenAI API Key", type="password",
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value="")
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# 主界面
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col1, col2 = st.columns([2, 1])
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with col1:
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st.header("研究查询")
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query = st.text_area(
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"请输入您要研究的问题",
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placeholder="例如：2025年人工智能发展趋势",
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height=100
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)
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# 预设查询示例
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st.subheader("示例查询")
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example_queries = [
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"2025年人工智能发展趋势",
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"深度学习在医疗领域的应用",
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"区块链技术的最新发展",
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"可持续能源技术趋势",
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"量子计算的发展现状"
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]
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selected_example = st.selectbox("选择示例查询", ["自定义"] + example_queries)
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if selected_example != "自定义":
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query = selected_example
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with col2:
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st.header("状态信息")
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if 'agent' in st.session_state and hasattr(st.session_state.agent, 'state'):
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progress = st.session_state.agent.get_progress_summary()
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st.metric("总段落数", progress['total_paragraphs'])
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st.metric("已完成", progress['completed_paragraphs'])
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st.progress(progress['progress_percentage'] / 100)
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else:
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st.info("尚未开始研究")
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# 执行按钮
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col1, col2, col3 = st.columns([1, 1, 1])
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with col2:
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start_research = st.button("开始研究", type="primary", use_container_width=True)
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# 验证配置
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if start_research:
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if not query.strip():
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st.error("请输入研究查询")
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return
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if llm_provider == "openai" and not openai_key:
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st.error("请提供OpenAI API Key")
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return
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# 自动使用配置文件中的API密钥
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deepseek_key = DEEPSEEK_API_KEY
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tavily_key = TAVILY_API_KEY
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# 创建配置
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config = Config(
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deepseek_api_key=deepseek_key if llm_provider == "deepseek" else None,
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openai_api_key=openai_key if llm_provider == "openai" else None,
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tavily_api_key=tavily_key,
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default_llm_provider=llm_provider,
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deepseek_model=model_name if llm_provider == "deepseek" else "deepseek-chat",
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openai_model=model_name if llm_provider == "openai" else "gpt-4o-mini",
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max_reflections=max_reflections,
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max_content_length=max_content_length,
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output_dir="streamlit_reports"
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)
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# 执行研究
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execute_research(query, config)
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def execute_research(query: str, config: Config):
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"""执行研究"""
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try:
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# 创建进度条
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progress_bar = st.progress(0)
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status_text = st.empty()
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# 初始化Agent
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status_text.text("正在初始化Agent...")
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agent = DeepSearchAgent(config)
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st.session_state.agent = agent
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progress_bar.progress(10)
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# 生成报告结构
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status_text.text("正在生成报告结构...")
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agent._generate_report_structure(query)
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progress_bar.progress(20)
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# 处理段落
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total_paragraphs = len(agent.state.paragraphs)
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for i in range(total_paragraphs):
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status_text.text(f"正在处理段落 {i+1}/{total_paragraphs}: {agent.state.paragraphs[i].title}")
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# 初始搜索和总结
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agent._initial_search_and_summary(i)
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progress_value = 20 + (i + 0.5) / total_paragraphs * 60
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progress_bar.progress(int(progress_value))
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# 反思循环
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agent._reflection_loop(i)
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agent.state.paragraphs[i].research.mark_completed()
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progress_value = 20 + (i + 1) / total_paragraphs * 60
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progress_bar.progress(int(progress_value))
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# 生成最终报告
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status_text.text("正在生成最终报告...")
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final_report = agent._generate_final_report()
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progress_bar.progress(90)
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# 保存报告
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status_text.text("正在保存报告...")
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agent._save_report(final_report)
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progress_bar.progress(100)
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status_text.text("研究完成！")
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# 显示结果
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display_results(agent, final_report)
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except Exception as e:
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st.error(f"研究过程中发生错误: {str(e)}")
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def display_results(agent: DeepSearchAgent, final_report: str):
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"""显示研究结果"""
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st.header("研究结果")
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# 结果标签页
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tab1, tab2, tab3 = st.tabs(["最终报告", "详细信息", "下载"])
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with tab1:
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st.markdown(final_report)
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with tab2:
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# 段落详情
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st.subheader("段落详情")
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for i, paragraph in enumerate(agent.state.paragraphs):
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with st.expander(f"段落 {i+1}: {paragraph.title}"):
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st.write("**预期内容:**", paragraph.content)
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st.write("**最终内容:**", paragraph.research.latest_summary[:300] + "..."
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if len(paragraph.research.latest_summary) > 300
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else paragraph.research.latest_summary)
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st.write("**搜索次数:**", paragraph.research.get_search_count())
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st.write("**反思次数:**", paragraph.research.reflection_iteration)
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# 搜索历史
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st.subheader("搜索历史")
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all_searches = []
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for paragraph in agent.state.paragraphs:
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all_searches.extend(paragraph.research.search_history)
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if all_searches:
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for i, search in enumerate(all_searches):
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with st.expander(f"搜索 {i+1}: {search.query}"):
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st.write("**URL:**", search.url)
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st.write("**标题:**", search.title)
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st.write("**内容预览:**", search.content[:200] + "..." if len(search.content) > 200 else search.content)
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if search.score:
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st.write("**相关度评分:**", search.score)
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with tab3:
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# 下载选项
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st.subheader("下载报告")
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# Markdown下载
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st.download_button(
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label="下载Markdown报告",
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data=final_report,
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file_name=f"deep_search_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.md",
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mime="text/markdown"
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)
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# JSON状态下载
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state_json = agent.state.to_json()
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st.download_button(
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label="下载状态文件",
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data=state_json,
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file_name=f"deep_search_state_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
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mime="application/json"
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)
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if __name__ == "__main__":
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main()
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