""" Streamlit Web界面 为Deep Search 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 src import DeepSearchAgent, Config from config import DEEPSEEK_API_KEY, TAVILY_API_KEY def main(): """主函数""" st.set_page_config( page_title="Deep Search Agent", page_icon="🔍", layout="wide" ) st.title("Deep Search Agent") st.markdown("基于DeepSeek的无框架深度搜索AI代理") # 侧边栏配置 with st.sidebar: st.header("配置") # 显示当前API密钥状态 st.subheader("API密钥状态") if DEEPSEEK_API_KEY: st.success("DeepSeek API Key: 已配置") else: st.error("DeepSeek API Key: 未配置") if TAVILY_API_KEY: st.success("Tavily API Key: 已配置") else: st.error("Tavily API Key: 未配置") # 高级配置 st.subheader("高级配置") max_reflections = st.slider("反思次数", 1, 5, 2) max_search_results = st.slider("搜索结果数", 1, 10, 3) max_content_length = st.number_input("最大内容长度", 1000, 50000, 20000) # 模型选择 llm_provider = st.selectbox("LLM提供商", ["deepseek", "openai"]) if llm_provider == "deepseek": model_name = st.selectbox("DeepSeek模型", ["deepseek-chat"]) else: model_name = st.selectbox("OpenAI模型", ["gpt-4o-mini", "gpt-4o"]) openai_key = st.text_input("OpenAI API Key", type="password", value="") # 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 ) # 预设查询示例 st.subheader("示例查询") example_queries = [ "2025年人工智能发展趋势", "深度学习在医疗领域的应用", "区块链技术的最新发展", "可持续能源技术趋势", "量子计算的发展现状" ] selected_example = st.selectbox("选择示例查询", ["自定义"] + example_queries) if selected_example != "自定义": query = selected_example 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, col2, col3 = st.columns([1, 1, 1]) with col2: 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 if not DEEPSEEK_API_KEY and llm_provider == "deepseek": st.error("请在config.py中配置DeepSeek API Key") return if not TAVILY_API_KEY: st.error("请在config.py中配置Tavily API Key") return if llm_provider == "openai" and not openai_key: st.error("请提供OpenAI API Key") return # 创建配置 config = Config( deepseek_api_key=DEEPSEEK_API_KEY if llm_provider == "deepseek" else None, openai_api_key=openai_key if llm_provider == "openai" else None, tavily_api_key=TAVILY_API_KEY, default_llm_provider=llm_provider, deepseek_model=model_name if llm_provider == "deepseek" else "deepseek-chat", openai_model=model_name if llm_provider == "openai" else "gpt-4o-mini", max_reflections=max_reflections, max_search_results=max_search_results, max_content_length=max_content_length, output_dir="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 tab1, tab2, tab3 = st.tabs(["最终报告", "详细信息", "下载"]) 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) with tab3: # 下载选项 st.subheader("下载报告") # Markdown下载 st.download_button( label="下载Markdown报告", data=final_report, file_name=f"deep_search_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.md", mime="text/markdown" ) # JSON状态下载 state_json = agent.state.to_json() st.download_button( label="下载状态文件", data=state_json, file_name=f"deep_search_state_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json", mime="application/json" ) if __name__ == "__main__": main()