insight_engine_streamlit_app.py
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"""
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 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="Deep Search Agent",
page_icon="🔍",
layout="wide"
)
st.title("Insight Engine Agent")
st.markdown("基于DeepSeek的本地舆情数据库深度分析AI代理")
# 侧边栏配置
with st.sidebar:
st.header("配置")
# 模型选择
llm_provider = st.selectbox("LLM提供商", ["deepseek", "openai", "kimi"])
# 高级配置
st.subheader("高级配置")
max_reflections = st.slider("反思次数", 1, 5, 2)
# 根据选择的模型动态调整默认值
if llm_provider == "kimi":
default_content_length = 500000 # Kimi支持长文本，使用更大的默认值
max_limit = 1000000 # 提高上限
st.info("💡 Kimi模型支持超长文本处理，建议使用更大的内容长度以充分利用其能力")
else:
default_content_length = 200000
max_limit = 500000
max_content_length = st.number_input("最大内容长度", 10000, max_limit, default_content_length)
# 初始化所有可能的变量
openai_key = ""
kimi_key = ""
if llm_provider == "deepseek":
model_name = st.selectbox("DeepSeek模型", ["deepseek-chat"])
elif llm_provider == "openai":
model_name = st.selectbox("OpenAI模型", ["gpt-4o-mini", "gpt-4o"])
openai_key = st.text_input("OpenAI API Key", type="password",
value="")
else: # kimi
model_name = st.selectbox("Kimi模型", ["kimi-k2-0711-preview"])
kimi_key = st.text_input("Kimi 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 llm_provider == "openai" and not openai_key:
st.error("请提供OpenAI API Key")
return
if llm_provider == "kimi" and not kimi_key and not KIMI_API_KEY:
st.error("请提供Kimi API Key或在配置文件中设置KIMI_API_KEY")
return
# 自动使用配置文件中的API密钥和数据库配置
deepseek_key = DEEPSEEK_API_KEY
kimi_key_final = kimi_key if kimi_key else KIMI_API_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
# 创建配置
config = Config(
deepseek_api_key=deepseek_key if llm_provider == "deepseek" else None,
openai_api_key=openai_key if llm_provider == "openai" else None,
kimi_api_key=kimi_key_final if llm_provider == "kimi" else None,
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=model_name if llm_provider == "deepseek" else "deepseek-chat",
openai_model=model_name if llm_provider == "openai" else "gpt-4o-mini",
kimi_model=model_name if llm_provider == "kimi" else "kimi-k2-0711-preview",
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
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()