263 lines
10 KiB
Python
263 lines
10 KiB
Python
"""
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多 Agent 决策工作坊 - 主应用
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基于DeepSeek API的多角色AI代理辩论系统
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"""
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import streamlit as st
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import os
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from openai import OpenAI
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from dotenv import load_dotenv
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import asyncio
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import json
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from typing import List, Dict, Any
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from datetime import datetime
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# 加载环境变量
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load_dotenv()
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class MultiAgentDecisionWorkshop:
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def __init__(self):
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"""初始化多代理决策工作坊"""
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self.client = OpenAI(
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api_key=os.getenv("DEEPSEEK_API_KEY"),
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base_url="https://api.deepseek.com"
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)
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# 定义4个专业角色代理
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self.agents = {
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"技术专家": {
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"role": "system",
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"content": "你是一位资深技术专家，擅长从技术可行性、实现难度、技术风险等角度分析问题。你的观点务实、注重细节。"
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},
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"商业分析师": {
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"role": "system",
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"content": "你是一位经验丰富的商业分析师，擅长从市场机会、商业模式、投资回报等商业角度分析问题。你的观点注重商业价值。"
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},
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"用户体验师": {
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"role": "system",
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"content": "你是一位专业的用户体验设计师，擅长从用户需求、易用性、用户体验等角度分析问题。你的观点以用户为中心。"
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},
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"风险顾问": {
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"role": "system",
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"content": "你是一位谨慎的风险顾问，擅长识别潜在风险、评估不确定性、提出风险缓解方案。你的观点保守但务实。"
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}
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}
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def initialize_debate(self, question: str) -> Dict[str, Any]:
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"""初始化辩论话题"""
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return {
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"question": question,
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"rounds": 0,
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"max_rounds": 3,
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"current_speaker": "技术专家",
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"debate_history": [],
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"start_time": datetime.now()
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}
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def get_agent_response(self, agent_name: str, debate_context: str) -> str:
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"""获取单个代理的回应"""
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try:
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response = self.client.chat.completions.create(
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model="deepseek-chat",
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messages=[
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self.agents[agent_name],
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{
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"role": "user",
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"content": f"辩论话题：{debate_context}\n\n请从你的专业角度发表观点，并回应其他代理的论点。"
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}
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],
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temperature=0.7,
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max_tokens=500
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)
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return response.choices[0].message.content
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except Exception as e:
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return f"{agent_name}暂时无法回应：{str(e)}"
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def conduct_debate_round(self, debate_state: Dict[str, Any]) -> Dict[str, Any]:
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"""进行一轮辩论"""
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current_speaker = debate_state["current_speaker"]
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# 构建辩论上下文
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context = f"问题：{debate_state['question']}\n"
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if debate_state["debate_history"]:
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context += "之前的讨论：\n"
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for entry in debate_state["debate_history"][-3:]: # 只取最近3条
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context += f"{entry['speaker']}：{entry['content']}\n"
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# 获取当前发言者的回应
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response = self.get_agent_response(current_speaker, context)
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# 更新辩论历史
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debate_state["debate_history"].append({
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"speaker": current_speaker,
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"content": response,
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"timestamp": datetime.now().strftime("%H:%M:%S")
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})
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# 切换到下一个发言者
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agent_names = list(self.agents.keys())
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current_index = agent_names.index(current_speaker)
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next_index = (current_index + 1) % len(agent_names)
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debate_state["current_speaker"] = agent_names[next_index]
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debate_state["rounds"] += 1
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return debate_state
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def generate_decision_summary(self, debate_history: List[Dict]) -> str:
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"""生成决策要点总结"""
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try:
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# 构建辩论内容摘要
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debate_content = "辩论记录：\n"
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for entry in debate_history:
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debate_content += f"{entry['speaker']}：{entry['content']}\n"
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response = self.client.chat.completions.create(
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model="deepseek-chat",
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messages=[
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{
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"role": "system",
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"content": "你是一位专业的决策顾问，擅长从多角度辩论中提取关键观点、识别共识与分歧，并生成结构化的决策建议。"
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},
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{
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"role": "user",
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"content": f"请基于以下辩论记录，生成决策要点总结：\n\n{debate_content}\n\n请提供：\n1. 关键观点汇总\n2. 主要共识领域\n3. 重要分歧点\n4. 风险评估\n5. 行动建议"
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}
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],
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temperature=0.5,
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max_tokens=800
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)
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return response.choices[0].message.content
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except Exception as e:
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return f"总结生成失败：{str(e)}"
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def main():
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"""主应用函数"""
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st.set_page_config(
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page_title="多 Agent 决策工作坊",
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page_icon="🤖",
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layout="wide"
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)
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st.title("🤖 多 Agent 决策工作坊")
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st.markdown("### 通过AI代理的多角度辩论，助力智能决策")
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# 初始化工作坊
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if "workshop" not in st.session_state:
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st.session_state.workshop = MultiAgentDecisionWorkshop()
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if "debate_state" not in st.session_state:
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st.session_state.debate_state = None
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if "debate_in_progress" not in st.session_state:
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st.session_state.debate_in_progress = False
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# 侧边栏 - 控制面板
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with st.sidebar:
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st.header("控制面板")
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# 决策问题输入
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question = st.text_area(
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"请输入决策问题：",
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placeholder="例如：我们应该开发一个新的移动应用吗？",
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height=100
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)
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# 开始辩论按钮
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if st.button("🚀 开始辩论", type="primary", use_container_width=True):
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if question.strip():
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st.session_state.debate_state = st.session_state.workshop.initialize_debate(question)
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st.session_state.debate_in_progress = True
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st.rerun()
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else:
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st.warning("请输入决策问题")
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# 辩论控制按钮
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if st.session_state.debate_in_progress:
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col1, col2 = st.columns(2)
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with col1:
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if st.button("⏭️ 下一轮", use_container_width=True):
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if st.session_state.debate_state["rounds"] < st.session_state.debate_state["max_rounds"]:
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st.session_state.debate_state = st.session_state.workshop.conduct_debate_round(
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st.session_state.debate_state
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)
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st.rerun()
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else:
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st.session_state.debate_in_progress = False
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st.rerun()
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with col2:
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if st.button("⏹️ 结束辩论", use_container_width=True):
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st.session_state.debate_in_progress = False
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st.rerun()
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st.markdown("---")
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st.markdown("### 参与代理")
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for agent_name in st.session_state.workshop.agents.keys():
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st.markdown(f"- {agent_name}")
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# 主内容区
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col1, col2 = st.columns([2, 1])
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with col1:
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st.header("辩论过程")
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if st.session_state.debate_state:
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# 显示辩论进度
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progress = min(st.session_state.debate_state["rounds"] / st.session_state.debate_state["max_rounds"], 1.0)
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st.progress(progress, text=f"辩论进度：{st.session_state.debate_state['rounds']}/{st.session_state.debate_state['max_rounds']}轮")
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# 显示辩论历史
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for entry in st.session_state.debate_state["debate_history"]:
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with st.chat_message("user", avatar=f"👤"):
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st.markdown(f"**{entry['speaker']}** ({entry['timestamp']})")
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st.write(entry["content"])
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# 显示当前状态
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if st.session_state.debate_in_progress:
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st.info(f"🔄 等待 {st.session_state.debate_state['current_speaker']} 发言...")
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else:
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st.success("✅ 辩论已完成！")
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# 生成总结
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if st.button("📊 生成决策总结"):
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with st.spinner("正在生成决策要点..."):
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summary = st.session_state.workshop.generate_decision_summary(
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st.session_state.debate_state["debate_history"]
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)
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st.session_state.summary = summary
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if "summary" in st.session_state:
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st.markdown("### 决策要点总结")
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st.write(st.session_state.summary)
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else:
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st.info("👆 请在左侧输入决策问题并开始辩论")
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with col2:
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st.header("决策助手")
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# 快速决策模板
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st.markdown("### 快速决策模板")
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templates = [
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"产品功能优先级排序",
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"技术方案选择评估",
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"市场进入策略分析",
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"团队资源分配决策"
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]
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for template in templates:
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if st.button(template, use_container_width=True):
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st.session_state.debate_state = st.session_state.workshop.initialize_debate(template)
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st.session_state.debate_in_progress = True
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st.rerun()
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st.markdown("---")
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st.markdown("### 使用说明")
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st.markdown("""
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1. 输入决策问题
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2. 点击开始辩论
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3. 观察AI代理的多角度分析
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4. 生成决策要点总结
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5. 基于共识制定行动计划
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""")
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if __name__ == "__main__":
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main() |