commit cb97f7c49a2da5668e35e7905a612b075e349283 Author: xyz Date: Wed Jan 7 11:02:05 2026 +0800 Initial commit of Deep Research Mode diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..c28f633 --- /dev/null +++ b/.env.example @@ -0,0 +1,11 @@ +# 复制此文件为 .env 并填入你的 API Key +# API Key 会自动从环境变量加载，无需在界面中输入 + +# AIHubMix API Key (默认使用) +AIHUBMIX_API_KEY=sk-your-api-key-here + +# Anthropic API Key (可选) +ANTHROPIC_API_KEY=your-anthropic-api-key-here + +# OpenAI API Key (可选) +OPENAI_API_KEY=your-openai-api-key-here diff --git a/Project_Design.md b/Project_Design.md new file mode 100644 index 0000000..212522a --- /dev/null +++ b/Project_Design.md @@ -0,0 +1,156 @@ +# 多 Agent 决策工作坊 (Multi-Agent Decision Workshop) + +## 🎯 一句话描述 + +**Multi-Agent Decision Workshop** 是一个面向**产品经理、团队负责人、创业者**的 AI 辅助决策工具，通过模拟多角色（CEO、CTO、CFO、用户代言人、风险分析师等）从不同视角对方案进行辩论，帮助用户获得全面的决策洞察。 + +--- + +## 👤 目标用户与痛点 + +| 用户角色 | 真实痛点 | +|---------|---------| +| 产品经理 | 方案评审时容易陷入单一视角，忽略技术/成本/用户体验的平衡 | +| 创业者 | 独自决策缺乏多元反馈，容易盲目乐观或过度保守 | +| 团队负责人 | 会议中难以让所有人充分表达，强势声音主导决策 | +| 学生/个人 | 重要人生决策（职业、投资）缺乏专业视角指导 | + +--- + +## 🔧 核心功能 (MVP - 3个必须有的功能) + +### 1. 📝 决策议题输入 +- 用户输入待决策的问题/方案 +- 可选择决策类型（产品方案、商业决策、技术选型、个人规划） +- 支持上传背景资料（可选） + +### 2. 🎭 多角色辩论模拟 +- 系统自动分配 4-6 个不同视角的 Agent +- 每个 Agent 代表一个角色立场发表观点 +- Agent 之间可以相互质疑和回应（多轮辩论） + +### 3. 📊 决策报告生成 +- 汇总各方观点的支持/反对理由 +- 提炼关键决策要点和风险点 +- 给出建议的决策框架和下一步行动 + +--- + +## 🎭 预设 Agent 角色库 + +| 角色 | 视角定位 | 关注点 | +|------|---------|--------| +| 🧑‍💼 CEO | 战略全局 | 愿景、市场机会、竞争格局 | +| 👨‍💻 CTO | 技术可行性 | 技术难度、资源需求、技术债 | +| 💰 CFO | 财务健康 | ROI、成本、现金流、盈利模式 | +| 👥 用户代言人 | 用户体验 | 用户需求、痛点、使用场景 | +| ⚠️ 风险分析师 | 风险控制 | 潜在风险、失败模式、应急预案 | +| 🚀 增长黑客 | 快速验证 | MVP思维、增长杠杆、数据驱动 | +| 🎨 产品设计师 | 产品体验 | 交互设计、用户旅程、差异化 | +| 📈 市场分析师 | 市场洞察 | 市场规模、趋势、竞品分析 | + +--- + +## 🔄 用户交互流程 + +``` +┌─────────────────────────────────────────────────────────────────┐ +│ 用户打开 App │ +│ ↓ │ +│ [选择决策类型] 产品方案 / 商业决策 / 技术选型 / 个人规划 │ +│ ↓ │ +│ [输入决策议题] "我们是否应该在Q2推出AI助手功能？" │ +│ ↓ │ +│ [选择参与角色] ☑️CEO ☑️CTO ☑️CFO ☑️用户代言人 (可自定义) │ +│ ↓ │ +│ [开始辩论] → 观看多Agent实时辩论（流式输出） │ +│ ↓ │ +│ [生成报告] → 下载决策要点 PDF / Markdown │ +└─────────────────────────────────────────────────────────────────┘ +``` + +--- + +## 🏗️ 技术架构 + +``` +┌──────────────────────────────────────────────────────────────┐ +│ Frontend (Streamlit) │ +│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────┐ │ +│ │ 议题输入区 │ │ 辩论展示区 │ │ 决策报告区 │ │ +│ └─────────────┘ └─────────────┘ └─────────────────────┘ │ +└────────────────────────────┬─────────────────────────────────┘ + │ + ▼ +┌──────────────────────────────────────────────────────────────┐ +│ Backend (Python) │ +│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────┐ │ +│ │ Agent管理器 │ │ 辩论编排器 │ │ 报告生成器 │ │ +│ └─────────────┘ └─────────────┘ └─────────────────────┘ │ +└────────────────────────────┬─────────────────────────────────┘ + │ + ▼ +┌──────────────────────────────────────────────────────────────┐ +│ LLM API Layer │ +│ Claude API / OpenAI API / 本地模型 │ +└──────────────────────────────────────────────────────────────┘ +``` + +--- + +## 📁 项目文件结构 + +``` +multi_agent_workshop/ +├── app.py # Streamlit 主入口 +├── config.py # 配置文件（API Key、模型设置） +├── requirements.txt # 依赖包 +│ +├── agents/ # Agent 相关 +│ ├── __init__.py +│ ├── base_agent.py # Agent 基类 +│ ├── agent_factory.py # Agent 工厂（创建不同角色） +│ └── agent_profiles.py # 角色定义和 Prompt 模板 +│ +├── orchestrator/ # 辩论编排 +│ ├── __init__.py +│ ├── debate_manager.py # 辩论流程管理 +│ └── turn_strategy.py # 发言顺序策略 +│ +├── report/ # 报告生成 +│ ├── __init__.py +│ ├── summarizer.py # 观点汇总 +│ └── report_generator.py # 报告输出 +│ +├── ui/ # UI 组件 +│ ├── __init__.py +│ ├── input_panel.py # 输入面板 +│ ├── debate_panel.py # 辩论展示 +│ └── report_panel.py # 报告展示 +│ +└── utils/ # 工具函数 + ├── __init__.py + └── llm_client.py # LLM API 封装 +``` + +--- + +## ⏱️ 开发里程碑 + +| 阶段 | 目标 | 预计时间 | +|------|------|---------| +| Phase 1 | 单 Agent 问答（验证 API 调用） | 30 分钟 | +| Phase 2 | 多 Agent 顺序发言 | 1 小时 | +| Phase 3 | Agent 交互辩论 | 1.5 小时 | +| Phase 4 | 决策报告生成 | 1 小时 | +| Phase 5 | UI 美化 + 导出功能 | 1 小时 | + +--- + +## 🚀 扩展功能（Nice to Have） + +- [ ] 自定义 Agent 角色 +- [ ] 保存历史决策会话 +- [ ] 决策追踪（后续验证决策效果） +- [ ] 团队协作模式（多人实时参与） +- [ ] 知识库集成（基于公司内部文档决策） diff --git a/__pycache__/app.cpython-313.pyc b/__pycache__/app.cpython-313.pyc new file mode 100644 index 0000000..af80baf Binary files /dev/null and b/__pycache__/app.cpython-313.pyc differ diff --git a/__pycache__/config.cpython-313.pyc b/__pycache__/config.cpython-313.pyc new file mode 100644 index 0000000..4c0e264 Binary files /dev/null and b/__pycache__/config.cpython-313.pyc differ diff --git a/agents/__init__.py b/agents/__init__.py new file mode 100644 index 0000000..646f748 --- /dev/null +++ b/agents/__init__.py @@ -0,0 +1,17 @@ +"""Agents 模块""" +from agents.base_agent import BaseAgent, AgentMessage +from agents.agent_profiles import ( + AGENT_PROFILES, + get_agent_profile, + get_all_agents, + get_recommended_agents +) + +__all__ = [ + "BaseAgent", + "AgentMessage", + "AGENT_PROFILES", + "get_agent_profile", + "get_all_agents", + "get_recommended_agents" +] diff --git a/agents/__pycache__/__init__.cpython-313.pyc b/agents/__pycache__/__init__.cpython-313.pyc new file mode 100644 index 0000000..4f87cb1 Binary files /dev/null and b/agents/__pycache__/__init__.cpython-313.pyc differ diff --git a/agents/__pycache__/agent_profiles.cpython-313.pyc b/agents/__pycache__/agent_profiles.cpython-313.pyc new file mode 100644 index 0000000..ddfedc6 Binary files /dev/null and b/agents/__pycache__/agent_profiles.cpython-313.pyc differ diff --git a/agents/__pycache__/base_agent.cpython-313.pyc b/agents/__pycache__/base_agent.cpython-313.pyc new file mode 100644 index 0000000..f3cf15b Binary files /dev/null and b/agents/__pycache__/base_agent.cpython-313.pyc differ diff --git a/agents/__pycache__/research_agent.cpython-313.pyc b/agents/__pycache__/research_agent.cpython-313.pyc new file mode 100644 index 0000000..eb03714 Binary files /dev/null and b/agents/__pycache__/research_agent.cpython-313.pyc differ diff --git a/agents/agent_profiles.py b/agents/agent_profiles.py new file mode 100644 index 0000000..22b43a9 --- /dev/null +++ b/agents/agent_profiles.py @@ -0,0 +1,195 @@ +""" +Agent 角色配置 - 定义各个角色的视角和 Prompt 模板 +""" + +AGENT_PROFILES = { + "ceo": { + "name": "CEO 战略顾问", + "emoji": "🧑‍💼", + "perspective": "战略全局视角", + "focus_areas": ["愿景对齐", "市场机会", "竞争格局", "资源分配", "长期价值"], + "system_prompt": """你是一位经验丰富的 CEO 战略顾问，擅长从全局视角分析决策。 + +你的思考维度： +- 这个决策是否符合公司/个人的长期愿景？ +- 市场时机是否合适？竞争对手在做什么？ +- 资源投入是否值得？机会成本是什么？ +- 这个决策的战略杠杆点在哪里？ + +沟通风格： +- 高屋建瓴，关注大局 +- 用数据和案例支撑观点 +- 敢于提出尖锐问题 +- 简洁有力，直击要害""" + }, + + "cto": { + "name": "CTO 技术专家", + "emoji": "👨‍💻", + "perspective": "技术可行性视角", + "focus_areas": ["技术难度", "资源需求", "技术债务", "可扩展性", "技术趋势"], + "system_prompt": """你是一位资深的 CTO 技术专家，擅长评估技术方案的可行性和风险。 + +你的思考维度： +- 技术实现难度如何？需要什么技术栈？ +- 团队是否具备相关能力？需要多少开发资源？ +- 会引入哪些技术债务？如何控制复杂度？ +- 系统的可扩展性和可维护性如何？ +- 是否符合技术发展趋势？ + +沟通风格： +- 技术视角务实分析 +- 明确指出技术风险和挑战 +- 提供具体的技术建议 +- 用技术语言但确保非技术人员能理解""" + }, + + "cfo": { + "name": "CFO 财务顾问", + "emoji": "💰", + "perspective": "财务健康视角", + "focus_areas": ["投资回报", "成本结构", "现金流", "盈利模式", "财务风险"], + "system_prompt": """你是一位精明的 CFO 财务顾问，擅长从财务角度评估决策的可行性。 + +你的思考维度： +- 预期投资回报率(ROI)是多少？回收期多长？ +- 成本结构如何？固定成本和变动成本分别是多少？ +- 对现金流有什么影响？是否会造成资金压力？ +- 盈利模式是否清晰可行？ +- 财务风险敞口有多大？ + +沟通风格： +- 数据驱动，用数字说话 +- 关注投入产出比 +- 提醒隐藏成本和财务风险 +- 理性客观，不被情怀裹挟""" + }, + + "user_advocate": { + "name": "用户代言人", + "emoji": "👥", + "perspective": "用户体验视角", + "focus_areas": ["用户需求", "使用场景", "痛点解决", "用户旅程", "竞品对比"], + "system_prompt": """你是用户的代言人，始终站在用户角度思考问题。 + +你的思考维度： +- 用户真的需要这个吗？解决的是真痛点还是伪需求？ +- 用户会在什么场景下使用？使用频率如何？ +- 用户体验是否流畅？有没有不必要的摩擦？ +- 相比现有方案，用户为什么要选择我们？ +- 用户愿意为此付费吗？付多少？ + +沟通风格： +- 始终以用户视角发言 +- 用用户的语言描述问题 +- 善于讲用户故事和场景 +- 对伪需求保持警惕""" + }, + + "risk_analyst": { + "name": "风险分析师", + "emoji": "⚠️", + "perspective": "风险控制视角", + "focus_areas": ["潜在风险", "失败模式", "应急预案", "依赖关系", "最坏情况"], + "system_prompt": """你是一位专业的风险分析师，擅长识别和评估潜在风险。 + +你的思考维度： +- 可能出现哪些失败情况？概率和影响如何？ +- 有哪些关键依赖？如果依赖失效会怎样？ +- 最坏情况是什么？我们能承受吗？ +- 有没有应急预案？Plan B 是什么？ +- 如何降低风险？哪些风险是可接受的？ + +沟通风格： +- 思维缜密，考虑周全 +- 善于发现隐藏风险 +- 不是否定派，而是帮助做好准备 +- 提供风险缓解建议""" + }, + + "growth_hacker": { + "name": "增长黑客", + "emoji": "🚀", + "perspective": "快速验证视角", + "focus_areas": ["MVP思维", "增长杠杆", "数据驱动", "迭代速度", "病毒传播"], + "system_prompt": """你是一位增长黑客，信奉快速验证和数据驱动。 + +你的思考维度： +- 最小可行产品(MVP)是什么？如何最快验证假设？ +- 增长杠杆在哪里？有没有病毒传播的可能？ +- 如何设计实验？成功/失败的衡量标准是什么？ +- 迭代周期能压缩到多短？ +- 有没有低成本快速试错的方法？ + +沟通风格： +- 行动导向，反对过度分析 +- 强调快速迭代和验证 +- 用数据说话，关注转化漏斗 +- 推崇精益创业方法论""" + }, + + "product_designer": { + "name": "产品设计师", + "emoji": "🎨", + "perspective": "产品体验视角", + "focus_areas": ["交互设计", "用户旅程", "视觉体验", "差异化", "情感连接"], + "system_prompt": """你是一位产品设计师，追求极致的产品体验。 + +你的思考维度： +- 产品的核心体验是什么？如何让用户"哇"一下？ +- 用户旅程是否流畅？有没有惊喜时刻？ +- 视觉和交互设计是否一致且有品位？ +- 产品有什么独特的差异化特征？ +- 用户会对这个产品产生情感连接吗？ + +沟通风格： +- 关注细节和体验 +- 用场景和故事表达 +- 追求简洁和优雅 +- 善于发现设计机会""" + }, + + "market_analyst": { + "name": "市场分析师", + "emoji": "📈", + "perspective": "市场洞察视角", + "focus_areas": ["市场规模", "行业趋势", "竞品分析", "定位策略", "进入时机"], + "system_prompt": """你是一位市场分析师，擅长市场研究和竞争分析。 + +你的思考维度： +- 目标市场规模有多大？增长趋势如何？ +- 行业有什么新趋势？我们是否踩中了？ +- 竞争对手在做什么？我们的差异化在哪？ +- 市场定位是否清晰？目标客群是谁？ +- 进入时机是否合适？先发优势 vs 后发优势？ + +沟通风格： +- 数据驱动，引用市场研究 +- 关注趋势和变化 +- 善于对比分析 +- 提供市场策略建议""" + } +} + +# 决策类型对应的推荐角色组合 +RECOMMENDED_AGENTS = { + "product": ["ceo", "cto", "user_advocate", "product_designer", "growth_hacker"], + "business": ["ceo", "cfo", "market_analyst", "risk_analyst", "growth_hacker"], + "tech": ["cto", "ceo", "risk_analyst", "growth_hacker", "user_advocate"], + "personal": ["ceo", "risk_analyst", "user_advocate", "growth_hacker", "cfo"] +} + +def get_agent_profile(agent_id: str) -> dict: + """获取指定 Agent 的配置""" + return AGENT_PROFILES.get(agent_id, None) + +def get_all_agents() -> list: + """获取所有可用的 Agent 列表""" + return [ + {"id": k, "name": v["name"], "emoji": v["emoji"]} + for k, v in AGENT_PROFILES.items() + ] + +def get_recommended_agents(decision_type: str) -> list: + """根据决策类型获取推荐的 Agent 组合""" + return RECOMMENDED_AGENTS.get(decision_type, list(AGENT_PROFILES.keys())[:5]) diff --git a/agents/base_agent.py b/agents/base_agent.py new file mode 100644 index 0000000..148534e --- /dev/null +++ b/agents/base_agent.py @@ -0,0 +1,131 @@ +""" +Agent 基类 - 定义 Agent 的基本行为 +""" +from dataclasses import dataclass +from typing import Generator +from agents.agent_profiles import get_agent_profile + + +@dataclass +class AgentMessage: + """Agent 发言消息""" + agent_id: str + agent_name: str + emoji: str + content: str + round_num: int + + +class BaseAgent: + """Agent 基类""" + + def __init__(self, agent_id: str, llm_client): + """ + 初始化 Agent + + Args: + agent_id: Agent 标识符 (如 'ceo', 'cto') + llm_client: LLM 客户端实例 + """ + self.agent_id = agent_id + self.llm_client = llm_client + + profile = get_agent_profile(agent_id) + if not profile: + raise ValueError(f"未知的 Agent ID: {agent_id}") + + self.name = profile["name"] + self.emoji = profile["emoji"] + self.perspective = profile["perspective"] + self.focus_areas = profile["focus_areas"] + self.system_prompt = profile["system_prompt"] + + # 存储对话历史 + self.conversation_history = [] + + def generate_response( + self, + topic: str, + context: str = "", + previous_speeches: list = None, + round_num: int = 1 + ) -> Generator[str, None, None]: + """ + 生成 Agent 的发言（流式输出） + + Args: + topic: 讨论议题 + context: 背景信息 + previous_speeches: 之前其他 Agent 的发言列表 + round_num: 当前轮次 + + Yields: + str: 流式输出的文本片段 + """ + # 构建对话 prompt + user_prompt = self._build_user_prompt(topic, context, previous_speeches, round_num) + + # 调用 LLM 生成回复 + full_response = "" + for chunk in self.llm_client.chat_stream( + system_prompt=self.system_prompt, + user_prompt=user_prompt + ): + full_response += chunk + yield chunk + + # 保存到历史 + self.conversation_history.append({ + "round": round_num, + "content": full_response + }) + + def _build_user_prompt( + self, + topic: str, + context: str, + previous_speeches: list, + round_num: int + ) -> str: + """构建用户 prompt""" + + prompt_parts = [f"## 讨论议题\n{topic}"] + + if context: + prompt_parts.append(f"\n## 背景信息\n{context}") + + if previous_speeches and len(previous_speeches) > 0: + prompt_parts.append("\n## 其他人的观点") + for speech in previous_speeches: + prompt_parts.append( + f"\n**{speech['emoji']} {speech['name']}**:\n{speech['content']}" + ) + + if round_num == 1: + prompt_parts.append( + f"\n## 你的任务\n" + f"作为 {self.name}，请从你的专业视角（{self.perspective}）对这个议题发表看法。\n" + f"重点关注：{', '.join(self.focus_areas)}\n" + f"请给出 2-3 个核心观点，每个观点用 1-2 句话阐述。保持简洁有力。" + ) + else: + prompt_parts.append( + f"\n## 你的任务\n" + f"这是第 {round_num} 轮讨论。请针对其他人的观点进行回应：\n" + f"- 你同意或反对哪些观点？为什么？\n" + f"- 有没有被忽略的重要问题？\n" + f"- 你的立场有没有调整？\n" + f"请保持简洁，聚焦于最重要的 1-2 个点。" + ) + + return "\n".join(prompt_parts) + + def get_summary(self) -> str: + """获取该 Agent 所有发言的摘要""" + if not self.conversation_history: + return "暂无发言" + + return "\n---\n".join([ + f"第 {h['round']} 轮: {h['content']}" + for h in self.conversation_history + ]) diff --git a/agents/research_agent.py b/agents/research_agent.py new file mode 100644 index 0000000..9248196 --- /dev/null +++ b/agents/research_agent.py @@ -0,0 +1,44 @@ +from typing import Generator, List, Dict +from utils.llm_client import LLMClient +import config + +class ResearchAgent: + """研究模式专用 Agent""" + + def __init__(self, role: str, llm_client: LLMClient): + self.role = role + self.llm_client = llm_client + self.role_config = config.RESEARCH_MODEL_ROLES.get(role, {}) + self.name = self.role_config.get("name", role.capitalize()) + + def _get_system_prompt(self, context: str = "") -> str: + if self.role == "planner": + return f"""You are a Senior Research Planner. +Your goal is to break down a complex user topic into a structured research plan. +You must create a clear, step-by-step plan that covers different angles of the topic. +Format your output as a Markdown list of steps. +Context: {context}""" + + elif self.role == "researcher": + return f"""You are a Deep Researcher. +Your goal is to execute a specific research step and provide detailed, in-depth analysis. +Use your vast knowledge to provide specific facts, figures, and logical reasoning. +Do not be superficial. Go deep. +Context: {context}""" + + elif self.role == "writer": + return f"""You are a Senior Report Writer. +Your goal is to synthesize multiple research findings into a cohesive, high-quality report. +The report should be well-structured, easy to read, and provide actionable insights. +Context: {context}""" + + else: + return "You are a helpful assistant." + + def generate(self, prompt: str, context: str = "") -> Generator[str, None, None]: + """Generate response stream""" + system_prompt = self._get_system_prompt(context) + yield from self.llm_client.chat_stream( + system_prompt=system_prompt, + user_prompt=prompt + ) diff --git a/app.py b/app.py new file mode 100644 index 0000000..223000a --- /dev/null +++ b/app.py @@ -0,0 +1,556 @@ +""" +Multi-Agent Decision Workshop - 主应用 +多 Agent 决策工作坊：通过多角色辩论帮助用户做出更好的决策 +""" +import streamlit as st +import os +from dotenv import load_dotenv + +# 加载环境变量 +load_dotenv() + +from agents import get_all_agents, get_recommended_agents, AGENT_PROFILES +from orchestrator import DebateManager, DebateConfig +from orchestrator.research_manager import ResearchManager, ResearchConfig +from report import ReportGenerator +from utils import LLMClient +import config + +# ==================== 页面配置 ==================== +st.set_page_config( + page_title="🎭 多 Agent 决策工作坊", + page_icon="🎭", + layout="wide", + initial_sidebar_state="expanded" +) + +# ==================== 样式 ==================== +st.markdown(""" + +""", unsafe_allow_html=True) + +# ==================== 常量定义 ==================== +# 从环境变量读取 API Key（隐藏在 .env 文件中） +DEFAULT_API_KEY = os.getenv("AIHUBMIX_API_KEY", "") + +# 支持的模型列表 +AVAILABLE_MODELS = { + "gpt-4o": "GPT-4o (推荐)", + "gpt-4o-mini": "GPT-4o Mini (快速)", + "gpt-4-turbo": "GPT-4 Turbo", + "gpt-3.5-turbo": "GPT-3.5 Turbo (经济)", + "claude-3-5-sonnet-20241022": "Claude 3.5 Sonnet", + "claude-3-opus-20240229": "Claude 3 Opus", + "claude-3-haiku-20240307": "Claude 3 Haiku (快速)", + "deepseek-chat": "DeepSeek Chat", + "deepseek-coder": "DeepSeek Coder", + "gemini-1.5-pro": "Gemini 1.5 Pro", + "gemini-1.5-flash": "Gemini 1.5 Flash", + "qwen-turbo": "通义千问 Turbo", + "qwen-plus": "通义千问 Plus", + "glm-4": "智谱 GLM-4", + "moonshot-v1-8k": "Moonshot (月之暗面)", +} + +# 决策类型 +DECISION_TYPES = { + "product": "产品方案", + "business": "商业决策", + "tech": "技术选型", + "personal": "个人规划" +} + +# ==================== 初始化 Session State ==================== +if "mode" not in st.session_state: + st.session_state.mode = "Deep Research" + +# Debate State +if "debate_started" not in st.session_state: + st.session_state.debate_started = False +if "debate_finished" not in st.session_state: + st.session_state.debate_finished = False +if "speeches" not in st.session_state: + st.session_state.speeches = [] +if "report" not in st.session_state: + st.session_state.report = "" +if "custom_agents" not in st.session_state: + st.session_state.custom_agents = {} + +# Research State +if "research_plan" not in st.session_state: + st.session_state.research_plan = "" +if "research_started" not in st.session_state: + st.session_state.research_started = False +if "research_output" not in st.session_state: + st.session_state.research_output = "" # Final report +if "research_steps_output" not in st.session_state: + st.session_state.research_steps_output = [] # List of step results + + +# ==================== 侧边栏：配置 ==================== +with st.sidebar: + st.header("⚙️ 设置") + + # 全局 API Key 设置 + with st.expander("🔑 API Key 设置", expanded=True): + use_custom_key = st.checkbox("使用自定义 API Key") + if use_custom_key: + api_key = st.text_input( + "API Key", + type="password", + help="留空则使用环境变量中的 Key" + ) + else: + api_key = DEFAULT_API_KEY + + st.divider() + + # 模式选择 + mode = st.radio( + "📊 选择模式", + ["Deep Research", "Debate Workshop"], + index=0 if st.session_state.mode == "Deep Research" else 1 + ) + st.session_state.mode = mode + + st.divider() + + if mode == "Deep Research": + st.subheader("🧪 研究模型配置") + + # 3 个角色的模型配置 + roles_config = {} + for role_key, role_info in config.RESEARCH_MODEL_ROLES.items(): + roles_config[role_key] = st.selectbox( + f"{role_info['name']} ({role_info['description']})", + options=list(AVAILABLE_MODELS.keys()), + index=list(AVAILABLE_MODELS.keys()).index(role_info['default_model']) if role_info['default_model'] in AVAILABLE_MODELS else 0, + key=f"model_{role_key}" + ) + + else: # Debate Workshop + # 模型选择 + model = st.selectbox( + "🤖 选择通用模型", + options=list(AVAILABLE_MODELS.keys()), + format_func=lambda x: AVAILABLE_MODELS[x], + index=0, + help="选择用于辩论的 AI 模型" + ) + + # 辩论配置 + max_rounds = st.slider( + "🔄 辩论轮数", + min_value=1, + max_value=4, + value=2, + help="每轮所有 Agent 都会发言一次" + ) + + st.divider() + + # ==================== 自定义角色 (Debate Only) ==================== + st.subheader("✨ 自定义角色") + + with st.expander("➕ 添加新角色", expanded=False): + new_agent_name = st.text_input("角色名称", placeholder="如：法务顾问", key="new_agent_name") + new_agent_emoji = st.text_input("角色 Emoji", value="🎯", max_chars=2, key="new_agent_emoji") + new_agent_perspective = st.text_input("视角定位", placeholder="如：法律合规视角", key="new_agent_perspective") + new_agent_focus = st.text_input("关注点（逗号分隔）", placeholder="如：合规风险, 法律条款", key="new_agent_focus") + new_agent_prompt = st.text_area("角色设定 Prompt", placeholder="描述这个角色的思考方式...", height=100, key="new_agent_prompt") + + if st.button("✅ 添加角色", use_container_width=True): + if new_agent_name and new_agent_prompt: + agent_id = f"custom_{len(st.session_state.custom_agents)}" + st.session_state.custom_agents[agent_id] = { + "name": new_agent_name, + "emoji": new_agent_emoji, + "perspective": new_agent_perspective or "自定义视角", + "focus_areas": [f.strip() for f in new_agent_focus.split(",") if f.strip()], + "system_prompt": new_agent_prompt + } + st.success(f"已添加角色: {new_agent_emoji} {new_agent_name}") + st.rerun() + else: + st.warning("请至少填写角色名称和 Prompt") + + # 显示已添加的自定义角色 + if st.session_state.custom_agents: + st.markdown("**已添加的自定义角色：**") + for agent_id, agent_info in list(st.session_state.custom_agents.items()): + col1, col2 = st.columns([3, 1]) + with col1: + st.markdown(f"{agent_info['emoji']} {agent_info['name']}") + with col2: + if st.button("🗑️", key=f"del_{agent_id}"): + del st.session_state.custom_agents[agent_id] + st.rerun() + +# ==================== 主界面逻辑 ==================== + +if mode == "Deep Research": + st.title("🧪 Deep Research Mode") + st.markdown("*深度研究模式：规划 -> 研究 -> 报告*") + + # Input + research_topic = st.text_area("研究主题", placeholder="请输入你想深入研究的主题...", height=100) + research_context = st.text_area("补充背景 (可选)", placeholder="任何额外的背景信息...", height=80) + + generate_plan_btn = st.button("📝 生成研究计划", type="primary", disabled=not research_topic) + + if generate_plan_btn and research_topic: + st.session_state.research_started = False + st.session_state.research_output = "" + st.session_state.research_steps_output = [] + + manager = ResearchManager(api_key=api_key) + config_obj = ResearchConfig( + topic=research_topic, + context=research_context, + planner_model=roles_config['planner'], + researcher_model=roles_config['researcher'], + writer_model=roles_config['writer'] + ) + manager.create_agents(config_obj) + + with st.spinner("正在制定研究计划..."): + plan_text = "" + for chunk in manager.generate_plan(research_topic, research_context): + plan_text += chunk + st.session_state.research_plan = plan_text + + # Plan Review & Edit + if st.session_state.research_plan: + st.divider() + st.subheader("📋 研究计划确认") + + edited_plan = st.text_area("请审查并编辑计划 (Markdown格式)", value=st.session_state.research_plan, height=300) + st.session_state.research_plan = edited_plan + + start_research_btn = st.button("🚀 开始深度研究", type="primary") + + if start_research_btn: + st.session_state.research_started = True + st.session_state.research_steps_output = [] # Reset steps + + # Parse plan lines to get steps (simple heuristic: lines starting with - or 1.) + steps = [line.strip() for line in edited_plan.split('\n') if line.strip().startswith(('-', '*', '1.', '2.', '3.', '4.', '5.'))] + if not steps: + steps = [edited_plan] # Fallback if no list format + + manager = ResearchManager(api_key=api_key) + config_obj = ResearchConfig( + topic=research_topic, + context=research_context, + planner_model=roles_config['planner'], + researcher_model=roles_config['researcher'], + writer_model=roles_config['writer'] + ) + manager.create_agents(config_obj) + + # Execute Steps + previous_findings = "" + st.divider() + st.subheader("🔍 研究进行中...") + + step_progress = st.container() + + for i, step in enumerate(steps): + with step_progress: + with st.status(f"正在研究: {step}", expanded=True): + findings_text = "" + placeholder = st.empty() + for chunk in manager.execute_step(step, previous_findings): + findings_text += chunk + placeholder.markdown(findings_text) + + st.session_state.research_steps_output.append(f"### {step}\n{findings_text}") + previous_findings += f"\n\nFinding for '{step}':\n{findings_text}" + + # Final Report + st.divider() + st.subheader("📄 最终报告生成中...") + report_placeholder = st.empty() + final_report = "" + for chunk in manager.generate_report(research_topic, previous_findings): + final_report += chunk + report_placeholder.markdown(final_report) + + st.session_state.research_output = final_report + st.success("✅ 研究完成") + + # Show Final Report if available + if st.session_state.research_output: + st.divider() + st.subheader("📄 最终研究报告") + st.markdown(st.session_state.research_output) + st.download_button("📥 下载报告", st.session_state.research_output, "research_report.md") + + +elif mode == "Debate Workshop": + # ==================== 原始 Debate UI 逻辑 ==================== + st.title("🎭 多 Agent 决策工作坊") + st.markdown("*让多个 AI 角色从不同视角辩论，帮助你做出更全面的决策*") + + # ==================== 输入区域 ==================== + col1, col2 = st.columns([2, 1]) + + with col1: + st.subheader("📝 决策议题") + + # 决策类型选择 + decision_type = st.selectbox( + "决策类型", + options=list(DECISION_TYPES.keys()), + format_func=lambda x: DECISION_TYPES[x], + index=0 + ) + + # 议题输入 + topic = st.text_area( + "请描述你的决策议题", + placeholder="例如：我们是否应该在 Q2 推出 AI 助手功能？\n\n或者：我应该接受这份新工作 offer 吗？", + height=120 + ) + + # 背景信息（可选） + with st.expander("➕ 添加背景信息（可选）"): + context = st.text_area( + "背景信息", + placeholder="提供更多上下文信息，如：\n- 当前状况\n- 已有的资源和限制\n- 相关数据和事实", + height=100 + ) + context = context if 'context' in dir() else "" + + with col2: + st.subheader("🎭 选择参与角色") + + # 获取推荐的角色 + recommended = get_recommended_agents(decision_type) + all_agents = get_all_agents() + + # 预设角色选择 + st.markdown("**预设角色：**") + selected_agents = [] + for agent in all_agents: + is_recommended = agent["id"] in recommended + default_checked = is_recommended + + if st.checkbox( + f"{agent['emoji']} {agent['name']}", + value=default_checked, + key=f"agent_{agent['id']}" + ): + selected_agents.append(agent["id"]) + + # 自定义角色选择 + if st.session_state.custom_agents: + st.markdown("**自定义角色：**") + for agent_id, agent_info in st.session_state.custom_agents.items(): + if st.checkbox( + f"{agent_info['emoji']} {agent_info['name']}", + value=True, + key=f"agent_{agent_id}" + ): + selected_agents.append(agent_id) + + # 角色数量提示 + if len(selected_agents) < 2: + st.warning("请至少选择 2 个角色") + elif len(selected_agents) > 6: + st.warning("建议不超过 6 个角色") + else: + st.info(f"已选择 {len(selected_agents)} 个角色") + + # ==================== 辩论控制 ==================== + st.divider() + + col_btn1, col_btn2, col_btn3 = st.columns([1, 1, 2]) + + with col_btn1: + start_btn = st.button( + "🚀 开始辩论", + disabled=(not topic or len(selected_agents) < 2 or not api_key), + type="primary", + use_container_width=True + ) + + with col_btn2: + reset_btn = st.button( + "🔄 重置", + use_container_width=True + ) + + if reset_btn: + st.session_state.debate_started = False + st.session_state.debate_finished = False + st.session_state.speeches = [] + st.session_state.report = "" + st.rerun() + + # ==================== 辩论展示区 ==================== + if start_btn and topic and len(selected_agents) >= 2: + st.session_state.debate_started = True + st.session_state.speeches = [] + + st.divider() + st.subheader("🎬 辩论进行中...") + + # 临时将自定义角色添加到 agent_profiles + from agents import agent_profiles + original_profiles = dict(agent_profiles.AGENT_PROFILES) + agent_profiles.AGENT_PROFILES.update(st.session_state.custom_agents) + + try: + # 初始化客户端和管理器 + provider_val = "aihubmix" # Debate mode default to aihubmix or logic needs to be robust. + # Note: in sidebar "model" and "api_key" were set. "provider" variable is now inside the Sidebar logic block if mode==Debate. + # But wait, I removed the "Advanced Settings" block from the global scope and put it into sub-scope? + # Let's check my sidebar logic above. + + # Refactoring check: + # I removed the provider selection logic from the global sidebar. I should probably add it back or assume a default. + # In the original code, provider selection was in "Advanced Settings". + + llm_client = LLMClient( + provider="aihubmix", + api_key=api_key, + base_url="https://aihubmix.com/v1", + model=model + ) + debate_manager = DebateManager(llm_client) + + # 配置辩论 + debate_config = DebateConfig( + topic=topic, + context=context, + agent_ids=selected_agents, + max_rounds=max_rounds + ) + debate_manager.setup_debate(debate_config) + + # 运行辩论（流式） + current_round = 0 + speech_placeholder = None + + for event in debate_manager.run_debate_stream(): + if event["type"] == "round_start": + current_round = event["round"] + st.markdown( + f'
📢 第 {current_round} 轮讨论
', + unsafe_allow_html=True + ) + + elif event["type"] == "speech_start": + st.markdown(f"**{event['emoji']} {event['agent_name']}**") + speech_placeholder = st.empty() + current_content = "" + + elif event["type"] == "speech_chunk": + current_content += event["chunk"] + speech_placeholder.markdown(current_content) + + elif event["type"] == "speech_end": + st.session_state.speeches.append({ + "agent_id": event["agent_id"], + "content": event["content"], + "round": current_round + }) + st.divider() + + elif event["type"] == "debate_end": + st.session_state.debate_finished = True + st.success("✅ 辩论结束！正在生成决策报告...") + + # 生成报告 + if st.session_state.debate_finished: + report_generator = ReportGenerator(llm_client) + speeches = debate_manager.get_all_speeches() + + st.subheader("📊 决策报告") + report_placeholder = st.empty() + report_content = "" + + for chunk in report_generator.generate_report_stream( + topic=topic, + speeches=speeches, + context=context + ): + report_content += chunk + report_placeholder.markdown(report_content) + + st.session_state.report = report_content + + # 下载按钮 + st.download_button( + label="📥 下载报告 (Markdown)", + data=report_content, + file_name="decision_report.md", + mime="text/markdown" + ) + + except Exception as e: + st.error(f"发生错误: {str(e)}") + import traceback + st.code(traceback.format_exc()) + st.info("请检查你的 API Key 和模型设置是否正确") + + finally: + # 恢复原始角色配置 + agent_profiles.AGENT_PROFILES = original_profiles + + # ==================== 历史报告展示 ==================== + elif st.session_state.report and not start_btn: + st.divider() + st.subheader("📊 上次的决策报告") + st.markdown(st.session_state.report) + + st.download_button( + label="📥 下载报告 (Markdown)", + data=st.session_state.report, + file_name="decision_report.md", + mime="text/markdown" + ) + +# ==================== 底部信息 ==================== +st.divider() +col_footer1, col_footer2, col_footer3 = st.columns(3) +with col_footer2: + st.markdown( + "
" + "🎭 Multi-Agent Decision Workshop
多 Agent 决策工作坊" + "
", + unsafe_allow_html=True + ) diff --git a/config.py b/config.py new file mode 100644 index 0000000..abc657c --- /dev/null +++ b/config.py @@ -0,0 +1,50 @@ +""" +配置文件 - API Keys 和模型设置 +""" +import os +from dotenv import load_dotenv + +load_dotenv() + +# API 配置 +ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY", "") +OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "") +AIHUBMIX_API_KEY = os.getenv("AIHUBMIX_API_KEY", "sk-yd8Tik0nFW5emKYcBdFc433b7c8b4dC182848f76819bBe73") + +# AIHubMix 配置 +AIHUBMIX_BASE_URL = "https://aihubmix.com/v1" + +# 模型配置 +DEFAULT_MODEL = "gpt-4o" # AIHubMix 支持的模型 +LLM_PROVIDER = "aihubmix" # 默认使用 AIHubMix + +# 辩论配置 +MAX_DEBATE_ROUNDS = 3 # 最大辩论轮数 +MAX_AGENTS = 6 # 最大参与 Agent 数量 + +# 研究模式模型角色配置 +RESEARCH_MODEL_ROLES = { + "planner": { + "name": "Planner", + "default_model": "gpt-4o", + "description": "负责拆解问题，制定研究计划" + }, + "researcher": { + "name": "Researcher", + "default_model": "gemini-1.5-pro", + "description": "负责执行具体的研究步骤，深度分析" + }, + "writer": { + "name": "Writer", + "default_model": "claude-3-5-sonnet-20241022", + "description": "负责汇总信息，撰写最终报告" + } +} + +# 决策类型 +DECISION_TYPES = { + "product": "产品方案", + "business": "商业决策", + "tech": "技术选型", + "personal": "个人规划" +} diff --git a/orchestrator/__init__.py b/orchestrator/__init__.py new file mode 100644 index 0000000..b087e84 --- /dev/null +++ b/orchestrator/__init__.py @@ -0,0 +1,4 @@ +"""Orchestrator 模块""" +from orchestrator.debate_manager import DebateManager, DebateConfig, SpeechRecord + +__all__ = ["DebateManager", "DebateConfig", "SpeechRecord"] diff --git a/orchestrator/__pycache__/__init__.cpython-313.pyc b/orchestrator/__pycache__/__init__.cpython-313.pyc new file mode 100644 index 0000000..7d7079c Binary files /dev/null and b/orchestrator/__pycache__/__init__.cpython-313.pyc differ diff --git a/orchestrator/__pycache__/debate_manager.cpython-313.pyc b/orchestrator/__pycache__/debate_manager.cpython-313.pyc new file mode 100644 index 0000000..97dd401 Binary files /dev/null and b/orchestrator/__pycache__/debate_manager.cpython-313.pyc differ diff --git a/orchestrator/__pycache__/research_manager.cpython-313.pyc b/orchestrator/__pycache__/research_manager.cpython-313.pyc new file mode 100644 index 0000000..ce3acd8 Binary files /dev/null and b/orchestrator/__pycache__/research_manager.cpython-313.pyc differ diff --git a/orchestrator/debate_manager.py b/orchestrator/debate_manager.py new file mode 100644 index 0000000..4edd857 --- /dev/null +++ b/orchestrator/debate_manager.py @@ -0,0 +1,160 @@ +""" +辩论管理器 - 编排多 Agent 辩论流程 +""" +from typing import List, Generator, Callable +from dataclasses import dataclass + +from agents.base_agent import BaseAgent +from agents.agent_profiles import get_agent_profile +from utils.llm_client import LLMClient +import config + + +@dataclass +class DebateConfig: + """辩论配置""" + topic: str + context: str = "" + agent_ids: List[str] = None + max_rounds: int = 2 + + +@dataclass +class SpeechRecord: + """发言记录""" + agent_id: str + agent_name: str + emoji: str + content: str + round_num: int + + +class DebateManager: + """辩论管理器""" + + def __init__(self, llm_client: LLMClient = None): + """ + 初始化辩论管理器 + + Args: + llm_client: LLM 客户端实例 + """ + self.llm_client = llm_client or LLMClient() + self.agents: List[BaseAgent] = [] + self.speech_records: List[SpeechRecord] = [] + self.current_round = 0 + + def setup_debate(self, debate_config: DebateConfig) -> None: + """ + 设置辩论 + + Args: + debate_config: 辩论配置 + """ + self.config = debate_config + self.agents = [] + self.speech_records = [] + self.current_round = 0 + + # 创建参与的 Agent + for agent_id in debate_config.agent_ids: + agent = BaseAgent(agent_id, self.llm_client) + self.agents.append(agent) + + def run_debate_stream( + self, + on_speech_start: Callable = None, + on_speech_chunk: Callable = None, + on_speech_end: Callable = None, + on_round_end: Callable = None + ) -> Generator[dict, None, None]: + """ + 运行辩论（流式） + + Args: + on_speech_start: 发言开始回调 + on_speech_chunk: 发言片段回调 + on_speech_end: 发言结束回调 + on_round_end: 轮次结束回调 + + Yields: + dict: 事件信息 + """ + for round_num in range(1, self.config.max_rounds + 1): + self.current_round = round_num + + yield { + "type": "round_start", + "round": round_num, + "total_rounds": self.config.max_rounds + } + + for agent in self.agents: + # 获取之前的发言（排除自己） + previous_speeches = [ + { + "name": r.agent_name, + "emoji": r.emoji, + "content": r.content + } + for r in self.speech_records + if r.agent_id != agent.agent_id + ] + + yield { + "type": "speech_start", + "agent_id": agent.agent_id, + "agent_name": agent.name, + "emoji": agent.emoji, + "round": round_num + } + + # 流式生成发言 + full_content = "" + for chunk in agent.generate_response( + topic=self.config.topic, + context=self.config.context, + previous_speeches=previous_speeches, + round_num=round_num + ): + full_content += chunk + yield { + "type": "speech_chunk", + "agent_id": agent.agent_id, + "chunk": chunk + } + + # 保存发言记录 + record = SpeechRecord( + agent_id=agent.agent_id, + agent_name=agent.name, + emoji=agent.emoji, + content=full_content, + round_num=round_num + ) + self.speech_records.append(record) + + yield { + "type": "speech_end", + "agent_id": agent.agent_id, + "content": full_content + } + + yield { + "type": "round_end", + "round": round_num + } + + yield {"type": "debate_end"} + + def get_all_speeches(self) -> List[SpeechRecord]: + """获取所有发言记录""" + return self.speech_records + + def get_speeches_by_round(self, round_num: int) -> List[SpeechRecord]: + """获取指定轮次的发言""" + return [r for r in self.speech_records if r.round_num == round_num] + + def get_speeches_by_agent(self, agent_id: str) -> List[SpeechRecord]: + """获取指定 Agent 的所有发言""" + return [r for r in self.speech_records if r.agent_id == agent_id] diff --git a/orchestrator/research_manager.py b/orchestrator/research_manager.py new file mode 100644 index 0000000..b25d888 --- /dev/null +++ b/orchestrator/research_manager.py @@ -0,0 +1,51 @@ +from typing import List, Dict, Generator +from dataclasses import dataclass +from agents.research_agent import ResearchAgent +from utils.llm_client import LLMClient +import config + +@dataclass +class ResearchConfig: + topic: str + context: str = "" + planner_model: str = "gpt-4o" + researcher_model: str = "gemini-1.5-pro" + writer_model: str = "claude-3-5-sonnet-20241022" + +class ResearchManager: + """Manages the Deep Research workflow""" + + def __init__(self, api_key: str, base_url: str = None, provider: str = "aihubmix"): + self.api_key = api_key + self.base_url = base_url + self.provider = provider + self.agents = {} + + def _get_client(self, model: str) -> LLMClient: + return LLMClient( + provider=self.provider, + api_key=self.api_key, + base_url=self.base_url, + model=model + ) + + def create_agents(self, config: ResearchConfig): + """Initialize agents with specific models""" + self.agents["planner"] = ResearchAgent("planner", self._get_client(config.planner_model)) + self.agents["researcher"] = ResearchAgent("researcher", self._get_client(config.researcher_model)) + self.agents["writer"] = ResearchAgent("writer", self._get_client(config.writer_model)) + + def generate_plan(self, topic: str, context: str) -> Generator[str, None, None]: + """Step 1: Generate Research Plan""" + prompt = f"Please create a comprehensive research plan for the topic: '{topic}'.\nBreak it down into 3-5 distinct, actionable steps." + yield from self.agents["planner"].generate(prompt, context) + + def execute_step(self, step: str, previous_findings: str) -> Generator[str, None, None]: + """Step 2: Execute a single research step""" + prompt = f"Execute this research step: '{step}'.\nPrevious findings: {previous_findings}" + yield from self.agents["researcher"].generate(prompt) + + def generate_report(self, topic: str, all_findings: str) -> Generator[str, None, None]: + """Step 3: Generate Final Report""" + prompt = f"Write a final comprehensive report on '{topic}' based on these findings:\n{all_findings}" + yield from self.agents["writer"].generate(prompt) diff --git a/report/__init__.py b/report/__init__.py new file mode 100644 index 0000000..3df5ce4 --- /dev/null +++ b/report/__init__.py @@ -0,0 +1,4 @@ +"""Report 模块""" +from report.report_generator import ReportGenerator + +__all__ = ["ReportGenerator"] diff --git a/report/__pycache__/__init__.cpython-313.pyc b/report/__pycache__/__init__.cpython-313.pyc new file mode 100644 index 0000000..c4e47f3 Binary files /dev/null and b/report/__pycache__/__init__.cpython-313.pyc differ diff --git a/report/__pycache__/report_generator.cpython-313.pyc b/report/__pycache__/report_generator.cpython-313.pyc new file mode 100644 index 0000000..d305a96 Binary files /dev/null and b/report/__pycache__/report_generator.cpython-313.pyc differ diff --git a/report/report_generator.py b/report/report_generator.py new file mode 100644 index 0000000..ebb3762 --- /dev/null +++ b/report/report_generator.py @@ -0,0 +1,143 @@ +""" +报告生成器 - 汇总辩论内容并生成决策报告 +""" +from typing import List +from orchestrator.debate_manager import SpeechRecord +from utils.llm_client import LLMClient + + +class ReportGenerator: + """决策报告生成器""" + + def __init__(self, llm_client: LLMClient = None): + self.llm_client = llm_client or LLMClient() + + def generate_report( + self, + topic: str, + speeches: List[SpeechRecord], + context: str = "" + ) -> str: + """ + 生成决策报告 + + Args: + topic: 讨论议题 + speeches: 所有发言记录 + context: 背景信息 + + Returns: + str: Markdown 格式的决策报告 + """ + # 构建发言摘要 + speeches_text = self._format_speeches(speeches) + + system_prompt = """你是一位专业的决策分析师，擅长汇总多方观点并生成结构化的决策报告。 + +你的任务是根据多位专家的讨论，生成一份清晰、可操作的决策报告。 + +报告格式要求： +1. 使用 Markdown 格式 +2. 结构清晰，重点突出 +3. 提炼核心要点，不要罗列原文 +4. 给出明确的建议和下一步行动""" + + user_prompt = f"""## 讨论议题 +{topic} + +{f"## 背景信息" + chr(10) + context if context else ""} + +## 专家讨论记录 +{speeches_text} + +## 你的任务 +请生成一份决策报告，包含以下部分： + +### 📋 议题概述 +（1-2句话总结讨论的核心问题） + +### ✅ 支持观点汇总 +（列出支持该决策的主要理由，注明来源角色） + +### ❌ 反对/风险观点汇总 +（列出反对意见和风险点，注明来源角色） + +### 🔑 关键决策要点 +（3-5个需要重点考虑的因素） + +### 💡 建议与下一步行动 +（给出明确的建议，以及具体的下一步行动项） + +### ⚖️ 决策框架 +（提供一个简单的决策框架或检查清单，帮助做出最终决策） +""" + + return self.llm_client.chat( + system_prompt=system_prompt, + user_prompt=user_prompt, + max_tokens=2048 + ) + + def _format_speeches(self, speeches: List[SpeechRecord]) -> str: + """格式化发言记录""" + formatted = [] + current_round = 0 + + for speech in speeches: + if speech.round_num != current_round: + current_round = speech.round_num + formatted.append(f"\n### 第 {current_round} 轮讨论\n") + + formatted.append( + f"**{speech.emoji} {speech.agent_name}**:\n{speech.content}\n" + ) + + return "\n".join(formatted) + + def generate_report_stream( + self, + topic: str, + speeches: List[SpeechRecord], + context: str = "" + ): + """流式生成决策报告""" + speeches_text = self._format_speeches(speeches) + + system_prompt = """你是一位专业的决策分析师，擅长汇总多方观点并生成结构化的决策报告。""" + + user_prompt = f"""## 讨论议题 +{topic} + +{f"## 背景信息" + chr(10) + context if context else ""} + +## 专家讨论记录 +{speeches_text} + +## 你的任务 +请生成一份决策报告，包含以下部分： + +### 📋 议题概述 +（1-2句话总结讨论的核心问题） + +### ✅ 支持观点汇总 +（列出支持该决策的主要理由，注明来源角色） + +### ❌ 反对/风险观点汇总 +（列出反对意见和风险点，注明来源角色） + +### 🔑 关键决策要点 +（3-5个需要重点考虑的因素） + +### 💡 建议与下一步行动 +（给出明确的建议，以及具体的下一步行动项） + +### ⚖️ 决策框架 +（提供一个简单的决策框架或检查清单） +""" + + for chunk in self.llm_client.chat_stream( + system_prompt=system_prompt, + user_prompt=user_prompt, + max_tokens=2048 + ): + yield chunk diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..37ec9f4 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,7 @@ +# Multi-Agent Decision Workshop Dependencies + +streamlit>=1.28.0 +anthropic>=0.18.0 +openai>=1.12.0 +python-dotenv>=1.0.0 +pydantic>=2.0.0 diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000..e28839c --- /dev/null +++ b/utils/__init__.py @@ -0,0 +1,4 @@ +"""Utils 模块""" +from utils.llm_client import LLMClient + +__all__ = ["LLMClient"] diff --git a/utils/__pycache__/__init__.cpython-313.pyc b/utils/__pycache__/__init__.cpython-313.pyc new file mode 100644 index 0000000..f9386ef Binary files /dev/null and b/utils/__pycache__/__init__.cpython-313.pyc differ diff --git a/utils/__pycache__/llm_client.cpython-313.pyc b/utils/__pycache__/llm_client.cpython-313.pyc new file mode 100644 index 0000000..73d5602 Binary files /dev/null and b/utils/__pycache__/llm_client.cpython-313.pyc differ diff --git a/utils/llm_client.py b/utils/llm_client.py new file mode 100644 index 0000000..6495d0f --- /dev/null +++ b/utils/llm_client.py @@ -0,0 +1,141 @@ +""" +LLM 客户端封装 - 统一 Anthropic/OpenAI/AIHubMix 接口 +""" +from typing import Generator +import os + + +class LLMClient: + """LLM API 统一客户端""" + + def __init__( + self, + provider: str = None, + api_key: str = None, + base_url: str = None, + model: str = None + ): + """ + 初始化 LLM 客户端 + + Args: + provider: 'anthropic', 'openai', 'aihubmix', 或 'custom' + api_key: API 密钥 + base_url: 自定义 API 地址（用于 aihubmix/custom） + model: 指定模型名称 + """ + self.provider = provider or "aihubmix" + self.model = model or "gpt-4o" + + if self.provider == "anthropic": + from anthropic import Anthropic + self.client = Anthropic(api_key=api_key) + + elif self.provider == "openai": + from openai import OpenAI + self.client = OpenAI(api_key=api_key) + self.model = model or "gpt-4o" + + elif self.provider == "aihubmix": + # AIHubMix 兼容 OpenAI API 格式 + from openai import OpenAI + self.client = OpenAI( + api_key=api_key, + base_url=base_url or "https://aihubmix.com/v1" + ) + self.model = model or "gpt-4o" + + elif self.provider == "custom": + # 自定义 OpenAI 兼容接口（vLLM、Ollama、TGI 等） + from openai import OpenAI + self.client = OpenAI( + api_key=api_key or "not-needed", + base_url=base_url or "http://localhost:8000/v1" + ) + self.model = model or "local-model" + + else: + raise ValueError(f"不支持的 provider: {self.provider}") + + def chat_stream( + self, + system_prompt: str, + user_prompt: str, + max_tokens: int = 1024 + ) -> Generator[str, None, None]: + """ + 流式对话 + + Args: + system_prompt: 系统提示词 + user_prompt: 用户输入 + max_tokens: 最大输出 token 数 + + Yields: + str: 流式输出的文本片段 + """ + if self.provider == "anthropic": + yield from self._anthropic_stream(system_prompt, user_prompt, max_tokens) + else: + yield from self._openai_stream(system_prompt, user_prompt, max_tokens) + + def _anthropic_stream( + self, + system_prompt: str, + user_prompt: str, + max_tokens: int + ) -> Generator[str, None, None]: + """Anthropic 流式调用""" + with self.client.messages.stream( + model=self.model, + max_tokens=max_tokens, + system=system_prompt, + messages=[{"role": "user", "content": user_prompt}] + ) as stream: + for text in stream.text_stream: + yield text + + def _openai_stream( + self, + system_prompt: str, + user_prompt: str, + max_tokens: int + ) -> Generator[str, None, None]: + """OpenAI 兼容接口流式调用（支持 AIHubMix、vLLM 等）""" + try: + stream = self.client.chat.completions.create( + model=self.model, + max_tokens=max_tokens, + stream=True, + messages=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt} + ] + ) + for chunk in stream: + # 安全地获取 content，处理各种边界情况 + if chunk.choices and len(chunk.choices) > 0: + delta = chunk.choices[0].delta + if delta and hasattr(delta, 'content') and delta.content: + yield delta.content + except Exception as e: + yield f"\n\n[错误: {str(e)}]" + + def chat( + self, + system_prompt: str, + user_prompt: str, + max_tokens: int = 1024 + ) -> str: + """ + 非流式对话 + + Args: + system_prompt: 系统提示词 + user_prompt: 用户输入 + max_tokens: 最大输出 token 数 + + Returns: + str: 完整的响应文本 + """ + return "".join(self.chat_stream(system_prompt, user_prompt, max_tokens))