From cb97f7c49a2da5668e35e7905a612b075e349283 Mon Sep 17 00:00:00 2001 From: xyz Date: Wed, 7 Jan 2026 11:02:05 +0800 Subject: [PATCH] Initial commit of Deep Research Mode --- .env.example | 11 + Project_Design.md | 156 +++++ __pycache__/app.cpython-313.pyc | Bin 0 -> 21825 bytes __pycache__/config.cpython-313.pyc | Bin 0 -> 937 bytes agents/__init__.py | 17 + agents/__pycache__/__init__.cpython-313.pyc | Bin 0 -> 480 bytes .../agent_profiles.cpython-313.pyc | Bin 0 -> 7176 bytes agents/__pycache__/base_agent.cpython-313.pyc | Bin 0 -> 5005 bytes .../research_agent.cpython-313.pyc | Bin 0 -> 2797 bytes agents/agent_profiles.py | 195 ++++++ agents/base_agent.py | 131 +++++ agents/research_agent.py | 44 ++ app.py | 556 ++++++++++++++++++ config.py | 50 ++ orchestrator/__init__.py | 4 + .../__pycache__/__init__.cpython-313.pyc | Bin 0 -> 344 bytes .../debate_manager.cpython-313.pyc | Bin 0 -> 6029 bytes .../research_manager.cpython-313.pyc | Bin 0 -> 4072 bytes orchestrator/debate_manager.py | 160 +++++ orchestrator/research_manager.py | 51 ++ report/__init__.py | 4 + report/__pycache__/__init__.cpython-313.pyc | Bin 0 -> 287 bytes .../report_generator.cpython-313.pyc | Bin 0 -> 4954 bytes report/report_generator.py | 143 +++++ requirements.txt | 7 + utils/__init__.py | 4 + utils/__pycache__/__init__.cpython-313.pyc | Bin 0 -> 271 bytes utils/__pycache__/llm_client.cpython-313.pyc | Bin 0 -> 5294 bytes utils/llm_client.py | 141 +++++ 29 files changed, 1674 insertions(+) create mode 100644 .env.example create mode 100644 Project_Design.md create mode 100644 __pycache__/app.cpython-313.pyc create mode 100644 __pycache__/config.cpython-313.pyc create mode 100644 agents/__init__.py create mode 100644 agents/__pycache__/__init__.cpython-313.pyc create mode 100644 agents/__pycache__/agent_profiles.cpython-313.pyc create mode 100644 agents/__pycache__/base_agent.cpython-313.pyc create mode 100644 agents/__pycache__/research_agent.cpython-313.pyc create mode 100644 agents/agent_profiles.py create mode 100644 agents/base_agent.py create mode 100644 agents/research_agent.py create mode 100644 app.py create mode 100644 config.py create mode 100644 orchestrator/__init__.py create mode 100644 orchestrator/__pycache__/__init__.cpython-313.pyc create mode 100644 orchestrator/__pycache__/debate_manager.cpython-313.pyc create mode 100644 orchestrator/__pycache__/research_manager.cpython-313.pyc create mode 100644 orchestrator/debate_manager.py create mode 100644 orchestrator/research_manager.py create mode 100644 report/__init__.py create mode 100644 report/__pycache__/__init__.cpython-313.pyc create mode 100644 report/__pycache__/report_generator.cpython-313.pyc create mode 100644 report/report_generator.py create mode 100644 requirements.txt create mode 100644 utils/__init__.py create mode 100644 utils/__pycache__/__init__.cpython-313.pyc create mode 100644 utils/__pycache__/llm_client.cpython-313.pyc create mode 100644 utils/llm_client.py 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 +- 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+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 0000000000000000000000000000000000000000..4f87cb18f8b8842c76f27eea388f5bda007f5be3 GIT binary patch literal 480 zcmZuuyH3L}6m^_u3$3USOblJKG!qg7s#HWlq);A#iNOku2@y$L*{Mpq@ezoLfek(h zFANAIHbe$i>@)}=afjpk@HyvN&TF+Q5D~RbBVPgFMHW8+%OqbD$qn!T1)k!;p5j6Z zU6ra$S*tzG)v4|p)NoB|x)!yXzyY3isL)EN+s3FS=$#O!8Ed{h+`r!4zDr>n^2&zK za53TY#j%GO^FwTFyx!UF_K(rQ(f-!XZr9_D5NAk?(hLnLIget@>qRS$XKlupWliyb zBneIf9F%?ApsKK=ngm^fA;FYj3FzBZZXy(r0Ybb{Sg_WaSZv1sCG(|n_-76FpQaS_ z#b)RcX)RU6+em^W(RBVxwE!z}HiP(SJe+d#GDSu|~IrkC;8hM@lQJ@9-&6!K) zMs>Xx(B-{zX6DS9`OeJTqsOJC+a0*xDu2J`*iRjfKhjI?DzzRS|0f;>9A1a!@H)LN 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z(6S86YWejxv_vcHvn=^MdOPbat3eqTF7=4#!*<59>b?UD4Q|im{{h=9NrVlcj@T1G z!p#<-V+-n7wh)X?L8aoBbsa_N5hXU04KX4zmK<6TdKZ18@964!YFxQyLTbW1M}Lpz z`+fbXy>mOe2&NnS4>{d`DxET}@h*aC4#Z))RlYrTr+7!)K0ZHs;(_Eu?AWNZSI&lM z(Fve5u*g>#Bqr^L8`^@pyt4Y|6yHrgq}bD8xsu5pKg;Y3F=8*Wy}*;{$_IdSHAPW= ZK!fvW@O!lTTU2 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 0000000000000000000000000000000000000000..7d7079ccfc225460cdb52c1c1e392445c2fff146 GIT binary patch literal 344 zcmYk2u}Z{15QcY?oF_+8S&7XRHfgdCpxBryq7s9Z#SoJ<2`1SvNjNW^4TMH12g;oF#GM!^p-UtDjuARY^l!o!Tg8zC;D9_?npq81T@f&sHdUk z>8N`K8dJhZp!pO>iEbP1mTEo4DCKp7VIy$*`Skd)xc?eZwk|D~Uxp1o4$Cm%D9x2U 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""" + 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 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-> 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 0000000000000000000000000000000000000000..f9386efc2526a062c9446ceded5fefebbe2d72d4 GIT binary patch literal 271 zcmey&%ge<81o?W=nZZE%F^B^LOi;#WDIjAiLoh=TLoj17lQ*LmQxTH_Lol;GV-a&Y zizaI+4^Tm9NoG#5!m||%pU$2B5~Nd;@fN3#kFRr1W@=tZ5i?N4Pm|>qdwhIKesX;L zErC*y2ECk|-1uaK5|DgiPELIMN`}uMLvGpXhZd(673)`2R#jE$yX2SW<>V)(6zk`f 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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))