From 9dda9308680c9d3edd912d6d987f755e8d964a02 Mon Sep 17 00:00:00 2001 From: xyz Date: Wed, 7 Jan 2026 12:59:56 +0800 Subject: [PATCH] feat: Implement Multi-Model Council mode with 3-expert workflow --- README.md | 83 +++++++- __pycache__/app.cpython-313.pyc | Bin 21825 -> 22130 bytes __pycache__/config.cpython-313.pyc | Bin 937 -> 1881 bytes agents/__pycache__/base_agent.cpython-313.pyc | Bin 5005 -> 5344 bytes .../research_agent.cpython-313.pyc | Bin 2797 -> 2940 bytes agents/base_agent.py | 7 + agents/research_agent.py | 33 +-- app.py | 193 +++++++++--------- config.py | 33 ++- .../debate_manager.cpython-313.pyc | Bin 6029 -> 6308 bytes .../research_manager.cpython-313.pyc | Bin 4072 -> 4779 bytes orchestrator/debate_manager.py | 9 +- orchestrator/research_manager.py | 58 ++++-- 13 files changed, 271 insertions(+), 145 deletions(-) diff --git a/README.md b/README.md index 42331e8..c161857 100644 --- a/README.md +++ b/README.md @@ -1 +1,82 @@ -# multi-agent \ No newline at end of file +# Multi-Agent Decision Workshop & Deep Research + +这是一个基于多智能体（Multi-Agent）的决策辅助和深度研究系统。它包含两个核心模式： +1. **Deep Research Mode (Deep Research)**: 模仿 Gemini 研究模式，通过规划、执行、撰写三个阶段进行深度分析。 +2. **Debate Workshop (辩论工作坊)**: 让多个 AI 角色从不同视角辩论，帮助你做出更全面的决策。 + +## ✨ 功能特性 + +- **双模式切换**: 侧边栏一键切换 "Deep Research" 和 "Debate Workshop"。 +- **自定义模型角色**: + - 在 Deep Research 模式下，可以分别指定 `Planner` (规划者), `Researcher` (研究员), `Writer` (作家) 使用不同的 LLM。 +- **多模型支持**: 支持 OpenAI (GPT-4o), Anthropic (Claude 3.5), Gemini 等主流模型。 +- **交互式研究**: 生成研究计划后，用户可以介入修改，确保研究方向正确。 +- **流式输出**: 实时展示研究进度和辩论过程。 + +## 🛠️ 安装与使用 + +### 1. 克隆项目 + +```bash +git clone https://github.com/HomoDeusss/multi-agent.git +cd multi-agent +``` + +### 2. 安装依赖 + +确保你安装了 Python 3.8+。 + +```bash +pip install -r requirements.txt +``` + +### 3. 配置 API Key + +你可以通过以下两种方式配置 API Key： + +**方式 A: 创建 `.env` 文件 (推荐)** +复制 `.env.example` 为 `.env`，并填入你的 API Key。 + +```bash +cp .env.example .env +``` + +编辑 `.env` 文件: +```env +AIHUBMIX_API_KEY=your_api_key_here +``` + +**方式 B: 在 UI 中输入** +启动应用后，在侧边栏的 "设置" -> "API Key" 输入框中填入。 + +### 4. 启动应用 + +运行 Streamlit 应用： + +```bash +streamlit run app.py +``` + +会自动在浏览器打开 `http://localhost:8501`。 + +## 📖 使用指南 + +### 🧪 Deep Research Mode (深度研究模式) +1. 在侧边栏选择模式为 **"Deep Research"**。 +2. 在 "研究模型配置" 中，为 Planner, Researcher, Writer 选择合适的模型（推荐分别使用 GPT-4o, Gemini-1.5-pro, Claude-3.5-sonnet）。 +3. 输入你的**研究主题** (例如: "2025年量子计算商业化前景")。 +4. 点击 **"生成研究计划"**。 +5. 系统生成计划后，你可以直接在文本框中**修改计划步骤**。 +6. 点击 **"开始深度研究"**，观察 Agent 逐步执行研究任务。 +7. 下载最终生成的 Markdown 报告。 + +### 🎭 Debate Workshop (辩论工作坊) +1. 在侧边栏选择模式为 **"Debate Workshop"**。 +2. 输入**决策议题** (例如: "我是否应该辞职创业？")。 +3. 选择参与辩论的 **AI 角色** (如: CEO, 风险控制专家, 职业顾问)。 +4. 点击 **"开始辩论"**。 +5. 观看不同角色之间的唇枪舌战，最后生成综合决策建议。 + +## 📝 License + +[MIT License](LICENSE) \ No newline at end of file diff --git a/__pycache__/app.cpython-313.pyc b/__pycache__/app.cpython-313.pyc index af80baf3b6faecc674d22c4f5f43fd6ce2a3f917..e2ee526d6d0e925abca61c95b5b7c03231781633 100644 GIT binary patch delta 8905 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z2=j+gH_|)q((LKfTxtD_r$z){U#UOM>-5?5d$%#n<4j1Sk~w-1$-wp)FD+)P1)ABLcGe$cjkX2sl%P str: + """获取当前使用的模型名称""" + if hasattr(self.llm_client, "model"): + return self.llm_client.model + return "Unknown Model" def generate_response( self, diff --git a/agents/research_agent.py b/agents/research_agent.py index 9248196..d68d79b 100644 --- a/agents/research_agent.py +++ b/agents/research_agent.py @@ -1,4 +1,4 @@ -from typing import Generator, List, Dict +from typing import Generator from utils.llm_client import LLMClient import config @@ -11,25 +11,28 @@ class ResearchAgent: self.role_config = config.RESEARCH_MODEL_ROLES.get(role, {}) self.name = self.role_config.get("name", role.capitalize()) + @property + def model_name(self) -> str: + return self.llm_client.model + 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. + if self.role == "expert_a": + return f"""You are Expert A, a Senior Analyst. +Your goal is to provide a deep, foundational analysis of the user's topic. +Structure your thinking clearly. Propose a solid initial framework or solution. 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. + elif self.role == "expert_b": + return f"""You are Expert B, a Critical Reviewer. +Your goal is to find flaws, risks, and missed opportunities in Expert A's analysis. +Be constructive but rigorous. Don't just agree; add value by challenging assumptions. 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. + elif self.role == "expert_c": + return f"""You are Expert C, a Senior Strategist. +Your goal is to synthesize the final output. +Combine the structural strength of Expert A with the critical insights of Expert B. +Produce a final, polished, comprehensive plan or report. Context: {context}""" else: diff --git a/app.py b/app.py index 223000a..3f5f2d5 100644 --- a/app.py +++ b/app.py @@ -66,23 +66,7 @@ st.markdown(""" 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)", -} +from config import AVAILABLE_MODELS, RESEARCH_MODEL_ROLES # 决策类型 DECISION_TYPES = { @@ -221,17 +205,17 @@ with st.sidebar: # ==================== 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*") + st.title("🧪 Multi-Model Council") + st.markdown("*多模型智囊团：分析 (Expert A) -> 批判 (Expert B) -> 决策 (Expert C)*") # 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_topic = st.text_area("研究/决策主题", placeholder="请输入你想深入研究或决策的主题...", 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) + start_research_btn = st.button("🚀 开始多模型协作", type="primary", disabled=not research_topic) - if generate_plan_btn and research_topic: - st.session_state.research_started = False + if start_research_btn and research_topic: + st.session_state.research_started = True st.session_state.research_output = "" st.session_state.research_steps_output = [] @@ -239,84 +223,70 @@ if mode == "Deep Research": config_obj = ResearchConfig( topic=research_topic, context=research_context, - planner_model=roles_config['planner'], - researcher_model=roles_config['researcher'], - writer_model=roles_config['writer'] + expert_a_model=roles_config['expert_a'], + expert_b_model=roles_config['expert_b'], + expert_c_model=roles_config['expert_c'] ) 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") + st.subheader("🧠 智囊团思考中...") - 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 + # Collaborative Execution + current_step_name = "" + current_step_content = "" + step_placeholder = st.empty() + status_container = st.status("正在初始化...", expanded=True) - 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 + try: + for event in manager.collaborate(research_topic, research_context): + if event["type"] == "step_start": + current_step_name = event["step"] + current_agent = event["agent"] + current_model = event["model"] + status_container.update(label=f"🔄 {current_step_name} [{current_agent}] ({current_model})", state="running") + step_placeholder = st.empty() + current_step_content = "" + + elif event["type"] == "content": + current_step_content += event["content"] + step_placeholder.markdown(f"**Thinking...**\n\n{current_step_content}") + + elif event["type"] == "step_end": + # Save step result + st.session_state.research_steps_output.append({ + "step": current_step_name, + "output": event["output"] + }) + status_container.write(f"### {current_step_name}\n{event['output']}") + status_container.update(label=f"✅ {current_step_name} 完成", state="running") - # 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 + status_container.update(label="✅ 所有步骤完成", state="complete", expanded=False) - 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) + # The last step output is the final plan + if st.session_state.research_steps_output: + final_plan = st.session_state.research_steps_output[-1]["output"] + st.session_state.research_output = final_plan + st.success("✅ 综合方案生成完毕") - st.session_state.research_output = final_report - st.success("✅ 研究完成") - + except Exception as e: + st.error(f"发生错误: {str(e)}") + import traceback + st.code(traceback.format_exc()) + # Show Final Report if available if st.session_state.research_output: st.divider() - st.subheader("📄 最终研究报告") + st.subheader("📄 最终综合方案") st.markdown(st.session_state.research_output) - st.download_button("📥 下载报告", st.session_state.research_output, "research_report.md") + st.download_button("📥 下载方案", st.session_state.research_output, "comprehensive_plan.md") + + # Show breakdown history + with st.expander("查看完整思考过程"): + for step in st.session_state.research_steps_output: + st.markdown(f"### {step['step']}") + st.markdown(step['output']) + st.divider() elif mode == "Debate Workshop": @@ -386,6 +356,23 @@ elif mode == "Debate Workshop": ): selected_agents.append(agent_id) + # 自定义模型配置 (Advanced) + agent_model_map = {} + with st.expander("🛠️ 为每个角色指定模型 (可选)"): + for agent_id in selected_agents: + # Find agent name + agent_name = next((a['name'] for a in all_agents if a['id'] == agent_id), agent_id) + if agent_id in st.session_state.custom_agents: + agent_name = st.session_state.custom_agents[agent_id]['name'] + + agent_model = st.selectbox( + f"{agent_name} 的模型", + options=list(AVAILABLE_MODELS.keys()), + index=list(AVAILABLE_MODELS.keys()).index(model) if model in AVAILABLE_MODELS else 0, + key=f"model_for_{agent_id}" + ) + agent_model_map[agent_id] = agent_model + # 角色数量提示 if len(selected_agents) < 2: st.warning("请至少选择 2 个角色") @@ -434,22 +421,25 @@ elif mode == "Debate Workshop": 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". - + # 初始化默认客户端 llm_client = LLMClient( provider="aihubmix", api_key=api_key, base_url="https://aihubmix.com/v1", model=model ) + + # 初始化特定角色的客户端 + agent_clients = {} + for ag_id, ag_model in agent_model_map.items(): + if ag_model != model: # Only create new client if different from default + agent_clients[ag_id] = LLMClient( + provider="aihubmix", + api_key=api_key, + base_url="https://aihubmix.com/v1", + model=ag_model + ) + debate_manager = DebateManager(llm_client) # 配置辩论 @@ -457,7 +447,8 @@ elif mode == "Debate Workshop": topic=topic, context=context, agent_ids=selected_agents, - max_rounds=max_rounds + max_rounds=max_rounds, + agent_clients=agent_clients ) debate_manager.setup_debate(debate_config) @@ -474,7 +465,9 @@ elif mode == "Debate Workshop": ) elif event["type"] == "speech_start": - st.markdown(f"**{event['emoji']} {event['agent_name']}**") + # 显示模型名称 + model_display = f" ({event.get('model_name', 'Unknown')})" + st.markdown(f"**{event['emoji']} {event['agent_name']}**{model_display}", unsafe_allow_html=True) speech_placeholder = st.empty() current_content = "" diff --git a/config.py b/config.py index abc657c..15db778 100644 --- a/config.py +++ b/config.py @@ -18,26 +18,41 @@ AIHUBMIX_BASE_URL = "https://aihubmix.com/v1" DEFAULT_MODEL = "gpt-4o" # AIHubMix 支持的模型 LLM_PROVIDER = "aihubmix" # 默认使用 AIHubMix +# 支持的模型列表 +AVAILABLE_MODELS = { + "gpt-4o": "GPT-4o (OpenAI)", + "gpt-4o-mini": "GPT-4o Mini (OpenAI)", + "claude-3-5-sonnet-20241022": "Claude 3.5 Sonnet (Anthropic)", + "claude-3-opus-20240229": "Claude 3 Opus (Anthropic)", + "gemini-1.5-pro": "Gemini 1.5 Pro (Google)", + "gemini-1.5-flash": "Gemini 1.5 Flash (Google)", + "deepseek-chat": "DeepSeek V3 (DeepSeek)", + "deepseek-reasoner": "DeepSeek R1 (DeepSeek)", + "llama-3.3-70b-instruct": "Llama 3.3 70B (Meta)", + "qwen-2.5-72b-instruct": "Qwen 2.5 72B (Alibaba)", + "mistral-large-latest": "Mistral Large (Mistral)", +} + # 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", + "expert_a": { + "name": "Expert A (Analyst)", "default_model": "gpt-4o", - "description": "负责拆解问题，制定研究计划" + "description": "负责初步分析，提出核心观点和方案" }, - "researcher": { - "name": "Researcher", + "expert_b": { + "name": "Expert B (Critique)", "default_model": "gemini-1.5-pro", - "description": "负责执行具体的研究步骤，深度分析" + "description": "负责批判性分析，指出潜在问题和漏洞" }, - "writer": { - "name": "Writer", + "expert_c": { + "name": "Expert C (Synthesizer)", "default_model": "claude-3-5-sonnet-20241022", - "description": "负责汇总信息，撰写最终报告" + "description": "负责综合各方观点，生成最终决策方案" } } diff --git a/orchestrator/__pycache__/debate_manager.cpython-313.pyc b/orchestrator/__pycache__/debate_manager.cpython-313.pyc index 97dd40182d8bb52817791069e83480f3ef80c55f..85373e6a5647e7bcade5c1ae6be7bc4266497a05 100644 GIT binary patch delta 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-106,6 +112,7 @@ class DebateManager: "agent_id": agent.agent_id, "agent_name": agent.name, "emoji": agent.emoji, + "model_name": agent.model_name, "round": round_num } diff --git a/orchestrator/research_manager.py b/orchestrator/research_manager.py index b25d888..5fe3f3c 100644 --- a/orchestrator/research_manager.py +++ b/orchestrator/research_manager.py @@ -8,12 +8,12 @@ import config 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" + expert_a_model: str = "gpt-4o" + expert_b_model: str = "gemini-1.5-pro" + expert_c_model: str = "claude-3-5-sonnet-20241022" class ResearchManager: - """Manages the Deep Research workflow""" + """Manages the Multi-Model Council workflow""" def __init__(self, api_key: str, base_url: str = None, provider: str = "aihubmix"): self.api_key = api_key @@ -31,21 +31,41 @@ class ResearchManager: 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)) + self.agents["expert_a"] = ResearchAgent("expert_a", self._get_client(config.expert_a_model)) + self.agents["expert_b"] = ResearchAgent("expert_b", self._get_client(config.expert_b_model)) + self.agents["expert_c"] = ResearchAgent("expert_c", self._get_client(config.expert_c_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 collaborate(self, topic: str, context: str) -> Generator[Dict[str, str], None, None]: + """ + Execute the collaborative research process: + 1. Expert A: Propose Analysis + 2. Expert B: Critique + 3. Expert C: Synthesis & Final Plan + """ + + # Step 1: Expert A Analysis + findings_a = "" + yield {"type": "step_start", "step": "Expert A Analysis", "agent": self.agents["expert_a"].name, "model": self.agents["expert_a"].model_name} + prompt_a = f"Please provide a comprehensive analysis and initial proposal for the topic: '{topic}'.\nContext: {context}" + for chunk in self.agents["expert_a"].generate(prompt_a, context): + findings_a += chunk + yield {"type": "content", "content": chunk} + yield {"type": "step_end", "output": findings_a} - 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) + # Step 2: Expert B Critique + findings_b = "" + yield {"type": "step_start", "step": "Expert B Critique", "agent": self.agents["expert_b"].name, "model": self.agents["expert_b"].model_name} + prompt_b = f"Review Expert A's proposal on '{topic}'. Critique it, find gaps, and suggest improvements.\nExpert A's Proposal:\n{findings_a}" + for chunk in self.agents["expert_b"].generate(prompt_b, context): + findings_b += chunk + yield {"type": "content", "content": chunk} + yield {"type": "step_end", "output": findings_b} - 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) + # Step 3: Expert C Synthesis + findings_c = "" + yield {"type": "step_start", "step": "Expert C Synthesis", "agent": self.agents["expert_c"].name, "model": self.agents["expert_c"].model_name} + prompt_c = f"Synthesize a final comprehensive plan for '{topic}' based on Expert A's proposal and Expert B's critique.\nExpert A:\n{findings_a}\nExpert B:\n{findings_b}" + for chunk in self.agents["expert_c"].generate(prompt_c, context): + findings_c += chunk + yield {"type": "content", "content": chunk} + yield {"type": "step_end", "output": findings_c}