281 lines
8.9 KiB
Python
281 lines
8.9 KiB
Python
"""
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AutoGen SDLC 多智能体协同系统 - 主程序入口
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实现端到端软件交付的自动化流程
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"""
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import os
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import sys
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from pathlib import Path
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from typing import Dict, Any, Optional, List
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import json
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# 添加项目根目录到路径
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sys.path.insert(0, str(Path(__file__).parent))
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from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager
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from config.llm_config import (
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get_llm_config,
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PM_PROMPT,
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QA_PROMPT,
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DEV_PROMPT,
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ORCH_PROMPT
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)
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from utils.logger import get_logger
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from utils.callback_handler import get_callback_handler
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class AutoGenSDLCSystem:
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"""AutoGen SDLC 多智能体协同系统"""
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def __init__(
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self,
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api_key: Optional[str] = None,
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base_url: Optional[str] = None,
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model: str = "qwen3.5-flash",
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workspace_dir: str = "workspace"
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):
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"""
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初始化 SDLC 系统
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Args:
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api_key: API Key，默认从环境变量读取
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base_url: API Base URL
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model: 模型名称
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workspace_dir: 工作目录
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"""
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# 配置 LLM
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self.api_key = api_key or os.getenv("DASHSCOPE_API_KEY", "")
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self.base_url = base_url or "https://dashscope.aliyuncs.com/compatible-mode/v1"
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self.model = model
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if not self.api_key:
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raise ValueError("请设置 DASHSCOPE_API_KEY 环境变量或传入 api_key 参数")
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self.llm_config = get_llm_config(
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model=model,
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api_key=self.api_key,
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base_url=self.base_url
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)
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# 初始化日志和回调
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self.logger = get_logger()
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self.callback_handler = get_callback_handler()
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# 创建工作目录
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self.workspace_dir = Path(workspace_dir)
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self.workspace_dir.mkdir(parents=True, exist_ok=True)
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# 创建 Agent
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self._create_agents()
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# 创建 GroupChat
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self.groupchat = None
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self.manager = None
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def _create_agents(self):
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"""创建所有 Agent"""
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# PM Agent
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self.pm_agent = AssistantAgent(
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name="PM_Agent",
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system_message=PM_PROMPT,
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llm_config=self.llm_config,
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description="资深软件产品经理，负责需求分析和 SRS 生成",
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human_input_mode="NEVER"
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)
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# QA Agent
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self.qa_agent = AssistantAgent(
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name="QA_Agent",
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system_message=QA_PROMPT,
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llm_config=self.llm_config,
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description="资深测试工程师，负责测试用例设计",
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human_input_mode="NEVER"
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)
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# Dev Agent
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self.dev_agent = AssistantAgent(
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name="Dev_Agent",
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system_message=DEV_PROMPT,
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llm_config=self.llm_config,
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description="资深软件工程师，负责代码实现",
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human_input_mode="NEVER"
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)
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# Orchestrator Agent
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self.orchestrator = AssistantAgent(
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name="Orchestrator",
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system_message=ORCH_PROMPT,
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llm_config=self.llm_config,
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description="多智能体协调器，负责流程控制和验证",
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human_input_mode="NEVER"
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)
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# User Proxy（用于执行代码）
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self.user_proxy = UserProxyAgent(
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name="User_Proxy",
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human_input_mode="NEVER", # 修复：Web 环境不支持 TERMINAL
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max_consecutive_auto_reply=0,
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code_execution_config={
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"work_dir": str(self.workspace_dir),
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"use_docker": False,
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},
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is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE")
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)
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self.logger.log_event("agents_created", "所有 Agent 已创建", {
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"agents": ["PM_Agent", "QA_Agent", "Dev_Agent", "Orchestrator", "User_Proxy"]
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})
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def create_groupchat(self, max_round: int = 20):
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"""
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创建 GroupChat
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Args:
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max_round: 最大对话轮数
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"""
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self.groupchat = GroupChat(
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agents=[self.pm_agent, self.qa_agent, self.dev_agent,
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self.orchestrator, self.user_proxy],
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messages=[],
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max_round=max_round,
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speaker_selection_method="round_robin" # 轮流发言确保流程可控
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)
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self.manager = GroupChatManager(
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groupchat=self.groupchat,
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llm_config=self.llm_config
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)
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self.logger.log_event("groupchat_created", "GroupChat 已创建", {
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"max_round": max_round
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})
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def run_workflow(self, user_requirement: str, max_round: int = 20) -> Dict[str, Any]:
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"""
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运行完整的 SDLC 工作流
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Args:
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user_requirement: 用户需求描述
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max_round: 最大对话轮数
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Returns:
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工作流结果
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"""
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self.logger.log_event("workflow_started", "SDLC 工作流启动", {
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"requirement": user_requirement
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})
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# 创建 GroupChat
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self.create_groupchat(max_round)
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# 构建初始消息
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initial_message = f"""
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请启动完整的 SDLC 流程，开发以下功能：
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【用户需求】
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{user_requirement}
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【工作流程】
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1. PM_Agent: 分析需求，生成 SRS 文档
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2. QA_Agent: 根据 SRS 设计测试用例
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3. Dev_Agent: 根据 SRS 和测试用例编写代码
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4. User_Proxy: 执行测试验证
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5. Orchestrator: 汇总结果并生成最终报告
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请各 Agent 按顺序协作完成。每个步骤完成后，Orchestrator 进行验证。
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如果测试失败，Dev_Agent 需要修复代码直到测试通过。
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开始工作！
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"""
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try:
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# 启动对话
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chat_result = self.user_proxy.initiate_chat(
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self.manager,
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message=initial_message,
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max_turns=max_round,
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summary_method="reflection_with_llm"
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)
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# 记录结果
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self.logger.log_event(
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"workflow_completed",
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"SDLC 工作流完成",
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{"chat_summary": chat_result.summary if hasattr(chat_result, 'summary') else "完成"}
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)
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# 导出对话历史
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for msg in self.groupchat.messages:
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self.logger.log_message(
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agent_name=msg.get("name", "Unknown"),
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message=msg.get("content", ""),
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role=msg.get("role", "assistant")
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)
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return {
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"success": True,
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"summary": chat_result.summary if hasattr(chat_result, 'summary') else "工作流完成",
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"messages": self.groupchat.messages,
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"workspace": str(self.workspace_dir)
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}
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except Exception as e:
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self.logger.log_event("workflow_error", f"工作流执行出错：{str(e)}")
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return {
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"success": False,
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"error": str(e),
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"messages": self.groupchat.messages if self.groupchat else []
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}
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def export_conversation(self, output_path: Optional[str] = None) -> str:
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"""导出对话历史"""
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return self.logger.export_to_json(output_path)
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def export_report(self, output_path: Optional[str] = None) -> str:
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"""导出 Markdown 格式报告"""
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return self.logger.export_to_markdown(output_path)
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def main():
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"""主函数 - 演示模式"""
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print("=" * 60)
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print("AutoGen SDLC 多智能体协同系统")
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print("=" * 60)
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# 检查 API Key
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api_key = os.getenv("DASHSCOPE_API_KEY")
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if not api_key:
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print("\n❌ 错误：未设置 DASHSCOPE_API_KEY 环境变量")
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print("请运行：export DASHSCOPE_API_KEY='your_api_key'")
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return
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# 创建系统实例
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system = AutoGenSDLCSystem(api_key=api_key)
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# 演示用例
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demo_requirement = "我需要一个电池健康状态 (SOH) 预测 API，能够接收电池的电压、电流、温度数据，输出健康度百分比"
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print(f"\n📋 演示需求：{demo_requirement}")
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print("\n🚀 启动 SDLC 工作流...\n")
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# 运行工作流
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result = system.run_workflow(demo_requirement, max_round=15)
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# 输出结果
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print("\n" + "=" * 60)
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if result["success"]:
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print("✅ 工作流成功完成！")
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print(f"📄 摘要：{result['summary'][:200]}...")
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print(f"📂 工作目录：{result['workspace']}")
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else:
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print(f"❌ 工作流失败：{result.get('error', '未知错误')}")
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# 导出报告
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report_path = system.export_report()
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print(f"📊 对话报告已导出：{report_path}")
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print("\n" + "=" * 60)
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if __name__ == "__main__":
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main()
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