293 lines
7.7 KiB
Python
293 lines
7.7 KiB
Python
"""
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Orchestrator Agent - 协调器智能体
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负责流程调度与最终验证
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"""
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from autogen import AssistantAgent
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from typing import Dict, Any, Optional, List
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import os
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from pathlib import Path
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from config.llm_config import get_agent_llm_config, ORCH_PROMPT
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class OrchestratorAgent:
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"""协调器 Agent，负责多智能体协同和流程控制"""
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def __init__(self, llm_config: Optional[Dict] = None):
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"""
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初始化 Orchestrator Agent
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Args:
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llm_config: LLM 配置
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"""
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self.llm_config = llm_config or get_agent_llm_config("Orchestrator")
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self.agent = 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="多智能体系统协调器",
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human_input_mode="NEVER"
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)
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self.workspace_dir = Path("workspace")
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self.workspace_dir.mkdir(exist_ok=True)
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# 流程状态
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self.workflow_state: Dict[str, Any] = {
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"current_step": 0,
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"total_steps": 5,
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"status": "pending",
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"artifacts": {}
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}
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def start_workflow(self, user_requirement: str) -> Dict[str, Any]:
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"""
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启动完整的工作流程
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Args:
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user_requirement: 用户需求
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Returns:
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工作流状态
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"""
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self.workflow_state = {
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"current_step": 1,
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"total_steps": 5,
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"status": "in_progress",
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"user_requirement": user_requirement,
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"artifacts": {}
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}
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return self.workflow_state
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def validate_srs(self, srs_content: str) -> Dict[str, Any]:
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"""
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验证 SRS 文档的完整性
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Args:
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srs_content: SRS 文档内容
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Returns:
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验证结果
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"""
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prompt = f"""
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请验证以下 SRS 文档的完整性：
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{self._truncate(srs_content, 3000)}
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检查清单：
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1. ✅ 包含功能性需求列表
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2. ✅ 包含非功能性需求
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3. ✅ 包含验收标准
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4. ✅ 包含风险分析
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5. ✅ 需求具有唯一 ID
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请输出验证报告，指出缺失或不完整的部分。
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"""
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response = self.agent.generate_reply(
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messages=[{"role": "user", "content": prompt}]
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)
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validation_report = response if isinstance(response, str) else str(response)
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# 保存验证报告
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report_file = self.workspace_dir / "srs_validation.md"
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with open(report_file, 'w', encoding='utf-8') as f:
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f.write(validation_report)
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return {
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"valid": "✅" in validation_report and "❌" not in validation_report,
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"report": validation_report,
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"file": str(report_file)
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}
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def validate_tests(self, test_code: str, srs_content: str) -> Dict[str, Any]:
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"""
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验证测试用例的覆盖率
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Args:
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test_code: 测试代码
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srs_content: SRS 文档
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Returns:
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验证结果
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"""
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prompt = f"""
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请验证测试用例是否覆盖了所有 SRS 需求：
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【SRS 需求】
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{self._truncate(srs_content, 2000)}
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【测试代码】
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{self._truncate(test_code, 2000)}
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检查清单：
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1. ✅ 每个功能需求都有对应测试
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2. ✅ 包含边界情况测试
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3. ✅ 包含异常场景测试
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4. ✅ 测试可执行且独立
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5. ✅ 遵循 TDD 原则
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请输出验证报告。
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"""
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response = self.agent.generate_reply(
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messages=[{"role": "user", "content": prompt}]
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)
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validation_report = response if isinstance(response, str) else str(response)
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return {
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"valid": "✅" in validation_report,
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"report": validation_report
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}
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def validate_code(self, code: str, srs_content: str, test_result: Dict) -> Dict[str, Any]:
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"""
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验证代码质量
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Args:
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code: 源代码
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srs_content: SRS 文档
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test_result: 测试结果
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Returns:
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验证结果
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"""
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prompt = f"""
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请验证代码质量：
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【SRS 需求】
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{self._truncate(srs_content, 1500)}
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【代码】
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{self._truncate(code, 2000)}
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【测试结果】
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{test_result}
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检查清单：
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1. ✅ 实现所有功能需求
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2. ✅ 通过所有测试用例
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3. ✅ 代码符合规范（MISRA-C/PEP8）
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4. ✅ 包含完整文档
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5. ✅ 无安全漏洞
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6. ✅ 性能满足要求
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请输出代码质量验证报告。
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"""
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response = self.agent.generate_reply(
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messages=[{"role": "user", "content": prompt}]
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)
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validation_report = response if isinstance(response, str) else str(response)
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return {
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"valid": test_result.get("success", False) and "✅" in validation_report,
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"report": validation_report
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}
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def generate_final_report(self) -> str:
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"""
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生成最终项目总结报告
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Returns:
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最终报告内容
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"""
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prompt = """
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请生成项目最终总结报告，包含：
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1. 项目概述
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2. 交付物清单：
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- SRS 文档
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- 测试用例
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- 源代码
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3. 质量指标：
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- 测试覆盖率
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- 代码质量评分
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4. 合规性说明：
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- ISO 26262
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- MISRA-C
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- ASPICE
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5. 后续建议
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请基于 workspace 目录下的所有文件生成完整报告。
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"""
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response = self.agent.generate_reply(
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messages=[{"role": "user", "content": prompt}]
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)
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final_report = response if isinstance(response, str) else str(response)
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# 保存最终报告
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report_file = self.workspace_dir / "FINAL_REPORT.md"
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with open(report_file, 'w', encoding='utf-8') as f:
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f.write(final_report)
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print(f"✅ 最终报告已生成：{report_file}")
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return final_report
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def request_human_approval(
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self,
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approval_type: str,
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description: str,
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data: Dict[str, Any]
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) -> bool:
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"""
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请求人工确认（需要前端配合实现）
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Args:
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approval_type: 确认类型
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description: 确认描述
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data: 相关数据
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Returns:
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用户是否批准
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"""
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# 这里只是标记，实际的前端交互由 Streamlit 处理
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print(f"\n⚠️ 需要人工确认：{description}")
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print(f"类型：{approval_type}")
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print(f"数据：{data}\n")
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# 在命令行模式下，可以简单询问用户
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# 在 GUI 模式下，这会触发前端弹窗
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try:
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response = input("是否批准？(y/n): ").lower()
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return response == 'y'
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except:
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# 非交互模式下默认批准
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return True
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def _truncate(self, text: str, max_length: int) -> str:
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"""截断文本"""
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if len(text) <= max_length:
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return text
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return text[:max_length] + "... [内容已截断]"
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def create_orchestrator_agent(llm_config: Optional[Dict] = None) -> AssistantAgent:
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"""
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创建 Orchestrator Agent（AutoGen 原生格式）
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Args:
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llm_config: LLM 配置
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Returns:
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AutoGen AssistantAgent 实例
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"""
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config = llm_config or get_agent_llm_config("Orchestrator")
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agent = AssistantAgent(
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name="Orchestrator",
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system_message=ORCH_PROMPT,
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llm_config=config,
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description="多智能体系统协调器",
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human_input_mode="NEVER"
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)
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return agent
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