154 lines
5.3 KiB
Python
154 lines
5.3 KiB
Python
"""
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封装 ADK Agent 调用
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将 Dev Agent 的执行包装为可被任务管理器调用的异步函数
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"""
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import os
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import sys
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# 确保项目根目录在 path 里
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PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
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if PROJECT_ROOT not in sys.path:
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sys.path.insert(0, PROJECT_ROOT)
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from google.adk.runners import Runner
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from google.adk.sessions import InMemorySessionService
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from google.genai import types
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class AgentRunner:
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def __init__(self):
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# 延迟导入 agent，避免循环导入
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from agents.my_agent.agent import root_agent
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self.agent = root_agent
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self.session_service = InMemorySessionService()
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self._runner: Runner = None
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def _get_runner(self) -> Runner:
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if self._runner is None:
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self._runner = Runner(
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agent=self.agent,
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app_name="dev_agent_server",
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session_service=self.session_service,
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auto_create_session=True,
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)
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return self._runner
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async def run_task(self, task: dict) -> dict:
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"""
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执行一个开发任务，返回结构化结果
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Args:
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task: 任务字典，包含 description, project_path, requirements 等
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Returns:
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结构化的任务结果
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"""
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task_id = task["id"]
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description = task["description"]
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project_path = task.get("project_path", "")
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requirements = task.get("requirements", "")
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# 构建给 agent 的提示词
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prompt = self._build_prompt(description, project_path, requirements)
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print(f"[AgentRunner] 执行任务 {task_id}: {description[:60]}...")
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runner = self._get_runner()
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session_id = f"task_{task_id}"
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user_id = "task_manager"
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all_text = [] # 收集所有 model 消息中的文本
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tool_calls = [] # 收集所有 function_call
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final_text = [] # 最终响应的文本
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event_count = 0
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error_msg = None
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try:
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async for event in runner.run_async(
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user_id=user_id,
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session_id=session_id,
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new_message=types.Content(
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role="user",
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parts=[types.Part(text=prompt)],
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),
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):
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event_count += 1
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# 跳过没有 content 的事件
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if not event.content:
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continue
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# 只收集 model 角色的消息（不是 user / function_response 等）
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content = event.content
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role = getattr(content, "role", "")
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if role != "model":
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continue
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# 遍历 parts 收集文本和 function_call
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parts = getattr(content, "parts", [])
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for part in parts:
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# 文本
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if hasattr(part, "text") and part.text:
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all_text.append(part.text)
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# 最终回复（没有 function_call 的 model 消息）
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if event.is_final_response():
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final_text.append(part.text)
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# function_call
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if hasattr(part, "function_call") and part.function_call:
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fc = part.function_call
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tool_calls.append({
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"name": fc.name,
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"args": dict(fc.args) if hasattr(fc, "args") else {},
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})
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result_text = "\n".join(final_text) if final_text else "\n".join(all_text)
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status = "success" if result_text.strip() else "empty"
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except Exception as e:
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error_msg = f"{type(e).__name__}: {e}"
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result_text = f"任务执行出错：{error_msg}"
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status = "error"
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return {
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"summary": self._extract_summary(result_text),
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"full_response": result_text,
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"tool_calls_count": len(tool_calls),
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"tool_calls_sample": tool_calls[:10],
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"event_count": event_count,
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"status": status,
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"error": error_msg,
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}
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def _build_prompt(self, description: str, project_path: str, requirements: str) -> str:
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"""构建给 agent 的任务指令"""
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parts = [
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"你需要完成以下开发任务：",
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"",
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f"**任务描述：**{description}",
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]
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if project_path:
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parts.append(f"**项目路径：**{project_path}")
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if requirements:
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parts.append(f"**额外要求：**{requirements}")
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parts.extend([
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"",
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"请按照你的工作流程执行：",
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"1. 浏览项目结构，理解上下文",
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"2. 编写或修改代码",
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"3. 运行编译/构建验证",
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"4. 完成后，按照你规定的报告格式输出结果",
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"",
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"请开始执行。",
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])
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return "\n".join(parts)
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def _extract_summary(self, text: str) -> str:
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"""从 agent 回复中提取摘要"""
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# 简单处理：取前 1000 字符作为摘要
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if not text:
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return "(无响应)"
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if len(text) <= 1000:
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return text
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return text[:1000] + "...（已截断）"
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