import json import uuid from abc import ABCMeta, abstractmethod from typing import Any, Optional from langchain.tools import BaseTool from pydantic import PrivateAttr from app.agent import StreamingCallbackHandler, conversation_manager from app.chain import ChainBase from app.log import logger from app.schemas import Notification class ToolChain(ChainBase): pass class MoviePilotTool(BaseTool, metaclass=ABCMeta): """ MoviePilot专用工具基类 """ _session_id: str = PrivateAttr() _user_id: str = PrivateAttr() _channel: str = PrivateAttr(default=None) _source: str = PrivateAttr(default=None) _username: str = PrivateAttr(default=None) _callback_handler: StreamingCallbackHandler = PrivateAttr(default=None) def __init__(self, session_id: str, user_id: str, **kwargs): super().__init__(**kwargs) self._session_id = session_id self._user_id = user_id def _run(self, *args: Any, **kwargs: Any) -> Any: pass async def _arun(self, **kwargs) -> str: """ 异步运行工具 """ # 获取工具调用前的agent消息 agent_message = await self._callback_handler.get_message() # 生成唯一的工具调用ID call_id = f"call_{str(uuid.uuid4())[:16]}" # 记忆工具调用 await conversation_manager.add_conversation( session_id=self._session_id, user_id=self._user_id, role="tool_call", content=agent_message, metadata={ "call_id": call_id, "tool_name": self.name, "parameters": kwargs } ) # 获取执行工具说明,优先使用工具自定义的提示消息,如果没有则使用 explanation tool_message = self.get_tool_message(**kwargs) if not tool_message: explanation = kwargs.get("explanation") if explanation: tool_message = explanation # 合并agent消息和工具执行消息,一起发送 messages = [] if agent_message: messages.append(agent_message) if tool_message: messages.append(f"⚙️ => {tool_message}") # 发送合并后的消息 if messages: merged_message = "\n\n".join(messages) await self.send_tool_message(merged_message, title="MoviePilot助手") logger.debug(f'Executing tool {self.name} with args: {kwargs}') # 执行工具,捕获异常确保结果总是被存储到记忆中 try: result = await self.run(**kwargs) logger.debug(f'Tool {self.name} executed with result: {result}') except Exception as e: # 记录异常详情 error_message = f"工具执行异常 ({type(e).__name__}): {str(e)}" logger.error(f'Tool {self.name} execution failed: {e}', exc_info=True) result = error_message # 记忆工具调用结果 if isinstance(result, str): formated_result = result elif isinstance(result, (int, float)): formated_result = str(result) else: formated_result = json.dumps(result, ensure_ascii=False, indent=2) await conversation_manager.add_conversation( session_id=self._session_id, user_id=self._user_id, role="tool_result", content=formated_result, metadata={ "call_id": call_id, "tool_name": self.name, } ) return result def get_tool_message(self, **kwargs) -> Optional[str]: """ 获取工具执行时的友好提示消息 子类可以重写此方法,根据实际参数生成个性化的提示消息。 如果返回 None 或空字符串,将回退使用 explanation 参数。 Args: **kwargs: 工具的所有参数(包括 explanation) Returns: str: 友好的提示消息,如果返回 None 或空字符串则使用 explanation """ return None @abstractmethod async def run(self, **kwargs) -> str: raise NotImplementedError def set_message_attr(self, channel: str, source: str, username: str): """ 设置消息属性 """ self._channel = channel self._source = source self._username = username def set_callback_handler(self, callback_handler: StreamingCallbackHandler): """ 设置回调处理器 """ self._callback_handler = callback_handler async def send_tool_message(self, message: str, title: str = ""): """ 发送工具消息 """ await ToolChain().async_post_message( Notification( channel=self._channel, source=self._source, userid=self._user_id, username=self._username, title=title, text=message ) )