657 lines
30 KiB
Python
657 lines
30 KiB
Python
import asyncio
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import contextlib
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import time
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import traceback
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from collections import deque
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from typing import List, Optional, Dict, Any, Deque, Callable, Awaitable
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from src.chat.message_receive.chat_stream import ChatStream
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from src.chat.message_receive.chat_stream import chat_manager
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from rich.traceback import install
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from src.chat.utils.prompt_builder import global_prompt_manager
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from src.common.logger_manager import get_logger
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from src.chat.utils.timer_calculator import Timer
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from src.chat.heart_flow.observation.observation import Observation
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from src.chat.focus_chat.heartFC_Cycleinfo import CycleDetail
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from src.chat.focus_chat.info.info_base import InfoBase
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from src.chat.focus_chat.info_processors.chattinginfo_processor import ChattingInfoProcessor
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from src.chat.focus_chat.info_processors.mind_processor import MindProcessor
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from src.chat.focus_chat.info_processors.working_memory_processor import WorkingMemoryProcessor
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# from src.chat.focus_chat.info_processors.action_processor import ActionProcessor
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from src.chat.heart_flow.observation.hfcloop_observation import HFCloopObservation
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from src.chat.heart_flow.observation.working_observation import WorkingMemoryObservation
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from src.chat.heart_flow.observation.structure_observation import StructureObservation
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from src.chat.heart_flow.observation.actions_observation import ActionObservation
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from src.chat.focus_chat.info_processors.tool_processor import ToolProcessor
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from src.chat.focus_chat.expressors.default_expressor import DefaultExpressor
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from src.chat.focus_chat.memory_activator import MemoryActivator
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from src.chat.focus_chat.info_processors.base_processor import BaseProcessor
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from src.chat.focus_chat.info_processors.self_processor import SelfProcessor
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from src.chat.focus_chat.planners.planner import ActionPlanner
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from src.chat.focus_chat.planners.modify_actions import ActionModifier
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from src.chat.focus_chat.planners.action_manager import ActionManager
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from src.chat.focus_chat.working_memory.working_memory import WorkingMemory
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from src.config.config import global_config
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install(extra_lines=3)
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# 定义处理器映射：键是处理器名称，值是 (处理器类, 可选的配置键名)
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# 如果配置键名为 None，则该处理器默认启用且不能通过 focus_chat_processor 配置禁用
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PROCESSOR_CLASSES = {
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"ChattingInfoProcessor": (ChattingInfoProcessor, None),
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"MindProcessor": (MindProcessor, "mind_processor"),
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"ToolProcessor": (ToolProcessor, "tool_use_processor"),
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"WorkingMemoryProcessor": (WorkingMemoryProcessor, "working_memory_processor"),
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"SelfProcessor": (SelfProcessor, "self_identify_processor"),
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}
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logger = get_logger("hfc") # Logger Name Changed
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async def _handle_cycle_delay(action_taken_this_cycle: bool, cycle_start_time: float, log_prefix: str):
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"""处理循环延迟"""
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cycle_duration = time.monotonic() - cycle_start_time
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try:
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sleep_duration = 0.0
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if not action_taken_this_cycle and cycle_duration < 1:
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sleep_duration = 1 - cycle_duration
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elif cycle_duration < 0.2:
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sleep_duration = 0.2
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if sleep_duration > 0:
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await asyncio.sleep(sleep_duration)
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except asyncio.CancelledError:
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logger.info(f"{log_prefix} Sleep interrupted, loop likely cancelling.")
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raise
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class HeartFChatting:
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"""
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管理一个连续的Focus Chat循环
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用于在特定聊天流中生成回复。
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其生命周期现在由其关联的 SubHeartflow 的 FOCUSED 状态控制。
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"""
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def __init__(
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self,
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chat_id: str,
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observations: list[Observation],
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on_stop_focus_chat: Optional[Callable[[], Awaitable[None]]] = None,
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):
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"""
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HeartFChatting 初始化函数
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参数:
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chat_id: 聊天流唯一标识符(如stream_id)
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observations: 关联的观察列表
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on_stop_focus_chat: 当收到stop_focus_chat命令时调用的回调函数
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"""
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# 基础属性
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self.stream_id: str = chat_id # 聊天流ID
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self.chat_stream: Optional[ChatStream] = None # 关联的聊天流
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self.log_prefix: str = str(chat_id) # Initial default, will be updated
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self.hfcloop_observation = HFCloopObservation(observe_id=self.stream_id)
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self.chatting_observation = observations[0]
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self.structure_observation = StructureObservation(observe_id=self.stream_id)
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self.memory_activator = MemoryActivator()
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self.working_memory = WorkingMemory(chat_id=self.stream_id)
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self.working_observation = WorkingMemoryObservation(
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observe_id=self.stream_id, working_memory=self.working_memory
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)
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# 根据配置文件和默认规则确定启用的处理器
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self.enabled_processor_names: List[str] = []
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config_processor_settings = global_config.focus_chat_processor
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for proc_name, (_proc_class, config_key) in PROCESSOR_CLASSES.items():
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if config_key: # 此处理器可通过配置控制
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if getattr(config_processor_settings, config_key, True): # 默认启用 (如果配置中未指定该键)
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self.enabled_processor_names.append(proc_name)
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else: # 此处理器不在配置映射中 (config_key is None)，默认启用
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self.enabled_processor_names.append(proc_name)
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logger.info(f"{self.log_prefix} 将启用的处理器: {self.enabled_processor_names}")
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self.processors: List[BaseProcessor] = []
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self._register_default_processors()
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self.expressor = DefaultExpressor(chat_id=self.stream_id)
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self.action_manager = ActionManager()
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self.action_planner = ActionPlanner(log_prefix=self.log_prefix, action_manager=self.action_manager)
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self.action_modifier = ActionModifier(action_manager=self.action_manager)
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self.action_observation = ActionObservation(observe_id=self.stream_id)
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self.action_observation.set_action_manager(self.action_manager)
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self.all_observations = observations
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# 初始化状态控制
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self._initialized = False
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self._processing_lock = asyncio.Lock()
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# 循环控制内部状态
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self._loop_active: bool = False # 循环是否正在运行
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self._loop_task: Optional[asyncio.Task] = None # 主循环任务
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# 添加循环信息管理相关的属性
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self._cycle_counter = 0
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self._cycle_history: Deque[CycleDetail] = deque(maxlen=10) # 保留最近10个循环的信息
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self._current_cycle_detail: Optional[CycleDetail] = None
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self._shutting_down: bool = False # 关闭标志位
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# 存储回调函数
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self.on_stop_focus_chat = on_stop_focus_chat
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async def _initialize(self) -> bool:
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"""
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执行懒初始化操作
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功能:
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1. 获取聊天类型(群聊/私聊)和目标信息
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2. 获取聊天流对象
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3. 设置日志前缀
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返回:
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bool: 初始化是否成功
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注意:
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- 如果已经初始化过会直接返回True
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- 需要获取chat_stream对象才能继续后续操作
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"""
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# 如果已经初始化过，直接返回成功
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if self._initialized:
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return True
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try:
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await self.expressor.initialize()
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self.chat_stream = await asyncio.to_thread(chat_manager.get_stream, self.stream_id)
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self.expressor.chat_stream = self.chat_stream
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self.log_prefix = f"[{chat_manager.get_stream_name(self.stream_id) or self.stream_id}]"
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except Exception as e:
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logger.error(f"[HFC:{self.stream_id}] 初始化HFC时发生错误: {e}")
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return False
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# 标记初始化完成
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self._initialized = True
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logger.debug(f"{self.log_prefix} 初始化完成，准备开始处理消息")
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return True
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def _register_default_processors(self):
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"""根据 self.enabled_processor_names 注册信息处理器"""
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self.processors = [] # 清空已有的
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for name in self.enabled_processor_names: # 'name' is "ChattingInfoProcessor", etc.
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processor_info = PROCESSOR_CLASSES.get(name) # processor_info is (ProcessorClass, config_key)
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if processor_info:
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processor_actual_class = processor_info[0] # 获取实际的类定义
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# 根据处理器类名判断是否需要 subheartflow_id
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if name in ["MindProcessor", "ToolProcessor", "WorkingMemoryProcessor", "SelfProcessor"]:
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self.processors.append(processor_actual_class(subheartflow_id=self.stream_id))
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elif name == "ChattingInfoProcessor":
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self.processors.append(processor_actual_class())
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else:
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# 对于PROCESSOR_CLASSES中定义但此处未明确处理构造的处理器
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# (例如, 新增了一个处理器到PROCESSOR_CLASSES, 它不需要id, 也不叫ChattingInfoProcessor)
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try:
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self.processors.append(processor_actual_class()) # 尝试无参构造
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logger.debug(f"{self.log_prefix} 注册处理器 {name} (尝试无参构造).")
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except TypeError:
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logger.error(
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f"{self.log_prefix} 处理器 {name} 构造失败。它可能需要参数（如 subheartflow_id）但未在注册逻辑中明确处理。"
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)
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else:
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# 这理论上不应该发生，因为 enabled_processor_names 是从 PROCESSOR_CLASSES 的键生成的
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logger.warning(
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f"{self.log_prefix} 在 PROCESSOR_CLASSES 中未找到名为 '{name}' 的处理器定义，将跳过注册。"
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)
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if self.processors:
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logger.info(
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f"{self.log_prefix} 已根据配置和默认规则注册处理器: {[p.__class__.__name__ for p in self.processors]}"
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)
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else:
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logger.warning(f"{self.log_prefix} 没有注册任何处理器。这可能是由于配置错误或所有处理器都被禁用了。")
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async def start(self):
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"""
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启动 HeartFChatting 的主循环。
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注意：调用此方法前必须确保已经成功初始化。
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"""
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logger.info(f"{self.log_prefix} 开始认真聊天(HFC)...")
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await self._start_loop_if_needed()
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async def _start_loop_if_needed(self):
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"""检查是否需要启动主循环，如果未激活则启动。"""
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# 如果循环已经激活，直接返回
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if self._loop_active:
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return
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# 标记为活动状态，防止重复启动
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self._loop_active = True
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# 检查是否已有任务在运行（理论上不应该，因为 _loop_active=False）
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if self._loop_task and not self._loop_task.done():
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logger.warning(f"{self.log_prefix} 发现之前的循环任务仍在运行（不符合预期）。取消旧任务。")
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self._loop_task.cancel()
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try:
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# 等待旧任务确实被取消
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await asyncio.wait_for(self._loop_task, timeout=0.5)
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except (asyncio.CancelledError, asyncio.TimeoutError):
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pass # 忽略取消或超时错误
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self._loop_task = None # 清理旧任务引用
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self._loop_task = asyncio.create_task(self._run_focus_chat())
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self._loop_task.add_done_callback(self._handle_loop_completion)
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def _handle_loop_completion(self, task: asyncio.Task):
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"""当 _hfc_loop 任务完成时执行的回调。"""
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try:
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exception = task.exception()
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if exception:
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logger.error(f"{self.log_prefix} HeartFChatting: 脱离了聊天(异常): {exception}")
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logger.error(traceback.format_exc()) # Log full traceback for exceptions
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else:
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logger.info(f"{self.log_prefix} HeartFChatting: 脱离了聊天 (外部停止)")
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except asyncio.CancelledError:
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logger.info(f"{self.log_prefix} HeartFChatting: 脱离了聊天(任务取消)")
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finally:
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self._loop_active = False
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self._loop_task = None
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if self._processing_lock.locked():
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logger.warning(f"{self.log_prefix} HeartFChatting: 处理锁在循环结束时仍被锁定，强制释放。")
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self._processing_lock.release()
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async def _run_focus_chat(self):
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"""主循环，持续进行计划并可能回复消息，直到被外部取消。"""
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try:
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while True: # 主循环
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logger.debug(f"{self.log_prefix} 开始第{self._cycle_counter}次循环")
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if self._shutting_down:
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logger.info(f"{self.log_prefix} 检测到关闭标志，退出 Focus Chat 循环。")
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break
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# 创建新的循环信息
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self._cycle_counter += 1
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self._current_cycle_detail = CycleDetail(self._cycle_counter)
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self._current_cycle_detail.prefix = self.log_prefix
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# 初始化周期状态
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cycle_timers = {}
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loop_cycle_start_time = time.monotonic()
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# 执行规划和处理阶段
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async with self._get_cycle_context():
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thinking_id = "tid" + str(round(time.time(), 2))
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self._current_cycle_detail.set_thinking_id(thinking_id)
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# 主循环：思考->决策->执行
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async with global_prompt_manager.async_message_scope(self.chat_stream.context.get_template_name()):
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logger.debug(f"模板 {self.chat_stream.context.get_template_name()}")
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loop_info = await self._observe_process_plan_action_loop(cycle_timers, thinking_id)
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if loop_info["loop_action_info"]["command"] == "stop_focus_chat":
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logger.info(f"{self.log_prefix} 麦麦决定停止专注聊天")
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# 如果设置了回调函数，则调用它
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if self.on_stop_focus_chat:
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try:
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await self.on_stop_focus_chat()
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logger.info(f"{self.log_prefix} 成功调用回调函数处理停止专注聊天")
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except Exception as e:
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logger.error(f"{self.log_prefix} 调用停止专注聊天回调函数时出错: {e}")
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logger.error(traceback.format_exc())
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break
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self._current_cycle_detail.set_loop_info(loop_info)
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self.hfcloop_observation.add_loop_info(self._current_cycle_detail)
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self._current_cycle_detail.timers = cycle_timers
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# 防止循环过快消耗资源
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await _handle_cycle_delay(
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loop_info["loop_action_info"]["action_taken"], loop_cycle_start_time, self.log_prefix
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)
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# 完成当前循环并保存历史
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self._current_cycle_detail.complete_cycle()
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self._cycle_history.append(self._current_cycle_detail)
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# 记录循环信息和计时器结果
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timer_strings = []
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for name, elapsed in cycle_timers.items():
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formatted_time = f"{elapsed * 1000:.2f}毫秒" if elapsed < 1 else f"{elapsed:.2f}秒"
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timer_strings.append(f"{name}: {formatted_time}")
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# 新增：输出每个处理器的耗时
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processor_time_costs = self._current_cycle_detail.loop_processor_info.get("processor_time_costs", {})
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processor_time_strings = []
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for pname, ptime in processor_time_costs.items():
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formatted_ptime = f"{ptime * 1000:.2f}毫秒" if ptime < 1 else f"{ptime:.2f}秒"
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processor_time_strings.append(f"{pname}: {formatted_ptime}")
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processor_time_log = (
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("\n各处理器耗时: " + "; ".join(processor_time_strings)) if processor_time_strings else ""
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)
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logger.info(
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f"{self.log_prefix} 第{self._current_cycle_detail.cycle_id}次思考,"
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f"耗时: {self._current_cycle_detail.end_time - self._current_cycle_detail.start_time:.1f}秒, "
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f"动作: {self._current_cycle_detail.loop_plan_info['action_result']['action_type']}"
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+ (f"\n详情: {'; '.join(timer_strings)}" if timer_strings else "")
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+ processor_time_log
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)
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await asyncio.sleep(global_config.focus_chat.think_interval)
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except asyncio.CancelledError:
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# 设置了关闭标志位后被取消是正常流程
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if not self._shutting_down:
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logger.warning(f"{self.log_prefix} 麦麦Focus聊天模式意外被取消")
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else:
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logger.info(f"{self.log_prefix} 麦麦已离开Focus聊天模式")
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except Exception as e:
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logger.error(f"{self.log_prefix} 麦麦Focus聊天模式意外错误: {e}")
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print(traceback.format_exc())
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@contextlib.asynccontextmanager
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async def _get_cycle_context(self):
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"""
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循环周期的上下文管理器
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用于确保资源的正确获取和释放：
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1. 获取处理锁
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2. 执行操作
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3. 释放锁
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"""
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acquired = False
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try:
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await self._processing_lock.acquire()
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acquired = True
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yield acquired
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finally:
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if acquired and self._processing_lock.locked():
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self._processing_lock.release()
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async def _process_processors(
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self, observations: List[Observation], running_memorys: List[Dict[str, Any]]
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) -> tuple[List[InfoBase], Dict[str, float]]:
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# 记录并行任务开始时间
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parallel_start_time = time.time()
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logger.debug(f"{self.log_prefix} 开始信息处理器并行任务")
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processor_tasks = []
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task_to_name_map = {}
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processor_time_costs = {} # 新增: 记录每个处理器耗时
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for processor in self.processors:
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processor_name = processor.__class__.log_prefix
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async def run_with_timeout(proc=processor):
|
||
return await asyncio.wait_for(
|
||
proc.process_info(observations=observations, running_memorys=running_memorys),
|
||
timeout=global_config.focus_chat.processor_max_time,
|
||
)
|
||
|
||
task = asyncio.create_task(run_with_timeout())
|
||
processor_tasks.append(task)
|
||
task_to_name_map[task] = processor_name
|
||
logger.debug(f"{self.log_prefix} 启动处理器任务: {processor_name}")
|
||
|
||
pending_tasks = set(processor_tasks)
|
||
all_plan_info: List[InfoBase] = []
|
||
|
||
while pending_tasks:
|
||
done, pending_tasks = await asyncio.wait(pending_tasks, return_when=asyncio.FIRST_COMPLETED)
|
||
|
||
for task in done:
|
||
processor_name = task_to_name_map[task]
|
||
task_completed_time = time.time()
|
||
duration_since_parallel_start = task_completed_time - parallel_start_time
|
||
|
||
try:
|
||
result_list = await task
|
||
logger.info(f"{self.log_prefix} 处理器 {processor_name} 已完成!")
|
||
if result_list is not None:
|
||
all_plan_info.extend(result_list)
|
||
else:
|
||
logger.warning(f"{self.log_prefix} 处理器 {processor_name} 返回了 None")
|
||
# 记录耗时
|
||
processor_time_costs[processor_name] = duration_since_parallel_start
|
||
except asyncio.TimeoutError:
|
||
logger.info(
|
||
f"{self.log_prefix} 处理器 {processor_name} 超时（>{global_config.focus_chat.processor_max_time}s），已跳过"
|
||
)
|
||
processor_time_costs[processor_name] = global_config.focus_chat.processor_max_time
|
||
except Exception as e:
|
||
logger.error(
|
||
f"{self.log_prefix} 处理器 {processor_name} 执行失败，耗时 (自并行开始): {duration_since_parallel_start:.2f}秒. 错误: {e}",
|
||
exc_info=True,
|
||
)
|
||
traceback.print_exc()
|
||
processor_time_costs[processor_name] = duration_since_parallel_start
|
||
|
||
if pending_tasks:
|
||
current_progress_time = time.time()
|
||
elapsed_for_log = current_progress_time - parallel_start_time
|
||
pending_names_for_log = [task_to_name_map[t] for t in pending_tasks]
|
||
logger.info(
|
||
f"{self.log_prefix} 信息处理已进行 {elapsed_for_log:.2f}秒，待完成任务: {', '.join(pending_names_for_log)}"
|
||
)
|
||
|
||
# 所有任务完成后的最终日志
|
||
parallel_end_time = time.time()
|
||
total_duration = parallel_end_time - parallel_start_time
|
||
logger.info(f"{self.log_prefix} 所有处理器任务全部完成，总耗时: {total_duration:.2f}秒")
|
||
# logger.debug(f"{self.log_prefix} 所有信息处理器处理后的信息: {all_plan_info}")
|
||
|
||
return all_plan_info, processor_time_costs
|
||
|
||
async def _observe_process_plan_action_loop(self, cycle_timers: dict, thinking_id: str) -> dict:
|
||
try:
|
||
with Timer("观察", cycle_timers):
|
||
await self.chatting_observation.observe()
|
||
await self.working_observation.observe()
|
||
await self.hfcloop_observation.observe()
|
||
await self.structure_observation.observe()
|
||
observations: List[Observation] = []
|
||
observations.append(self.chatting_observation)
|
||
observations.append(self.working_observation)
|
||
observations.append(self.hfcloop_observation)
|
||
observations.append(self.structure_observation)
|
||
|
||
loop_observation_info = {
|
||
"observations": observations,
|
||
}
|
||
|
||
self.all_observations = observations
|
||
|
||
with Timer("调整动作", cycle_timers):
|
||
# 处理特殊的观察
|
||
await self.action_modifier.modify_actions(observations=observations)
|
||
await self.action_observation.observe()
|
||
observations.append(self.action_observation)
|
||
|
||
# 根据配置决定是否并行执行回忆和处理器阶段
|
||
# print(global_config.focus_chat.parallel_processing)
|
||
if global_config.focus_chat.parallel_processing:
|
||
# 并行执行回忆和处理器阶段
|
||
with Timer("并行回忆和处理", cycle_timers):
|
||
memory_task = asyncio.create_task(self.memory_activator.activate_memory(observations))
|
||
processor_task = asyncio.create_task(self._process_processors(observations, []))
|
||
|
||
# 等待两个任务完成
|
||
running_memorys, (all_plan_info, processor_time_costs) = await asyncio.gather(
|
||
memory_task, processor_task
|
||
)
|
||
else:
|
||
# 串行执行
|
||
with Timer("回忆", cycle_timers):
|
||
running_memorys = await self.memory_activator.activate_memory(observations)
|
||
|
||
with Timer("执行 信息处理器", cycle_timers):
|
||
all_plan_info, processor_time_costs = await self._process_processors(observations, running_memorys)
|
||
|
||
loop_processor_info = {
|
||
"all_plan_info": all_plan_info,
|
||
"processor_time_costs": processor_time_costs,
|
||
}
|
||
|
||
with Timer("规划器", cycle_timers):
|
||
plan_result = await self.action_planner.plan(all_plan_info, running_memorys)
|
||
|
||
loop_plan_info = {
|
||
"action_result": plan_result.get("action_result", {}),
|
||
"current_mind": plan_result.get("current_mind", ""),
|
||
"observed_messages": plan_result.get("observed_messages", ""),
|
||
}
|
||
|
||
with Timer("执行动作", cycle_timers):
|
||
action_type, action_data, reasoning = (
|
||
plan_result.get("action_result", {}).get("action_type", "error"),
|
||
plan_result.get("action_result", {}).get("action_data", {}),
|
||
plan_result.get("action_result", {}).get("reasoning", "未提供理由"),
|
||
)
|
||
|
||
if action_type == "reply":
|
||
action_str = "回复"
|
||
elif action_type == "no_reply":
|
||
action_str = "不回复"
|
||
else:
|
||
action_str = action_type
|
||
|
||
logger.debug(f"{self.log_prefix} 麦麦想要：'{action_str}', 原因'{reasoning}'")
|
||
|
||
success, reply_text, command = await self._handle_action(
|
||
action_type, reasoning, action_data, cycle_timers, thinking_id
|
||
)
|
||
|
||
loop_action_info = {
|
||
"action_taken": success,
|
||
"reply_text": reply_text,
|
||
"command": command,
|
||
"taken_time": time.time(),
|
||
}
|
||
|
||
loop_info = {
|
||
"loop_observation_info": loop_observation_info,
|
||
"loop_processor_info": loop_processor_info,
|
||
"loop_plan_info": loop_plan_info,
|
||
"loop_action_info": loop_action_info,
|
||
}
|
||
|
||
return loop_info
|
||
|
||
except Exception as e:
|
||
logger.error(f"{self.log_prefix} FOCUS聊天处理失败: {e}")
|
||
logger.error(traceback.format_exc())
|
||
return {
|
||
"loop_observation_info": {},
|
||
"loop_processor_info": {},
|
||
"loop_plan_info": {},
|
||
"loop_action_info": {"action_taken": False, "reply_text": "", "command": ""},
|
||
}
|
||
|
||
async def _handle_action(
|
||
self,
|
||
action: str,
|
||
reasoning: str,
|
||
action_data: dict,
|
||
cycle_timers: dict,
|
||
thinking_id: str,
|
||
) -> tuple[bool, str, str]:
|
||
"""
|
||
处理规划动作，使用动作工厂创建相应的动作处理器
|
||
|
||
参数:
|
||
action: 动作类型
|
||
reasoning: 决策理由
|
||
action_data: 动作数据，包含不同动作需要的参数
|
||
cycle_timers: 计时器字典
|
||
thinking_id: 思考ID
|
||
|
||
返回:
|
||
tuple[bool, str, str]: (是否执行了动作, 思考消息ID, 命令)
|
||
"""
|
||
try:
|
||
# 使用工厂创建动作处理器实例
|
||
try:
|
||
action_handler = self.action_manager.create_action(
|
||
action_name=action,
|
||
action_data=action_data,
|
||
reasoning=reasoning,
|
||
cycle_timers=cycle_timers,
|
||
thinking_id=thinking_id,
|
||
observations=self.all_observations,
|
||
expressor=self.expressor,
|
||
chat_stream=self.chat_stream,
|
||
log_prefix=self.log_prefix,
|
||
shutting_down=self._shutting_down,
|
||
)
|
||
except Exception as e:
|
||
logger.error(f"{self.log_prefix} 创建动作处理器时出错: {e}")
|
||
traceback.print_exc()
|
||
return False, "", ""
|
||
|
||
if not action_handler:
|
||
logger.warning(f"{self.log_prefix} 未能创建动作处理器: {action}, 原因: {reasoning}")
|
||
return False, "", ""
|
||
|
||
# 处理动作并获取结果
|
||
result = await action_handler.handle_action()
|
||
if len(result) == 3:
|
||
success, reply_text, command = result
|
||
else:
|
||
success, reply_text = result
|
||
command = ""
|
||
logger.debug(
|
||
f"{self.log_prefix} 麦麦执行了'{action}', 原因'{reasoning}'，返回结果'{success}', '{reply_text}', '{command}'"
|
||
)
|
||
return success, reply_text, command
|
||
|
||
except Exception as e:
|
||
logger.error(f"{self.log_prefix} 处理{action}时出错: {e}")
|
||
traceback.print_exc()
|
||
return False, "", ""
|
||
|
||
async def shutdown(self):
|
||
"""优雅关闭HeartFChatting实例，取消活动循环任务"""
|
||
logger.info(f"{self.log_prefix} 正在关闭HeartFChatting...")
|
||
self._shutting_down = True # <-- 在开始关闭时设置标志位
|
||
|
||
# 取消循环任务
|
||
if self._loop_task and not self._loop_task.done():
|
||
logger.info(f"{self.log_prefix} 正在取消HeartFChatting循环任务")
|
||
self._loop_task.cancel()
|
||
try:
|
||
await asyncio.wait_for(self._loop_task, timeout=1.0)
|
||
logger.info(f"{self.log_prefix} HeartFChatting循环任务已取消")
|
||
except (asyncio.CancelledError, asyncio.TimeoutError):
|
||
pass
|
||
except Exception as e:
|
||
logger.error(f"{self.log_prefix} 取消循环任务出错: {e}")
|
||
else:
|
||
logger.info(f"{self.log_prefix} 没有活动的HeartFChatting循环任务")
|
||
|
||
# 清理状态
|
||
self._loop_active = False
|
||
self._loop_task = None
|
||
if self._processing_lock.locked():
|
||
self._processing_lock.release()
|
||
logger.warning(f"{self.log_prefix} 已释放处理锁")
|
||
|
||
logger.info(f"{self.log_prefix} HeartFChatting关闭完成")
|
||
|
||
def get_cycle_history(self, last_n: Optional[int] = None) -> List[Dict[str, Any]]:
|
||
"""获取循环历史记录
|
||
|
||
参数:
|
||
last_n: 获取最近n个循环的信息，如果为None则获取所有历史记录
|
||
|
||
返回:
|
||
List[Dict[str, Any]]: 循环历史记录列表
|
||
"""
|
||
history = list(self._cycle_history)
|
||
if last_n is not None:
|
||
history = history[-last_n:]
|
||
return [cycle.to_dict() for cycle in history]
|