f515bf679e
原有的 breaking 模式等待逻辑过于复杂，它会累积新消息的兴趣值，并根据动态阈值（受专注度和发言频率影响）决定何时响应。这种机制可能导致响应延迟且行为难以预测。 本次重构完全移除了该等待与累积机制。现在，系统一旦收到任何新消息，就会立即处理并计算其平均兴趣值，从而确保了响应的及时性和逻辑的简洁性。
575 lines
24 KiB
Python
575 lines
24 KiB
Python
import asyncio
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import time
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import traceback
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import random
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from typing import Optional, List, Dict, Any
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from collections import deque
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from src.common.logger import get_logger
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from src.config.config import global_config
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from src.person_info.relationship_builder_manager import relationship_builder_manager
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from src.chat.express.expression_learner import expression_learner_manager
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from src.chat.chat_loop.sleep_manager.sleep_manager import SleepManager, SleepState
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from src.plugin_system.apis import message_api
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from .hfc_context import HfcContext
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from .energy_manager import EnergyManager
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from .proactive.proactive_thinker import ProactiveThinker
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from .cycle_processor import CycleProcessor
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from .response_handler import ResponseHandler
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from .cycle_tracker import CycleTracker
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from .sleep_manager.wakeup_manager import WakeUpManager
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from .proactive.events import ProactiveTriggerEvent
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logger = get_logger("hfc")
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class HeartFChatting:
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def __init__(self, chat_id: str):
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"""
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初始化心跳聊天管理器
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Args:
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chat_id: 聊天ID标识符
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功能说明:
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- 创建聊天上下文和所有子管理器
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- 初始化循环跟踪器、响应处理器、循环处理器等核心组件
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- 设置能量管理器、主动思考器和普通模式处理器
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- 初始化聊天模式并记录初始化完成日志
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"""
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self.context = HfcContext(chat_id)
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self.context.new_message_queue = asyncio.Queue()
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self.cycle_tracker = CycleTracker(self.context)
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self.response_handler = ResponseHandler(self.context)
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self.cycle_processor = CycleProcessor(self.context, self.response_handler, self.cycle_tracker)
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self.energy_manager = EnergyManager(self.context)
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self.proactive_thinker = ProactiveThinker(self.context, self.cycle_processor)
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self.wakeup_manager = WakeUpManager(self.context)
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self.sleep_manager = SleepManager()
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# 将唤醒度管理器设置到上下文中
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self.context.wakeup_manager = self.wakeup_manager
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self.context.energy_manager = self.energy_manager
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self.context.sleep_manager = self.sleep_manager
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# 将HeartFChatting实例设置到上下文中，以便其他组件可以调用其方法
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self.context.chat_instance = self
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self._loop_task: Optional[asyncio.Task] = None
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self._proactive_monitor_task: Optional[asyncio.Task] = None
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# 记录最近3次的兴趣度
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self.recent_interest_records: deque = deque(maxlen=3)
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self._initialize_chat_mode()
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logger.info(f"{self.context.log_prefix} HeartFChatting 初始化完成")
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def _initialize_chat_mode(self):
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"""
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初始化聊天模式
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功能说明:
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- 检测是否为群聊环境
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- 根据全局配置设置强制聊天模式
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- 在focus模式下设置能量值为35
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- 在normal模式下设置能量值为15
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- 如果是auto模式则保持默认设置
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"""
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is_group_chat = self.context.chat_stream.group_info is not None if self.context.chat_stream else False
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if is_group_chat and global_config.chat.group_chat_mode != "auto":
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self.context.energy_value = 25
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async def start(self):
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"""
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启动心跳聊天系统
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功能说明:
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- 检查是否已经在运行，避免重复启动
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- 初始化关系构建器和表达学习器
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- 启动能量管理器和主动思考器
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- 创建主聊天循环任务并设置完成回调
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- 记录启动完成日志
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"""
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if self.context.running:
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return
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self.context.running = True
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self.context.relationship_builder = relationship_builder_manager.get_or_create_builder(self.context.stream_id)
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self.context.expression_learner = await expression_learner_manager.get_expression_learner(self.context.stream_id)
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# 启动主动思考监视器
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if global_config.chat.enable_proactive_thinking:
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self._proactive_monitor_task = asyncio.create_task(self._proactive_monitor_loop())
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self._proactive_monitor_task.add_done_callback(self._handle_proactive_monitor_completion)
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logger.info(f"{self.context.log_prefix} 主动思考监视器已启动")
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await self.wakeup_manager.start()
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self._loop_task = asyncio.create_task(self._main_chat_loop())
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self._loop_task.add_done_callback(self._handle_loop_completion)
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logger.info(f"{self.context.log_prefix} HeartFChatting 启动完成")
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async def add_message(self, message: Dict[str, Any]):
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"""从外部接收新消息并放入队列"""
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await self.context.new_message_queue.put(message)
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async def stop(self):
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"""
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停止心跳聊天系统
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功能说明:
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- 检查是否正在运行，避免重复停止
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- 设置运行状态为False
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- 停止能量管理器和主动思考器
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- 取消主聊天循环任务
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- 记录停止完成日志
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"""
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if not self.context.running:
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return
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self.context.running = False
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# 停止主动思考监视器
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if self._proactive_monitor_task and not self._proactive_monitor_task.done():
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self._proactive_monitor_task.cancel()
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await asyncio.sleep(0)
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logger.info(f"{self.context.log_prefix} 主动思考监视器已停止")
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await self.wakeup_manager.stop()
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if self._loop_task and not self._loop_task.done():
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self._loop_task.cancel()
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await asyncio.sleep(0)
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logger.info(f"{self.context.log_prefix} HeartFChatting 已停止")
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def _handle_loop_completion(self, task: asyncio.Task):
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"""
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处理主循环任务完成
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Args:
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task: 完成的异步任务对象
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功能说明:
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- 处理任务异常完成的情况
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- 区分正常停止和异常终止
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- 记录相应的日志信息
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- 处理取消任务的情况
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"""
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try:
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if exception := task.exception():
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logger.error(f"{self.context.log_prefix} HeartFChatting: 脱离了聊天(异常): {exception}")
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logger.error(traceback.format_exc())
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else:
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logger.info(f"{self.context.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.context.log_prefix} HeartFChatting: 结束了聊天")
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def _handle_proactive_monitor_completion(self, task: asyncio.Task):
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"""
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处理主动思考监视器任务完成
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Args:
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task: 完成的异步任务对象
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功能说明:
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- 处理任务异常完成的情况
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- 记录任务正常结束或被取消的日志
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"""
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try:
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if exception := task.exception():
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logger.error(f"{self.context.log_prefix} 主动思考监视器异常: {exception}")
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else:
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logger.info(f"{self.context.log_prefix} 主动思考监视器正常结束")
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except asyncio.CancelledError:
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logger.info(f"{self.context.log_prefix} 主动思考监视器被取消")
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async def _proactive_monitor_loop(self):
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"""
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主动思考监视器循环
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功能说明:
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- 定期检查是否需要进行主动思考
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- 计算聊天沉默时间，并与动态思考间隔比较
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- 当沉默时间超过阈值时，触发主动思考
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- 处理思考过程中的异常
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"""
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while self.context.running:
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await asyncio.sleep(15)
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if not self._should_enable_proactive_thinking():
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continue
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current_time = time.time()
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silence_duration = current_time - self.context.last_message_time
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target_interval = self._get_dynamic_thinking_interval()
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if silence_duration >= target_interval:
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try:
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formatted_time = self._format_duration(silence_duration)
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event = ProactiveTriggerEvent(
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source="silence_monitor",
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reason=f"聊天已沉默 {formatted_time}",
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metadata={"silence_duration": silence_duration},
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)
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await self.proactive_thinker.think(event)
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self.context.last_message_time = current_time
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except Exception as e:
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logger.error(f"{self.context.log_prefix} 主动思考触发执行出错: {e}")
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logger.error(traceback.format_exc())
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def _should_enable_proactive_thinking(self) -> bool:
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"""
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判断是否应启用主动思考
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Returns:
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bool: 如果应启用主动思考则返回True，否则返回False
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功能说明:
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- 检查全局配置和特定聊天设置
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- 支持按群聊和私聊分别配置
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- 支持白名单模式，只在特定聊天中启用
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"""
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if not self.context.chat_stream:
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return False
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is_group_chat = self.context.chat_stream.group_info is not None
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if is_group_chat and not global_config.chat.proactive_thinking_in_group:
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return False
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if not is_group_chat and not global_config.chat.proactive_thinking_in_private:
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return False
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stream_parts = self.context.stream_id.split(":")
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current_chat_identifier = f"{stream_parts}:{stream_parts}" if len(stream_parts) >= 2 else self.context.stream_id
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enable_list = getattr(
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global_config.chat,
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"proactive_thinking_enable_in_groups" if is_group_chat else "proactive_thinking_enable_in_private",
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[],
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)
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return not enable_list or current_chat_identifier in enable_list
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def _get_dynamic_thinking_interval(self) -> float:
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"""
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获取动态思考间隔时间
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Returns:
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float: 思考间隔秒数
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功能说明:
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- 尝试从timing_utils导入正态分布间隔函数
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- 根据配置计算动态间隔，增加随机性
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- 在无法导入或计算出错时，回退到固定的间隔
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"""
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try:
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from src.utils.timing_utils import get_normal_distributed_interval
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base_interval = global_config.chat.proactive_thinking_interval
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delta_sigma = getattr(global_config.chat, "delta_sigma", 120)
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if base_interval <= 0:
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base_interval = abs(base_interval)
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if delta_sigma < 0:
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delta_sigma = abs(delta_sigma)
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if base_interval == 0 and delta_sigma == 0:
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return 300
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if delta_sigma == 0:
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return base_interval
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sigma_percentage = delta_sigma / base_interval if base_interval > 0 else delta_sigma / 1000
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return get_normal_distributed_interval(base_interval, sigma_percentage, 1, 86400, use_3sigma_rule=True)
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except ImportError:
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logger.warning(f"{self.context.log_prefix} timing_utils不可用，使用固定间隔")
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return max(300, abs(global_config.chat.proactive_thinking_interval))
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except Exception as e:
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logger.error(f"{self.context.log_prefix} 动态间隔计算出错: {e}，使用固定间隔")
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return max(300, abs(global_config.chat.proactive_thinking_interval))
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@staticmethod
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def _format_duration(seconds: float) -> str:
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"""
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格式化时长为可读字符串
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Args:
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seconds: 时长秒数
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Returns:
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str: 格式化后的字符串 (例如 "1小时2分3秒")
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"""
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hours = int(seconds // 3600)
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minutes = int((seconds % 3600) // 60)
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secs = int(seconds % 60)
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parts = []
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if hours > 0:
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parts.append(f"{hours}小时")
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if minutes > 0:
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parts.append(f"{minutes}分")
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if secs > 0 or not parts:
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parts.append(f"{secs}秒")
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return "".join(parts)
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async def _main_chat_loop(self):
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"""
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主聊天循环
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功能说明:
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- 持续运行聊天处理循环
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- 只有在有新消息时才进行思考循环
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- 无新消息时等待新消息到达（由主动思考系统单独处理主动发言）
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- 处理取消和异常情况
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- 在异常时尝试重新启动循环
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"""
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try:
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while self.context.running:
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has_new_messages = await self._loop_body()
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if has_new_messages:
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# 有新消息时，继续快速检查是否还有更多消息
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await asyncio.sleep(1)
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else:
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# 无新消息时，等待较长时间再检查
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# 这里只是为了定期检查系统状态，不进行思考循环
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# 真正的新消息响应依赖于消息到达时的通知
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await asyncio.sleep(1.0)
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except asyncio.CancelledError:
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logger.info(f"{self.context.log_prefix} 麦麦已关闭聊天")
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except Exception:
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logger.error(f"{self.context.log_prefix} 麦麦聊天意外错误，将于3s后尝试重新启动")
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print(traceback.format_exc())
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await asyncio.sleep(3)
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self._loop_task = asyncio.create_task(self._main_chat_loop())
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logger.error(f"{self.context.log_prefix} 结束了当前聊天循环")
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async def _loop_body(self) -> bool:
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"""
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单次循环体处理
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Returns:
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bool: 是否处理了新消息
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功能说明:
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- 检查是否处于睡眠模式，如果是则处理唤醒度逻辑
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- 获取最近的新消息（过滤机器人自己的消息和命令）
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- 只有在有新消息时才进行思考循环处理
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- 更新最后消息时间和读取时间
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- 根据当前聊天模式执行不同的处理逻辑
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- FOCUS模式：直接处理所有消息并检查退出条件
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- NORMAL模式：检查进入FOCUS模式的条件，并通过normal_mode_handler处理消息
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"""
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# --- 核心状态更新 ---
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await self.sleep_manager.update_sleep_state(self.wakeup_manager)
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current_sleep_state = self.sleep_manager.get_current_sleep_state()
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is_sleeping = current_sleep_state == SleepState.SLEEPING
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is_in_insomnia = current_sleep_state == SleepState.INSOMNIA
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# 核心修复：在睡眠模式(包括失眠)下获取消息时，不过滤命令消息，以确保@消息能被接收
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filter_command_flag = not (is_sleeping or is_in_insomnia)
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# 从队列中获取所有待处理的新消息
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recent_messages = []
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while not self.context.new_message_queue.empty():
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recent_messages.append(await self.context.new_message_queue.get())
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has_new_messages = bool(recent_messages)
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new_message_count = len(recent_messages)
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# 只有在有新消息时才进行思考循环处理
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if has_new_messages:
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self.context.last_message_time = time.time()
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self.context.last_read_time = time.time()
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# --- 专注模式安静群组检查 ---
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quiet_groups = global_config.chat.focus_mode_quiet_groups
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if quiet_groups and self.context.chat_stream:
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is_group_chat = self.context.chat_stream.group_info is not None
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if is_group_chat:
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try:
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platform = self.context.chat_stream.platform
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group_id = self.context.chat_stream.group_info.group_id
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# 兼容不同QQ适配器的平台名称
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is_qq_platform = platform in ["qq", "napcat"]
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current_chat_identifier = f"{platform}:{group_id}"
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config_identifier_for_qq = f"qq:{group_id}"
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is_in_quiet_list = (current_chat_identifier in quiet_groups or
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(is_qq_platform and config_identifier_for_qq in quiet_groups))
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if is_in_quiet_list:
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is_mentioned_in_batch = False
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for msg in recent_messages:
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if msg.get("is_mentioned"):
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is_mentioned_in_batch = True
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break
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if not is_mentioned_in_batch:
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logger.info(f"{self.context.log_prefix} 在专注安静模式下，因未被提及而忽略了消息。")
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return True # 消耗消息但不做回复
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except Exception as e:
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logger.error(f"{self.context.log_prefix} 检查专注安静群组时出错: {e}")
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# 处理唤醒度逻辑
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if current_sleep_state in [SleepState.SLEEPING, SleepState.PREPARING_SLEEP, SleepState.INSOMNIA]:
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self._handle_wakeup_messages(recent_messages)
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# 再次获取最新状态，因为 handle_wakeup 可能导致状态变为 WOKEN_UP
|
||
current_sleep_state = self.sleep_manager.get_current_sleep_state()
|
||
|
||
if current_sleep_state == SleepState.SLEEPING:
|
||
# 只有在纯粹的 SLEEPING 状态下才跳过消息处理
|
||
return True
|
||
|
||
if current_sleep_state == SleepState.WOKEN_UP:
|
||
logger.info(f"{self.context.log_prefix} 从睡眠中被唤醒，将处理积压的消息。")
|
||
|
||
# 根据聊天模式处理新消息
|
||
should_process, interest_value = await self._should_process_messages(recent_messages)
|
||
if not should_process:
|
||
# 消息数量不足或兴趣不够，等待
|
||
await asyncio.sleep(0.5)
|
||
return True # Skip rest of the logic for this iteration
|
||
|
||
# Messages should be processed
|
||
action_type = await self.cycle_processor.observe(interest_value=interest_value)
|
||
|
||
# 尝试触发表达学习
|
||
if self.context.expression_learner:
|
||
try:
|
||
await self.context.expression_learner.trigger_learning_for_chat()
|
||
except Exception as e:
|
||
logger.error(f"{self.context.log_prefix} 表达学习触发失败: {e}")
|
||
|
||
# 管理no_reply计数器
|
||
if action_type != "no_reply":
|
||
self.recent_interest_records.clear()
|
||
self.context.no_reply_consecutive = 0
|
||
logger.debug(f"{self.context.log_prefix} 执行了{action_type}动作，重置no_reply计数器")
|
||
else: # action_type == "no_reply"
|
||
self.context.no_reply_consecutive += 1
|
||
self._determine_form_type()
|
||
|
||
# 在一轮动作执行完毕后，增加睡眠压力
|
||
if self.context.energy_manager and global_config.sleep_system.enable_insomnia_system:
|
||
if action_type not in ["no_reply", "no_action"]:
|
||
self.context.energy_manager.increase_sleep_pressure()
|
||
|
||
# 如果成功观察，增加能量值并重置累积兴趣值
|
||
self.context.energy_value += 1 / global_config.chat.focus_value
|
||
# 重置累积兴趣值，因为消息已经被成功处理
|
||
self.context.breaking_accumulated_interest = 0.0
|
||
logger.info(
|
||
f"{self.context.log_prefix} 能量值增加，当前能量值：{self.context.energy_value:.1f}，重置累积兴趣值"
|
||
)
|
||
|
||
# 更新上一帧的睡眠状态
|
||
self.context.was_sleeping = is_sleeping
|
||
|
||
# --- 重新入睡逻辑 ---
|
||
# 如果被吵醒了，并且在一定时间内没有新消息，则尝试重新入睡
|
||
if self.sleep_manager.get_current_sleep_state() == SleepState.WOKEN_UP and not has_new_messages:
|
||
re_sleep_delay = global_config.sleep_system.re_sleep_delay_minutes * 60
|
||
# 使用 last_message_time 来判断空闲时间
|
||
if time.time() - self.context.last_message_time > re_sleep_delay:
|
||
logger.info(
|
||
f"{self.context.log_prefix} 已被唤醒且超过 {re_sleep_delay / 60} 分钟无新消息，尝试重新入睡。"
|
||
)
|
||
self.sleep_manager.reset_sleep_state_after_wakeup()
|
||
|
||
# 保存HFC上下文状态
|
||
self.context.save_context_state()
|
||
|
||
return has_new_messages
|
||
|
||
def _handle_wakeup_messages(self, messages):
|
||
"""
|
||
处理休眠状态下的消息，累积唤醒度
|
||
|
||
Args:
|
||
messages: 消息列表
|
||
|
||
功能说明:
|
||
- 区分私聊和群聊消息
|
||
- 检查群聊消息是否艾特了机器人
|
||
- 调用唤醒度管理器累积唤醒度
|
||
- 如果达到阈值则唤醒并进入愤怒状态
|
||
"""
|
||
if not self.wakeup_manager:
|
||
return
|
||
|
||
is_private_chat = self.context.chat_stream.group_info is None if self.context.chat_stream else False
|
||
|
||
for message in messages:
|
||
is_mentioned = False
|
||
|
||
# 检查群聊消息是否艾特了机器人
|
||
if not is_private_chat:
|
||
# 最终修复：直接使用消息对象中由上游处理好的 is_mention 字段。
|
||
# 该字段在 message.py 的 MessageRecv._process_single_segment 中被设置。
|
||
if message.get("is_mentioned"):
|
||
is_mentioned = True
|
||
|
||
# 累积唤醒度
|
||
woke_up = self.wakeup_manager.add_wakeup_value(is_private_chat, is_mentioned)
|
||
|
||
if woke_up:
|
||
logger.info(f"{self.context.log_prefix} 被消息吵醒，进入愤怒状态！")
|
||
break
|
||
|
||
def _determine_form_type(self) -> str:
|
||
"""判断使用哪种形式的no_reply"""
|
||
# 检查是否启用breaking模式
|
||
if not getattr(global_config.chat, "enable_breaking_mode", False):
|
||
logger.info(f"{self.context.log_prefix} breaking模式已禁用，使用waiting形式")
|
||
self.context.focus_energy = 1
|
||
return "waiting"
|
||
|
||
# 如果连续no_reply次数少于3次，使用waiting形式
|
||
if self.context.no_reply_consecutive <= 3:
|
||
self.context.focus_energy = 1
|
||
return "waiting"
|
||
else:
|
||
# 使用累积兴趣值而不是最近3次的记录
|
||
total_interest = self.context.breaking_accumulated_interest
|
||
|
||
# 计算调整后的阈值
|
||
adjusted_threshold = 1 / global_config.chat.get_current_talk_frequency(self.context.stream_id)
|
||
|
||
logger.info(
|
||
f"{self.context.log_prefix} 累积兴趣值: {total_interest:.2f}, 调整后阈值: {adjusted_threshold:.2f}"
|
||
)
|
||
|
||
# 如果累积兴趣值小于阈值，进入breaking形式
|
||
if total_interest < adjusted_threshold:
|
||
logger.info(f"{self.context.log_prefix} 累积兴趣度不足，进入breaking形式")
|
||
self.context.focus_energy = random.randint(3, 6)
|
||
return "breaking"
|
||
else:
|
||
logger.info(f"{self.context.log_prefix} 累积兴趣度充足，使用waiting形式")
|
||
self.context.focus_energy = 1
|
||
return "waiting"
|
||
|
||
async def _should_process_messages(self, new_message: List[Dict[str, Any]]) -> tuple[bool, float]:
|
||
"""
|
||
统一判断是否应该处理消息的函数
|
||
根据当前循环模式和消息内容决定是否继续处理
|
||
"""
|
||
if not new_message:
|
||
return False, 0.0
|
||
|
||
# 计算平均兴趣值
|
||
total_interest = 0.0
|
||
message_count = 0
|
||
for msg_dict in new_message:
|
||
interest_value = msg_dict.get("interest_value", 0.0)
|
||
if msg_dict.get("processed_plain_text", ""):
|
||
total_interest += interest_value
|
||
message_count += 1
|
||
|
||
avg_interest = total_interest / message_count if message_count > 0 else 0.0
|
||
|
||
logger.info(f"{self.context.log_prefix} 收到 {len(new_message)} 条新消息，立即处理！平均兴趣值: {avg_interest:.2f}")
|
||
return True, avg_interest
|