d25c6ab452
Updated logic in heartFC_chat.py and relationship_manager.py to prioritize 'chat_info_platform', then 'user_platform', and finally a default when determining platform information. Added a fallback to 'unknown' in get_person_id if platform is None, improving robustness when platform data is missing. Co-Authored-By: tt-P607 <68868379+tt-P607@users.noreply.github.com>
1055 lines
47 KiB
Python
1055 lines
47 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 List, Optional, Dict, Any, Tuple
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from rich.traceback import install
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from src.config.config import global_config
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from src.common.logger import get_logger
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from src.chat.message_receive.chat_stream import ChatStream, get_chat_manager
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from src.chat.utils.prompt_builder import global_prompt_manager
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from src.chat.utils.timer_calculator import Timer
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from src.chat.planner_actions.planner import ActionPlanner
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from src.chat.planner_actions.action_modifier import ActionModifier
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from src.chat.planner_actions.action_manager import ActionManager
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from src.chat.chat_loop.hfc_utils import CycleDetail
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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.person_info.person_info import get_person_info_manager
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from src.plugin_system.base.component_types import ActionInfo, ChatMode, EventType
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from src.plugin_system.core import events_manager
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from src.plugin_system.apis import generator_api, send_api, message_api, database_api
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from src.chat.willing.willing_manager import get_willing_manager
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from src.mais4u.mai_think import mai_thinking_manager
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from src.mais4u.constant_s4u import ENABLE_S4U
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from src.chat.chat_loop.hfc_utils import send_typing, stop_typing
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ERROR_LOOP_INFO = {
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"loop_plan_info": {
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"action_result": {
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"action_type": "error",
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"action_data": {},
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"reasoning": "循环处理失败",
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},
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},
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"loop_action_info": {
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"action_taken": False,
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"reply_text": "",
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"command": "",
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"taken_time": time.time(),
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},
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}
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NO_ACTION = {
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"action_result": {
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"action_type": "no_action",
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"action_data": {},
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"reasoning": "规划器初始化默认",
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"is_parallel": True,
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},
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"chat_context": "",
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"action_prompt": "",
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}
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install(extra_lines=3)
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# 注释：原来的动作修改超时常量已移除，因为改为顺序执行
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logger = get_logger("hfc") # Logger Name Changed
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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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):
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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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on_stop_focus_chat: 当收到stop_focus_chat命令时调用的回调函数
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performance_version: 性能记录版本号，用于区分不同启动版本
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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: ChatStream = get_chat_manager().get_stream(self.stream_id) # type: ignore
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if not self.chat_stream:
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raise ValueError(f"无法找到聊天流: {self.stream_id}")
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self.log_prefix = f"[{get_chat_manager().get_stream_name(self.stream_id) or self.stream_id}]"
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self.relationship_builder = relationship_builder_manager.get_or_create_builder(self.stream_id)
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self.expression_learner = expression_learner_manager.get_expression_learner(self.stream_id)
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self.loop_mode = ChatMode.NORMAL # 初始循环模式为普通模式
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self.last_action = "no_action"
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self.action_manager = ActionManager()
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self.action_planner = ActionPlanner(chat_id=self.stream_id, action_manager=self.action_manager)
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self.action_modifier = ActionModifier(action_manager=self.action_manager, chat_id=self.stream_id)
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# 循环控制内部状态
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self.running: bool = False
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self._loop_task: Optional[asyncio.Task] = None # 主循环任务
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self._energy_task: Optional[asyncio.Task] = None
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# 添加循环信息管理相关的属性
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self.history_loop: List[CycleDetail] = []
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self._cycle_counter = 0
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self._current_cycle_detail: CycleDetail = None # type: ignore
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self.reply_timeout_count = 0
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self.plan_timeout_count = 0
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self.last_read_time = time.time() - 1
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self.willing_manager = get_willing_manager()
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logger.info(f"{self.log_prefix} HeartFChatting 初始化完成")
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self.energy_value = 5
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# 根据配置初始化聊天模式和能量值
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is_group_chat = self.chat_stream.group_info is not None
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if is_group_chat and global_config.chat.group_chat_mode != "auto":
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if global_config.chat.group_chat_mode == "focus":
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self.loop_mode = ChatMode.FOCUS
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self.energy_value = 35
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logger.info(f"{self.log_prefix} 群聊强制专注模式已启用，能量值设置为35")
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elif global_config.chat.group_chat_mode == "normal":
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self.loop_mode = ChatMode.NORMAL
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self.energy_value = 15
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logger.info(f"{self.log_prefix} 群聊强制普通模式已启用，能量值设置为15")
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self.focus_energy = 1
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# 能量值日志时间控制
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self.last_energy_log_time = 0 # 上次记录能量值日志的时间
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self.energy_log_interval = 90 # 能量值日志间隔（秒）
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# 主动思考功能相关属性
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self.last_message_time = time.time() # 最后一条消息的时间
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self._proactive_thinking_task: Optional[asyncio.Task] = None # 主动思考任务
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async def start(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.running:
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logger.debug(f"{self.log_prefix} HeartFChatting 已激活，无需重复启动")
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return
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try:
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# 标记为活动状态，防止重复启动
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self.running = True
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self._energy_task = asyncio.create_task(self._energy_loop())
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self._energy_task.add_done_callback(self._handle_energy_completion)
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# 启动主动思考任务（仅在群聊且启用的情况下）
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if (global_config.chat.enable_proactive_thinking and
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self.chat_stream.group_info is not None):
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self._proactive_thinking_task = asyncio.create_task(self._proactive_thinking_loop())
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self._proactive_thinking_task.add_done_callback(self._handle_proactive_thinking_completion)
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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.log_prefix} HeartFChatting 启动完成")
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except Exception as e:
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# 启动失败时重置状态
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self.running = False
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self._loop_task = None
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logger.error(f"{self.log_prefix} HeartFChatting 启动失败: {e}")
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raise
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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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if exception := task.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: 结束了聊天")
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def start_cycle(self):
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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.thinking_id = f"tid{str(round(time.time(), 2))}"
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cycle_timers = {}
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return cycle_timers, self._current_cycle_detail.thinking_id
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def end_cycle(self, loop_info, cycle_timers):
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self._current_cycle_detail.set_loop_info(loop_info)
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self.history_loop.append(self._current_cycle_detail)
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self._current_cycle_detail.timers = cycle_timers
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self._current_cycle_detail.end_time = time.time()
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def _handle_energy_completion(self, task: asyncio.Task):
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"""当 energy_loop 任务完成时执行的回调。"""
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try:
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if exception := task.exception():
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logger.error(f"{self.log_prefix} 能量循环异常: {exception}")
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else:
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logger.info(f"{self.log_prefix} 能量循环正常结束")
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except asyncio.CancelledError:
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logger.info(f"{self.log_prefix} 能量循环被取消")
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def _handle_proactive_thinking_completion(self, task: asyncio.Task):
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"""当 proactive_thinking_loop 任务完成时执行的回调。"""
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try:
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if exception := task.exception():
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logger.error(f"{self.log_prefix} 主动思考循环异常: {exception}")
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else:
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logger.info(f"{self.log_prefix} 主动思考循环正常结束")
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except asyncio.CancelledError:
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logger.info(f"{self.log_prefix} 主动思考循环被取消")
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"""处理能量循环任务的完成"""
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if task.cancelled():
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logger.info(f"{self.log_prefix} 能量循环任务被取消")
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elif task.exception():
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logger.error(f"{self.log_prefix} 能量循环任务发生异常: {task.exception()}")
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def _should_log_energy(self) -> bool:
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"""判断是否应该记录能量值日志（基于时间间隔控制）"""
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current_time = time.time()
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if current_time - self.last_energy_log_time >= self.energy_log_interval:
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self.last_energy_log_time = current_time
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return True
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return False
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def _log_energy_change(self, action: str, reason: str = ""):
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"""记录能量值变化日志（受时间间隔控制）"""
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if self._should_log_energy():
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if reason:
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logger.info(f"{self.log_prefix} {action}，{reason}，当前能量值：{self.energy_value:.1f}")
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else:
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logger.info(f"{self.log_prefix} {action}，当前能量值：{self.energy_value:.1f}")
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else:
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# 仍然以debug级别记录，便于调试
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if reason:
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logger.debug(f"{self.log_prefix} {action}，{reason}，当前能量值：{self.energy_value:.1f}")
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else:
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logger.debug(f"{self.log_prefix} {action}，当前能量值：{self.energy_value:.1f}")
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async def _energy_loop(self):
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while self.running:
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await asyncio.sleep(10)
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# 检查是否为群聊且配置了强制模式
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is_group_chat = self.chat_stream.group_info is not None
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if is_group_chat and global_config.chat.group_chat_mode != "auto":
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# 强制模式下固定能量值和聊天模式
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if global_config.chat.group_chat_mode == "focus":
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self.loop_mode = ChatMode.FOCUS
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self.energy_value = 35 # 强制设置为35
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elif global_config.chat.group_chat_mode == "normal":
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self.loop_mode = ChatMode.NORMAL
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self.energy_value = 15 # 强制设置为15
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continue # 跳过正常的能量值衰减逻辑
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# 原有的自动模式逻辑
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if self.loop_mode == ChatMode.NORMAL:
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self.energy_value -= 0.3
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self.energy_value = max(self.energy_value, 0.3)
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if self.loop_mode == ChatMode.FOCUS:
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self.energy_value -= 0.6
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self.energy_value = max(self.energy_value, 0.3)
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async def _proactive_thinking_loop(self):
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"""主动思考循环，仅在focus模式下生效"""
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while self.running:
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await asyncio.sleep(30) # 每30秒检查一次
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# 只在focus模式下进行主动思考
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if self.loop_mode != ChatMode.FOCUS:
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continue
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current_time = time.time()
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silence_duration = current_time - self.last_message_time
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# 检查是否达到主动思考的时间间隔
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if silence_duration >= global_config.chat.proactive_thinking_interval:
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try:
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await self._execute_proactive_thinking(silence_duration)
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# 重置计时器，避免频繁触发
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self.last_message_time = current_time
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except Exception as e:
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logger.error(f"{self.log_prefix} 主动思考执行出错: {e}")
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logger.error(traceback.format_exc())
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def _format_duration(self, seconds: float) -> str:
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"""格式化时间间隔为易读格式"""
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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: # 如果没有小时和分钟，显示秒
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parts.append(f"{secs}秒")
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return "".join(parts)
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async def _execute_proactive_thinking(self, silence_duration: float):
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"""执行主动思考"""
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formatted_time = self._format_duration(silence_duration)
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logger.info(f"{self.log_prefix} 触发主动思考，已沉默{formatted_time}")
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try:
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# 构建主动思考的prompt
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proactive_prompt = global_config.chat.proactive_thinking_prompt_template.format(
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time=formatted_time
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)
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# 创建一个虚拟的消息数据用于主动思考
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"""
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因为主动思考是在没有用户消息的情况下触发的
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但规划器仍然需要一个"消息"作为输入来工作
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所以需要"伪造"一个消息来触发思考流程，本质上是系统与自己的对话，让AI能够主动思考和决策。
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"""
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thinking_message = {
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"processed_plain_text": proactive_prompt,
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"user_id": "system_proactive_thinking",
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"user_platform": "system",
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"timestamp": time.time(),
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"message_type": "proactive_thinking",
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"user_nickname": "系统主动思考",
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"chat_info_platform": "system",
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"message_id": f"proactive_{int(time.time())}"
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}
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# 使用现有的_observe方法来处理主动思考
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# 这样可以复用现有的完整思考流程
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logger.info(f"{self.log_prefix} 开始主动思考...")
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await self._observe(message_data=thinking_message)
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logger.info(f"{self.log_prefix} 主动思考完成")
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except Exception as e:
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logger.error(f"{self.log_prefix} 主动思考执行异常: {e}")
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logger.error(traceback.format_exc())
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def print_cycle_info(self, cycle_timers):
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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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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, " # type: ignore
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f"选择动作: {self._current_cycle_detail.loop_plan_info.get('action_result', {}).get('action_type', '未知动作')}"
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+ (f"\n详情: {'; '.join(timer_strings)}" if timer_strings else "")
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)
|
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|
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|
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async def _loopbody(self):
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||
recent_messages_dict = message_api.get_messages_by_time_in_chat(
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chat_id=self.stream_id,
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start_time=self.last_read_time,
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end_time=time.time(),
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limit = 10,
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limit_mode="latest",
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||
filter_mai=True,
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filter_command=True,
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)
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new_message_count = len(recent_messages_dict)
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# 如果有新消息，更新最后消息时间（用于主动思考计时）
|
||
if new_message_count > 0:
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||
current_time = time.time()
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self.last_message_time = current_time
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|
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|
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if self.loop_mode == ChatMode.FOCUS:
|
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# focus模式下，在有新消息时进行观察思考
|
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# 主动思考由独立的 _proactive_thinking_loop 处理
|
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if new_message_count > 0:
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self.last_read_time = time.time()
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|
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if await self._observe():
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# 在强制模式下，能量值不会因观察而增加
|
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is_group_chat = self.chat_stream.group_info is not None
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if not (is_group_chat and global_config.chat.group_chat_mode != "auto"):
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||
self.energy_value += 1 / global_config.chat.focus_value
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self._log_energy_change("能量值增加")
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|
||
# 检查是否应该退出专注模式
|
||
# 如果开启了强制私聊专注模式且当前为私聊，则不允许退出专注状态
|
||
is_private_chat = self.chat_stream.group_info is None
|
||
is_group_chat = self.chat_stream.group_info is not None
|
||
|
||
if global_config.chat.force_focus_private and is_private_chat:
|
||
# 强制私聊专注模式下，保持专注状态，但重置能量值防止过低
|
||
if self.energy_value <= 1:
|
||
self.energy_value = 5 # 重置为较低但足够的能量值
|
||
return True
|
||
|
||
# 群聊强制专注模式下，不允许退出专注状态
|
||
if is_group_chat and global_config.chat.group_chat_mode == "focus":
|
||
return True
|
||
|
||
if self.energy_value <= 1:
|
||
self.energy_value = 1
|
||
self.loop_mode = ChatMode.NORMAL
|
||
return True
|
||
|
||
return True
|
||
elif self.loop_mode == ChatMode.NORMAL:
|
||
# 检查是否应该强制进入专注模式（私聊且开启强制专注）
|
||
is_private_chat = self.chat_stream.group_info is None
|
||
is_group_chat = self.chat_stream.group_info is not None
|
||
|
||
if global_config.chat.force_focus_private and is_private_chat:
|
||
self.loop_mode = ChatMode.FOCUS
|
||
self.energy_value = 10 # 设置初始能量值
|
||
return True
|
||
|
||
# 群聊强制普通模式下，不允许进入专注状态
|
||
if is_group_chat and global_config.chat.group_chat_mode == "normal":
|
||
# 在强制普通模式下，即使满足条件也不进入专注模式
|
||
pass
|
||
elif global_config.chat.focus_value != 0:
|
||
if new_message_count > 3 / pow(global_config.chat.focus_value, 0.5):
|
||
self.loop_mode = ChatMode.FOCUS
|
||
self.energy_value = (
|
||
10 + (new_message_count / (3 / pow(global_config.chat.focus_value, 0.5))) * 10
|
||
)
|
||
return True
|
||
|
||
if self.energy_value >= 30:
|
||
self.loop_mode = ChatMode.FOCUS
|
||
return True
|
||
|
||
if new_message_count >= self.focus_energy:
|
||
earliest_messages_data = recent_messages_dict[0]
|
||
self.last_read_time = earliest_messages_data.get("time")
|
||
|
||
if_think = await self.normal_response(earliest_messages_data)
|
||
|
||
# 在强制模式下，能量值变化逻辑需要特殊处理
|
||
is_group_chat = self.chat_stream.group_info is not None
|
||
if is_group_chat and global_config.chat.group_chat_mode != "auto":
|
||
# 强制模式下不改变能量值
|
||
pass
|
||
elif if_think:
|
||
factor = max(global_config.chat.focus_value, 0.1)
|
||
self.energy_value *= 1.1 * factor
|
||
self._log_energy_change("进行了思考，能量值按倍数增加")
|
||
else:
|
||
self.energy_value += 0.1 * global_config.chat.focus_value
|
||
self._log_energy_change("没有进行思考，能量值线性增加")
|
||
|
||
# 这个可以保持debug级别，因为它是总结性信息
|
||
logger.debug(f"{self.log_prefix} 当前能量值：{self.energy_value:.1f}")
|
||
return True
|
||
|
||
await asyncio.sleep(0.5)
|
||
|
||
return True
|
||
|
||
async def build_reply_to_str(self, message_data: dict):
|
||
person_info_manager = get_person_info_manager()
|
||
|
||
# 获取平台信息，优先使用chat_info_platform，如果为None则使用user_platform
|
||
platform = message_data.get("chat_info_platform") or message_data.get("user_platform") or self.chat_stream.platform
|
||
user_id = message_data.get("user_id")
|
||
|
||
person_id = person_info_manager.get_person_id(platform, user_id)
|
||
person_name = await person_info_manager.get_value(person_id, "person_name")
|
||
return f"{person_name}:{message_data.get('processed_plain_text')}"
|
||
|
||
async def _send_and_store_reply(
|
||
self,
|
||
response_set,
|
||
reply_to_str,
|
||
loop_start_time,
|
||
action_message,
|
||
cycle_timers: Dict[str, float],
|
||
thinking_id,
|
||
plan_result,
|
||
) -> Tuple[Dict[str, Any], str, Dict[str, float]]:
|
||
with Timer("回复发送", cycle_timers):
|
||
reply_text = await self._send_response(response_set, reply_to_str, loop_start_time, action_message)
|
||
|
||
# 存储reply action信息
|
||
person_info_manager = get_person_info_manager()
|
||
|
||
# 获取平台信息，优先使用chat_info_platform，如果为空则使用user_platform
|
||
platform = action_message.get("chat_info_platform") or action_message.get("user_platform") or self.chat_stream.platform
|
||
user_id = action_message.get("user_id", "")
|
||
|
||
person_id = person_info_manager.get_person_id(platform, user_id)
|
||
person_name = await person_info_manager.get_value(person_id, "person_name")
|
||
action_prompt_display = f"你对{person_name}进行了回复：{reply_text}"
|
||
|
||
await database_api.store_action_info(
|
||
chat_stream=self.chat_stream,
|
||
action_build_into_prompt=False,
|
||
action_prompt_display=action_prompt_display,
|
||
action_done=True,
|
||
thinking_id=thinking_id,
|
||
action_data={"reply_text": reply_text, "reply_to": reply_to_str},
|
||
action_name="reply",
|
||
)
|
||
|
||
# 构建循环信息
|
||
loop_info: Dict[str, Any] = {
|
||
"loop_plan_info": {
|
||
"action_result": plan_result.get("action_result", {}),
|
||
},
|
||
"loop_action_info": {
|
||
"action_taken": True,
|
||
"reply_text": reply_text,
|
||
"command": "",
|
||
"taken_time": time.time(),
|
||
},
|
||
}
|
||
|
||
return loop_info, reply_text, cycle_timers
|
||
|
||
async def _observe(self, message_data: Optional[Dict[str, Any]] = None) -> bool:
|
||
if not message_data:
|
||
message_data = {}
|
||
action_type = "no_action"
|
||
reply_text = "" # 初始化reply_text变量，避免UnboundLocalError
|
||
gen_task = None # 初始化gen_task变量，避免UnboundLocalError
|
||
reply_to_str = "" # 初始化reply_to_str变量
|
||
|
||
# 创建新的循环信息
|
||
cycle_timers, thinking_id = self.start_cycle()
|
||
|
||
logger.info(f"{self.log_prefix} 开始第{self._cycle_counter}次思考[模式：{self.loop_mode}]")
|
||
|
||
if ENABLE_S4U:
|
||
await send_typing()
|
||
|
||
async with global_prompt_manager.async_message_scope(self.chat_stream.context.get_template_name()):
|
||
loop_start_time = time.time()
|
||
await self.relationship_builder.build_relation()
|
||
await self.expression_learner.trigger_learning_for_chat()
|
||
|
||
available_actions = {}
|
||
|
||
# 第一步：动作修改
|
||
with Timer("动作修改", cycle_timers):
|
||
try:
|
||
await self.action_modifier.modify_actions()
|
||
available_actions = self.action_manager.get_using_actions()
|
||
except Exception as e:
|
||
logger.error(f"{self.log_prefix} 动作修改失败: {e}")
|
||
|
||
# 检查是否在normal模式下没有可用动作（除了reply相关动作）
|
||
skip_planner = False
|
||
if self.loop_mode == ChatMode.NORMAL:
|
||
# 过滤掉reply相关的动作，检查是否还有其他动作
|
||
non_reply_actions = {
|
||
k: v for k, v in available_actions.items() if k not in ["reply", "no_reply", "no_action"]
|
||
}
|
||
|
||
if not non_reply_actions:
|
||
skip_planner = True
|
||
logger.info(f"{self.log_prefix} Normal模式下没有可用动作，直接回复")
|
||
|
||
# 直接设置为reply动作
|
||
action_type = "reply"
|
||
reasoning = ""
|
||
action_data = {"loop_start_time": loop_start_time}
|
||
is_parallel = False
|
||
|
||
# 构建plan_result用于后续处理
|
||
plan_result = {
|
||
"action_result": {
|
||
"action_type": action_type,
|
||
"action_data": action_data,
|
||
"reasoning": reasoning,
|
||
"timestamp": time.time(),
|
||
"is_parallel": is_parallel,
|
||
},
|
||
"action_prompt": "",
|
||
}
|
||
target_message = message_data
|
||
|
||
# 如果normal模式且不跳过规划器，开始一个回复生成进程，先准备好回复（其实是和planer同时进行的）
|
||
if not skip_planner:
|
||
reply_to_str = await self.build_reply_to_str(message_data)
|
||
gen_task = asyncio.create_task(
|
||
self._generate_response(
|
||
message_data=message_data,
|
||
available_actions=available_actions,
|
||
reply_to=reply_to_str,
|
||
request_type="chat.replyer.normal",
|
||
)
|
||
)
|
||
|
||
if not skip_planner:
|
||
planner_info = self.action_planner.get_necessary_info()
|
||
prompt_info = await self.action_planner.build_planner_prompt(
|
||
is_group_chat=planner_info[0],
|
||
chat_target_info=planner_info[1],
|
||
current_available_actions=planner_info[2],
|
||
)
|
||
if not await events_manager.handle_mai_events(
|
||
EventType.ON_PLAN, None, prompt_info[0], None, self.chat_stream.stream_id
|
||
):
|
||
return False
|
||
with Timer("规划器", cycle_timers):
|
||
plan_result, target_message = await self.action_planner.plan(mode=self.loop_mode)
|
||
|
||
action_result: Dict[str, Any] = plan_result.get("action_result", {}) # type: ignore
|
||
action_type, action_data, reasoning, is_parallel = (
|
||
action_result.get("action_type", "error"),
|
||
action_result.get("action_data", {}),
|
||
action_result.get("reasoning", "未提供理由"),
|
||
action_result.get("is_parallel", True),
|
||
)
|
||
|
||
action_data["loop_start_time"] = loop_start_time
|
||
|
||
# 在私聊的专注模式下，如果规划动作为no_reply，则强制改为reply
|
||
is_private_chat = self.chat_stream.group_info is None
|
||
if self.loop_mode == ChatMode.FOCUS and is_private_chat and action_type == "no_reply":
|
||
action_type = "reply"
|
||
logger.info(f"{self.log_prefix} 私聊专注模式下强制回复")
|
||
|
||
if action_type == "reply":
|
||
logger.info(f"{self.log_prefix}{global_config.bot.nickname} 决定进行回复")
|
||
elif is_parallel:
|
||
logger.info(f"{self.log_prefix}{global_config.bot.nickname} 决定进行回复, 同时执行{action_type}动作")
|
||
else:
|
||
# 只有在gen_task存在时才进行相关操作
|
||
if gen_task:
|
||
if not gen_task.done():
|
||
gen_task.cancel()
|
||
logger.debug(f"{self.log_prefix} 已取消预生成的回复任务")
|
||
logger.info(
|
||
f"{self.log_prefix}{global_config.bot.nickname} 原本想要回复，但选择执行{action_type}，不发表回复"
|
||
)
|
||
elif generation_result := gen_task.result():
|
||
content = " ".join([item[1] for item in generation_result if item[0] == "text"])
|
||
logger.debug(f"{self.log_prefix} 预生成的回复任务已完成")
|
||
logger.info(
|
||
f"{self.log_prefix}{global_config.bot.nickname} 原本想要回复：{content}，但选择执行{action_type}，不发表回复"
|
||
)
|
||
else:
|
||
logger.warning(f"{self.log_prefix} 预生成的回复任务未生成有效内容")
|
||
|
||
action_message = target_message or message_data
|
||
if action_type == "reply":
|
||
# 等待回复生成完毕
|
||
if self.loop_mode == ChatMode.NORMAL:
|
||
# 只有在gen_task存在时才等待
|
||
if not gen_task:
|
||
reply_to_str = await self.build_reply_to_str(message_data)
|
||
gen_task = asyncio.create_task(
|
||
self._generate_response(
|
||
message_data=message_data,
|
||
available_actions=available_actions,
|
||
reply_to=reply_to_str,
|
||
request_type="chat.replyer.normal",
|
||
)
|
||
)
|
||
|
||
gather_timeout = global_config.chat.thinking_timeout
|
||
try:
|
||
response_set = await asyncio.wait_for(gen_task, timeout=gather_timeout)
|
||
except asyncio.TimeoutError:
|
||
logger.warning(f"{self.log_prefix} 回复生成超时>{global_config.chat.thinking_timeout}s，已跳过")
|
||
response_set = None
|
||
|
||
# 模型炸了或超时，没有回复内容生成
|
||
if not response_set:
|
||
logger.warning(f"{self.log_prefix}模型未生成回复内容")
|
||
return False
|
||
else:
|
||
logger.info(f"{self.log_prefix}{global_config.bot.nickname} 决定进行回复 (focus模式)")
|
||
|
||
# 构建reply_to字符串
|
||
reply_to_str = await self.build_reply_to_str(action_message)
|
||
|
||
# 生成回复
|
||
with Timer("回复生成", cycle_timers):
|
||
response_set = await self._generate_response(
|
||
message_data=action_message,
|
||
available_actions=available_actions,
|
||
reply_to=reply_to_str,
|
||
request_type="chat.replyer.focus",
|
||
)
|
||
|
||
if not response_set:
|
||
logger.warning(f"{self.log_prefix}模型未生成回复内容")
|
||
return False
|
||
|
||
loop_info, reply_text, cycle_timers = await self._send_and_store_reply(
|
||
response_set, reply_to_str, loop_start_time, action_message, cycle_timers, thinking_id, plan_result
|
||
)
|
||
|
||
return True
|
||
|
||
else:
|
||
# 并行执行：同时进行回复发送和动作执行
|
||
# 先置空防止未定义错误
|
||
background_reply_task = None
|
||
background_action_task = None
|
||
# 如果是并行执行且在normal模式下，需要等待预生成的回复任务完成并发送回复
|
||
if self.loop_mode == ChatMode.NORMAL and is_parallel and gen_task:
|
||
|
||
async def handle_reply_task() -> Tuple[Optional[Dict[str, Any]], str, Dict[str, float]]:
|
||
# 等待预生成的回复任务完成
|
||
gather_timeout = global_config.chat.thinking_timeout
|
||
try:
|
||
response_set = await asyncio.wait_for(gen_task, timeout=gather_timeout)
|
||
|
||
except asyncio.TimeoutError:
|
||
logger.warning(
|
||
f"{self.log_prefix} 并行执行：回复生成超时>{global_config.chat.thinking_timeout}s，已跳过"
|
||
)
|
||
return None, "", {}
|
||
except asyncio.CancelledError:
|
||
logger.debug(f"{self.log_prefix} 并行执行：回复生成任务已被取消")
|
||
return None, "", {}
|
||
|
||
if not response_set:
|
||
logger.warning(f"{self.log_prefix} 模型超时或生成回复内容为空")
|
||
return None, "", {}
|
||
|
||
reply_to_str = await self.build_reply_to_str(action_message)
|
||
loop_info, reply_text, cycle_timers_reply = await self._send_and_store_reply(
|
||
response_set,
|
||
reply_to_str,
|
||
loop_start_time,
|
||
action_message,
|
||
cycle_timers,
|
||
thinking_id,
|
||
plan_result,
|
||
)
|
||
return loop_info, reply_text, cycle_timers_reply
|
||
|
||
# 执行回复任务并赋值到变量
|
||
background_reply_task = asyncio.create_task(handle_reply_task())
|
||
|
||
# 动作执行任务
|
||
async def handle_action_task():
|
||
with Timer("动作执行", cycle_timers):
|
||
success, reply_text, command = await self._handle_action(
|
||
action_type, reasoning, action_data, cycle_timers, thinking_id, action_message
|
||
)
|
||
return success, reply_text, command
|
||
|
||
# 执行动作任务并赋值到变量
|
||
background_action_task = asyncio.create_task(handle_action_task())
|
||
|
||
reply_loop_info = None
|
||
reply_text_from_reply = ""
|
||
action_success = False
|
||
action_reply_text = ""
|
||
action_command = ""
|
||
|
||
# 并行执行所有任务
|
||
if background_reply_task:
|
||
results = await asyncio.gather(
|
||
background_reply_task, background_action_task, return_exceptions=True
|
||
)
|
||
# 处理回复任务结果
|
||
reply_result = results[0]
|
||
if isinstance(reply_result, BaseException):
|
||
logger.error(f"{self.log_prefix} 回复任务执行异常: {reply_result}")
|
||
elif reply_result and reply_result[0] is not None:
|
||
reply_loop_info, reply_text_from_reply, _ = reply_result
|
||
|
||
# 处理动作任务结果
|
||
action_task_result = results[1]
|
||
if isinstance(action_task_result, BaseException):
|
||
logger.error(f"{self.log_prefix} 动作任务执行异常: {action_task_result}")
|
||
else:
|
||
action_success, action_reply_text, action_command = action_task_result
|
||
else:
|
||
results = await asyncio.gather(background_action_task, return_exceptions=True)
|
||
# 只有动作任务
|
||
action_task_result = results[0]
|
||
if isinstance(action_task_result, BaseException):
|
||
logger.error(f"{self.log_prefix} 动作任务执行异常: {action_task_result}")
|
||
else:
|
||
action_success, action_reply_text, action_command = action_task_result
|
||
|
||
# 构建最终的循环信息
|
||
if reply_loop_info:
|
||
# 如果有回复信息，使用回复的loop_info作为基础
|
||
loop_info = reply_loop_info
|
||
# 更新动作执行信息
|
||
loop_info["loop_action_info"].update(
|
||
{
|
||
"action_taken": action_success,
|
||
"command": action_command,
|
||
"taken_time": time.time(),
|
||
}
|
||
)
|
||
reply_text = reply_text_from_reply
|
||
else:
|
||
# 没有回复信息，构建纯动作的loop_info
|
||
loop_info = {
|
||
"loop_plan_info": {
|
||
"action_result": plan_result.get("action_result", {}),
|
||
},
|
||
"loop_action_info": {
|
||
"action_taken": action_success,
|
||
"reply_text": action_reply_text,
|
||
"command": action_command,
|
||
"taken_time": time.time(),
|
||
},
|
||
}
|
||
reply_text = action_reply_text
|
||
|
||
self.last_action = action_type
|
||
|
||
if ENABLE_S4U:
|
||
await stop_typing()
|
||
await mai_thinking_manager.get_mai_think(self.stream_id).do_think_after_response(reply_text)
|
||
|
||
self.end_cycle(loop_info, cycle_timers)
|
||
self.print_cycle_info(cycle_timers)
|
||
|
||
if self.loop_mode == ChatMode.NORMAL:
|
||
await self.willing_manager.after_generate_reply_handle(message_data.get("message_id", ""))
|
||
|
||
# 管理动作状态：当执行了非no_reply动作时进行记录
|
||
if action_type != "no_reply" and action_type != "no_action":
|
||
logger.info(f"{self.log_prefix} 执行了{action_type}动作")
|
||
return True
|
||
elif action_type == "no_action":
|
||
logger.info(f"{self.log_prefix} 执行了回复动作")
|
||
|
||
return True
|
||
|
||
async def _main_chat_loop(self):
|
||
"""主循环，持续进行计划并可能回复消息，直到被外部取消。"""
|
||
try:
|
||
while self.running:
|
||
# 主循环
|
||
success = await self._loopbody()
|
||
await asyncio.sleep(0.1)
|
||
if not success:
|
||
break
|
||
except asyncio.CancelledError:
|
||
# 设置了关闭标志位后被取消是正常流程
|
||
logger.info(f"{self.log_prefix} 麦麦已关闭聊天")
|
||
except Exception:
|
||
logger.error(f"{self.log_prefix} 麦麦聊天意外错误，将于3s后尝试重新启动")
|
||
print(traceback.format_exc())
|
||
await asyncio.sleep(3)
|
||
self._loop_task = asyncio.create_task(self._main_chat_loop())
|
||
logger.error(f"{self.log_prefix} 结束了当前聊天循环")
|
||
|
||
async def _handle_action(
|
||
self,
|
||
action: str,
|
||
reasoning: str,
|
||
action_data: dict,
|
||
cycle_timers: Dict[str, float],
|
||
thinking_id: str,
|
||
action_message: dict,
|
||
) -> 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,
|
||
chat_stream=self.chat_stream,
|
||
log_prefix=self.log_prefix,
|
||
action_message=action_message,
|
||
)
|
||
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}")
|
||
return False, "", ""
|
||
|
||
# 处理动作并获取结果
|
||
result = await action_handler.handle_action()
|
||
success, reply_text = result
|
||
command = ""
|
||
|
||
if reply_text == "timeout":
|
||
self.reply_timeout_count += 1
|
||
if self.reply_timeout_count > 5:
|
||
logger.warning(
|
||
f"[{self.log_prefix} ] 连续回复超时次数过多，{global_config.chat.thinking_timeout}秒 内大模型没有返回有效内容，请检查你的api是否速度过慢或配置错误。建议不要使用推理模型，推理模型生成速度过慢。或者尝试拉高thinking_timeout参数，这可能导致回复时间过长。"
|
||
)
|
||
logger.warning(f"{self.log_prefix} 回复生成超时{global_config.chat.thinking_timeout}s，已跳过")
|
||
return False, "", ""
|
||
|
||
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 normal_response(self, message_data: dict) -> bool:
|
||
"""
|
||
处理接收到的消息。
|
||
在"兴趣"模式下，判断是否回复并生成内容。
|
||
"""
|
||
|
||
interested_rate = message_data.get("interest_value") or 0.0
|
||
|
||
self.willing_manager.setup(message_data, self.chat_stream)
|
||
|
||
reply_probability = await self.willing_manager.get_reply_probability(message_data.get("message_id", ""))
|
||
|
||
talk_frequency = -1.00
|
||
|
||
if reply_probability < 1: # 简化逻辑，如果未提及 (reply_probability 为 0)，则获取意愿概率
|
||
additional_config = message_data.get("additional_config", {})
|
||
if additional_config and "maimcore_reply_probability_gain" in additional_config:
|
||
reply_probability += additional_config["maimcore_reply_probability_gain"]
|
||
reply_probability = min(max(reply_probability, 0), 1) # 确保概率在 0-1 之间
|
||
|
||
talk_frequency = global_config.chat.get_current_talk_frequency(self.stream_id)
|
||
reply_probability = talk_frequency * reply_probability
|
||
|
||
# 处理表情包
|
||
if message_data.get("is_emoji") or message_data.get("is_picid"):
|
||
reply_probability = 0
|
||
|
||
# 打印消息信息
|
||
mes_name = self.chat_stream.group_info.group_name if self.chat_stream.group_info else "私聊"
|
||
|
||
# logger.info(f"[{mes_name}] 当前聊天频率: {talk_frequency:.2f},兴趣值: {interested_rate:.2f},回复概率: {reply_probability * 100:.1f}%")
|
||
|
||
if reply_probability > 0.05:
|
||
logger.info(
|
||
f"[{mes_name}]"
|
||
f"{message_data.get('user_nickname')}:"
|
||
f"{message_data.get('processed_plain_text')}[兴趣:{interested_rate:.2f}][回复概率:{reply_probability * 100:.1f}%]"
|
||
)
|
||
|
||
if random.random() < reply_probability:
|
||
await self.willing_manager.before_generate_reply_handle(message_data.get("message_id", ""))
|
||
await self._observe(message_data=message_data)
|
||
return True
|
||
|
||
# 意愿管理器：注销当前message信息 (无论是否回复，只要处理过就删除)
|
||
self.willing_manager.delete(message_data.get("message_id", ""))
|
||
return False
|
||
|
||
async def _generate_response(
|
||
self,
|
||
message_data: dict,
|
||
available_actions: Optional[Dict[str, ActionInfo]],
|
||
reply_to: str,
|
||
request_type: str = "chat.replyer.normal",
|
||
) -> Optional[list]:
|
||
"""生成普通回复"""
|
||
try:
|
||
success, reply_set, _ = await generator_api.generate_reply(
|
||
chat_stream=self.chat_stream,
|
||
reply_to=reply_to,
|
||
available_actions=available_actions,
|
||
enable_tool=global_config.tool.enable_tool,
|
||
request_type=request_type,
|
||
from_plugin=False,
|
||
)
|
||
|
||
if not success or not reply_set:
|
||
logger.info(f"对 {message_data.get('processed_plain_text')} 的回复生成失败")
|
||
return None
|
||
|
||
return reply_set
|
||
|
||
except Exception as e:
|
||
logger.error(f"{self.log_prefix}回复生成出现错误：{str(e)} {traceback.format_exc()}")
|
||
return None
|
||
|
||
async def _send_response(self, reply_set, reply_to, thinking_start_time, message_data) -> str:
|
||
current_time = time.time()
|
||
new_message_count = message_api.count_new_messages(
|
||
chat_id=self.chat_stream.stream_id, start_time=thinking_start_time, end_time=current_time
|
||
)
|
||
platform = message_data.get("user_platform", "")
|
||
user_id = message_data.get("user_id", "")
|
||
reply_to_platform_id = f"{platform}:{user_id}"
|
||
|
||
need_reply = new_message_count >= random.randint(2, 4)
|
||
|
||
if need_reply:
|
||
logger.info(f"{self.log_prefix} 从思考到回复，共有{new_message_count}条新消息，使用引用回复")
|
||
else:
|
||
logger.info(f"{self.log_prefix} 从思考到回复，共有{new_message_count}条新消息，不使用引用回复")
|
||
|
||
reply_text = ""
|
||
|
||
# 检查是否为主动思考且决定沉默
|
||
is_proactive_thinking = message_data.get("message_type") == "proactive_thinking"
|
||
|
||
first_replied = False
|
||
for reply_seg in reply_set:
|
||
data = reply_seg[1]
|
||
reply_text += data
|
||
|
||
# 如果是主动思考且回复内容是"沉默"，则不发送消息
|
||
if is_proactive_thinking and data.strip() == "沉默":
|
||
logger.info(f"{self.log_prefix} 主动思考决定保持沉默，不发送消息")
|
||
continue
|
||
|
||
if not first_replied:
|
||
if need_reply:
|
||
await send_api.text_to_stream(
|
||
text=data,
|
||
stream_id=self.chat_stream.stream_id,
|
||
reply_to=reply_to,
|
||
reply_to_platform_id=reply_to_platform_id,
|
||
typing=False,
|
||
)
|
||
else:
|
||
await send_api.text_to_stream(
|
||
text=data,
|
||
stream_id=self.chat_stream.stream_id,
|
||
reply_to_platform_id=reply_to_platform_id,
|
||
typing=False,
|
||
)
|
||
first_replied = True
|
||
else:
|
||
await send_api.text_to_stream(
|
||
text=data,
|
||
stream_id=self.chat_stream.stream_id,
|
||
reply_to_platform_id=reply_to_platform_id,
|
||
typing=True,
|
||
)
|
||
|
||
return reply_text
|