838 lines
35 KiB
Python
838 lines
35 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 collections import deque
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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.utils.chat_message_builder import get_raw_msg_by_timestamp_with_chat
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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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# no_reply逻辑已集成到heartFC_chat.py中，不再需要导入
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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.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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self.focus_energy = 1
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self.no_reply_consecutive = 0
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# 最近三次no_reply的新消息兴趣度记录
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self.recent_interest_records: deque = deque(maxlen=3)
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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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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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if exception := task.exception():
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logger.error(f"{self.log_prefix} HeartFChatting: 能量循环异常: {exception}")
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logger.error(traceback.format_exc())
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else:
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logger.info(f"{self.log_prefix} HeartFChatting: 能量循环完成")
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async def _energy_loop(self):
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while self.running:
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await asyncio.sleep(12)
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self.energy_value -= 0.5
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self.energy_value = max(self.energy_value, 0.3)
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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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# 获取动作类型，兼容新旧格式
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action_type = "未知动作"
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if hasattr(self, '_current_cycle_detail') and self._current_cycle_detail:
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loop_plan_info = self._current_cycle_detail.loop_plan_info
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if isinstance(loop_plan_info, dict):
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action_result = loop_plan_info.get('action_result', {})
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if isinstance(action_result, dict):
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# 旧格式：action_result是字典
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action_type = action_result.get('action_type', '未知动作')
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elif isinstance(action_result, list) and action_result:
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# 新格式：action_result是actions列表
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action_type = action_result[0].get('action_type', '未知动作')
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elif isinstance(loop_plan_info, list) and loop_plan_info:
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# 直接是actions列表的情况
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action_type = loop_plan_info[0].get('action_type', '未知动作')
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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: {action_type}"
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+ (f"\n详情: {'; '.join(timer_strings)}" if timer_strings else "")
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)
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def _determine_form_type(self) -> str:
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"""判断使用哪种形式的no_reply"""
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# 如果连续no_reply次数少于3次，使用waiting形式
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if self.no_reply_consecutive <= 3:
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self.focus_energy = 1
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else:
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# 计算最近三次记录的兴趣度总和
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total_recent_interest = sum(self.recent_interest_records)
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# 计算调整后的阈值
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adjusted_threshold = 3 / global_config.chat.get_current_talk_frequency(self.stream_id)
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logger.info(f"{self.log_prefix} 最近三次兴趣度总和: {total_recent_interest:.2f}, 调整后阈值: {adjusted_threshold:.2f}")
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# 如果兴趣度总和小于阈值，进入breaking形式
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if total_recent_interest < adjusted_threshold:
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logger.info(f"{self.log_prefix} 兴趣度不足，进入breaking形式")
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self.focus_energy = random.randint(3, 6)
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else:
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logger.info(f"{self.log_prefix} 兴趣度充足")
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self.focus_energy = 1
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async def _should_process_messages(self, new_message: List[Dict[str, Any]]) -> tuple[bool,float]:
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"""
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判断是否应该处理消息
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Args:
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new_message: 新消息列表
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mode: 当前聊天模式
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Returns:
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bool: 是否应该处理消息
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"""
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new_message_count = len(new_message)
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# talk_frequency = global_config.chat.get_current_talk_frequency(self.stream_id)
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modified_exit_count_threshold = self.focus_energy / global_config.chat.focus_value
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total_interest = 0.0
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for msg_dict in new_message:
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interest_value = msg_dict.get("interest_value", 0.0)
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if msg_dict.get("processed_plain_text", ""):
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total_interest += interest_value
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if new_message_count >= modified_exit_count_threshold:
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# 记录兴趣度到列表
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self.recent_interest_records.append(total_interest)
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logger.info(
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f"{self.log_prefix} 累计消息数量达到{new_message_count}条(>{modified_exit_count_threshold:.1f})，结束等待"
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)
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logger.info(self.last_read_time)
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logger.info(new_message)
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return True,total_interest/new_message_count
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# 检查累计兴趣值
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if new_message_count > 0:
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# 只在兴趣值变化时输出log
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if not hasattr(self, "_last_accumulated_interest") or total_interest != self._last_accumulated_interest:
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logger.info(f"{self.log_prefix} breaking形式当前累计兴趣值: {total_interest:.2f}, 专注度: {global_config.chat.focus_value:.1f}")
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self._last_accumulated_interest = total_interest
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if total_interest >= 3 / global_config.chat.focus_value:
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# 记录兴趣度到列表
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self.recent_interest_records.append(total_interest)
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logger.info(
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f"{self.log_prefix} 累计兴趣值达到{total_interest:.2f}(>{3 / global_config.chat.focus_value})，结束等待"
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)
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return True,total_interest/new_message_count
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# 每10秒输出一次等待状态
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if int(time.time() - self.last_read_time) > 0 and int(time.time() - self.last_read_time) % 10 == 0:
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logger.info(
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f"{self.log_prefix} 已等待{time.time() - self.last_read_time:.0f}秒，累计{new_message_count}条消息，累计兴趣{total_interest:.1f}，继续等待..."
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)
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await asyncio.sleep(0.5)
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return False,0.0
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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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# 统一的消息处理逻辑
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should_process,interest_value = await self._should_process_messages(recent_messages_dict)
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if should_process:
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# earliest_message_data = recent_messages_dict[0]
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# self.last_read_time = earliest_message_data.get("time")
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self.last_read_time = time.time()
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await self._observe(interest_value = interest_value)
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else:
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# Normal模式：消息数量不足，等待
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await asyncio.sleep(0.5)
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return True
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return True
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async def build_reply_to_str(self, message_data: dict):
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person_info_manager = get_person_info_manager()
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# 获取 platform，如果不存在则从 chat_stream 获取，如果还是 None 则使用默认值
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platform = message_data.get("chat_info_platform")
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if platform is None:
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platform = getattr(self.chat_stream, "platform", "unknown")
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person_id = person_info_manager.get_person_id(
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platform, # type: ignore
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message_data.get("user_id"), # type: ignore
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)
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person_name = await person_info_manager.get_value(person_id, "person_name")
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return f"{person_name}:{message_data.get('processed_plain_text')}"
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async def _send_and_store_reply(
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self,
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response_set,
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reply_to_str,
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loop_start_time,
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action_message,
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cycle_timers: Dict[str, float],
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thinking_id,
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actions,
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) -> Tuple[Dict[str, Any], str, Dict[str, float]]:
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with Timer("回复发送", cycle_timers):
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reply_text = await self._send_response(response_set, reply_to_str, loop_start_time, action_message)
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# 存储reply action信息
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person_info_manager = get_person_info_manager()
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# 获取 platform，如果不存在则从 chat_stream 获取，如果还是 None 则使用默认值
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platform = action_message.get("chat_info_platform")
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if platform is None:
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platform = getattr(self.chat_stream, "platform", "unknown")
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person_id = person_info_manager.get_person_id(
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platform,
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action_message.get("user_id", ""),
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)
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person_name = await person_info_manager.get_value(person_id, "person_name")
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action_prompt_display = f"你对{person_name}进行了回复：{reply_text}"
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await database_api.store_action_info(
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chat_stream=self.chat_stream,
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action_build_into_prompt=False,
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action_prompt_display=action_prompt_display,
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action_done=True,
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thinking_id=thinking_id,
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action_data={"reply_text": reply_text, "reply_to": reply_to_str},
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action_name="reply",
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)
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# 构建循环信息
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loop_info: Dict[str, Any] = {
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"loop_plan_info": {
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"action_result": actions,
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},
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"loop_action_info": {
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"action_taken": True,
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"reply_text": 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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return loop_info, reply_text, cycle_timers
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async def _observe(self,interest_value:float = 0.0) -> bool:
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action_type = "no_action"
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reply_text = "" # 初始化reply_text变量，避免UnboundLocalError
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reply_to_str = "" # 初始化reply_to_str变量
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# 根据interest_value计算概率，决定使用哪种planner模式
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# interest_value越高，越倾向于使用Normal模式
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import random
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import math
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# 使用sigmoid函数将interest_value转换为概率
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# 当interest_value为0时，概率接近0（使用Focus模式）
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# 当interest_value很高时，概率接近1（使用Normal模式）
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def calculate_normal_mode_probability(interest_val: float) -> float:
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# 使用sigmoid函数，调整参数使概率分布更合理
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# 当interest_value = 0时，概率约为0.1
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# 当interest_value = 1时，概率约为0.5
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# 当interest_value = 2时，概率约为0.8
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# 当interest_value = 3时，概率约为0.95
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k = 2.0 # 控制曲线陡峭程度
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x0 = 1.0 # 控制曲线中心点
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return 1.0 / (1.0 + math.exp(-k * (interest_val - x0)))
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normal_mode_probability = calculate_normal_mode_probability(interest_value) / global_config.chat.get_current_talk_frequency(self.stream_id)
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|
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# 根据概率决定使用哪种模式
|
||
if random.random() < normal_mode_probability:
|
||
mode = ChatMode.NORMAL
|
||
logger.info(f"{self.log_prefix} 基于兴趣值 {interest_value:.2f}，概率 {normal_mode_probability:.2f}，选择Normal planner模式")
|
||
else:
|
||
mode = ChatMode.FOCUS
|
||
logger.info(f"{self.log_prefix} 基于兴趣值 {interest_value:.2f}，概率 {normal_mode_probability:.2f}，选择Focus planner模式")
|
||
|
||
# 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")
|
||
|
||
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}")
|
||
|
||
# 执行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):
|
||
actions, _= await self.action_planner.plan(
|
||
mode=mode,
|
||
loop_start_time=loop_start_time,
|
||
available_actions=available_actions,
|
||
)
|
||
|
||
# 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),
|
||
# )
|
||
|
||
|
||
# 3. 并行执行所有动作
|
||
async def execute_action(action_info):
|
||
"""执行单个动作的通用函数"""
|
||
try:
|
||
if action_info["action_type"] == "no_reply":
|
||
# 直接处理no_reply逻辑，不再通过动作系统
|
||
reason = action_info.get("reasoning", "选择不回复")
|
||
logger.info(f"{self.log_prefix} 选择不回复，原因: {reason}")
|
||
|
||
# 存储no_reply信息到数据库
|
||
await database_api.store_action_info(
|
||
chat_stream=self.chat_stream,
|
||
action_build_into_prompt=False,
|
||
action_prompt_display=reason,
|
||
action_done=True,
|
||
thinking_id=thinking_id,
|
||
action_data={"reason": reason},
|
||
action_name="no_reply",
|
||
)
|
||
|
||
return {
|
||
"action_type": "no_reply",
|
||
"success": True,
|
||
"reply_text": "",
|
||
"command": ""
|
||
}
|
||
elif action_info["action_type"] != "reply":
|
||
# 执行普通动作
|
||
with Timer("动作执行", cycle_timers):
|
||
success, reply_text, command = await self._handle_action(
|
||
action_info["action_type"],
|
||
action_info["reasoning"],
|
||
action_info["action_data"],
|
||
cycle_timers,
|
||
thinking_id,
|
||
action_info["action_message"]
|
||
)
|
||
return {
|
||
"action_type": action_info["action_type"],
|
||
"success": success,
|
||
"reply_text": reply_text,
|
||
"command": command
|
||
}
|
||
else:
|
||
# 执行回复动作
|
||
reply_to_str = await self.build_reply_to_str(action_info["action_message"])
|
||
|
||
|
||
# 生成回复
|
||
gather_timeout = global_config.chat.thinking_timeout
|
||
try:
|
||
response_set = await asyncio.wait_for(
|
||
self._generate_response(
|
||
message_data=action_info["action_message"],
|
||
available_actions=action_info["available_actions"],
|
||
reply_to=reply_to_str,
|
||
request_type="chat.replyer",
|
||
),
|
||
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 {
|
||
"action_type": "reply",
|
||
"success": False,
|
||
"reply_text": "",
|
||
"loop_info": 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 {
|
||
"action_type": "reply",
|
||
"success": False,
|
||
"reply_text": "",
|
||
"loop_info": 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 {
|
||
"action_type": "reply",
|
||
"success": False,
|
||
"reply_text": "",
|
||
"loop_info": None
|
||
}
|
||
|
||
loop_info, reply_text, cycle_timers_reply = await self._send_and_store_reply(
|
||
response_set,
|
||
reply_to_str,
|
||
loop_start_time,
|
||
action_info["action_message"],
|
||
cycle_timers,
|
||
thinking_id,
|
||
actions,
|
||
)
|
||
return {
|
||
"action_type": "reply",
|
||
"success": True,
|
||
"reply_text": reply_text,
|
||
"loop_info": loop_info
|
||
}
|
||
except Exception as e:
|
||
logger.error(f"{self.log_prefix} 执行动作时出错: {e}")
|
||
logger.error(f"{self.log_prefix} 错误信息: {traceback.format_exc()}")
|
||
return {
|
||
"action_type": action_info["action_type"],
|
||
"success": False,
|
||
"reply_text": "",
|
||
"loop_info": None,
|
||
"error": str(e)
|
||
}
|
||
|
||
# 创建所有动作的后台任务
|
||
# print(actions)
|
||
|
||
action_tasks = [asyncio.create_task(execute_action(action)) for action in actions]
|
||
|
||
# 并行执行所有任务
|
||
results = await asyncio.gather(*action_tasks, return_exceptions=True)
|
||
|
||
# 处理执行结果
|
||
reply_loop_info = None
|
||
reply_text_from_reply = ""
|
||
action_success = False
|
||
action_reply_text = ""
|
||
action_command = ""
|
||
|
||
for i, result in enumerate(results):
|
||
if isinstance(result, BaseException):
|
||
logger.error(f"{self.log_prefix} 动作执行异常: {result}")
|
||
continue
|
||
|
||
action_info = actions[i]
|
||
if result["action_type"] != "reply":
|
||
action_success = result["success"]
|
||
action_reply_text = result["reply_text"]
|
||
action_command = result.get("command", "")
|
||
elif result["action_type"] == "reply":
|
||
if result["success"]:
|
||
reply_loop_info = result["loop_info"]
|
||
reply_text_from_reply = result["reply_text"]
|
||
else:
|
||
logger.warning(f"{self.log_prefix} 回复动作执行失败")
|
||
|
||
# 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": actions,
|
||
},
|
||
"loop_action_info": {
|
||
"action_taken": action_success,
|
||
"reply_text": action_reply_text,
|
||
"command": action_command,
|
||
"taken_time": time.time(),
|
||
},
|
||
}
|
||
reply_text = action_reply_text
|
||
|
||
|
||
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)
|
||
|
||
# await self.willing_manager.after_generate_reply_handle(message_data.get("message_id", ""))
|
||
|
||
action_type = actions[0]["action_type"] if actions else "no_action"
|
||
|
||
# 管理no_reply计数器：当执行了非no_reply动作时，重置计数器
|
||
if action_type != "no_reply":
|
||
# no_reply逻辑已集成到heartFC_chat.py中，直接重置计数器
|
||
self.recent_interest_records.clear()
|
||
self.no_reply_consecutive = 0
|
||
logger.debug(f"{self.log_prefix} 执行了{action_type}动作，重置no_reply计数器")
|
||
return True
|
||
|
||
if action_type == "no_reply":
|
||
self.no_reply_consecutive += 1
|
||
self._determine_form_type()
|
||
|
||
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 _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")
|
||
|
||
reply_text = ""
|
||
first_replied = False
|
||
for reply_seg in reply_set:
|
||
data = reply_seg[1]
|
||
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,
|
||
)
|
||
reply_text += data
|
||
|
||
return reply_text
|