615 lines
25 KiB
Python
615 lines
25 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
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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.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.person_info.person_info import get_person_info_manager
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from src.plugin_system.base.component_types import ActionInfo, ChatMode
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from src.plugin_system.apis import generator_api, send_api, message_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 maim_message.message_base import GroupInfo
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from src.mais4u.constant_s4u import ENABLE_S4U
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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.loop_mode = ChatMode.NORMAL # 初始循环模式为普通模式
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# 新增：消息计数器和疲惫阈值
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self._message_count = 0 # 发送的消息计数
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self._message_threshold = max(10, int(30 * global_config.chat.focus_value))
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self._fatigue_triggered = False # 是否已触发疲惫退出
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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_amplifier = 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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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(10)
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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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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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async def _loopbody(self):
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if self.loop_mode == ChatMode.FOCUS:
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if await self._observe():
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self.energy_value -= 1 * global_config.chat.focus_value
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else:
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self.energy_value -= 3 * global_config.chat.focus_value
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if self.energy_value <= 1:
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self.energy_value = 1
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self.loop_mode = ChatMode.NORMAL
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return True
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return True
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elif self.loop_mode == ChatMode.NORMAL:
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new_messages_data = get_raw_msg_by_timestamp_with_chat(
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chat_id=self.stream_id,
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timestamp_start=self.last_read_time,
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timestamp_end=time.time(),
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limit=10,
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limit_mode="earliest",
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filter_bot=True,
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)
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if len(new_messages_data) > 3 * global_config.chat.focus_value:
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self.loop_mode = ChatMode.FOCUS
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self.energy_value = 10 + (len(new_messages_data) / (3 * global_config.chat.focus_value)) * 10
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return True
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if self.energy_value >= 30 * global_config.chat.focus_value:
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self.loop_mode = ChatMode.FOCUS
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return True
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if new_messages_data:
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earliest_messages_data = new_messages_data[0]
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self.last_read_time = earliest_messages_data.get("time")
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if_think = await self.normal_response(earliest_messages_data)
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if if_think:
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factor = max(global_config.chat.focus_value, 0.1)
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self.energy_value *= 1.1 / factor
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logger.info(f"{self.log_prefix} 麦麦进行了思考，能量值按倍数增加，当前能量值：{self.energy_value}")
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else:
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self.energy_value += 0.1 / global_config.chat.focus_value
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logger.info(f"{self.log_prefix} 麦麦没有进行思考，能量值线性增加，当前能量值：{self.energy_value}")
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logger.debug(f"{self.log_prefix} 当前能量值：{self.energy_value}")
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return True
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await asyncio.sleep(1)
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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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person_id = person_info_manager.get_person_id(
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message_data.get("chat_info_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_typing(self):
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group_info = GroupInfo(platform="amaidesu_default", group_id="114514", group_name="内心")
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chat = await get_chat_manager().get_or_create_stream(
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platform="amaidesu_default",
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user_info=None,
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group_info=group_info,
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)
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await send_api.custom_to_stream(
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message_type="state", content="typing", stream_id=chat.stream_id, storage_message=False
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)
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async def stop_typing(self):
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group_info = GroupInfo(platform="amaidesu_default", group_id="114514", group_name="内心")
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chat = await get_chat_manager().get_or_create_stream(
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platform="amaidesu_default",
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user_info=None,
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group_info=group_info,
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)
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await send_api.custom_to_stream(
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message_type="state", content="stop_typing", stream_id=chat.stream_id, storage_message=False
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)
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async def _observe(self, message_data: Optional[Dict[str, Any]] = None):
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# sourcery skip: hoist-statement-from-if, merge-comparisons, reintroduce-else
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if not message_data:
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message_data = {}
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action_type = "no_action"
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# 创建新的循环信息
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cycle_timers, thinking_id = self.start_cycle()
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logger.info(f"{self.log_prefix} 开始第{self._cycle_counter}次思考[模式：{self.loop_mode}]")
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if ENABLE_S4U:
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await self.send_typing()
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async with global_prompt_manager.async_message_scope(self.chat_stream.context.get_template_name()):
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loop_start_time = time.time()
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await self.relationship_builder.build_relation()
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available_actions = {}
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# 第一步：动作修改
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with Timer("动作修改", cycle_timers):
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try:
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await self.action_modifier.modify_actions()
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available_actions = self.action_manager.get_using_actions()
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except Exception as e:
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logger.error(f"{self.log_prefix} 动作修改失败: {e}")
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# 如果normal，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）
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if self.loop_mode == ChatMode.NORMAL:
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reply_to_str = await self.build_reply_to_str(message_data)
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gen_task = asyncio.create_task(self._generate_response(message_data, available_actions, reply_to_str))
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with Timer("规划器", cycle_timers):
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plan_result, target_message = await self.action_planner.plan(mode=self.loop_mode)
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action_result: dict = plan_result.get("action_result", {}) # type: ignore
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action_type, action_data, reasoning, is_parallel = (
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action_result.get("action_type", "error"),
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action_result.get("action_data", {}),
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action_result.get("reasoning", "未提供理由"),
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action_result.get("is_parallel", True),
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)
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action_data["loop_start_time"] = loop_start_time
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if self.loop_mode == ChatMode.NORMAL:
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if action_type == "no_action":
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logger.info(f"[{self.log_prefix}] {global_config.bot.nickname} 决定进行回复")
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elif is_parallel:
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logger.info(
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f"[{self.log_prefix}] {global_config.bot.nickname} 决定进行回复, 同时执行{action_type}动作"
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)
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else:
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logger.info(f"[{self.log_prefix}] {global_config.bot.nickname} 决定执行{action_type}动作")
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if action_type == "no_action":
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# 等待回复生成完毕
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gather_timeout = global_config.chat.thinking_timeout
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try:
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response_set = await asyncio.wait_for(gen_task, timeout=gather_timeout)
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except asyncio.TimeoutError:
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response_set = None
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if response_set:
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content = " ".join([item[1] for item in response_set if item[0] == "text"])
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# 模型炸了，没有回复内容生成
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if not response_set:
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logger.warning(f"[{self.log_prefix}] 模型未生成回复内容")
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return False
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elif action_type not in ["no_action"] and not is_parallel:
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logger.info(
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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"
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)
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return False
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logger.info(f"[{self.log_prefix}] {global_config.bot.nickname} 决定的回复内容: {content}")
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# 发送回复 (不再需要传入 chat)
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reply_text = await self._send_response(response_set, reply_to_str, loop_start_time,message_data)
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if ENABLE_S4U:
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await self.stop_typing()
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await mai_thinking_manager.get_mai_think(self.stream_id).do_think_after_response(reply_text)
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return True
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else:
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action_message: Dict[str, Any] = message_data or target_message # type: ignore
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# 动作执行计时
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with Timer("动作执行", cycle_timers):
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success, reply_text, command = await self._handle_action(
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action_type, reasoning, action_data, cycle_timers, thinking_id, action_message
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)
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loop_info = {
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"loop_plan_info": {
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"action_result": plan_result.get("action_result", {}),
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},
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"loop_action_info": {
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"action_taken": success,
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"reply_text": reply_text,
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"command": command,
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"taken_time": time.time(),
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},
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}
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if loop_info["loop_action_info"]["command"] == "stop_focus_chat":
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logger.info(f"{self.log_prefix} 麦麦决定停止专注聊天")
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return False
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# 停止该聊天模式的循环
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self.end_cycle(loop_info, cycle_timers)
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self.print_cycle_info(cycle_timers)
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if self.loop_mode == ChatMode.NORMAL:
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await self.willing_manager.after_generate_reply_handle(message_data.get("message_id", ""))
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if action_type != "no_reply" and action_type != "no_action":
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return True
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return True
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async def _main_chat_loop(self):
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"""主循环，持续进行计划并可能回复消息，直到被外部取消。"""
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try:
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while self.running: # 主循环
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success = await self._loopbody()
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await asyncio.sleep(0.1)
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if not success:
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break
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logger.info(f"{self.log_prefix} 麦麦已强制离开聊天")
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except asyncio.CancelledError:
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# 设置了关闭标志位后被取消是正常流程
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logger.info(f"{self.log_prefix} 麦麦已关闭聊天")
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except Exception:
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logger.error(f"{self.log_prefix} 麦麦聊天意外错误")
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print(traceback.format_exc())
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# 理论上不能到这里
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logger.error(f"{self.log_prefix} 麦麦聊天意外错误，结束了聊天循环")
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||
async def _handle_action(
|
||
self,
|
||
action: str,
|
||
reasoning: str,
|
||
action_data: dict,
|
||
cycle_timers: dict,
|
||
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_amplifier
|
||
|
||
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
|
||
) -> 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_in_normal_chat,
|
||
request_type="chat.replyer.normal",
|
||
)
|
||
|
||
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):
|
||
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)
|
||
|
||
logger.info(
|
||
f"{self.log_prefix} 从思考到回复，共有{new_message_count}条新消息，{'使用' if need_reply else '不使用'}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
|