0ffcae4d44
删除了 `ChatMood` 类中的 `is_angry_from_wakeup` 属性，并移除了 `MoodManager` 中所有与之相关的方法，包括： - `reset_mood_by_chat_id` - `set_angry_from_wakeup` - `clear_angry_from_wakeup` - `start_insomnia` - `stop_insomnia` - `get_angry_prompt_addition` 这些逻辑现在由更通用的状态机和动作系统处理，简化了情绪模块的职责，使其更专注于核心情绪值的管理。
287 lines
10 KiB
Python
287 lines
10 KiB
Python
import math
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import random
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import time
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from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_by_timestamp_with_chat_inclusive
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from src.chat.utils.prompt import Prompt, global_prompt_manager
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from src.common.data_models.database_data_model import DatabaseMessages
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from src.common.logger import get_logger
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from src.config.config import global_config, model_config
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from src.llm_models.utils_model import LLMRequest
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from src.manager.async_task_manager import AsyncTask, async_task_manager
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logger = get_logger("mood")
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def init_prompt():
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Prompt(
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"""
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{chat_talking_prompt}
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以上是群里正在进行的聊天记录
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{identity_block}
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你刚刚的情绪状态是：{mood_state}
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现在，发送了消息，引起了你的注意，你对其进行了阅读和思考，请你输出一句话描述你新的情绪状态
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请只输出情绪状态，不要输出其他内容：
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""",
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"change_mood_prompt",
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)
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Prompt(
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"""
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{chat_talking_prompt}
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以上是群里最近的聊天记录
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{identity_block}
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你之前的情绪状态是：{mood_state}
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距离你上次关注群里消息已经过去了一段时间，你冷静了下来，请你输出一句话描述你现在的情绪状态
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请只输出情绪状态，不要输出其他内容：
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""",
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"regress_mood_prompt",
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)
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class ChatMood:
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def __init__(self, chat_id: str):
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self.chat_id: str = chat_id
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# 这些将在异步初始化中设置
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self.chat_stream = None # type: ignore
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self.log_prefix = f"[{chat_id}]"
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self._initialized = False
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self.mood_state: str = "感觉很平静"
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async def _initialize(self):
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"""异步初始化方法"""
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if not self._initialized:
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try:
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from src.chat.message_receive.chat_stream import get_chat_manager
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chat_manager = get_chat_manager()
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self.chat_stream = await chat_manager.get_stream(self.chat_id)
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if not self.chat_stream:
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# 如果找不到聊天流，使用基础日志前缀但不抛出异常
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self.log_prefix = f"[{self.chat_id}]"
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logger.warning(f"Chat stream for chat_id {self.chat_id} not found during mood initialization")
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else:
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self.log_prefix = f"[{self.chat_stream.group_info.group_name if self.chat_stream.group_info else self.chat_stream.user_info.user_nickname}]"
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# 初始化回归计数
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if not hasattr(self, "regression_count"):
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self.regression_count = 0
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# 初始化情绪模型
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if not hasattr(self, "mood_model"):
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self.mood_model = LLMRequest(model_set=model_config.model_task_config.emotion, request_type="mood")
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# 初始化最后变化时间
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if not hasattr(self, "last_change_time"):
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self.last_change_time = 0
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self._initialized = True
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logger.debug(f"{self.log_prefix} 情绪系统初始化完成")
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except Exception as e:
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logger.error(f"情绪系统初始化失败: {e}")
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# 设置基础初始化状态，避免重复尝试
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self.log_prefix = f"[{self.chat_id}]"
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self._initialized = True
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if not hasattr(self, "regression_count"):
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self.regression_count = 0
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if not hasattr(self, "mood_model"):
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self.mood_model = LLMRequest(model_set=model_config.model_task_config.emotion, request_type="mood")
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if not hasattr(self, "last_change_time"):
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self.last_change_time = 0
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async def update_mood_by_message(self, message: DatabaseMessages, interested_rate: float):
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# 确保异步初始化已完成
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await self._initialize()
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# 如果当前聊天处于失眠状态，则锁定情绪，不允许更新
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if self.chat_id in mood_manager.insomnia_chats:
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logger.debug(f"{self.log_prefix} 处于失眠状态，情绪已锁定，跳过更新。")
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return
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self.regression_count = 0
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# 使用 DatabaseMessages 的时间字段
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message_time = message.time
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# 防止负时间差
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during_last_time = max(0, message_time - self.last_change_time)
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base_probability = 0.05
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time_multiplier = 4 * (1 - math.exp(-0.01 * during_last_time))
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if interested_rate <= 0:
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interest_multiplier = 0
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else:
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interest_multiplier = 2 * math.pow(interested_rate, 0.25)
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logger.debug(
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f"base_probability: {base_probability}, time_multiplier: {time_multiplier}, interest_multiplier: {interest_multiplier}"
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)
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update_probability = global_config.mood.mood_update_threshold * min(
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1.0, base_probability * time_multiplier * interest_multiplier
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)
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if random.random() > update_probability:
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logger.debug(f"{self.log_prefix} 情绪更新概率未达到阈值，跳过更新。概率: {update_probability:.3f}")
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return
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logger.debug(
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f"{self.log_prefix} 更新情绪状态，感兴趣度: {interested_rate:.2f}, 更新概率: {update_probability:.2f}"
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)
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message_list_before_now = await get_raw_msg_by_timestamp_with_chat_inclusive(
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chat_id=self.chat_id,
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timestamp_start=self.last_change_time,
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timestamp_end=message_time,
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limit=int(global_config.chat.max_context_size / 3),
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limit_mode="last",
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)
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chat_talking_prompt = await build_readable_messages(
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message_list_before_now,
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replace_bot_name=True,
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merge_messages=False,
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timestamp_mode="normal_no_YMD",
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read_mark=0.0,
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truncate=True,
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show_actions=True,
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)
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bot_name = global_config.bot.nickname
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if global_config.bot.alias_names:
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bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}"
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else:
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bot_nickname = ""
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prompt_personality = global_config.personality.personality_core
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identity_block = f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}："
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prompt = await global_prompt_manager.format_prompt(
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"change_mood_prompt",
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chat_talking_prompt=chat_talking_prompt,
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identity_block=identity_block,
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mood_state=self.mood_state,
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)
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response, (reasoning_content, _, _) = await self.mood_model.generate_response_async(
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prompt=prompt, temperature=0.7
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)
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if global_config.debug.show_prompt:
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logger.debug(f"{self.log_prefix} prompt: {prompt}")
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logger.debug(f"{self.log_prefix} response: {response}")
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logger.debug(f"{self.log_prefix} reasoning_content: {reasoning_content}")
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logger.info(f"{self.log_prefix} 情绪状态更新为: {response}")
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self.mood_state = response
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self.last_change_time = message_time
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async def regress_mood(self):
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message_time = time.time()
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message_list_before_now = await get_raw_msg_by_timestamp_with_chat_inclusive(
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chat_id=self.chat_id,
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timestamp_start=self.last_change_time,
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timestamp_end=message_time,
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limit=15,
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limit_mode="last",
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)
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chat_talking_prompt = await build_readable_messages(
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message_list_before_now,
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replace_bot_name=True,
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merge_messages=False,
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timestamp_mode="normal_no_YMD",
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read_mark=0.0,
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truncate=True,
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show_actions=True,
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)
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bot_name = global_config.bot.nickname
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if global_config.bot.alias_names:
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bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}"
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else:
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bot_nickname = ""
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prompt_personality = global_config.personality.personality_core
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identity_block = f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}："
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prompt = await global_prompt_manager.format_prompt(
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"regress_mood_prompt",
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chat_talking_prompt=chat_talking_prompt,
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identity_block=identity_block,
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mood_state=self.mood_state,
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)
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response, (reasoning_content, _, _) = await self.mood_model.generate_response_async(
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prompt=prompt, temperature=0.7
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)
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if global_config.debug.show_prompt:
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logger.debug(f"{self.log_prefix} prompt: {prompt}")
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logger.debug(f"{self.log_prefix} response: {response}")
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logger.debug(f"{self.log_prefix} reasoning_content: {reasoning_content}")
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logger.info(f"{self.log_prefix} 情绪状态转变为: {response}")
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self.mood_state = response
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self.regression_count += 1
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class MoodRegressionTask(AsyncTask):
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def __init__(self, mood_manager: "MoodManager"):
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super().__init__(task_name="MoodRegressionTask", run_interval=30)
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self.mood_manager = mood_manager
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async def run(self):
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logger.debug("开始情绪回归任务...")
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now = time.time()
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for mood in self.mood_manager.mood_list:
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if mood.last_change_time == 0:
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continue
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if now - mood.last_change_time > 180:
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if mood.regression_count >= 3:
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continue
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logger.debug(f"{mood.log_prefix} 开始情绪回归, 第 {mood.regression_count + 1} 次")
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await mood.regress_mood()
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class MoodManager:
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def __init__(self):
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self.mood_list: list[ChatMood] = []
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"""当前情绪状态"""
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self.task_started: bool = False
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self.insomnia_chats: set[str] = set() # 正在失眠的聊天ID列表
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async def start(self):
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"""启动情绪回归后台任务"""
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if self.task_started:
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return
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logger.info("启动情绪回归任务...")
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task = MoodRegressionTask(self)
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await async_task_manager.add_task(task)
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self.task_started = True
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logger.info("情绪回归任务已启动")
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def get_mood_by_chat_id(self, chat_id: str) -> ChatMood:
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for mood in self.mood_list:
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if mood.chat_id == chat_id:
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return mood
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new_mood = ChatMood(chat_id)
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self.mood_list.append(new_mood)
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return new_mood
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init_prompt()
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mood_manager = MoodManager()
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"""全局情绪管理器"""
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