mirror of https://github.com/Mai-with-u/MaiBot.git
457 lines
17 KiB
Python
457 lines
17 KiB
Python
import asyncio
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import json
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import time
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from src.chat.message_receive.message import MessageRecv
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from src.llm_models.utils_model import LLMRequest
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from src.common.logger import get_logger
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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.config.config import global_config, model_config
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from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
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from src.manager.async_task_manager import AsyncTask, async_task_manager
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from src.plugin_system.apis import send_api
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from src.mais4u.s4u_config import s4u_config
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"""
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情绪管理系统使用说明：
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1. 情绪数值系统：
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- 情绪包含四个维度：joy(喜), anger(怒), sorrow(哀), fear(惧)
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- 每个维度的取值范围为1-10
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- 当情绪发生变化时，会自动发送到ws端处理
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2. 情绪更新机制：
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- 接收到新消息时会更新情绪状态
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- 定期进行情绪回归（冷静下来）
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- 每次情绪变化都会发送到ws端，格式为：
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type: "emotion"
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data: {"joy": 5, "anger": 1, "sorrow": 1, "fear": 1}
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3. ws端处理：
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- 本地只负责情绪计算和发送情绪数值
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- 表情渲染和动作由ws端根据情绪数值处理
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"""
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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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以上是直播间里正在进行的对话
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{indentify_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，不要输出任何其他内容
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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_vtb",
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)
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Prompt(
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"""
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{chat_talking_prompt}
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以上是直播间里最近的对话
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{indentify_block}
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你之前的情绪状态是：{mood_state}
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距离你上次关注直播间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_vtb",
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)
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Prompt(
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"""
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{chat_talking_prompt}
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以上是直播间里正在进行的对话
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{indentify_block}
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你刚刚的情绪状态是：{mood_state}
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具体来说，从1-10分，你的情绪状态是：
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喜(Joy): {joy}
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怒(Anger): {anger}
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哀(Sorrow): {sorrow}
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惧(Fear): {fear}
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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。请基于对话内容，评估你新的情绪状态。
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请以JSON格式输出你新的情绪状态，包含"喜怒哀惧"四个维度，每个维度的取值范围为1-10。
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键值请使用英文: "joy", "anger", "sorrow", "fear".
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例如: {{"joy": 5, "anger": 1, "sorrow": 1, "fear": 1}}
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不要输出任何其他内容，只输出JSON。
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""",
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"change_mood_numerical_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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以上是直播间里最近的对话
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{indentify_block}
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你之前的情绪状态是：{mood_state}
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具体来说，从1-10分，你的情绪状态是：
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喜(Joy): {joy}
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怒(Anger): {anger}
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哀(Sorrow): {sorrow}
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惧(Fear): {fear}
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距离你上次关注直播间消息已经过去了一段时间，你冷静了下来。请基于此，评估你现在的情绪状态。
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请以JSON格式输出你新的情绪状态，包含"喜怒哀惧"四个维度，每个维度的取值范围为1-10。
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键值请使用英文: "joy", "anger", "sorrow", "fear".
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例如: {{"joy": 5, "anger": 1, "sorrow": 1, "fear": 1}}
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不要输出任何其他内容，只输出JSON。
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""",
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"regress_mood_numerical_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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self.mood_state: str = "感觉很平静"
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self.mood_values: dict[str, int] = {"joy": 5, "anger": 1, "sorrow": 1, "fear": 1}
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self.regression_count: int = 0
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self.mood_model = LLMRequest(model_set=model_config.model_task_config.emotion, request_type="mood_text")
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self.mood_model_numerical = LLMRequest(
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model_set=model_config.model_task_config.emotion, request_type="mood_numerical"
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)
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self.last_change_time: float = 0
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# 发送初始情绪状态到ws端
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asyncio.create_task(self.send_emotion_update(self.mood_values))
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def _parse_numerical_mood(self, response: str) -> dict[str, int] | None:
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try:
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# The LLM might output markdown with json inside
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if "```json" in response:
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response = response.split("```json")[1].split("```")[0]
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elif "```" in response:
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response = response.split("```")[1].split("```")[0]
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data = json.loads(response)
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# Validate
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required_keys = {"joy", "anger", "sorrow", "fear"}
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if not required_keys.issubset(data.keys()):
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logger.warning(f"Numerical mood response missing keys: {response}")
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return None
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for key in required_keys:
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value = data[key]
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if not isinstance(value, int) or not (1 <= value <= 10):
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logger.warning(f"Numerical mood response invalid value for {key}: {value} in {response}")
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return None
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return {key: data[key] for key in required_keys}
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except json.JSONDecodeError:
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logger.warning(f"Failed to parse numerical mood JSON: {response}")
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return None
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except Exception as e:
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logger.error(f"Error parsing numerical mood: {e}, response: {response}")
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return None
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async def update_mood_by_message(self, message: MessageRecv):
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self.regression_count = 0
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message_time: float = message.message_info.time # type: ignore
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message_list_before_now = 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=10,
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limit_mode="last",
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)
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chat_talking_prompt = build_readable_messages(
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message_list_before_now,
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replace_bot_name=True,
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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
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indentify_block = f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}："
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async def _update_text_mood():
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prompt = await global_prompt_manager.format_prompt(
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"change_mood_prompt_vtb",
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chat_talking_prompt=chat_talking_prompt,
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indentify_block=indentify_block,
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mood_state=self.mood_state,
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)
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logger.debug(f"text mood prompt: {prompt}")
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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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logger.info(f"text mood response: {response}")
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logger.debug(f"text mood reasoning_content: {reasoning_content}")
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return response
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async def _update_numerical_mood():
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prompt = await global_prompt_manager.format_prompt(
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"change_mood_numerical_prompt",
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chat_talking_prompt=chat_talking_prompt,
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indentify_block=indentify_block,
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mood_state=self.mood_state,
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joy=self.mood_values["joy"],
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anger=self.mood_values["anger"],
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sorrow=self.mood_values["sorrow"],
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fear=self.mood_values["fear"],
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)
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logger.debug(f"numerical mood prompt: {prompt}")
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response, (reasoning_content, _, _) = await self.mood_model_numerical.generate_response_async(
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prompt=prompt, temperature=0.4
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)
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logger.info(f"numerical mood response: {response}")
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logger.debug(f"numerical mood reasoning_content: {reasoning_content}")
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return self._parse_numerical_mood(response)
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results = await asyncio.gather(_update_text_mood(), _update_numerical_mood())
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text_mood_response, numerical_mood_response = results
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if text_mood_response:
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self.mood_state = text_mood_response
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if numerical_mood_response:
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_old_mood_values = self.mood_values.copy()
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self.mood_values = numerical_mood_response
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# 发送情绪更新到ws端
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await self.send_emotion_update(self.mood_values)
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logger.info(f"[{self.chat_id}] 情绪变化: {_old_mood_values} -> {self.mood_values}")
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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 = 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=5,
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limit_mode="last",
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)
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chat_talking_prompt = build_readable_messages(
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message_list_before_now,
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replace_bot_name=True,
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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
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indentify_block = f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}："
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async def _regress_text_mood():
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prompt = await global_prompt_manager.format_prompt(
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"regress_mood_prompt_vtb",
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chat_talking_prompt=chat_talking_prompt,
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indentify_block=indentify_block,
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mood_state=self.mood_state,
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)
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logger.debug(f"text regress prompt: {prompt}")
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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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logger.info(f"text regress response: {response}")
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logger.debug(f"text regress reasoning_content: {reasoning_content}")
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return response
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async def _regress_numerical_mood():
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prompt = await global_prompt_manager.format_prompt(
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"regress_mood_numerical_prompt",
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chat_talking_prompt=chat_talking_prompt,
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indentify_block=indentify_block,
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mood_state=self.mood_state,
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joy=self.mood_values["joy"],
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anger=self.mood_values["anger"],
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sorrow=self.mood_values["sorrow"],
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fear=self.mood_values["fear"],
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)
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logger.debug(f"numerical regress prompt: {prompt}")
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response, (reasoning_content, _, _) = await self.mood_model_numerical.generate_response_async(
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prompt=prompt,
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temperature=0.4,
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)
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logger.info(f"numerical regress response: {response}")
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logger.debug(f"numerical regress reasoning_content: {reasoning_content}")
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return self._parse_numerical_mood(response)
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results = await asyncio.gather(_regress_text_mood(), _regress_numerical_mood())
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text_mood_response, numerical_mood_response = results
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if text_mood_response:
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self.mood_state = text_mood_response
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if numerical_mood_response:
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_old_mood_values = self.mood_values.copy()
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self.mood_values = numerical_mood_response
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# 发送情绪更新到ws端
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await self.send_emotion_update(self.mood_values)
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logger.info(f"[{self.chat_id}] 情绪回归: {_old_mood_values} -> {self.mood_values}")
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self.regression_count += 1
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async def send_emotion_update(self, mood_values: dict[str, int]):
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"""发送情绪更新到ws端"""
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emotion_data = {
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"joy": mood_values.get("joy", 5),
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"anger": mood_values.get("anger", 1),
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"sorrow": mood_values.get("sorrow", 1),
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"fear": mood_values.get("fear", 1),
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}
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await send_api.custom_to_stream(
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message_type="emotion",
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content=emotion_data,
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stream_id=self.chat_id,
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storage_message=False,
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show_log=True,
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)
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logger.info(f"[{self.chat_id}] 发送情绪更新: {emotion_data}")
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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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self.run_count = 0
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async def run(self):
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self.run_count += 1
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logger.info(f"[回归任务] 第{self.run_count}次检查，当前管理{len(self.mood_manager.mood_list)}个聊天的情绪状态")
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now = time.time()
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regression_executed = 0
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for mood in self.mood_manager.mood_list:
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chat_info = f"chat {mood.chat_id}"
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if mood.last_change_time == 0:
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logger.debug(f"[回归任务] {chat_info} 尚未有情绪变化，跳过回归")
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continue
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time_since_last_change = now - mood.last_change_time
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# 检查是否有极端情绪需要快速回归
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high_emotions = {k: v for k, v in mood.mood_values.items() if v >= 8}
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has_extreme_emotion = len(high_emotions) > 0
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# 回归条件：1. 正常时间间隔(120s) 或 2. 有极端情绪且距上次变化>=30s
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should_regress = False
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regress_reason = ""
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if time_since_last_change > 120:
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should_regress = True
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regress_reason = f"常规回归(距上次变化{int(time_since_last_change)}秒)"
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elif has_extreme_emotion and time_since_last_change > 30:
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should_regress = True
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high_emotion_str = ", ".join([f"{k}={v}" for k, v in high_emotions.items()])
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regress_reason = f"极端情绪快速回归({high_emotion_str}, 距上次变化{int(time_since_last_change)}秒)"
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if should_regress:
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if mood.regression_count >= 3:
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logger.debug(f"[回归任务] {chat_info} 已达到最大回归次数(3次)，停止回归")
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continue
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logger.info(
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f"[回归任务] {chat_info} 开始情绪回归 ({regress_reason}，第{mood.regression_count + 1}次回归)"
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)
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await mood.regress_mood()
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regression_executed += 1
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else:
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if has_extreme_emotion:
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remaining_time = 5 - time_since_last_change
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high_emotion_str = ", ".join([f"{k}={v}" for k, v in high_emotions.items()])
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logger.debug(
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f"[回归任务] {chat_info} 存在极端情绪({high_emotion_str})，距离快速回归还需等待{int(remaining_time)}秒"
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)
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else:
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remaining_time = 120 - time_since_last_change
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logger.debug(f"[回归任务] {chat_info} 距离回归还需等待{int(remaining_time)}秒")
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if regression_executed > 0:
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logger.info(f"[回归任务] 本次执行了{regression_executed}个聊天的情绪回归")
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else:
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logger.debug("[回归任务] 本次没有符合回归条件的聊天")
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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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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("启动情绪管理任务...")
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# 启动情绪回归任务
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regression_task = MoodRegressionTask(self)
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await async_task_manager.add_task(regression_task)
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self.task_started = True
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logger.info("情绪管理任务已启动（情绪回归）")
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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
|
||
|
||
def reset_mood_by_chat_id(self, chat_id: str):
|
||
for mood in self.mood_list:
|
||
if mood.chat_id == chat_id:
|
||
mood.mood_state = "感觉很平静"
|
||
mood.mood_values = {"joy": 5, "anger": 1, "sorrow": 1, "fear": 1}
|
||
mood.regression_count = 0
|
||
# 发送重置后的情绪状态到ws端
|
||
asyncio.create_task(mood.send_emotion_update(mood.mood_values))
|
||
return
|
||
|
||
# 如果没有找到现有的mood，创建新的
|
||
new_mood = ChatMood(chat_id)
|
||
self.mood_list.append(new_mood)
|
||
# 发送初始情绪状态到ws端
|
||
asyncio.create_task(new_mood.send_emotion_update(new_mood.mood_values))
|
||
|
||
|
||
if s4u_config.enable_s4u:
|
||
init_prompt()
|
||
mood_manager = MoodManager()
|
||
else:
|
||
mood_manager = None
|
||
|
||
"""全局情绪管理器"""
|