534 lines
23 KiB
Python
534 lines
23 KiB
Python
from .observation import Observation
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import asyncio
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from src.plugins.moods.moods import MoodManager
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from src.plugins.models.utils_model import LLM_request
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from src.plugins.config.config import global_config
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import re
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import time
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from src.plugins.schedule.schedule_generator import bot_schedule
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from src.plugins.memory_system.Hippocampus import HippocampusManager
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from src.common.logger import get_module_logger, LogConfig, SUB_HEARTFLOW_STYLE_CONFIG # noqa: E402
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from src.plugins.chat.utils import get_embedding
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from src.common.database import db
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from typing import Union
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from src.individuality.individuality import Individuality
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import random
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from src.plugins.chat.chat_stream import ChatStream
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from src.plugins.person_info.relationship_manager import relationship_manager
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from src.plugins.chat.utils import get_recent_group_speaker
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subheartflow_config = LogConfig(
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# 使用海马体专用样式
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console_format=SUB_HEARTFLOW_STYLE_CONFIG["console_format"],
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file_format=SUB_HEARTFLOW_STYLE_CONFIG["file_format"],
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)
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logger = get_module_logger("subheartflow", config=subheartflow_config)
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class CurrentState:
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def __init__(self):
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self.willing = 0
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self.current_state_info = ""
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self.mood_manager = MoodManager()
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self.mood = self.mood_manager.get_prompt()
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def update_current_state_info(self):
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self.current_state_info = self.mood_manager.get_current_mood()
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class SubHeartflow:
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def __init__(self, subheartflow_id):
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self.subheartflow_id = subheartflow_id
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self.current_mind = ""
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self.past_mind = []
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self.current_state: CurrentState = CurrentState()
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self.llm_model = LLM_request(
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model=global_config.llm_sub_heartflow, temperature=0.2, max_tokens=600, request_type="sub_heart_flow"
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)
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self.main_heartflow_info = ""
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self.last_reply_time = time.time()
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self.last_active_time = time.time() # 添加最后激活时间
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if not self.current_mind:
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self.current_mind = "你什么也没想"
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self.is_active = False
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self.observations: list[Observation] = []
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self.running_knowledges = []
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self.bot_name = global_config.BOT_NICKNAME
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def add_observation(self, observation: Observation):
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"""添加一个新的observation对象到列表中，如果已存在相同id的observation则不添加"""
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# 查找是否存在相同id的observation
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for existing_obs in self.observations:
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if existing_obs.observe_id == observation.observe_id:
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# 如果找到相同id的observation，直接返回
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return
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# 如果没有找到相同id的observation，则添加新的
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self.observations.append(observation)
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def remove_observation(self, observation: Observation):
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"""从列表中移除一个observation对象"""
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if observation in self.observations:
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self.observations.remove(observation)
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def get_all_observations(self) -> list[Observation]:
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"""获取所有observation对象"""
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return self.observations
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def clear_observations(self):
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"""清空所有observation对象"""
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self.observations.clear()
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async def subheartflow_start_working(self):
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while True:
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current_time = time.time()
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if (
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current_time - self.last_reply_time > global_config.sub_heart_flow_freeze_time
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): # 120秒无回复/不在场，冻结
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self.is_active = False
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await asyncio.sleep(global_config.sub_heart_flow_update_interval) # 每60秒检查一次
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else:
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self.is_active = True
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self.last_active_time = current_time # 更新最后激活时间
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self.current_state.update_current_state_info()
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# await self.do_a_thinking()
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# await self.judge_willing()
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await asyncio.sleep(global_config.sub_heart_flow_update_interval)
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# 检查是否超过10分钟没有激活
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if (
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current_time - self.last_active_time > global_config.sub_heart_flow_stop_time
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): # 5分钟无回复/不在场，销毁
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logger.info(f"子心流 {self.subheartflow_id} 已经5分钟没有激活，正在销毁...")
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break # 退出循环，销毁自己
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async def do_observe(self):
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observation = self.observations[0]
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await observation.observe()
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async def do_thinking_before_reply(self, message_txt:str, sender_name:str, chat_stream:ChatStream):
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current_thinking_info = self.current_mind
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mood_info = self.current_state.mood
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# mood_info = "你很生气，很愤怒"
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observation = self.observations[0]
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chat_observe_info = observation.observe_info
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# print(f"chat_observe_info：{chat_observe_info}")
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# 开始构建prompt
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prompt_personality = f"你的名字是{self.bot_name},你"
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# person
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individuality = Individuality.get_instance()
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personality_core = individuality.personality.personality_core
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prompt_personality += personality_core
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personality_sides = individuality.personality.personality_sides
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random.shuffle(personality_sides)
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prompt_personality += f",{personality_sides[0]}"
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identity_detail = individuality.identity.identity_detail
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random.shuffle(identity_detail)
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prompt_personality += f",{identity_detail[0]}"
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# 关系
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who_chat_in_group = [
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(chat_stream.user_info.platform, chat_stream.user_info.user_id, chat_stream.user_info.user_nickname)
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]
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who_chat_in_group += get_recent_group_speaker(
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chat_stream.stream_id,
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(chat_stream.user_info.platform, chat_stream.user_info.user_id),
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limit=global_config.MAX_CONTEXT_SIZE,
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)
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relation_prompt = ""
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for person in who_chat_in_group:
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relation_prompt += await relationship_manager.build_relationship_info(person)
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relation_prompt_all = (
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f"{relation_prompt}关系等级越大，关系越好，请分析聊天记录，"
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f"根据你和说话者{sender_name}的关系和态度进行回复，明确你的立场和情感。"
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)
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# 调取记忆
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related_memory = await HippocampusManager.get_instance().get_memory_from_text(
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text=chat_observe_info, max_memory_num=2, max_memory_length=2, max_depth=3, fast_retrieval=False
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)
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if related_memory:
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related_memory_info = ""
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for memory in related_memory:
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related_memory_info += memory[1]
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else:
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related_memory_info = ""
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related_info, grouped_results = await self.get_prompt_info(chat_observe_info + message_txt, 0.4)
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# print(related_info)
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for _topic, results in grouped_results.items():
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for result in results:
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# print(result)
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self.running_knowledges.append(result)
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# print(f"相关记忆：{related_memory_info}")
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schedule_info = bot_schedule.get_current_num_task(num=1, time_info=False)
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prompt = ""
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# prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n"
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prompt += f"{relation_prompt_all}\n"
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prompt += f"{prompt_personality}\n"
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# prompt += f"你刚刚在做的事情是：{schedule_info}\n"
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# if related_memory_info:
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# prompt += f"你想起来你之前见过的回忆：{related_memory_info}。\n以上是你的回忆，不一定是目前聊天里的人说的，也不一定是现在发生的事情，请记住。\n"
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# if related_info:
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# prompt += f"你想起你知道：{related_info}\n"
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prompt += f"刚刚你的想法是{current_thinking_info}。如果有新的内容，记得转换话题\n"
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prompt += "-----------------------------------\n"
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prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n"
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prompt += f"你现在{mood_info}\n"
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prompt += f"你注意到{sender_name}刚刚说：{message_txt}\n"
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prompt += "现在你接下去继续思考，产生新的想法，不要分点输出，输出连贯的内心独白，不要太长，"
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prompt += "思考时可以想想如何对群聊内容进行回复。回复的要求是：平淡一些，简短一些，说中文，不要刻意突出自身学科背景，尽量不要说你说过的话 ，注意只输出回复内容。"
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prompt += "请注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，"
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prompt += f"记得结合上述的消息，生成符合内心想法的内心独白，文字不要浮夸，注意你就是{self.bot_name}，{self.bot_name}指的就是你。"
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try:
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response, reasoning_content = await self.llm_model.generate_response_async(prompt)
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except Exception as e:
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logger.error(f"回复前内心独白获取失败: {e}")
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response = ""
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self.update_current_mind(response)
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self.current_mind = response
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logger.info(f"prompt:\n{prompt}\n")
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logger.info(f"麦麦的思考前脑内状态：{self.current_mind}")
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return self.current_mind ,self.past_mind
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async def do_thinking_after_reply(self, reply_content, chat_talking_prompt):
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# print("麦麦回复之后脑袋转起来了")
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# 开始构建prompt
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prompt_personality = f"你的名字是{self.bot_name},你"
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# person
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individuality = Individuality.get_instance()
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personality_core = individuality.personality.personality_core
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prompt_personality += personality_core
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personality_sides = individuality.personality.personality_sides
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random.shuffle(personality_sides)
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prompt_personality += f",{personality_sides[0]}"
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identity_detail = individuality.identity.identity_detail
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random.shuffle(identity_detail)
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prompt_personality += f",{identity_detail[0]}"
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current_thinking_info = self.current_mind
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mood_info = self.current_state.mood
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observation = self.observations[0]
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chat_observe_info = observation.observe_info
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message_new_info = chat_talking_prompt
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reply_info = reply_content
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# schedule_info = bot_schedule.get_current_num_task(num=1, time_info=False)
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prompt = ""
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# prompt += f"你现在正在做的事情是：{schedule_info}\n"
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prompt += f"{prompt_personality}\n"
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prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n"
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prompt += f"刚刚你的想法是{current_thinking_info}。"
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prompt += f"你现在看到了网友们发的新消息:{message_new_info}\n"
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prompt += f"你刚刚回复了群友们:{reply_info}"
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prompt += f"你现在{mood_info}"
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prompt += "现在你接下去继续思考，产生新的想法，记得保留你刚刚的想法，不要分点输出，输出连贯的内心独白"
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prompt += "不要太长，但是记得结合上述的消息，要记得你的人设，关注聊天和新内容，关注你回复的内容，不要思考太多:"
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try:
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response, reasoning_content = await self.llm_model.generate_response_async(prompt)
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except Exception as e:
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logger.error(f"回复后内心独白获取失败: {e}")
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response = ""
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self.update_current_mind(response)
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self.current_mind = response
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logger.info(f"麦麦回复后的脑内状态：{self.current_mind}")
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self.last_reply_time = time.time()
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async def judge_willing(self):
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# 开始构建prompt
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prompt_personality = "你"
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# person
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individuality = Individuality.get_instance()
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personality_core = individuality.personality.personality_core
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prompt_personality += personality_core
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personality_sides = individuality.personality.personality_sides
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random.shuffle(personality_sides)
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prompt_personality += f",{personality_sides[0]}"
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identity_detail = individuality.identity.identity_detail
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random.shuffle(identity_detail)
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prompt_personality += f",{identity_detail[0]}"
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# print("麦麦闹情绪了1")
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current_thinking_info = self.current_mind
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mood_info = self.current_state.mood
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# print("麦麦闹情绪了2")
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prompt = ""
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prompt += f"{prompt_personality}\n"
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prompt += "现在你正在上网，和qq群里的网友们聊天"
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prompt += f"你现在的想法是{current_thinking_info}。"
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prompt += f"你现在{mood_info}。"
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prompt += "现在请你思考，你想不想发言或者回复，请你输出一个数字，1-10，1表示非常不想，10表示非常想。"
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prompt += "请你用<>包裹你的回复意愿，输出<1>表示不想回复，输出<10>表示非常想回复。请你考虑，你完全可以不回复"
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try:
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response, reasoning_content = await self.llm_model.generate_response_async(prompt)
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# 解析willing值
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willing_match = re.search(r"<(\d+)>", response)
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except Exception as e:
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logger.error(f"意愿判断获取失败: {e}")
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willing_match = None
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if willing_match:
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self.current_state.willing = int(willing_match.group(1))
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else:
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self.current_state.willing = 0
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return self.current_state.willing
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def update_current_mind(self, response):
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self.past_mind.append(self.current_mind)
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self.current_mind = response
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async def get_prompt_info(self, message: str, threshold: float):
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start_time = time.time()
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related_info = ""
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logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
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# 1. 先从LLM获取主题，类似于记忆系统的做法
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topics = []
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# try:
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# # 先尝试使用记忆系统的方法获取主题
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# hippocampus = HippocampusManager.get_instance()._hippocampus
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# topic_num = min(5, max(1, int(len(message) * 0.1)))
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# topics_response = await hippocampus.llm_topic_judge.generate_response(hippocampus.find_topic_llm(message, topic_num))
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# # 提取关键词
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# topics = re.findall(r"<([^>]+)>", topics_response[0])
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# if not topics:
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# topics = []
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# else:
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# topics = [
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# topic.strip()
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# for topic in ",".join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
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# if topic.strip()
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# ]
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# logger.info(f"从LLM提取的主题: {', '.join(topics)}")
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# except Exception as e:
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# logger.error(f"从LLM提取主题失败: {str(e)}")
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# # 如果LLM提取失败，使用jieba分词提取关键词作为备选
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# words = jieba.cut(message)
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# topics = [word for word in words if len(word) > 1][:5]
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# logger.info(f"使用jieba提取的主题: {', '.join(topics)}")
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# 如果无法提取到主题，直接使用整个消息
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if not topics:
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logger.debug("未能提取到任何主题，使用整个消息进行查询")
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embedding = await get_embedding(message, request_type="info_retrieval")
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if not embedding:
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logger.error("获取消息嵌入向量失败")
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return ""
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related_info = self.get_info_from_db(embedding, limit=3, threshold=threshold)
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logger.info(f"知识库检索完成，总耗时: {time.time() - start_time:.3f}秒")
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return related_info, {}
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# 2. 对每个主题进行知识库查询
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logger.info(f"开始处理{len(topics)}个主题的知识库查询")
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# 优化：批量获取嵌入向量，减少API调用
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embeddings = {}
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topics_batch = [topic for topic in topics if len(topic) > 0]
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if message: # 确保消息非空
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topics_batch.append(message)
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# 批量获取嵌入向量
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embed_start_time = time.time()
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for text in topics_batch:
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if not text or len(text.strip()) == 0:
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continue
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try:
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embedding = await get_embedding(text, request_type="info_retrieval")
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if embedding:
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embeddings[text] = embedding
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else:
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logger.warning(f"获取'{text}'的嵌入向量失败")
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except Exception as e:
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logger.error(f"获取'{text}'的嵌入向量时发生错误: {str(e)}")
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logger.info(f"批量获取嵌入向量完成，耗时: {time.time() - embed_start_time:.3f}秒")
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if not embeddings:
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logger.error("所有嵌入向量获取失败")
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return ""
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# 3. 对每个主题进行知识库查询
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all_results = []
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query_start_time = time.time()
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# 首先添加原始消息的查询结果
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if message in embeddings:
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original_results = self.get_info_from_db(embeddings[message], limit=3, threshold=threshold, return_raw=True)
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if original_results:
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for result in original_results:
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result["topic"] = "原始消息"
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all_results.extend(original_results)
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logger.info(f"原始消息查询到{len(original_results)}条结果")
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# 然后添加每个主题的查询结果
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for topic in topics:
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if not topic or topic not in embeddings:
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continue
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try:
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topic_results = self.get_info_from_db(embeddings[topic], limit=3, threshold=threshold, return_raw=True)
|
||
if topic_results:
|
||
# 添加主题标记
|
||
for result in topic_results:
|
||
result["topic"] = topic
|
||
all_results.extend(topic_results)
|
||
logger.info(f"主题'{topic}'查询到{len(topic_results)}条结果")
|
||
except Exception as e:
|
||
logger.error(f"查询主题'{topic}'时发生错误: {str(e)}")
|
||
|
||
logger.info(f"知识库查询完成，耗时: {time.time() - query_start_time:.3f}秒，共获取{len(all_results)}条结果")
|
||
|
||
# 4. 去重和过滤
|
||
process_start_time = time.time()
|
||
unique_contents = set()
|
||
filtered_results = []
|
||
for result in all_results:
|
||
content = result["content"]
|
||
if content not in unique_contents:
|
||
unique_contents.add(content)
|
||
filtered_results.append(result)
|
||
|
||
# 5. 按相似度排序
|
||
filtered_results.sort(key=lambda x: x["similarity"], reverse=True)
|
||
|
||
# 6. 限制总数量（最多10条）
|
||
filtered_results = filtered_results[:10]
|
||
logger.info(
|
||
f"结果处理完成，耗时: {time.time() - process_start_time:.3f}秒，过滤后剩余{len(filtered_results)}条结果"
|
||
)
|
||
|
||
# 7. 格式化输出
|
||
if filtered_results:
|
||
format_start_time = time.time()
|
||
grouped_results = {}
|
||
for result in filtered_results:
|
||
topic = result["topic"]
|
||
if topic not in grouped_results:
|
||
grouped_results[topic] = []
|
||
grouped_results[topic].append(result)
|
||
|
||
# 按主题组织输出
|
||
for topic, results in grouped_results.items():
|
||
related_info += f"【主题: {topic}】\n"
|
||
for _i, result in enumerate(results, 1):
|
||
_similarity = result["similarity"]
|
||
content = result["content"].strip()
|
||
# 调试：为内容添加序号和相似度信息
|
||
# related_info += f"{i}. [{similarity:.2f}] {content}\n"
|
||
related_info += f"{content}\n"
|
||
related_info += "\n"
|
||
|
||
logger.info(f"格式化输出完成，耗时: {time.time() - format_start_time:.3f}秒")
|
||
|
||
logger.info(f"知识库检索总耗时: {time.time() - start_time:.3f}秒")
|
||
return related_info, grouped_results
|
||
|
||
def get_info_from_db(
|
||
self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
|
||
) -> Union[str, list]:
|
||
if not query_embedding:
|
||
return "" if not return_raw else []
|
||
# 使用余弦相似度计算
|
||
pipeline = [
|
||
{
|
||
"$addFields": {
|
||
"dotProduct": {
|
||
"$reduce": {
|
||
"input": {"$range": [0, {"$size": "$embedding"}]},
|
||
"initialValue": 0,
|
||
"in": {
|
||
"$add": [
|
||
"$$value",
|
||
{
|
||
"$multiply": [
|
||
{"$arrayElemAt": ["$embedding", "$$this"]},
|
||
{"$arrayElemAt": [query_embedding, "$$this"]},
|
||
]
|
||
},
|
||
]
|
||
},
|
||
}
|
||
},
|
||
"magnitude1": {
|
||
"$sqrt": {
|
||
"$reduce": {
|
||
"input": "$embedding",
|
||
"initialValue": 0,
|
||
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
|
||
}
|
||
}
|
||
},
|
||
"magnitude2": {
|
||
"$sqrt": {
|
||
"$reduce": {
|
||
"input": query_embedding,
|
||
"initialValue": 0,
|
||
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
|
||
}
|
||
}
|
||
},
|
||
}
|
||
},
|
||
{"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]}}},
|
||
{
|
||
"$match": {
|
||
"similarity": {"$gte": threshold} # 只保留相似度大于等于阈值的结果
|
||
}
|
||
},
|
||
{"$sort": {"similarity": -1}},
|
||
{"$limit": limit},
|
||
{"$project": {"content": 1, "similarity": 1}},
|
||
]
|
||
|
||
results = list(db.knowledges.aggregate(pipeline))
|
||
logger.debug(f"知识库查询结果数量: {len(results)}")
|
||
|
||
if not results:
|
||
return "" if not return_raw else []
|
||
|
||
if return_raw:
|
||
return results
|
||
else:
|
||
# 返回所有找到的内容，用换行分隔
|
||
return "\n".join(str(result["content"]) for result in results)
|
||
|
||
|
||
# subheartflow = SubHeartflow()
|