153 lines
6.2 KiB
Python
153 lines
6.2 KiB
Python
# 定义了来自外部世界的信息
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# 外部世界可以是某个聊天 不同平台的聊天 也可以是任意媒体
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from datetime import datetime
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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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from src.common.database import db
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from src.individuality.individuality import Individuality
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import random
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# 所有观察的基类
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class Observation:
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def __init__(self, observe_type, observe_id):
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self.observe_info = ""
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self.observe_type = observe_type
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self.observe_id = observe_id
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self.last_observe_time = datetime.now().timestamp() # 初始化为当前时间
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# 聊天观察
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class ChattingObservation(Observation):
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def __init__(self, chat_id):
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super().__init__("chat", chat_id)
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self.chat_id = chat_id
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self.talking_message = []
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self.talking_message_str = ""
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self.name = global_config.BOT_NICKNAME
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self.nick_name = global_config.BOT_ALIAS_NAMES
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self.observe_times = 0
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self.summary_count = 0 # 30秒内的更新次数
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self.max_update_in_30s = 2 # 30秒内最多更新2次
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self.last_summary_time = 0 # 上次更新summary的时间
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self.sub_observe = None
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self.llm_summary = LLM_request(
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model=global_config.llm_observation, temperature=0.7, max_tokens=300, request_type="chat_observation"
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)
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# 进行一次观察 返回观察结果observe_info
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async def observe(self):
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# 查找新消息，限制最多30条
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new_messages = list(
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db.messages.find({"chat_id": self.chat_id, "time": {"$gt": self.last_observe_time}})
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.sort("time", 1)
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.limit(20)
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) # 按时间正序排列，最多20条
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if not new_messages:
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return self.observe_info # 没有新消息，返回上次观察结果
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# 将新消息转换为字符串格式
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new_messages_str = ""
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for msg in new_messages:
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if "detailed_plain_text" in msg:
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new_messages_str += f"{msg['detailed_plain_text']}"
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# print(f"new_messages_str：{new_messages_str}")
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# 将新消息添加到talking_message，同时保持列表长度不超过20条
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self.talking_message.extend(new_messages)
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if len(self.talking_message) > 20:
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self.talking_message = self.talking_message[-20:] # 只保留最新的20条
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self.translate_message_list_to_str()
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# 更新观察次数
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self.observe_times += 1
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self.last_observe_time = new_messages[-1]["time"]
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# 检查是否需要更新summary
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current_time = int(datetime.now().timestamp())
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if current_time - self.last_summary_time >= 30: # 如果超过30秒，重置计数
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self.summary_count = 0
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self.last_summary_time = current_time
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if self.summary_count < self.max_update_in_30s: # 如果30秒内更新次数小于2次
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await self.update_talking_summary(new_messages_str)
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self.summary_count += 1
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return self.observe_info
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async def carefully_observe(self):
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# 查找新消息，限制最多40条
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new_messages = list(
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db.messages.find({"chat_id": self.chat_id, "time": {"$gt": self.last_observe_time}})
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.sort("time", 1)
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.limit(30)
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) # 按时间正序排列，最多30条
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if not new_messages:
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return self.observe_info # 没有新消息，返回上次观察结果
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# 将新消息转换为字符串格式
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new_messages_str = ""
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for msg in new_messages:
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if "detailed_plain_text" in msg:
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new_messages_str += f"{msg['detailed_plain_text']}\n"
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# 将新消息添加到talking_message，同时保持列表长度不超过30条
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self.talking_message.extend(new_messages)
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if len(self.talking_message) > 30:
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self.talking_message = self.talking_message[-30:] # 只保留最新的30条
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self.translate_message_list_to_str()
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# 更新观察次数
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self.observe_times += 1
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self.last_observe_time = new_messages[-1]["time"]
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await self.update_talking_summary(new_messages_str)
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return self.observe_info
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async def update_talking_summary(self, new_messages_str):
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# 基于已经有的talking_summary，和新的talking_message，生成一个summary
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# print(f"更新聊天总结：{self.talking_summary}")
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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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personality_info = prompt_personality
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prompt = ""
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prompt += f"{personality_info}，请注意识别你自己的聊天发言"
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prompt += f"你的名字叫：{self.name}，你的昵称是：{self.nick_name}\n"
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prompt += f"你正在参与一个qq群聊的讨论，你记得这个群之前在聊的内容是：{self.observe_info}\n"
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prompt += f"现在群里的群友们产生了新的讨论，有了新的发言，具体内容如下：{new_messages_str}\n"
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prompt += """以上是群里在进行的聊天，请你对这个聊天内容进行总结，总结内容要包含聊天的大致内容，
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以及聊天中的一些重要信息，注意识别你自己的发言，记得不要分点，不要太长，精简的概括成一段文本\n"""
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prompt += "总结概括："
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self.observe_info, reasoning_content = await self.llm_summary.generate_response_async(prompt)
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print(f"prompt：{prompt}")
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print(f"self.observe_info：{self.observe_info}")
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def translate_message_list_to_str(self):
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self.talking_message_str = ""
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for message in self.talking_message:
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self.talking_message_str += message["detailed_plain_text"]
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