mirror of https://github.com/Mai-with-u/MaiBot.git
170 lines
7.3 KiB
Python
170 lines
7.3 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.common.logger import get_module_logger
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import traceback
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logger = get_module_logger("observation")
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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.max_now_obs_len = global_config.observation_context_size
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self.overlap_len = global_config.compressed_length
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self.mid_memorys = []
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self.max_mid_memory_len = global_config.compress_length_limit
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self.mid_memory_info = ""
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self.now_message_info = ""
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self.updating_old = False
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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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def get_observe_info(self, ids=None):
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if ids:
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mid_memory_str = ""
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for id in ids:
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print(f"id：{id}")
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try:
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for mid_memory in self.mid_memorys:
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if mid_memory["id"] == id:
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mid_memory_by_id = mid_memory
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msg_str = ""
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for msg in mid_memory_by_id["messages"]:
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msg_str += f"{msg['detailed_plain_text']}"
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# time_diff = int((datetime.now().timestamp() - mid_memory_by_id["created_at"]) / 60)
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# mid_memory_str += f"距离现在{time_diff}分钟前：\n{msg_str}\n"
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mid_memory_str += f"{msg_str}\n"
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except Exception as e:
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logger.error(f"获取mid_memory_id失败: {e}")
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traceback.print_exc()
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# print(f"获取mid_memory_id失败: {e}")
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return self.now_message_info
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return mid_memory_str + "现在群里正在聊：\n" + self.now_message_info
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else:
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return self.now_message_info
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async def observe(self):
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# 查找新消息
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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}}).sort("time", 1)
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) # 按时间正序排列
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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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self.last_observe_time = new_messages[-1]["time"]
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self.talking_message.extend(new_messages)
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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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if len(self.talking_message) > self.max_now_obs_len and not self.updating_old:
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self.updating_old = True
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# 计算需要保留的消息数量
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keep_messages_count = self.max_now_obs_len - self.overlap_len
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# 提取所有超出保留数量的最老消息
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oldest_messages = self.talking_message[:-keep_messages_count]
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self.talking_message = self.talking_message[-keep_messages_count:]
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oldest_messages_str = "\n".join([msg["detailed_plain_text"] for msg in oldest_messages])
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oldest_timestamps = [msg["time"] for msg in oldest_messages]
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# 调用 LLM 总结主题
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prompt = f"请总结以下聊天记录的主题：\n{oldest_messages_str}\n主题,用一句话概括包括人物事件和主要信息，不要分点："
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try:
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summary, _ = await self.llm_summary.generate_response_async(prompt)
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except Exception as e:
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print(f"总结主题失败: {e}")
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summary = "无法总结主题"
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mid_memory = {
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"id": str(int(datetime.now().timestamp())),
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"theme": summary,
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"messages": oldest_messages,
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"timestamps": oldest_timestamps,
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"chat_id": self.chat_id,
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"created_at": datetime.now().timestamp(),
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}
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# print(f"mid_memory：{mid_memory}")
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# 存入内存中的 mid_memorys
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self.mid_memorys.append(mid_memory)
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if len(self.mid_memorys) > self.max_mid_memory_len:
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self.mid_memorys.pop(0)
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mid_memory_str = "之前聊天的内容概括是：\n"
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for mid_memory in self.mid_memorys:
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time_diff = int((datetime.now().timestamp() - mid_memory["created_at"]) / 60)
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mid_memory_str += f"距离现在{time_diff}分钟前(聊天记录id:{mid_memory['id']})：{mid_memory['theme']}\n"
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self.mid_memory_info = mid_memory_str
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self.updating_old = False
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# print(f"处理后self.talking_message：{self.talking_message}")
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now_message_str = ""
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now_message_str += self.translate_message_list_to_str(talking_message=self.talking_message)
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self.now_message_info = now_message_str
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logger.debug(f"压缩早期记忆：{self.mid_memory_info}\n现在聊天内容：{self.now_message_info}")
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async def update_talking_summary(self, new_messages_str):
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prompt = ""
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# prompt += f"{personality_info}"
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prompt += f"你的名字叫：{self.name}\n，标识'{self.name}'的都是你自己说的话"
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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，目前最新讨论的话题
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以及聊天中的一些重要信息，记得不要分点，精简的概括成一段文本\n"""
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prompt += "总结概括："
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try:
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updated_observe_info, reasoning_content = await self.llm_summary.generate_response_async(prompt)
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except Exception as e:
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print(f"获取总结失败: {e}")
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updated_observe_info = ""
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return updated_observe_info
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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, talking_message):
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talking_message_str = ""
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for message in talking_message:
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talking_message_str += message["detailed_plain_text"]
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return talking_message_str
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