300 lines
13 KiB
Python
300 lines
13 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 LLMRequest
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from src.config.config import global_config
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from src.common.logger_manager import get_logger
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import traceback
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from src.plugins.utils.chat_message_builder import (
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get_raw_msg_before_timestamp_with_chat,
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build_readable_messages,
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get_raw_msg_by_timestamp_with_chat,
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num_new_messages_since,
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get_person_id_list,
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)
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from src.plugins.utils.prompt_builder import Prompt, global_prompt_manager
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from typing import Optional
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import difflib
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from src.plugins.chat.message import MessageRecv # 添加 MessageRecv 导入
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# Import the new utility function
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from .utils_chat import get_chat_type_and_target_info
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logger = get_logger("observation")
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# --- Define Prompt Templates for Chat Summary ---
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Prompt(
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"""这是qq群聊的聊天记录，请总结以下聊天记录的主题：
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{chat_logs}
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请用一句话概括，包括人物、事件和主要信息，不要分点。""",
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"chat_summary_group_prompt", # Template for group chat
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)
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Prompt(
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"""这是你和{chat_target}的私聊记录，请总结以下聊天记录的主题：
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{chat_logs}
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请用一句话概括，包括事件，时间，和主要信息，不要分点。""",
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"chat_summary_private_prompt", # Template for private chat
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)
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# --- End Prompt Template Definition ---
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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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async def observe(self):
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pass
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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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# --- Initialize attributes (defaults) ---
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self.is_group_chat: bool = False
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self.chat_target_info: Optional[dict] = None
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# --- End Initialization ---
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# --- Other attributes initialized in __init__ ---
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self.talking_message = []
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self.talking_message_str = ""
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self.talking_message_str_truncate = ""
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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.person_list = []
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self.llm_summary = LLMRequest(
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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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async def initialize(self):
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# --- Use utility function to determine chat type and fetch info ---
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self.is_group_chat, self.chat_target_info = await get_chat_type_and_target_info(self.chat_id)
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# logger.debug(f"is_group_chat: {self.is_group_chat}")
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# logger.debug(f"chat_target_info: {self.chat_target_info}")
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# --- End using utility function ---
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# Fetch initial messages (existing logic)
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initial_messages = get_raw_msg_before_timestamp_with_chat(self.chat_id, self.last_observe_time, 10)
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self.talking_message = initial_messages
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self.talking_message_str = await build_readable_messages(self.talking_message)
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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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return self.talking_message_str
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return mid_memory_str + "现在群里正在聊：\n" + self.talking_message_str
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else:
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return self.talking_message_str
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async def observe(self):
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# 自上一次观察的新消息
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new_messages_list = get_raw_msg_by_timestamp_with_chat(
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chat_id=self.chat_id,
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timestamp_start=self.last_observe_time,
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timestamp_end=datetime.now().timestamp(),
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limit=self.max_now_obs_len,
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limit_mode="latest",
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)
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last_obs_time_mark = self.last_observe_time
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if new_messages_list:
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self.last_observe_time = new_messages_list[-1]["time"]
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self.talking_message.extend(new_messages_list)
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if len(self.talking_message) > self.max_now_obs_len:
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# 计算需要移除的消息数量，保留最新的 max_now_obs_len 条
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messages_to_remove_count = len(self.talking_message) - self.max_now_obs_len
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oldest_messages = self.talking_message[:messages_to_remove_count]
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self.talking_message = self.talking_message[messages_to_remove_count:] # 保留后半部分，即最新的
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oldest_messages_str = await build_readable_messages(
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messages=oldest_messages, timestamp_mode="normal", read_mark=0
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)
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# --- Build prompt using template ---
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prompt = None # Initialize prompt as None
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try:
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# 构建 Prompt - 根据 is_group_chat 选择模板
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if self.is_group_chat:
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prompt_template_name = "chat_summary_group_prompt"
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prompt = await global_prompt_manager.format_prompt(
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prompt_template_name, chat_logs=oldest_messages_str
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)
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else:
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# For private chat, add chat_target to the prompt variables
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prompt_template_name = "chat_summary_private_prompt"
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# Determine the target name for the prompt
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chat_target_name = "对方" # Default fallback
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if self.chat_target_info:
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# Prioritize person_name, then nickname
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chat_target_name = (
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self.chat_target_info.get("person_name")
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or self.chat_target_info.get("user_nickname")
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or chat_target_name
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)
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# Format the private chat prompt
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prompt = await global_prompt_manager.format_prompt(
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prompt_template_name,
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# Assuming the private prompt template uses {chat_target}
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chat_target=chat_target_name,
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chat_logs=oldest_messages_str,
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)
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except Exception as e:
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logger.error(f"构建总结 Prompt 失败 for chat {self.chat_id}: {e}")
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# prompt remains None
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summary = "没有主题的闲聊" # 默认值
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if prompt: # Check if prompt was built successfully
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try:
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summary_result, _, _ = await self.llm_summary.generate_response(prompt)
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if summary_result: # 确保结果不为空
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summary = summary_result
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except Exception as e:
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logger.error(f"总结主题失败 for chat {self.chat_id}: {e}")
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# 保留默认总结 "没有主题的闲聊"
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else:
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logger.warning(f"因 Prompt 构建失败，跳过 LLM 总结 for chat {self.chat_id}")
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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, # 存储原始消息对象
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"readable_messages": oldest_messages_str,
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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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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) # 移除最旧的
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mid_memory_str = "之前聊天的内容概述是：\n"
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for mid_memory_item in self.mid_memorys: # 重命名循环变量以示区分
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time_diff = int((datetime.now().timestamp() - mid_memory_item["created_at"]) / 60)
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mid_memory_str += (
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f"距离现在{time_diff}分钟前(聊天记录id:{mid_memory_item['id']})：{mid_memory_item['theme']}\n"
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)
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self.mid_memory_info = mid_memory_str
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self.talking_message_str = await build_readable_messages(
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messages=self.talking_message,
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timestamp_mode="lite",
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read_mark=last_obs_time_mark,
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)
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self.talking_message_str_truncate = await build_readable_messages(
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messages=self.talking_message,
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timestamp_mode="normal",
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read_mark=last_obs_time_mark,
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truncate=True,
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)
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self.person_list = await get_person_id_list(self.talking_message)
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# print(f"self.11111person_list: {self.person_list}")
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logger.trace(
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f"Chat {self.chat_id} - 压缩早期记忆：{self.mid_memory_info}\n现在聊天内容：{self.talking_message_str}"
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)
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async def find_best_matching_message(self, search_str: str, min_similarity: float = 0.6) -> Optional[MessageRecv]:
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"""
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在 talking_message 中查找与 search_str 最匹配的消息。
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Args:
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search_str: 要搜索的字符串。
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min_similarity: 要求的最低相似度（0到1之间）。
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Returns:
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匹配的 MessageRecv 实例，如果找不到则返回 None。
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"""
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best_match_score = -1.0
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best_match_dict = None
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if not self.talking_message:
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logger.debug(f"Chat {self.chat_id}: talking_message is empty, cannot find match for '{search_str}'")
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return None
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for message_dict in self.talking_message:
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try:
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# 临时创建 MessageRecv 以处理文本
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temp_msg = MessageRecv(message_dict)
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await temp_msg.process() # 处理消息以获取 processed_plain_text
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current_text = temp_msg.processed_plain_text
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if not current_text: # 跳过没有文本内容的消息
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continue
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# 计算相似度
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matcher = difflib.SequenceMatcher(None, search_str, current_text)
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score = matcher.ratio()
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# logger.debug(f"Comparing '{search_str}' with '{current_text}', score: {score}") # 可选：用于调试
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if score > best_match_score:
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best_match_score = score
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best_match_dict = message_dict
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except Exception as e:
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logger.error(f"Error processing message for matching in chat {self.chat_id}: {e}", exc_info=True)
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continue # 继续处理下一条消息
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if best_match_dict is not None and best_match_score >= min_similarity:
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logger.debug(f"Found best match for '{search_str}' with score {best_match_score:.2f}")
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try:
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final_msg = MessageRecv(best_match_dict)
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await final_msg.process()
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# 确保 MessageRecv 实例有关联的 chat_stream
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if hasattr(self, "chat_stream"):
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final_msg.update_chat_stream(self.chat_stream)
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else:
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logger.warning(
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f"ChattingObservation instance for chat {self.chat_id} does not have a chat_stream attribute set."
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)
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return final_msg
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except Exception as e:
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logger.error(f"Error creating final MessageRecv for chat {self.chat_id}: {e}", exc_info=True)
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return None
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else:
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logger.debug(
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f"No suitable match found for '{search_str}' in chat {self.chat_id} (best score: {best_match_score:.2f}, threshold: {min_similarity})"
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)
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return None
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async def has_new_messages_since(self, timestamp: float) -> bool:
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"""检查指定时间戳之后是否有新消息"""
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count = num_new_messages_since(chat_id=self.chat_id, timestamp_start=timestamp)
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return count > 0
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