mirror of https://github.com/Mai-with-u/MaiBot.git
Merge branch 'dev' of https://github.com/MaiM-with-u/MaiBot into dev
墨梓柒
commit
51bf81f1e2
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|
@ -29,7 +29,7 @@ from src.chat.utils.chat_message_builder import (
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build_readable_messages_with_id,
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get_raw_msg_before_timestamp_with_chat,
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)
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from src.chat.utils.chat_history_summarizer import ChatHistorySummarizer
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from src.hippo_memorizer.chat_history_summarizer import ChatHistorySummarizer
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if TYPE_CHECKING:
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from src.common.data_models.database_data_model import DatabaseMessages
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|
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@ -222,7 +222,8 @@ class ActionPlanner:
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# 非no_reply动作需要target_message_id
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target_message = None
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if target_message_id := action_json.get("target_message_id"):
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target_message_id = action_json.get("target_message_id")
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if target_message_id:
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# 根据target_message_id查找原始消息
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target_message = self.find_message_by_id(target_message_id, message_id_list)
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if target_message is None:
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@ -233,6 +234,20 @@ class ActionPlanner:
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target_message = message_id_list[-1][1]
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logger.debug(f"{self.log_prefix}动作'{action}'缺少target_message_id，使用最新消息作为target_message")
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if (
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action != "no_reply"
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and target_message is not None
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and self._is_message_from_self(target_message)
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):
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logger.info(
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f"{self.log_prefix}Planner选择了自己的消息 {target_message_id or target_message.message_id} 作为目标，强制使用 no_reply"
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)
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reasoning = (
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f"目标消息 {target_message_id or target_message.message_id} 来自机器人自身，违反不回复自身消息规则。原始理由: {reasoning}"
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)
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action = "no_reply"
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target_message = None
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# 验证action是否可用
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available_action_names = [action_name for action_name, _ in current_available_actions]
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internal_action_names = ["no_reply", "reply", "wait_time", "no_reply_until_call"]
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@ -277,6 +292,17 @@ class ActionPlanner:
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return action_planner_infos
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def _is_message_from_self(self, message: "DatabaseMessages") -> bool:
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"""判断消息是否由机器人自身发送"""
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try:
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return (
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str(message.user_info.user_id) == str(global_config.bot.qq_account)
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and (message.user_info.platform or "") == (global_config.bot.platform or "")
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)
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except AttributeError:
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logger.warning(f"{self.log_prefix}检测消息发送者失败，缺少必要字段")
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return False
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async def plan(
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self,
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available_actions: Dict[str, ActionInfo],
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@ -1,493 +0,0 @@
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"""
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聊天内容概括器
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用于累积、打包和压缩聊天记录
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"""
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import asyncio
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import json
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import time
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from typing import List, Optional, Set
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from dataclasses import dataclass
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from src.common.logger import get_logger
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from src.common.data_models.database_data_model import DatabaseMessages
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from src.config.config import global_config, model_config
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from src.llm_models.utils_model import LLMRequest
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from src.plugin_system.apis import message_api
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from src.chat.utils.chat_message_builder import build_readable_messages
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from src.person_info.person_info import Person
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from src.chat.message_receive.chat_stream import get_chat_manager
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logger = get_logger("chat_history_summarizer")
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@dataclass
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class MessageBatch:
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"""消息批次"""
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messages: List[DatabaseMessages]
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start_time: float
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end_time: float
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is_preparing: bool = False # 是否处于准备结束模式
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class ChatHistorySummarizer:
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"""聊天内容概括器"""
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def __init__(self, chat_id: str, check_interval: int = 60):
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"""
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初始化聊天内容概括器
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Args:
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chat_id: 聊天ID
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check_interval: 定期检查间隔（秒），默认60秒
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"""
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self.chat_id = chat_id
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self._chat_display_name = self._get_chat_display_name()
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self.log_prefix = f"[{self._chat_display_name}]"
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# 记录时间点，用于计算新消息
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self.last_check_time = time.time()
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# 当前累积的消息批次
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self.current_batch: Optional[MessageBatch] = None
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# LLM请求器，用于压缩聊天内容
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self.summarizer_llm = LLMRequest(
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model_set=model_config.model_task_config.utils, request_type="chat_history_summarizer"
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)
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# 后台循环相关
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self.check_interval = check_interval # 检查间隔（秒）
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self._periodic_task: Optional[asyncio.Task] = None
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self._running = False
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def _get_chat_display_name(self) -> str:
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"""获取聊天显示名称"""
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try:
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chat_name = get_chat_manager().get_stream_name(self.chat_id)
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if chat_name:
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return chat_name
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# 如果获取失败，使用简化的chat_id显示
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if len(self.chat_id) > 20:
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return f"{self.chat_id[:8]}..."
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return self.chat_id
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except Exception:
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# 如果获取失败，使用简化的chat_id显示
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if len(self.chat_id) > 20:
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return f"{self.chat_id[:8]}..."
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return self.chat_id
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async def process(self, current_time: Optional[float] = None):
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"""
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处理聊天内容概括
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Args:
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current_time: 当前时间戳，如果为None则使用time.time()
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"""
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if current_time is None:
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current_time = time.time()
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try:
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# 获取从上次检查时间到当前时间的新消息
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new_messages = message_api.get_messages_by_time_in_chat(
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chat_id=self.chat_id,
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start_time=self.last_check_time,
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end_time=current_time,
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limit=0,
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limit_mode="latest",
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filter_mai=False, # 不过滤bot消息，因为需要检查bot是否发言
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filter_command=False,
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)
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if not new_messages:
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# 没有新消息，检查是否需要打包
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if self.current_batch and self.current_batch.messages:
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await self._check_and_package(current_time)
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self.last_check_time = current_time
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return
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logger.debug(
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f"{self.log_prefix} 开始处理聊天概括，时间窗口: {self.last_check_time:.2f} -> {current_time:.2f}"
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)
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# 有新消息，更新最后检查时间
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self.last_check_time = current_time
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# 如果有当前批次，添加新消息
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if self.current_batch:
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before_count = len(self.current_batch.messages)
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self.current_batch.messages.extend(new_messages)
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self.current_batch.end_time = current_time
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logger.info(f"{self.log_prefix} 更新聊天话题: {before_count} -> {len(self.current_batch.messages)} 条消息")
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else:
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# 创建新批次
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self.current_batch = MessageBatch(
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messages=new_messages,
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start_time=new_messages[0].time if new_messages else current_time,
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end_time=current_time,
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)
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logger.info(f"{self.log_prefix} 新建聊天话题: {len(new_messages)} 条消息")
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# 检查是否需要打包
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await self._check_and_package(current_time)
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except Exception as e:
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logger.error(f"{self.log_prefix} 处理聊天内容概括时出错: {e}")
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import traceback
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traceback.print_exc()
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async def _check_and_package(self, current_time: float):
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"""检查是否需要打包"""
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if not self.current_batch or not self.current_batch.messages:
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return
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messages = self.current_batch.messages
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message_count = len(messages)
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last_message_time = messages[-1].time if messages else current_time
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time_since_last_message = current_time - last_message_time
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# 格式化时间差显示
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if time_since_last_message < 60:
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time_str = f"{time_since_last_message:.1f}秒"
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elif time_since_last_message < 3600:
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time_str = f"{time_since_last_message / 60:.1f}分钟"
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else:
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time_str = f"{time_since_last_message / 3600:.1f}小时"
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preparing_status = "是" if self.current_batch.is_preparing else "否"
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logger.info(
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f"{self.log_prefix} 批次状态检查 | 消息数: {message_count} | 距最后消息: {time_str} | 准备结束模式: {preparing_status}"
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)
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# 检查打包条件
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should_package = False
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# 条件1: 消息长度超过120，直接打包
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if message_count >= 120:
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should_package = True
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logger.info(f"{self.log_prefix} 触发打包条件: 消息数量达到 {message_count} 条（阈值: 120条）")
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# 条件2: 最后一条消息的时间和当前时间差>600秒，直接打包
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elif time_since_last_message > 600:
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should_package = True
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logger.info(f"{self.log_prefix} 触发打包条件: 距最后消息 {time_str}（阈值: 10分钟）")
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# 条件3: 消息长度超过100，进入准备结束模式
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elif message_count > 100:
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if not self.current_batch.is_preparing:
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self.current_batch.is_preparing = True
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logger.info(f"{self.log_prefix} 消息数量 {message_count} 条超过阈值（100条），进入准备结束模式")
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# 在准备结束模式下，如果最后一条消息的时间和当前时间差>10秒，就打包
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if time_since_last_message > 10:
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should_package = True
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logger.info(f"{self.log_prefix} 触发打包条件: 准备结束模式下，距最后消息 {time_str}（阈值: 10秒）")
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if should_package:
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await self._package_and_store()
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async def _package_and_store(self):
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"""打包并存储聊天记录"""
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if not self.current_batch or not self.current_batch.messages:
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return
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messages = self.current_batch.messages
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start_time = self.current_batch.start_time
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end_time = self.current_batch.end_time
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logger.info(
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f"{self.log_prefix} 开始打包批次 | 消息数: {len(messages)} | 时间范围: {start_time:.2f} - {end_time:.2f}"
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)
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||||
# 检查是否有bot发言
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# 第一条消息前推600s到最后一条消息的时间内
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check_start_time = max(start_time - 600, 0)
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check_end_time = end_time
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# 使用包含边界的时间范围查询
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bot_messages = message_api.get_messages_by_time_in_chat_inclusive(
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chat_id=self.chat_id,
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start_time=check_start_time,
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end_time=check_end_time,
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limit=0,
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limit_mode="latest",
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filter_mai=False,
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||||
filter_command=False,
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||||
)
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||||
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||||
# 检查是否有bot的发言
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||||
has_bot_message = False
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||||
bot_user_id = str(global_config.bot.qq_account)
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||||
for msg in bot_messages:
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||||
if msg.user_info.user_id == bot_user_id:
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||||
has_bot_message = True
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||||
break
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||||
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||||
if not has_bot_message:
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||||
logger.info(
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||||
f"{self.log_prefix} 批次内无Bot发言，丢弃批次 | 检查时间范围: {check_start_time:.2f} - {check_end_time:.2f}"
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||||
)
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||||
self.current_batch = None
|
||||
return
|
||||
|
||||
# 有bot发言，进行压缩和存储
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||||
try:
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||||
# 构建对话原文
|
||||
original_text = build_readable_messages(
|
||||
messages=messages,
|
||||
replace_bot_name=True,
|
||||
timestamp_mode="normal_no_YMD",
|
||||
read_mark=0.0,
|
||||
truncate=False,
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||||
show_actions=False,
|
||||
)
|
||||
|
||||
# 获取参与的所有人的昵称
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||||
participants_set: Set[str] = set()
|
||||
for msg in messages:
|
||||
# 使用 msg.user_platform（扁平化字段）或 msg.user_info.platform
|
||||
platform = (
|
||||
getattr(msg, "user_platform", None)
|
||||
or (msg.user_info.platform if msg.user_info else None)
|
||||
or msg.chat_info.platform
|
||||
)
|
||||
person = Person(platform=platform, user_id=msg.user_info.user_id)
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||||
person_name = person.person_name
|
||||
if person_name:
|
||||
participants_set.add(person_name)
|
||||
participants = list(participants_set)
|
||||
logger.info(f"{self.log_prefix} 批次参与者: {', '.join(participants) if participants else '未知'}")
|
||||
|
||||
# 使用LLM压缩聊天内容
|
||||
success, theme, keywords, summary = await self._compress_with_llm(original_text)
|
||||
|
||||
if not success:
|
||||
logger.warning(f"{self.log_prefix} LLM压缩失败，不存储到数据库 | 消息数: {len(messages)}")
|
||||
# 清空当前批次，避免重复处理
|
||||
self.current_batch = None
|
||||
return
|
||||
|
||||
logger.info(
|
||||
f"{self.log_prefix} LLM压缩完成 | 主题: {theme} | 关键词数: {len(keywords)} | 概括长度: {len(summary)} 字"
|
||||
)
|
||||
|
||||
# 存储到数据库
|
||||
await self._store_to_database(
|
||||
start_time=start_time,
|
||||
end_time=end_time,
|
||||
original_text=original_text,
|
||||
participants=participants,
|
||||
theme=theme,
|
||||
keywords=keywords,
|
||||
summary=summary,
|
||||
)
|
||||
|
||||
logger.info(f"{self.log_prefix} 成功打包并存储聊天记录 | 消息数: {len(messages)} | 主题: {theme}")
|
||||
|
||||
# 清空当前批次
|
||||
self.current_batch = None
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 打包和存储聊天记录时出错: {e}")
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
# 出错时也清空批次，避免重复处理
|
||||
self.current_batch = None
|
||||
|
||||
async def _compress_with_llm(self, original_text: str) -> tuple[bool, str, List[str], str]:
|
||||
"""
|
||||
使用LLM压缩聊天内容
|
||||
|
||||
Returns:
|
||||
tuple[bool, str, List[str], str]: (是否成功, 主题, 关键词列表, 概括)
|
||||
"""
|
||||
prompt = f"""请对以下聊天记录进行概括，提取以下信息：
|
||||
|
||||
1. 主题：这段对话的主要内容，一个简短的标题（不超过20字）
|
||||
2. 关键词：这段对话的关键词，用列表形式返回（3-10个关键词）
|
||||
3. 概括：对这段话的平文本概括（50-200字）
|
||||
|
||||
请以JSON格式返回，格式如下：
|
||||
{{
|
||||
"theme": "主题",
|
||||
"keywords": ["关键词1", "关键词2", ...],
|
||||
"summary": "概括内容"
|
||||
}}
|
||||
|
||||
聊天记录：
|
||||
{original_text}
|
||||
|
||||
请直接返回JSON，不要包含其他内容。"""
|
||||
|
||||
try:
|
||||
response, _ = await self.summarizer_llm.generate_response_async(
|
||||
prompt=prompt,
|
||||
temperature=0.3,
|
||||
max_tokens=500,
|
||||
)
|
||||
|
||||
# 解析JSON响应
|
||||
import re
|
||||
|
||||
# 移除可能的markdown代码块标记
|
||||
json_str = response.strip()
|
||||
json_str = re.sub(r"^```json\s*", "", json_str, flags=re.MULTILINE)
|
||||
json_str = re.sub(r"^```\s*", "", json_str, flags=re.MULTILINE)
|
||||
json_str = json_str.strip()
|
||||
|
||||
# 尝试找到JSON对象的开始和结束位置
|
||||
# 查找第一个 { 和最后一个匹配的 }
|
||||
start_idx = json_str.find("{")
|
||||
if start_idx == -1:
|
||||
raise ValueError("未找到JSON对象开始标记")
|
||||
|
||||
# 从后往前查找最后一个 }
|
||||
end_idx = json_str.rfind("}")
|
||||
if end_idx == -1 or end_idx <= start_idx:
|
||||
raise ValueError("未找到JSON对象结束标记")
|
||||
|
||||
# 提取JSON字符串
|
||||
json_str = json_str[start_idx : end_idx + 1]
|
||||
|
||||
# 尝试解析JSON
|
||||
try:
|
||||
result = json.loads(json_str)
|
||||
except json.JSONDecodeError:
|
||||
# 如果解析失败，尝试修复字符串值中的中文引号
|
||||
# 简单方法：将字符串值中的中文引号替换为转义的英文引号
|
||||
# 使用状态机方法：遍历字符串，在字符串值内部替换中文引号
|
||||
fixed_chars = []
|
||||
in_string = False
|
||||
escape_next = False
|
||||
i = 0
|
||||
while i < len(json_str):
|
||||
char = json_str[i]
|
||||
if escape_next:
|
||||
fixed_chars.append(char)
|
||||
escape_next = False
|
||||
elif char == "\\":
|
||||
fixed_chars.append(char)
|
||||
escape_next = True
|
||||
elif char == '"' and not escape_next:
|
||||
fixed_chars.append(char)
|
||||
in_string = not in_string
|
||||
elif in_string and (char == '"' or char == '"'):
|
||||
# 在字符串值内部，将中文引号替换为转义的英文引号
|
||||
fixed_chars.append('\\"')
|
||||
else:
|
||||
fixed_chars.append(char)
|
||||
i += 1
|
||||
|
||||
json_str = "".join(fixed_chars)
|
||||
# 再次尝试解析
|
||||
result = json.loads(json_str)
|
||||
|
||||
theme = result.get("theme", "未命名对话")
|
||||
keywords = result.get("keywords", [])
|
||||
summary = result.get("summary", "无概括")
|
||||
|
||||
# 确保keywords是列表
|
||||
if isinstance(keywords, str):
|
||||
keywords = [keywords]
|
||||
|
||||
return True, theme, keywords, summary
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} LLM压缩聊天内容时出错: {e}")
|
||||
logger.error(f"{self.log_prefix} LLM响应: {response if 'response' in locals() else 'N/A'}")
|
||||
# 返回失败标志和默认值
|
||||
return False, "未命名对话", [], "压缩失败，无法生成概括"
|
||||
|
||||
async def _store_to_database(
|
||||
self,
|
||||
start_time: float,
|
||||
end_time: float,
|
||||
original_text: str,
|
||||
participants: List[str],
|
||||
theme: str,
|
||||
keywords: List[str],
|
||||
summary: str,
|
||||
):
|
||||
"""存储到数据库"""
|
||||
try:
|
||||
from src.common.database.database_model import ChatHistory
|
||||
from src.plugin_system.apis import database_api
|
||||
|
||||
# 准备数据
|
||||
data = {
|
||||
"chat_id": self.chat_id,
|
||||
"start_time": start_time,
|
||||
"end_time": end_time,
|
||||
"original_text": original_text,
|
||||
"participants": json.dumps(participants, ensure_ascii=False),
|
||||
"theme": theme,
|
||||
"keywords": json.dumps(keywords, ensure_ascii=False),
|
||||
"summary": summary,
|
||||
"count": 0,
|
||||
}
|
||||
|
||||
# 使用db_save存储（使用start_time和chat_id作为唯一标识）
|
||||
# 由于可能有多条记录，我们使用组合键，但peewee不支持，所以使用start_time作为唯一标识
|
||||
# 但为了避免冲突，我们使用组合键：chat_id + start_time
|
||||
# 由于peewee不支持组合键，我们直接创建新记录（不提供key_field和key_value）
|
||||
saved_record = await database_api.db_save(
|
||||
ChatHistory,
|
||||
data=data,
|
||||
)
|
||||
|
||||
if saved_record:
|
||||
logger.debug(f"{self.log_prefix} 成功存储聊天历史记录到数据库")
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix} 存储聊天历史记录到数据库失败")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 存储到数据库时出错: {e}")
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
raise
|
||||
|
||||
async def start(self):
|
||||
"""启动后台定期检查循环"""
|
||||
if self._running:
|
||||
logger.warning(f"{self.log_prefix} 后台循环已在运行，无需重复启动")
|
||||
return
|
||||
|
||||
self._running = True
|
||||
self._periodic_task = asyncio.create_task(self._periodic_check_loop())
|
||||
logger.info(f"{self.log_prefix} 已启动后台定期检查循环 | 检查间隔: {self.check_interval}秒")
|
||||
|
||||
async def stop(self):
|
||||
"""停止后台定期检查循环"""
|
||||
self._running = False
|
||||
if self._periodic_task:
|
||||
self._periodic_task.cancel()
|
||||
try:
|
||||
await self._periodic_task
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
self._periodic_task = None
|
||||
logger.info(f"{self.log_prefix} 已停止后台定期检查循环")
|
||||
|
||||
async def _periodic_check_loop(self):
|
||||
"""后台定期检查循环"""
|
||||
try:
|
||||
while self._running:
|
||||
# 执行一次检查
|
||||
await self.process()
|
||||
|
||||
# 等待指定间隔后再次检查
|
||||
await asyncio.sleep(self.check_interval)
|
||||
except asyncio.CancelledError:
|
||||
logger.info(f"{self.log_prefix} 后台检查循环被取消")
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 后台检查循环出错: {e}")
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
self._running = False
|
||||
|
|
@ -372,6 +372,7 @@ class ChatHistory(BaseModel):
|
|||
theme = TextField() # 主题：这段对话的主要内容，一个简短的标题
|
||||
keywords = TextField() # 关键词：这段对话的关键词，JSON格式存储
|
||||
summary = TextField() # 概括：对这段话的平文本概括
|
||||
key_point = TextField(null=True) # 关键信息：话题中的关键信息点，JSON格式存储
|
||||
count = IntegerField(default=0) # 被检索次数
|
||||
forget_times = IntegerField(default=0) # 被遗忘检查的次数
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,898 @@
|
|||
"""
|
||||
聊天内容概括器
|
||||
用于累积、打包和压缩聊天记录
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import time
|
||||
import re
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional, Set
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
from src.common.logger import get_logger
|
||||
from src.common.data_models.database_data_model import DatabaseMessages
|
||||
from src.config.config import global_config, model_config
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.plugin_system.apis import message_api
|
||||
from src.chat.utils.chat_message_builder import build_readable_messages
|
||||
from src.person_info.person_info import Person
|
||||
from src.chat.message_receive.chat_stream import get_chat_manager
|
||||
from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
|
||||
|
||||
logger = get_logger("chat_history_summarizer")
|
||||
|
||||
HIPPO_CACHE_DIR = Path(__file__).resolve().parents[2] / "data" / "hippo_memorizer"
|
||||
|
||||
|
||||
def init_prompt():
|
||||
"""初始化提示词模板"""
|
||||
|
||||
topic_analysis_prompt = """
|
||||
【历史话题标题列表】（仅标题，不含具体内容）：
|
||||
{history_topics_block}
|
||||
|
||||
【本次聊天记录】（每条消息前有编号，用于后续引用）：
|
||||
{messages_block}
|
||||
|
||||
请完成以下任务：
|
||||
**识别话题**
|
||||
1. 识别【本次聊天记录】中正在进行的一个或多个话题；
|
||||
2. 判断【历史话题标题列表】中的话题是否在【本次聊天记录】中出现，如果出现，则直接使用该历史话题标题字符串；
|
||||
|
||||
**选取消息**
|
||||
1. 对于每个话题（新话题或历史话题），从上述带编号的消息中选出与该话题强相关的消息编号列表；
|
||||
2. 每个话题用一句话清晰地描述正在发生的事件，必须包含时间（大致即可）、人物、主要事件和主题，保证精准且有区分度；
|
||||
|
||||
请先输出一段简短思考，说明有什么话题，哪些是不包含在历史话题中的，哪些是包含在历史话题中的，并说明为什么；
|
||||
然后严格以 JSON 格式输出【本次聊天记录】中涉及的话题，格式如下：
|
||||
[
|
||||
{{
|
||||
"topic": "话题",
|
||||
"message_indices": [1, 2, 5]
|
||||
}},
|
||||
...
|
||||
]
|
||||
"""
|
||||
Prompt(topic_analysis_prompt, "hippo_topic_analysis_prompt")
|
||||
|
||||
topic_summary_prompt = """
|
||||
请基于以下话题，对聊天记录片段进行概括，提取以下信息：
|
||||
|
||||
**话题**：{topic}
|
||||
|
||||
**要求**：
|
||||
1. 关键词：提取与话题相关的关键词，用列表形式返回（3-10个关键词）
|
||||
2. 概括：对这段话的平文本概括（50-200字），要求：
|
||||
- 仔细地转述发生的事件和聊天内容；
|
||||
- 可以适当摘取聊天记录中的原文；
|
||||
- 重点突出事件的发展过程和结果；
|
||||
- 围绕话题这个中心进行概括。
|
||||
3. 关键信息：提取话题中的关键信息点，用列表形式返回（3-8个关键信息点），每个关键信息点应该简洁明了。
|
||||
|
||||
请以JSON格式返回，格式如下：
|
||||
{{
|
||||
"keywords": ["关键词1", "关键词2", ...],
|
||||
"summary": "概括内容",
|
||||
"key_point": ["关键信息1", "关键信息2", ...]
|
||||
}}
|
||||
|
||||
聊天记录：
|
||||
{original_text}
|
||||
|
||||
请直接返回JSON，不要包含其他内容。
|
||||
"""
|
||||
Prompt(topic_summary_prompt, "hippo_topic_summary_prompt")
|
||||
|
||||
|
||||
@dataclass
|
||||
class MessageBatch:
|
||||
"""消息批次（用于触发话题检查的原始消息累积）"""
|
||||
|
||||
messages: List[DatabaseMessages]
|
||||
start_time: float
|
||||
end_time: float
|
||||
|
||||
|
||||
@dataclass
|
||||
class TopicCacheItem:
|
||||
"""
|
||||
话题缓存项
|
||||
|
||||
Attributes:
|
||||
topic: 话题标题（一句话描述时间、人物、事件和主题）
|
||||
messages: 与该话题相关的消息字符串列表（已经通过 build 函数转成可读文本）
|
||||
participants: 涉及到的发言人昵称集合
|
||||
no_update_checks: 连续多少次“检查”没有新增内容
|
||||
"""
|
||||
|
||||
topic: str
|
||||
messages: List[str] = field(default_factory=list)
|
||||
participants: Set[str] = field(default_factory=set)
|
||||
no_update_checks: int = 0
|
||||
|
||||
|
||||
class ChatHistorySummarizer:
|
||||
"""聊天内容概括器"""
|
||||
|
||||
def __init__(self, chat_id: str, check_interval: int = 60):
|
||||
"""
|
||||
初始化聊天内容概括器
|
||||
|
||||
Args:
|
||||
chat_id: 聊天ID
|
||||
check_interval: 定期检查间隔（秒），默认60秒
|
||||
"""
|
||||
self.chat_id = chat_id
|
||||
self._chat_display_name = self._get_chat_display_name()
|
||||
self.log_prefix = f"[{self._chat_display_name}]"
|
||||
|
||||
# 记录时间点，用于计算新消息
|
||||
self.last_check_time = time.time()
|
||||
|
||||
# 记录上一次话题检查的时间，用于判断是否需要触发检查
|
||||
self.last_topic_check_time = time.time()
|
||||
|
||||
# 当前累积的消息批次
|
||||
self.current_batch: Optional[MessageBatch] = None
|
||||
|
||||
# 话题缓存：topic_str -> TopicCacheItem
|
||||
# 在内存中维护，并通过本地文件实时持久化
|
||||
self.topic_cache: Dict[str, TopicCacheItem] = {}
|
||||
self._safe_chat_id = self._sanitize_chat_id(self.chat_id)
|
||||
self._topic_cache_file = HIPPO_CACHE_DIR / f"{self._safe_chat_id}.json"
|
||||
# 注意：批次加载需要异步查询消息，所以在 start() 中调用
|
||||
|
||||
# LLM请求器，用于压缩聊天内容
|
||||
self.summarizer_llm = LLMRequest(
|
||||
model_set=model_config.model_task_config.utils, request_type="chat_history_summarizer"
|
||||
)
|
||||
|
||||
# 后台循环相关
|
||||
self.check_interval = check_interval # 检查间隔（秒）
|
||||
self._periodic_task: Optional[asyncio.Task] = None
|
||||
self._running = False
|
||||
|
||||
def _get_chat_display_name(self) -> str:
|
||||
"""获取聊天显示名称"""
|
||||
try:
|
||||
chat_name = get_chat_manager().get_stream_name(self.chat_id)
|
||||
if chat_name:
|
||||
return chat_name
|
||||
# 如果获取失败，使用简化的chat_id显示
|
||||
if len(self.chat_id) > 20:
|
||||
return f"{self.chat_id[:8]}..."
|
||||
return self.chat_id
|
||||
except Exception:
|
||||
# 如果获取失败，使用简化的chat_id显示
|
||||
if len(self.chat_id) > 20:
|
||||
return f"{self.chat_id[:8]}..."
|
||||
return self.chat_id
|
||||
|
||||
def _sanitize_chat_id(self, chat_id: str) -> str:
|
||||
"""用于生成可作为文件名的 chat_id"""
|
||||
return re.sub(r"[^a-zA-Z0-9_.-]", "_", chat_id)
|
||||
|
||||
def _load_topic_cache_from_disk(self):
|
||||
"""在启动时加载本地话题缓存（同步部分），支持重启后继续"""
|
||||
try:
|
||||
if not self._topic_cache_file.exists():
|
||||
return
|
||||
|
||||
with self._topic_cache_file.open("r", encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
|
||||
self.last_topic_check_time = data.get("last_topic_check_time", self.last_topic_check_time)
|
||||
topics_data = data.get("topics", {})
|
||||
loaded_count = 0
|
||||
for topic, payload in topics_data.items():
|
||||
self.topic_cache[topic] = TopicCacheItem(
|
||||
topic=topic,
|
||||
messages=payload.get("messages", []),
|
||||
participants=set(payload.get("participants", [])),
|
||||
no_update_checks=payload.get("no_update_checks", 0),
|
||||
)
|
||||
loaded_count += 1
|
||||
|
||||
if loaded_count:
|
||||
logger.info(f"{self.log_prefix} 已加载 {loaded_count} 个话题缓存，继续追踪")
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 加载话题缓存失败: {e}")
|
||||
|
||||
async def _load_batch_from_disk(self):
|
||||
"""在启动时加载聊天批次，支持重启后继续"""
|
||||
try:
|
||||
if not self._topic_cache_file.exists():
|
||||
return
|
||||
|
||||
with self._topic_cache_file.open("r", encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
|
||||
batch_data = data.get("current_batch")
|
||||
if not batch_data:
|
||||
return
|
||||
|
||||
start_time = batch_data.get("start_time")
|
||||
end_time = batch_data.get("end_time")
|
||||
if not start_time or not end_time:
|
||||
return
|
||||
|
||||
# 根据时间范围重新查询消息
|
||||
messages = message_api.get_messages_by_time_in_chat(
|
||||
chat_id=self.chat_id,
|
||||
start_time=start_time,
|
||||
end_time=end_time,
|
||||
limit=0,
|
||||
limit_mode="latest",
|
||||
filter_mai=False,
|
||||
filter_command=False,
|
||||
)
|
||||
|
||||
if messages:
|
||||
self.current_batch = MessageBatch(
|
||||
messages=messages,
|
||||
start_time=start_time,
|
||||
end_time=end_time,
|
||||
)
|
||||
logger.info(f"{self.log_prefix} 已恢复聊天批次，包含 {len(messages)} 条消息")
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 加载聊天批次失败: {e}")
|
||||
|
||||
def _persist_topic_cache(self):
|
||||
"""实时持久化话题缓存和聊天批次，避免重启后丢失"""
|
||||
try:
|
||||
# 如果既没有话题缓存也没有批次，删除缓存文件
|
||||
if not self.topic_cache and not self.current_batch:
|
||||
if self._topic_cache_file.exists():
|
||||
self._topic_cache_file.unlink()
|
||||
return
|
||||
|
||||
HIPPO_CACHE_DIR.mkdir(parents=True, exist_ok=True)
|
||||
data = {
|
||||
"chat_id": self.chat_id,
|
||||
"last_topic_check_time": self.last_topic_check_time,
|
||||
"topics": {
|
||||
topic: {
|
||||
"messages": item.messages,
|
||||
"participants": list(item.participants),
|
||||
"no_update_checks": item.no_update_checks,
|
||||
}
|
||||
for topic, item in self.topic_cache.items()
|
||||
},
|
||||
}
|
||||
|
||||
# 保存当前批次的时间范围（如果有）
|
||||
if self.current_batch:
|
||||
data["current_batch"] = {
|
||||
"start_time": self.current_batch.start_time,
|
||||
"end_time": self.current_batch.end_time,
|
||||
}
|
||||
|
||||
with self._topic_cache_file.open("w", encoding="utf-8") as f:
|
||||
json.dump(data, f, ensure_ascii=False, indent=2)
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 持久化话题缓存失败: {e}")
|
||||
|
||||
async def process(self, current_time: Optional[float] = None):
|
||||
"""
|
||||
处理聊天内容概括
|
||||
|
||||
Args:
|
||||
current_time: 当前时间戳，如果为None则使用time.time()
|
||||
"""
|
||||
if current_time is None:
|
||||
current_time = time.time()
|
||||
|
||||
try:
|
||||
# 获取从上次检查时间到当前时间的新消息
|
||||
new_messages = message_api.get_messages_by_time_in_chat(
|
||||
chat_id=self.chat_id,
|
||||
start_time=self.last_check_time,
|
||||
end_time=current_time,
|
||||
limit=0,
|
||||
limit_mode="latest",
|
||||
filter_mai=False, # 不过滤bot消息，因为需要检查bot是否发言
|
||||
filter_command=False,
|
||||
)
|
||||
|
||||
if not new_messages:
|
||||
# 没有新消息，检查是否需要进行“话题检查”
|
||||
if self.current_batch and self.current_batch.messages:
|
||||
await self._check_and_run_topic_check(current_time)
|
||||
self.last_check_time = current_time
|
||||
return
|
||||
|
||||
logger.debug(
|
||||
f"{self.log_prefix} 开始处理聊天概括，时间窗口: {self.last_check_time:.2f} -> {current_time:.2f}"
|
||||
)
|
||||
|
||||
# 有新消息，更新最后检查时间
|
||||
self.last_check_time = current_time
|
||||
|
||||
# 如果有当前批次，添加新消息
|
||||
if self.current_batch:
|
||||
before_count = len(self.current_batch.messages)
|
||||
self.current_batch.messages.extend(new_messages)
|
||||
self.current_batch.end_time = current_time
|
||||
logger.info(f"{self.log_prefix} 更新聊天检查批次: {before_count} -> {len(self.current_batch.messages)} 条消息")
|
||||
# 更新批次后持久化
|
||||
self._persist_topic_cache()
|
||||
else:
|
||||
# 创建新批次
|
||||
self.current_batch = MessageBatch(
|
||||
messages=new_messages,
|
||||
start_time=new_messages[0].time if new_messages else current_time,
|
||||
end_time=current_time,
|
||||
)
|
||||
logger.info(f"{self.log_prefix} 新建聊天检查批次: {len(new_messages)} 条消息")
|
||||
# 创建批次后持久化
|
||||
self._persist_topic_cache()
|
||||
|
||||
# 检查是否需要触发“话题检查”
|
||||
await self._check_and_run_topic_check(current_time)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 处理聊天内容概括时出错: {e}")
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
|
||||
async def _check_and_run_topic_check(self, current_time: float):
|
||||
"""
|
||||
检查是否需要进行一次“话题检查”
|
||||
|
||||
触发条件：
|
||||
- 当前批次消息数 >= 100，或者
|
||||
- 距离上一次检查的时间 > 3600 秒（1小时）
|
||||
"""
|
||||
if not self.current_batch or not self.current_batch.messages:
|
||||
return
|
||||
|
||||
messages = self.current_batch.messages
|
||||
message_count = len(messages)
|
||||
time_since_last_check = current_time - self.last_topic_check_time
|
||||
|
||||
# 格式化时间差显示
|
||||
if time_since_last_check < 60:
|
||||
time_str = f"{time_since_last_check:.1f}秒"
|
||||
elif time_since_last_check < 3600:
|
||||
time_str = f"{time_since_last_check / 60:.1f}分钟"
|
||||
else:
|
||||
time_str = f"{time_since_last_check / 3600:.1f}小时"
|
||||
|
||||
logger.info(
|
||||
f"{self.log_prefix} 批次状态检查 | 消息数: {message_count} | 距上次检查: {time_str}"
|
||||
)
|
||||
|
||||
# 检查“话题检查”触发条件
|
||||
should_check = False
|
||||
|
||||
# 条件1: 消息数量 >= 100，触发一次检查
|
||||
if message_count >= 50:
|
||||
should_check = True
|
||||
logger.info(f"{self.log_prefix} 触发检查条件: 消息数量达到 {message_count} 条（阈值: 100条）")
|
||||
|
||||
# 条件2: 距离上一次检查 > 3600 秒（1小时），触发一次检查
|
||||
elif time_since_last_check > 1200:
|
||||
should_check = True
|
||||
logger.info(f"{self.log_prefix} 触发检查条件: 距上次检查 {time_str}（阈值: 1小时）")
|
||||
|
||||
if should_check:
|
||||
await self._run_topic_check_and_update_cache(messages)
|
||||
# 本批次已经被处理为话题信息，可以清空
|
||||
self.current_batch = None
|
||||
# 更新上一次检查时间，并持久化
|
||||
self.last_topic_check_time = current_time
|
||||
self._persist_topic_cache()
|
||||
|
||||
async def _run_topic_check_and_update_cache(self, messages: List[DatabaseMessages]):
|
||||
"""
|
||||
执行一次“话题检查”：
|
||||
1. 首先确认这段消息里是否有 Bot 发言，没有则直接丢弃本次批次；
|
||||
2. 将消息编号并转成字符串，构造 LLM Prompt；
|
||||
3. 把历史话题标题列表放入 Prompt，要求 LLM：
|
||||
- 识别当前聊天中的话题（1 个或多个）；
|
||||
- 为每个话题选出相关消息编号；
|
||||
- 若话题属于历史话题，则沿用原话题标题；
|
||||
4. LLM 返回 JSON：多个 {topic, message_indices}；
|
||||
5. 更新本地话题缓存，并根据规则触发“话题打包存储”。
|
||||
"""
|
||||
if not messages:
|
||||
return
|
||||
|
||||
start_time = messages[0].time
|
||||
end_time = messages[-1].time
|
||||
|
||||
logger.info(
|
||||
f"{self.log_prefix} 开始话题检查 | 消息数: {len(messages)} | 时间范围: {start_time:.2f} - {end_time:.2f}"
|
||||
)
|
||||
|
||||
# 1. 检查当前批次内是否有 bot 发言（只检查当前批次，不往前推）
|
||||
# 原因：我们要记录的是 bot 参与过的对话片段，如果当前批次内 bot 没有发言，
|
||||
# 说明 bot 没有参与这段对话，不应该记录
|
||||
bot_user_id = str(global_config.bot.qq_account)
|
||||
has_bot_message = False
|
||||
|
||||
for msg in messages:
|
||||
if msg.user_info.user_id == bot_user_id:
|
||||
has_bot_message = True
|
||||
break
|
||||
|
||||
if not has_bot_message:
|
||||
logger.info(
|
||||
f"{self.log_prefix} 当前批次内无 Bot 发言，丢弃本次检查 | 时间范围: {start_time:.2f} - {end_time:.2f}"
|
||||
)
|
||||
return
|
||||
|
||||
# 2. 构造编号后的消息字符串和参与者信息
|
||||
numbered_lines, index_to_msg_str, index_to_msg_text, index_to_participants = self._build_numbered_messages_for_llm(messages)
|
||||
|
||||
# 3. 调用 LLM 识别话题，并得到 topic -> indices
|
||||
existing_topics = list(self.topic_cache.keys())
|
||||
success, topic_to_indices = await self._analyze_topics_with_llm(
|
||||
numbered_lines=numbered_lines,
|
||||
existing_topics=existing_topics,
|
||||
)
|
||||
|
||||
if not success or not topic_to_indices:
|
||||
logger.warning(f"{self.log_prefix} 话题识别失败或无有效话题，本次检查忽略")
|
||||
# 即使识别失败，也认为是一次“检查”，但不更新 no_update_checks（保持原状）
|
||||
return
|
||||
|
||||
# 4. 统计哪些话题在本次检查中有新增内容
|
||||
updated_topics: Set[str] = set()
|
||||
|
||||
for topic, indices in topic_to_indices.items():
|
||||
if not indices:
|
||||
continue
|
||||
|
||||
item = self.topic_cache.get(topic)
|
||||
if not item:
|
||||
# 新话题
|
||||
item = TopicCacheItem(topic=topic)
|
||||
self.topic_cache[topic] = item
|
||||
|
||||
# 收集属于该话题的消息文本（不带编号）
|
||||
topic_msg_texts: List[str] = []
|
||||
new_participants: Set[str] = set()
|
||||
for idx in indices:
|
||||
msg_text = index_to_msg_text.get(idx)
|
||||
if not msg_text:
|
||||
continue
|
||||
topic_msg_texts.append(msg_text)
|
||||
new_participants.update(index_to_participants.get(idx, set()))
|
||||
|
||||
if not topic_msg_texts:
|
||||
continue
|
||||
|
||||
# 将本次检查中属于该话题的所有消息合并为一个字符串（不带编号）
|
||||
merged_text = "\n".join(topic_msg_texts)
|
||||
item.messages.append(merged_text)
|
||||
item.participants.update(new_participants)
|
||||
# 本次检查中该话题有更新，重置计数
|
||||
item.no_update_checks = 0
|
||||
updated_topics.add(topic)
|
||||
|
||||
# 5. 对于本次没有更新的历史话题，no_update_checks + 1
|
||||
for topic, item in list(self.topic_cache.items()):
|
||||
if topic not in updated_topics:
|
||||
item.no_update_checks += 1
|
||||
|
||||
# 6. 检查是否有话题需要打包存储
|
||||
topics_to_finalize: List[str] = []
|
||||
for topic, item in self.topic_cache.items():
|
||||
if item.no_update_checks >= 3:
|
||||
logger.info(f"{self.log_prefix} 话题[{topic}] 连续 5 次检查无新增内容，触发打包存储")
|
||||
topics_to_finalize.append(topic)
|
||||
continue
|
||||
if len(item.messages) > 8:
|
||||
logger.info(f"{self.log_prefix} 话题[{topic}] 消息条数超过 30，触发打包存储")
|
||||
topics_to_finalize.append(topic)
|
||||
|
||||
for topic in topics_to_finalize:
|
||||
item = self.topic_cache.get(topic)
|
||||
if not item:
|
||||
continue
|
||||
try:
|
||||
await self._finalize_and_store_topic(
|
||||
topic=topic,
|
||||
item=item,
|
||||
# 这里的时间范围尽量覆盖最近一次检查的区间
|
||||
start_time=start_time,
|
||||
end_time=end_time,
|
||||
)
|
||||
finally:
|
||||
# 无论成功与否，都从缓存中删除，避免重复
|
||||
self.topic_cache.pop(topic, None)
|
||||
|
||||
def _build_numbered_messages_for_llm(
|
||||
self, messages: List[DatabaseMessages]
|
||||
) -> tuple[List[str], Dict[int, str], Dict[int, str], Dict[int, Set[str]]]:
|
||||
"""
|
||||
将消息转为带编号的字符串，供 LLM 选择使用。
|
||||
|
||||
返回:
|
||||
numbered_lines: ["1. xxx", "2. yyy", ...] # 带编号，用于 LLM 选择
|
||||
index_to_msg_str: idx -> "idx. xxx" # 带编号，用于 LLM 选择
|
||||
index_to_msg_text: idx -> "xxx" # 不带编号，用于最终存储
|
||||
index_to_participants: idx -> {nickname1, nickname2, ...}
|
||||
"""
|
||||
numbered_lines: List[str] = []
|
||||
index_to_msg_str: Dict[int, str] = {}
|
||||
index_to_msg_text: Dict[int, str] = {} # 不带编号的消息文本
|
||||
index_to_participants: Dict[int, Set[str]] = {}
|
||||
|
||||
for idx, msg in enumerate(messages, start=1):
|
||||
# 使用 build_readable_messages 生成可读文本
|
||||
try:
|
||||
text = build_readable_messages(
|
||||
messages=[msg],
|
||||
replace_bot_name=True,
|
||||
timestamp_mode="normal_no_YMD",
|
||||
read_mark=0.0,
|
||||
truncate=False,
|
||||
show_actions=False,
|
||||
).strip()
|
||||
except Exception:
|
||||
# 回退到简单文本
|
||||
text = getattr(msg, "processed_plain_text", "") or ""
|
||||
|
||||
# 获取发言人昵称
|
||||
participants: Set[str] = set()
|
||||
try:
|
||||
platform = (
|
||||
getattr(msg, "user_platform", None)
|
||||
or (msg.user_info.platform if msg.user_info else None)
|
||||
or msg.chat_info.platform
|
||||
)
|
||||
user_id = msg.user_info.user_id if msg.user_info else None
|
||||
if platform and user_id:
|
||||
person = Person(platform=platform, user_id=user_id)
|
||||
if person.person_name:
|
||||
participants.add(person.person_name)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# 带编号的字符串（用于 LLM 选择）
|
||||
line = f"{idx}. {text}"
|
||||
numbered_lines.append(line)
|
||||
index_to_msg_str[idx] = line
|
||||
# 不带编号的文本（用于最终存储）
|
||||
index_to_msg_text[idx] = text
|
||||
index_to_participants[idx] = participants
|
||||
|
||||
return numbered_lines, index_to_msg_str, index_to_msg_text, index_to_participants
|
||||
|
||||
async def _analyze_topics_with_llm(
|
||||
self,
|
||||
numbered_lines: List[str],
|
||||
existing_topics: List[str],
|
||||
) -> tuple[bool, Dict[str, List[int]]]:
|
||||
"""
|
||||
使用 LLM 识别本次检查中的话题，并为每个话题选择相关消息编号。
|
||||
|
||||
要求：
|
||||
- 话题用一句话清晰描述正在发生的事件，包括时间、人物、主要事件和主题；
|
||||
- 可以有 1 个或多个话题；
|
||||
- 若某个话题与历史话题列表中的某个话题是同一件事，请直接使用历史话题的字符串；
|
||||
- 输出 JSON，格式：
|
||||
[
|
||||
{
|
||||
"topic": "话题标题字符串",
|
||||
"message_indices": [1, 2, 5]
|
||||
},
|
||||
...
|
||||
]
|
||||
"""
|
||||
if not numbered_lines:
|
||||
return False, {}
|
||||
|
||||
history_topics_block = (
|
||||
"\n".join(f"- {t}" for t in existing_topics) if existing_topics else "（当前无历史话题）"
|
||||
)
|
||||
messages_block = "\n".join(numbered_lines)
|
||||
|
||||
prompt = await global_prompt_manager.format_prompt(
|
||||
"hippo_topic_analysis_prompt",
|
||||
history_topics_block=history_topics_block,
|
||||
messages_block=messages_block,
|
||||
)
|
||||
|
||||
try:
|
||||
response, _ = await self.summarizer_llm.generate_response_async(
|
||||
prompt=prompt,
|
||||
temperature=0.2,
|
||||
max_tokens=800,
|
||||
)
|
||||
|
||||
import re
|
||||
logger.info(f"{self.log_prefix} 话题识别LLM Prompt: {prompt}")
|
||||
logger.info(f"{self.log_prefix} 话题识别LLM Response: {response}")
|
||||
|
||||
json_str = response.strip()
|
||||
# 移除可能的 markdown 代码块标记
|
||||
json_str = re.sub(r"^```json\s*", "", json_str, flags=re.MULTILINE)
|
||||
json_str = re.sub(r"^```\s*", "", json_str, flags=re.MULTILINE)
|
||||
json_str = json_str.strip()
|
||||
|
||||
# 尝试直接解析为 JSON 数组
|
||||
result = json.loads(json_str)
|
||||
|
||||
if not isinstance(result, list):
|
||||
logger.error(f"{self.log_prefix} 话题识别返回的 JSON 不是列表: {result}")
|
||||
return False, {}
|
||||
|
||||
topic_to_indices: Dict[str, List[int]] = {}
|
||||
for item in result:
|
||||
if not isinstance(item, dict):
|
||||
continue
|
||||
topic = item.get("topic")
|
||||
indices = item.get("message_indices") or item.get("messages") or []
|
||||
if not topic or not isinstance(topic, str):
|
||||
continue
|
||||
if isinstance(indices, list):
|
||||
valid_indices: List[int] = []
|
||||
for v in indices:
|
||||
try:
|
||||
iv = int(v)
|
||||
if iv > 0:
|
||||
valid_indices.append(iv)
|
||||
except (TypeError, ValueError):
|
||||
continue
|
||||
if valid_indices:
|
||||
topic_to_indices[topic] = valid_indices
|
||||
|
||||
return True, topic_to_indices
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 话题识别 LLM 调用或解析失败: {e}")
|
||||
logger.error(f"{self.log_prefix} LLM响应: {response if 'response' in locals() else 'N/A'}")
|
||||
return False, {}
|
||||
|
||||
async def _finalize_and_store_topic(
|
||||
self,
|
||||
topic: str,
|
||||
item: TopicCacheItem,
|
||||
start_time: float,
|
||||
end_time: float,
|
||||
):
|
||||
"""
|
||||
对某个话题进行最终打包存储：
|
||||
1. 将 messages(list[str]) 拼接为 original_text；
|
||||
2. 使用 LLM 对 original_text 进行总结，得到 summary 和 keywords，theme 直接使用话题字符串；
|
||||
3. 写入数据库 ChatHistory；
|
||||
4. 完成后，调用方会从缓存中删除该话题。
|
||||
"""
|
||||
if not item.messages:
|
||||
logger.info(f"{self.log_prefix} 话题[{topic}] 无消息内容，跳过打包")
|
||||
return
|
||||
|
||||
original_text = "\n".join(item.messages)
|
||||
|
||||
logger.info(
|
||||
f"{self.log_prefix} 开始打包话题[{topic}] | 消息数: {len(item.messages)} | 时间范围: {start_time:.2f} - {end_time:.2f}"
|
||||
)
|
||||
|
||||
# 使用 LLM 进行总结（基于话题名）
|
||||
success, keywords, summary, key_point = await self._compress_with_llm(original_text, topic)
|
||||
if not success:
|
||||
logger.warning(f"{self.log_prefix} 话题[{topic}] LLM 概括失败，不写入数据库")
|
||||
return
|
||||
|
||||
participants = list(item.participants)
|
||||
|
||||
await self._store_to_database(
|
||||
start_time=start_time,
|
||||
end_time=end_time,
|
||||
original_text=original_text,
|
||||
participants=participants,
|
||||
theme=topic, # 主题直接使用话题名
|
||||
keywords=keywords,
|
||||
summary=summary,
|
||||
key_point=key_point,
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"{self.log_prefix} 话题[{topic}] 成功打包并存储 | 消息数: {len(item.messages)} | 参与者数: {len(participants)}"
|
||||
)
|
||||
|
||||
async def _compress_with_llm(self, original_text: str, topic: str) -> tuple[bool, List[str], str, List[str]]:
|
||||
"""
|
||||
使用LLM压缩聊天内容（用于单个话题的最终总结）
|
||||
|
||||
Args:
|
||||
original_text: 聊天记录原文
|
||||
topic: 话题名称
|
||||
|
||||
Returns:
|
||||
tuple[bool, List[str], str, List[str]]: (是否成功, 关键词列表, 概括, 关键信息列表)
|
||||
"""
|
||||
prompt = await global_prompt_manager.format_prompt(
|
||||
"hippo_topic_summary_prompt",
|
||||
topic=topic,
|
||||
original_text=original_text,
|
||||
)
|
||||
|
||||
try:
|
||||
response, _ = await self.summarizer_llm.generate_response_async(
|
||||
prompt=prompt,
|
||||
temperature=0.3,
|
||||
max_tokens=500,
|
||||
)
|
||||
|
||||
# 解析JSON响应
|
||||
import re
|
||||
|
||||
# 移除可能的markdown代码块标记
|
||||
json_str = response.strip()
|
||||
json_str = re.sub(r"^```json\s*", "", json_str, flags=re.MULTILINE)
|
||||
json_str = re.sub(r"^```\s*", "", json_str, flags=re.MULTILINE)
|
||||
json_str = json_str.strip()
|
||||
|
||||
# 尝试找到JSON对象的开始和结束位置
|
||||
# 查找第一个 { 和最后一个匹配的 }
|
||||
start_idx = json_str.find("{")
|
||||
if start_idx == -1:
|
||||
raise ValueError("未找到JSON对象开始标记")
|
||||
|
||||
# 从后往前查找最后一个 }
|
||||
end_idx = json_str.rfind("}")
|
||||
if end_idx == -1 or end_idx <= start_idx:
|
||||
raise ValueError("未找到JSON对象结束标记")
|
||||
|
||||
# 提取JSON字符串
|
||||
json_str = json_str[start_idx : end_idx + 1]
|
||||
|
||||
# 尝试解析JSON
|
||||
try:
|
||||
result = json.loads(json_str)
|
||||
except json.JSONDecodeError:
|
||||
# 如果解析失败，尝试修复字符串值中的中文引号
|
||||
# 简单方法：将字符串值中的中文引号替换为转义的英文引号
|
||||
# 使用状态机方法：遍历字符串，在字符串值内部替换中文引号
|
||||
fixed_chars = []
|
||||
in_string = False
|
||||
escape_next = False
|
||||
i = 0
|
||||
while i < len(json_str):
|
||||
char = json_str[i]
|
||||
if escape_next:
|
||||
fixed_chars.append(char)
|
||||
escape_next = False
|
||||
elif char == "\\":
|
||||
fixed_chars.append(char)
|
||||
escape_next = True
|
||||
elif char == '"' and not escape_next:
|
||||
fixed_chars.append(char)
|
||||
in_string = not in_string
|
||||
elif in_string and (char == '"' or char == '"'):
|
||||
# 在字符串值内部，将中文引号替换为转义的英文引号
|
||||
fixed_chars.append('\\"')
|
||||
else:
|
||||
fixed_chars.append(char)
|
||||
i += 1
|
||||
|
||||
json_str = "".join(fixed_chars)
|
||||
# 再次尝试解析
|
||||
result = json.loads(json_str)
|
||||
|
||||
keywords = result.get("keywords", [])
|
||||
summary = result.get("summary", "无概括")
|
||||
key_point = result.get("key_point", [])
|
||||
|
||||
# 确保keywords和key_point是列表
|
||||
if isinstance(keywords, str):
|
||||
keywords = [keywords]
|
||||
if isinstance(key_point, str):
|
||||
key_point = [key_point]
|
||||
|
||||
return True, keywords, summary, key_point
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} LLM压缩聊天内容时出错: {e}")
|
||||
logger.error(f"{self.log_prefix} LLM响应: {response if 'response' in locals() else 'N/A'}")
|
||||
# 返回失败标志和默认值
|
||||
return False, [], "压缩失败，无法生成概括", []
|
||||
|
||||
async def _store_to_database(
|
||||
self,
|
||||
start_time: float,
|
||||
end_time: float,
|
||||
original_text: str,
|
||||
participants: List[str],
|
||||
theme: str,
|
||||
keywords: List[str],
|
||||
summary: str,
|
||||
key_point: Optional[List[str]] = None,
|
||||
):
|
||||
"""存储到数据库"""
|
||||
try:
|
||||
from src.common.database.database_model import ChatHistory
|
||||
from src.plugin_system.apis import database_api
|
||||
|
||||
# 准备数据
|
||||
data = {
|
||||
"chat_id": self.chat_id,
|
||||
"start_time": start_time,
|
||||
"end_time": end_time,
|
||||
"original_text": original_text,
|
||||
"participants": json.dumps(participants, ensure_ascii=False),
|
||||
"theme": theme,
|
||||
"keywords": json.dumps(keywords, ensure_ascii=False),
|
||||
"summary": summary,
|
||||
"count": 0,
|
||||
}
|
||||
|
||||
# 存储 key_point（如果存在）
|
||||
if key_point is not None:
|
||||
data["key_point"] = json.dumps(key_point, ensure_ascii=False)
|
||||
|
||||
# 使用db_save存储（使用start_time和chat_id作为唯一标识）
|
||||
# 由于可能有多条记录，我们使用组合键，但peewee不支持，所以使用start_time作为唯一标识
|
||||
# 但为了避免冲突，我们使用组合键：chat_id + start_time
|
||||
# 由于peewee不支持组合键，我们直接创建新记录（不提供key_field和key_value）
|
||||
saved_record = await database_api.db_save(
|
||||
ChatHistory,
|
||||
data=data,
|
||||
)
|
||||
|
||||
if saved_record:
|
||||
logger.debug(f"{self.log_prefix} 成功存储聊天历史记录到数据库")
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix} 存储聊天历史记录到数据库失败")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 存储到数据库时出错: {e}")
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
raise
|
||||
|
||||
async def start(self):
|
||||
"""启动后台定期检查循环"""
|
||||
if self._running:
|
||||
logger.warning(f"{self.log_prefix} 后台循环已在运行，无需重复启动")
|
||||
return
|
||||
|
||||
# 加载聊天批次（如果有）
|
||||
await self._load_batch_from_disk()
|
||||
|
||||
self._running = True
|
||||
self._periodic_task = asyncio.create_task(self._periodic_check_loop())
|
||||
logger.info(f"{self.log_prefix} 已启动后台定期检查循环 | 检查间隔: {self.check_interval}秒")
|
||||
|
||||
async def stop(self):
|
||||
"""停止后台定期检查循环"""
|
||||
self._running = False
|
||||
if self._periodic_task:
|
||||
self._periodic_task.cancel()
|
||||
try:
|
||||
await self._periodic_task
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
self._periodic_task = None
|
||||
logger.info(f"{self.log_prefix} 已停止后台定期检查循环")
|
||||
|
||||
async def _periodic_check_loop(self):
|
||||
"""后台定期检查循环"""
|
||||
try:
|
||||
while self._running:
|
||||
# 执行一次检查
|
||||
await self.process()
|
||||
|
||||
# 等待指定间隔后再次检查
|
||||
await asyncio.sleep(self.check_interval)
|
||||
except asyncio.CancelledError:
|
||||
logger.info(f"{self.log_prefix} 后台检查循环被取消")
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 后台检查循环出错: {e}")
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
self._running = False
|
||||
|
||||
|
||||
init_prompt()
|
||||
|
||||
Loading…
Reference in New Issue