Merge branch 'refactor' into rel_fix
This commit is contained in:
meng_xi_pan
@@ -1,8 +1,8 @@
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from .emoji_manager import emoji_manager
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from .relationship_manager import relationship_manager
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from ..relationship.relationship_manager import relationship_manager
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from .chat_stream import chat_manager
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from .message_sender import message_manager
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from .storage import MessageStorage
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from ..storage.storage import MessageStorage
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from .auto_speak import auto_speak_manager
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@@ -8,7 +8,7 @@ from .message import MessageSending, MessageThinking, MessageSet, MessageRecv
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from ..message.message_base import UserInfo, Seg
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from .message_sender import message_manager
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from ..moods.moods import MoodManager
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from .llm_generator import ResponseGenerator
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from ..chat_module.reasoning_chat.reasoning_generator import ResponseGenerator
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from src.common.logger import get_module_logger
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from src.heart_flow.heartflow import heartflow
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from ...common.database import db
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@@ -1,26 +1,14 @@
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import re
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import time
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from random import random
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from ..memory_system.Hippocampus import HippocampusManager
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from ..moods.moods import MoodManager # 导入情绪管理器
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from ..config.config import global_config
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from .emoji_manager import emoji_manager # 导入表情包管理器
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from .llm_generator import ResponseGenerator
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from .message import MessageSending, MessageRecv, MessageThinking, MessageSet
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from ..chat_module.reasoning_chat.reasoning_generator import ResponseGenerator
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from .chat_stream import chat_manager
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from .message_sender import message_manager # 导入新的消息管理器
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from .relationship_manager import relationship_manager
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from .storage import MessageStorage
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from .utils import is_mentioned_bot_in_message, get_recent_group_detailed_plain_text
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from .utils_image import image_path_to_base64
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from ..willing.willing_manager import willing_manager # 导入意愿管理器
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from ..message import UserInfo, Seg
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from ..storage.storage import MessageStorage # 修改导入路径
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from src.heart_flow.heartflow import heartflow
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from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
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from ..chat_module.think_flow_chat.think_flow_chat import ThinkFlowChat
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from ..chat_module.reasoning_chat.reasoning_chat import ReasoningChat
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# 定义日志配置
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chat_config = LogConfig(
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@@ -41,329 +29,42 @@ class ChatBot:
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self._started = False
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self.mood_manager = MoodManager.get_instance() # 获取情绪管理器单例
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self.mood_manager.start_mood_update() # 启动情绪更新
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self.think_flow_chat = ThinkFlowChat()
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self.reasoning_chat = ReasoningChat()
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async def _ensure_started(self):
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"""确保所有任务已启动"""
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if not self._started:
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self._started = True
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async def _create_thinking_message(self, message, chat, userinfo, messageinfo):
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"""创建思考消息
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Args:
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message: 接收到的消息
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chat: 聊天流对象
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userinfo: 用户信息对象
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messageinfo: 消息信息对象
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Returns:
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str: thinking_id
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"""
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bot_user_info = UserInfo(
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user_id=global_config.BOT_QQ,
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user_nickname=global_config.BOT_NICKNAME,
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platform=messageinfo.platform,
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)
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thinking_time_point = round(time.time(), 2)
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thinking_id = "mt" + str(thinking_time_point)
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thinking_message = MessageThinking(
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message_id=thinking_id,
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chat_stream=chat,
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bot_user_info=bot_user_info,
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reply=message,
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thinking_start_time=thinking_time_point,
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)
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message_manager.add_message(thinking_message)
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willing_manager.change_reply_willing_sent(chat)
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return thinking_id
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async def message_process(self, message_data: str) -> None:
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"""处理转化后的统一格式消息
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1. 过滤消息
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2. 记忆激活
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3. 意愿激活
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4. 生成回复并发送
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5. 更新关系
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6. 更新情绪
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根据global_config.response_mode选择不同的回复模式：
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1. heart_flow模式：使用思维流系统进行回复
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- 包含思维流状态管理
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- 在回复前进行观察和状态更新
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- 回复后更新思维流状态
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2. reasoning模式：使用推理系统进行回复
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- 直接使用意愿管理器计算回复概率
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- 没有思维流相关的状态管理
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- 更简单直接的回复逻辑
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两种模式都包含：
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- 消息过滤
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- 记忆激活
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- 意愿计算
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- 消息生成和发送
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- 表情包处理
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- 性能计时
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"""
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timing_results = {} # 用于收集所有计时结果
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response_set = None # 初始化response_set变量
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message = MessageRecv(message_data)
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groupinfo = message.message_info.group_info
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userinfo = message.message_info.user_info
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messageinfo = message.message_info
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# 消息过滤，涉及到config有待更新
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# 创建聊天流
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chat = await chat_manager.get_or_create_stream(
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platform=messageinfo.platform,
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user_info=userinfo,
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group_info=groupinfo,
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)
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message.update_chat_stream(chat)
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# 创建 心流与chat的观察
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heartflow.create_subheartflow(chat.stream_id)
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await message.process()
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# 过滤词/正则表达式过滤
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if self._check_ban_words(message.processed_plain_text, chat, userinfo) or self._check_ban_regex(
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message.raw_message, chat, userinfo
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):
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return
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await self.storage.store_message(message, chat)
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timer1 = time.time()
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interested_rate = 0
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interested_rate = await HippocampusManager.get_instance().get_activate_from_text(
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message.processed_plain_text, fast_retrieval=True
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)
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timer2 = time.time()
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timing_results["记忆激活"] = timer2 - timer1
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is_mentioned = is_mentioned_bot_in_message(message)
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if global_config.enable_think_flow:
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current_willing_old = willing_manager.get_willing(chat_stream=chat)
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current_willing_new = (heartflow.get_subheartflow(chat.stream_id).current_state.willing - 5) / 4
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print(f"旧回复意愿：{current_willing_old}，新回复意愿：{current_willing_new}")
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current_willing = (current_willing_old + current_willing_new) / 2
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if global_config.response_mode == "heart_flow":
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await self.think_flow_chat.process_message(message_data)
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elif global_config.response_mode == "reasoning":
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await self.reasoning_chat.process_message(message_data)
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else:
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current_willing = willing_manager.get_willing(chat_stream=chat)
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willing_manager.set_willing(chat.stream_id, current_willing)
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timer1 = time.time()
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reply_probability = await willing_manager.change_reply_willing_received(
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chat_stream=chat,
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is_mentioned_bot=is_mentioned,
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config=global_config,
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is_emoji=message.is_emoji,
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interested_rate=interested_rate,
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sender_id=str(message.message_info.user_info.user_id),
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)
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timer2 = time.time()
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timing_results["意愿激活"] = timer2 - timer1
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# 神秘的消息流数据结构处理
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if chat.group_info:
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mes_name = chat.group_info.group_name
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else:
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mes_name = "私聊"
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if message.message_info.additional_config:
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if "maimcore_reply_probability_gain" in message.message_info.additional_config.keys():
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reply_probability += message.message_info.additional_config["maimcore_reply_probability_gain"]
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# 打印收到的信息的信息
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current_time = time.strftime("%H:%M:%S", time.localtime(messageinfo.time))
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logger.info(
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f"[{current_time}][{mes_name}]"
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f"{chat.user_info.user_nickname}:"
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f"{message.processed_plain_text}[回复意愿:{current_willing:.2f}][概率:{reply_probability * 100:.1f}%]"
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)
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do_reply = False
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# 开始组织语言
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if random() < reply_probability:
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do_reply = True
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timer1 = time.time()
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thinking_id = await self._create_thinking_message(message, chat, userinfo, messageinfo)
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timer2 = time.time()
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timing_results["创建思考消息"] = timer2 - timer1
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timer1 = time.time()
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await heartflow.get_subheartflow(chat.stream_id).do_observe()
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timer2 = time.time()
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timing_results["观察"] = timer2 - timer1
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timer1 = time.time()
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await heartflow.get_subheartflow(chat.stream_id).do_thinking_before_reply(message.processed_plain_text)
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timer2 = time.time()
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timing_results["思考前脑内状态"] = timer2 - timer1
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timer1 = time.time()
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response_set, undivided_response = await self.gpt.generate_response(message)
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timer2 = time.time()
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timing_results["生成回复"] = timer2 - timer1
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if not response_set:
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logger.info("为什么生成回复失败？")
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return
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# 发送消息
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timer1 = time.time()
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await self._send_response_messages(message, chat, response_set, thinking_id)
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timer2 = time.time()
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timing_results["发送消息"] = timer2 - timer1
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# 处理表情包
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timer1 = time.time()
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await self._handle_emoji(message, chat, response_set)
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timer2 = time.time()
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timing_results["处理表情包"] = timer2 - timer1
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timer1 = time.time()
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await self._update_using_response(message, response_set)
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timer2 = time.time()
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timing_results["更新心流"] = timer2 - timer1
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# 在最后统一输出所有计时结果
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if do_reply:
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timing_str = " | ".join([f"{step}: {duration:.2f}秒" for step, duration in timing_results.items()])
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trigger_msg = message.processed_plain_text
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response_msg = " ".join(response_set) if response_set else "无回复"
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logger.info(f"触发消息: {trigger_msg[:20]}... | 生成消息: {response_msg[:20]}... | 性能计时: {timing_str}")
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# 更新情绪和关系
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await self._update_emotion_and_relationship(message, chat, undivided_response)
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async def _update_using_response(self, message, response_set):
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# 更新心流状态
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stream_id = message.chat_stream.stream_id
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chat_talking_prompt = ""
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if stream_id:
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chat_talking_prompt = get_recent_group_detailed_plain_text(
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stream_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True
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)
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await heartflow.get_subheartflow(stream_id).do_thinking_after_reply(response_set, chat_talking_prompt)
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async def _send_response_messages(self, message, chat, response_set, thinking_id):
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container = message_manager.get_container(chat.stream_id)
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thinking_message = None
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# logger.info(f"开始发送消息准备")
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for msg in container.messages:
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if isinstance(msg, MessageThinking) and msg.message_info.message_id == thinking_id:
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thinking_message = msg
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container.messages.remove(msg)
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break
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if not thinking_message:
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logger.warning("未找到对应的思考消息，可能已超时被移除")
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return
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# logger.info(f"开始发送消息")
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thinking_start_time = thinking_message.thinking_start_time
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message_set = MessageSet(chat, thinking_id)
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mark_head = False
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for msg in response_set:
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message_segment = Seg(type="text", data=msg)
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bot_message = MessageSending(
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message_id=thinking_id,
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chat_stream=chat,
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bot_user_info=UserInfo(
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user_id=global_config.BOT_QQ,
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user_nickname=global_config.BOT_NICKNAME,
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platform=message.message_info.platform,
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),
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sender_info=message.message_info.user_info,
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message_segment=message_segment,
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reply=message,
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is_head=not mark_head,
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is_emoji=False,
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thinking_start_time=thinking_start_time,
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)
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if not mark_head:
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mark_head = True
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message_set.add_message(bot_message)
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# logger.info(f"开始添加发送消息")
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message_manager.add_message(message_set)
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async def _handle_emoji(self, message, chat, response):
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"""处理表情包
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Args:
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message: 接收到的消息
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chat: 聊天流对象
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response: 生成的回复
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"""
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if random() < global_config.emoji_chance:
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emoji_raw = await emoji_manager.get_emoji_for_text(response)
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if emoji_raw:
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emoji_path, description = emoji_raw
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emoji_cq = image_path_to_base64(emoji_path)
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thinking_time_point = round(message.message_info.time, 2)
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message_segment = Seg(type="emoji", data=emoji_cq)
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bot_message = MessageSending(
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message_id="mt" + str(thinking_time_point),
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chat_stream=chat,
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bot_user_info=UserInfo(
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user_id=global_config.BOT_QQ,
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user_nickname=global_config.BOT_NICKNAME,
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platform=message.message_info.platform,
|
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),
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sender_info=message.message_info.user_info,
|
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message_segment=message_segment,
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reply=message,
|
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is_head=False,
|
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is_emoji=True,
|
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)
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message_manager.add_message(bot_message)
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|
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async def _update_emotion_and_relationship(self, message, chat, undivided_response):
|
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"""更新情绪和关系
|
||||
|
||||
Args:
|
||||
message: 接收到的消息
|
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chat: 聊天流对象
|
||||
undivided_response: 生成的未分割回复
|
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"""
|
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stance, emotion = await self.gpt._get_emotion_tags(undivided_response, message.processed_plain_text)
|
||||
await relationship_manager.calculate_update_relationship_value(chat_stream=chat, label=emotion, stance=stance)
|
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self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor)
|
||||
|
||||
def _check_ban_words(self, text: str, chat, userinfo) -> bool:
|
||||
"""检查消息中是否包含过滤词
|
||||
|
||||
Args:
|
||||
text: 要检查的文本
|
||||
chat: 聊天流对象
|
||||
userinfo: 用户信息对象
|
||||
|
||||
Returns:
|
||||
bool: 如果包含过滤词返回True，否则返回False
|
||||
"""
|
||||
for word in global_config.ban_words:
|
||||
if word in text:
|
||||
logger.info(
|
||||
f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}"
|
||||
)
|
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logger.info(f"[过滤词识别]消息中含有{word}，filtered")
|
||||
return True
|
||||
return False
|
||||
|
||||
def _check_ban_regex(self, text: str, chat, userinfo) -> bool:
|
||||
"""检查消息是否匹配过滤正则表达式
|
||||
|
||||
Args:
|
||||
text: 要检查的文本
|
||||
chat: 聊天流对象
|
||||
userinfo: 用户信息对象
|
||||
|
||||
Returns:
|
||||
bool: 如果匹配过滤正则返回True，否则返回False
|
||||
"""
|
||||
for pattern in global_config.ban_msgs_regex:
|
||||
if re.search(pattern, text):
|
||||
logger.info(
|
||||
f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}"
|
||||
)
|
||||
logger.info(f"[正则表达式过滤]消息匹配到{pattern}，filtered")
|
||||
return True
|
||||
return False
|
||||
logger.error(f"未知的回复模式，请检查配置文件！！: {global_config.response_mode}")
|
||||
|
||||
|
||||
# 创建全局ChatBot实例
|
||||
|
||||
@@ -1,229 +0,0 @@
|
||||
import time
|
||||
from typing import List, Optional, Tuple, Union
|
||||
|
||||
|
||||
from ...common.database import db
|
||||
from ..models.utils_model import LLM_request
|
||||
from ..config.config import global_config
|
||||
from .message import MessageRecv, MessageThinking, Message
|
||||
from .prompt_builder import prompt_builder
|
||||
from .utils import process_llm_response
|
||||
from src.common.logger import get_module_logger, LogConfig, LLM_STYLE_CONFIG
|
||||
|
||||
# 定义日志配置
|
||||
llm_config = LogConfig(
|
||||
# 使用消息发送专用样式
|
||||
console_format=LLM_STYLE_CONFIG["console_format"],
|
||||
file_format=LLM_STYLE_CONFIG["file_format"],
|
||||
)
|
||||
|
||||
logger = get_module_logger("llm_generator", config=llm_config)
|
||||
|
||||
|
||||
class ResponseGenerator:
|
||||
def __init__(self):
|
||||
self.model_reasoning = LLM_request(
|
||||
model=global_config.llm_reasoning,
|
||||
temperature=0.7,
|
||||
max_tokens=3000,
|
||||
request_type="response",
|
||||
)
|
||||
self.model_normal = LLM_request(
|
||||
model=global_config.llm_normal, temperature=0.8, max_tokens=256, request_type="response"
|
||||
)
|
||||
|
||||
self.model_sum = LLM_request(
|
||||
model=global_config.llm_summary_by_topic, temperature=0.7, max_tokens=3000, request_type="relation"
|
||||
)
|
||||
self.current_model_type = "r1" # 默认使用 R1
|
||||
self.current_model_name = "unknown model"
|
||||
|
||||
async def generate_response(self, message: MessageThinking) -> Optional[Union[str, List[str]]]:
|
||||
"""根据当前模型类型选择对应的生成函数"""
|
||||
# 从global_config中获取模型概率值并选择模型
|
||||
# if random.random() < global_config.MODEL_R1_PROBABILITY:
|
||||
# self.current_model_type = "深深地"
|
||||
# current_model = self.model_reasoning
|
||||
# else:
|
||||
# self.current_model_type = "浅浅的"
|
||||
# current_model = self.model_normal
|
||||
|
||||
# logger.info(
|
||||
# f"{self.current_model_type}思考:{message.processed_plain_text[:30] + '...' if len(message.processed_plain_text) > 30 else message.processed_plain_text}"
|
||||
# ) # noqa: E501
|
||||
|
||||
|
||||
logger.info(
|
||||
f"思考:{message.processed_plain_text[:30] + '...' if len(message.processed_plain_text) > 30 else message.processed_plain_text}"
|
||||
)
|
||||
|
||||
current_model = self.model_normal
|
||||
model_response = await self._generate_response_with_model(message, current_model)
|
||||
undivided_response = model_response
|
||||
|
||||
# print(f"raw_content: {model_response}")
|
||||
|
||||
if model_response:
|
||||
logger.info(f"{global_config.BOT_NICKNAME}的回复是：{model_response}")
|
||||
model_response = await self._process_response(model_response)
|
||||
|
||||
return model_response, undivided_response
|
||||
else:
|
||||
logger.info(f"{self.current_model_type}思考，失败")
|
||||
return None, None
|
||||
|
||||
async def _generate_response_with_model(self, message: MessageThinking, model: LLM_request):
|
||||
sender_name = ""
|
||||
if message.chat_stream.user_info.user_cardname and message.chat_stream.user_info.user_nickname:
|
||||
sender_name = (
|
||||
f"[({message.chat_stream.user_info.user_id}){message.chat_stream.user_info.user_nickname}]"
|
||||
f"{message.chat_stream.user_info.user_cardname}"
|
||||
)
|
||||
elif message.chat_stream.user_info.user_nickname:
|
||||
sender_name = f"({message.chat_stream.user_info.user_id}){message.chat_stream.user_info.user_nickname}"
|
||||
else:
|
||||
sender_name = f"用户({message.chat_stream.user_info.user_id})"
|
||||
|
||||
logger.debug("开始使用生成回复-2")
|
||||
# 构建prompt
|
||||
timer1 = time.time()
|
||||
prompt = await prompt_builder._build_prompt(
|
||||
message.chat_stream,
|
||||
message_txt=message.processed_plain_text,
|
||||
sender_name=sender_name,
|
||||
stream_id=message.chat_stream.stream_id,
|
||||
)
|
||||
timer2 = time.time()
|
||||
logger.info(f"构建prompt时间: {timer2 - timer1}秒")
|
||||
|
||||
try:
|
||||
content, reasoning_content, self.current_model_name = await model.generate_response(prompt)
|
||||
except Exception:
|
||||
logger.exception("生成回复时出错")
|
||||
return None
|
||||
|
||||
# 保存到数据库
|
||||
self._save_to_db(
|
||||
message=message,
|
||||
sender_name=sender_name,
|
||||
prompt=prompt,
|
||||
content=content,
|
||||
reasoning_content=reasoning_content,
|
||||
# reasoning_content_check=reasoning_content_check if global_config.enable_kuuki_read else ""
|
||||
)
|
||||
|
||||
return content
|
||||
|
||||
# def _save_to_db(self, message: Message, sender_name: str, prompt: str, prompt_check: str,
|
||||
# content: str, content_check: str, reasoning_content: str, reasoning_content_check: str):
|
||||
def _save_to_db(
|
||||
self,
|
||||
message: MessageRecv,
|
||||
sender_name: str,
|
||||
prompt: str,
|
||||
content: str,
|
||||
reasoning_content: str,
|
||||
):
|
||||
"""保存对话记录到数据库"""
|
||||
db.reasoning_logs.insert_one(
|
||||
{
|
||||
"time": time.time(),
|
||||
"chat_id": message.chat_stream.stream_id,
|
||||
"user": sender_name,
|
||||
"message": message.processed_plain_text,
|
||||
"model": self.current_model_name,
|
||||
"reasoning": reasoning_content,
|
||||
"response": content,
|
||||
"prompt": prompt,
|
||||
}
|
||||
)
|
||||
|
||||
async def _get_emotion_tags(self, content: str, processed_plain_text: str):
|
||||
"""提取情感标签，结合立场和情绪"""
|
||||
try:
|
||||
# 构建提示词，结合回复内容、被回复的内容以及立场分析
|
||||
prompt = f"""
|
||||
请严格根据以下对话内容，完成以下任务：
|
||||
1. 判断回复者对被回复者观点的直接立场：
|
||||
- "支持"：明确同意或强化被回复者观点
|
||||
- "反对"：明确反驳或否定被回复者观点
|
||||
- "中立"：不表达明确立场或无关回应
|
||||
2. 从"开心,愤怒,悲伤,惊讶,平静,害羞,恐惧,厌恶,困惑"中选出最匹配的1个情感标签
|
||||
3. 按照"立场-情绪"的格式直接输出结果，例如："反对-愤怒"
|
||||
|
||||
对话示例：
|
||||
被回复：「A就是笨」
|
||||
回复：「A明明很聪明」 → 反对-愤怒
|
||||
|
||||
当前对话：
|
||||
被回复：「{processed_plain_text}」
|
||||
回复：「{content}」
|
||||
|
||||
输出要求：
|
||||
- 只需输出"立场-情绪"结果，不要解释
|
||||
- 严格基于文字直接表达的对立关系判断
|
||||
"""
|
||||
|
||||
# 调用模型生成结果
|
||||
result, _, _ = await self.model_sum.generate_response(prompt)
|
||||
result = result.strip()
|
||||
|
||||
# 解析模型输出的结果
|
||||
if "-" in result:
|
||||
stance, emotion = result.split("-", 1)
|
||||
valid_stances = ["支持", "反对", "中立"]
|
||||
valid_emotions = ["开心", "愤怒", "悲伤", "惊讶", "害羞", "平静", "恐惧", "厌恶", "困惑"]
|
||||
if stance in valid_stances and emotion in valid_emotions:
|
||||
return stance, emotion # 返回有效的立场-情绪组合
|
||||
else:
|
||||
logger.debug(f"无效立场-情感组合:{result}")
|
||||
return "中立", "平静" # 默认返回中立-平静
|
||||
else:
|
||||
logger.debug(f"立场-情感格式错误:{result}")
|
||||
return "中立", "平静" # 格式错误时返回默认值
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"获取情感标签时出错: {e}")
|
||||
return "中立", "平静" # 出错时返回默认值
|
||||
|
||||
async def _process_response(self, content: str) -> Tuple[List[str], List[str]]:
|
||||
"""处理响应内容，返回处理后的内容和情感标签"""
|
||||
if not content:
|
||||
return None, []
|
||||
|
||||
processed_response = process_llm_response(content)
|
||||
|
||||
# print(f"得到了处理后的llm返回{processed_response}")
|
||||
|
||||
return processed_response
|
||||
|
||||
|
||||
class InitiativeMessageGenerate:
|
||||
def __init__(self):
|
||||
self.model_r1 = LLM_request(model=global_config.llm_reasoning, temperature=0.7)
|
||||
self.model_v3 = LLM_request(model=global_config.llm_normal, temperature=0.7)
|
||||
self.model_r1_distill = LLM_request(model=global_config.llm_reasoning_minor, temperature=0.7)
|
||||
|
||||
def gen_response(self, message: Message):
|
||||
topic_select_prompt, dots_for_select, prompt_template = prompt_builder._build_initiative_prompt_select(
|
||||
message.group_id
|
||||
)
|
||||
content_select, reasoning, _ = self.model_v3.generate_response(topic_select_prompt)
|
||||
logger.debug(f"{content_select} {reasoning}")
|
||||
topics_list = [dot[0] for dot in dots_for_select]
|
||||
if content_select:
|
||||
if content_select in topics_list:
|
||||
select_dot = dots_for_select[topics_list.index(content_select)]
|
||||
else:
|
||||
return None
|
||||
else:
|
||||
return None
|
||||
prompt_check, memory = prompt_builder._build_initiative_prompt_check(select_dot[1], prompt_template)
|
||||
content_check, reasoning_check, _ = self.model_v3.generate_response(prompt_check)
|
||||
logger.info(f"{content_check} {reasoning_check}")
|
||||
if "yes" not in content_check.lower():
|
||||
return None
|
||||
prompt = prompt_builder._build_initiative_prompt(select_dot, prompt_template, memory)
|
||||
content, reasoning = self.model_r1.generate_response_async(prompt)
|
||||
logger.debug(f"[DEBUG] {content} {reasoning}")
|
||||
return content
|
||||
@@ -7,7 +7,7 @@ from ...common.database import db
|
||||
from ..message.api import global_api
|
||||
from .message import MessageSending, MessageThinking, MessageSet
|
||||
|
||||
from .storage import MessageStorage
|
||||
from ..storage.storage import MessageStorage
|
||||
from ..config.config import global_config
|
||||
from .utils import truncate_message, calculate_typing_time
|
||||
|
||||
|
||||
@@ -1,290 +0,0 @@
|
||||
import random
|
||||
import time
|
||||
from typing import Optional
|
||||
|
||||
from ...common.database import db
|
||||
from ..memory_system.Hippocampus import HippocampusManager
|
||||
from ..moods.moods import MoodManager
|
||||
from ..schedule.schedule_generator import bot_schedule
|
||||
from ..config.config import global_config
|
||||
from .utils import get_embedding, get_recent_group_detailed_plain_text, get_recent_group_speaker
|
||||
from .chat_stream import chat_manager
|
||||
from src.common.logger import get_module_logger
|
||||
from .relationship_manager import relationship_manager
|
||||
|
||||
from src.heart_flow.heartflow import heartflow
|
||||
|
||||
logger = get_module_logger("prompt")
|
||||
|
||||
|
||||
class PromptBuilder:
|
||||
def __init__(self):
|
||||
self.prompt_built = ""
|
||||
self.activate_messages = ""
|
||||
|
||||
async def _build_prompt(
|
||||
self, chat_stream, message_txt: str, sender_name: str = "某人", stream_id: Optional[int] = None
|
||||
) -> tuple[str, str]:
|
||||
|
||||
current_mind_info = heartflow.get_subheartflow(stream_id).current_mind
|
||||
|
||||
# 开始构建prompt
|
||||
|
||||
# 关系
|
||||
who_chat_in_group = [(chat_stream.user_info.platform,
|
||||
chat_stream.user_info.user_id,
|
||||
chat_stream.user_info.user_nickname)]
|
||||
who_chat_in_group += get_recent_group_speaker(
|
||||
stream_id,
|
||||
(chat_stream.user_info.platform, chat_stream.user_info.user_id),
|
||||
limit=global_config.MAX_CONTEXT_SIZE,
|
||||
)
|
||||
|
||||
relation_prompt = ""
|
||||
for person in who_chat_in_group:
|
||||
relation_prompt += await relationship_manager.build_relationship_info(person)
|
||||
|
||||
relation_prompt_all = (
|
||||
f"{relation_prompt}关系等级越大，关系越好，请分析聊天记录，"
|
||||
f"根据你和说话者{sender_name}的关系和态度进行回复，明确你的立场和情感。"
|
||||
)
|
||||
|
||||
# 心情
|
||||
mood_manager = MoodManager.get_instance()
|
||||
mood_prompt = mood_manager.get_prompt()
|
||||
|
||||
logger.info(f"心情prompt: {mood_prompt}")
|
||||
|
||||
# 日程构建
|
||||
# schedule_prompt = f'''你现在正在做的事情是：{bot_schedule.get_current_num_task(num = 1,time_info = False)}'''
|
||||
|
||||
# 获取聊天上下文
|
||||
chat_in_group = True
|
||||
chat_talking_prompt = ""
|
||||
if stream_id:
|
||||
chat_talking_prompt = get_recent_group_detailed_plain_text(
|
||||
stream_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True
|
||||
)
|
||||
chat_stream = chat_manager.get_stream(stream_id)
|
||||
if chat_stream.group_info:
|
||||
chat_talking_prompt = chat_talking_prompt
|
||||
else:
|
||||
chat_in_group = False
|
||||
chat_talking_prompt = chat_talking_prompt
|
||||
# print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}")
|
||||
|
||||
# 类型
|
||||
if chat_in_group:
|
||||
chat_target = "你正在qq群里聊天，下面是群里在聊的内容："
|
||||
chat_target_2 = "和群里聊天"
|
||||
else:
|
||||
chat_target = f"你正在和{sender_name}聊天，这是你们之前聊的内容："
|
||||
chat_target_2 = f"和{sender_name}私聊"
|
||||
|
||||
# 关键词检测与反应
|
||||
keywords_reaction_prompt = ""
|
||||
for rule in global_config.keywords_reaction_rules:
|
||||
if rule.get("enable", False):
|
||||
if any(keyword in message_txt.lower() for keyword in rule.get("keywords", [])):
|
||||
logger.info(
|
||||
f"检测到以下关键词之一：{rule.get('keywords', [])}，触发反应：{rule.get('reaction', '')}"
|
||||
)
|
||||
keywords_reaction_prompt += rule.get("reaction", "") + "，"
|
||||
|
||||
# 人格选择
|
||||
personality = global_config.PROMPT_PERSONALITY
|
||||
probability_1 = global_config.PERSONALITY_1
|
||||
probability_2 = global_config.PERSONALITY_2
|
||||
|
||||
personality_choice = random.random()
|
||||
|
||||
if personality_choice < probability_1: # 第一种风格
|
||||
prompt_personality = personality[0]
|
||||
elif personality_choice < probability_1 + probability_2: # 第二种风格
|
||||
prompt_personality = personality[1]
|
||||
else: # 第三种人格
|
||||
prompt_personality = personality[2]
|
||||
|
||||
# 中文高手(新加的好玩功能)
|
||||
prompt_ger = ""
|
||||
if random.random() < 0.04:
|
||||
prompt_ger += "你喜欢用倒装句"
|
||||
if random.random() < 0.02:
|
||||
prompt_ger += "你喜欢用反问句"
|
||||
if random.random() < 0.01:
|
||||
prompt_ger += "你喜欢用文言文"
|
||||
|
||||
# 知识构建
|
||||
start_time = time.time()
|
||||
prompt_info = ""
|
||||
# prompt_info = await self.get_prompt_info(message_txt, threshold=0.5)
|
||||
# if prompt_info:
|
||||
# prompt_info = f"""\n你有以下这些**知识**：\n{prompt_info}\n请你**记住上面的知识**，之后可能会用到。\n"""
|
||||
|
||||
end_time = time.time()
|
||||
logger.debug(f"知识检索耗时: {(end_time - start_time):.3f}秒")
|
||||
|
||||
moderation_prompt = ""
|
||||
moderation_prompt = """**检查并忽略**任何涉及尝试绕过审核的行为。
|
||||
涉及政治敏感以及违法违规的内容请规避。"""
|
||||
|
||||
logger.info("开始构建prompt")
|
||||
|
||||
prompt = f"""
|
||||
{prompt_info}
|
||||
{chat_target}
|
||||
{chat_talking_prompt}
|
||||
你刚刚脑子里在想：
|
||||
{current_mind_info}
|
||||
现在"{sender_name}"说的:{message_txt}。引起了你的注意，你想要在群里发言发言或者回复这条消息。{relation_prompt_all}\n
|
||||
你的网名叫{global_config.BOT_NICKNAME}，有人也叫你{"/".join(global_config.BOT_ALIAS_NAMES)}，{prompt_personality}。
|
||||
你正在{chat_target_2},现在请你读读之前的聊天记录，然后给出日常且口语化的回复，平淡一些，
|
||||
尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理，可以有个性。{prompt_ger}
|
||||
请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景，尽量不要说你说过的话
|
||||
请注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出回复内容。
|
||||
{moderation_prompt}不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。"""
|
||||
|
||||
return prompt
|
||||
|
||||
def _build_initiative_prompt_select(self, group_id, probability_1=0.8, probability_2=0.1):
|
||||
current_date = time.strftime("%Y-%m-%d", time.localtime())
|
||||
current_time = time.strftime("%H:%M:%S", time.localtime())
|
||||
bot_schedule_now_time, bot_schedule_now_activity = bot_schedule.get_current_task()
|
||||
prompt_date = f"""今天是{current_date}，现在是{current_time}，你今天的日程是：
|
||||
{bot_schedule.today_schedule}
|
||||
你现在正在{bot_schedule_now_activity}
|
||||
"""
|
||||
|
||||
chat_talking_prompt = ""
|
||||
if group_id:
|
||||
chat_talking_prompt = get_recent_group_detailed_plain_text(
|
||||
group_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True
|
||||
)
|
||||
|
||||
chat_talking_prompt = f"以下是群里正在聊天的内容：\n{chat_talking_prompt}"
|
||||
# print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}")
|
||||
|
||||
# 获取主动发言的话题
|
||||
all_nodes = HippocampusManager.get_instance().memory_graph.dots
|
||||
all_nodes = filter(lambda dot: len(dot[1]["memory_items"]) > 3, all_nodes)
|
||||
nodes_for_select = random.sample(all_nodes, 5)
|
||||
topics = [info[0] for info in nodes_for_select]
|
||||
|
||||
# 激活prompt构建
|
||||
activate_prompt = ""
|
||||
activate_prompt = "以上是群里正在进行的聊天。"
|
||||
personality = global_config.PROMPT_PERSONALITY
|
||||
prompt_personality = ""
|
||||
personality_choice = random.random()
|
||||
if personality_choice < probability_1: # 第一种人格
|
||||
prompt_personality = f"""{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[0]}"""
|
||||
elif personality_choice < probability_1 + probability_2: # 第二种人格
|
||||
prompt_personality = f"""{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}"""
|
||||
else: # 第三种人格
|
||||
prompt_personality = f"""{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[2]}"""
|
||||
|
||||
topics_str = ",".join(f'"{topics}"')
|
||||
prompt_for_select = (
|
||||
f"你现在想在群里发言，回忆了一下，想到几个话题，分别是{topics_str}，综合当前状态以及群内气氛，"
|
||||
f"请你在其中选择一个合适的话题，注意只需要输出话题，除了话题什么也不要输出(双引号也不要输出)"
|
||||
)
|
||||
|
||||
prompt_initiative_select = f"{prompt_date}\n{prompt_personality}\n{prompt_for_select}"
|
||||
prompt_regular = f"{prompt_date}\n{prompt_personality}"
|
||||
|
||||
return prompt_initiative_select, nodes_for_select, prompt_regular
|
||||
|
||||
def _build_initiative_prompt_check(self, selected_node, prompt_regular):
|
||||
memory = random.sample(selected_node["memory_items"], 3)
|
||||
memory = "\n".join(memory)
|
||||
prompt_for_check = (
|
||||
f"{prompt_regular}你现在想在群里发言，回忆了一下，想到一个话题,是{selected_node['concept']}，"
|
||||
f"关于这个话题的记忆有\n{memory}\n，以这个作为主题发言合适吗？请在把握群里的聊天内容的基础上，"
|
||||
f"综合群内的氛围，如果认为应该发言请输出yes，否则输出no，请注意是决定是否需要发言，而不是编写回复内容，"
|
||||
f"除了yes和no不要输出任何回复内容。"
|
||||
)
|
||||
return prompt_for_check, memory
|
||||
|
||||
def _build_initiative_prompt(self, selected_node, prompt_regular, memory):
|
||||
prompt_for_initiative = (
|
||||
f"{prompt_regular}你现在想在群里发言，回忆了一下，想到一个话题,是{selected_node['concept']}，"
|
||||
f"关于这个话题的记忆有\n{memory}\n，请在把握群里的聊天内容的基础上，综合群内的氛围，"
|
||||
f"以日常且口语化的口吻，简短且随意一点进行发言，不要说的太有条理，可以有个性。"
|
||||
f"记住不要输出多余内容(包括前后缀，冒号和引号，括号，表情,@等)"
|
||||
)
|
||||
return prompt_for_initiative
|
||||
|
||||
async def get_prompt_info(self, message: str, threshold: float):
|
||||
related_info = ""
|
||||
logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
|
||||
embedding = await get_embedding(message, request_type="prompt_build")
|
||||
related_info += self.get_info_from_db(embedding, limit=1, threshold=threshold)
|
||||
|
||||
return related_info
|
||||
|
||||
def get_info_from_db(self, query_embedding: list, limit: int = 1, threshold: float = 0.5) -> str:
|
||||
if not query_embedding:
|
||||
return ""
|
||||
# 使用余弦相似度计算
|
||||
pipeline = [
|
||||
{
|
||||
"$addFields": {
|
||||
"dotProduct": {
|
||||
"$reduce": {
|
||||
"input": {"$range": [0, {"$size": "$embedding"}]},
|
||||
"initialValue": 0,
|
||||
"in": {
|
||||
"$add": [
|
||||
"$$value",
|
||||
{
|
||||
"$multiply": [
|
||||
{"$arrayElemAt": ["$embedding", "$$this"]},
|
||||
{"$arrayElemAt": [query_embedding, "$$this"]},
|
||||
]
|
||||
},
|
||||
]
|
||||
},
|
||||
}
|
||||
},
|
||||
"magnitude1": {
|
||||
"$sqrt": {
|
||||
"$reduce": {
|
||||
"input": "$embedding",
|
||||
"initialValue": 0,
|
||||
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
|
||||
}
|
||||
}
|
||||
},
|
||||
"magnitude2": {
|
||||
"$sqrt": {
|
||||
"$reduce": {
|
||||
"input": query_embedding,
|
||||
"initialValue": 0,
|
||||
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
|
||||
}
|
||||
}
|
||||
},
|
||||
}
|
||||
},
|
||||
{"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]}}},
|
||||
{
|
||||
"$match": {
|
||||
"similarity": {"$gte": threshold} # 只保留相似度大于等于阈值的结果
|
||||
}
|
||||
},
|
||||
{"$sort": {"similarity": -1}},
|
||||
{"$limit": limit},
|
||||
{"$project": {"content": 1, "similarity": 1}},
|
||||
]
|
||||
|
||||
results = list(db.knowledges.aggregate(pipeline))
|
||||
# print(f"\033[1;34m[调试]\033[0m获取知识库内容结果: {results}")
|
||||
|
||||
if not results:
|
||||
return ""
|
||||
|
||||
# 返回所有找到的内容，用换行分隔
|
||||
return "\n".join(str(result["content"]) for result in results)
|
||||
|
||||
|
||||
prompt_builder = PromptBuilder()
|
||||
@@ -1,187 +0,0 @@
|
||||
from src.common.logger import get_module_logger, LogConfig, RELATION_STYLE_CONFIG
|
||||
from .chat_stream import ChatStream
|
||||
import math
|
||||
from bson.decimal128 import Decimal128
|
||||
from .person_info import person_info_manager
|
||||
import time
|
||||
|
||||
relationship_config = LogConfig(
|
||||
# 使用关系专用样式
|
||||
console_format=RELATION_STYLE_CONFIG["console_format"],
|
||||
file_format=RELATION_STYLE_CONFIG["file_format"],
|
||||
)
|
||||
logger = get_module_logger("rel_manager", config=relationship_config)
|
||||
|
||||
class RelationshipManager:
|
||||
def __init__(self):
|
||||
self.positive_feedback_dict = {} # 正反馈系统
|
||||
|
||||
def positive_feedback_sys(self, person_id, value, label: str, stance: str):
|
||||
"""正反馈系统"""
|
||||
|
||||
positive_list = [
|
||||
"开心",
|
||||
"惊讶",
|
||||
"害羞",
|
||||
]
|
||||
|
||||
negative_list = [
|
||||
"愤怒",
|
||||
"悲伤",
|
||||
"恐惧",
|
||||
"厌恶",
|
||||
]
|
||||
|
||||
if person_id not in self.positive_feedback_dict:
|
||||
self.positive_feedback_dict[person_id] = 0
|
||||
|
||||
if label in positive_list and stance != "反对":
|
||||
if 7 > self.positive_feedback_dict[person_id] >= 0:
|
||||
self.positive_feedback_dict[person_id] += 1
|
||||
elif self.positive_feedback_dict[person_id] < 0:
|
||||
self.positive_feedback_dict[person_id] = 0
|
||||
return value
|
||||
elif label in negative_list and stance != "支持":
|
||||
if -7 < self.positive_feedback_dict[person_id] <= 0:
|
||||
self.positive_feedback_dict[person_id] -= 1
|
||||
elif self.positive_feedback_dict[person_id] > 0:
|
||||
self.positive_feedback_dict[person_id] = 0
|
||||
return value
|
||||
else:
|
||||
return value
|
||||
|
||||
gain_coefficient = [1.0, 1.1, 1.2, 1.4, 1.7, 1.9, 2.0]
|
||||
value *= gain_coefficient[abs(self.positive_feedback_dict[person_id])-1]
|
||||
if abs(self.positive_feedback_dict[person_id]) - 1:
|
||||
logger.info(f"触发增益，当前增益系数：{gain_coefficient[abs(self.positive_feedback_dict[person_id])-1]}")
|
||||
|
||||
return value
|
||||
|
||||
|
||||
async def calculate_update_relationship_value(self, chat_stream: ChatStream, label: str, stance: str) -> None:
|
||||
"""计算并变更关系值
|
||||
新的关系值变更计算方式：
|
||||
将关系值限定在-1000到1000
|
||||
对于关系值的变更，期望：
|
||||
1.向两端逼近时会逐渐减缓
|
||||
2.关系越差，改善越难，关系越好，恶化越容易
|
||||
3.人维护关系的精力往往有限，所以当高关系值用户越多，对于中高关系值用户增长越慢
|
||||
4.连续正面或负面情感会正反馈
|
||||
"""
|
||||
stancedict = {
|
||||
"支持": 0,
|
||||
"中立": 1,
|
||||
"反对": 2,
|
||||
}
|
||||
|
||||
valuedict = {
|
||||
"开心": 1.5,
|
||||
"愤怒": -2.0,
|
||||
"悲伤": -0.5,
|
||||
"惊讶": 0.6,
|
||||
"害羞": 2.0,
|
||||
"平静": 0.3,
|
||||
"恐惧": -1.5,
|
||||
"厌恶": -1.0,
|
||||
"困惑": 0.5,
|
||||
}
|
||||
|
||||
person_id = person_info_manager.get_person_id(chat_stream.user_info.platform, chat_stream.user_info.user_id)
|
||||
data = {
|
||||
"platform" : chat_stream.user_info.platform,
|
||||
"user_id" : chat_stream.user_info.user_id,
|
||||
"nickname" : chat_stream.user_info.user_nickname,
|
||||
"konw_time" : int(time.time())
|
||||
}
|
||||
old_value = await person_info_manager.get_value(person_id, "relationship_value")
|
||||
old_value = self.ensure_float(old_value, person_id)
|
||||
|
||||
if old_value > 1000:
|
||||
old_value = 1000
|
||||
elif old_value < -1000:
|
||||
old_value = -1000
|
||||
|
||||
value = valuedict[label]
|
||||
if old_value >= 0:
|
||||
if valuedict[label] >= 0 and stancedict[stance] != 2:
|
||||
value = value * math.cos(math.pi * old_value / 2000)
|
||||
if old_value > 500:
|
||||
rdict = await person_info_manager.get_specific_value_list("relationship_value", lambda x: x > 700)
|
||||
high_value_count = len(rdict)
|
||||
if old_value > 700:
|
||||
value *= 3 / (high_value_count + 2) # 排除自己
|
||||
else:
|
||||
value *= 3 / (high_value_count + 3)
|
||||
elif valuedict[label] < 0 and stancedict[stance] != 0:
|
||||
value = value * math.exp(old_value / 2000)
|
||||
else:
|
||||
value = 0
|
||||
elif old_value < 0:
|
||||
if valuedict[label] >= 0 and stancedict[stance] != 2:
|
||||
value = value * math.exp(old_value / 2000)
|
||||
elif valuedict[label] < 0 and stancedict[stance] != 0:
|
||||
value = value * math.cos(math.pi * old_value / 2000)
|
||||
else:
|
||||
value = 0
|
||||
|
||||
value = self.positive_feedback_sys(person_id, value, label, stance)
|
||||
|
||||
level_num = self.calculate_level_num(old_value + value)
|
||||
relationship_level = ["厌恶", "冷漠", "一般", "友好", "喜欢", "暧昧"]
|
||||
logger.info(
|
||||
f"当前关系: {relationship_level[level_num]}, "
|
||||
f"关系值: {old_value:.2f}, "
|
||||
f"当前立场情感: {stance}-{label}, "
|
||||
f"变更: {value:+.5f}"
|
||||
)
|
||||
|
||||
await person_info_manager.update_one_field(person_id, "relationship_value", old_value + value, data)
|
||||
|
||||
async def build_relationship_info(self, person) -> str:
|
||||
person_id = person_info_manager.get_person_id(person[0], person[1])
|
||||
relationship_value = await person_info_manager.get_value(person_id, "relationship_value")
|
||||
level_num = self.calculate_level_num(relationship_value)
|
||||
relationship_level = ["厌恶", "冷漠", "一般", "友好", "喜欢", "暧昧"]
|
||||
relation_prompt2_list = [
|
||||
"厌恶回应",
|
||||
"冷淡回复",
|
||||
"保持理性",
|
||||
"愿意回复",
|
||||
"积极回复",
|
||||
"无条件支持",
|
||||
]
|
||||
|
||||
return (
|
||||
f"你对昵称为'({person[1]}){person[2]}'的用户的态度为{relationship_level[level_num]}，"
|
||||
f"回复态度为{relation_prompt2_list[level_num]}，关系等级为{level_num}。"
|
||||
)
|
||||
|
||||
def calculate_level_num(self, relationship_value) -> int:
|
||||
"""关系等级计算"""
|
||||
if -1000 <= relationship_value < -227:
|
||||
level_num = 0
|
||||
elif -227 <= relationship_value < -73:
|
||||
level_num = 1
|
||||
elif -73 <= relationship_value < 227:
|
||||
level_num = 2
|
||||
elif 227 <= relationship_value < 587:
|
||||
level_num = 3
|
||||
elif 587 <= relationship_value < 900:
|
||||
level_num = 4
|
||||
elif 900 <= relationship_value <= 1000:
|
||||
level_num = 5
|
||||
else:
|
||||
level_num = 5 if relationship_value > 1000 else 0
|
||||
return level_num
|
||||
|
||||
def ensure_float(elsf, value, person_id):
|
||||
"""确保返回浮点数，转换失败返回0.0"""
|
||||
if isinstance(value, float):
|
||||
return value
|
||||
try:
|
||||
return float(value.to_decimal() if isinstance(value, Decimal128) else value)
|
||||
except (ValueError, TypeError, AttributeError):
|
||||
logger.warning(f"[关系管理] {person_id}值转换失败（原始值：{value}），已重置为0")
|
||||
return 0.0
|
||||
|
||||
relationship_manager = RelationshipManager()
|
||||
@@ -1,52 +0,0 @@
|
||||
from typing import Union
|
||||
|
||||
from ...common.database import db
|
||||
from .message import MessageSending, MessageRecv
|
||||
from .chat_stream import ChatStream
|
||||
from src.common.logger import get_module_logger
|
||||
|
||||
logger = get_module_logger("message_storage")
|
||||
|
||||
|
||||
class MessageStorage:
|
||||
async def store_message(self, message: Union[MessageSending, MessageRecv], chat_stream: ChatStream) -> None:
|
||||
"""存储消息到数据库"""
|
||||
try:
|
||||
message_data = {
|
||||
"message_id": message.message_info.message_id,
|
||||
"time": message.message_info.time,
|
||||
"chat_id": chat_stream.stream_id,
|
||||
"chat_info": chat_stream.to_dict(),
|
||||
"user_info": message.message_info.user_info.to_dict(),
|
||||
"processed_plain_text": message.processed_plain_text,
|
||||
"detailed_plain_text": message.detailed_plain_text,
|
||||
"memorized_times": message.memorized_times,
|
||||
}
|
||||
db.messages.insert_one(message_data)
|
||||
except Exception:
|
||||
logger.exception("存储消息失败")
|
||||
|
||||
async def store_recalled_message(self, message_id: str, time: str, chat_stream: ChatStream) -> None:
|
||||
"""存储撤回消息到数据库"""
|
||||
if "recalled_messages" not in db.list_collection_names():
|
||||
db.create_collection("recalled_messages")
|
||||
else:
|
||||
try:
|
||||
message_data = {
|
||||
"message_id": message_id,
|
||||
"time": time,
|
||||
"stream_id": chat_stream.stream_id,
|
||||
}
|
||||
db.recalled_messages.insert_one(message_data)
|
||||
except Exception:
|
||||
logger.exception("存储撤回消息失败")
|
||||
|
||||
async def remove_recalled_message(self, time: str) -> None:
|
||||
"""删除撤回消息"""
|
||||
try:
|
||||
db.recalled_messages.delete_many({"time": {"$lt": time - 300}})
|
||||
except Exception:
|
||||
logger.exception("删除撤回消息失败")
|
||||
|
||||
|
||||
# 如果需要其他存储相关的函数，可以在这里添加
|
||||
@@ -1,49 +0,0 @@
|
||||
from typing import List, Optional
|
||||
|
||||
|
||||
from ..models.utils_model import LLM_request
|
||||
from ..config.config import global_config
|
||||
from src.common.logger import get_module_logger, LogConfig, TOPIC_STYLE_CONFIG
|
||||
|
||||
# 定义日志配置
|
||||
topic_config = LogConfig(
|
||||
# 使用海马体专用样式
|
||||
console_format=TOPIC_STYLE_CONFIG["console_format"],
|
||||
file_format=TOPIC_STYLE_CONFIG["file_format"],
|
||||
)
|
||||
|
||||
logger = get_module_logger("topic_identifier", config=topic_config)
|
||||
|
||||
|
||||
class TopicIdentifier:
|
||||
def __init__(self):
|
||||
self.llm_topic_judge = LLM_request(model=global_config.llm_topic_judge, request_type="topic")
|
||||
|
||||
async def identify_topic_llm(self, text: str) -> Optional[List[str]]:
|
||||
"""识别消息主题，返回主题列表"""
|
||||
|
||||
prompt = f"""判断这条消息的主题，如果没有明显主题请回复"无主题"，要求：
|
||||
1. 主题通常2-4个字，必须简短，要求精准概括，不要太具体。
|
||||
2. 建议给出多个主题，之间用英文逗号分割。只输出主题本身就好，不要有前后缀。
|
||||
|
||||
消息内容：{text}"""
|
||||
|
||||
# 使用 LLM_request 类进行请求
|
||||
topic, _, _ = await self.llm_topic_judge.generate_response(prompt)
|
||||
|
||||
if not topic:
|
||||
logger.error("LLM API 返回为空")
|
||||
return None
|
||||
|
||||
# 直接在这里处理主题解析
|
||||
if not topic or topic == "无主题":
|
||||
return None
|
||||
|
||||
# 解析主题字符串为列表
|
||||
topic_list = [t.strip() for t in topic.split(",") if t.strip()]
|
||||
|
||||
logger.info(f"主题: {topic_list}")
|
||||
return topic_list if topic_list else None
|
||||
|
||||
|
||||
topic_identifier = TopicIdentifier()
|
||||
@@ -366,6 +366,7 @@ def calculate_typing_time(input_string: str, chinese_time: float = 0.2, english_
|
||||
total_time += chinese_time
|
||||
else: # 其他字符（如英文）
|
||||
total_time += english_time
|
||||
|
||||
return total_time + 0.3 # 加上回车时间
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user