Merge branch 'SengokuCola:debug' into debug
This commit is contained in:
Rikki
GitHub
@@ -2,9 +2,8 @@ import asyncio
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import time
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from loguru import logger
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from nonebot import get_driver, on_command, on_message, require
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from nonebot import get_driver, on_message, require
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from nonebot.adapters.onebot.v11 import Bot, GroupMessageEvent, Message, MessageSegment
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from nonebot.rule import to_me
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from nonebot.typing import T_State
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from ...common.database import Database
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@@ -16,6 +15,10 @@ from .config import global_config
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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 .willing_manager import willing_manager
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from ..memory_system.memory import hippocampus, memory_graph
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from .bot import ChatBot
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from .message_sender import message_manager, message_sender
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# 创建LLM统计实例
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llm_stats = LLMStatistics("llm_statistics.txt")
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@@ -35,19 +38,13 @@ Database.initialize(
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password=config.MONGODB_PASSWORD,
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auth_source=config.MONGODB_AUTH_SOURCE
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)
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print("\033[1;32m[初始化数据库完成]\033[0m")
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logger.success("初始化数据库成功")
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# 导入其他模块
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from ..memory_system.memory import hippocampus, memory_graph
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from .bot import ChatBot
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# from .message_send_control import message_sender
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from .message_sender import message_manager, message_sender
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# 初始化表情管理器
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emoji_manager.initialize()
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print(f"\033[1;32m正在唤醒{global_config.BOT_NICKNAME}......\033[0m")
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logger.debug(f"正在唤醒{global_config.BOT_NICKNAME}......")
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# 创建机器人实例
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chat_bot = ChatBot()
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# 注册群消息处理器
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@@ -61,12 +58,12 @@ async def start_background_tasks():
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"""启动后台任务"""
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# 启动LLM统计
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llm_stats.start()
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logger.success("[初始化]LLM统计功能已启动")
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logger.success("LLM统计功能启动成功")
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# 初始化并启动情绪管理器
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mood_manager = MoodManager.get_instance()
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mood_manager.start_mood_update(update_interval=global_config.mood_update_interval)
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logger.success("[初始化]情绪管理器已启动")
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logger.success("情绪管理器启动成功")
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# 只启动表情包管理任务
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asyncio.create_task(emoji_manager.start_periodic_check(interval_MINS=global_config.EMOJI_CHECK_INTERVAL))
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@@ -77,7 +74,7 @@ async def start_background_tasks():
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@driver.on_startup
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async def init_relationships():
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"""在 NoneBot2 启动时初始化关系管理器"""
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print("\033[1;32m[初始化]\033[0m 正在加载用户关系数据...")
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logger.debug("正在加载用户关系数据...")
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await relationship_manager.load_all_relationships()
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asyncio.create_task(relationship_manager._start_relationship_manager())
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@@ -86,19 +83,19 @@ async def init_relationships():
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async def _(bot: Bot):
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"""Bot连接成功时的处理"""
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global _message_manager_started
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print(f"\033[1;38;5;208m-----------{global_config.BOT_NICKNAME}成功连接！-----------\033[0m")
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logger.debug(f"-----------{global_config.BOT_NICKNAME}成功连接！-----------")
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await willing_manager.ensure_started()
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message_sender.set_bot(bot)
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print("\033[1;38;5;208m-----------消息发送器已启动！-----------\033[0m")
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logger.success("-----------消息发送器已启动！-----------")
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if not _message_manager_started:
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asyncio.create_task(message_manager.start_processor())
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_message_manager_started = True
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print("\033[1;38;5;208m-----------消息处理器已启动！-----------\033[0m")
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logger.success("-----------消息处理器已启动！-----------")
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asyncio.create_task(emoji_manager._periodic_scan(interval_MINS=global_config.EMOJI_REGISTER_INTERVAL))
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print("\033[1;38;5;208m-----------开始偷表情包！-----------\033[0m")
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logger.success("-----------开始偷表情包！-----------")
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@group_msg.handle()
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@@ -110,13 +107,15 @@ async def _(bot: Bot, event: GroupMessageEvent, state: T_State):
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@scheduler.scheduled_job("interval", seconds=global_config.build_memory_interval, id="build_memory")
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async def build_memory_task():
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"""每build_memory_interval秒执行一次记忆构建"""
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print(
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"\033[1;32m[记忆构建]\033[0m -------------------------------------------开始构建记忆-------------------------------------------")
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logger.debug(
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"[记忆构建]"
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"------------------------------------开始构建记忆--------------------------------------")
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start_time = time.time()
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await hippocampus.operation_build_memory(chat_size=20)
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end_time = time.time()
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print(
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f"\033[1;32m[记忆构建]\033[0m -------------------------------------------记忆构建完成：耗时: {end_time - start_time:.2f} 秒-------------------------------------------")
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logger.success(
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f"[记忆构建]--------------------------记忆构建完成：耗时: {end_time - start_time:.2f} "
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"秒-------------------------------------------")
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@scheduler.scheduled_job("interval", seconds=global_config.forget_memory_interval, id="forget_memory")
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@@ -31,10 +31,10 @@ 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.emoji_chance = 0.2 # 发送表情包的基础概率
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# self.message_streams = MessageStreamContainer()
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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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@@ -42,26 +42,26 @@ class ChatBot:
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async def handle_message(self, event: GroupMessageEvent, bot: Bot) -> None:
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"""处理收到的群消息"""
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if event.group_id not in global_config.talk_allowed_groups:
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return
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self.bot = bot # 更新 bot 实例
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if event.user_id in global_config.ban_user_id:
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return
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group_info = await bot.get_group_info(group_id=event.group_id)
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sender_info = await bot.get_group_member_info(group_id=event.group_id, user_id=event.user_id, no_cache=True)
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await relationship_manager.update_relationship(user_id = event.user_id, data = sender_info)
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await relationship_manager.update_relationship_value(user_id = event.user_id, relationship_value = 0.5)
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await relationship_manager.update_relationship(user_id=event.user_id, data=sender_info)
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await relationship_manager.update_relationship_value(user_id=event.user_id, relationship_value=0.5)
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message = Message(
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group_id=event.group_id,
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user_id=event.user_id,
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message_id=event.message_id,
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user_cardname=sender_info['card'],
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raw_message=str(event.original_message),
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raw_message=str(event.original_message),
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plain_text=event.get_plaintext(),
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reply_message=event.reply,
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)
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@@ -70,26 +70,26 @@ class ChatBot:
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# 过滤词
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for word in global_config.ban_words:
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if word in message.detailed_plain_text:
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logger.info(f"\033[1;32m[{message.group_name}]{message.user_nickname}:\033[0m {message.processed_plain_text}")
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logger.info(f"\033[1;32m[过滤词识别]\033[0m 消息中含有{word}，filtered")
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logger.info(
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f"[{message.group_name}]{message.user_nickname}:{message.processed_plain_text}")
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logger.info(f"[过滤词识别]消息中含有{word}，filtered")
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return
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current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(message.time))
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# topic=await topic_identifier.identify_topic_llm(message.processed_plain_text)
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topic = ''
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interested_rate = 0
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interested_rate = await hippocampus.memory_activate_value(message.processed_plain_text)/100
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print(f"\033[1;32m[记忆激活]\033[0m 对{message.processed_plain_text}的激活度:---------------------------------------{interested_rate}\n")
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interested_rate = await hippocampus.memory_activate_value(message.processed_plain_text) / 100
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logger.debug(f"对{message.processed_plain_text}"
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f"的激活度:{interested_rate}")
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# logger.info(f"\033[1;32m[主题识别]\033[0m 使用{global_config.topic_extract}主题: {topic}")
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await self.storage.store_message(message, topic[0] if topic else None)
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is_mentioned = is_mentioned_bot_in_txt(message.processed_plain_text)
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reply_probability = willing_manager.change_reply_willing_received(
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event.group_id,
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event.group_id,
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topic[0] if topic else None,
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is_mentioned,
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global_config,
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@@ -98,25 +98,24 @@ class ChatBot:
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interested_rate
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)
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current_willing = willing_manager.get_willing(event.group_id)
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print(f"\033[1;32m[{current_time}][{message.group_name}]{message.user_nickname}:\033[0m {message.processed_plain_text}\033[1;36m[回复意愿:{current_willing:.2f}][概率:{reply_probability * 100:.1f}%]\033[0m")
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logger.info(
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f"[{current_time}][{message.group_name}]{message.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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response = ""
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if random() < reply_probability:
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tinking_time_point = round(time.time(), 2)
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think_id = 'mt' + str(tinking_time_point)
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thinking_message = Message_Thinking(message=message,message_id=think_id)
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thinking_message = Message_Thinking(message=message, message_id=think_id)
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message_manager.add_message(thinking_message)
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willing_manager.change_reply_willing_sent(thinking_message.group_id)
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response,raw_content = await self.gpt.generate_response(message)
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response, raw_content = await self.gpt.generate_response(message)
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if response:
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container = message_manager.get_container(event.group_id)
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thinking_message = None
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@@ -127,27 +126,28 @@ class ChatBot:
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container.messages.remove(msg)
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# print(f"\033[1;32m[思考消息删除]\033[0m 已找到思考消息对象，开始删除")
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break
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# 如果找不到思考消息，直接返回
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if not thinking_message:
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print(f"\033[1;33m[警告]\033[0m 未找到对应的思考消息，可能已超时被移除")
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logger.warning(f"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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#记录开始思考的时间，避免从思考到回复的时间太久
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# 记录开始思考的时间，避免从思考到回复的时间太久
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thinking_start_time = thinking_message.thinking_start_time
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message_set = MessageSet(event.group_id, global_config.BOT_QQ, think_id) # 发送消息的id和产生发送消息的message_thinking是一致的
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#计算打字时间，1是为了模拟打字，2是避免多条回复乱序
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message_set = MessageSet(event.group_id, global_config.BOT_QQ,
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think_id) # 发送消息的id和产生发送消息的message_thinking是一致的
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# 计算打字时间，1是为了模拟打字，2是避免多条回复乱序
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accu_typing_time = 0
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# print(f"\033[1;32m[开始回复]\033[0m 开始将回复1载入发送容器")
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mark_head = False
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for msg in response:
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# print(f"\033[1;32m[回复内容]\033[0m {msg}")
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#通过时间改变时间戳
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# 通过时间改变时间戳
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typing_time = calculate_typing_time(msg)
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accu_typing_time += typing_time
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timepoint = tinking_time_point + accu_typing_time
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bot_message = Message_Sending(
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group_id=event.group_id,
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user_id=global_config.BOT_QQ,
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@@ -157,8 +157,8 @@ class ChatBot:
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processed_plain_text=msg,
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user_nickname=global_config.BOT_NICKNAME,
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group_name=message.group_name,
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time=timepoint, #记录了回复生成的时间
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thinking_start_time=thinking_start_time, #记录了思考开始的时间
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time=timepoint, # 记录了回复生成的时间
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thinking_start_time=thinking_start_time, # 记录了思考开始的时间
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reply_message_id=message.message_id
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)
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await bot_message.initialize()
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@@ -166,27 +166,27 @@ class ChatBot:
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bot_message.is_head = True
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mark_head = True
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message_set.add_message(bot_message)
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#message_set 可以直接加入 message_manager
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# message_set 可以直接加入 message_manager
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# print(f"\033[1;32m[回复]\033[0m 将回复载入发送容器")
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message_manager.add_message(message_set)
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bot_response_time = tinking_time_point
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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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# 检查是否 <没有找到> emoji
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if emoji_raw != None:
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emoji_path,discription = emoji_raw
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emoji_path, discription = emoji_raw
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emoji_cq = CQCode.create_emoji_cq(emoji_path)
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if random() < 0.5:
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bot_response_time = tinking_time_point - 1
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else:
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bot_response_time = bot_response_time + 1
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bot_message = Message_Sending(
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group_id=event.group_id,
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user_id=global_config.BOT_QQ,
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@@ -206,8 +206,8 @@ class ChatBot:
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await bot_message.initialize()
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message_manager.add_message(bot_message)
|
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emotion = await self.gpt._get_emotion_tags(raw_content)
|
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print(f"为 '{response}' 获取到的情感标签为：{emotion}")
|
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valuedict={
|
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logger.debug(f"为 '{response}' 获取到的情感标签为：{emotion}")
|
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valuedict = {
|
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'happy': 0.5,
|
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'angry': -1,
|
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'sad': -0.5,
|
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@@ -216,11 +216,13 @@ class ChatBot:
|
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'fearful': -0.7,
|
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'neutral': 0.1
|
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}
|
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await relationship_manager.update_relationship_value(message.user_id, relationship_value=valuedict[emotion[0]])
|
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await relationship_manager.update_relationship_value(message.user_id,
|
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relationship_value=valuedict[emotion[0]])
|
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# 使用情绪管理器更新情绪
|
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self.mood_manager.update_mood_from_emotion(emotion[0], global_config.mood_intensity_factor)
|
||||
|
||||
|
||||
# willing_manager.change_reply_willing_after_sent(event.group_id)
|
||||
|
||||
|
||||
# 创建全局ChatBot实例
|
||||
chat_bot = ChatBot()
|
||||
chat_bot = ChatBot()
|
||||
|
||||
@@ -135,7 +135,7 @@ class BotConfig:
|
||||
try:
|
||||
config_version: str = toml["inner"]["version"]
|
||||
except KeyError as e:
|
||||
logger.error(f"配置文件中 inner 段 不存在 {e}, 这是错误的配置文件")
|
||||
logger.error(f"配置文件中 inner 段 不存在, 这是错误的配置文件")
|
||||
raise KeyError(f"配置文件中 inner 段 不存在 {e}, 这是错误的配置文件")
|
||||
else:
|
||||
toml["inner"] = {"version": "0.0.0"}
|
||||
@@ -162,7 +162,7 @@ class BotConfig:
|
||||
personality_config = parent['personality']
|
||||
personality = personality_config.get('prompt_personality')
|
||||
if len(personality) >= 2:
|
||||
logger.info(f"载入自定义人格:{personality}")
|
||||
logger.debug(f"载入自定义人格:{personality}")
|
||||
config.PROMPT_PERSONALITY = personality_config.get('prompt_personality', config.PROMPT_PERSONALITY)
|
||||
logger.info(f"载入自定义日程prompt:{personality_config.get('prompt_schedule', config.PROMPT_SCHEDULE_GEN)}")
|
||||
config.PROMPT_SCHEDULE_GEN = personality_config.get('prompt_schedule', config.PROMPT_SCHEDULE_GEN)
|
||||
@@ -246,11 +246,11 @@ class BotConfig:
|
||||
try:
|
||||
cfg_target[i] = cfg_item[i]
|
||||
except KeyError as e:
|
||||
logger.error(f"{item} 中的必要字段 {e} 不存在，请检查")
|
||||
logger.error(f"{item} 中的必要字段不存在，请检查")
|
||||
raise KeyError(f"{item} 中的必要字段 {e} 不存在，请检查")
|
||||
|
||||
provider = cfg_item.get("provider")
|
||||
if provider == None:
|
||||
if provider is None:
|
||||
logger.error(f"provider 字段在模型配置 {item} 中不存在，请检查")
|
||||
raise KeyError(f"provider 字段在模型配置 {item} 中不存在，请检查")
|
||||
|
||||
|
||||
@@ -4,6 +4,7 @@ import os
|
||||
import time
|
||||
from dataclasses import dataclass
|
||||
from typing import Dict, Optional
|
||||
from loguru import logger
|
||||
|
||||
import requests
|
||||
|
||||
@@ -151,11 +152,11 @@ class CQCode:
|
||||
|
||||
except (requests.exceptions.SSLError, requests.exceptions.HTTPError) as e:
|
||||
if retry == max_retries - 1:
|
||||
print(f"\033[1;31m[致命错误]\033[0m 最终请求失败: {str(e)}")
|
||||
logger.error(f"最终请求失败: {str(e)}")
|
||||
time.sleep(1.5 ** retry) # 指数退避
|
||||
|
||||
except Exception as e:
|
||||
print(f"\033[1;33m[未知错误]\033[0m {str(e)}")
|
||||
logger.exception(f"[未知错误]")
|
||||
return None
|
||||
|
||||
return None
|
||||
@@ -194,7 +195,7 @@ class CQCode:
|
||||
description, _ = await self._llm.generate_response_for_image(prompt, image_base64)
|
||||
return f"[表情包：{description}]"
|
||||
except Exception as e:
|
||||
print(f"\033[1;31m[错误]\033[0m AI接口调用失败: {str(e)}")
|
||||
logger.exception(f"AI接口调用失败: {str(e)}")
|
||||
return "[表情包]"
|
||||
|
||||
async def get_image_description(self, image_base64: str) -> str:
|
||||
@@ -205,7 +206,7 @@ class CQCode:
|
||||
description, _ = await self._llm.generate_response_for_image(prompt, image_base64)
|
||||
return f"[图片：{description}]"
|
||||
except Exception as e:
|
||||
print(f"\033[1;31m[错误]\033[0m AI接口调用失败: {str(e)}")
|
||||
logger.exception(f"AI接口调用失败: {str(e)}")
|
||||
return "[图片]"
|
||||
|
||||
async def translate_forward(self) -> str:
|
||||
@@ -222,7 +223,7 @@ class CQCode:
|
||||
try:
|
||||
messages = ast.literal_eval(content)
|
||||
except ValueError as e:
|
||||
print(f"\033[1;31m[错误]\033[0m 解析转发消息内容失败: {str(e)}")
|
||||
logger.error(f"解析转发消息内容失败: {str(e)}")
|
||||
return '[转发消息]'
|
||||
|
||||
# 处理每条消息
|
||||
@@ -277,11 +278,11 @@ class CQCode:
|
||||
|
||||
# 合并所有消息
|
||||
combined_messages = '\n'.join(formatted_messages)
|
||||
print(f"\033[1;34m[调试信息]\033[0m 合并后的转发消息: {combined_messages}")
|
||||
logger.debug(f"合并后的转发消息: {combined_messages}")
|
||||
return f"[转发消息:\n{combined_messages}]"
|
||||
|
||||
except Exception as e:
|
||||
print(f"\033[1;31m[错误]\033[0m 处理转发消息失败: {str(e)}")
|
||||
logger.exception("处理转发消息失败")
|
||||
return '[转发消息]'
|
||||
|
||||
async def translate_reply(self) -> str:
|
||||
@@ -307,7 +308,7 @@ class CQCode:
|
||||
return f"[回复 {self.reply_message.sender.nickname} 的消息: {message_obj.processed_plain_text}]"
|
||||
|
||||
else:
|
||||
print("\033[1;31m[错误]\033[0m 回复消息的sender.user_id为空")
|
||||
logger.error("回复消息的sender.user_id为空")
|
||||
return '[回复某人消息]'
|
||||
|
||||
@staticmethod
|
||||
|
||||
@@ -21,24 +21,26 @@ config = driver.config
|
||||
class EmojiManager:
|
||||
_instance = None
|
||||
EMOJI_DIR = "data/emoji" # 表情包存储目录
|
||||
|
||||
|
||||
def __new__(cls):
|
||||
if cls._instance is None:
|
||||
cls._instance = super().__new__(cls)
|
||||
cls._instance.db = None
|
||||
cls._instance._initialized = False
|
||||
return cls._instance
|
||||
|
||||
|
||||
def __init__(self):
|
||||
self.db = Database.get_instance()
|
||||
self._scan_task = None
|
||||
self.vlm = LLM_request(model=global_config.vlm, temperature=0.3, max_tokens=1000)
|
||||
self.llm_emotion_judge = LLM_request(model=global_config.llm_emotion_judge, max_tokens=60,temperature=0.8) #更高的温度，更少的token（后续可以根据情绪来调整温度）
|
||||
|
||||
self.llm_emotion_judge = LLM_request(model=global_config.llm_normal_minor, max_tokens=60,
|
||||
temperature=0.8) # 更高的温度，更少的token（后续可以根据情绪来调整温度）
|
||||
|
||||
|
||||
def _ensure_emoji_dir(self):
|
||||
"""确保表情存储目录存在"""
|
||||
os.makedirs(self.EMOJI_DIR, exist_ok=True)
|
||||
|
||||
|
||||
def initialize(self):
|
||||
"""初始化数据库连接和表情目录"""
|
||||
if not self._initialized:
|
||||
@@ -50,15 +52,15 @@ class EmojiManager:
|
||||
# 启动时执行一次完整性检查
|
||||
self.check_emoji_file_integrity()
|
||||
except Exception as e:
|
||||
logger.error(f"初始化表情管理器失败: {str(e)}")
|
||||
|
||||
logger.exception(f"初始化表情管理器失败")
|
||||
|
||||
def _ensure_db(self):
|
||||
"""确保数据库已初始化"""
|
||||
if not self._initialized:
|
||||
self.initialize()
|
||||
if not self._initialized:
|
||||
raise RuntimeError("EmojiManager not initialized")
|
||||
|
||||
|
||||
def _ensure_emoji_collection(self):
|
||||
"""确保emoji集合存在并创建索引
|
||||
|
||||
@@ -76,7 +78,7 @@ class EmojiManager:
|
||||
self.db.db.emoji.create_index([('embedding', '2dsphere')])
|
||||
self.db.db.emoji.create_index([('tags', 1)])
|
||||
self.db.db.emoji.create_index([('filename', 1)], unique=True)
|
||||
|
||||
|
||||
def record_usage(self, emoji_id: str):
|
||||
"""记录表情使用次数"""
|
||||
try:
|
||||
@@ -86,8 +88,8 @@ class EmojiManager:
|
||||
{'$inc': {'usage_count': 1}}
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"记录表情使用失败: {str(e)}")
|
||||
|
||||
logger.exception(f"记录表情使用失败")
|
||||
|
||||
async def get_emoji_for_text(self, text: str) -> Optional[str]:
|
||||
"""根据文本内容获取相关表情包
|
||||
Args:
|
||||
@@ -102,9 +104,9 @@ class EmojiManager:
|
||||
"""
|
||||
try:
|
||||
self._ensure_db()
|
||||
|
||||
|
||||
# 获取文本的embedding
|
||||
text_for_search= await self._get_kimoji_for_text(text)
|
||||
text_for_search = await self._get_kimoji_for_text(text)
|
||||
if not text_for_search:
|
||||
logger.error("无法获取文本的情绪")
|
||||
return None
|
||||
@@ -112,15 +114,15 @@ class EmojiManager:
|
||||
if not text_embedding:
|
||||
logger.error("无法获取文本的embedding")
|
||||
return None
|
||||
|
||||
|
||||
try:
|
||||
# 获取所有表情包
|
||||
all_emojis = list(self.db.db.emoji.find({}, {'_id': 1, 'path': 1, 'embedding': 1, 'discription': 1}))
|
||||
|
||||
|
||||
if not all_emojis:
|
||||
logger.warning("数据库中没有任何表情包")
|
||||
return None
|
||||
|
||||
|
||||
# 计算余弦相似度并排序
|
||||
def cosine_similarity(v1, v2):
|
||||
if not v1 or not v2:
|
||||
@@ -131,42 +133,43 @@ class EmojiManager:
|
||||
if norm_v1 == 0 or norm_v2 == 0:
|
||||
return 0
|
||||
return dot_product / (norm_v1 * norm_v2)
|
||||
|
||||
|
||||
# 计算所有表情包与输入文本的相似度
|
||||
emoji_similarities = [
|
||||
(emoji, cosine_similarity(text_embedding, emoji.get('embedding', [])))
|
||||
for emoji in all_emojis
|
||||
]
|
||||
|
||||
|
||||
# 按相似度降序排序
|
||||
emoji_similarities.sort(key=lambda x: x[1], reverse=True)
|
||||
|
||||
|
||||
# 获取前3个最相似的表情包
|
||||
top_3_emojis = emoji_similarities[:3]
|
||||
|
||||
|
||||
if not top_3_emojis:
|
||||
logger.warning("未找到匹配的表情包")
|
||||
return None
|
||||
|
||||
|
||||
# 从前3个中随机选择一个
|
||||
selected_emoji, similarity = random.choice(top_3_emojis)
|
||||
|
||||
|
||||
if selected_emoji and 'path' in selected_emoji:
|
||||
# 更新使用次数
|
||||
self.db.db.emoji.update_one(
|
||||
{'_id': selected_emoji['_id']},
|
||||
{'$inc': {'usage_count': 1}}
|
||||
)
|
||||
logger.success(f"找到匹配的表情包: {selected_emoji.get('discription', '无描述')} (相似度: {similarity:.4f})")
|
||||
logger.success(
|
||||
f"找到匹配的表情包: {selected_emoji.get('discription', '无描述')} (相似度: {similarity:.4f})")
|
||||
# 稍微改一下文本描述，不然容易产生幻觉，描述已经包含 表情包 了
|
||||
return selected_emoji['path'],"[ %s ]" % selected_emoji.get('discription', '无描述')
|
||||
|
||||
return selected_emoji['path'], "[ %s ]" % selected_emoji.get('discription', '无描述')
|
||||
|
||||
except Exception as search_error:
|
||||
logger.error(f"搜索表情包失败: {str(search_error)}")
|
||||
return None
|
||||
|
||||
|
||||
return None
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取表情包失败: {str(e)}")
|
||||
return None
|
||||
@@ -175,39 +178,39 @@ class EmojiManager:
|
||||
"""获取表情包的标签"""
|
||||
try:
|
||||
prompt = '这是一个表情包，使用中文简洁的描述一下表情包的内容和表情包所表达的情感'
|
||||
|
||||
|
||||
content, _ = await self.vlm.generate_response_for_image(prompt, image_base64)
|
||||
logger.debug(f"输出描述: {content}")
|
||||
return content
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取标签失败: {str(e)}")
|
||||
return None
|
||||
|
||||
|
||||
async def _check_emoji(self, image_base64: str) -> str:
|
||||
try:
|
||||
prompt = f'这是一个表情包，请回答这个表情包是否满足\"{global_config.EMOJI_CHECK_PROMPT}\"的要求，是则回答是，否则回答否，不要出现任何其他内容'
|
||||
|
||||
|
||||
content, _ = await self.vlm.generate_response_for_image(prompt, image_base64)
|
||||
logger.debug(f"输出描述: {content}")
|
||||
return content
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取标签失败: {str(e)}")
|
||||
return None
|
||||
|
||||
async def _get_kimoji_for_text(self, text:str):
|
||||
|
||||
async def _get_kimoji_for_text(self, text: str):
|
||||
try:
|
||||
prompt = f'这是{global_config.BOT_NICKNAME}将要发送的消息内容:\n{text}\n若要为其配上表情包，请你输出这个表情包应该表达怎样的情感，应该给人什么样的感觉，不要太简洁也不要太长，注意不要输出任何对消息内容的分析内容，只输出\"一种什么样的感觉\"中间的形容词部分。'
|
||||
|
||||
|
||||
content, _ = await self.llm_emotion_judge.generate_response_async(prompt)
|
||||
logger.info(f"输出描述: {content}")
|
||||
return content
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取标签失败: {str(e)}")
|
||||
return None
|
||||
|
||||
|
||||
async def scan_new_emojis(self):
|
||||
"""扫描新的表情包"""
|
||||
try:
|
||||
@@ -215,22 +218,23 @@ class EmojiManager:
|
||||
os.makedirs(emoji_dir, exist_ok=True)
|
||||
|
||||
# 获取所有支持的图片文件
|
||||
files_to_process = [f for f in os.listdir(emoji_dir) if f.lower().endswith(('.jpg', '.jpeg', '.png', '.gif'))]
|
||||
|
||||
files_to_process = [f for f in os.listdir(emoji_dir) if
|
||||
f.lower().endswith(('.jpg', '.jpeg', '.png', '.gif'))]
|
||||
|
||||
for filename in files_to_process:
|
||||
image_path = os.path.join(emoji_dir, filename)
|
||||
|
||||
|
||||
# 检查是否已经注册过
|
||||
existing_emoji = self.db.db['emoji'].find_one({'filename': filename})
|
||||
if existing_emoji:
|
||||
continue
|
||||
|
||||
|
||||
# 压缩图片并获取base64编码
|
||||
image_base64 = image_path_to_base64(image_path)
|
||||
if image_base64 is None:
|
||||
os.remove(image_path)
|
||||
continue
|
||||
|
||||
|
||||
# 获取表情包的描述
|
||||
discription = await self._get_emoji_discription(image_base64)
|
||||
if global_config.EMOJI_CHECK:
|
||||
@@ -248,30 +252,28 @@ class EmojiManager:
|
||||
emoji_record = {
|
||||
'filename': filename,
|
||||
'path': image_path,
|
||||
'embedding':embedding,
|
||||
'embedding': embedding,
|
||||
'discription': discription,
|
||||
'timestamp': int(time.time())
|
||||
}
|
||||
|
||||
|
||||
# 保存到数据库
|
||||
self.db.db['emoji'].insert_one(emoji_record)
|
||||
logger.success(f"注册新表情包: {filename}")
|
||||
logger.info(f"描述: {discription}")
|
||||
else:
|
||||
logger.warning(f"跳过表情包: {filename}")
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"扫描表情包失败: {str(e)}")
|
||||
logger.error(traceback.format_exc())
|
||||
|
||||
logger.exception(f"扫描表情包失败")
|
||||
|
||||
async def _periodic_scan(self, interval_MINS: int = 10):
|
||||
"""定期扫描新表情包"""
|
||||
while True:
|
||||
print("\033[1;36m[表情包]\033[0m 开始扫描新表情包...")
|
||||
logger.info("开始扫描新表情包...")
|
||||
await self.scan_new_emojis()
|
||||
await asyncio.sleep(interval_MINS * 60) # 每600秒扫描一次
|
||||
|
||||
|
||||
def check_emoji_file_integrity(self):
|
||||
"""检查表情包文件完整性
|
||||
如果文件已被删除，则从数据库中移除对应记录
|
||||
@@ -282,7 +284,7 @@ class EmojiManager:
|
||||
all_emojis = list(self.db.db.emoji.find())
|
||||
removed_count = 0
|
||||
total_count = len(all_emojis)
|
||||
|
||||
|
||||
for emoji in all_emojis:
|
||||
try:
|
||||
if 'path' not in emoji:
|
||||
@@ -290,27 +292,27 @@ class EmojiManager:
|
||||
self.db.db.emoji.delete_one({'_id': emoji['_id']})
|
||||
removed_count += 1
|
||||
continue
|
||||
|
||||
|
||||
if 'embedding' not in emoji:
|
||||
logger.warning(f"发现过时记录（缺少embedding字段），ID: {emoji.get('_id', 'unknown')}")
|
||||
self.db.db.emoji.delete_one({'_id': emoji['_id']})
|
||||
removed_count += 1
|
||||
continue
|
||||
|
||||
|
||||
# 检查文件是否存在
|
||||
if not os.path.exists(emoji['path']):
|
||||
logger.warning(f"表情包文件已被删除: {emoji['path']}")
|
||||
# 从数据库中删除记录
|
||||
result = self.db.db.emoji.delete_one({'_id': emoji['_id']})
|
||||
if result.deleted_count > 0:
|
||||
logger.success(f"成功删除数据库记录: {emoji['_id']}")
|
||||
logger.debug(f"成功删除数据库记录: {emoji['_id']}")
|
||||
removed_count += 1
|
||||
else:
|
||||
logger.error(f"删除数据库记录失败: {emoji['_id']}")
|
||||
except Exception as item_error:
|
||||
logger.error(f"处理表情包记录时出错: {str(item_error)}")
|
||||
continue
|
||||
|
||||
|
||||
# 验证清理结果
|
||||
remaining_count = self.db.db.emoji.count_documents({})
|
||||
if removed_count > 0:
|
||||
@@ -318,7 +320,7 @@ class EmojiManager:
|
||||
logger.info(f"清理前总数: {total_count} | 清理后总数: {remaining_count}")
|
||||
else:
|
||||
logger.info(f"已检查 {total_count} 个表情包记录")
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"检查表情包完整性失败: {str(e)}")
|
||||
logger.error(traceback.format_exc())
|
||||
@@ -329,6 +331,6 @@ class EmojiManager:
|
||||
await asyncio.sleep(interval_MINS * 60)
|
||||
|
||||
|
||||
|
||||
# 创建全局单例
|
||||
emoji_manager = EmojiManager()
|
||||
emoji_manager = EmojiManager()
|
||||
|
||||
|
||||
@@ -3,6 +3,7 @@ import time
|
||||
from typing import List, Optional, Tuple, Union
|
||||
|
||||
from nonebot import get_driver
|
||||
from loguru import logger
|
||||
|
||||
from ...common.database import Database
|
||||
from ..models.utils_model import LLM_request
|
||||
@@ -39,13 +40,13 @@ class ResponseGenerator:
|
||||
self.current_model_type = 'r1_distill'
|
||||
current_model = self.model_r1_distill
|
||||
|
||||
print(f"+++++++++++++++++{global_config.BOT_NICKNAME}{self.current_model_type}思考中+++++++++++++++++")
|
||||
logger.info(f"{global_config.BOT_NICKNAME}{self.current_model_type}思考中")
|
||||
|
||||
model_response = await self._generate_response_with_model(message, current_model)
|
||||
raw_content=model_response
|
||||
|
||||
if model_response:
|
||||
print(f'{global_config.BOT_NICKNAME}的回复是：{model_response}')
|
||||
logger.info(f'{global_config.BOT_NICKNAME}的回复是：{model_response}')
|
||||
model_response = await self._process_response(model_response)
|
||||
if model_response:
|
||||
|
||||
@@ -92,8 +93,8 @@ class ResponseGenerator:
|
||||
# 生成回复
|
||||
try:
|
||||
content, reasoning_content = await model.generate_response(prompt)
|
||||
except Exception as e:
|
||||
print(f"生成回复时出错: {e}")
|
||||
except Exception:
|
||||
logger.exception(f"生成回复时出错")
|
||||
return None
|
||||
|
||||
# 保存到数据库
|
||||
@@ -144,8 +145,8 @@ class ResponseGenerator:
|
||||
else:
|
||||
return ["neutral"]
|
||||
|
||||
except Exception as e:
|
||||
print(f"获取情感标签时出错: {e}")
|
||||
except Exception:
|
||||
logger.exception(f"获取情感标签时出错")
|
||||
return ["neutral"]
|
||||
|
||||
async def _process_response(self, content: str) -> Tuple[List[str], List[str]]:
|
||||
@@ -172,7 +173,7 @@ class InitiativeMessageGenerate:
|
||||
prompt_builder._build_initiative_prompt_select(message.group_id)
|
||||
)
|
||||
content_select, reasoning = self.model_v3.generate_response(topic_select_prompt)
|
||||
print(f"[DEBUG] {content_select} {reasoning}")
|
||||
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:
|
||||
@@ -185,12 +186,12 @@ class InitiativeMessageGenerate:
|
||||
select_dot[1], prompt_template
|
||||
)
|
||||
content_check, reasoning_check = self.model_v3.generate_response(prompt_check)
|
||||
print(f"[DEBUG] {content_check} {reasoning_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)
|
||||
print(f"[DEBUG] {content} {reasoning}")
|
||||
logger.debug(f"[DEBUG] {content} {reasoning}")
|
||||
return content
|
||||
|
||||
@@ -2,6 +2,7 @@ import asyncio
|
||||
import time
|
||||
from typing import Dict, List, Optional, Union
|
||||
|
||||
from loguru import logger
|
||||
from nonebot.adapters.onebot.v11 import Bot
|
||||
|
||||
from .cq_code import cq_code_tool
|
||||
@@ -13,45 +14,45 @@ from .config import global_config
|
||||
|
||||
class Message_Sender:
|
||||
"""发送器"""
|
||||
|
||||
def __init__(self):
|
||||
self.message_interval = (0.5, 1) # 消息间隔时间范围(秒)
|
||||
self.last_send_time = 0
|
||||
self._current_bot = None
|
||||
|
||||
|
||||
def set_bot(self, bot: Bot):
|
||||
"""设置当前bot实例"""
|
||||
self._current_bot = bot
|
||||
|
||||
|
||||
async def send_group_message(
|
||||
self,
|
||||
group_id: int,
|
||||
send_text: str,
|
||||
auto_escape: bool = False,
|
||||
reply_message_id: int = None,
|
||||
at_user_id: int = None
|
||||
self,
|
||||
group_id: int,
|
||||
send_text: str,
|
||||
auto_escape: bool = False,
|
||||
reply_message_id: int = None,
|
||||
at_user_id: int = None
|
||||
) -> None:
|
||||
|
||||
if not self._current_bot:
|
||||
raise RuntimeError("Bot未设置，请先调用set_bot方法设置bot实例")
|
||||
|
||||
|
||||
message = send_text
|
||||
|
||||
|
||||
# 如果需要回复
|
||||
if reply_message_id:
|
||||
reply_cq = cq_code_tool.create_reply_cq(reply_message_id)
|
||||
message = reply_cq + message
|
||||
|
||||
|
||||
# 如果需要at
|
||||
# if at_user_id:
|
||||
# at_cq = cq_code_tool.create_at_cq(at_user_id)
|
||||
# message = at_cq + " " + message
|
||||
|
||||
|
||||
|
||||
typing_time = calculate_typing_time(message)
|
||||
if typing_time > 10:
|
||||
typing_time = 10
|
||||
await asyncio.sleep(typing_time)
|
||||
|
||||
|
||||
# 发送消息
|
||||
try:
|
||||
await self._current_bot.send_group_msg(
|
||||
@@ -59,49 +60,49 @@ class Message_Sender:
|
||||
message=message,
|
||||
auto_escape=auto_escape
|
||||
)
|
||||
print(f"\033[1;34m[调试]\033[0m 发送消息{message}成功")
|
||||
logger.debug(f"发送消息{message}成功")
|
||||
except Exception as e:
|
||||
print(f"发生错误 {e}")
|
||||
print(f"\033[1;34m[调试]\033[0m 发送消息{message}失败")
|
||||
logger.exception(f"发送消息{message}失败")
|
||||
|
||||
|
||||
class MessageContainer:
|
||||
"""单个群的发送/思考消息容器"""
|
||||
|
||||
def __init__(self, group_id: int, max_size: int = 100):
|
||||
self.group_id = group_id
|
||||
self.max_size = max_size
|
||||
self.messages = []
|
||||
self.last_send_time = 0
|
||||
self.thinking_timeout = 20 # 思考超时时间（秒）
|
||||
|
||||
|
||||
def get_timeout_messages(self) -> List[Message_Sending]:
|
||||
"""获取所有超时的Message_Sending对象（思考时间超过30秒），按thinking_start_time排序"""
|
||||
current_time = time.time()
|
||||
timeout_messages = []
|
||||
|
||||
|
||||
for msg in self.messages:
|
||||
if isinstance(msg, Message_Sending):
|
||||
if current_time - msg.thinking_start_time > self.thinking_timeout:
|
||||
timeout_messages.append(msg)
|
||||
|
||||
|
||||
# 按thinking_start_time排序，时间早的在前面
|
||||
timeout_messages.sort(key=lambda x: x.thinking_start_time)
|
||||
|
||||
|
||||
return timeout_messages
|
||||
|
||||
|
||||
def get_earliest_message(self) -> Optional[Union[Message_Thinking, Message_Sending]]:
|
||||
"""获取thinking_start_time最早的消息对象"""
|
||||
if not self.messages:
|
||||
return None
|
||||
earliest_time = float('inf')
|
||||
earliest_message = None
|
||||
for msg in self.messages:
|
||||
for msg in self.messages:
|
||||
msg_time = msg.thinking_start_time
|
||||
if msg_time < earliest_time:
|
||||
earliest_time = msg_time
|
||||
earliest_message = msg
|
||||
earliest_message = msg
|
||||
return earliest_message
|
||||
|
||||
|
||||
def add_message(self, message: Union[Message_Thinking, Message_Sending]) -> None:
|
||||
"""添加消息到队列"""
|
||||
# print(f"\033[1;32m[添加消息]\033[0m 添加消息到对应群")
|
||||
@@ -110,7 +111,7 @@ class MessageContainer:
|
||||
self.messages.append(single_message)
|
||||
else:
|
||||
self.messages.append(message)
|
||||
|
||||
|
||||
def remove_message(self, message: Union[Message_Thinking, Message_Sending]) -> bool:
|
||||
"""移除消息，如果消息存在则返回True，否则返回False"""
|
||||
try:
|
||||
@@ -118,98 +119,104 @@ class MessageContainer:
|
||||
self.messages.remove(message)
|
||||
return True
|
||||
return False
|
||||
except Exception as e:
|
||||
print(f"\033[1;31m[错误]\033[0m 移除消息时发生错误: {e}")
|
||||
except Exception:
|
||||
logger.exception(f"移除消息时发生错误")
|
||||
return False
|
||||
|
||||
|
||||
def has_messages(self) -> bool:
|
||||
"""检查是否有待发送的消息"""
|
||||
return bool(self.messages)
|
||||
|
||||
|
||||
def get_all_messages(self) -> List[Union[Message, Message_Thinking]]:
|
||||
"""获取所有消息"""
|
||||
return list(self.messages)
|
||||
|
||||
|
||||
|
||||
class MessageManager:
|
||||
"""管理所有群的消息容器"""
|
||||
|
||||
def __init__(self):
|
||||
self.containers: Dict[int, MessageContainer] = {}
|
||||
self.storage = MessageStorage()
|
||||
self._running = True
|
||||
|
||||
|
||||
def get_container(self, group_id: int) -> MessageContainer:
|
||||
"""获取或创建群的消息容器"""
|
||||
if group_id not in self.containers:
|
||||
self.containers[group_id] = MessageContainer(group_id)
|
||||
return self.containers[group_id]
|
||||
|
||||
|
||||
def add_message(self, message: Union[Message_Thinking, Message_Sending, MessageSet]) -> None:
|
||||
container = self.get_container(message.group_id)
|
||||
container.add_message(message)
|
||||
|
||||
|
||||
async def process_group_messages(self, group_id: int):
|
||||
"""处理群消息"""
|
||||
# if int(time.time() / 3) == time.time() / 3:
|
||||
# print(f"\033[1;34m[调试]\033[0m 开始处理群{group_id}的消息")
|
||||
# print(f"\033[1;34m[调试]\033[0m 开始处理群{group_id}的消息")
|
||||
container = self.get_container(group_id)
|
||||
if container.has_messages():
|
||||
#最早的对象，可能是思考消息，也可能是发送消息
|
||||
message_earliest = container.get_earliest_message() #一个message_thinking or message_sending
|
||||
|
||||
#如果是思考消息
|
||||
# 最早的对象，可能是思考消息，也可能是发送消息
|
||||
message_earliest = container.get_earliest_message() # 一个message_thinking or message_sending
|
||||
|
||||
# 如果是思考消息
|
||||
if isinstance(message_earliest, Message_Thinking):
|
||||
#优先等待这条消息
|
||||
# 优先等待这条消息
|
||||
message_earliest.update_thinking_time()
|
||||
thinking_time = message_earliest.thinking_time
|
||||
print(f"\033[1;34m[调试]\033[0m 消息正在思考中，已思考{int(thinking_time)}秒\033[K\r", end='', flush=True)
|
||||
|
||||
print(f"消息正在思考中，已思考{int(thinking_time)}秒\r", end='', flush=True)
|
||||
|
||||
# 检查是否超时
|
||||
if thinking_time > global_config.thinking_timeout:
|
||||
print(f"\033[1;33m[警告]\033[0m 消息思考超时({thinking_time}秒)，移除该消息")
|
||||
logger.warning(f"消息思考超时({thinking_time}秒)，移除该消息")
|
||||
container.remove_message(message_earliest)
|
||||
else:# 如果不是message_thinking就只能是message_sending
|
||||
print(f"\033[1;34m[调试]\033[0m 消息'{message_earliest.processed_plain_text}'正在发送中")
|
||||
#直接发，等什么呢
|
||||
if message_earliest.is_head and message_earliest.update_thinking_time() >30:
|
||||
await message_sender.send_group_message(group_id, message_earliest.processed_plain_text, auto_escape=False, reply_message_id=message_earliest.reply_message_id)
|
||||
else: # 如果不是message_thinking就只能是message_sending
|
||||
logger.debug(f"消息'{message_earliest.processed_plain_text}'正在发送中")
|
||||
# 直接发，等什么呢
|
||||
if message_earliest.is_head and message_earliest.update_thinking_time() > 30:
|
||||
await message_sender.send_group_message(group_id, message_earliest.processed_plain_text,
|
||||
auto_escape=False,
|
||||
reply_message_id=message_earliest.reply_message_id)
|
||||
else:
|
||||
await message_sender.send_group_message(group_id, message_earliest.processed_plain_text, auto_escape=False)
|
||||
#移除消息
|
||||
await message_sender.send_group_message(group_id, message_earliest.processed_plain_text,
|
||||
auto_escape=False)
|
||||
# 移除消息
|
||||
if message_earliest.is_emoji:
|
||||
message_earliest.processed_plain_text = "[表情包]"
|
||||
await self.storage.store_message(message_earliest, None)
|
||||
|
||||
|
||||
container.remove_message(message_earliest)
|
||||
|
||||
#获取并处理超时消息
|
||||
message_timeout = container.get_timeout_messages() #也许是一堆message_sending
|
||||
|
||||
# 获取并处理超时消息
|
||||
message_timeout = container.get_timeout_messages() # 也许是一堆message_sending
|
||||
if message_timeout:
|
||||
print(f"\033[1;34m[调试]\033[0m 发现{len(message_timeout)}条超时消息")
|
||||
logger.warning(f"发现{len(message_timeout)}条超时消息")
|
||||
for msg in message_timeout:
|
||||
if msg == message_earliest:
|
||||
continue # 跳过已经处理过的消息
|
||||
|
||||
|
||||
try:
|
||||
#发送
|
||||
if msg.is_head and msg.update_thinking_time() >30:
|
||||
await message_sender.send_group_message(group_id, msg.processed_plain_text, auto_escape=False, reply_message_id=msg.reply_message_id)
|
||||
# 发送
|
||||
if msg.is_head and msg.update_thinking_time() > 30:
|
||||
await message_sender.send_group_message(group_id, msg.processed_plain_text,
|
||||
auto_escape=False,
|
||||
reply_message_id=msg.reply_message_id)
|
||||
else:
|
||||
await message_sender.send_group_message(group_id, msg.processed_plain_text, auto_escape=False)
|
||||
|
||||
|
||||
#如果是表情包，则替换为"[表情包]"
|
||||
await message_sender.send_group_message(group_id, msg.processed_plain_text,
|
||||
auto_escape=False)
|
||||
|
||||
# 如果是表情包，则替换为"[表情包]"
|
||||
if msg.is_emoji:
|
||||
msg.processed_plain_text = "[表情包]"
|
||||
await self.storage.store_message(msg, None)
|
||||
|
||||
|
||||
# 安全地移除消息
|
||||
if not container.remove_message(msg):
|
||||
print("\033[1;33m[警告]\033[0m 尝试删除不存在的消息")
|
||||
except Exception as e:
|
||||
print(f"\033[1;31m[错误]\033[0m 处理超时消息时发生错误: {e}")
|
||||
logger.warning("尝试删除不存在的消息")
|
||||
except Exception:
|
||||
logger.exception(f"处理超时消息时发生错误")
|
||||
continue
|
||||
|
||||
|
||||
async def start_processor(self):
|
||||
"""启动消息处理器"""
|
||||
while self._running:
|
||||
@@ -217,9 +224,10 @@ class MessageManager:
|
||||
tasks = []
|
||||
for group_id in self.containers.keys():
|
||||
tasks.append(self.process_group_messages(group_id))
|
||||
|
||||
|
||||
await asyncio.gather(*tasks)
|
||||
|
||||
|
||||
# 创建全局消息管理器实例
|
||||
message_manager = MessageManager()
|
||||
# 创建全局发送器实例
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
import random
|
||||
import time
|
||||
from typing import Optional
|
||||
from loguru import logger
|
||||
|
||||
from ...common.database import Database
|
||||
from ..memory_system.memory import hippocampus, memory_graph
|
||||
@@ -16,13 +17,11 @@ class PromptBuilder:
|
||||
self.activate_messages = ''
|
||||
self.db = Database.get_instance()
|
||||
|
||||
|
||||
|
||||
async def _build_prompt(self,
|
||||
message_txt: str,
|
||||
sender_name: str = "某人",
|
||||
relationship_value: float = 0.0,
|
||||
group_id: Optional[int] = None) -> tuple[str, str]:
|
||||
async def _build_prompt(self,
|
||||
message_txt: str,
|
||||
sender_name: str = "某人",
|
||||
relationship_value: float = 0.0,
|
||||
group_id: Optional[int] = None) -> tuple[str, str]:
|
||||
"""构建prompt
|
||||
|
||||
Args:
|
||||
@@ -33,57 +32,56 @@ class PromptBuilder:
|
||||
|
||||
Returns:
|
||||
str: 构建好的prompt
|
||||
"""
|
||||
#先禁用关系
|
||||
"""
|
||||
# 先禁用关系
|
||||
if 0 > 30:
|
||||
relation_prompt = "关系特别特别好，你很喜欢喜欢他"
|
||||
relation_prompt_2 = "热情发言或者回复"
|
||||
elif 0 <-20:
|
||||
elif 0 < -20:
|
||||
relation_prompt = "关系很差，你很讨厌他"
|
||||
relation_prompt_2 = "骂他"
|
||||
else:
|
||||
relation_prompt = "关系一般"
|
||||
relation_prompt_2 = "发言或者回复"
|
||||
|
||||
#开始构建prompt
|
||||
|
||||
|
||||
#心情
|
||||
|
||||
# 开始构建prompt
|
||||
|
||||
# 心情
|
||||
mood_manager = MoodManager.get_instance()
|
||||
mood_prompt = mood_manager.get_prompt()
|
||||
|
||||
|
||||
#日程构建
|
||||
|
||||
# 日程构建
|
||||
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()
|
||||
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：\n{bot_schedule.today_schedule}\n你现在正在{bot_schedule_now_activity}\n'''
|
||||
|
||||
#知识构建
|
||||
# 知识构建
|
||||
start_time = time.time()
|
||||
|
||||
|
||||
prompt_info = ''
|
||||
promt_info_prompt = ''
|
||||
prompt_info = await self.get_prompt_info(message_txt,threshold=0.5)
|
||||
prompt_info = await self.get_prompt_info(message_txt, threshold=0.5)
|
||||
if prompt_info:
|
||||
prompt_info = f'''\n----------------------------------------------------\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----------------------------------------------------\n'''
|
||||
|
||||
prompt_info = f'''你有以下这些[知识]：{prompt_info}请你记住上面的[
|
||||
知识]，之后可能会用到-'''
|
||||
|
||||
end_time = time.time()
|
||||
print(f"\033[1;32m[知识检索]\033[0m 耗时: {(end_time - start_time):.3f}秒")
|
||||
|
||||
logger.debug(f"知识检索耗时: {(end_time - start_time):.3f}秒")
|
||||
|
||||
# 获取聊天上下文
|
||||
chat_talking_prompt = ''
|
||||
if group_id:
|
||||
chat_talking_prompt = get_recent_group_detailed_plain_text(self.db, group_id, limit=global_config.MAX_CONTEXT_SIZE,combine = True)
|
||||
|
||||
chat_talking_prompt = get_recent_group_detailed_plain_text(self.db, 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}"
|
||||
|
||||
|
||||
|
||||
|
||||
# 使用新的记忆获取方法
|
||||
memory_prompt = ''
|
||||
start_time = time.time()
|
||||
|
||||
|
||||
# 调用 hippocampus 的 get_relevant_memories 方法
|
||||
relevant_memories = await hippocampus.get_relevant_memories(
|
||||
text=message_txt,
|
||||
@@ -91,30 +89,28 @@ class PromptBuilder:
|
||||
similarity_threshold=0.4,
|
||||
max_memory_num=5
|
||||
)
|
||||
|
||||
|
||||
if relevant_memories:
|
||||
# 格式化记忆内容
|
||||
memory_items = []
|
||||
for memory in relevant_memories:
|
||||
memory_items.append(f"关于「{memory['topic']}」的记忆：{memory['content']}")
|
||||
|
||||
|
||||
memory_prompt = "看到这些聊天，你想起来：\n" + "\n".join(memory_items) + "\n"
|
||||
|
||||
|
||||
# 打印调试信息
|
||||
print("\n\033[1;32m[记忆检索]\033[0m 找到以下相关记忆：")
|
||||
logger.debug("[记忆检索]找到以下相关记忆：")
|
||||
for memory in relevant_memories:
|
||||
print(f"- 主题「{memory['topic']}」[相似度: {memory['similarity']:.2f}]: {memory['content']}")
|
||||
|
||||
logger.debug(f"- 主题「{memory['topic']}」[相似度: {memory['similarity']:.2f}]: {memory['content']}")
|
||||
|
||||
end_time = time.time()
|
||||
print(f"\033[1;32m[回忆耗时]\033[0m 耗时: {(end_time - start_time):.3f}秒")
|
||||
|
||||
|
||||
|
||||
#激活prompt构建
|
||||
logger.info(f"回忆耗时: {(end_time - start_time):.3f}秒")
|
||||
|
||||
# 激活prompt构建
|
||||
activate_prompt = ''
|
||||
activate_prompt = f"以上是群里正在进行的聊天，{memory_prompt} 现在昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意,你和他{relation_prompt},{mood_prompt},你想要{relation_prompt_2}。"
|
||||
|
||||
#检测机器人相关词汇，改为关键词检测与反应功能了，提取到全局配置中
|
||||
activate_prompt = f"以上是群里正在进行的聊天，{memory_prompt} 现在昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意,你和他{relation_prompt},{mood_prompt},你想要{relation_prompt_2}。"
|
||||
|
||||
# 检测机器人相关词汇，改为关键词检测与反应功能了，提取到全局配置中
|
||||
# bot_keywords = ['人机', 'bot', '机器', '入机', 'robot', '机器人']
|
||||
# is_bot = any(keyword in message_txt.lower() for keyword in bot_keywords)
|
||||
# if is_bot:
|
||||
@@ -127,12 +123,11 @@ class PromptBuilder:
|
||||
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", [])):
|
||||
print(f"检测到以下关键词之一：{rule.get('keywords', [])}，触发反应：{rule.get('reaction', '')}")
|
||||
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
|
||||
# 人格选择
|
||||
personality = global_config.PROMPT_PERSONALITY
|
||||
probability_1 = global_config.PERSONALITY_1
|
||||
probability_2 = global_config.PERSONALITY_2
|
||||
probability_3 = global_config.PERSONALITY_3
|
||||
@@ -150,8 +145,8 @@ class PromptBuilder:
|
||||
prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[2]}, 你正在浏览qq群，{promt_info_prompt},
|
||||
现在请你给出日常且口语化的回复，请表现你自己的见解，不要一昧迎合，尽量简短一些。{keywords_reaction_prompt}
|
||||
请你表达自己的见解和观点。可以有个性。'''
|
||||
|
||||
#中文高手(新加的好玩功能)
|
||||
|
||||
# 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'
|
||||
@@ -159,23 +154,23 @@ class PromptBuilder:
|
||||
prompt_ger += '你喜欢用反问句'
|
||||
if random.random() < 0.01:
|
||||
prompt_ger += '你喜欢用文言文'
|
||||
|
||||
#额外信息要求
|
||||
extra_info = '''但是记得回复平淡一些，简短一些，尤其注意在没明确提到时不要过多提及自身的背景, 不要直接回复别人发的表情包，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)，只需要输出回复内容就好，不要输出其他任何内容'''
|
||||
|
||||
#合并prompt
|
||||
|
||||
# 额外信息要求
|
||||
extra_info = '''但是记得回复平淡一些，简短一些，尤其注意在没明确提到时不要过多提及自身的背景, 不要直接回复别人发的表情包，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)，只需要输出回复内容就好，不要输出其他任何内容'''
|
||||
|
||||
# 合并prompt
|
||||
prompt = ""
|
||||
prompt += f"{prompt_info}\n"
|
||||
prompt += f"{prompt_date}\n"
|
||||
prompt += f"{chat_talking_prompt}\n"
|
||||
prompt += f"{chat_talking_prompt}\n"
|
||||
prompt += f"{prompt_personality}\n"
|
||||
prompt += f"{prompt_ger}\n"
|
||||
prompt += f"{extra_info}\n"
|
||||
|
||||
'''读空气prompt处理'''
|
||||
activate_prompt_check=f"以上是群里正在进行的聊天，昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意,你和他{relation_prompt}，你想要{relation_prompt_2}，但是这不一定是合适的时机，请你决定是否要回应这条消息。"
|
||||
prompt += f"{extra_info}\n"
|
||||
|
||||
'''读空气prompt处理'''
|
||||
activate_prompt_check = f"以上是群里正在进行的聊天，昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意,你和他{relation_prompt}，你想要{relation_prompt_2}，但是这不一定是合适的时机，请你决定是否要回应这条消息。"
|
||||
prompt_personality_check = ''
|
||||
extra_check_info=f"请注意把握群里的聊天内容的基础上，综合群内的氛围，例如，和{global_config.BOT_NICKNAME}相关的话题要积极回复,如果是at自己的消息一定要回复，如果自己正在和别人聊天一定要回复，其他话题如果合适搭话也可以回复，如果认为应该回复请输出yes，否则输出no，请注意是决定是否需要回复，而不是编写回复内容，除了yes和no不要输出任何回复内容。"
|
||||
extra_check_info = f"请注意把握群里的聊天内容的基础上，综合群内的氛围，例如，和{global_config.BOT_NICKNAME}相关的话题要积极回复,如果是at自己的消息一定要回复，如果自己正在和别人聊天一定要回复，其他话题如果合适搭话也可以回复，如果认为应该回复请输出yes，否则输出no，请注意是决定是否需要回复，而不是编写回复内容，除了yes和no不要输出任何回复内容。"
|
||||
if personality_choice < probability_1: # 第一种人格
|
||||
prompt_personality_check = f'''你的网名叫{global_config.BOT_NICKNAME}，{personality[0]}, 你正在浏览qq群，{promt_info_prompt} {activate_prompt_check} {extra_check_info}'''
|
||||
elif personality_choice < probability_1 + probability_2: # 第二种人格
|
||||
@@ -183,34 +178,36 @@ class PromptBuilder:
|
||||
else: # 第三种人格
|
||||
prompt_personality_check = f'''你的网名叫{global_config.BOT_NICKNAME}，{personality[2]}, 你正在浏览qq群，{promt_info_prompt} {activate_prompt_check} {extra_check_info}'''
|
||||
|
||||
prompt_check_if_response=f"{prompt_info}\n{prompt_date}\n{chat_talking_prompt}\n{prompt_personality_check}"
|
||||
|
||||
return prompt,prompt_check_if_response
|
||||
|
||||
def _build_initiative_prompt_select(self,group_id):
|
||||
prompt_check_if_response = f"{prompt_info}\n{prompt_date}\n{chat_talking_prompt}\n{prompt_personality_check}"
|
||||
|
||||
return prompt, prompt_check_if_response
|
||||
|
||||
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()
|
||||
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：\n{bot_schedule.today_schedule}\n你现在正在{bot_schedule_now_activity}\n'''
|
||||
|
||||
chat_talking_prompt = ''
|
||||
if group_id:
|
||||
chat_talking_prompt = get_recent_group_detailed_plain_text(self.db, group_id, limit=global_config.MAX_CONTEXT_SIZE,combine = True)
|
||||
|
||||
chat_talking_prompt = get_recent_group_detailed_plain_text(self.db, 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}")
|
||||
# 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=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]
|
||||
infos=[info[1] for info in nodes_for_select]
|
||||
all_nodes = 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]
|
||||
infos = [info[1] for info in nodes_for_select]
|
||||
|
||||
#激活prompt构建
|
||||
# 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
|
||||
personality = global_config.PROMPT_PERSONALITY
|
||||
prompt_personality = ''
|
||||
personality_choice = random.random()
|
||||
if personality_choice < probability_1: # 第一种人格
|
||||
@@ -219,32 +216,31 @@ class PromptBuilder:
|
||||
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，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']}，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，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，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']}，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，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。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)"
|
||||
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，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']}，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，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，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']}，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，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。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):
|
||||
async def get_prompt_info(self, message: str, threshold: float):
|
||||
related_info = ''
|
||||
print(f"\033[1;34m[调试]\033[0m 获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
|
||||
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)
|
||||
related_info += self.get_info_from_db(embedding,threshold=threshold)
|
||||
|
||||
related_info += self.get_info_from_db(embedding, threshold=threshold)
|
||||
|
||||
return related_info
|
||||
|
||||
def get_info_from_db(self, query_embedding: list, limit: int = 1, threshold: float = 0.5) -> str:
|
||||
@@ -305,14 +301,15 @@ class PromptBuilder:
|
||||
{"$limit": limit},
|
||||
{"$project": {"content": 1, "similarity": 1}}
|
||||
]
|
||||
|
||||
|
||||
results = list(self.db.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()
|
||||
|
||||
|
||||
prompt_builder = PromptBuilder()
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
import asyncio
|
||||
from loguru import logger
|
||||
from typing import Optional
|
||||
|
||||
from ...common.database import Database
|
||||
@@ -8,9 +9,10 @@ class Impression:
|
||||
traits: str = None
|
||||
called: str = None
|
||||
know_time: float = None
|
||||
|
||||
|
||||
relationship_value: float = None
|
||||
|
||||
|
||||
class Relationship:
|
||||
user_id: int = None
|
||||
# impression: Impression = None
|
||||
@@ -21,7 +23,7 @@ class Relationship:
|
||||
nickname: str = None
|
||||
relationship_value: float = None
|
||||
saved = False
|
||||
|
||||
|
||||
def __init__(self, user_id: int, data=None, **kwargs):
|
||||
if isinstance(data, dict):
|
||||
# 如果输入是字典，使用字典解析
|
||||
@@ -39,14 +41,12 @@ class Relationship:
|
||||
self.nickname = kwargs.get('nickname')
|
||||
self.relationship_value = kwargs.get('relationship_value', 0.0)
|
||||
self.saved = kwargs.get('saved', False)
|
||||
|
||||
|
||||
|
||||
|
||||
class RelationshipManager:
|
||||
def __init__(self):
|
||||
self.relationships: dict[int, Relationship] = {}
|
||||
|
||||
self.relationships: dict[int, Relationship] = {}
|
||||
|
||||
async def update_relationship(self, user_id: int, data=None, **kwargs):
|
||||
# 检查是否在内存中已存在
|
||||
relationship = self.relationships.get(user_id)
|
||||
@@ -62,7 +62,8 @@ class RelationshipManager:
|
||||
setattr(relationship, key, value)
|
||||
else:
|
||||
# 如果不存在，创建新对象
|
||||
relationship = Relationship(user_id, data=data) if isinstance(data, dict) else Relationship(user_id, **kwargs)
|
||||
relationship = Relationship(user_id, data=data) if isinstance(data, dict) else Relationship(user_id,
|
||||
**kwargs)
|
||||
self.relationships[user_id] = relationship
|
||||
|
||||
# 更新 id_name_nickname_table
|
||||
@@ -71,9 +72,9 @@ class RelationshipManager:
|
||||
# 保存到数据库
|
||||
await self.storage_relationship(relationship)
|
||||
relationship.saved = True
|
||||
|
||||
|
||||
return relationship
|
||||
|
||||
|
||||
async def update_relationship_value(self, user_id: int, **kwargs):
|
||||
# 检查是否在内存中已存在
|
||||
relationship = self.relationships.get(user_id)
|
||||
@@ -85,31 +86,30 @@ class RelationshipManager:
|
||||
relationship.saved = True
|
||||
return relationship
|
||||
else:
|
||||
print(f"\033[1;31m[关系管理]\033[0m 用户 {user_id} 不存在，无法更新")
|
||||
logger.warning(f"用户 {user_id} 不存在，无法更新")
|
||||
return None
|
||||
|
||||
|
||||
|
||||
def get_relationship(self, user_id: int) -> Optional[Relationship]:
|
||||
"""获取用户关系对象"""
|
||||
if user_id in self.relationships:
|
||||
return self.relationships[user_id]
|
||||
else:
|
||||
return 0
|
||||
|
||||
|
||||
async def load_relationship(self, data: dict) -> Relationship:
|
||||
"""从数据库加载或创建新的关系对象"""
|
||||
"""从数据库加载或创建新的关系对象"""
|
||||
rela = Relationship(user_id=data['user_id'], data=data)
|
||||
rela.saved = True
|
||||
self.relationships[rela.user_id] = rela
|
||||
return rela
|
||||
|
||||
|
||||
async def load_all_relationships(self):
|
||||
"""加载所有关系对象"""
|
||||
db = Database.get_instance()
|
||||
all_relationships = db.db.relationships.find({})
|
||||
for data in all_relationships:
|
||||
await self.load_relationship(data)
|
||||
|
||||
|
||||
async def _start_relationship_manager(self):
|
||||
"""每5分钟自动保存一次关系数据"""
|
||||
db = Database.get_instance()
|
||||
@@ -119,23 +119,23 @@ class RelationshipManager:
|
||||
for data in all_relationships:
|
||||
user_id = data['user_id']
|
||||
relationship = await self.load_relationship(data)
|
||||
self.relationships[user_id] = relationship
|
||||
print(f"\033[1;32m[关系管理]\033[0m 已加载 {len(self.relationships)} 条关系记录")
|
||||
|
||||
self.relationships[user_id] = relationship
|
||||
logger.debug(f"已加载 {len(self.relationships)} 条关系记录")
|
||||
|
||||
while True:
|
||||
print("\033[1;32m[关系管理]\033[0m 正在自动保存关系")
|
||||
logger.debug("正在自动保存关系")
|
||||
await asyncio.sleep(300) # 等待300秒(5分钟)
|
||||
await self._save_all_relationships()
|
||||
|
||||
|
||||
async def _save_all_relationships(self):
|
||||
"""将所有关系数据保存到数据库"""
|
||||
"""将所有关系数据保存到数据库"""
|
||||
# 保存所有关系数据
|
||||
for userid, relationship in self.relationships.items():
|
||||
if not relationship.saved:
|
||||
relationship.saved = True
|
||||
await self.storage_relationship(relationship)
|
||||
|
||||
async def storage_relationship(self,relationship: Relationship):
|
||||
|
||||
async def storage_relationship(self, relationship: Relationship):
|
||||
"""
|
||||
将关系记录存储到数据库中
|
||||
"""
|
||||
@@ -145,7 +145,7 @@ class RelationshipManager:
|
||||
gender = relationship.gender
|
||||
age = relationship.age
|
||||
saved = relationship.saved
|
||||
|
||||
|
||||
db = Database.get_instance()
|
||||
db.db.relationships.update_one(
|
||||
{'user_id': user_id},
|
||||
@@ -158,7 +158,7 @@ class RelationshipManager:
|
||||
}},
|
||||
upsert=True
|
||||
)
|
||||
|
||||
|
||||
def get_name(self, user_id: int) -> str:
|
||||
# 确保user_id是整数类型
|
||||
user_id = int(user_id)
|
||||
@@ -169,4 +169,4 @@ class RelationshipManager:
|
||||
return "某人"
|
||||
|
||||
|
||||
relationship_manager = RelationshipManager()
|
||||
relationship_manager = RelationshipManager()
|
||||
|
||||
@@ -2,12 +2,13 @@ from typing import Optional
|
||||
|
||||
from ...common.database import Database
|
||||
from .message import Message
|
||||
from loguru import logger
|
||||
|
||||
|
||||
class MessageStorage:
|
||||
def __init__(self):
|
||||
self.db = Database.get_instance()
|
||||
|
||||
|
||||
async def store_message(self, message: Message, topic: Optional[str] = None) -> None:
|
||||
"""存储消息到数据库"""
|
||||
try:
|
||||
@@ -41,9 +42,9 @@ class MessageStorage:
|
||||
"topic": topic,
|
||||
"detailed_plain_text": message.detailed_plain_text,
|
||||
}
|
||||
|
||||
self.db.db.messages.insert_one(message_data)
|
||||
except Exception as e:
|
||||
print(f"\033[1;31m[错误]\033[0m 存储消息失败: {e}")
|
||||
|
||||
# 如果需要其他存储相关的函数，可以在这里添加
|
||||
self.db.db.messages.insert_one(message_data)
|
||||
except Exception:
|
||||
logger.exception(f"存储消息失败")
|
||||
|
||||
# 如果需要其他存储相关的函数，可以在这里添加
|
||||
|
||||
@@ -4,9 +4,11 @@ from nonebot import get_driver
|
||||
|
||||
from ..models.utils_model import LLM_request
|
||||
from .config import global_config
|
||||
from loguru import logger
|
||||
|
||||
driver = get_driver()
|
||||
config = driver.config
|
||||
config = driver.config
|
||||
|
||||
|
||||
class TopicIdentifier:
|
||||
def __init__(self):
|
||||
@@ -23,19 +25,20 @@ class TopicIdentifier:
|
||||
|
||||
# 使用 LLM_request 类进行请求
|
||||
topic, _ = await self.llm_topic_judge.generate_response(prompt)
|
||||
|
||||
|
||||
if not topic:
|
||||
print("\033[1;31m[错误]\033[0m LLM API 返回为空")
|
||||
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()]
|
||||
|
||||
print(f"\033[1;32m[主题识别]\033[0m 主题: {topic_list}")
|
||||
|
||||
logger.info(f"主题: {topic_list}")
|
||||
return topic_list if topic_list else None
|
||||
|
||||
topic_identifier = TopicIdentifier()
|
||||
|
||||
topic_identifier = TopicIdentifier()
|
||||
|
||||
@@ -7,6 +7,7 @@ from typing import Dict, List
|
||||
import jieba
|
||||
import numpy as np
|
||||
from nonebot import get_driver
|
||||
from loguru import logger
|
||||
|
||||
from ..models.utils_model import LLM_request
|
||||
from ..utils.typo_generator import ChineseTypoGenerator
|
||||
@@ -39,16 +40,16 @@ def combine_messages(messages: List[Message]) -> str:
|
||||
|
||||
|
||||
def db_message_to_str(message_dict: Dict) -> str:
|
||||
print(f"message_dict: {message_dict}")
|
||||
logger.debug(f"message_dict: {message_dict}")
|
||||
time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(message_dict["time"]))
|
||||
try:
|
||||
name = "[(%s)%s]%s" % (
|
||||
message_dict['user_id'], message_dict.get("user_nickname", ""), message_dict.get("user_cardname", ""))
|
||||
message_dict['user_id'], message_dict.get("user_nickname", ""), message_dict.get("user_cardname", ""))
|
||||
except:
|
||||
name = message_dict.get("user_nickname", "") or f"用户{message_dict['user_id']}"
|
||||
content = message_dict.get("processed_plain_text", "")
|
||||
result = f"[{time_str}] {name}: {content}\n"
|
||||
print(f"result: {result}")
|
||||
logger.debug(f"result: {result}")
|
||||
return result
|
||||
|
||||
|
||||
@@ -182,7 +183,7 @@ async def get_recent_group_messages(db, group_id: int, limit: int = 12) -> list:
|
||||
await msg.initialize()
|
||||
message_objects.append(msg)
|
||||
except KeyError:
|
||||
print("[WARNING] 数据库中存在无效的消息")
|
||||
logger.warning("数据库中存在无效的消息")
|
||||
continue
|
||||
|
||||
# 按时间正序排列
|
||||
@@ -298,11 +299,10 @@ def split_into_sentences_w_remove_punctuation(text: str) -> List[str]:
|
||||
sentence = sentence.replace('，', ' ').replace(',', ' ')
|
||||
sentences_done.append(sentence)
|
||||
|
||||
print(f"处理后的句子: {sentences_done}")
|
||||
logger.info(f"处理后的句子: {sentences_done}")
|
||||
return sentences_done
|
||||
|
||||
|
||||
|
||||
def random_remove_punctuation(text: str) -> str:
|
||||
"""随机处理标点符号，模拟人类打字习惯
|
||||
|
||||
@@ -330,11 +330,10 @@ def random_remove_punctuation(text: str) -> str:
|
||||
return result
|
||||
|
||||
|
||||
|
||||
def process_llm_response(text: str) -> List[str]:
|
||||
# processed_response = process_text_with_typos(content)
|
||||
if len(text) > 200:
|
||||
print(f"回复过长 ({len(text)} 字符)，返回默认回复")
|
||||
logger.warning(f"回复过长 ({len(text)} 字符)，返回默认回复")
|
||||
return ['懒得说']
|
||||
# 处理长消息
|
||||
typo_generator = ChineseTypoGenerator(
|
||||
@@ -354,9 +353,9 @@ def process_llm_response(text: str) -> List[str]:
|
||||
else:
|
||||
sentences.append(sentence)
|
||||
# 检查分割后的消息数量是否过多（超过3条）
|
||||
|
||||
|
||||
if len(sentences) > 5:
|
||||
print(f"分割后消息数量过多 ({len(sentences)} 条)，返回默认回复")
|
||||
logger.warning(f"分割后消息数量过多 ({len(sentences)} 条)，返回默认回复")
|
||||
return [f'{global_config.BOT_NICKNAME}不知道哦']
|
||||
|
||||
return sentences
|
||||
@@ -378,15 +377,15 @@ def calculate_typing_time(input_string: str, chinese_time: float = 0.4, english_
|
||||
mood_arousal = mood_manager.current_mood.arousal
|
||||
# 映射到0.5到2倍的速度系数
|
||||
typing_speed_multiplier = 1.5 ** mood_arousal # 唤醒度为1时速度翻倍,为-1时速度减半
|
||||
chinese_time *= 1/typing_speed_multiplier
|
||||
english_time *= 1/typing_speed_multiplier
|
||||
chinese_time *= 1 / typing_speed_multiplier
|
||||
english_time *= 1 / typing_speed_multiplier
|
||||
# 计算中文字符数
|
||||
chinese_chars = sum(1 for char in input_string if '\u4e00' <= char <= '\u9fff')
|
||||
|
||||
|
||||
# 如果只有一个中文字符，使用3倍时间
|
||||
if chinese_chars == 1 and len(input_string.strip()) == 1:
|
||||
return chinese_time * 3 + 0.3 # 加上回车时间
|
||||
|
||||
|
||||
# 正常计算所有字符的输入时间
|
||||
total_time = 0.0
|
||||
for char in input_string:
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
import asyncio
|
||||
from loguru import logger
|
||||
from .config import global_config
|
||||
|
||||
|
||||
@@ -7,74 +8,100 @@ class WillingManager:
|
||||
self.group_reply_willing = {} # 存储每个群的回复意愿
|
||||
self._decay_task = None
|
||||
self._started = False
|
||||
|
||||
self.min_reply_willing = 0.01
|
||||
self.attenuation_coefficient = 0.75
|
||||
|
||||
async def _decay_reply_willing(self):
|
||||
"""定期衰减回复意愿"""
|
||||
while True:
|
||||
await asyncio.sleep(5)
|
||||
for group_id in self.group_reply_willing:
|
||||
self.group_reply_willing[group_id] = max(0, self.group_reply_willing[group_id] * 0.6)
|
||||
|
||||
self.group_reply_willing[group_id] = max(
|
||||
self.min_reply_willing,
|
||||
self.group_reply_willing[group_id] * self.attenuation_coefficient
|
||||
)
|
||||
|
||||
def get_willing(self, group_id: int) -> float:
|
||||
"""获取指定群组的回复意愿"""
|
||||
return self.group_reply_willing.get(group_id, 0)
|
||||
|
||||
|
||||
def set_willing(self, group_id: int, willing: float):
|
||||
"""设置指定群组的回复意愿"""
|
||||
self.group_reply_willing[group_id] = willing
|
||||
|
||||
def change_reply_willing_received(self, group_id: int, topic: str, is_mentioned_bot: bool, config, user_id: int = None, is_emoji: bool = False, interested_rate: float = 0) -> float:
|
||||
"""改变指定群组的回复意愿并返回回复概率"""
|
||||
|
||||
def change_reply_willing_received(self, group_id: int, topic: str, is_mentioned_bot: bool, config,
|
||||
user_id: int = None, is_emoji: bool = False, interested_rate: float = 0) -> float:
|
||||
|
||||
# 若非目标回复群组，则直接return
|
||||
if group_id not in config.talk_allowed_groups:
|
||||
reply_probability = 0
|
||||
return reply_probability
|
||||
|
||||
current_willing = self.group_reply_willing.get(group_id, 0)
|
||||
|
||||
# print(f"初始意愿: {current_willing}")
|
||||
if is_mentioned_bot and current_willing < 1.0:
|
||||
current_willing += 0.9
|
||||
print(f"被提及, 当前意愿: {current_willing}")
|
||||
elif is_mentioned_bot:
|
||||
current_willing += 0.05
|
||||
print(f"被重复提及, 当前意愿: {current_willing}")
|
||||
|
||||
|
||||
logger.debug(f"[{group_id}]的初始回复意愿: {current_willing}")
|
||||
|
||||
# 根据消息类型（被cue/表情包）调控
|
||||
if is_mentioned_bot:
|
||||
current_willing = min(
|
||||
3.0,
|
||||
current_willing + 0.9
|
||||
)
|
||||
logger.debug(f"被提及, 当前意愿: {current_willing}")
|
||||
|
||||
if is_emoji:
|
||||
current_willing *= 0.1
|
||||
print(f"表情包, 当前意愿: {current_willing}")
|
||||
|
||||
print(f"放大系数_interested_rate: {global_config.response_interested_rate_amplifier}")
|
||||
interested_rate *= global_config.response_interested_rate_amplifier #放大回复兴趣度
|
||||
if interested_rate > 0.4:
|
||||
# print(f"兴趣度: {interested_rate}, 当前意愿: {current_willing}")
|
||||
current_willing += interested_rate-0.4
|
||||
|
||||
current_willing *= global_config.response_willing_amplifier #放大回复意愿
|
||||
# print(f"放大系数_willing: {global_config.response_willing_amplifier}, 当前意愿: {current_willing}")
|
||||
|
||||
reply_probability = max((current_willing - 0.45) * 2, 0)
|
||||
if group_id not in config.talk_allowed_groups:
|
||||
current_willing = 0
|
||||
reply_probability = 0
|
||||
|
||||
logger.debug(f"表情包, 当前意愿: {current_willing}")
|
||||
|
||||
# 兴趣放大系数，若兴趣 > 0.4则增加回复概率
|
||||
interested_rate_amplifier = global_config.response_interested_rate_amplifier
|
||||
logger.debug(f"放大系数_interested_rate: {interested_rate_amplifier}")
|
||||
interested_rate *= interested_rate_amplifier
|
||||
|
||||
current_willing += max(
|
||||
0.0,
|
||||
interested_rate - 0.4
|
||||
)
|
||||
|
||||
# 回复意愿系数调控，独立乘区
|
||||
willing_amplifier = max(
|
||||
global_config.response_willing_amplifier,
|
||||
self.min_reply_willing
|
||||
)
|
||||
current_willing *= willing_amplifier
|
||||
logger.debug(f"放大系数_willing: {global_config.response_willing_amplifier}, 当前意愿: {current_willing}")
|
||||
|
||||
# 回复概率迭代，保底0.01回复概率
|
||||
reply_probability = max(
|
||||
(current_willing - 0.45) * 2,
|
||||
self.min_reply_willing
|
||||
)
|
||||
|
||||
# 降低目标低频群组回复概率
|
||||
down_frequency_rate = max(
|
||||
1.0,
|
||||
global_config.down_frequency_rate
|
||||
)
|
||||
if group_id in config.talk_frequency_down_groups:
|
||||
reply_probability = reply_probability / global_config.down_frequency_rate
|
||||
reply_probability = reply_probability / down_frequency_rate
|
||||
|
||||
reply_probability = min(reply_probability, 1)
|
||||
if reply_probability < 0:
|
||||
reply_probability = 0
|
||||
|
||||
|
||||
|
||||
self.group_reply_willing[group_id] = min(current_willing, 3.0)
|
||||
logger.debug(f"当前群组{group_id}回复概率：{reply_probability}")
|
||||
return reply_probability
|
||||
|
||||
|
||||
def change_reply_willing_sent(self, group_id: int):
|
||||
"""开始思考后降低群组的回复意愿"""
|
||||
current_willing = self.group_reply_willing.get(group_id, 0)
|
||||
self.group_reply_willing[group_id] = max(0, current_willing - 2)
|
||||
|
||||
|
||||
def change_reply_willing_after_sent(self, group_id: int):
|
||||
"""发送消息后提高群组的回复意愿"""
|
||||
current_willing = self.group_reply_willing.get(group_id, 0)
|
||||
if current_willing < 1:
|
||||
self.group_reply_willing[group_id] = min(1, current_willing + 0.2)
|
||||
|
||||
|
||||
async def ensure_started(self):
|
||||
"""确保衰减任务已启动"""
|
||||
if not self._started:
|
||||
@@ -82,5 +109,6 @@ class WillingManager:
|
||||
self._decay_task = asyncio.create_task(self._decay_reply_willing())
|
||||
self._started = True
|
||||
|
||||
|
||||
# 创建全局实例
|
||||
willing_manager = WillingManager()
|
||||
willing_manager = WillingManager()
|
||||
|
||||
Reference in New Issue
Block a user