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Merge pull request #155 from KX76/fix/20250310-logger-optimize

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Fix/20250310 logger optimize
This commit is contained in:
HYY
2025-03-10 11:51:48 +08:00
committed by GitHub
parent 5c7881133e a43f9495ea
commit b039bd3abc
23 changed files with 834 additions and 798 deletions
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3
bot.py
View File

@@ -100,7 +100,7 @@ def load_logger():
"#777777>|</> <cyan>{name:.<8}</cyan>:<cyan>{function:.<8}</cyan>:<cyan>{line: >4}</cyan> <fg "
"#777777>-</> <level>{message}</level>",
colorize=True,
level=os.getenv("LOG_LEVEL", "INFO") # 根据环境设置日志级别，默认为INFO
level=os.getenv("LOG_LEVEL", "DEBUG") # 根据环境设置日志级别，默认为INFO
)
@@ -149,6 +149,7 @@ if __name__ == "__main__":
init_config()
init_env()
load_env()
load_logger()
env_config = {key: os.getenv(key) for key in os.environ}
scan_provider(env_config)

49
src/gui/reasoning_gui.py
View File

@@ -5,6 +5,9 @@ import threading
import time
from datetime import datetime
from typing import Dict, List
from loguru import logger
from typing import Optional
from pymongo import MongoClient
import customtkinter as ctk
from dotenv import load_dotenv
@@ -17,23 +20,20 @@ root_dir = os.path.abspath(os.path.join(current_dir, '..', '..'))
# 加载环境变量
if os.path.exists(os.path.join(root_dir, '.env.dev')):
load_dotenv(os.path.join(root_dir, '.env.dev'))
print("成功加载开发环境配置")
logger.info("成功加载开发环境配置")
elif os.path.exists(os.path.join(root_dir, '.env.prod')):
load_dotenv(os.path.join(root_dir, '.env.prod'))
print("成功加载生产环境配置")
logger.info("成功加载生产环境配置")
else:
print("未找到环境配置文件")
logger.error("未找到环境配置文件")
sys.exit(1)
from typing import Optional
from pymongo import MongoClient
class Database:
_instance: Optional["Database"] = None
def __init__(self, host: str, port: int, db_name: str, username: str = None, password: str = None, auth_source: str = None):
def __init__(self, host: str, port: int, db_name: str, username: str = None, password: str = None,
auth_source: str = None):
if username and password:
self.client = MongoClient(
host=host,
@@ -47,7 +47,8 @@ class Database:
self.db = self.client[db_name]
@classmethod
def initialize(cls, host: str, port: int, db_name: str, username: str = None, password: str = None, auth_source: str = None) -> "Database":
def initialize(cls, host: str, port: int, db_name: str, username: str = None, password: str = None,
auth_source: str = None) -> "Database":
if cls._instance is None:
cls._instance = cls(host, port, db_name, username, password, auth_source)
return cls._instance
@@ -59,12 +60,11 @@ class Database:
return cls._instance
class ReasoningGUI:
def __init__(self):
# 记录启动时间戳，转换为Unix时间戳
self.start_timestamp = datetime.now().timestamp()
print(f"程序启动时间戳: {self.start_timestamp}")
logger.info(f"程序启动时间戳: {self.start_timestamp}")
# 设置主题
ctk.set_appearance_mode("dark")
@@ -79,15 +79,15 @@ class ReasoningGUI:
# 初始化数据库连接
try:
self.db = Database.get_instance().db
print("数据库连接成功")
logger.success("数据库连接成功")
except RuntimeError:
print("数据库未初始化，正在尝试初始化...")
logger.warning("数据库未初始化，正在尝试初始化...")
try:
Database.initialize("127.0.0.1", 27017, "maimai_bot")
self.db = Database.get_instance().db
print("数据库初始化成功")
except Exception as e:
print(f"数据库初始化失败: {e}")
logger.success("数据库初始化成功")
except Exception:
logger.exception(f"数据库初始化失败")
sys.exit(1)
# 存储群组数据
@@ -285,12 +285,12 @@ class ReasoningGUI:
try:
# 从数据库获取最新数据，只获取启动时间之后的记录
query = {"time": {"$gt": self.start_timestamp}}
print(f"查询条件: {query}")
logger.debug(f"查询条件: {query}")
# 先获取一条记录检查时间格式
sample = self.db.reasoning_logs.find_one()
if sample:
print(f"样本记录时间格式: {type(sample['time'])} 值: {sample['time']}")
logger.debug(f"样本记录时间格式: {type(sample['time'])} 值: {sample['time']}")
cursor = self.db.reasoning_logs.find(query).sort("time", -1)
new_data = {}
@@ -299,7 +299,7 @@ class ReasoningGUI:
for item in cursor:
# 调试输出
if total_count == 0:
print(f"记录时间: {item['time']}, 类型: {type(item['time'])}")
logger.debug(f"记录时间: {item['time']}, 类型: {type(item['time'])}")
total_count += 1
group_id = str(item.get('group_id', 'unknown'))
@@ -312,7 +312,7 @@ class ReasoningGUI:
elif isinstance(item['time'], datetime):
time_obj = item['time']
else:
print(f"未知的时间格式: {type(item['time'])}")
logger.warning(f"未知的时间格式: {type(item['time'])}")
time_obj = datetime.now() # 使用当前时间作为后备
new_data[group_id].append({
@@ -325,12 +325,12 @@ class ReasoningGUI:
'prompt': item.get('prompt', '') # 添加prompt字段
})
print(f"从数据库加载了 {total_count} 条记录，分布在 {len(new_data)} 个群组中")
logger.info(f"从数据库加载了 {total_count} 条记录，分布在 {len(new_data)} 个群组中")
# 更新数据
if new_data != self.group_data:
self.group_data = new_data
print("数据已更新，正在刷新显示...")
logger.info("数据已更新，正在刷新显示...")
# 将更新任务添加到队列
self.update_queue.put({'type': 'update_group_list'})
if self.group_data:
@@ -341,8 +341,8 @@ class ReasoningGUI:
'type': 'update_display',
'group_id': self.selected_group_id
})
except Exception as e:
print(f"自动更新出错: {e}")
except Exception:
logger.exception(f"自动更新出错")
# 每5秒更新一次
time.sleep(5)
@@ -371,6 +371,5 @@ def main():
app.run()
if __name__ == "__main__":
main()

41
src/plugins/chat/__init__.py
View File

@@ -2,9 +2,8 @@ import asyncio
import time
from loguru import logger
from nonebot import get_driver, on_command, on_message, require
from nonebot import get_driver, on_message, require
from nonebot.adapters.onebot.v11 import Bot, GroupMessageEvent, Message, MessageSegment
from nonebot.rule import to_me
from nonebot.typing import T_State
from ...common.database import Database
@@ -16,6 +15,10 @@ from .config import global_config
from .emoji_manager import emoji_manager
from .relationship_manager import relationship_manager
from .willing_manager import willing_manager
from ..memory_system.memory import hippocampus, memory_graph
from .bot import ChatBot
from .message_sender import message_manager, message_sender
# 创建LLM统计实例
llm_stats = LLMStatistics("llm_statistics.txt")
@@ -35,19 +38,13 @@ Database.initialize(
password=config.MONGODB_PASSWORD,
auth_source=config.MONGODB_AUTH_SOURCE
)
print("\033[1;32m[初始化数据库完成]\033[0m")
logger.success("初始化数据库成功")
# 导入其他模块
from ..memory_system.memory import hippocampus, memory_graph
from .bot import ChatBot
# from .message_send_control import message_sender
from .message_sender import message_manager, message_sender
# 初始化表情管理器
emoji_manager.initialize()
print(f"\033[1;32m正在唤醒{global_config.BOT_NICKNAME}......\033[0m")
logger.debug(f"正在唤醒{global_config.BOT_NICKNAME}......")
# 创建机器人实例
chat_bot = ChatBot()
# 注册群消息处理器
@@ -61,12 +58,12 @@ async def start_background_tasks():
"""启动后台任务"""
# 启动LLM统计
llm_stats.start()
logger.success("[初始化]LLM统计功能已启动")
logger.success("LLM统计功能启动成功")
# 初始化并启动情绪管理器
mood_manager = MoodManager.get_instance()
mood_manager.start_mood_update(update_interval=global_config.mood_update_interval)
logger.success("[初始化]情绪管理器已启动")
logger.success("情绪管理器启动成功")
# 只启动表情包管理任务
asyncio.create_task(emoji_manager.start_periodic_check(interval_MINS=global_config.EMOJI_CHECK_INTERVAL))
@@ -77,7 +74,7 @@ async def start_background_tasks():
@driver.on_startup
async def init_relationships():
"""在 NoneBot2 启动时初始化关系管理器"""
print("\033[1;32m[初始化]\033[0m 正在加载用户关系数据...")
logger.debug("正在加载用户关系数据...")
await relationship_manager.load_all_relationships()
asyncio.create_task(relationship_manager._start_relationship_manager())
@@ -86,19 +83,19 @@ async def init_relationships():
async def _(bot: Bot):
"""Bot连接成功时的处理"""
global _message_manager_started
print(f"\033[1;38;5;208m-----------{global_config.BOT_NICKNAME}成功连接！-----------\033[0m")
logger.debug(f"-----------{global_config.BOT_NICKNAME}成功连接！-----------")
await willing_manager.ensure_started()
message_sender.set_bot(bot)
print("\033[1;38;5;208m-----------消息发送器已启动！-----------\033[0m")
logger.success("-----------消息发送器已启动！-----------")
if not _message_manager_started:
asyncio.create_task(message_manager.start_processor())
_message_manager_started = True
print("\033[1;38;5;208m-----------消息处理器已启动！-----------\033[0m")
logger.success("-----------消息处理器已启动！-----------")
asyncio.create_task(emoji_manager._periodic_scan(interval_MINS=global_config.EMOJI_REGISTER_INTERVAL))
print("\033[1;38;5;208m-----------开始偷表情包！-----------\033[0m")
logger.success("-----------开始偷表情包！-----------")
@group_msg.handle()
@@ -110,13 +107,15 @@ async def _(bot: Bot, event: GroupMessageEvent, state: T_State):
@scheduler.scheduled_job("interval", seconds=global_config.build_memory_interval, id="build_memory")
async def build_memory_task():
"""每build_memory_interval秒执行一次记忆构建"""
print(
"\033[1;32m[记忆构建]\033[0m -------------------------------------------开始构建记忆-------------------------------------------")
logger.debug(
"[记忆构建]"
"------------------------------------开始构建记忆--------------------------------------")
start_time = time.time()
await hippocampus.operation_build_memory(chat_size=20)
end_time = time.time()
print(
f"\033[1;32m[记忆构建]\033[0m -------------------------------------------记忆构建完成：耗时: {end_time - start_time:.2f} 秒-------------------------------------------")
logger.success(
f"[记忆构建]--------------------------记忆构建完成：耗时: {end_time - start_time:.2f} "
"秒-------------------------------------------")
@scheduler.scheduled_job("interval", seconds=global_config.forget_memory_interval, id="forget_memory")

28
src/plugins/chat/bot.py
View File

@@ -70,19 +70,19 @@ class ChatBot:
# 过滤词
for word in global_config.ban_words:
if word in message.detailed_plain_text:
logger.info(f"\033[1;32m[{message.group_name}]{message.user_nickname}:\033[0m {message.processed_plain_text}")
logger.info(f"\033[1;32m[过滤词识别]\033[0m 消息中含有{word}，filtered")
logger.info(
f"[{message.group_name}]{message.user_nickname}:{message.processed_plain_text}")
logger.info(f"[过滤词识别]消息中含有{word}，filtered")
return
current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(message.time))
# topic=await topic_identifier.identify_topic_llm(message.processed_plain_text)
topic = ''
interested_rate = 0
interested_rate = await hippocampus.memory_activate_value(message.processed_plain_text) / 100
print(f"\033[1;32m[记忆激活]\033[0m 对{message.processed_plain_text}的激活度:---------------------------------------{interested_rate}\n")
logger.debug(f"对{message.processed_plain_text}"
f"的激活度:{interested_rate}")
# logger.info(f"\033[1;32m[主题识别]\033[0m 使用{global_config.topic_extract}主题: {topic}")
await self.storage.store_message(message, topic[0] if topic else None)
@@ -99,14 +99,13 @@ class ChatBot:
)
current_willing = willing_manager.get_willing(event.group_id)
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")
logger.info(
f"[{current_time}][{message.group_name}]{message.user_nickname}:"
f"{message.processed_plain_text}[回复意愿:{current_willing:.2f}][概率:{reply_probability * 100:.1f}%]")
response = ""
if random() < reply_probability:
tinking_time_point = round(time.time(), 2)
think_id = 'mt' + str(tinking_time_point)
thinking_message = Message_Thinking(message=message, message_id=think_id)
@@ -130,12 +129,13 @@ class ChatBot:
# 如果找不到思考消息，直接返回
if not thinking_message:
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")
logger.warning(f"未找到对应的思考消息，可能已超时被移除")
return
# 记录开始思考的时间，避免从思考到回复的时间太久
thinking_start_time = thinking_message.thinking_start_time
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
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
# 计算打字时间，1是为了模拟打字，2是避免多条回复乱序
accu_typing_time = 0
@@ -206,7 +206,7 @@ class ChatBot:
await bot_message.initialize()
message_manager.add_message(bot_message)
emotion = await self.gpt._get_emotion_tags(raw_content)
print(f"为 '{response}' 获取到的情感标签为：{emotion}")
logger.debug(f"为 '{response}' 获取到的情感标签为：{emotion}")
valuedict = {
'happy': 0.5,
'angry': -1,
@@ -216,11 +216,13 @@ class ChatBot:
'fearful': -0.7,
'neutral': 0.1
}
await relationship_manager.update_relationship_value(message.user_id, relationship_value=valuedict[emotion[0]])
await relationship_manager.update_relationship_value(message.user_id,
relationship_value=valuedict[emotion[0]])
# 使用情绪管理器更新情绪
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()

8
src/plugins/chat/config.py
View File

@@ -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")

17
src/plugins/chat/cq_code.py
View File

@@ -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

24
src/plugins/chat/emoji_manager.py
View File

@@ -33,7 +33,9 @@ class EmojiManager:
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"""
@@ -50,7 +52,7 @@ 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"""
@@ -86,7 +88,7 @@ class EmojiManager:
{'$inc': {'usage_count': 1}}
)
except Exception as e:
logger.error(f"记录表情使用失败: {str(e)}")
logger.exception(f"记录表情使用失败")
async def get_emoji_for_text(self, text: str) -> Optional[str]:
"""根据文本内容获取相关表情包
@@ -157,7 +159,8 @@ class EmojiManager:
{'_id': selected_emoji['_id']},
{'$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')
@@ -215,7 +218,8 @@ 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)
@@ -260,17 +264,15 @@ class EmojiManager:
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
@@ -302,7 +304,7 @@ class EmojiManager:
# 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']}")
@@ -328,6 +330,6 @@ class EmojiManager:
await asyncio.sleep(interval_MINS * 60)
# 创建全局单例
emoji_manager = EmojiManager()

19
src/plugins/chat/llm_generator.py
View File

@@ -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

44
src/plugins/chat/message_sender.py
View File

@@ -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
@@ -13,6 +14,7 @@ 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
@@ -46,7 +48,6 @@ class Message_Sender:
# 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
@@ -59,14 +60,14 @@ 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
@@ -118,8 +119,8 @@ 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:
@@ -133,6 +134,7 @@ class MessageContainer:
class MessageManager:
"""管理所有群的消息容器"""
def __init__(self):
self.containers: Dict[int, MessageContainer] = {}
self.storage = MessageStorage()
@@ -162,19 +164,22 @@ class MessageManager:
# 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")
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)
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)
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]"
@@ -185,7 +190,7 @@ class MessageManager:
# 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
@@ -193,10 +198,12 @@ class MessageManager:
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)
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)
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:
@@ -205,9 +212,9 @@ class MessageManager:
# 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):
@@ -220,6 +227,7 @@ class MessageManager:
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

37
src/plugins/chat/prompt_builder.py
View File

@@ -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
@@ -16,8 +17,6 @@ class PromptBuilder:
self.activate_messages = ''
self.db = Database.get_instance()
async def _build_prompt(self,
message_txt: str,
sender_name: str = "某人",
@@ -47,12 +46,10 @@ class PromptBuilder:
# 开始构建prompt
# 心情
mood_manager = MoodManager.get_instance()
mood_prompt = mood_manager.get_prompt()
# 日程构建
current_date = time.strftime("%Y-%m-%d", time.localtime())
current_time = time.strftime("%H:%M:%S", time.localtime())
@@ -66,20 +63,21 @@ class PromptBuilder:
promt_info_prompt = ''
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()
@@ -101,14 +99,12 @@ class PromptBuilder:
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")
logger.info(f"回忆耗时: {(end_time - start_time):.3f}秒")
# 激活prompt构建
activate_prompt = ''
@@ -127,10 +123,9 @@ 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
probability_1 = global_config.PERSONALITY_1
@@ -187,7 +182,7 @@ class PromptBuilder:
return prompt, prompt_check_if_response
def _build_initiative_prompt_select(self,group_id):
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()
@@ -195,7 +190,9 @@ class PromptBuilder:
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}")
@@ -238,10 +235,9 @@ class PromptBuilder:
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):
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)
@@ -315,4 +311,5 @@ class PromptBuilder:
# 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()

14
src/plugins/chat/relationship_manager.py
View File

@@ -1,4 +1,5 @@
import asyncio
from loguru import logger
from typing import Optional
from ...common.database import Database
@@ -11,6 +12,7 @@ class Impression:
relationship_value: float = None
class Relationship:
user_id: int = None
# impression: Impression = None
@@ -41,8 +43,6 @@ class Relationship:
self.saved = kwargs.get('saved', False)
class RelationshipManager:
def __init__(self):
self.relationships: dict[int, Relationship] = {}
@@ -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
@@ -85,10 +86,9 @@ 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:
@@ -120,10 +120,10 @@ class RelationshipManager:
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")
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()

5
src/plugins/chat/storage.py
View File

@@ -2,6 +2,7 @@ from typing import Optional
from ...common.database import Database
from .message import Message
from loguru import logger
class MessageStorage:
@@ -43,7 +44,7 @@ class MessageStorage:
}
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}")
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

7
src/plugins/chat/topic_identifier.py
View File

@@ -4,10 +4,12 @@ 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
class TopicIdentifier:
def __init__(self):
self.llm_topic_judge = LLM_request(model=global_config.llm_topic_judge)
@@ -25,7 +27,7 @@ class TopicIdentifier:
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
@@ -35,7 +37,8 @@ class TopicIdentifier:
# 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()

15
src/plugins/chat/utils.py
View File

@@ -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
@@ -39,7 +40,7 @@ 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" % (
@@ -48,7 +49,7 @@ def db_message_to_str(message_dict: Dict) -> str:
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(
@@ -356,7 +355,7 @@ def process_llm_response(text: str) -> List[str]:
# 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

9
src/plugins/chat/willing_manager.py
View File

@@ -1,5 +1,6 @@
import asyncio
from .config import global_config
from loguru import logger
class WillingManager:
@@ -30,16 +31,16 @@ class WillingManager:
# 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}")
logger.info(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.info(f"被重复提及, 当前意愿: {current_willing}")
if is_emoji:
current_willing *= 0.1
print(f"表情包, 当前意愿: {current_willing}")
logger.info(f"表情包, 当前意愿: {current_willing}")
print(f"放大系数_interested_rate: {global_config.response_interested_rate_amplifier}")
logger.debug(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}")

25
src/plugins/memory_system/draw_memory.py
View File

@@ -7,6 +7,7 @@ import jieba
import matplotlib.pyplot as plt
import networkx as nx
from dotenv import load_dotenv
from loguru import logger
sys.path.append("C:/GitHub/MaiMBot") # 添加项目根目录到 Python 路径
from src.common.database import Database # 使用正确的导入语法
@@ -99,18 +100,20 @@ class Memory_graph:
# 返回所有节点对应的 Memory_dot 对象
return [self.get_dot(node) for node in self.G.nodes()]
def get_random_chat_from_db(self, length: int, timestamp: str):
# 从数据库中根据时间戳获取离其最近的聊天记录
chat_text = ''
closest_record = self.db.db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)]) # 调试输出
print(f"距离time最近的消息时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(closest_record['time'])))}")
logger.info(
f"距离time最近的消息时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(closest_record['time'])))}")
if closest_record:
closest_time = closest_record['time']
group_id = closest_record['group_id'] # 获取groupid
# 获取该时间戳之后的length条消息，且groupid相同
chat_record = list(self.db.db.messages.find({"time": {"$gt": closest_time}, "group_id": group_id}).sort('time', 1).limit(length))
chat_record = list(
self.db.db.messages.find({"time": {"$gt": closest_time}, "group_id": group_id}).sort('time', 1).limit(
length))
for record in chat_record:
time_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(record['time'])))
try:
@@ -179,30 +182,31 @@ def main():
break
first_layer_items, second_layer_items = memory_graph.get_related_item(query)
if first_layer_items or second_layer_items:
print("\n第一层记忆：")
logger.debug("第一层记忆：")
for item in first_layer_items:
print(item)
print("\n第二层记忆：")
logger.debug(item)
logger.debug("第二层记忆：")
for item in second_layer_items:
print(item)
logger.debug(item)
else:
print("未找到相关记忆。")
logger.debug("未找到相关记忆。")
def segment_text(text):
seg_text = list(jieba.cut(text))
return seg_text
def find_topic(text, topic_num):
prompt = f'这是一段文字：{text}。请你从这段话中总结出{topic_num}个话题，帮我列出来，用逗号隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要告诉我其他内容。'
return prompt
def topic_what(text, topic):
prompt = f'这是一段文字：{text}。我想知道这记忆里有什么关于{topic}的话题，帮我总结成一句自然的话，可以包含时间和人物。只输出这句话就好'
return prompt
def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = False):
# 设置中文字体
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
@@ -226,7 +230,7 @@ def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = Fal
# 如果过滤后没有节点，则返回
if len(H.nodes()) == 0:
print("过滤后没有符合条件的节点可显示")
logger.debug("过滤后没有符合条件的节点可显示")
return
# 保存图到本地
@@ -287,6 +291,5 @@ def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = Fal
plt.show()
if __name__ == "__main__":
main()

59
src/plugins/memory_system/memory.py
View File

@@ -7,6 +7,7 @@ import time
import jieba
import networkx as nx
from loguru import logger
from ...common.database import Database # 使用正确的导入语法
from ..chat.config import global_config
from ..chat.utils import (
@@ -223,17 +224,18 @@ class Hippocampus:
for msg in messages:
input_text += f"{msg['text']}\n"
print(input_text)
logger.debug(input_text)
topic_num = self.calculate_topic_num(input_text, compress_rate)
topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(input_text, topic_num))
# 过滤topics
filter_keywords = global_config.memory_ban_words
topics = [topic.strip() for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
topics = [topic.strip() for topic in
topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
filtered_topics = [topic for topic in topics if not any(keyword in topic for keyword in filter_keywords)]
print(f"过滤后话题: {filtered_topics}")
logger.info(f"过滤后话题: {filtered_topics}")
# 创建所有话题的请求任务
tasks = []
@@ -257,7 +259,9 @@ class Hippocampus:
topic_by_length = text.count('\n') * compress_rate
topic_by_information_content = max(1, min(5, int((information_content - 3) * 2)))
topic_num = int((topic_by_length + topic_by_information_content) / 2)
print(f"topic_by_length: {topic_by_length}, topic_by_information_content: {topic_by_information_content}, topic_num: {topic_num}")
logger.debug(
f"topic_by_length: {topic_by_length}, topic_by_information_content: {topic_by_information_content}, "
f"topic_num: {topic_num}")
return topic_num
async def operation_build_memory(self, chat_size=20):
@@ -272,22 +276,22 @@ class Hippocampus:
bar_length = 30
filled_length = int(bar_length * i // len(memory_sample))
bar = '█' * filled_length + '-' * (bar_length - filled_length)
print(f"\n进度: [{bar}] {progress:.1f}% ({i}/{len(memory_sample)})")
logger.debug(f"进度: [{bar}] {progress:.1f}% ({i}/{len(memory_sample)})")
# 生成压缩后记忆 ,表现为 (话题,记忆) 的元组
compressed_memory = set()
compress_rate = 0.1
compressed_memory = await self.memory_compress(input_text, compress_rate)
print(f"\033[1;33m压缩后记忆数量\033[0m: {len(compressed_memory)}")
logger.info(f"压缩后记忆数量: {len(compressed_memory)}")
# 将记忆加入到图谱中
for topic, memory in compressed_memory:
print(f"\033[1;32m添加节点\033[0m: {topic}")
logger.info(f"添加节点: {topic}")
self.memory_graph.add_dot(topic, memory)
all_topics.append(topic) # 收集所有话题
for i in range(len(all_topics)):
for j in range(i + 1, len(all_topics)):
print(f"\033[1;32m连接节点\033[0m: {all_topics[i]} 和 {all_topics[j]}")
logger.info(f"连接节点: {all_topics[i]} 和 {all_topics[j]}")
self.memory_graph.connect_dot(all_topics[i], all_topics[j])
self.sync_memory_to_db()
@@ -448,14 +452,14 @@ class Hippocampus:
removed_item = self.memory_graph.forget_topic(node)
if removed_item:
forgotten_nodes.append((node, removed_item))
print(f"遗忘节点 {node} 的记忆: {removed_item}")
logger.debug(f"遗忘节点 {node} 的记忆: {removed_item}")
# 同步到数据库
if forgotten_nodes:
self.sync_memory_to_db()
print(f"完成遗忘操作，共遗忘 {len(forgotten_nodes)} 个节点的记忆")
logger.debug(f"完成遗忘操作，共遗忘 {len(forgotten_nodes)} 个节点的记忆")
else:
print("本次检查没有节点满足遗忘条件")
logger.debug("本次检查没有节点满足遗忘条件")
async def merge_memory(self, topic):
"""
@@ -478,8 +482,8 @@ class Hippocampus:
# 拼接成文本
merged_text = "\n".join(selected_memories)
print(f"\n[合并记忆] 话题: {topic}")
print(f"选择的记忆:\n{merged_text}")
logger.debug(f"\n[合并记忆] 话题: {topic}")
logger.debug(f"选择的记忆:\n{merged_text}")
# 使用memory_compress生成新的压缩记忆
compressed_memories = await self.memory_compress(selected_memories, 0.1)
@@ -491,11 +495,11 @@ class Hippocampus:
# 添加新的压缩记忆
for _, compressed_memory in compressed_memories:
memory_items.append(compressed_memory)
print(f"添加压缩记忆: {compressed_memory}")
logger.info(f"添加压缩记忆: {compressed_memory}")
# 更新节点的记忆项
self.memory_graph.G.nodes[topic]['memory_items'] = memory_items
print(f"完成记忆合并，当前记忆数量: {len(memory_items)}")
logger.debug(f"完成记忆合并，当前记忆数量: {len(memory_items)}")
async def operation_merge_memory(self, percentage=0.1):
"""
@@ -521,16 +525,16 @@ class Hippocampus:
# 如果内容数量超过100，进行合并
if content_count > 100:
print(f"\n检查节点: {node}, 当前记忆数量: {content_count}")
logger.debug(f"检查节点: {node}, 当前记忆数量: {content_count}")
await self.merge_memory(node)
merged_nodes.append(node)
# 同步到数据库
if merged_nodes:
self.sync_memory_to_db()
print(f"\n完成记忆合并操作，共处理 {len(merged_nodes)} 个节点")
logger.debug(f"完成记忆合并操作，共处理 {len(merged_nodes)} 个节点")
else:
print("\n本次检查没有需要合并的节点")
logger.debug("本次检查没有需要合并的节点")
def find_topic_llm(self, text, topic_num):
prompt = f'这是一段文字：{text}。请你从这段话中总结出{topic_num}个关键的概念，可以是名词，动词，或者特定人物，帮我列出来，用逗号,隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要有序号，不要告诉我其他内容。'
@@ -551,7 +555,8 @@ class Hippocampus:
"""
topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, 5))
# print(f"话题: {topics_response[0]}")
topics = [topic.strip() for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
topics = [topic.strip() for topic in
topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
# print(f"话题: {topics}")
return topics
@@ -624,7 +629,7 @@ class Hippocampus:
async def memory_activate_value(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.3) -> int:
"""计算输入文本对记忆的激活程度"""
print(f"\033[1;32m[记忆激活]\033[0m 识别主题: {await self._identify_topics(text)}")
logger.info(f"识别主题: {await self._identify_topics(text)}")
# 识别主题
identified_topics = await self._identify_topics(text)
@@ -655,7 +660,8 @@ class Hippocampus:
penalty = 1.0 / (1 + math.log(content_count + 1))
activation = int(score * 50 * penalty)
print(f"\033[1;32m[记忆激活]\033[0m 单主题「{topic}」- 相似度: {score:.3f}, 内容数: {content_count}, 激活值: {activation}")
logger.info(
f"[记忆激活]单主题「{topic}」- 相似度: {score:.3f}, 内容数: {content_count}, 激活值: {activation}")
return activation
# 计算关键词匹配率，同时考虑内容数量
@@ -682,7 +688,8 @@ class Hippocampus:
matched_topics.add(input_topic)
adjusted_sim = sim * penalty
topic_similarities[input_topic] = max(topic_similarities.get(input_topic, 0), adjusted_sim)
print(f"\033[1;32m[记忆激活]\033[0m 主题「{input_topic}」-> 「{memory_topic}」(内容数: {content_count}, 相似度: {adjusted_sim:.3f})")
logger.info(
f"[记忆激活]主题「{input_topic}」-> 「{memory_topic}」(内容数: {content_count}, 相似度: {adjusted_sim:.3f})")
# 计算主题匹配率和平均相似度
topic_match = len(matched_topics) / len(identified_topics)
@@ -690,11 +697,13 @@ class Hippocampus:
# 计算最终激活值
activation = int((topic_match + average_similarities) / 2 * 100)
print(f"\033[1;32m[记忆激活]\033[0m 匹配率: {topic_match:.3f}, 平均相似度: {average_similarities:.3f}, 激活值: {activation}")
logger.info(
f"[记忆激活]匹配率: {topic_match:.3f}, 平均相似度: {average_similarities:.3f}, 激活值: {activation}")
return activation
async def get_relevant_memories(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.4, max_memory_num: int = 5) -> list:
async def get_relevant_memories(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.4,
max_memory_num: int = 5) -> list:
"""根据输入文本获取相关的记忆内容"""
# 识别主题
identified_topics = await self._identify_topics(text)
@@ -764,4 +773,4 @@ hippocampus = Hippocampus(memory_graph)
hippocampus.sync_memory_from_db()
end_time = time.time()
print(f"\033[32m[加载海马体耗时: {end_time - start_time:.2f} 秒]\033[0m")
logger.success(f"加载海马体耗时: {end_time - start_time:.2f} 秒")

2
src/plugins/memory_system/memory_manual_build.py
View File

@@ -743,7 +743,7 @@ class Hippocampus:
async def memory_activate_value(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.3) -> int:
"""计算输入文本对记忆的激活程度"""
print(f"\033[1;32m[记忆激活]\033[0m 识别主题: {await self._identify_topics(text)}")
logger.info(f"[记忆激活]识别主题: {await self._identify_topics(text)}")
identified_topics = await self._identify_topics(text)
if not identified_topics:

38
src/plugins/models/utils_model.py
View File

@@ -45,7 +45,7 @@ class LLM_request:
self.db.db.llm_usage.create_index([("user_id", 1)])
self.db.db.llm_usage.create_index([("request_type", 1)])
except Exception as e:
logger.error(f"创建数据库索引失败: {e}")
logger.error(f"创建数据库索引失败")
def _record_usage(self, prompt_tokens: int, completion_tokens: int, total_tokens: int,
user_id: str = "system", request_type: str = "chat",
@@ -79,8 +79,8 @@ class LLM_request:
f"提示词: {prompt_tokens}, 完成: {completion_tokens}, "
f"总计: {total_tokens}"
)
except Exception as e:
logger.error(f"记录token使用情况失败: {e}")
except Exception:
logger.error(f"记录token使用情况失败")
def _calculate_cost(self, prompt_tokens: int, completion_tokens: int) -> float:
"""计算API调用成本
@@ -143,9 +143,9 @@ class LLM_request:
# 判断是否为流式
stream_mode = self.params.get("stream", False)
if self.params.get("stream", False) is True:
logger.info(f"进入流式输出模式，发送请求到URL: {api_url}")
logger.debug(f"进入流式输出模式，发送请求到URL: {api_url}")
else:
logger.info(f"发送请求到URL: {api_url}")
logger.debug(f"发送请求到URL: {api_url}")
logger.info(f"使用模型: {self.model_name}")
# 构建请求体
@@ -184,13 +184,15 @@ class LLM_request:
logger.error(f"错误码: {response.status} - {error_code_mapping.get(response.status)}")
if response.status == 403:
# 尝试降级Pro模型
if self.model_name.startswith("Pro/") and self.base_url == "https://api.siliconflow.cn/v1/":
if self.model_name.startswith(
"Pro/") and self.base_url == "https://api.siliconflow.cn/v1/":
old_model_name = self.model_name
self.model_name = self.model_name[4:] # 移除"Pro/"前缀
logger.warning(f"检测到403错误，模型从 {old_model_name} 降级为 {self.model_name}")
# 对全局配置进行更新
if hasattr(global_config, 'llm_normal') and global_config.llm_normal.get('name') == old_model_name:
if hasattr(global_config, 'llm_normal') and global_config.llm_normal.get(
'name') == old_model_name:
global_config.llm_normal['name'] = self.model_name
logger.warning(f"已将全局配置中的 llm_normal 模型降级")
@@ -224,8 +226,8 @@ class LLM_request:
if delta_content is None:
delta_content = ""
accumulated_content += delta_content
except Exception as e:
logger.error(f"解析流式输出错误: {e}")
except Exception:
logger.exception(f"解析流式输出错")
content = accumulated_content
reasoning_content = ""
think_match = re.search(r'<think>(.*?)</think>', content, re.DOTALL)
@@ -233,12 +235,15 @@ class LLM_request:
reasoning_content = think_match.group(1).strip()
content = re.sub(r'<think>.*?</think>', '', content, flags=re.DOTALL).strip()
# 构造一个伪result以便调用自定义响应处理器或默认处理器
result = {"choices": [{"message": {"content": content, "reasoning_content": reasoning_content}}]}
return response_handler(result) if response_handler else self._default_response_handler(result, user_id, request_type, endpoint)
result = {
"choices": [{"message": {"content": content, "reasoning_content": reasoning_content}}]}
return response_handler(result) if response_handler else self._default_response_handler(
result, user_id, request_type, endpoint)
else:
result = await response.json()
# 使用自定义处理器或默认处理
return response_handler(result) if response_handler else self._default_response_handler(result, user_id, request_type, endpoint)
return response_handler(result) if response_handler else self._default_response_handler(
result, user_id, request_type, endpoint)
except Exception as e:
if retry < policy["max_retries"] - 1:
@@ -262,7 +267,8 @@ class LLM_request:
# 复制一份参数，避免直接修改原始数据
new_params = dict(params)
# 定义需要转换的模型列表
models_needing_transformation = ["o3-mini", "o1-mini", "o1-preview", "o1-2024-12-17", "o1-preview-2024-09-12", "o3-mini-2025-01-31", "o1-mini-2024-09-12"]
models_needing_transformation = ["o3-mini", "o1-mini", "o1-preview", "o1-2024-12-17", "o1-preview-2024-09-12",
"o3-mini-2025-01-31", "o1-mini-2024-09-12"]
if self.model_name.lower() in models_needing_transformation:
# 删除 'temprature' 参数（如果存在）
new_params.pop("temperature", None)
@@ -298,11 +304,11 @@ class LLM_request:
**params_copy
}
# 如果 payload 中依然存在 max_tokens 且需要转换，在这里进行再次检查
if self.model_name.lower() in ["o3-mini", "o1-mini", "o1-preview", "o1-2024-12-17", "o1-preview-2024-09-12", "o3-mini-2025-01-31", "o1-mini-2024-09-12"] and "max_tokens" in payload:
if self.model_name.lower() in ["o3-mini", "o1-mini", "o1-preview", "o1-2024-12-17", "o1-preview-2024-09-12",
"o3-mini-2025-01-31", "o1-mini-2024-09-12"] and "max_tokens" in payload:
payload["max_completion_tokens"] = payload.pop("max_tokens")
return payload
def _default_response_handler(self, result: dict, user_id: str = "system",
request_type: str = "chat", endpoint: str = "/chat/completions") -> Tuple:
"""默认响应解析"""
@@ -404,6 +410,7 @@ class LLM_request:
Returns:
list: embedding向量，如果失败则返回None
"""
def embedding_handler(result):
"""处理响应"""
if "data" in result and len(result["data"]) > 0:
@@ -425,4 +432,3 @@ class LLM_request:
response_handler=embedding_handler
)
return embedding

4
src/plugins/moods/moods.py
View File

@@ -4,7 +4,7 @@ import time
from dataclasses import dataclass
from ..chat.config import global_config
from loguru import logger
@dataclass
class MoodState:
@@ -210,7 +210,7 @@ class MoodManager:
def print_mood_status(self) -> None:
"""打印当前情绪状态"""
print(f"\033[1;35m[情绪状态]\033[0m 愉悦度: {self.current_mood.valence:.2f}, "
logger.info(f"[情绪状态]愉悦度: {self.current_mood.valence:.2f}, "
f"唤醒度: {self.current_mood.arousal:.2f}, "
f"心情: {self.current_mood.text}")

12
src/plugins/schedule/schedule_generator.py
View File

@@ -57,12 +57,12 @@ class ScheduleGenerator:
existing_schedule = self.db.db.schedule.find_one({"date": date_str})
if existing_schedule:
logger.info(f"{date_str}的日程已存在:")
logger.debug(f"{date_str}的日程已存在:")
schedule_text = existing_schedule["schedule"]
# print(self.schedule_text)
elif read_only == False:
logger.info(f"{date_str}的日程不存在，准备生成新的日程。")
elif not read_only:
logger.debug(f"{date_str}的日程不存在，准备生成新的日程。")
prompt = f"""我是{global_config.BOT_NICKNAME}，{global_config.PROMPT_SCHEDULE_GEN}，请为我生成{date_str}（{weekday}）的日程安排，包括：""" + \
"""
1. 早上的学习和工作安排
@@ -78,7 +78,7 @@ class ScheduleGenerator:
schedule_text = "生成日程时出错了"
# print(self.schedule_text)
else:
logger.info(f"{date_str}的日程不存在。")
logger.debug(f"{date_str}的日程不存在。")
schedule_text = "忘了"
return schedule_text, None
@@ -154,10 +154,10 @@ class ScheduleGenerator:
logger.warning("今日日程有误，将在下次运行时重新生成")
self.db.db.schedule.delete_one({"date": datetime.datetime.now().strftime("%Y-%m-%d")})
else:
logger.info("\n=== 今日日程安排 ===")
logger.info("=== 今日日程安排 ===")
for time_str, activity in self.today_schedule.items():
logger.info(f"时间[{time_str}]: 活动[{activity}]")
logger.info("==================\n")
logger.info("==================")
# def main():

5
src/plugins/utils/statistic.py
View File

@@ -3,6 +3,7 @@ import time
from collections import defaultdict
from datetime import datetime, timedelta
from typing import Any, Dict
from loguru import logger
from ...common.database import Database
@@ -153,8 +154,8 @@ class LLMStatistics:
try:
all_stats = self._collect_all_statistics()
self._save_statistics(all_stats)
except Exception as e:
print(f"\033[1;31m[错误]\033[0m 统计数据处理失败: {e}")
except Exception:
logger.exception(f"统计数据处理失败")
# 等待1分钟
for _ in range(60):

19
src/test/typo.py
View File

@@ -11,6 +11,8 @@ from pathlib import Path
import random
import math
import time
from loguru import logger
class ChineseTypoGenerator:
def __init__(self,
@@ -36,7 +38,7 @@ class ChineseTypoGenerator:
self.max_freq_diff = max_freq_diff
# 加载数据
print("正在加载汉字数据库，请稍候...")
logger.debug("正在加载汉字数据库，请稍候...")
self.pinyin_dict = self._create_pinyin_dict()
self.char_frequency = self._load_or_create_char_frequency()
@@ -399,9 +401,10 @@ class ChineseTypoGenerator:
for key, value in kwargs.items():
if hasattr(self, key):
setattr(self, key, value)
print(f"参数 {key} 已设置为 {value}")
logger.debug(f"参数 {key} 已设置为 {value}")
else:
print(f"警告: 参数 {key} 不存在")
logger.warning(f"警告: 参数 {key} 不存在")
def main():
# 创建错别字生成器实例
@@ -420,18 +423,18 @@ def main():
typo_sentence, typo_info = typo_generator.create_typo_sentence(sentence)
# 打印结果
print("\n原句：", sentence)
print("错字版：", typo_sentence)
logger.debug("原句：", sentence)
logger.debug("错字版：", typo_sentence)
# 打印错别字信息
if typo_info:
print("\n错别字信息：")
print(typo_generator.format_typo_info(typo_info))
logger.debug(f"错别字信息：{typo_generator.format_typo_info(typo_info)})")
# 计算并打印总耗时
end_time = time.time()
total_time = end_time - start_time
print(f"\n总耗时：{total_time:.2f}秒")
logger.debug(f"总耗时：{total_time:.2f}秒")
if __name__ == "__main__":
main()