mirror of https://github.com/Mai-with-u/MaiBot.git
Merge branch 'MaiM-with-u:main-fix' into main-fix
Bakadax
GitHub
commit
b42f8eed9c
|
|
@ -75,22 +75,22 @@ conda activate maimbot
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|||
pip install -r requirements.txt
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||||
```
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||||
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### 2️⃣ **然后你需要启动MongoDB数据库，来存储信息**
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### 3️⃣ **然后你需要启动MongoDB数据库，来存储信息**
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- 安装并启动MongoDB服务
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- 默认连接本地27017端口
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### 3️⃣ **配置NapCat，让麦麦bot与qq取得联系**
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### 4️⃣ **配置NapCat，让麦麦bot与qq取得联系**
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- 安装并登录NapCat（用你的qq小号）
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- 添加反向WS: `ws://127.0.0.1:8080/onebot/v11/ws`
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### 4️⃣ **配置文件设置，让麦麦Bot正常工作**
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### 5️⃣ **配置文件设置，让麦麦Bot正常工作**
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- 修改环境配置文件：`.env.prod`
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- 修改机器人配置文件：`bot_config.toml`
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### 5️⃣ **启动麦麦机器人**
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### 6️⃣ **启动麦麦机器人**
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- 打开命令行，cd到对应路径
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@ -104,7 +104,7 @@ nb run
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python bot.py
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```
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### 6️⃣ **其他组件(可选)**
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### 7️⃣ **其他组件(可选)**
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- `run_thingking.bat`: 启动可视化推理界面（未完善）
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- 直接运行 knowledge.py生成知识库
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|
|
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@ -86,6 +86,25 @@ MEMORY_STYLE_CONFIG = {
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},
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}
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#MOOD
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MOOD_STYLE_CONFIG = {
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"advanced": {
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"console_format": (
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"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
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"<level>{level: <8}</level> | "
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"<cyan>{extra[module]: <12}</cyan> | "
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"<light-green>心情</light-green> | "
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"<level>{message}</level>"
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),
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"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 心情 | {message}"),
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},
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"simple": {
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"console_format": ("<green>{time:MM-DD HH:mm}</green> | <light-green>心情</light-green> | {message}"),
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"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 心情 | {message}"),
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},
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}
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SENDER_STYLE_CONFIG = {
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"advanced": {
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"console_format": (
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@ -163,7 +182,7 @@ TOPIC_STYLE_CONFIG = TOPIC_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else TOPIC_ST
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SENDER_STYLE_CONFIG = SENDER_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else SENDER_STYLE_CONFIG["advanced"]
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LLM_STYLE_CONFIG = LLM_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else LLM_STYLE_CONFIG["advanced"]
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CHAT_STYLE_CONFIG = CHAT_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else CHAT_STYLE_CONFIG["advanced"]
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MOOD_STYLE_CONFIG = MOOD_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else MOOD_STYLE_CONFIG["advanced"]
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def is_registered_module(record: dict) -> bool:
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"""检查是否为已注册的模块"""
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|
|
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@ -150,7 +150,7 @@ async def merge_memory_task():
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# print("\033[1;32m[记忆整合]\033[0m 记忆整合完成")
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@scheduler.scheduled_job("interval", seconds=30, id="print_mood")
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@scheduler.scheduled_job("interval", seconds=15, id="print_mood")
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async def print_mood_task():
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"""每30秒打印一次情绪状态"""
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mood_manager = MoodManager.get_instance()
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|
|
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@ -4,9 +4,14 @@ import time
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from dataclasses import dataclass
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from ..chat.config import global_config
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from src.common.logger import get_module_logger
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from src.common.logger import get_module_logger, LogConfig, MOOD_STYLE_CONFIG
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logger = get_module_logger("mood_manager")
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mood_config = LogConfig(
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# 使用海马体专用样式
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console_format=MOOD_STYLE_CONFIG["console_format"],
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file_format=MOOD_STYLE_CONFIG["file_format"],
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)
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logger = get_module_logger("mood_manager", config=mood_config)
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@dataclass
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|
|
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@ -54,7 +54,9 @@ def send_heartbeat(server_url, client_id):
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sys = platform.system()
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try:
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headers = {"Client-ID": client_id, "User-Agent": f"HeartbeatClient/{client_id[:8]}"}
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data = json.dumps({"system": sys})
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data = json.dumps(
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{"system": sys, "Version": global_config.MAI_VERSION},
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)
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response = requests.post(f"{server_url}/api/clients", headers=headers, data=data)
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if response.status_code == 201:
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@ -92,9 +94,9 @@ class HeartbeatThread(threading.Thread):
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logger.info(f"{self.interval}秒后发送下一次心跳...")
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else:
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logger.info(f"{self.interval}秒后重试...")
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self.last_heartbeat_time = time.time()
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# 使用可中断的等待代替 sleep
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# 每秒检查一次是否应该停止或发送心跳
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remaining_wait = self.interval
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@ -104,7 +106,7 @@ class HeartbeatThread(threading.Thread):
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if self.stop_event.wait(wait_time):
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break # 如果事件被设置，立即退出等待
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remaining_wait -= wait_time
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# 检查是否由于外部原因导致间隔异常延长
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if time.time() - self.last_heartbeat_time >= self.interval * 1.5:
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logger.warning("检测到心跳间隔异常延长，立即发送心跳")
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|
|
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|
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@ -1,191 +0,0 @@
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import datetime
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import json
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import re
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import os
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import sys
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from typing import Dict, Union
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||||
|
||||
|
||||
# 添加项目根目录到 Python 路径
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||||
root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
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sys.path.append(root_path)
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|
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from src.common.database import db # noqa: E402
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from src.common.logger import get_module_logger # noqa: E402
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from src.plugins.schedule.offline_llm import LLMModel # noqa: E402
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from src.plugins.chat.config import global_config # noqa: E402
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||||
|
||||
logger = get_module_logger("scheduler")
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||||
|
||||
|
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class ScheduleGenerator:
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enable_output: bool = True
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|
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def __init__(self):
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# 使用离线LLM模型
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self.llm_scheduler = LLMModel(model_name="Pro/deepseek-ai/DeepSeek-V3", temperature=0.9)
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self.today_schedule_text = ""
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self.today_schedule = {}
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self.tomorrow_schedule_text = ""
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self.tomorrow_schedule = {}
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self.yesterday_schedule_text = ""
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||||
self.yesterday_schedule = {}
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async def initialize(self):
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today = datetime.datetime.now()
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tomorrow = datetime.datetime.now() + datetime.timedelta(days=1)
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yesterday = datetime.datetime.now() - datetime.timedelta(days=1)
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self.today_schedule_text, self.today_schedule = await self.generate_daily_schedule(target_date=today)
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self.tomorrow_schedule_text, self.tomorrow_schedule = await self.generate_daily_schedule(
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target_date=tomorrow, read_only=True
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)
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self.yesterday_schedule_text, self.yesterday_schedule = await self.generate_daily_schedule(
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target_date=yesterday, read_only=True
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)
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|
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async def generate_daily_schedule(
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self, target_date: datetime.datetime = None, read_only: bool = False
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) -> Dict[str, str]:
|
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date_str = target_date.strftime("%Y-%m-%d")
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weekday = target_date.strftime("%A")
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||||
|
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schedule_text = str
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existing_schedule = db.schedule.find_one({"date": date_str})
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if existing_schedule:
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if self.enable_output:
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logger.debug(f"{date_str}的日程已存在:")
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schedule_text = existing_schedule["schedule"]
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# print(self.schedule_text)
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elif not read_only:
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logger.debug(f"{date_str}的日程不存在，准备生成新的日程。")
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prompt = (
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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："""
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+ """
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1. 早上的学习和工作安排
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2. 下午的活动和任务
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||||
3. 晚上的计划和休息时间
|
||||
请按照时间顺序列出具体时间点和对应的活动，用一个时间点而不是时间段来表示时间，用JSON格式返回日程表，
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||||
仅返回内容，不要返回注释，不要添加任何markdown或代码块样式，时间采用24小时制，
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||||
格式为{"时间": "活动","时间": "活动",...}。"""
|
||||
)
|
||||
|
||||
try:
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||||
schedule_text, _ = self.llm_scheduler.generate_response(prompt)
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||||
db.schedule.insert_one({"date": date_str, "schedule": schedule_text})
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||||
self.enable_output = True
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||||
except Exception as e:
|
||||
logger.error(f"生成日程失败: {str(e)}")
|
||||
schedule_text = "生成日程时出错了"
|
||||
# print(self.schedule_text)
|
||||
else:
|
||||
if self.enable_output:
|
||||
logger.debug(f"{date_str}的日程不存在。")
|
||||
schedule_text = "忘了"
|
||||
|
||||
return schedule_text, None
|
||||
|
||||
schedule_form = self._parse_schedule(schedule_text)
|
||||
return schedule_text, schedule_form
|
||||
|
||||
def _parse_schedule(self, schedule_text: str) -> Union[bool, Dict[str, str]]:
|
||||
"""解析日程文本，转换为时间和活动的字典"""
|
||||
try:
|
||||
reg = r"\{(.|\r|\n)+\}"
|
||||
matched = re.search(reg, schedule_text)[0]
|
||||
schedule_dict = json.loads(matched)
|
||||
return schedule_dict
|
||||
except json.JSONDecodeError:
|
||||
logger.exception("解析日程失败: {}".format(schedule_text))
|
||||
return False
|
||||
|
||||
def _parse_time(self, time_str: str) -> str:
|
||||
"""解析时间字符串，转换为时间"""
|
||||
return datetime.datetime.strptime(time_str, "%H:%M")
|
||||
|
||||
def get_current_task(self) -> str:
|
||||
"""获取当前时间应该进行的任务"""
|
||||
current_time = datetime.datetime.now().strftime("%H:%M")
|
||||
|
||||
# 找到最接近当前时间的任务
|
||||
closest_time = None
|
||||
min_diff = float("inf")
|
||||
|
||||
# 检查今天的日程
|
||||
if not self.today_schedule:
|
||||
return "摸鱼"
|
||||
for time_str in self.today_schedule.keys():
|
||||
diff = abs(self._time_diff(current_time, time_str))
|
||||
if closest_time is None or diff < min_diff:
|
||||
closest_time = time_str
|
||||
min_diff = diff
|
||||
|
||||
# 检查昨天的日程中的晚间任务
|
||||
if self.yesterday_schedule:
|
||||
for time_str in self.yesterday_schedule.keys():
|
||||
if time_str >= "20:00": # 只考虑晚上8点之后的任务
|
||||
# 计算与昨天这个时间点的差异（需要加24小时）
|
||||
diff = abs(self._time_diff(current_time, time_str))
|
||||
if diff < min_diff:
|
||||
closest_time = time_str
|
||||
min_diff = diff
|
||||
return closest_time, self.yesterday_schedule[closest_time]
|
||||
|
||||
if closest_time:
|
||||
return closest_time, self.today_schedule[closest_time]
|
||||
return "摸鱼"
|
||||
|
||||
def _time_diff(self, time1: str, time2: str) -> int:
|
||||
"""计算两个时间字符串之间的分钟差"""
|
||||
if time1 == "24:00":
|
||||
time1 = "23:59"
|
||||
if time2 == "24:00":
|
||||
time2 = "23:59"
|
||||
t1 = datetime.datetime.strptime(time1, "%H:%M")
|
||||
t2 = datetime.datetime.strptime(time2, "%H:%M")
|
||||
diff = int((t2 - t1).total_seconds() / 60)
|
||||
# 考虑时间的循环性
|
||||
if diff < -720:
|
||||
diff += 1440 # 加一天的分钟
|
||||
elif diff > 720:
|
||||
diff -= 1440 # 减一天的分钟
|
||||
# print(f"时间1[{time1}]: 时间2[{time2}]，差值[{diff}]分钟")
|
||||
return diff
|
||||
|
||||
def print_schedule(self):
|
||||
"""打印完整的日程安排"""
|
||||
if not self._parse_schedule(self.today_schedule_text):
|
||||
logger.warning("今日日程有误，将在下次运行时重新生成")
|
||||
db.schedule.delete_one({"date": datetime.datetime.now().strftime("%Y-%m-%d")})
|
||||
else:
|
||||
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("==================")
|
||||
self.enable_output = False
|
||||
|
||||
|
||||
async def main():
|
||||
# 使用示例
|
||||
scheduler = ScheduleGenerator()
|
||||
await scheduler.initialize()
|
||||
scheduler.print_schedule()
|
||||
print("\n当前任务：")
|
||||
print(await scheduler.get_current_task())
|
||||
|
||||
print("昨天日程：")
|
||||
print(scheduler.yesterday_schedule)
|
||||
print("今天日程：")
|
||||
print(scheduler.today_schedule)
|
||||
print("明天日程：")
|
||||
print(scheduler.tomorrow_schedule)
|
||||
|
||||
# 当作为组件导入时使用的实例
|
||||
bot_schedule = ScheduleGenerator()
|
||||
|
||||
if __name__ == "__main__":
|
||||
import asyncio
|
||||
# 当直接运行此文件时执行
|
||||
asyncio.run(main())
|
||||
|
|
@ -0,0 +1,222 @@
|
|||
import datetime
|
||||
import json
|
||||
import re
|
||||
import os
|
||||
import sys
|
||||
from typing import Dict, Union
|
||||
# 添加项目根目录到 Python 路径
|
||||
root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
|
||||
sys.path.append(root_path)
|
||||
|
||||
from src.common.database import db # noqa: E402
|
||||
from src.common.logger import get_module_logger # noqa: E402
|
||||
from src.plugins.schedule.offline_llm import LLMModel # noqa: E402
|
||||
|
||||
logger = get_module_logger("scheduler")
|
||||
|
||||
|
||||
class ScheduleGenerator:
|
||||
enable_output: bool = True
|
||||
|
||||
def __init__(self, name: str = "bot_name", personality: str = "你是一个爱国爱党的新时代青年", behavior: str = "你非常外向，喜欢尝试新事物和人交流"):
|
||||
# 使用离线LLM模型
|
||||
self.llm_scheduler = LLMModel(model_name="Pro/deepseek-ai/DeepSeek-V3", temperature=0.9)
|
||||
|
||||
self.today_schedule_text = ""
|
||||
self.today_done_list = []
|
||||
|
||||
self.yesterday_schedule_text = ""
|
||||
self.yesterday_done_list = []
|
||||
|
||||
self.name = name
|
||||
self.personality = personality
|
||||
self.behavior = behavior
|
||||
|
||||
self.start_time = datetime.datetime.now()
|
||||
|
||||
async def mai_schedule_start(self):
|
||||
"""启动日程系统，每5分钟执行一次move_doing，并在日期变化时重新检查日程"""
|
||||
try:
|
||||
logger.info(f"日程系统启动/刷新时间: {self.start_time.strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
# 初始化日程
|
||||
await self.check_and_create_today_schedule()
|
||||
self.print_schedule()
|
||||
|
||||
while True:
|
||||
current_time = datetime.datetime.now()
|
||||
|
||||
# 检查是否需要重新生成日程（日期变化）
|
||||
if current_time.date() != self.start_time.date():
|
||||
logger.info("检测到日期变化，重新生成日程")
|
||||
self.start_time = current_time
|
||||
await self.check_and_create_today_schedule()
|
||||
self.print_schedule()
|
||||
|
||||
# 执行当前活动
|
||||
current_activity = await self.move_doing()
|
||||
logger.info(f"当前活动: {current_activity}")
|
||||
|
||||
# 等待5分钟
|
||||
await asyncio.sleep(300) # 300秒 = 5分钟
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"日程系统运行时出错: {str(e)}")
|
||||
logger.exception("详细错误信息：")
|
||||
|
||||
async def check_and_create_today_schedule(self):
|
||||
"""检查昨天的日程，并确保今天有日程安排
|
||||
|
||||
Returns:
|
||||
tuple: (today_schedule_text, today_schedule) 今天的日程文本和解析后的日程字典
|
||||
"""
|
||||
today = datetime.datetime.now()
|
||||
yesterday = today - datetime.timedelta(days=1)
|
||||
|
||||
# 先检查昨天的日程
|
||||
self.yesterday_schedule_text, self.yesterday_done_list = self.load_schedule_from_db(yesterday)
|
||||
if self.yesterday_schedule_text:
|
||||
logger.debug(f"已加载{yesterday.strftime('%Y-%m-%d')}的日程")
|
||||
|
||||
# 检查今天的日程
|
||||
self.today_schedule_text, self.today_done_list = self.load_schedule_from_db(today)
|
||||
if not self.today_schedule_text:
|
||||
logger.info(f"{today.strftime('%Y-%m-%d')}的日程不存在，准备生成新的日程")
|
||||
self.today_schedule_text = await self.generate_daily_schedule(target_date=today)
|
||||
|
||||
self.save_today_schedule_to_db()
|
||||
|
||||
def construct_daytime_prompt(self, target_date: datetime.datetime):
|
||||
date_str = target_date.strftime("%Y-%m-%d")
|
||||
weekday = target_date.strftime("%A")
|
||||
|
||||
prompt = f"我是{self.name}，{self.personality}，{self.behavior}"
|
||||
prompt += f"我昨天的日程是：{self.yesterday_schedule_text}\n"
|
||||
prompt += f"请为我生成{date_str}（{weekday}）的日程安排，结合我的个人特点和行为习惯\n"
|
||||
prompt += "推测我的日程安排，包括我一天都在做什么，有什么发现和思考，具体一些，详细一些，记得写明时间\n"
|
||||
prompt += "直接返回我的日程，不要输出其他内容："
|
||||
return prompt
|
||||
|
||||
def construct_doing_prompt(self,time: datetime.datetime):
|
||||
now_time = time.strftime("%H:%M")
|
||||
previous_doing = self.today_done_list[-20:] if len(self.today_done_list) > 20 else self.today_done_list
|
||||
prompt = f"我是{self.name}，{self.personality}，{self.behavior}"
|
||||
prompt += f"我今天的日程是：{self.today_schedule_text}\n"
|
||||
prompt += f"我之前做了的事情是：{previous_doing}\n"
|
||||
prompt += f"现在是{now_time}，结合我的个人特点和行为习惯,"
|
||||
prompt += "推测我现在做什么，具体一些，详细一些\n"
|
||||
prompt += "直接返回我在做的事情，不要输出其他内容："
|
||||
return prompt
|
||||
|
||||
async def generate_daily_schedule(
|
||||
self, target_date: datetime.datetime = None,) -> Dict[str, str]:
|
||||
daytime_prompt = self.construct_daytime_prompt(target_date)
|
||||
daytime_response, _ = await self.llm_scheduler.generate_response(daytime_prompt)
|
||||
return daytime_response
|
||||
|
||||
def _time_diff(self, time1: str, time2: str) -> int:
|
||||
"""计算两个时间字符串之间的分钟差"""
|
||||
if time1 == "24:00":
|
||||
time1 = "23:59"
|
||||
if time2 == "24:00":
|
||||
time2 = "23:59"
|
||||
t1 = datetime.datetime.strptime(time1, "%H:%M")
|
||||
t2 = datetime.datetime.strptime(time2, "%H:%M")
|
||||
diff = int((t2 - t1).total_seconds() / 60)
|
||||
# 考虑时间的循环性
|
||||
if diff < -720:
|
||||
diff += 1440 # 加一天的分钟
|
||||
elif diff > 720:
|
||||
diff -= 1440 # 减一天的分钟
|
||||
# print(f"时间1[{time1}]: 时间2[{time2}]，差值[{diff}]分钟")
|
||||
return diff
|
||||
|
||||
def print_schedule(self):
|
||||
"""打印完整的日程安排"""
|
||||
if not self.today_schedule_text:
|
||||
logger.warning("今日日程有误，将在下次运行时重新生成")
|
||||
db.schedule.delete_one({"date": datetime.datetime.now().strftime("%Y-%m-%d")})
|
||||
else:
|
||||
logger.info("=== 今日日程安排 ===")
|
||||
logger.info(self.today_schedule_text)
|
||||
logger.info("==================")
|
||||
self.enable_output = False
|
||||
|
||||
async def update_today_done_list(self):
|
||||
# 更新数据库中的 today_done_list
|
||||
today_str = datetime.datetime.now().strftime("%Y-%m-%d")
|
||||
existing_schedule = db.schedule.find_one({"date": today_str})
|
||||
|
||||
if existing_schedule:
|
||||
# 更新数据库中的 today_done_list
|
||||
db.schedule.update_one(
|
||||
{"date": today_str},
|
||||
{"$set": {"today_done_list": self.today_done_list}}
|
||||
)
|
||||
logger.debug(f"已更新{today_str}的已完成活动列表")
|
||||
else:
|
||||
logger.warning(f"未找到{today_str}的日程记录")
|
||||
|
||||
async def move_doing(self):
|
||||
current_time = datetime.datetime.now()
|
||||
time_str = current_time.strftime("%H:%M")
|
||||
doing_prompt = self.construct_doing_prompt(current_time)
|
||||
doing_response, _ = await self.llm_scheduler.generate_response(doing_prompt)
|
||||
self.today_done_list.append(current_time,time_str + "在" + doing_response)
|
||||
|
||||
await self.update_today_done_list()
|
||||
|
||||
return doing_response
|
||||
|
||||
|
||||
|
||||
|
||||
def save_today_schedule_to_db(self):
|
||||
"""保存日程到数据库，同时初始化 today_done_list"""
|
||||
date_str = datetime.datetime.now().strftime("%Y-%m-%d")
|
||||
schedule_data = {
|
||||
"date": date_str,
|
||||
"schedule": self.today_schedule_text,
|
||||
"today_done_list": self.today_done_list if hasattr(self, 'today_done_list') else []
|
||||
}
|
||||
# 使用 upsert 操作，如果存在则更新，不存在则插入
|
||||
db.schedule.update_one(
|
||||
{"date": date_str},
|
||||
{"$set": schedule_data},
|
||||
upsert=True
|
||||
)
|
||||
logger.debug(f"已保存{date_str}的日程到数据库")
|
||||
|
||||
def load_schedule_from_db(self, date: datetime.datetime):
|
||||
"""从数据库加载日程，同时加载 today_done_list"""
|
||||
date_str = date.strftime("%Y-%m-%d")
|
||||
existing_schedule = db.schedule.find_one({"date": date_str})
|
||||
|
||||
if existing_schedule:
|
||||
schedule_text = existing_schedule["schedule"]
|
||||
return schedule_text, existing_schedule.get("today_done_list", [])
|
||||
else:
|
||||
logger.debug(f"{date_str}的日程不存在")
|
||||
return None, None
|
||||
|
||||
async def main():
|
||||
# 使用示例
|
||||
scheduler = ScheduleGenerator(name="麦麦", personality="你叫麦麦，你19岁，是一个大二的女大学生，你有一头黑色短发，你会刷贴吧，你现在在学习心理学", behavior="你比较内向")
|
||||
await scheduler.check_and_create_today_schedule()
|
||||
scheduler.print_schedule()
|
||||
print("\n当前任务：")
|
||||
print(await scheduler.get_current_task())
|
||||
|
||||
print("昨天日程：")
|
||||
print(scheduler.yesterday_schedule)
|
||||
print("今天日程：")
|
||||
print(scheduler.today_schedule)
|
||||
print("明天日程：")
|
||||
print(scheduler.tomorrow_schedule)
|
||||
|
||||
# 当作为组件导入时使用的实例
|
||||
bot_schedule = ScheduleGenerator()
|
||||
|
||||
if __name__ == "__main__":
|
||||
import asyncio
|
||||
# 当直接运行此文件时执行
|
||||
asyncio.run(main())
|
||||
|
|
@ -0,0 +1,617 @@
|
|||
import tomli
|
||||
import sys
|
||||
import re
|
||||
from pathlib import Path
|
||||
from typing import Dict, Any, List, Set, Tuple
|
||||
|
||||
def load_toml_file(file_path: str) -> Dict[str, Any]:
|
||||
"""加载TOML文件"""
|
||||
try:
|
||||
with open(file_path, "rb") as f:
|
||||
return tomli.load(f)
|
||||
except Exception as e:
|
||||
print(f"错误: 无法加载配置文件 {file_path}: {str(e)} 请检查文件是否存在或者他妈的有没有东西没写值")
|
||||
sys.exit(1)
|
||||
|
||||
def load_env_file(file_path: str) -> Dict[str, str]:
|
||||
"""加载.env文件中的环境变量"""
|
||||
env_vars = {}
|
||||
try:
|
||||
with open(file_path, 'r', encoding='utf-8') as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if not line or line.startswith('#'):
|
||||
continue
|
||||
if '=' in line:
|
||||
key, value = line.split('=', 1)
|
||||
key = key.strip()
|
||||
value = value.strip()
|
||||
|
||||
# 处理注释
|
||||
if '#' in value:
|
||||
value = value.split('#', 1)[0].strip()
|
||||
|
||||
# 处理引号
|
||||
if (value.startswith('"') and value.endswith('"')) or \
|
||||
(value.startswith("'") and value.endswith("'")):
|
||||
value = value[1:-1]
|
||||
|
||||
env_vars[key] = value
|
||||
return env_vars
|
||||
except Exception as e:
|
||||
print(f"警告: 无法加载.env文件 {file_path}: {str(e)}")
|
||||
return {}
|
||||
|
||||
def check_required_sections(config: Dict[str, Any]) -> List[str]:
|
||||
"""检查必要的配置段是否存在"""
|
||||
required_sections = [
|
||||
"inner", "bot", "personality", "message", "emoji",
|
||||
"cq_code", "response", "willing", "memory", "mood",
|
||||
"groups", "model"
|
||||
]
|
||||
missing_sections = []
|
||||
|
||||
for section in required_sections:
|
||||
if section not in config:
|
||||
missing_sections.append(section)
|
||||
|
||||
return missing_sections
|
||||
|
||||
def check_probability_sum(config: Dict[str, Any]) -> List[Tuple[str, float]]:
|
||||
"""检查概率总和是否为1"""
|
||||
errors = []
|
||||
|
||||
# 检查人格概率
|
||||
if "personality" in config:
|
||||
personality = config["personality"]
|
||||
prob_sum = sum([
|
||||
personality.get("personality_1_probability", 0),
|
||||
personality.get("personality_2_probability", 0),
|
||||
personality.get("personality_3_probability", 0)
|
||||
])
|
||||
if abs(prob_sum - 1.0) > 0.001: # 允许有小数点精度误差
|
||||
errors.append(("人格概率总和", prob_sum))
|
||||
|
||||
# 检查响应模型概率
|
||||
if "response" in config:
|
||||
response = config["response"]
|
||||
model_prob_sum = sum([
|
||||
response.get("model_r1_probability", 0),
|
||||
response.get("model_v3_probability", 0),
|
||||
response.get("model_r1_distill_probability", 0)
|
||||
])
|
||||
if abs(model_prob_sum - 1.0) > 0.001:
|
||||
errors.append(("响应模型概率总和", model_prob_sum))
|
||||
|
||||
return errors
|
||||
|
||||
def check_probability_range(config: Dict[str, Any]) -> List[Tuple[str, float]]:
|
||||
"""检查概率值是否在0-1范围内"""
|
||||
errors = []
|
||||
|
||||
# 收集所有概率值
|
||||
prob_fields = []
|
||||
|
||||
# 人格概率
|
||||
if "personality" in config:
|
||||
personality = config["personality"]
|
||||
prob_fields.extend([
|
||||
("personality.personality_1_probability", personality.get("personality_1_probability")),
|
||||
("personality.personality_2_probability", personality.get("personality_2_probability")),
|
||||
("personality.personality_3_probability", personality.get("personality_3_probability"))
|
||||
])
|
||||
|
||||
# 消息概率
|
||||
if "message" in config:
|
||||
message = config["message"]
|
||||
prob_fields.append(("message.emoji_chance", message.get("emoji_chance")))
|
||||
|
||||
# 响应模型概率
|
||||
if "response" in config:
|
||||
response = config["response"]
|
||||
prob_fields.extend([
|
||||
("response.model_r1_probability", response.get("model_r1_probability")),
|
||||
("response.model_v3_probability", response.get("model_v3_probability")),
|
||||
("response.model_r1_distill_probability", response.get("model_r1_distill_probability"))
|
||||
])
|
||||
|
||||
# 情绪衰减率
|
||||
if "mood" in config:
|
||||
mood = config["mood"]
|
||||
prob_fields.append(("mood.mood_decay_rate", mood.get("mood_decay_rate")))
|
||||
|
||||
# 中文错别字概率
|
||||
if "chinese_typo" in config and config["chinese_typo"].get("enable", False):
|
||||
typo = config["chinese_typo"]
|
||||
prob_fields.extend([
|
||||
("chinese_typo.error_rate", typo.get("error_rate")),
|
||||
("chinese_typo.tone_error_rate", typo.get("tone_error_rate")),
|
||||
("chinese_typo.word_replace_rate", typo.get("word_replace_rate"))
|
||||
])
|
||||
|
||||
# 检查所有概率值是否在0-1范围内
|
||||
for field_name, value in prob_fields:
|
||||
if value is not None and (value < 0 or value > 1):
|
||||
errors.append((field_name, value))
|
||||
|
||||
return errors
|
||||
|
||||
def check_model_configurations(config: Dict[str, Any], env_vars: Dict[str, str]) -> List[str]:
|
||||
"""检查模型配置是否完整，并验证provider是否正确"""
|
||||
errors = []
|
||||
|
||||
if "model" not in config:
|
||||
return ["缺少[model]部分"]
|
||||
|
||||
required_models = [
|
||||
"llm_reasoning", "llm_reasoning_minor", "llm_normal",
|
||||
"llm_normal_minor", "llm_emotion_judge", "llm_topic_judge",
|
||||
"llm_summary_by_topic", "vlm", "embedding"
|
||||
]
|
||||
|
||||
# 从环境变量中提取有效的API提供商
|
||||
valid_providers = set()
|
||||
for key in env_vars:
|
||||
if key.endswith('_BASE_URL'):
|
||||
provider_name = key.replace('_BASE_URL', '')
|
||||
valid_providers.add(provider_name)
|
||||
|
||||
# 将provider名称标准化以便比较
|
||||
provider_mapping = {
|
||||
"SILICONFLOW": ["SILICONFLOW", "SILICON_FLOW", "SILICON-FLOW"],
|
||||
"CHAT_ANY_WHERE": ["CHAT_ANY_WHERE", "CHAT-ANY-WHERE", "CHATANYWHERE"],
|
||||
"DEEP_SEEK": ["DEEP_SEEK", "DEEP-SEEK", "DEEPSEEK"]
|
||||
}
|
||||
|
||||
# 创建反向映射表，用于检查错误拼写
|
||||
reverse_mapping = {}
|
||||
for standard, variants in provider_mapping.items():
|
||||
for variant in variants:
|
||||
reverse_mapping[variant.upper()] = standard
|
||||
|
||||
for model_name in required_models:
|
||||
# 检查model下是否有对应子部分
|
||||
if model_name not in config["model"]:
|
||||
errors.append(f"缺少[model.{model_name}]配置")
|
||||
else:
|
||||
model_config = config["model"][model_name]
|
||||
if "name" not in model_config:
|
||||
errors.append(f"[model.{model_name}]缺少name属性")
|
||||
|
||||
if "provider" not in model_config:
|
||||
errors.append(f"[model.{model_name}]缺少provider属性")
|
||||
else:
|
||||
provider = model_config["provider"].upper()
|
||||
|
||||
# 检查拼写错误
|
||||
for known_provider, correct_provider in reverse_mapping.items():
|
||||
# 使用模糊匹配检测拼写错误
|
||||
if provider != known_provider and _similar_strings(provider, known_provider) and provider not in reverse_mapping:
|
||||
errors.append(f"[model.{model_name}]的provider '{model_config['provider']}' 可能拼写错误，应为 '{known_provider}'")
|
||||
break
|
||||
|
||||
return errors
|
||||
|
||||
def _similar_strings(s1: str, s2: str) -> bool:
|
||||
"""简单检查两个字符串是否相似（用于检测拼写错误）"""
|
||||
# 如果两个字符串长度相差过大，则认为不相似
|
||||
if abs(len(s1) - len(s2)) > 2:
|
||||
return False
|
||||
|
||||
# 计算相同字符的数量
|
||||
common_chars = sum(1 for c1, c2 in zip(s1, s2) if c1 == c2)
|
||||
# 如果相同字符比例超过80%，则认为相似
|
||||
return common_chars / max(len(s1), len(s2)) > 0.8
|
||||
|
||||
def check_api_providers(config: Dict[str, Any], env_vars: Dict[str, str]) -> List[str]:
|
||||
"""检查配置文件中的API提供商是否与环境变量中的一致"""
|
||||
errors = []
|
||||
|
||||
if "model" not in config:
|
||||
return ["缺少[model]部分"]
|
||||
|
||||
# 从环境变量中提取有效的API提供商
|
||||
valid_providers = {}
|
||||
for key in env_vars:
|
||||
if key.endswith('_BASE_URL'):
|
||||
provider_name = key.replace('_BASE_URL', '')
|
||||
base_url = env_vars[key]
|
||||
valid_providers[provider_name] = {
|
||||
"base_url": base_url,
|
||||
"key": env_vars.get(f"{provider_name}_KEY", "")
|
||||
}
|
||||
|
||||
# 检查配置文件中使用的所有提供商
|
||||
used_providers = set()
|
||||
for model_category, model_config in config["model"].items():
|
||||
if "provider" in model_config:
|
||||
provider = model_config["provider"]
|
||||
used_providers.add(provider)
|
||||
|
||||
# 检查此提供商是否在环境变量中定义
|
||||
normalized_provider = provider.replace(" ", "_").upper()
|
||||
found = False
|
||||
for env_provider in valid_providers:
|
||||
if normalized_provider == env_provider:
|
||||
found = True
|
||||
break
|
||||
# 尝试更宽松的匹配（例如SILICONFLOW可能匹配SILICON_FLOW）
|
||||
elif normalized_provider.replace("_", "") == env_provider.replace("_", ""):
|
||||
found = True
|
||||
errors.append(f"提供商 '{provider}' 在环境变量中的名称是 '{env_provider}', 建议统一命名")
|
||||
break
|
||||
|
||||
if not found:
|
||||
errors.append(f"提供商 '{provider}' 在环境变量中未定义")
|
||||
|
||||
# 特别检查常见的拼写错误
|
||||
for provider in used_providers:
|
||||
if provider.upper() == "SILICONFOLW":
|
||||
errors.append(f"提供商 'SILICONFOLW' 存在拼写错误，应为 'SILICONFLOW'")
|
||||
|
||||
return errors
|
||||
|
||||
def check_groups_configuration(config: Dict[str, Any]) -> List[str]:
|
||||
"""检查群组配置"""
|
||||
errors = []
|
||||
|
||||
if "groups" not in config:
|
||||
return ["缺少[groups]部分"]
|
||||
|
||||
groups = config["groups"]
|
||||
|
||||
# 检查talk_allowed是否为列表
|
||||
if "talk_allowed" not in groups:
|
||||
errors.append("缺少groups.talk_allowed配置")
|
||||
elif not isinstance(groups["talk_allowed"], list):
|
||||
errors.append("groups.talk_allowed应该是一个列表")
|
||||
else:
|
||||
# 检查talk_allowed是否包含默认示例值123
|
||||
if 123 in groups["talk_allowed"]:
|
||||
errors.append({
|
||||
"main": "groups.talk_allowed中存在默认示例值'123'，请修改为真实的群号",
|
||||
"details": [
|
||||
f" 当前值: {groups['talk_allowed']}",
|
||||
f" '123'为示例值，需要替换为真实群号"
|
||||
]
|
||||
})
|
||||
|
||||
# 检查是否存在重复的群号
|
||||
talk_allowed = groups["talk_allowed"]
|
||||
duplicates = []
|
||||
seen = set()
|
||||
for gid in talk_allowed:
|
||||
if gid in seen and gid not in duplicates:
|
||||
duplicates.append(gid)
|
||||
seen.add(gid)
|
||||
|
||||
if duplicates:
|
||||
errors.append({
|
||||
"main": "groups.talk_allowed中存在重复的群号",
|
||||
"details": [f" 重复的群号: {duplicates}"]
|
||||
})
|
||||
|
||||
# 检查其他群组配置
|
||||
if "talk_frequency_down" in groups and not isinstance(groups["talk_frequency_down"], list):
|
||||
errors.append("groups.talk_frequency_down应该是一个列表")
|
||||
|
||||
if "ban_user_id" in groups and not isinstance(groups["ban_user_id"], list):
|
||||
errors.append("groups.ban_user_id应该是一个列表")
|
||||
|
||||
return errors
|
||||
|
||||
def check_keywords_reaction(config: Dict[str, Any]) -> List[str]:
|
||||
"""检查关键词反应配置"""
|
||||
errors = []
|
||||
|
||||
if "keywords_reaction" not in config:
|
||||
return ["缺少[keywords_reaction]部分"]
|
||||
|
||||
kr = config["keywords_reaction"]
|
||||
|
||||
# 检查enable字段
|
||||
if "enable" not in kr:
|
||||
errors.append("缺少keywords_reaction.enable配置")
|
||||
|
||||
# 检查规则配置
|
||||
if "rules" not in kr:
|
||||
errors.append("缺少keywords_reaction.rules配置")
|
||||
elif not isinstance(kr["rules"], list):
|
||||
errors.append("keywords_reaction.rules应该是一个列表")
|
||||
else:
|
||||
for i, rule in enumerate(kr["rules"]):
|
||||
if "enable" not in rule:
|
||||
errors.append(f"关键词规则 #{i+1} 缺少enable字段")
|
||||
if "keywords" not in rule:
|
||||
errors.append(f"关键词规则 #{i+1} 缺少keywords字段")
|
||||
elif not isinstance(rule["keywords"], list):
|
||||
errors.append(f"关键词规则 #{i+1} 的keywords应该是一个列表")
|
||||
if "reaction" not in rule:
|
||||
errors.append(f"关键词规则 #{i+1} 缺少reaction字段")
|
||||
|
||||
return errors
|
||||
|
||||
def check_willing_mode(config: Dict[str, Any]) -> List[str]:
|
||||
"""检查回复意愿模式配置"""
|
||||
errors = []
|
||||
|
||||
if "willing" not in config:
|
||||
return ["缺少[willing]部分"]
|
||||
|
||||
willing = config["willing"]
|
||||
|
||||
if "willing_mode" not in willing:
|
||||
errors.append("缺少willing.willing_mode配置")
|
||||
elif willing["willing_mode"] not in ["classical", "dynamic", "custom"]:
|
||||
errors.append(f"willing.willing_mode值无效: {willing['willing_mode']}, 应为classical/dynamic/custom")
|
||||
|
||||
return errors
|
||||
|
||||
def check_memory_config(config: Dict[str, Any]) -> List[str]:
|
||||
"""检查记忆系统配置"""
|
||||
errors = []
|
||||
|
||||
if "memory" not in config:
|
||||
return ["缺少[memory]部分"]
|
||||
|
||||
memory = config["memory"]
|
||||
|
||||
# 检查必要的参数
|
||||
required_fields = [
|
||||
"build_memory_interval", "memory_compress_rate",
|
||||
"forget_memory_interval", "memory_forget_time",
|
||||
"memory_forget_percentage"
|
||||
]
|
||||
|
||||
for field in required_fields:
|
||||
if field not in memory:
|
||||
errors.append(f"缺少memory.{field}配置")
|
||||
|
||||
# 检查参数值的有效性
|
||||
if "memory_compress_rate" in memory and (memory["memory_compress_rate"] <= 0 or memory["memory_compress_rate"] > 1):
|
||||
errors.append(f"memory.memory_compress_rate值无效: {memory['memory_compress_rate']}, 应在0-1之间")
|
||||
|
||||
if "memory_forget_percentage" in memory and (memory["memory_forget_percentage"] <= 0 or memory["memory_forget_percentage"] > 1):
|
||||
errors.append(f"memory.memory_forget_percentage值无效: {memory['memory_forget_percentage']}, 应在0-1之间")
|
||||
|
||||
return errors
|
||||
|
||||
def check_personality_config(config: Dict[str, Any]) -> List[str]:
|
||||
"""检查人格配置"""
|
||||
errors = []
|
||||
|
||||
if "personality" not in config:
|
||||
return ["缺少[personality]部分"]
|
||||
|
||||
personality = config["personality"]
|
||||
|
||||
# 检查prompt_personality是否存在且为数组
|
||||
if "prompt_personality" not in personality:
|
||||
errors.append("缺少personality.prompt_personality配置")
|
||||
elif not isinstance(personality["prompt_personality"], list):
|
||||
errors.append("personality.prompt_personality应该是一个数组")
|
||||
else:
|
||||
# 检查数组长度
|
||||
if len(personality["prompt_personality"]) < 1:
|
||||
errors.append(f"personality.prompt_personality数组长度不足，当前长度: {len(personality['prompt_personality'])}, 需要至少1项")
|
||||
else:
|
||||
# 模板默认值
|
||||
template_values = [
|
||||
"用一句话或几句话描述性格特点和其他特征",
|
||||
"用一句话或几句话描述性格特点和其他特征",
|
||||
"例如，是一个热爱国家热爱党的新时代好青年"
|
||||
]
|
||||
|
||||
# 检查是否仍然使用默认模板值
|
||||
error_details = []
|
||||
for i, (current, template) in enumerate(zip(personality["prompt_personality"][:3], template_values)):
|
||||
if current == template:
|
||||
error_details.append({
|
||||
"main": f"personality.prompt_personality第{i+1}项仍使用默认模板值，请自定义",
|
||||
"details": [
|
||||
f" 当前值: '{current}'",
|
||||
f" 请不要使用模板值: '{template}'"
|
||||
]
|
||||
})
|
||||
|
||||
# 将错误添加到errors列表
|
||||
for error in error_details:
|
||||
errors.append(error)
|
||||
|
||||
return errors
|
||||
|
||||
def check_bot_config(config: Dict[str, Any]) -> List[str]:
|
||||
"""检查机器人基础配置"""
|
||||
errors = []
|
||||
infos = []
|
||||
|
||||
if "bot" not in config:
|
||||
return ["缺少[bot]部分"]
|
||||
|
||||
bot = config["bot"]
|
||||
|
||||
# 检查QQ号是否为默认值或测试值
|
||||
if "qq" not in bot:
|
||||
errors.append("缺少bot.qq配置")
|
||||
elif bot["qq"] == 1 or bot["qq"] == 123:
|
||||
errors.append(f"QQ号 '{bot['qq']}' 似乎是默认值或测试值，请设置为真实的QQ号")
|
||||
else:
|
||||
infos.append(f"当前QQ号: {bot['qq']}")
|
||||
|
||||
# 检查昵称是否设置
|
||||
if "nickname" not in bot or not bot["nickname"]:
|
||||
errors.append("缺少bot.nickname配置或昵称为空")
|
||||
elif bot["nickname"]:
|
||||
infos.append(f"当前昵称: {bot['nickname']}")
|
||||
|
||||
# 检查别名是否为列表
|
||||
if "alias_names" in bot and not isinstance(bot["alias_names"], list):
|
||||
errors.append("bot.alias_names应该是一个列表")
|
||||
|
||||
return errors, infos
|
||||
|
||||
def format_results(all_errors):
|
||||
"""格式化检查结果"""
|
||||
sections_errors, prob_sum_errors, prob_range_errors, model_errors, api_errors, groups_errors, kr_errors, willing_errors, memory_errors, personality_errors, bot_results = all_errors
|
||||
bot_errors, bot_infos = bot_results
|
||||
|
||||
if not any([sections_errors, prob_sum_errors, prob_range_errors, model_errors, api_errors, groups_errors, kr_errors, willing_errors, memory_errors, personality_errors, bot_errors]):
|
||||
result = "✅ 配置文件检查通过，未发现问题。"
|
||||
|
||||
# 添加机器人信息
|
||||
if bot_infos:
|
||||
result += "\n\n【机器人信息】"
|
||||
for info in bot_infos:
|
||||
result += f"\n - {info}"
|
||||
|
||||
return result
|
||||
|
||||
output = []
|
||||
output.append("❌ 配置文件检查发现以下问题:")
|
||||
|
||||
if sections_errors:
|
||||
output.append("\n【缺失的配置段】")
|
||||
for section in sections_errors:
|
||||
output.append(f" - {section}")
|
||||
|
||||
if prob_sum_errors:
|
||||
output.append("\n【概率总和错误】(应为1.0)")
|
||||
for name, value in prob_sum_errors:
|
||||
output.append(f" - {name}: {value:.4f}")
|
||||
|
||||
if prob_range_errors:
|
||||
output.append("\n【概率值范围错误】(应在0-1之间)")
|
||||
for name, value in prob_range_errors:
|
||||
output.append(f" - {name}: {value}")
|
||||
|
||||
if model_errors:
|
||||
output.append("\n【模型配置错误】")
|
||||
for error in model_errors:
|
||||
output.append(f" - {error}")
|
||||
|
||||
if api_errors:
|
||||
output.append("\n【API提供商错误】")
|
||||
for error in api_errors:
|
||||
output.append(f" - {error}")
|
||||
|
||||
if groups_errors:
|
||||
output.append("\n【群组配置错误】")
|
||||
for error in groups_errors:
|
||||
if isinstance(error, dict):
|
||||
output.append(f" - {error['main']}")
|
||||
for detail in error['details']:
|
||||
output.append(f"{detail}")
|
||||
else:
|
||||
output.append(f" - {error}")
|
||||
|
||||
if kr_errors:
|
||||
output.append("\n【关键词反应配置错误】")
|
||||
for error in kr_errors:
|
||||
output.append(f" - {error}")
|
||||
|
||||
if willing_errors:
|
||||
output.append("\n【回复意愿配置错误】")
|
||||
for error in willing_errors:
|
||||
output.append(f" - {error}")
|
||||
|
||||
if memory_errors:
|
||||
output.append("\n【记忆系统配置错误】")
|
||||
for error in memory_errors:
|
||||
output.append(f" - {error}")
|
||||
|
||||
if personality_errors:
|
||||
output.append("\n【人格配置错误】")
|
||||
for error in personality_errors:
|
||||
if isinstance(error, dict):
|
||||
output.append(f" - {error['main']}")
|
||||
for detail in error['details']:
|
||||
output.append(f"{detail}")
|
||||
else:
|
||||
output.append(f" - {error}")
|
||||
|
||||
if bot_errors:
|
||||
output.append("\n【机器人基础配置错误】")
|
||||
for error in bot_errors:
|
||||
output.append(f" - {error}")
|
||||
|
||||
# 添加机器人信息，即使有错误
|
||||
if bot_infos:
|
||||
output.append("\n【机器人信息】")
|
||||
for info in bot_infos:
|
||||
output.append(f" - {info}")
|
||||
|
||||
return "\n".join(output)
|
||||
|
||||
def main():
|
||||
# 获取配置文件路径
|
||||
config_path = Path("config/bot_config.toml")
|
||||
env_path = Path(".env.prod")
|
||||
|
||||
if not config_path.exists():
|
||||
print(f"错误: 找不到配置文件 {config_path}")
|
||||
return
|
||||
|
||||
if not env_path.exists():
|
||||
print(f"警告: 找不到环境变量文件 {env_path}, 将跳过API提供商检查")
|
||||
env_vars = {}
|
||||
else:
|
||||
env_vars = load_env_file(env_path)
|
||||
|
||||
# 加载配置文件
|
||||
config = load_toml_file(config_path)
|
||||
|
||||
# 运行各种检查
|
||||
sections_errors = check_required_sections(config)
|
||||
prob_sum_errors = check_probability_sum(config)
|
||||
prob_range_errors = check_probability_range(config)
|
||||
model_errors = check_model_configurations(config, env_vars)
|
||||
api_errors = check_api_providers(config, env_vars)
|
||||
groups_errors = check_groups_configuration(config)
|
||||
kr_errors = check_keywords_reaction(config)
|
||||
willing_errors = check_willing_mode(config)
|
||||
memory_errors = check_memory_config(config)
|
||||
personality_errors = check_personality_config(config)
|
||||
bot_results = check_bot_config(config)
|
||||
|
||||
# 格式化并打印结果
|
||||
all_errors = (sections_errors, prob_sum_errors, prob_range_errors, model_errors, api_errors, groups_errors, kr_errors, willing_errors, memory_errors, personality_errors, bot_results)
|
||||
result = format_results(all_errors)
|
||||
print("📋 机器人配置检查结果:")
|
||||
print(result)
|
||||
|
||||
# 综合评估
|
||||
total_errors = 0
|
||||
|
||||
# 解包bot_results
|
||||
bot_errors, _ = bot_results
|
||||
|
||||
# 计算普通错误列表的长度
|
||||
for errors in [sections_errors, model_errors, api_errors, groups_errors, kr_errors, willing_errors, memory_errors, bot_errors]:
|
||||
total_errors += len(errors)
|
||||
|
||||
# 计算元组列表的长度（概率相关错误）
|
||||
total_errors += len(prob_sum_errors)
|
||||
total_errors += len(prob_range_errors)
|
||||
|
||||
# 特殊处理personality_errors和groups_errors
|
||||
for errors_list in [personality_errors, groups_errors]:
|
||||
for error in errors_list:
|
||||
if isinstance(error, dict):
|
||||
# 每个字典表示一个错误，而不是每行都算一个
|
||||
total_errors += 1
|
||||
else:
|
||||
total_errors += 1
|
||||
|
||||
if total_errors > 0:
|
||||
print(f"\n总计发现 {total_errors} 个配置问题。")
|
||||
print("\n建议：")
|
||||
print("1. 修复所有错误后再运行机器人")
|
||||
print("2. 特别注意拼写错误，例如不！要！写！错！别！字！！！！！")
|
||||
print("3. 确保所有API提供商名称与环境变量中一致")
|
||||
print("4. 检查概率值设置，确保总和为1")
|
||||
else:
|
||||
print("\n您的配置文件完全正确！机器人可以正常运行。")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
input("\n按任意键退出...")
|
||||
Loading…
Reference in New Issue