464 lines
17 KiB
Python
464 lines
17 KiB
Python
from peewee import Model, DoubleField, IntegerField, BooleanField, TextField, FloatField, DateTimeField
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from .database import db
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import datetime
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from src.common.logger import get_logger
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logger = get_logger("database_model")
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# 请在此处定义您的数据库实例。
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# 您需要取消注释并配置适合您的数据库的部分。
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# 例如，对于 SQLite:
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# db = SqliteDatabase('MaiBot.db')
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#
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# 对于 PostgreSQL:
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# db = PostgresqlDatabase('your_db_name', user='your_user', password='your_password',
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# host='localhost', port=5432)
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#
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# 对于 MySQL:
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# db = MySQLDatabase('your_db_name', user='your_user', password='your_password',
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# host='localhost', port=3306)
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# 定义一个基础模型是一个好习惯，所有其他模型都应继承自它。
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# 这允许您在一个地方为所有模型指定数据库。
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class BaseModel(Model):
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class Meta:
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# 将下面的 'db' 替换为您实际的数据库实例变量名。
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database = db # 例如: database = my_actual_db_instance
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pass # 在用户定义数据库实例之前，此处为占位符
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class ChatStreams(BaseModel):
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"""
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用于存储流式记录数据的模型，类似于提供的 MongoDB 结构。
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"""
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# stream_id: "a544edeb1a9b73e3e1d77dff36e41264"
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# 假设 stream_id 是唯一的，并为其创建索引以提高查询性能。
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stream_id = TextField(unique=True, index=True)
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# create_time: 1746096761.4490178 (时间戳，精确到小数点后7位)
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# DoubleField 用于存储浮点数，适合此类时间戳。
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create_time = DoubleField()
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# group_info 字段:
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# platform: "qq"
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# group_id: "941657197"
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# group_name: "测试"
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group_platform = TextField(null=True) # 群聊信息可能不存在
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group_id = TextField(null=True)
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group_name = TextField(null=True)
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# last_active_time: 1746623771.4825106 (时间戳，精确到小数点后7位)
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last_active_time = DoubleField()
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# platform: "qq" (顶层平台字段)
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platform = TextField()
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# user_info 字段:
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# platform: "qq"
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# user_id: "1787882683"
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# user_nickname: "墨梓柒(IceSakurary)"
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# user_cardname: ""
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user_platform = TextField()
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user_id = TextField()
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user_nickname = TextField()
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# user_cardname 可能为空字符串或不存在，设置 null=True 更具灵活性。
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user_cardname = TextField(null=True)
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class Meta:
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# 如果 BaseModel.Meta.database 已设置，则此模型将继承该数据库配置。
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# 如果不使用带有数据库实例的 BaseModel，或者想覆盖它，
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# 请取消注释并在下面设置数据库实例：
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# database = db
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table_name = "chat_streams" # 可选：明确指定数据库中的表名
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class LLMUsage(BaseModel):
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"""
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用于存储 API 使用日志数据的模型。
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"""
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model_name = TextField(index=True) # 添加索引
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user_id = TextField(index=True) # 添加索引
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request_type = TextField(index=True) # 添加索引
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endpoint = TextField()
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prompt_tokens = IntegerField()
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completion_tokens = IntegerField()
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total_tokens = IntegerField()
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cost = DoubleField()
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status = TextField()
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timestamp = DateTimeField(index=True) # 更改为 DateTimeField 并添加索引
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class Meta:
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# 如果 BaseModel.Meta.database 已设置，则此模型将继承该数据库配置。
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# database = db
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table_name = "llm_usage"
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class Emoji(BaseModel):
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"""表情包"""
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full_path = TextField(unique=True, index=True) # 文件的完整路径 (包括文件名)
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format = TextField() # 图片格式
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emoji_hash = TextField(index=True) # 表情包的哈希值
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description = TextField() # 表情包的描述
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query_count = IntegerField(default=0) # 查询次数（用于统计表情包被查询描述的次数）
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is_registered = BooleanField(default=False) # 是否已注册
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is_banned = BooleanField(default=False) # 是否被禁止注册
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# emotion: list[str] # 表情包的情感标签 - 存储为文本，应用层处理序列化/反序列化
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emotion = TextField(null=True)
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record_time = FloatField() # 记录时间（被创建的时间）
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register_time = FloatField(null=True) # 注册时间（被注册为可用表情包的时间）
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usage_count = IntegerField(default=0) # 使用次数（被使用的次数）
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last_used_time = FloatField(null=True) # 上次使用时间
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class Meta:
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# database = db # 继承自 BaseModel
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table_name = "emoji"
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class Messages(BaseModel):
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"""
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用于存储消息数据的模型。
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"""
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message_id = TextField(index=True) # 消息 ID (更改自 IntegerField)
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time = DoubleField() # 消息时间戳
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chat_id = TextField(index=True) # 对应的 ChatStreams stream_id
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# 从 chat_info 扁平化而来的字段
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chat_info_stream_id = TextField()
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chat_info_platform = TextField()
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chat_info_user_platform = TextField()
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chat_info_user_id = TextField()
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chat_info_user_nickname = TextField()
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chat_info_user_cardname = TextField(null=True)
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chat_info_group_platform = TextField(null=True) # 群聊信息可能不存在
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chat_info_group_id = TextField(null=True)
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chat_info_group_name = TextField(null=True)
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chat_info_create_time = DoubleField()
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chat_info_last_active_time = DoubleField()
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# 从顶层 user_info 扁平化而来的字段 (消息发送者信息)
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user_platform = TextField()
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user_id = TextField()
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user_nickname = TextField()
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user_cardname = TextField(null=True)
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processed_plain_text = TextField(null=True) # 处理后的纯文本消息
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display_message = TextField(null=True) # 显示的消息
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detailed_plain_text = TextField(null=True) # 详细的纯文本消息
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memorized_times = IntegerField(default=0) # 被记忆的次数
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class Meta:
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# database = db # 继承自 BaseModel
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table_name = "messages"
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class ActionRecords(BaseModel):
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"""
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用于存储动作记录数据的模型。
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"""
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action_id = TextField(index=True) # 消息 ID (更改自 IntegerField)
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time = DoubleField() # 消息时间戳
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action_name = TextField()
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action_data = TextField()
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action_done = BooleanField(default=False)
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action_build_into_prompt = BooleanField(default=False)
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action_prompt_display = TextField()
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chat_id = TextField(index=True) # 对应的 ChatStreams stream_id
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chat_info_stream_id = TextField()
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chat_info_platform = TextField()
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class Meta:
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# database = db # 继承自 BaseModel
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table_name = "action_records"
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class Images(BaseModel):
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"""
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用于存储图像信息的模型。
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"""
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image_id = TextField(default="") # 图片唯一ID
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emoji_hash = TextField(index=True) # 图像的哈希值
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description = TextField(null=True) # 图像的描述
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path = TextField(unique=True) # 图像文件的路径
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# base64 = TextField() # 图片的base64编码
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count = IntegerField(default=1) # 图片被引用的次数
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timestamp = FloatField() # 时间戳
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type = TextField() # 图像类型，例如 "emoji"
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vlm_processed = BooleanField(default=False) # 是否已经过VLM处理
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class Meta:
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table_name = "images"
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class ImageDescriptions(BaseModel):
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"""
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用于存储图像描述信息的模型。
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"""
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type = TextField() # 类型，例如 "emoji"
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image_description_hash = TextField(index=True) # 图像的哈希值
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description = TextField() # 图像的描述
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timestamp = FloatField() # 时间戳
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class Meta:
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# database = db # 继承自 BaseModel
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table_name = "image_descriptions"
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class OnlineTime(BaseModel):
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"""
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用于存储在线时长记录的模型。
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"""
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# timestamp: "$date": "2025-05-01T18:52:18.191Z" (存储为字符串)
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timestamp = TextField(default=datetime.datetime.now) # 时间戳
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duration = IntegerField() # 时长，单位分钟
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start_timestamp = DateTimeField(default=datetime.datetime.now)
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end_timestamp = DateTimeField(index=True)
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class Meta:
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# database = db # 继承自 BaseModel
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table_name = "online_time"
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class PersonInfo(BaseModel):
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"""
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用于存储个人信息数据的模型。
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"""
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person_id = TextField(unique=True, index=True) # 个人唯一ID
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person_name = TextField(null=True) # 个人名称 (允许为空)
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name_reason = TextField(null=True) # 名称设定的原因
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platform = TextField() # 平台
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user_id = TextField(index=True) # 用户ID
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nickname = TextField() # 用户昵称
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impression = TextField(null=True) # 个人印象
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short_impression = TextField(null=True) # 个人印象的简短描述
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points = TextField(null=True) # 个人印象的点
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forgotten_points = TextField(null=True) # 被遗忘的点
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info_list = TextField(null=True) # 与Bot的互动
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know_times = FloatField(null=True) # 认识时间 (时间戳)
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know_since = FloatField(null=True) # 首次印象总结时间
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last_know = FloatField(null=True) # 最后一次印象总结时间
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familiarity_value = IntegerField(null=True, default=0) # 熟悉度，0-100，从完全陌生到非常熟悉
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liking_value = IntegerField(null=True, default=50) # 好感度，0-100，从非常厌恶到十分喜欢
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class Meta:
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# database = db # 继承自 BaseModel
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table_name = "person_info"
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class Knowledges(BaseModel):
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"""
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用于存储知识库条目的模型。
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"""
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content = TextField() # 知识内容的文本
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embedding = TextField() # 知识内容的嵌入向量，存储为 JSON 字符串的浮点数列表
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# 可以添加其他元数据字段，如 source, create_time 等
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class Meta:
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# database = db # 继承自 BaseModel
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table_name = "knowledges"
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class ThinkingLog(BaseModel):
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chat_id = TextField(index=True)
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trigger_text = TextField(null=True)
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response_text = TextField(null=True)
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# Store complex dicts/lists as JSON strings
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trigger_info_json = TextField(null=True)
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response_info_json = TextField(null=True)
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timing_results_json = TextField(null=True)
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chat_history_json = TextField(null=True)
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chat_history_in_thinking_json = TextField(null=True)
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chat_history_after_response_json = TextField(null=True)
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heartflow_data_json = TextField(null=True)
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reasoning_data_json = TextField(null=True)
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# Add a timestamp for the log entry itself
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# Ensure you have: from peewee import DateTimeField
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# And: import datetime
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created_at = DateTimeField(default=datetime.datetime.now)
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class Meta:
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table_name = "thinking_logs"
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class RecalledMessages(BaseModel):
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"""
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用于存储撤回消息记录的模型。
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"""
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message_id = TextField(index=True) # 被撤回的消息 ID
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time = DoubleField() # 撤回操作发生的时间戳
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stream_id = TextField() # 对应的 ChatStreams stream_id
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class Meta:
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table_name = "recalled_messages"
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class GraphNodes(BaseModel):
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"""
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用于存储记忆图节点的模型
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"""
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concept = TextField(unique=True, index=True) # 节点概念
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memory_items = TextField() # JSON格式存储的记忆列表
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hash = TextField() # 节点哈希值
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created_time = FloatField() # 创建时间戳
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last_modified = FloatField() # 最后修改时间戳
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class Meta:
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table_name = "graph_nodes"
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class GraphEdges(BaseModel):
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"""
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用于存储记忆图边的模型
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"""
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source = TextField(index=True) # 源节点
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target = TextField(index=True) # 目标节点
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strength = IntegerField() # 连接强度
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hash = TextField() # 边哈希值
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created_time = FloatField() # 创建时间戳
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last_modified = FloatField() # 最后修改时间戳
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class Meta:
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table_name = "graph_edges"
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def create_tables():
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"""
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创建所有在模型中定义的数据库表。
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"""
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with db:
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db.create_tables(
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[
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ChatStreams,
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LLMUsage,
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Emoji,
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Messages,
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Images,
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ImageDescriptions,
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OnlineTime,
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PersonInfo,
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Knowledges,
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ThinkingLog,
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RecalledMessages, # 添加新模型
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GraphNodes, # 添加图节点表
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GraphEdges, # 添加图边表
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ActionRecords, # 添加 ActionRecords 到初始化列表
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]
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)
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def initialize_database():
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"""
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检查所有定义的表是否存在，如果不存在则创建它们。
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检查所有表的所有字段是否存在，如果缺失则自动添加。
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"""
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models = [
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ChatStreams,
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LLMUsage,
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Emoji,
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Messages,
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Images,
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ImageDescriptions,
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OnlineTime,
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PersonInfo,
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Knowledges,
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ThinkingLog,
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RecalledMessages,
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GraphNodes,
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GraphEdges,
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ActionRecords, # 添加 ActionRecords 到初始化列表
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]
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try:
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with db: # 管理 table_exists 检查的连接
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for model in models:
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table_name = model._meta.table_name
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if not db.table_exists(model):
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logger.warning(f"表 '{table_name}' 未找到，正在创建...")
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db.create_tables([model])
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logger.info(f"表 '{table_name}' 创建成功")
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continue
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# 检查字段
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cursor = db.execute_sql(f"PRAGMA table_info('{table_name}')")
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existing_columns = {row[1] for row in cursor.fetchall()}
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model_fields = set(model._meta.fields.keys())
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# 检查并添加缺失字段（原有逻辑）
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missing_fields = model_fields - existing_columns
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if missing_fields:
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logger.warning(f"表 '{table_name}' 缺失字段: {missing_fields}")
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for field_name, field_obj in model._meta.fields.items():
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if field_name not in existing_columns:
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logger.info(f"表 '{table_name}' 缺失字段 '{field_name}'，正在添加...")
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field_type = field_obj.__class__.__name__
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sql_type = {
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"TextField": "TEXT",
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"IntegerField": "INTEGER",
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"FloatField": "FLOAT",
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"DoubleField": "DOUBLE",
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"BooleanField": "INTEGER",
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"DateTimeField": "DATETIME",
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}.get(field_type, "TEXT")
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alter_sql = f"ALTER TABLE {table_name} ADD COLUMN {field_name} {sql_type}"
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if field_obj.null:
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alter_sql += " NULL"
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else:
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alter_sql += " NOT NULL"
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if hasattr(field_obj, "default") and field_obj.default is not None:
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# 正确处理不同类型的默认值
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default_value = field_obj.default
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if isinstance(default_value, str):
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alter_sql += f" DEFAULT '{default_value}'"
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elif isinstance(default_value, bool):
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alter_sql += f" DEFAULT {int(default_value)}"
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else:
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alter_sql += f" DEFAULT {default_value}"
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try:
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db.execute_sql(alter_sql)
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logger.info(f"字段 '{field_name}' 添加成功")
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except Exception as e:
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logger.error(f"添加字段 '{field_name}' 失败: {e}")
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# 检查并删除多余字段（新增逻辑）
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extra_fields = existing_columns - model_fields
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if extra_fields:
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logger.warning(f"表 '{table_name}' 存在多余字段: {extra_fields}")
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for field_name in extra_fields:
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try:
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logger.warning(f"表 '{table_name}' 存在多余字段 '{field_name}'，正在尝试删除...")
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db.execute_sql(f"ALTER TABLE {table_name} DROP COLUMN {field_name}")
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logger.info(f"字段 '{field_name}' 删除成功")
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except Exception as e:
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logger.error(f"删除字段 '{field_name}' 失败: {e}")
|
||
except Exception as e:
|
||
logger.exception(f"检查表或字段是否存在时出错: {e}")
|
||
# 如果检查失败（例如数据库不可用），则退出
|
||
return
|
||
|
||
logger.info("数据库初始化完成")
|
||
|
||
|
||
# 模块加载时调用初始化函数
|
||
initialize_database()
|