749 lines
29 KiB
Python
749 lines
29 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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model_assign_name = TextField(null=True) # 添加索引
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model_api_provider = TextField(null=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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time_cost = DoubleField(null=True)
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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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reply_to = TextField(null=True)
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interest_value = DoubleField(null=True)
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key_words = TextField(null=True)
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key_words_lite = TextField(null=True)
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is_mentioned = BooleanField(null=True)
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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(null=True)
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user_id = TextField(null=True)
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user_nickname = TextField(null=True)
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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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memorized_times = IntegerField(default=0) # 被记忆的次数
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priority_mode = TextField(null=True)
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priority_info = TextField(null=True)
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additional_config = TextField(null=True)
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is_emoji = BooleanField(default=False)
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is_picid = BooleanField(default=False)
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is_command = BooleanField(default=False)
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is_notify = BooleanField(default=False)
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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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is_known = BooleanField(default=False) # 是否已认识
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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(null=True) # 用户昵称
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points = TextField(null=True) # 个人印象的点
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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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attitude_to_me = TextField(null=True) # 对bot的态度
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attitude_to_me_confidence = FloatField(null=True) # 对bot的态度置信度
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friendly_value = FloatField(null=True) # 对bot的友好程度
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friendly_value_confidence = FloatField(null=True) # 对bot的友好程度置信度
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rudeness = TextField(null=True) # 对bot的冒犯程度
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rudeness_confidence = FloatField(null=True) # 对bot的冒犯程度置信度
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neuroticism = TextField(null=True) # 对bot的神经质程度
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neuroticism_confidence = FloatField(null=True) # 对bot的神经质程度置信度
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conscientiousness = TextField(null=True) # 对bot的尽责程度
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conscientiousness_confidence = FloatField(null=True) # 对bot的尽责程度置信度
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likeness = TextField(null=True) # 对bot的相似程度
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likeness_confidence = FloatField(null=True) # 对bot的相似程度置信度
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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 GroupInfo(BaseModel):
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"""
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用于存储群组信息数据的模型。
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"""
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group_id = TextField(unique=True, index=True) # 群组唯一ID
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group_name = TextField(null=True) # 群组名称 (允许为空)
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platform = TextField() # 平台
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group_impression = TextField(null=True) # 群组印象
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member_list = TextField(null=True) # 群成员列表 (JSON格式)
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topic = TextField(null=True) # 群组基本信息
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create_time = FloatField(null=True) # 创建时间 (时间戳)
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last_active = FloatField(null=True) # 最后活跃时间
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member_count = IntegerField(null=True, default=0) # 成员数量
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class Meta:
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# database = db # 继承自 BaseModel
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table_name = "group_info"
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class Memory(BaseModel):
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memory_id = TextField(index=True)
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chat_id = TextField(null=True)
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memory_text = TextField(null=True)
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keywords = TextField(null=True)
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create_time = FloatField(null=True)
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last_view_time = FloatField(null=True)
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class Meta:
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table_name = "memory"
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class Expression(BaseModel):
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"""
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用于存储表达风格的模型。
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"""
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situation = TextField()
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style = TextField()
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count = FloatField()
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last_active_time = FloatField()
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chat_id = TextField(index=True)
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type = TextField()
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create_date = FloatField(null=True) # 创建日期，允许为空以兼容老数据
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class Meta:
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table_name = "expression"
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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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Expression,
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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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Memory,
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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(sync_constraints=False):
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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。
|
||
|
||
Args:
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sync_constraints (bool): 是否同步字段约束。默认为 False。
|
||
如果为 True，会检查并修复字段的 NULL 约束不一致问题。
|
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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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Expression,
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Memory,
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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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||
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if missing_fields := model_fields - existing_columns:
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logger.warning(f"表 '{table_name}' 缺失字段: {missing_fields}")
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||
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for field_name, field_obj in model._meta.fields.items():
|
||
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...")
|
||
field_type = field_obj.__class__.__name__
|
||
sql_type = {
|
||
"TextField": "TEXT",
|
||
"IntegerField": "INTEGER",
|
||
"FloatField": "FLOAT",
|
||
"DoubleField": "DOUBLE",
|
||
"BooleanField": "INTEGER",
|
||
"DateTimeField": "DATETIME",
|
||
}.get(field_type, "TEXT")
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||
alter_sql = f"ALTER TABLE {table_name} ADD COLUMN {field_name} {sql_type}"
|
||
alter_sql += " NULL" if field_obj.null else " NOT NULL"
|
||
if hasattr(field_obj, "default") and field_obj.default is not None:
|
||
# 正确处理不同类型的默认值，跳过lambda函数
|
||
default_value = field_obj.default
|
||
if callable(default_value):
|
||
# 跳过lambda函数或其他可调用对象，这些无法在SQL中表示
|
||
pass
|
||
elif isinstance(default_value, str):
|
||
alter_sql += f" DEFAULT '{default_value}'"
|
||
elif isinstance(default_value, bool):
|
||
alter_sql += f" DEFAULT {int(default_value)}"
|
||
else:
|
||
alter_sql += f" DEFAULT {default_value}"
|
||
try:
|
||
db.execute_sql(alter_sql)
|
||
logger.info(f"字段 '{field_name}' 添加成功")
|
||
except Exception as e:
|
||
logger.error(f"添加字段 '{field_name}' 失败: {e}")
|
||
|
||
# 检查并删除多余字段（新增逻辑）
|
||
extra_fields = existing_columns - model_fields
|
||
if extra_fields:
|
||
logger.warning(f"表 '{table_name}' 存在多余字段: {extra_fields}")
|
||
for field_name in extra_fields:
|
||
try:
|
||
logger.warning(f"表 '{table_name}' 存在多余字段 '{field_name}'，正在尝试删除...")
|
||
db.execute_sql(f"ALTER TABLE {table_name} DROP COLUMN {field_name}")
|
||
logger.info(f"字段 '{field_name}' 删除成功")
|
||
except Exception as e:
|
||
logger.error(f"删除字段 '{field_name}' 失败: {e}")
|
||
|
||
# 如果启用了约束同步，执行约束检查和修复
|
||
if sync_constraints:
|
||
logger.debug("开始同步数据库字段约束...")
|
||
sync_field_constraints()
|
||
logger.debug("数据库字段约束同步完成")
|
||
|
||
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")
|
||
|
||
|
||
def sync_field_constraints():
|
||
"""
|
||
同步数据库字段约束，确保现有数据库字段的 NULL 约束与模型定义一致。
|
||
如果发现不一致，会自动修复字段约束。
|
||
"""
|
||
|
||
models = [
|
||
ChatStreams,
|
||
LLMUsage,
|
||
Emoji,
|
||
Messages,
|
||
Images,
|
||
ImageDescriptions,
|
||
OnlineTime,
|
||
PersonInfo,
|
||
Expression,
|
||
Memory,
|
||
GraphNodes,
|
||
GraphEdges,
|
||
ActionRecords,
|
||
]
|
||
|
||
try:
|
||
with db:
|
||
for model in models:
|
||
table_name = model._meta.table_name
|
||
if not db.table_exists(model):
|
||
logger.warning(f"表 '{table_name}' 不存在，跳过约束检查")
|
||
continue
|
||
|
||
logger.debug(f"检查表 '{table_name}' 的字段约束...")
|
||
|
||
# 获取当前表结构信息
|
||
cursor = db.execute_sql(f"PRAGMA table_info('{table_name}')")
|
||
current_schema = {row[1]: {'type': row[2], 'notnull': bool(row[3]), 'default': row[4]}
|
||
for row in cursor.fetchall()}
|
||
|
||
# 检查每个模型字段的约束
|
||
constraints_to_fix = []
|
||
for field_name, field_obj in model._meta.fields.items():
|
||
if field_name not in current_schema:
|
||
continue # 字段不存在，跳过
|
||
|
||
current_notnull = current_schema[field_name]['notnull']
|
||
model_allows_null = field_obj.null
|
||
|
||
# 如果模型允许 null 但数据库字段不允许 null，需要修复
|
||
if model_allows_null and current_notnull:
|
||
constraints_to_fix.append({
|
||
'field_name': field_name,
|
||
'field_obj': field_obj,
|
||
'action': 'allow_null',
|
||
'current_constraint': 'NOT NULL',
|
||
'target_constraint': 'NULL'
|
||
})
|
||
logger.warning(f"字段 '{field_name}' 约束不一致: 模型允许NULL，但数据库为NOT NULL")
|
||
|
||
# 如果模型不允许 null 但数据库字段允许 null，也需要修复（但要小心）
|
||
elif not model_allows_null and not current_notnull:
|
||
constraints_to_fix.append({
|
||
'field_name': field_name,
|
||
'field_obj': field_obj,
|
||
'action': 'disallow_null',
|
||
'current_constraint': 'NULL',
|
||
'target_constraint': 'NOT NULL'
|
||
})
|
||
logger.warning(f"字段 '{field_name}' 约束不一致: 模型不允许NULL，但数据库允许NULL")
|
||
|
||
# 修复约束不一致的字段
|
||
if constraints_to_fix:
|
||
logger.info(f"表 '{table_name}' 需要修复 {len(constraints_to_fix)} 个字段约束")
|
||
_fix_table_constraints(table_name, model, constraints_to_fix)
|
||
else:
|
||
logger.debug(f"表 '{table_name}' 的字段约束已同步")
|
||
|
||
except Exception as e:
|
||
logger.exception(f"同步字段约束时出错: {e}")
|
||
|
||
|
||
def _fix_table_constraints(table_name, model, constraints_to_fix):
|
||
"""
|
||
修复表的字段约束。
|
||
对于 SQLite，由于不支持直接修改列约束，需要重建表。
|
||
"""
|
||
try:
|
||
# 备份表名
|
||
backup_table = f"{table_name}_backup_{int(datetime.datetime.now().timestamp())}"
|
||
|
||
logger.info(f"开始修复表 '{table_name}' 的字段约束...")
|
||
|
||
# 1. 创建备份表
|
||
db.execute_sql(f"CREATE TABLE {backup_table} AS SELECT * FROM {table_name}")
|
||
logger.info(f"已创建备份表 '{backup_table}'")
|
||
|
||
# 2. 删除原表
|
||
db.execute_sql(f"DROP TABLE {table_name}")
|
||
logger.info(f"已删除原表 '{table_name}'")
|
||
|
||
# 3. 重新创建表（使用当前模型定义）
|
||
db.create_tables([model])
|
||
logger.info(f"已重新创建表 '{table_name}' 使用新的约束")
|
||
|
||
# 4. 从备份表恢复数据
|
||
# 获取字段列表
|
||
fields = list(model._meta.fields.keys())
|
||
fields_str = ', '.join(fields)
|
||
|
||
# 对于需要从 NOT NULL 改为 NULL 的字段，直接复制数据
|
||
# 对于需要从 NULL 改为 NOT NULL 的字段，需要处理 NULL 值
|
||
insert_sql = f"INSERT INTO {table_name} ({fields_str}) SELECT {fields_str} FROM {backup_table}"
|
||
|
||
# 检查是否有字段需要从 NULL 改为 NOT NULL
|
||
null_to_notnull_fields = [
|
||
constraint['field_name'] for constraint in constraints_to_fix
|
||
if constraint['action'] == 'disallow_null'
|
||
]
|
||
|
||
if null_to_notnull_fields:
|
||
# 需要处理 NULL 值，为这些字段设置默认值
|
||
logger.warning(f"字段 {null_to_notnull_fields} 将从允许NULL改为不允许NULL，需要处理现有的NULL值")
|
||
|
||
# 构建更复杂的 SELECT 语句来处理 NULL 值
|
||
select_fields = []
|
||
for field_name in fields:
|
||
if field_name in null_to_notnull_fields:
|
||
field_obj = model._meta.fields[field_name]
|
||
# 根据字段类型设置默认值
|
||
if isinstance(field_obj, (TextField,)):
|
||
default_value = "''"
|
||
elif isinstance(field_obj, (IntegerField, FloatField, DoubleField)):
|
||
default_value = "0"
|
||
elif isinstance(field_obj, BooleanField):
|
||
default_value = "0"
|
||
elif isinstance(field_obj, DateTimeField):
|
||
default_value = f"'{datetime.datetime.now()}'"
|
||
else:
|
||
default_value = "''"
|
||
|
||
select_fields.append(f"COALESCE({field_name}, {default_value}) as {field_name}")
|
||
else:
|
||
select_fields.append(field_name)
|
||
|
||
select_str = ', '.join(select_fields)
|
||
insert_sql = f"INSERT INTO {table_name} ({fields_str}) SELECT {select_str} FROM {backup_table}"
|
||
|
||
db.execute_sql(insert_sql)
|
||
logger.info(f"已从备份表恢复数据到 '{table_name}'")
|
||
|
||
# 5. 验证数据完整性
|
||
original_count = db.execute_sql(f"SELECT COUNT(*) FROM {backup_table}").fetchone()[0]
|
||
new_count = db.execute_sql(f"SELECT COUNT(*) FROM {table_name}").fetchone()[0]
|
||
|
||
if original_count == new_count:
|
||
logger.info(f"数据完整性验证通过: {original_count} 行数据")
|
||
# 删除备份表
|
||
db.execute_sql(f"DROP TABLE {backup_table}")
|
||
logger.info(f"已删除备份表 '{backup_table}'")
|
||
else:
|
||
logger.error(f"数据完整性验证失败: 原始 {original_count} 行，新表 {new_count} 行")
|
||
logger.error(f"备份表 '{backup_table}' 已保留，请手动检查")
|
||
|
||
# 记录修复的约束
|
||
for constraint in constraints_to_fix:
|
||
logger.info(f"已修复字段 '{constraint['field_name']}': "
|
||
f"{constraint['current_constraint']} -> {constraint['target_constraint']}")
|
||
|
||
except Exception as e:
|
||
logger.exception(f"修复表 '{table_name}' 约束时出错: {e}")
|
||
# 尝试恢复
|
||
try:
|
||
if db.table_exists(backup_table):
|
||
logger.info(f"尝试从备份表 '{backup_table}' 恢复...")
|
||
db.execute_sql(f"DROP TABLE IF EXISTS {table_name}")
|
||
db.execute_sql(f"ALTER TABLE {backup_table} RENAME TO {table_name}")
|
||
logger.info(f"已从备份恢复表 '{table_name}'")
|
||
except Exception as restore_error:
|
||
logger.exception(f"恢复表失败: {restore_error}")
|
||
|
||
|
||
def check_field_constraints():
|
||
"""
|
||
检查但不修复字段约束，返回不一致的字段信息。
|
||
用于在修复前预览需要修复的内容。
|
||
"""
|
||
|
||
models = [
|
||
ChatStreams,
|
||
LLMUsage,
|
||
Emoji,
|
||
Messages,
|
||
Images,
|
||
ImageDescriptions,
|
||
OnlineTime,
|
||
PersonInfo,
|
||
Expression,
|
||
Memory,
|
||
GraphNodes,
|
||
GraphEdges,
|
||
ActionRecords,
|
||
]
|
||
|
||
inconsistencies = {}
|
||
|
||
try:
|
||
with db:
|
||
for model in models:
|
||
table_name = model._meta.table_name
|
||
if not db.table_exists(model):
|
||
continue
|
||
|
||
# 获取当前表结构信息
|
||
cursor = db.execute_sql(f"PRAGMA table_info('{table_name}')")
|
||
current_schema = {row[1]: {'type': row[2], 'notnull': bool(row[3]), 'default': row[4]}
|
||
for row in cursor.fetchall()}
|
||
|
||
table_inconsistencies = []
|
||
|
||
# 检查每个模型字段的约束
|
||
for field_name, field_obj in model._meta.fields.items():
|
||
if field_name not in current_schema:
|
||
continue
|
||
|
||
current_notnull = current_schema[field_name]['notnull']
|
||
model_allows_null = field_obj.null
|
||
|
||
if model_allows_null and current_notnull:
|
||
table_inconsistencies.append({
|
||
'field_name': field_name,
|
||
'issue': 'model_allows_null_but_db_not_null',
|
||
'model_constraint': 'NULL',
|
||
'db_constraint': 'NOT NULL',
|
||
'recommended_action': 'allow_null'
|
||
})
|
||
elif not model_allows_null and not current_notnull:
|
||
table_inconsistencies.append({
|
||
'field_name': field_name,
|
||
'issue': 'model_not_null_but_db_allows_null',
|
||
'model_constraint': 'NOT NULL',
|
||
'db_constraint': 'NULL',
|
||
'recommended_action': 'disallow_null'
|
||
})
|
||
|
||
if table_inconsistencies:
|
||
inconsistencies[table_name] = table_inconsistencies
|
||
|
||
except Exception as e:
|
||
logger.exception(f"检查字段约束时出错: {e}")
|
||
|
||
return inconsistencies
|
||
|
||
|
||
|
||
# 模块加载时调用初始化函数
|
||
initialize_database(sync_constraints=True) |