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48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
from pydantic import ConfigDict, Field, field_validator
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from backend.common.schema import SchemaBase
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from backend.plugin.code_generator.utils.type_conversion import sql_type_to_sqlalchemy
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class GenColumnSchemaBase(SchemaBase):
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"""代码生成模型基础模型"""
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name: str = Field(description='列名称')
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comment: str | None = Field(None, description='列描述')
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type: str = Field(description='SQLA 模型列类型')
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default: str | None = Field(None, description='列默认值')
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sort: int = Field(description='列排序')
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length: int = Field(description='列长度')
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is_pk: bool = Field(False, description='是否主键')
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is_nullable: bool = Field(False, description='是否可为空')
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gen_business_id: int = Field(description='代码生成业务ID')
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@field_validator('type')
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@classmethod
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def normalize_type(cls, v: str) -> str:
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"""规范化类型"""
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return sql_type_to_sqlalchemy(v)
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class CreateGenColumnParam(GenColumnSchemaBase):
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"""创建代码生成模型列参数"""
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class CreateGenColumnInternalParam(CreateGenColumnParam):
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"""创建代码生成模型列内部参数"""
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pd_type: str | None = Field(None, description='列类型对应的 pydantic 类型')
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class UpdateGenColumnParam(GenColumnSchemaBase):
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"""更新代码生成模型列参数"""
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class GetGenColumnDetail(GenColumnSchemaBase):
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"""获取代码生成模型列详情"""
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model_config = ConfigDict(from_attributes=True)
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id: int = Field(description='主键 ID')
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pd_type: str = Field(description='列类型对应的 pydantic 类型')
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