从Python数据类动态创建Pydantic模型

Question

我想从数据类动态创建Pydantic模型，类似于您可以从数据类动态创建棉花糖模式的方法，就像在marshmallow-dataclass或https://stevenloria.com/dynamic-schemas-in-marshmallow/中一样。有没有一个库或者简单的方法可以做到这一点？

一些背景知识--我更喜欢在我的业务逻辑中使用数据类，而不是直接使用Pydantic模型。我只在我的FastAPI应用程序中使用Pydantic模型来序列化/反序列化带有驼峰式大小写字段的数据。然而，我发现自己基本上重复了数据类的定义，这是很低效的。

示例输入：

from typing import List

from dataclasses import dataclass


@dataclass
class Item:
    id: int = None
    stuff: str = None
    height: float = None


@dataclass
class Bag:
    id: int = None
    name: str = None
    things: List[Item] = None


@dataclass
class Basket:
    id: int = None
    recipient: str = None
    bags: List[Bag] = None
    best_item: Item = None

所需输出：

from typing import List

from pydantic.main import BaseModel


def camel_case_converter(value: str):
    parts = value.lower().split('_')
    return parts[0] + ''.join(i.title() for i in parts[1:])


class CamelBaseModel(BaseModel):
    class Config:
        alias_generator = camel_case_converter


class Item(CamelBaseModel):
    id: int = None
    stuff: str = None
    height: float = None


class Bag(CamelBaseModel):
    id: int = None
    name: str = None
    things: List[Item] = None


class Basket(CamelBaseModel):
    id: int = None
    recipient: str = None
    bags: List[Bag] = None
    best_item: Item = None

Answer 1

也许是这样的？(来自https://github.com/samuelcolvin/pydantic/issues/1967#issuecomment-742698281)

from typing import Type

from pydantic import BaseModel
from pydantic.dataclasses import dataclass as pydantic_dataclass
from typing import List

from dataclasses import dataclass


def model_from_dataclass(kls: 'StdlibDataclass') -> Type[BaseModel]:
    """Converts a stdlib dataclass to a pydantic BaseModel"""
    return pydantic_dataclass(kls).__pydantic_model__

@dataclass
class Item:
    id: int = None
    stuff: str = None
    height: float = None

ItemBaseModel = model_from_dataclass(Item)