从 Python 数据类动态创建 Pydantic 模型

如何解决从 Python 数据类动态创建 Pydantic 模型

我想从数据类动态创建 Pydantic 模型，类似于如何从数据类动态创建棉花糖模式，如在 marshmallow-dataclass 或 https://stevenloria.com/dynamic-schemas-in-marshmallow/ 中。是否已经有图书馆或简单的方法来做到这一点？

一些背景 - 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。我使用 Pydantic 模型仅在我的 FastAPI 应用程序中使用驼峰式字段对数据进行序列化/反序列化。但是，我发现自己基本上重复了效率不高的数据类定义。

样本输入：

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

所需的输出：

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

解决方法

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)