从 Python 数据类动态创建 Pydantic 模型

Question

我想从一个数据类动态创建一个 Pydantic 模型，类似于从一个数据类动态创建一个棉花糖模式，如在 marshmallow-dataclass 或 https://stevenloria.com/dynamic-schemas-in-marshmallow/ 中。是否已经有一个库或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。我仅在我的 FastAPI 应用程序中使用 Pydantic 模型对带有驼峰式字段的数据进行序列化/反序列化。但是，我发现自己基本上是在复制效率不高的数据类定义。

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

期望的输出：

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

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)