Das ist dank **Pydantic** ebenfalls möglich, da es <a href="https://docs.pydantic.dev/latest/concepts/dataclasses/#use-of-stdlib-dataclasses-with-basemodel" class="external-link" target="_blank">`dataclasses` intern unterstützt</a>.

Auch wenn im obigen Code Pydantic nicht explizit vorkommt, verwendet FastAPI Pydantic, um diese Standard-Datenklassen in Pydantics eigene Variante von Datenklassen zu konvertieren.

So, even with the code above that doesn't use Pydantic explicitly, FastAPI is using Pydantic to convert those standard dataclasses to Pydantic's own flavor of dataclasses.

And of course, it supports the same:

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

/// info

Так что даже если в коде выше Pydantic не используется явно, FastAPI использует Pydantic, чтобы конвертировать стандартные dataclasses в собственный вариант dataclasses от Pydantic.

И, конечно, поддерживаются те же возможности:

- валидация данных

Então, mesmo com o código acima que não usa Pydantic explicitamente, o FastAPI está usando Pydantic para converter essas dataclasses padrão para a versão do Pydantic.

E claro, ele suporta o mesmo:

* validação de dados
* serialização de dados
* documentação de dados, etc.

Isso funciona da mesma forma que com os modelos Pydantic. E na verdade é alcançado da mesma maneira por baixo dos panos, usando Pydantic.

/// info | Informação

Así que, incluso con el código anterior que no usa Pydantic explícitamente, FastAPI está usando Pydantic para convertir esos dataclasses estándar en su propia versión de dataclasses de Pydantic.

Y por supuesto, soporta lo mismo:

* validación de datos

因此，即便上述代码没有显式使用 Pydantic，FastAPI 仍会使用 Pydantic 把标准数据类转换为 Pydantic 数据类（`dataclasses`）。

并且，它仍然支持以下功能：

* 数据验证
* 数据序列化
* 数据存档等

数据类的和运作方式与 Pydantic 模型相同。实际上，它的底层使用的也是 Pydantic。

/// info | 说明

注意，数据类不支持 Pydantic 模型的所有功能。

因此，开发时仍需要使用 Pydantic 模型。

但如果数据类很多，这一技巧能给 FastAPI 开发 Web API 增添不少助力。🤓

///

## `response_model` 使用数据类

在 `response_model` 参数中使用 `dataclasses`：

from fastapi.types import IncEx, ModelNameMap, UnionType
from pydantic import BaseModel, ConfigDict, Field, TypeAdapter, create_model
from pydantic import PydanticSchemaGenerationError as PydanticSchemaGenerationError
from pydantic import PydanticUndefinedAnnotation as PydanticUndefinedAnnotation
from pydantic import ValidationError as ValidationError
from pydantic._internal._schema_generation_shared import (  # type: ignore[attr-defined]

from typing import (
    Annotated,
    Any,
    Union,
)

from fastapi.types import UnionType
from pydantic import BaseModel
from pydantic.version import VERSION as PYDANTIC_VERSION
from starlette.datastructures import UploadFile
from typing_extensions import get_args, get_origin

# Copy from Pydantic v2, compatible with v1
if sys.version_info < (3, 10):

    Validator,
    annotation_is_pydantic_v1,
)
from fastapi.datastructures import DefaultPlaceholder, DefaultType
from fastapi.exceptions import FastAPIDeprecationWarning, PydanticV1NotSupportedError
from pydantic import BaseModel
from pydantic.fields import FieldInfo
from typing_extensions import Literal

from ._compat import v2

if TYPE_CHECKING:  # pragma: nocover
    from .routing import APIRoute

# Cache for `create_cloned_field`

from fastapi.types import IncEx
from pydantic import BaseModel
from pydantic.color import Color
from pydantic.networks import AnyUrl, NameEmail
from pydantic.types import SecretBytes, SecretStr
from pydantic_core import PydanticUndefinedType

from ._compat import (
    Url,
    is_pydantic_v1_model_instance,
)


# Taken from Pydantic v1 as is
def isoformat(o: Union[datetime.date, datetime.time]) -> str: