Sie können `response_model` in jeder möglichen *Pfadoperation* verwenden:

* `@app.get()`
* `@app.post()`
* `@app.put()`
* `@app.delete()`
* usw.

{* ../../docs_src/response_model/tutorial001_py310.py hl[17,22,24:27] *}

/// note | Hinweis

app = FastAPI()


@app.post("/sync/validated", response_model=ItemOut)
def sync_validated(item: ItemIn, dep: Annotated[int, Depends(dep_b)]):
    return ItemOut(name=item.name, value=item.value, dep=dep)


@app.get("/sync/dict-no-response-model")
def sync_dict_no_response_model():
    return {"name": "foo", "value": 123}


@app.get("/sync/dict-with-response-model", response_model=ItemOut)
def sync_dict_with_response_model(

Wenn Sie jedoch eine Menge Datenklassen herumliegen haben, ist dies ein guter Trick, um sie für eine Web-API mithilfe von FastAPI zu verwenden. 🤓

///

## Datenklassen in `response_model` { #dataclasses-in-response-model }

Sie können `dataclasses` auch im Parameter `response_model` verwenden:

{* ../../docs_src/dataclasses_/tutorial002_py310.py hl[1,6:12,18] *}

Die Datenklasse wird automatisch in eine Pydantic-Datenklasse konvertiert.

But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓

///

## Dataclasses in `response_model` { #dataclasses-in-response-model }

You can also use `dataclasses` in the `response_model` parameter:

{* ../../docs_src/dataclasses_/tutorial002_py310.py hl[1,6:12,18] *}

The dataclass will be automatically converted to a Pydantic dataclass.

@app.get("/response_model-no_annotation-return_invalid_dict", response_model=User)
def response_model_no_annotation_return_invalid_dict():
    return {"name": "John"}


@app.get("/response_model-no_annotation-return_invalid_model", response_model=User)
def response_model_no_annotation_return_invalid_model():
    return Item(name="Foo", price=42.0)


@app.get(
    "/response_model-no_annotation-return_dict_with_extra_data", response_model=User
)

        self.endpoint = endpoint
        if isinstance(response_model, DefaultPlaceholder):
            return_annotation = get_typed_return_annotation(endpoint)
            if lenient_issubclass(return_annotation, Response):
                response_model = None
            else:
                response_model = return_annotation
        self.response_model = response_model
        self.summary = summary

Mas se você tem um monte de dataclasses por aí, este é um truque legal para usá-las para alimentar uma API web usando FastAPI. 🤓

///

## Dataclasses em `response_model` { #dataclasses-in-response-model }

Você também pode usar `dataclasses` no parâmetro `response_model`:

{* ../../docs_src/dataclasses_/tutorial002_py310.py hl[1,6:12,18] *}

A dataclass será automaticamente convertida para uma dataclass Pydantic.

Pero si tienes un montón de dataclasses por ahí, este es un buen truco para usarlos para potenciar una API web usando FastAPI. 🤓

///

## Dataclasses en `response_model` { #dataclasses-in-response-model }

También puedes usar `dataclasses` en el parámetro `response_model`:

{* ../../docs_src/dataclasses_/tutorial002_py310.py hl[1,6:12,18] *}

El dataclass será automáticamente convertido a un dataclass de Pydantic.

Но если у вас уже есть набор dataclasses, это полезный приём — задействовать их для веб-API на FastAPI. 🤓

///

## Dataclasses в `response_model` { #dataclasses-in-response-model }

Вы также можете использовать `dataclasses` в параметре `response_model`:

{* ../../docs_src/dataclasses_/tutorial002_py310.py hl[1,6:12,18] *}

Этот dataclass будет автоматически преобразован в Pydantic dataclass.

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`：

{* ../../docs_src/dataclasses_/tutorial002.py hl[1,7:13,19] *}

本例把数据类自动转换为 Pydantic 数据类。

API 文档中也会显示相关概图：

<img src="/img/tutorial/dataclasses/image01.png">