### `response_model` Priority

If you declare both a return type and a `response_model`, the `response_model` will take priority and be used by FastAPI.

### `response_model_include` 和 `response_model_exclude`

你还可以使用*路径操作装饰器*的 `response_model_include` 和 `response_model_exclude` 参数。

它们接收一个由属性名称 `str` 组成的 `set` 来包含（忽略其他的）或者排除（包含其他的）这些属性。

如果你只有一个 Pydantic 模型，并且想要从输出中移除一些数据，则可以使用这种快捷方法。

!!! tip
    但是依然建议你使用上面提到的主意，使用多个类而不是这些参数。

```Python hl_lines="9 11 16"
{!../../../docs_src/response_model/tutorial003.py!}
```

ここでは、*path operation関数*がパスワードを含む同じ入力ユーザーを返しているにもかかわらず:

```Python hl_lines="24"
{!../../../docs_src/response_model/tutorial003.py!}
```

...`response_model`を`UserOut`と宣言したことで、パスワードが含まれていません:

```Python hl_lines="22"
{!../../../docs_src/response_model/tutorial003.py!}
```

### Приоритет `response_model`

Если одновременно указать аннотацию типа для ответа функции и параметр `response_model` - последний будет иметь больший приоритет и FastAPI будет использовать именно его.

### `response_model`-Priorität

Wenn sowohl Rückgabetyp als auch `response_model` deklariert sind, hat `response_model` die Priorität und wird von FastAPI bevorzugt verwendet.

    👉 ✔ `response_model_by_alias` 👈 👷 ➡.

=== "🐍 3️⃣.6️⃣ & 🔛"

    ```Python hl_lines="31  37"
    {!> ../../../docs_src/response_model/tutorial005.py!}
    ```

=== "🐍 3️⃣.1️⃣0️⃣ & 🔛"

    ```Python hl_lines="29  35"
    {!> ../../../docs_src/response_model/tutorial005_py310.py!}
    ```

!!! tip

```Python hl_lines="9  11  16"
{!../../../docs_src/response_model/tutorial003.py!}
```

여기서 *경로 작동 함수*가 비밀번호를 포함하는 동일한 입력 사용자를 반환할지라도:

```Python hl_lines="24"
{!../../../docs_src/response_model/tutorial003.py!}
```

...`response_model`을 `UserOut` 모델로 선언했기 때문에 비밀번호를 포함하지 않습니다:

```Python hl_lines="22"
{!../../../docs_src/response_model/tutorial003.py!}
```

            return name

    async def get_model_c() -> ModelC:
        return ModelC(username="test-user", password="test-password")

    @app.get("/model/{name}", response_model=ModelA)
    async def get_model_a(name: str, model_c=Depends(get_model_c)):
        return {"name": name, "description": "model-a-desc", "foo": model_c}

    client = TestClient(app)
    return client


@needs_pydanticv2

    if PYDANTIC_V2:
        kwargs.update({"mode": mode})
    else:
        kwargs.update(
            {
                "type_": type_,
                "class_validators": class_validators,
                "default": default,
                "required": required,
                "model_config": model_config,
                "alias": alias,
            }
        )
    try:

* 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
    Keep in mind that dataclasses can't do everything Pydantic models can do.

    So, you might still need to use Pydantic models.