`response_model` receives the same type you would declare for a Pydantic model field, so, it can be a Pydantic model, but it can also be, e.g. a `list` of Pydantic models, like `List[Item]`.

FastAPI will use this `response_model` to do all the data documentation, validation, etc. and also to **convert and filter the output data** to its type declaration.

!!! tip

=== "Python 3.10+"

    ```Python hl_lines="12"
    {!> ../../../docs_src/body_nested_models/tutorial002_py310.py!}
    ```

=== "Python 3.9+"

    ```Python hl_lines="14"
    {!> ../../../docs_src/body_nested_models/tutorial002_py39.py!}
    ```

=== "Python 3.8+"

    ```Python hl_lines="14"
    {!> ../../../docs_src/body_nested_models/tutorial002.py!}
    ```

## Set-Typen

### `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!}
```

* 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.

    # cloned_types caches already cloned types to support recursive models and improve
    # performance by avoiding unnecessary cloning
    if cloned_types is None:
        cloned_types = _CLONED_TYPES_CACHE

    original_type = field.type_
    if is_dataclass(original_type) and hasattr(original_type, "__pydantic_model__"):
        original_type = original_type.__pydantic_model__
    use_type = original_type

import binascii
from base64 import b64decode
from typing import Optional

from fastapi.exceptions import HTTPException
from fastapi.openapi.models import HTTPBase as HTTPBaseModel
from fastapi.openapi.models import HTTPBearer as HTTPBearerModel
from fastapi.security.base import SecurityBase
from fastapi.security.utils import get_authorization_scheme_param
from pydantic import BaseModel
from starlette.requests import Request

Tất cả validation được xử lí bằng những thiết lập tốt và mạnh mẽ của **Pydantic**.

### Bảo mật và xác thực

Bảo mật và xác thực đã tích hợp mà không làm tổn hại tới cơ sở dữ liệu hoặc data models.

Tất cả cơ chế bảo mật định nghĩa trong OpenAPI, bao gồm:

* HTTP Basic.
* **OAuth2** (với **JWT tokens**). Xem hướng dẫn [OAuth2 with JWT](tutorial/security/oauth2-jwt.md){.internal-link target=_blank}.

You can see those schemas because they were declared with the models in the app.

That information is available in the app's **OpenAPI schema**, and then shown in the API docs (by Swagger UI).

And that same information from the models that is included in OpenAPI is what can be used to **generate the client code**.

### Generate a TypeScript Client

Now that we have the app with the models, we can generate the client code for the frontend.

```Python hl_lines="1  10-13  21-24"
{!../../../docs_src/sql_databases/sql_app/models.py!}
```

### 创建关系

现在创建关系。

为此，我们使用SQLAlchemy  ORM提供的`relationship`。

这将或多或少会成为一种“神奇”属性，其中表示该表与其他相关的表中的值。

```Python hl_lines="2  15  26"
{!../../../docs_src/sql_databases/sql_app/models.py!}
```

当访问 user 中的属性`items`时，如 中`my_user.items`，它将有一个`Item`SQLAlchemy 模型列表（来自`items`表），这些模型具有指向`users`表中此记录的外键。