from . import models, schemas


def get_user(user_id: int):
    return models.User.filter(models.User.id == user_id).first()


def get_user_by_email(email: str):
    return models.User.filter(models.User.email == email).first()


def get_users(skip: int = 0, limit: int = 100):
    return list(models.User.select().offset(skip).limit(limit))


def create_user(user: schemas.UserCreate):

Das folgende sind technische Details, die Sie überspringen können, wenn sie für Sie nicht wichtig sind.

///

`RequestValidationError` ist eine Unterklasse von Pydantics <a href="https://pydantic-docs.helpmanual.io/usage/models/#error-handling" class="external-link" target="_blank">`ValidationError`</a>.

client = TestClient(app)


def test_get_enums_alexnet():
    response = client.get("/models/alexnet")
    assert response.status_code == 200
    assert response.json() == {"model_name": "alexnet", "message": "Deep Learning FTW!"}


def test_get_enums_lenet():
    response = client.get("/models/lenet")
    assert response.status_code == 200

```JSON
{
  "title": "towel",
  "size": "XL"
}
```

受信したボディを含むデータが無効であることを示すレスポンスが表示されます:

```JSON hl_lines="12 13 14 15"
{
  "detail": [
    {
      "loc": [
        "body",
        "size"
      ],
      "msg": "value is not a valid integer",
      "type": "type_error.integer"
    }
  ],
  "body": {
    "title": "towel",
    "size": "XL"
  }
}
```

* 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.

现在试着发送一个无效的 `item`，例如：

```JSON
{
  "title": "towel",
  "size": "XL"
}

```

收到的响应包含 `body` 信息，并说明数据是无效的：

```JSON hl_lines="12-15"
{
  "detail": [
    {
      "loc": [
        "body",
        "size"
      ],
      "msg": "value is not a valid integer",
      "type": "type_error.integer"
    }
  ],
  "body": {
    "title": "towel",
    "size": "XL"
  }
}

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.

We'll fix these things by adding a few **extra models**. Here's where SQLModel will shine. ✨

### Create Multiple Models

In **SQLModel**, any model class that has `table=True` is a **table model**.

And any model class that doesn't have `table=True` is a **data model**, these ones are actually just Pydantic models (with a couple of small extra features). 🤓

To distinguish from the models with all optional values for **updates** and models with required values for **creation**, you can use the ideas described in [Extra Models](extra-models.md){.internal-link target=_blank}.

                    "type": "type_error.integer",
                }
            ],
            "body": {"title": "towel", "size": "XL"},
        }
    )


def test_post():
    data = {"title": "towel", "size": 5}
    response = client.post("/items/", json=data)
    assert response.status_code == 200, response.text
    assert response.json() == data


def test_openapi_schema():