assert response.status_code == 422, response.text
    assert response.json() == IsDict(
        {
            "detail": [
                {
                    "type": "model_attributes_type",
                    "loc": ["body"],
                    "msg": "Input should be a valid dictionary or object to extract fields from",
                    "input": "name=Foo&price=50.5",
                }

    assert response.status_code == 422, response.text
    assert response.json() == IsDict(
        {
            "detail": [
                {
                    "type": "model_attributes_type",
                    "loc": ["body"],
                    "msg": "Input should be a valid dictionary or object to extract fields from",
                    "input": "name=Foo&price=50.5",
                }

* `tags: List[str] = []` has a default of an empty list: `[]`.

but you might want to omit them from the result if they were not actually stored.

For example, if you have models with many optional attributes in a NoSQL database, but you don't want to send very long JSON responses full of default values.

### Use the `response_model_exclude_unset` parameter

=== "Python 3.8+"

    ```Python hl_lines="9-11"
    {!> ../../../docs_src/body_nested_models/tutorial004.py!}
    ```

### Das Kindmodell als Typ verwenden

Und dann können wir es als Typ eines Attributes verwenden.

=== "Python 3.10+"

    ```Python hl_lines="18"
    {!> ../../../docs_src/body_nested_models/tutorial004_py310.py!}
    ```

=== "Python 3.9+"

    ```Python hl_lines="20"

#### Data "schema"

The term "schema" might also refer to the shape of some data, like a JSON content.

In that case, it would mean the JSON attributes, and data types they have, etc.

#### OpenAPI and JSON Schema

```Python hl_lines="4  7-8  18-19"
{!../../../docs_src/sql_databases/sql_app/models.py!}
```

The `__tablename__` attribute tells SQLAlchemy the name of the table to use in the database for each of these models.

### Create model attributes/columns

Now create all the model (class) attributes.

Each of these attributes represents a column in its corresponding database table.

We use `Column` from SQLAlchemy as the default value.

!!! check "Inspired **FastAPI** to"
    Define extra validations for data types using the "default" value of model attributes. This improves editor support, and it was not available in Pydantic before.

## Pydantic models

<a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a> is a Python library to perform data validation.

You declare the "shape" of the data as classes with attributes.

And each attribute has a type.

Then you create an instance of that class with some values and it will validate the values, convert them to the appropriate type (if that's the case) and give you an object with all the data.

* Per le richieste `PUT` su `/items/{item_id}`, leggerà il body come JSON, questo comprende:
    * verifica che la richiesta abbia un attributo obbligatorio `name` e che sia di tipo `str`.
    * verifica che la richiesta abbia un attributo obbligatorio `price` e che sia di tipo `float`.
    * verifica che la richiesta abbia un attributo opzionale `is_offer` e che sia di tipo `bool`, se presente.
    * Tutto questo funzionerebbe anche con oggetti JSON più complessi.

{!../../../docs_src/sql_databases_peewee/sql_app/models.py!}
```

!!! tip
    Peewee creates several magic attributes.

    It will automatically add an `id` attribute as an integer to be the primary key.

    It will chose the name of the tables based on the class names.

    For the `Item`, it will create an attribute `owner_id` with the integer ID of the `User`. But we don't declare it anywhere.

## Create the Pydantic models