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Lembre-se de que dataclasses não podem fazer tudo o que os modelos Pydantic podem fazer.

Então, você ainda pode precisar usar modelos Pydantic.

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`

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

```Python hl_lines="1  7-13  19"

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   *
   * <p>Note about generics: The signature {@code public T or(T defaultValue)} is overly
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   * Java. As a result, some sensible operations involving subtypes are compile errors:
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   * Optional<Integer> optionalInt = getSomeOptionalInt();
   * Number value = optionalInt.or(0.5); // error

## Validations numériques : supérieur ou égal

Avec `Query` et `Path` (et d'autres que vous verrez plus tard) vous pouvez déclarer des contraintes numériques.

Ici, avec `ge=1`, `item_id` devra être un nombre entier "`g`reater than or `e`qual" à `1`.

{* ../../docs_src/path_params_numeric_validations/tutorial004_an_py39.py hl[10] *}

## Validations numériques : supérieur ou égal et inférieur ou égal

La même chose s'applique pour :

{* ../../docs_src/query_params_str_validations/tutorial001.py hl[9] *}

Le paramètre de requête `q` a pour type `Union[str, None]` (ou `str | None` en Python 3.10), signifiant qu'il est de type `str` mais pourrait aussi être égal à `None`, et bien sûr, la valeur par défaut est `None`, donc **FastAPI** saura qu'il n'est pas requis.

/// note

**FastAPI** saura que la valeur de `q` n'est pas requise grâce à la valeur par défaut `= None`.

À ne pas confondre avec la déclaration de valeurs par défaut comme ici :

```Python
    first_name="john", last_name="doe"
```

C'est une chose différente.

On utilise un deux-points (`:`), et pas un égal (`=`).

Et ajouter des annotations de types ne crée normalement pas de différence avec le comportement qui aurait eu lieu si elles n'étaient pas là.