Nesse caso, existem 3 parâmetros de consulta:

* `needy`, um `str` obrigatório.
* `skip`, um `int` com o valor padrão `0`.
* `limit`, um `int` opcional.

/// tip | Dica

Você também poderia usar `Enum` da mesma forma que com [Path Parameters](path-params.md#valores-predefinidos){.internal-link target=_blank}.

///

## Upgrade der FastAPI-Versionen

Sie sollten Tests für Ihre Anwendung hinzufügen.

Mit **FastAPI** ist das sehr einfach (dank Starlette), schauen Sie sich die Dokumentation an: [Testen](../tutorial/testing.md){.internal-link target=_blank}

すべての属性にpython標準の型を使用します:

{* ../../docs_src/body/tutorial001.py hl[7:11] *}

クエリパラメータの宣言と同様に、モデル属性がデフォルト値をもつとき、必須な属性ではなくなります。それ以外は必須になります。オプショナルな属性にしたい場合は `None` を使用してください。

例えば、上記のモデルは以下の様なJSON「`オブジェクト`」(もしくはPythonの `dict` ) を宣言しています:

```JSON
{
    "name": "Foo",
    "description": "An optional description",
    "price": 45.2,
    "tax": 3.5
}
```

{* ../../docs_src/response_model/tutorial003_04.py hl[10] *}

...👉 ❌ ↩️ 🆎 ✍ 🚫 Pydantic 🆎 & 🚫 👁 `Response` 🎓 ⚖️ 🏿, ⚫️ 🇪🇺 (🙆 2️⃣) 🖖 `Response` & `dict`.

### ❎ 📨 🏷

▶️ ⚪️➡️ 🖼 🔛, 👆 5️⃣📆 🚫 💚 ✔️ 🔢 💽 🔬, 🧾, 🖥, ♒️. 👈 🎭 FastAPI.

✋️ 👆 💪 💚 🚧 📨 🆎 ✍ 🔢 🤚 🐕‍🦺 ⚪️➡️ 🧰 💖 👨‍🎨 & 🆎 ☑ (✅ ✍).

👉 💼, 👆 💪 ❎ 📨 🏷 ⚡ ⚒ `response_model=None`:

/// info

In Pydantic version 1 the configuration was done in an internal class `Config`, in Pydantic version 2 it's done in an attribute `model_config`. This attribute takes a `dict`, and to get autocompletion and inline errors you can import and use `SettingsConfigDict` to define that `dict`.

///

Here we define the config `env_file` inside of your Pydantic `Settings` class, and set the value to the filename with the dotenv file we want to use.

Por padrão, tudo **"simplesmente funciona"**.

### Validação

* Validação para a maioria dos (ou todos?) **tipos de dados** do Python, incluindo:
    * objetos JSON (`dict`).
    * arrays JSON (`list`), definindo tipos dos itens.
    * campos String (`str`), definindo tamanho mínimo e máximo.
    * Numbers (`int`, `float`) com valores mínimos e máximos, etc.

{* ../../docs_src/events/tutorial001.py hl[8] *}

In this case, the `startup` event handler function will initialize the items "database" (just a `dict`) with some values.

You can add more than one event handler function.

And your application won't start receiving requests until all the `startup` event handlers have completed.

## Eine Anwendung mit Callbacks

Sehen wir uns das alles anhand eines Beispiels an.

Stellen Sie sich vor, Sie entwickeln eine Anwendung, mit der Sie Rechnungen erstellen können.

Diese Rechnungen haben eine `id`, einen optionalen `title`, einen `customer` (Kunde) und ein `total` (Gesamtsumme).

  For the env param var_name, read from the environment or verify user input
  until receiving valid input. When done, set var_name in the environ_cp to its
  new value.

  Args:
    environ_cp: (Dict) copy of the os.environ.
    var_name: (String) string for name of environment variable, e.g. "TF_MYVAR".
    var_default: (String) default value string.
    ask_for_var: (String) string for how to ask for user input.

      "coverage" : {
        "per_day" : [ "linux" ]
      }
    } ]
  }, {
    "testId" : "org.gradle.performance.regression.corefeature.RichConsolePerformanceTest.clean assemble with rich console",
    "groups" : [ {
      "testProject" : "bigNative",
      "coverage" : {
        "per_day" : [ "linux" ]
      }
    }, {
      "testProject" : "largeJavaMultiProject",
      "coverage" : {