```Python
def get_settings():
    return Settings()
```

würden wir dieses Objekt für jeden Request erstellen und die `.env`-Datei für jeden Request lesen. ⚠️

Da wir jedoch den `@lru_cache`-Dekorator oben verwenden, wird das `Settings`-Objekt nur einmal erstellt, nämlich beim ersten Aufruf. ✔️

{* ../../docs_src/settings/app03_an_py310/main.py hl[1,11] *}

E pronto.

**2 linhas**.

E com a mesma forma e estrutura de todas as suas *funções de operação de rota*.

Você pode pensar nela como uma *função de operação de rota* sem o "decorador" (sem a linha `@app.get("/some-path")`).

E com qualquer retorno que você desejar.

Neste caso, a dependência espera por:

* Um parâmetro de consulta opcional `q` do tipo `str`.

compilation to JavaScript, it cannot rely on the types to define validation, serialization and documentation at the same time. Due to this and some design decisions, to get validation, serialization and automatic schema generation, it's needed to add decorators in many places. So, it becomes quite verbose.

It can't handle nested models very well. So, if the JSON body in the request is a JSON object that has inner fields that in turn are nested JSON objects, it cannot be properly documented and...

And the part after the `yield` will be executed **after** the application has finished.

### Async Context Manager { #async-context-manager }

If you check, the function is decorated with an `@asynccontextmanager`.

That converts the function into something called an "**async context manager**".

{* ../../docs_src/events/tutorial003_py310.py hl[1,13] *}

Das war's schon.

**Zwei Zeilen**.

Und sie hat die gleiche Form und Struktur wie alle Ihre *Pfadoperation-Funktionen*.

Sie können sie sich als *Pfadoperation-Funktion* ohne den „Dekorator“ (ohne `@app.get("/some-path")`) vorstellen.

Und sie kann alles zurückgeben, was Sie möchten.

In diesem Fall erwartet diese Abhängigkeit:

* Einen optionalen Query-Parameter `q`, der ein `str` ist.

This might all sound abstract. Don't worry. You'll see all this in action in the [Tutorial - User Guide](tutorial/index.md).

The important thing is that by using standard Python types, in a single place (instead of adding more classes, decorators, etc), **FastAPI** will do a lot of the work for you.

/// info

```Python
def get_settings():
    return Settings()
```

crearíamos ese objeto para cada request, y estaríamos leyendo el archivo `.env` para cada request. ⚠️

Pero como estamos usando el decorador `@lru_cache` encima, el objeto `Settings` se creará solo una vez, la primera vez que se llame. ✔️

{* ../../docs_src/settings/app03_an_py310/main.py hl[1,11] *}

Пример кода Bash...

```bash
# Вывести приветствие вселенной
echo "Hello universe"
```

...и пример вывода в консоли...

```console
$ <font color="#4E9A06">fastapi</font> run <u style="text-decoration-style:solid">main.py</u>
<span style="background-color:#009485"><font color="#D3D7CF"> FastAPI </font></span>  Starting server
        Searching for package file structure
```

...и ещё один пример вывода в консоли...

* **Extensible** :
    * Pydantic permet de définir des types de données personnalisés ou vous pouvez étendre la validation avec des méthodes sur un modèle décoré avec le décorateur de validation.

* [`@contextlib.asynccontextmanager`](https://docs.python.org/3/library/contextlib.html#contextlib.asynccontextmanager)

bir **FastAPI** dependency'si olarak kullanılabilir.

Hatta FastAPI bu iki decorator'ı içeride (internally) kullanır.

///

## `yield` ile Bir Veritabanı Dependency'si { #a-database-dependency-with-yield }