@pytest.fixture(name="client")
def get_client():
    from docs_src.query_params_str_validations.tutorial011_py310 import app

    client = TestClient(app)
    return client


@needs_py310
def test_multi_query_values(client: TestClient):
    url = "/items/?q=foo&q=bar"
    response = client.get(url)
    assert response.status_code == 200, response.text
    assert response.json() == {"q": ["foo", "bar"]}


@needs_py310

It is the recommended server for Starlette and **FastAPI**.

!!! check "**FastAPI** recommends it as"
    The main web server to run **FastAPI** applications.

    You can combine it with Gunicorn, to have an asynchronous multi-process server.

    Check more details in the [Deployment](deployment/index.md){.internal-link target=_blank} section.

## Benchmarks and speed

---

Essa poderia ser a história paralela equivalente aos hambúrgueres.

Para um exemplo "mais real", imagine um banco.

Até recentemente, a maioria dos bancos tinha muitos caixas e uma grande fila.

Todos os caixas fazendo todo o trabalho, um cliente após o outro.

E você tinha que esperar na fila por um longo tempo ou poderia perder a vez.

def create_item(item: Item):
    return item


@app.get("/items/")
def read_items() -> List[Item]:
    return [
        Item(
            name="Portal Gun",
            description="Device to travel through the multi-rick-verse",
        ),
        Item(name="Plumbus"),

def create_item(item: Item):
    return item


@app.get("/items/")
def read_items() -> list[Item]:
    return [
        Item(
            name="Portal Gun",
            description="Device to travel through the multi-rick-verse",
        ),
        Item(name="Plumbus"),

    url = "/items/"
    response = client.get(url)
    assert response.status_code == 200, response.text
    assert response.json() == {"q": ["foo", "bar"]}


@needs_py39
def test_multi_query_values(client: TestClient):
    url = "/items/?q=baz&q=foobar"
    response = client.get(url)
    assert response.status_code == 200, response.text
    assert response.json() == {"q": ["baz", "foobar"]}

@pytest.fixture(name="client")
def get_client():
    from docs_src.query_params_str_validations.tutorial011_an_py310 import app

    client = TestClient(app)
    return client


@needs_py310
def test_multi_query_values(client: TestClient):
    url = "/items/?q=foo&q=bar"
    response = client.get(url)
    assert response.status_code == 200, response.text
    assert response.json() == {"q": ["foo", "bar"]}


@needs_py310

@pytest.fixture(name="client")
def get_client():
    from docs_src.query_params_str_validations.tutorial011_py39 import app

    client = TestClient(app)
    return client


@needs_py39
def test_multi_query_values(client: TestClient):
    url = "/items/?q=foo&q=bar"
    response = client.get(url)
    assert response.status_code == 200, response.text
    assert response.json() == {"q": ["foo", "bar"]}


@needs_py39

    response = client.get("/items/")
    assert response.status_code == 200, response.text
    assert response.json() == [
        {
            "name": "Portal Gun",
            "description": "Device to travel through the multi-rick-verse",
        },
        {"name": "Plumbus", "description": None},
    ]


@needs_py310
@needs_pydanticv2
def test_openapi_schema(client: TestClient) -> None:
    response = client.get("/openapi.json")

<div class="termy">

```console
$ bash scripts/format-imports.sh
```

</div>

Como ele roda um comando após o outro, modificando e revertendo muitos arquivos, ele demora um pouco para concluir, então pode ser um pouco mais fácil utilizar `scripts/format.sh` frequentemente e `scripts/format-imports.sh` somente após "commitar uma branch".

## Documentação