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


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

    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_pydanticv2
def test_openapi_schema(client: TestClient) -> None:
    response = client.get("/openapi.json")

    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_py39
@needs_pydanticv2
def test_openapi_schema(client: TestClient) -> None:
    response = client.get("/openapi.json")

## Recap

You can use **Gunicorn** (or also Uvicorn) as a process manager with Uvicorn workers to take advantage of **multi-core CPUs**, to run **multiple processes in parallel**.

You could use these tools and ideas if you are setting up **your own deployment system** while taking care of the other deployment concepts yourself.

## Docker Image with Poetry

If you use <a href="https://python-poetry.org/" class="external-link" target="_blank">Poetry</a> to manage your project's dependencies, you could use Docker multi-stage building:

```{ .dockerfile .annotate }
# (1)
FROM python:3.9 as requirements-stage

# (2)
WORKDIR /tmp

# (3)
RUN pip install poetry

# (4)

!!! check "**FastAPI** le recommande comme"
Le serveur web principal pour exécuter les applications **FastAPI**.

    Vous pouvez le combiner avec Gunicorn, pour avoir un serveur multi-processus asynchrone.

    Pour plus de détails, consultez la section [Déploiement](deployment/index.md){.internal-link target=_blank}.

## Benchmarks et vitesse