app = FastAPI()


@app.get("/items/")
async def read_items(
    q: Union[str, None] = Query(
        default=None,
        title="Query string",
        description="Query string for the items to search in the database that have a good match",
        min_length=3,
    ),
):
    results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
    if q:
        results.update({"q": q})

@app.get("/items/")
async def read_items(
    q: Annotated[
        Union[str, None],
        Query(
            alias="item-query",
            title="Query string",
            description="Query string for the items to search in the database that have a good match",
            min_length=3,
            max_length=50,
            pattern="^fixedquery$",
            deprecated=True,
        ),
    ] = None,
):

Donc, pendant ce temps, l'ordinateur pourra effectuer d'autres tâches, pendant que "fichier-lent" 📝 se termine.

Ensuite l'ordinateur / le programme 🤖 reviendra à chaque fois qu'il en a la chance que ce soit parce qu'il attend à nouveau, ou car il 🤖 a fini tout le travail qu'il avait à faire. Il 🤖 regardera donc si les tâches qu'il attend ont terminé d'être effectuées.

### Internal

* 🔧 Update sponsors, remove Flint. PR [#10349](https://github.com/tiangolo/fastapi/pull/10349) by [@tiangolo](https://github.com/tiangolo).
* 🔧 Rename label "awaiting review" to "awaiting-review" to simplify search queries. PR [#10343](https://github.com/tiangolo/fastapi/pull/10343) by [@tiangolo](https://github.com/tiangolo).

                    "operationId": "read_items_items__get",
                    "parameters": [
                        {
                            "description": "Query string for the items to search in the database that have a good match",
                            "required": False,
                            "deprecated": True,
                            "schema": IsDict(
                                {

app = FastAPI()


@app.get("/items/")
async def read_items(
    q: Annotated[
        Union[str, None],
        Query(
            title="Query string",
            description="Query string for the items to search in the database that have a good match",
            min_length=3,
        ),
    ] = None,
):
    results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
    if q:
        results.update({"q": q})

app = FastAPI()


@app.get("/items/")
async def read_items(
    q: str | None = Query(
        default=None,
        title="Query string",
        description="Query string for the items to search in the database that have a good match",
        min_length=3,
    ),
):
    results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
    if q:
        results.update({"q": q})

app = FastAPI()


@app.get("/items/")
async def read_items(
    q: str | None = Query(
        default=None,
        alias="item-query",
        title="Query string",
        description="Query string for the items to search in the database that have a good match",
        min_length=3,
        max_length=50,
        pattern="^fixedquery$",
        deprecated=True,
    ),
):

"""


def generate_readme_content() -> str:
    en_index = en_docs_path / "docs" / "index.md"
    content = en_index.read_text("utf-8")
    match_pre = re.search(r"</style>\n\n", content)
    match_start = re.search(r"<!-- sponsors -->", content)
    match_end = re.search(r"<!-- /sponsors -->", content)
    sponsors_data_path = en_docs_path / "data" / "sponsors.yml"

    title: FastAPI video courses on demand from people you trust
    img: https://fastapi.tiangolo.com/img/sponsors/talkpython-v2.jpg
  - url: https://github.com/deepset-ai/haystack/
    title: Build powerful search from composable, open source building blocks
    img: https://fastapi.tiangolo.com/img/sponsors/haystack-fastapi.svg
  - url: https://databento.com/
    title: Pay as you go for market data