"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.get("/items/")
async def read_items(
    q: Union[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,
    ),
):

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,
):

### Modelle für maschinelles Lernen mit spaCy und FastAPI – Funktionen

* **spaCy** NER-Modellintegration.
* **Azure Cognitive Search**-Anforderungsformat integriert.
* **Produktionsbereit** Python-Webserver, verwendet Uvicorn und Gunicorn.
* **Azure DevOps** Kubernetes (AKS) CI/CD-Deployment integriert.

## Modèles d'apprentissage automatique avec spaCy et FastAPI - Fonctionnalités

* Intégration d'un modèle NER **spaCy**.
* Formatage de requête pour **Azure Cognitive Search**.
* Serveur Python web **prêt à utiliser en production** utilisant Uvicorn et Gunicorn.
* Déploiement CI/CD Kubernetes pour **Azure DevOps** (AKS).

* However, when a Markdown document is translated, the `#hash-parts` in links to its headings may change. Update these links if possible.
    * Search for such links in the translated document using the regex `#[^# ]`.
    * Search in all documents already translated into your language for `your-translated-document.md`. For example VS Code has an option "Edit" -> "Find in Files".

```Python
{!> ../../../docs_src/app_testing/app_b/test_main.py!}
```

Whenever you need the client to pass information in the request and you don't know how to, you can search (Google) how to do it in `httpx`, or even how to do it with `requests`, as HTTPX's design is based on Requests' design.

Then you just do the same in your tests.

E.g.:

It is used by many companies including Mozilla, Red Hat and Eventbrite.

It was one of the first examples of **automatic API documentation**, and this was specifically one of the first ideas that inspired "the search for" **FastAPI**.

!!! note
    Django REST Framework was created by Tom Christie. The same creator of Starlette and Uvicorn, on which **FastAPI** is based.


!!! check "Inspired **FastAPI** to"

In this case you will have to ensure that each operation ID is **unique** in some other way.

For example, you could make sure that each *path operation* has a tag, and then generate the operation ID based on the **tag** and the *path operation* **name** (the function name).

### Custom Generate Unique ID Function