Rufen Sie nun die Benutzerdaten aus der (gefakten) Datenbank ab, für diesen `username` aus dem Formularfeld.

Wenn es keinen solchen Benutzer gibt, geben wir die Fehlermeldung „Incorrect username or password“ zurück.

Für den Fehler verwenden wir die Exception `HTTPException`:

=== "Python 3.10+"

    ```Python hl_lines="3  79-81"
    {!> ../../../docs_src/security/tutorial003_an_py310.py!}
    ```

    ```

    </div>

=== "Windows PowerShell"

    <div class="termy">

    ```console
    $ .\env\Scripts\Activate.ps1
    ```

    </div>

=== "Windows Bash"

    Or if you use Bash for Windows (e.g. <a href="https://gitforwindows.org/" class="external-link" target="_blank">Git Bash</a>):

    <div class="termy">

    ```console
    $ source ./env/Scripts/activate
    ```

### Always `raise` in Dependencies with `yield` and `except`

If you catch an exception in a dependency with `yield`, unless you are raising another `HTTPException` or similar, you should re-raise the original exception.

                        str(additional_status_code).upper()
                    ) or http.client.responses.get(int(additional_status_code))
                    description = (
                        process_response.get("description")
                        or openapi_response.get("description")
                        or status_text
                        or "Additional Response"
                    )

            # `tests/test_infer_param_optionality.py` for an example.
            field_info = params.Path(annotation=use_annotation)
        elif is_uploadfile_or_nonable_uploadfile_annotation(
            type_annotation
        ) or is_uploadfile_sequence_annotation(type_annotation):
            field_info = params.File(annotation=use_annotation, default=default_value)

    These Pydantic models define more or less a "schema" (a valid data shape).

    So this will help us avoiding confusion while using both.

### Create initial Pydantic *models* / schemas

Create an `ItemBase` and `UserBase` Pydantic *models* (or let's say "schemas") to have common attributes while creating or reading data.

    # Contributors may wish to update or change these, e.g. to fix capitalization.
    language_names_path = Path(__file__).parent / "../docs/language_names.yml"
    local_language_names: Dict[str, str] = mkdocs.utils.yaml_load(
        language_names_path.read_text(encoding="utf-8")
    )
    for lang_path in get_lang_paths():
        if lang_path.name in {"en", "em"} or not lang_path.is_dir():
            continue

# Settings and Environment Variables

In many cases your application could need some external settings or configurations, for example secret keys, database credentials, credentials for email services, etc.

Most of these settings are variable (can change), like database URLs. And many could be sensitive, like secrets.

For this reason it's common to provide them in environment variables that are read by the application.

## Environment Variables

!!! tip

    return (
        _annotation_is_complex(annotation)
        or _annotation_is_complex(origin)
        or hasattr(origin, "__pydantic_core_schema__")
        or hasattr(origin, "__get_pydantic_core_schema__")
    )


def field_annotation_is_scalar(annotation: Any) -> bool:
    # handle Ellipsis here to make tuple[int, ...] work nicely
    return annotation is Ellipsis or not field_annotation_is_complex(annotation)

# If running behind a proxy like Nginx or Traefik add --proxy-headers
# CMD ["fastapi", "run", "app/main.py", "--port", "80", "--proxy-headers"]
```

</details>

## What is a Container

Containers (mainly Linux containers) are a very **lightweight** way to package applications including all their dependencies and necessary files while keeping them isolated from other containers (other applications or components) in the same system.