* Datenvalidierung
* Automatische Dokumentation

## Spezielle Typen und Validierungen

Abgesehen von normalen einfachen Typen, wie `str`, `int`, `float`, usw. können Sie komplexere einfache Typen verwenden, die von `str` erben.

    # to be read from files and other types, but I'm not even sure it's a good idea
    # to support it as a first class "feature"
    assert is_bytes_sequence_annotation(Union[List[str], List[bytes]])


def test_is_uploadfile_sequence_annotation():
    # For coverage
    # TODO: in theory this would allow declaring types that could be lists of UploadFile
    # and other types, but I'm not even sure it's a good idea to support it as a first

But at some point, there was no other option than creating something that provided all these features, taking the best ideas from previous tools, and combining them in the best way possible, using language features that weren't even available before (Python 3.6+ type hints).

## Previous tools

### <a href="https://www.djangoproject.com/" class="external-link" target="_blank">Django</a>

# Abhängigkeiten – `Depends()` und `Security()`

## `Depends()`

Abhängigkeiten werden hauptsächlich mit der speziellen Funktion `Depends()` behandelt, die ein Callable entgegennimmt.

Hier finden Sie deren Referenz und Parameter.

Sie können sie direkt von `fastapi` importieren:

```python
from fastapi import Depends
```

::: fastapi.Depends

## `Security()`

### `response_model` Priority

If you declare both a return type and a `response_model`, the `response_model` will take priority and be used by FastAPI.

This way you can add correct type annotations to your functions even when you are returning a type different than the response model, to be used by the editor and tools like mypy. And still you can have FastAPI do the data validation, documentation, etc. using the `response_model`.

    * If you didn't use FastAPI and used Starlette directly (or another tool, like Sanic, Flask, Responder, etc) you would have to implement all the data validation and serialization...

So, even with the code above that doesn't use Pydantic explicitly, FastAPI is using Pydantic to convert those standard dataclasses to Pydantic's own flavor of dataclasses.

And of course, it supports the same:

* data validation

In this case, the original path `/app` would actually be served at `/api/v1/app`.

Even though all your code is written assuming there's just `/app`.

```Python hl_lines="6"
{!../../../docs_src/behind_a_proxy/tutorial001.py!}
```

```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.:

* To pass a *path* or *query* parameter, add it to the URL itself.

```

!!! tip
    You could also access the value `"lenet"` with `ModelName.lenet.value`.

#### Return *enumeration members*

You can return *enum members* from your *path operation*, even nested in a JSON body (e.g. a `dict`).

They will be converted to their corresponding values (strings in this case) before returning them to the client:

```Python hl_lines="18  21  23"