With this, we get tooling support, from editors and mypy as this code is correct in terms of types, but we also get the data filtering from FastAPI.

How does this work? Let's check that out. 🤓

### Type Annotations and Tooling { #type-annotations-and-tooling }

First let's see how editors, mypy and other tools would see this.

<img src="/img/tutorial/body/image05.png">

/// tip

If you use <a href="https://www.jetbrains.com/pycharm/" class="external-link" target="_blank">PyCharm</a> as your editor, you can use the <a href="https://github.com/koxudaxi/pydantic-pycharm-plugin/" class="external-link" target="_blank">Pydantic PyCharm Plugin</a>.

It improves editor support for Pydantic models, with:

* auto-completion

{* ../../docs_src/response_status_code/tutorial002_py39.py hl[1,6] *}

Diese sind nur eine Annehmlichkeit, sie enthalten dieselbe Zahl, aber so können Sie die Autovervollständigung Ihres Editors verwenden, um sie zu finden:

<img src="/img/tutorial/response-status-code/image02.png">

/// note | Technische Details

Sie könnten auch `from starlette import status` verwenden.

By doing that, your editor can provide support even while processing items from the list:

<img src="/img/python-types/image05.png">

Without types, that's almost impossible to achieve.

Notice that the variable `item` is one of the elements in the list `items`.

And still, the editor knows it is a `str`, and provides support for that.

{* ../../docs_src/dependencies/tutorial001_an_py310.py hl[9] *}

But then we get a `dict` in the parameter `commons` of the *path operation function*.

And we know that editors can't provide a lot of support (like completion) for `dict`s, because they can't know their keys and value types.

We can do better...

## What makes a dependency { #what-makes-a-dependency }

<img src="/img/tutorial/body/image01.png">

Und werden auch in der API-Dokumentation innerhalb jeder *Pfadoperation*, die sie benötigt, verwendet:

<img src="/img/tutorial/body/image02.png">

## Editor-Unterstützung { #editor-support }

In Ihrem Editor erhalten Sie innerhalb Ihrer Funktion Typhinweise und Code-Vervollständigung überall (was nicht der Fall wäre, wenn Sie ein `dict` anstelle eines Pydantic-Modells erhalten hätten):

<img src="/img/tutorial/body/image05.png">

/// tip | Consejo

Si usas <a href="https://www.jetbrains.com/pycharm/" class="external-link" target="_blank">PyCharm</a> como tu editor, puedes usar el <a href="https://github.com/koxudaxi/pydantic-pycharm-plugin/" class="external-link" target="_blank">Pydantic PyCharm Plugin</a>.

Mejora el soporte del editor para modelos de Pydantic, con:

Com isso, temos suporte de ferramentas, de editores e mypy, pois este código está correto em termos de tipos, mas também obtemos a filtragem de dados do FastAPI.

Como isso funciona? Vamos verificar. 🤓

### Anotações de tipo e ferramentas { #type-annotations-and-tooling }

Primeiro, vamos ver como editores, mypy e outras ferramentas veriam isso.

<img src="/img/tutorial/body/image05.png">

/// tip | Dica

Se você utiliza o <a href="https://www.jetbrains.com/pycharm/" class="external-link" target="_blank">PyCharm</a> como editor, você pode utilizar o <a href="https://github.com/koxudaxi/pydantic-pycharm-plugin/" class="external-link" target="_blank">Plugin do Pydantic para o PyCharm </a>.

Melhora o suporte do editor para seus modelos Pydantic com:

{* ../../docs_src/response_model/tutorial003_01_py310.py hl[7:10,13:14,18] *}

Con esto, obtenemos soporte de las herramientas, de los editores y mypy ya que este código es correcto en términos de tipos, pero también obtenemos el filtrado de datos de FastAPI.