* This will be used by the **automatic docs**.
    * It will also be used by automatic client code generation tools.

But most importantly:

* It will **limit and filter** the output data to what is defined in the return type.
    * This is particularly important for **security**, we'll see more of that below.

## `response_model` Parameter

```Python hl_lines="9"
{!../../../docs_src/query_params_str_validations/tutorial009.py!}
```

## Déprécier des paramètres

Disons que vous ne vouliez plus utiliser ce paramètre désormais.

Il faut qu'il continue à exister pendant un certain temps car vos clients l'utilisent, mais vous voulez que la documentation mentionne clairement que ce paramètre est <abbr title="obsolète, recommandé de ne pas l'utiliser">déprécié</abbr>.

    It clearly inspired Uvicorn and Starlette, that are currently faster than Sanic in open benchmarks.

!!! check "Inspired **FastAPI** to"
    Find a way to have a crazy performance.

#### Tips and guidelines

Those two tasks are what **consume time the most**. That's the main work of maintaining FastAPI.

If you can help me with that, **you are helping me maintain FastAPI** and making sure it keeps **advancing faster and better**. 🚀

## Join the chat

Join the 👥 <a href="https://discord.gg/VQjSZaeJmf" class="external-link" target="_blank">Discord chat server</a> 👥 and hang out with others in the FastAPI community.

!!! tip

Import the `Response` class (sub-class) you want to use and declare it in the *path operation decorator*.

For large responses, returning a `Response` directly is much faster than returning a dictionary.

Still, in both situations, chances are that **FastAPI** will [still be faster](index.md#performance){.internal-link target=_blank} than (or at least comparable to) your previous framework.

### Dependencies

    * Django REST Framework
    * Starlette (sur lequel **FastAPI** est basé)
    * Uvicorn (utilisé par Starlette et **FastAPI**)

!!! check "A inspiré **FastAPI** à"
Exister.

    L'idée de déclarer plusieurs choses (validation des données, sérialisation et documentation) avec les mêmes types Python, tout en offrant un excellent support pour les éditeurs, était pour moi une idée brillante.