And of course, it supports the same:

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

!!! info
    Keep in mind that dataclasses can't do everything Pydantic models can do.

    So, you might still need to use Pydantic models.

But I'll show you how to improve that next. 🤓

## Custom Operation IDs and Better Method Names

You can **modify** the way these operation IDs are **generated** to make them simpler and have **simpler method names** in the clients.

    That way you tell the editor that you are intentionally returning anything. But FastAPI will still do the data documentation, validation, filtering, etc. with the `response_model`.

### `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.

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>

    ```

But that is still not that useful.

Let's make it give us the current user.

## Create a user model

First, let's create a Pydantic user model.

The same way we use Pydantic to declare bodies, we can use it anywhere else:

=== "Python 3.10+"

    ```Python hl_lines="5  12-16"
    {!> ../../../docs_src/security/tutorial002_an_py310.py!}
    ```

=== "Python 3.9+"

    * If you are comparing Starlette, compare it against Sanic, Flask, Django, etc. Web frameworks (or microframeworks).
* **FastAPI**:
    * The same way that Starlette uses Uvicorn and cannot be faster than it, **FastAPI** uses Starlette, so it cannot be faster than it.

This will **depend heavily** on the way you **deploy your system**, and it would probably be connected to the way you start programs, handling restarts, etc.

Here are some possible ideas:

* An "Init Container" in Kubernetes that runs before your app container
* A bash script that runs the previous steps and then starts your application
    * You would still need a way to start/restart *that* bash script, detect errors, etc.

<img src="/img/tutorial/path-params/image02.png">

The same way, there are many compatible tools. Including code generation tools for many languages.

## Pydantic

# About HTTPS

It is easy to assume that HTTPS is something that is just "enabled" or not.

But it is way more complex than that.

!!! tip
    If you are in a hurry or don't care, continue with the next sections for step by step instructions to set everything up with different techniques.

To **learn the basics of HTTPS**, from a consumer perspective, check <a href="https://howhttps.works/" class="external-link" target="_blank">https://howhttps.works/</a>.

The response would be something like:

```JSON
{
    "message": "Hello World",
    "root_path": "/api/v1"
}
```

### Setting the `root_path` in the FastAPI app

Alternatively, if you don't have a way to provide a command line option like `--root-path` or equivalent, you can set the `root_path` parameter when creating your FastAPI app:

```Python hl_lines="3"
{!../../../docs_src/behind_a_proxy/tutorial002.py!}
```