* **FastAPI**: (uses Starlette) an API microframework with several additional features for building APIs, with data validation, etc.

* **Uvicorn**:
    * Will have the best performance, as it doesn't have much extra code apart from the server itself.

In the docs UI at <a href="http://127.0.0.1:9999/api/v1/docs" class="external-link" target="_blank">http://127.0.0.1:9999/api/v1/docs</a> it would look like:

<img src="/img/tutorial/behind-a-proxy/image03.png">

!!! tip
    The docs UI will interact with the server that you select.

### Disable automatic server from `root_path`

<font color="#3465A4">INFO    </font> Searching for package file structure from directories with <font color="#3465A4">__init__.py</font> files
<font color="#3465A4">INFO    </font> Importing from <font color="#75507B">/home/user/code/</font><font color="#AD7FA8">awesomeapp</font>

 ╭─ <font color="#8AE234"><b>Python module file</b></font> ─╮
 │                      │

### What is "Mounting"

"Mounting" means adding a complete "independent" application in a specific path, that then takes care of handling all the sub-paths.

This is different from using an `APIRouter` as a mounted application is completely independent. The OpenAPI and docs from your main application won't include anything from the mounted application, etc.

### Create an `Enum` class

Import `Enum` and create a sub-class that inherits from `str` and from `Enum`.

By inheriting from `str` the API docs will be able to know that the values must be of type `string` and will be able to render correctly.

Then create class attributes with fixed values, which will be the available valid values:

For that, you use `app.add_middleware()` (as in the example for CORS).

```Python
from fastapi import FastAPI
from unicorn import UnicornMiddleware

app = FastAPI()

app.add_middleware(UnicornMiddleware, some_config="rainbow")
```

`app.add_middleware()` receives a middleware class as the first argument and any additional arguments to be passed to the middleware.

You can import it directly from `fastapi`:

```python
from fastapi import BackgroundTasks
```

    ```

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

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

You can declare a parameter in a *path operation function* or dependency to be of type `Request` and then you can access the raw request object directly, without any validation, etc.

You can import it directly from `fastapi`:

```python
from fastapi import Request
```

!!! tip

One of the main features needed by API systems is data "<abbr title="also called marshalling, conversion">serialization</abbr>" which is taking data from the code (Python) and converting it into something that can be sent through the network. For example, converting an object containing data from a database into a JSON object. Converting `datetime` objects into strings, etc.