* Portanto, o manuseio do certificado e da criptografia é feito antes do HTTP.
* O TCP não sabe sobre "domínios". Apenas sobre endereços IP.
    * As informações sobre o domínio solicitado vão nos dados HTTP.
* Os certificados HTTPS “certificam” um determinado domínio, mas o protocolo e a encriptação acontecem ao nível do TCP, antes de sabermos de que domínio se trata.

* It takes each **request** that comes to your application.
* It can then do something to that **request** or run any needed code.
* Then it passes the **request** to be processed by the rest of the application (by some *path operation*).
* It then takes the **response** generated by the application (by some *path operation*).

You can import it directly from `fastapi`:

```python
from fastapi import Request
```

!!! tip
    When you want to define dependencies that should be compatible with both HTTP and WebSockets, you can define a parameter that takes an `HTTPConnection` instead of a `Request` or a `WebSocket`.

### 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.

Para esos casos, puedes usar el `jsonable_encoder` para convertir tus datos antes de pasarlos a la respuesta:

```Python hl_lines="4 6 20 21"
{!../../../docs_src/response_directly/tutorial001.py!}
```

!!! note "Detalles Técnicos"

```Python
from unicorn import UnicornMiddleware

app = SomeASGIApp()

new_app = UnicornMiddleware(app, some_config="rainbow")
```

But FastAPI (actually Starlette) provides a simpler way to do it that makes sure that the internal middlewares to handle server errors and custom exception handlers work properly.

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

```Python
from fastapi import FastAPI

### <a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a>

Pydantic is a library to define data validation, serialization and documentation (using JSON Schema) based on Python type hints.

That makes it extremely intuitive.

It is comparable to Marshmallow. Although it's faster than Marshmallow in benchmarks. And as it is based on the same Python type hints, the editor support is great.

!!! check "**FastAPI** uses it to"

    ```Python hl_lines="25"
    {!> ../../../docs_src/security/tutorial002.py!}
    ```

## Get the user

`get_current_user` will use a (fake) utility function we created, that takes a token as a `str` and returns our Pydantic `User` model:

=== "Python 3.10+"

    ```Python hl_lines="19-22  26-27"
    {!> ../../../docs_src/security/tutorial002_an_py310.py!}
    ```

=== "Python 3.9+"

## Hypercorn with Trio

FastAPI uses a **unique ID** for each *path operation*, it is used for the **operation ID** and also for the names of any needed custom models, for requests or responses.

You can customize that function. It takes an `APIRoute` and outputs a string.

For example, here it is using the first tag (you will probably have only one tag) and the *path operation* name (the function name).