## Proxy with a stripped path prefix

Having a proxy with a stripped path prefix, in this case, means that you could declare a path at `/app` in your code, but then, you add a layer on top (the proxy) that would put your **FastAPI** application under a path like `/api/v1`.

In this case, the original path `/app` would actually be served at `/api/v1/app`.

```Python hl_lines="8"
{!../../../docs_src/first_steps/tutorial001.py!}
```

You can return a `dict`, `list`, singular values as `str`, `int`, etc.

You can also return Pydantic models (you'll see more about that later).

There are many other objects and models that will be automatically converted to JSON (including ORMs, etc). Try using your favorite ones, it's highly probable that they are already supported.

## Recap

Routes are declared in a single place, using functions declared in other places (instead of using decorators that can be placed right on top of the function that handles the endpoint). This is closer to how Django does it than to how Flask (and Starlette) does it. It separates in the code things that are relatively tightly coupled.

!!! check "Inspired **FastAPI** to"

### Return a Response Directly

The most common case would be [returning a Response directly as explained later in the advanced docs](../advanced/response-directly.md){.internal-link target=_blank}.

```Python hl_lines="8  10-11"
{!> ../../../docs_src/response_model/tutorial003_02.py!}
```

* Certificates have a **lifetime**.
    * They **expire**.
    * And then they need to be **renewed**, **acquired again** from the third party.
* The encryption of the connection happens at the **TCP level**.
    * That's one layer **below HTTP**.
    * So, the **certificate and encryption** handling is done **before HTTP**.
* **TCP doesn't know about "domains"**. Only about IP addresses.

    But it was updated to use Python-jose instead as it provides all the features from PyJWT plus some extras that you might need later when building integrations with other tools.

## Password hashing

"Hashing" means converting some content (a password in this case) into a sequence of bytes (just a string) that looks like gibberish.

    * One Uvicorn **process manager** would listen on the **IP** and **port**, and it would start **multiple Uvicorn worker processes**.
* **Kubernetes** and other distributed **container systems**
    * Something in the **Kubernetes** layer would listen on the **IP** and **port**. The replication would be by having **multiple containers**, each with **one Uvicorn process** running.
* **Cloud services** that handle this for you

### Resumen

En resumen, declaras los tipos de parámetros, body, etc. **una vez** como parámetros de la función.

Lo haces con tipos modernos estándar de Python.

No tienes que aprender una sintaxis nueva, los métodos o clases de una library específica, etc.

Solo **Python** estándar.

Por ejemplo, para un `int`:

```Python
item_id: int
```

o para un modelo más complejo de `Item`:

```Python
item: Item

For now, you just need to know that `Annotated` exists, and that it's standard Python. 😎

Later you will see how **powerful** it can be.

!!! tip

    * Porque las estructuras de datos de Pydantic son solo <abbr title='En español: ejemplares. Aunque a veces los llaman incorrectamente "instancias"'>instances</abbr> de clases que tu defines, el auto-completado, el linting, mypy y tu intuición deberían funcionar bien con tus datos validados.
* Valida **estructuras complejas**: