Just keep in mind that when you read "server" in general, it could refer to one of those two things.

And if you update that local FastAPI source code when you run that Python file again, it will use the fresh version of FastAPI you just edited.

That way, you don't have to "install" your local version to be able to test every change.

!!! note "Technical Details"
    This only happens when you install using this included `requirements.txt` instead of running `pip install fastapi` directly.

The `root_path` is used to handle these specific cases.

And it's also used internally when mounting sub-applications.

## 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`.

# `Request` class

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

    ```

!!! warning
    Notice that `Field` is imported directly from `pydantic`, not from `fastapi` as are all the rest (`Query`, `Path`, `Body`, etc).

## Declare model attributes

You can then use `Field` with model attributes:

=== "Python 3.10+"

    ```Python hl_lines="11-14"
    {!> ../../../docs_src/body_fields/tutorial001_an_py310.py!}
    ```

=== "Python 3.9+"

Several of these are explored in the next chapters of the tutorial.

## Order matters

When creating *path operations*, you can find situations where you have a fixed path.

Like `/users/me`, let's say that it's to get data about the current user.

And then you can also have a path `/users/{user_id}` to get data about a specific user by some user ID.

<img src="/img/tutorial/generate-clients/image01.png">

You can see those schemas because they were declared with the models in the app.

That information is available in the app's **OpenAPI schema**, and then shown in the API docs (by Swagger UI).

And that same information from the models that is included in OpenAPI is what can be used to **generate the client code**.

### Generate a TypeScript Client

But documentation is still missing. Then APISpec was created.

It is a plug-in for many frameworks (and there's a plug-in for Starlette too).

The way it works is that you write the definition of the schema using YAML format inside the docstring of each function handling a route.

And it generates OpenAPI schemas.

That's how it works in Flask, Starlette, Responder, etc.

```

</div>

If you create your app like:

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

And put it in a file `main.py`, then you would call `uvicorn` like:

<div class="termy">

```console
$ uvicorn main:my_awesome_api --reload

### `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.

This way you can add correct type annotations to your functions even when you are returning a type different than the response model, to be used by the editor and tools like mypy. And still you can have FastAPI do the data validation, documentation, etc. using the `response_model`.