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`.

<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.

### `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`.

                    "loc": ["query", "query"],
                    "msg": "Field required",
                    "input": None,
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {
            "detail": [
                {
                    "loc": ["query", "query"],
                    "msg": "field required",
                    "type": "value_error.missing",

                    "loc": ["body", "price"],
                    "msg": "Field required",
                    "input": {"name": "Foo"},
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {
            "detail": [
                {
                    "loc": ["body", "price"],
                    "msg": "field required",
                    "type": "value_error.missing",

                    "loc": ["body", "price"],
                    "msg": "Field required",
                    "input": {"name": "Foo"},
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {
            "detail": [
                {
                    "loc": ["body", "price"],
                    "msg": "field required",
                    "type": "value_error.missing",

                client an error.

                If `auto_error` is set to `False`, when the HTTP Basic authentication
                is not available, instead of erroring out, the dependency result will
                be `None`.

                This is useful when you want to have optional authentication.

                It is also useful when you want to have authentication that can be

                If `auto_error` is set to `False`, when the HTTP Authorization header
                is not available, instead of erroring out, the dependency result will
                be `None`.

                This is useful when you want to have optional authentication.

                It is also useful when you want to have authentication that can be