dependency: Annotated[
        Optional[Callable[..., Any]],
        Doc(
            """
            A "dependable" callable (like a function).

            Don't call it directly, FastAPI will call it for you, just pass the object
            directly.
            """
        ),
    ] = None,
    *,
    use_cache: Annotated[
        bool,
        Doc(
            """

```Python hl_lines="1"
{!../../../docs_src/first_steps/tutorial001.py!}
```

`FastAPI` is a Python class that provides all the functionality for your API.

!!! note "Technical Details"
    `FastAPI` is a class that inherits directly from `Starlette`.

    You can use all the <a href="https://www.starlette.io/" class="external-link" target="_blank">Starlette</a> functionality with `FastAPI` too.

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

When referring to the remote machine, it's common to call it **server**, but also **machine**, **VM** (virtual machine), **node**. Those all refer to some type of remote machine, normally running Linux, where you run programs.

## Install the Server Program

</div>

#### Generate Client Code

To generate the client code you can use the command line application `openapi-ts` that would now be installed.

Because it is installed in the local project, you probably wouldn't be able to call that command directly, but you would put it on your `package.json` file.

It could look like this:

```JSON  hl_lines="7"
{
  "name": "frontend-app",
  "version": "1.0.0",
  "description": "",

    ):
        self.model = HTTPBaseModel(scheme=scheme, description=description)
        self.scheme_name = scheme_name or self.__class__.__name__
        self.auto_error = auto_error

    async def __call__(
        self, request: Request
    ) -> Optional[HTTPAuthorizationCredentials]:
        authorization = request.headers.get("Authorization")
        scheme, credentials = get_authorization_scheme_param(authorization)

I have been avoiding the creation of a new framework for several years. First I tried to solve all the features covered by **FastAPI** using many different frameworks, plug-ins, and tools.

    * Will have the best performance, as it doesn't have much extra code apart from the server itself.
    * You wouldn't write an application in Uvicorn directly. That would mean that your code would have to include more or less, at least, all the code provided by Starlette (or **FastAPI**). And if you did that, your final application would have the same overhead as having used a framework and minimizing your app code and bugs.

`response_model` receives the same type you would declare for a Pydantic model field, so, it can be a Pydantic model, but it can also be, e.g. a `list` of Pydantic models, like `List[Item]`.

<a href="https://fastapi.tiangolo.com/fastapi-people/#sponsors" class="external-link" target="_blank">Altri sponsor</a>

## Recensioni

"_[...] I'm using **FastAPI** a ton these days. [...] I'm actually planning to use it for all of my team's **ML services at Microsoft**. Some of them are getting integrated into the core **Windows** product and some **Office** products._"

## Instale o FastAPI

O primeiro passo é instalar o FastAPI.

Para o tutorial, você deve querer instalá-lo com todas as dependências e recursos opicionais.

<div class="termy">

```console
$ pip install "fastapi[all]"

---> 100%
```

</div>

...isso também inclui o `uvicorn`, que você pode usar como o servidor que rodará seu código.

!!! note "Nota"
    Você também pode instalar parte por parte.