!!! tip
    **GraphQL** solves some very specific use cases.

    It has **advantages** and **disadvantages** when compared to common **web APIs**.

    Make sure you evaluate if the **benefits** for your use case compensate the **drawbacks**. 🤓

## GraphQL Libraries

Here are some of the **GraphQL** libraries that have **ASGI** support. You could use them with **FastAPI**:

You can declare the type of a path parameter in the function, using standard Python type annotations:

```Python hl_lines="7"
{!../../../docs_src/path_params/tutorial002.py!}
```

In this case, `item_id` is declared to be an `int`.

!!! check
    This will give you editor support inside of your function, with error checks, completion, etc.

In some cases, you might still have to use Pydantic's version of `dataclasses`. For example, if you have errors with the automatically generated API documentation.

In that case, you can simply swap the standard `dataclasses` with `pydantic.dataclasses`, which is a drop-in replacement:

```{ .python .annotate hl_lines="1  5  8-11  14-17  23-25  28" }
{!../../../docs_src/dataclasses/tutorial003.py!}
```

!!! tip
    The next sections are **not necessarily "advanced"**.

    And it's possible that for your use case, the solution is in one of them.

## Read the Tutorial first

The next sections assume you already read the main [Tutorial - User Guide: Security](../../tutorial/security/index.md){.internal-link target=_blank}.

Similar to the way you can [add `dependencies` to the *path operation decorators*](dependencies-in-path-operation-decorators.md){.internal-link target=_blank}, you can add them to the `FastAPI` application.

In that case, they will be applied to all the *path operations* in the application:

=== "Python 3.9+"

    ```Python hl_lines="16"
    {!> ../../../docs_src/dependencies/tutorial012_an_py39.py!}
    ```

=== "Python 3.8+"

!!! info
    A "bearer" token is not the only option.

    But it's the best one for our use case.

    And it might be the best for most use cases, unless you are an OAuth2 expert and know exactly why there's another option that suits better your needs.

    In that case, **FastAPI** also provides you with the tools to build it.

# OpenAPI

There are several utilities to handle OpenAPI.

    !!! tip
        Prefer to use the `Annotated` version if possible.

    ```Python hl_lines="19-21"
    {!> ../../../docs_src/body_multiple_params/tutorial001.py!}
    ```

!!! note
    Notice that, in this case, the `item` that would be taken from the body is optional. As it has a `None` default value.

## Multiple body parameters

And you can use any model or data for the security requirements (in this case, a Pydantic model `User`).

But you are not restricted to using some specific data model, class or type.

Do you want to have an `id` and `email` and not have any `username` in your model? Sure. You can use these same tools.

* Implement and use well-known cryptographic tools, like Passlib and JWT tokens, etc.
* Add more granular permission controls with OAuth2 scopes where needed.
* ...etc.

Nevertheless, you might have a very specific use case where you really need to disable the API docs for some environment (e.g. for production) or depending on configurations from environment variables.

## Conditional OpenAPI from settings and env vars