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

1. We still import `field` from standard `dataclasses`.

2. `pydantic.dataclasses` is a drop-in replacement for `dataclasses`.

3. The `Author` dataclass includes a list of `Item` dataclasses.

We can use **OAuth2** to build that with **FastAPI**.

But let's save you the time of reading the full long specification just to find those little pieces of information you need.

Let's use the tools provided by **FastAPI** to handle security.

## How it looks

Let's first just use the code and see how it works, and then we'll come back to understand what's happening.

We can also use this same approach to access the request body in an exception handler.

All we need to do is handle the request inside a `try`/`except` block:

```Python hl_lines="13  15"
{!../../../docs_src/custom_request_and_route/tutorial002.py!}
```

If an exception occurs, the`Request` instance will still be in scope, so we can read and make use of the request body when handling the error:

    "completed": False,
}
```

but it doesn't have the `id` field.

So we create a new `dict`, that contains the key-value pairs from `note.dict()` with:

```Python
{**note.dict()}
```

`**note.dict()` "unpacks" the key value pairs directly, so, `{**note.dict()}` would be, more or less, a copy of `note.dict()`.

And then, we extend that copy `dict`, adding another key-value pair: `"id": last_record_id`:

```Python

### Order of tags

The order of each tag metadata dictionary also defines the order shown in the docs UI.

For example, even though `users` would go after `items` in alphabetical order, it is shown before them, because we added their metadata as the first dictionary in the list.

## OpenAPI URL

By default, the OpenAPI schema is served at `/openapi.json`.

But you can configure it with the parameter `openapi_url`.

So, if we create a Pydantic object `user_in` like:

```Python
user_in = UserIn(username="john", password="secret", email="******@****.***")
```

and then we call:

```Python
user_dict = user_in.dict()
```

we now have a `dict` with the data in the variable `user_dict` (it's a `dict` instead of a Pydantic model object).

And if we call:

```Python
print(user_dict)
```

    ```Python hl_lines="14"
    {!> ../../../docs_src/body_nested_models/tutorial002.py!}
    ```

## Set types

But then we think about it, and realize that tags shouldn't repeat, they would probably be unique strings.

And Python has a special data type for sets of unique items, the `set`.

Then we can declare `tags` as a set of strings:

=== "Python 3.10+"

    ```Python hl_lines="12"

## Write the callback documentation code

This code won't be executed in your app, we only need it to *document* how that *external API* should look like.

But, you already know how to easily create automatic documentation for an API with **FastAPI**.

Or you might have a native mobile application that communicates with your WebSocket backend directly, in native code.

Or you might have any other way to communicate with the WebSocket endpoint.

---

But for this example, we'll use a very simple HTML document with some JavaScript, all inside a long string.

This, of course, is not optimal and you wouldn't use it for production.

In production you would have one of the options above.

Nevertheless, although we are not using the default integrated functionality, we are still using a Pydantic model to manually generate the JSON Schema for the data that we want to receive in YAML.

Then we use the request directly, and extract the body as `bytes`. This means that FastAPI won't even try to parse the request payload as JSON.