"/items?token=jessica", headers={"X-Token": "fake-super-secret-token"}
    )
    assert response.status_code == 200
    assert response.json() == {
        "plumbus": {"name": "Plumbus"},
        "gun": {"name": "Portal Gun"},
    }


@needs_py39
def test_items_with_no_token_jessica(client: TestClient):
    response = client.get("/items", headers={"X-Token": "fake-super-secret-token"})

But I'll show you how to improve that next. 🤓

## Custom Operation IDs and Better Method Names

You can **modify** the way these operation IDs are **generated** to make them simpler and have **simpler method names** in the clients.

from typing import Annotated

from fastapi import Header, HTTPException


async def get_token_header(x_token: Annotated[str, Header()]):
    if x_token != "fake-super-secret-token":
        raise HTTPException(status_code=400, detail="X-Token header invalid")


async def get_query_token(token: str):
    if token != "jessica":

## Create the database

Because now we are going to use a new database in a new file, we need to make sure we create the database with:

```Python
Base.metadata.create_all(bind=engine)
```

That is normally called in `main.py`, but the line in `main.py` uses the database file `sql_app.db`, and we need to make sure we create `test.db` for the tests.

So we add that line here, with the new file.

```Python hl_lines="16"

from dirty_equals import IsDict, IsOneOf
from fastapi.testclient import TestClient

from docs_src.security.tutorial005_an import (
    app,
    create_access_token,
    fake_users_db,
    get_password_hash,
    verify_password,
)

client = TestClient(app)


def get_access_token(username="johndoe", password="secret", scope=None):
    data = {"username": username, "password": password}
    if scope:
        data["scope"] = scope

This way, its schema will show up in the API docs user interface:

<img src="/img/tutorial/dataclasses/image01.png">

## Dataclasses in Nested Data Structures

You can also combine `dataclasses` with other type annotations to make nested data structures.

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.

!!! 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**:

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

Because *path operations* are evaluated in order, you need to make sure that the path for `/users/me` is declared before the one for `/users/{user_id}`:

```Python hl_lines="6  11"
{!../../../docs_src/path_params/tutorial003.py!}
```

These examples run the server program (e.g Uvicorn), starting **a single process**, listening on all the IPs (`0.0.0.0`) on a predefined port (e.g. `80`).

This is the basic idea. But you will probably want to take care of some additional things, like:

* Security - HTTPS
* Running on startup
* Restarts
* Replication (the number of processes running)
* Memory
* Previous steps before starting

This schema definition includes your API paths, the possible parameters they take, etc.

#### Data "schema"

The term "schema" might also refer to the shape of some data, like a JSON content.

In that case, it would mean the JSON attributes, and data types they have, etc.