FastAPI will use the `response_model` parameter (that includes dataclasses) to convert the response.

You can combine `dataclasses` with other type annotations in many different combinations to form complex data structures.

Check the in-code annotation tips above to see more specific details.

## Learn More

You can also combine `dataclasses` with other Pydantic models, inherit from them, include them in your own models, etc.

    * **TypeScript**.
    * Serveur Docker basé sur **Nginx** (configuré pour être facilement manipulé avec Vue-router).
    * Utilisation de *Docker multi-stage building*, pour ne pas avoir besoin de sauvegarder ou *commit* du code compilé.
    * Tests frontend exécutés à la compilation (pouvant être désactivés).
    * Fait aussi modulable que possible, pour pouvoir fonctionner comme tel, tout en pouvant être utilisé qu'en partie grâce à Vue CLI.

<img src="/img/tutorial/path-params/image03.png">

### Working with Python *enumerations*

The value of the *path parameter* will be an *enumeration member*.

#### Compare *enumeration members*

You can compare it with the *enumeration member* in your created enum `ModelName`:

```Python hl_lines="17"
{!../../../docs_src/path_params/tutorial005.py!}
```

#### Get the *enumeration value*

    * If you are comparing Uvicorn, compare it against Daphne, Hypercorn, uWSGI, etc. Application servers.
* **Starlette**:

    correct_username_bytes = b"stanleyjobson"
    is_correct_username = secrets.compare_digest(
        current_username_bytes, correct_username_bytes
    )
    current_password_bytes = credentials.password.encode("utf8")
    correct_password_bytes = b"swordfish"
    is_correct_password = secrets.compare_digest(
        current_password_bytes, correct_password_bytes
    )

      - name: Store coverage files
        uses: actions/upload-artifact@v3
        with:
          name: coverage
          path: coverage

  coverage-combine:
    needs: [test]
    runs-on: ubuntu-latest
    steps:
      - name: Dump GitHub context
        env:
          GITHUB_CONTEXT: ${{ toJson(github) }}
        run: echo "$GITHUB_CONTEXT"

    class Foo(BaseModel):
        model_config = ConfigDict(from_attributes=True)

    foo = Foo()
    config = _get_model_config(foo)
    assert config == {"from_attributes": True}


def test_complex():
    app = FastAPI()

    @app.post("/")
    def foo(foo: Union[str, List[int]]):
        return foo

    client = TestClient(app)

    response = client.post("/", json="bar")

#### Docker Compose

# GraphQL

As **FastAPI** is based on the **ASGI** standard, it's very easy to integrate any **GraphQL** library also compatible with ASGI.

You can combine normal FastAPI *path operations* with GraphQL on the same application.

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

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

!!! check "**FastAPI** recommends it as"
    The main web server to run **FastAPI** applications.

    You can combine it with Gunicorn, to have an asynchronous multi-process server.

    Check more details in the [Deployment](deployment/index.md){.internal-link target=_blank} section.

## Benchmarks and speed