fastapi = "fastapi.cli:main"

[tool.pdm]
version = { source = "file", path = "fastapi/__init__.py" }
distribution = true

[tool.pdm.build]
source-includes = [
    "tests/",
    "docs_src/",
    "requirements*.txt",
    "scripts/",
    # For a test
    "docs/en/docs/img/favicon.png",
    ]

[tool.tiangolo._internal-slim-build.packages.fastapi-slim.project]
name = "fastapi-slim"

[tool.mypy]

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 { #dataclasses-in-nested-data-structures }

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

-e .[all]
-r requirements-docs-tests.txt
pytest >=7.1.3,<9.0.0
coverage[toml] >= 6.5.0,< 8.0
mypy ==1.14.1
dirty-equals ==0.9.0
sqlmodel==0.0.27
flask >=1.1.2,<4.0.0
strawberry-graphql >=0.200.0,< 1.0.0
anyio[trio] >=3.2.1,<5.0.0
PyJWT==2.9.0
pyyaml >=5.3.1,<7.0.0
pwdlib[argon2] >=0.2.1
inline-snapshot>=0.21.1
pytest-codspeed==4.2.0
# types
types-ujson ==5.10.0.20240515

def test_multiple_path():
    app = FastAPI()

    @app.get("/test1")
    @app.get("/test2")
    async def test(var: Annotated[str, Query()] = "bar"):
        return {"foo": var}

    client = TestClient(app)
    response = client.get("/test1")
    assert response.status_code == 200
    assert response.json() == {"foo": "bar"}

    response = client.get("/test1", params={"var": "baz"})
    assert response.status_code == 200

* Durch die Verwendung einer Abhängigkeit können Sie das Testen vereinfachen.
* Sie können `.env`-Dateien damit verwenden.

            requirements**.txt
            pyproject.toml
      - name: Install Dependencies
        run: uv pip install -r requirements-tests.txt
      - run: mkdir coverage
      - name: Test
        if: matrix.codspeed != 'codspeed'
        run: bash scripts/test.sh
        env:
          COVERAGE_FILE: coverage/.coverage.${{ runner.os }}-py${{ matrix.python-version }}

Dessa forma, seu esquema aparecerá na interface de documentação da API:

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

## Dataclasses em Estruturas de Dados Aninhadas { #dataclasses-in-nested-data-structures }

Você também pode combinar `dataclasses` com outras anotações de tipo para criar estruturas de dados aninhadas.

De esta manera, su esquema aparecerá en la interfaz de usuario de la documentación de la API:

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

## Dataclasses en Estructuras de Datos Anidadas { #dataclasses-in-nested-data-structures }

También puedes combinar `dataclasses` con otras anotaciones de tipos para crear estructuras de datos anidadas.

Таким образом, его схема появится в интерфейсе документации API:

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

## Dataclasses во вложенных структурах данных { #dataclasses-in-nested-data-structures }

Вы также можете комбинировать `dataclasses` с другими аннотациями типов, чтобы создавать вложенные структуры данных.

Auf diese Weise wird deren Schema in der Benutzeroberfläche der API-Dokumentation angezeigt:

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

## Datenklassen in verschachtelten Datenstrukturen { #dataclasses-in-nested-data-structures }

Sie können `dataclasses` auch mit anderen Typannotationen kombinieren, um verschachtelte Datenstrukturen zu erstellen.