///

## Mehrere Body-Parameter { #multiple-body-parameters }

Im vorherigen Beispiel erwarteten die *Pfadoperationen* einen JSON-Body mit den Attributen eines `Item`s, etwa:

```JSON
{
    "name": "Foo",
    "description": "The pretender",
    "price": 42.0,
    "tax": 3.2
}
```

Aber Sie können auch mehrere Body-Parameter deklarieren, z. B. `item` und `user`:

///

## Múltiples parámetros del cuerpo { #multiple-body-parameters }

En el ejemplo anterior, las *path operations* esperarían un cuerpo JSON con los atributos de un `Item`, como:

```JSON
{
    "name": "Foo",
    "description": "The pretender",
    "price": 42.0,
    "tax": 3.2
}
```

    }
    response = client.post(
        "/user/",
        json=user_data,
    )
    assert response.status_code == 200, response.text
    assert response.json() == user_data


def test_openapi_schema(client: TestClient):
    response = client.get("/openapi.json")
    assert response.status_code == 200, response.text
    assert response.json() == {
        "openapi": "3.1.0",

	Host          string                        `json:"host"`
	Err           string                        `json:"err,omitempty"`
	WaitingDrives map[string]madmin.DiskMetrics `json:"waitingDrives,omitempty"`
}

type serviceResult struct {
	Action  madmin.ServiceAction `json:"action"`
	DryRun  bool                 `json:"dryRun"`
	Results []servicePeerResult  `json:"results,omitempty"`
}

Es wird getestet, ob der String eine gültige URL ist, und als solche wird er in JSON Schema / OpenAPI dokumentiert.

## Attribute mit Listen von Kindmodellen { #attributes-with-lists-of-submodels }

Sie können Pydantic-Modelle auch als Typen innerhalb von `list`, `set`, usw. verwenden:

{* ../../docs_src/body_nested_models/tutorial006_py310.py hl[18] *}

Das wird einen JSON-Body erwarten (konvertieren, validieren, dokumentieren, usw.) wie:

    return client


@needs_py310
def test_get(client: TestClient):
    response = client.get("/users")
    assert response.json() == {"username": "alice", "role": "admin"}


@needs_py310
def test_openapi_schema(client: TestClient):
    response = client.get("openapi.json")
    assert response.json() == snapshot(
        {
            "openapi": "3.1.0",
            "info": {"title": "FastAPI", "version": "0.1.0"},

    assert response.status_code == 200
    assert response.json() == {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}


def test_query_params_str_validations_item_query_fixedquery(client: TestClient):
    response = client.get("/items/", params={"item-query": "fixedquery"})
    assert response.status_code == 200
    assert response.json() == {
        "items": [{"item_id": "Foo"}, {"item_id": "Bar"}],

## Atributos con listas de submodelos { #attributes-with-lists-of-submodels }

También puedes usar modelos Pydantic como subtipos de `list`, `set`, etc.:

{* ../../docs_src/body_nested_models/tutorial006_py310.py hl[18] *}

Esto esperará (convertirá, validará, documentará, etc.) un cuerpo JSON como:

```JSON hl_lines="11"
{
    "name": "Foo",

    response = client.put(
        "/items/5",
        json={
            "name": "Foo",
            "description": "A very nice Item",
            "price": 35.4,
            "tax": 3.2,
        },
    )
    assert response.status_code == 200


def test_openapi_schema(client: TestClient):
    response = client.get("/openapi.json")
    assert response.status_code == 200, response.text

    response = client.put(
        "/items/5",
        json={
            "name": "Foo",
            "description": "A very nice Item",
            "price": 35.4,
            "tax": 3.2,
        },
    )
    assert response.status_code == 200


def test_openapi_schema(client: TestClient):
    response = client.get("/openapi.json")
    assert response.status_code == 200, response.text