///

## Embed a single body parameter { #embed-a-single-body-parameter }

Let's say you only have a single `item` body parameter from a Pydantic model `Item`.

By default, **FastAPI** will then expect its body directly.

But if you want it to expect a JSON with a key `item` and inside of it the model contents, as it does when you declare extra body parameters, you can use the special `Body` parameter `embed`:

```Python

///

/// note

Notice that the input model is still validated.

So, if you want to receive partial updates that can omit all the attributes, you need to have a model with all the attributes marked as optional (with default values or `None`).

To distinguish from the models with all optional values for **updates** and models with required values for **creation**, you can use the ideas described in [Extra Models](extra-models.md).

### `orjson` ou Modelo de Resposta { #orjson-or-response-model }

Se o que você procura é performance, provavelmente é melhor usar um [Modelo de Resposta](../tutorial/response-model.md) do que uma resposta com `orjson`.

You can see those schemas because they were declared with the models in the app.

That information is available in the app's **OpenAPI schema**, and then shown in the API docs.

That same information from the models that is included in OpenAPI is what can be used to **generate the client code**.

### Hey API { #hey-api }

				})
			}
		}
	})

	var models []User
	if err := DB.Where("name in (?)", []string{"find"}).Find(&models).Error; err != nil || len(models) != 3 {
		t.Errorf("errors happened when query find with in clause: %v, length: %v", err, len(models))
	} else {
		for idx, user := range users {
			t.Run("FindWithInClause#"+strconv.Itoa(idx+1), func(t *testing.T) {
				CheckUser(t, models[idx], user)
			})
		}
	}

**No está soportado** por Pydantic tener un modelo de Pydantic v2 con sus propios campos definidos como modelos de Pydantic v1 o viceversa.

```mermaid
graph TB
    subgraph "❌ Not Supported"
        direction TB
        subgraph V2["Pydantic v2 Model"]
            V1Field["Pydantic v1 Model"]
        end
        subgraph V1["Pydantic v1 Model"]
            V2Field["Pydantic v2 Model"]
        end
    end

### `orjson` ou Modèle de réponse { #orjson-or-response-model }

Si ce que vous recherchez est la performance, vous aurez probablement de meilleurs résultats en utilisant un [Modèle de réponse](../tutorial/response-model.md) qu'une réponse `orjson`.

Não é **suportado** pelo Pydantic ter um modelo do Pydantic v2 com campos próprios definidos como modelos do Pydantic v1, ou vice-versa.

```mermaid
graph TB
    subgraph "❌ Not Supported"
        direction TB
        subgraph V2["Pydantic v2 Model"]
            V1Field["Pydantic v1 Model"]
        end
        subgraph V1["Pydantic v1 Model"]
            V2Field["Pydantic v2 Model"]
        end
    end

## Other models { #other-models }

You can now get the current user directly in the *path operation functions* and deal with the security mechanisms at the **Dependency Injection** level, using `Depends`.

And you can use any model or data for the security requirements (in this case, a Pydantic model `User`).

But you are not restricted to using some specific data model, class or type.

Y luego en nuestro código, hacemos parse de ese contenido YAML directamente, y nuevamente estamos usando el mismo modelo Pydantic para validar el contenido YAML:

{* ../../docs_src/path_operation_advanced_configuration/tutorial007_py310.py hl[24:31] *}

/// tip | Consejo

Aquí reutilizamos el mismo modelo Pydantic.

Pero de la misma manera, podríamos haberlo validado de alguna otra forma.