- tutorial/query-param-models.md
    - tutorial/body-multiple-params.md
    - tutorial/body-fields.md
    - tutorial/body-nested-models.md
    - tutorial/schema-extra-example.md
    - tutorial/extra-data-types.md
    - tutorial/cookie-params.md
    - tutorial/header-params.md
    - tutorial/cookie-param-models.md
    - tutorial/header-param-models.md
    - tutorial/response-model.md

import binascii
from base64 import b64decode
from typing import Annotated, Optional

from annotated_doc import Doc
from fastapi.exceptions import HTTPException
from fastapi.openapi.models import HTTPBase as HTTPBaseModel
from fastapi.openapi.models import HTTPBearer as HTTPBearerModel
from fastapi.security.base import SecurityBase
from fastapi.security.utils import get_authorization_scheme_param
from pydantic import BaseModel

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

////

/// info | Información

En la versión 1 de Pydantic el método para obtener el JSON Schema para un modelo se llamaba `Item.schema()`, en la versión 2 de Pydantic, el método se llama `Item.model_json_schema()`.

///

Sin embargo, aunque no estamos usando la funcionalidad integrada por defecto, aún estamos usando un modelo Pydantic para generar manualmente el JSON Schema para los datos que queremos recibir en YAML.

////

/// info | Informação

Na versão 1 do Pydantic, o método para obter o JSON Schema de um modelo é `Item.schema()`, na versão 2 do Pydantic, o método é `Item.model_json_schema()`.

///

Entretanto, mesmo que não utilizemos a funcionalidade integrada por padrão, ainda estamos usando um modelo Pydantic para gerar um JSON Schema manualmente para os dados que queremos receber no formato YAML.

    "functionalTests": true,
    "crossVersionTests": false
  },
  {
    "name": "model-core",
    "path": "platforms/core-configuration/model-core",
    "unitTests": true,
    "functionalTests": true,
    "crossVersionTests": false
  },
  {
    "name": "model-groovy",
    "path": "platforms/core-configuration/model-groovy",
    "unitTests": true,
    "functionalTests": true,
    "crossVersionTests": false

    *   `Model.fit_generator`, `Model.evaluate_generator`,
        `Model.predict_generator`, `Model.train_on_batch`,
        `Model.test_on_batch`, and `Model.predict_on_batch` methods now respect
        the `run_eagerly` property, and will correctly run using `tf.function`
        by default. Note that `Model.fit_generator`, `Model.evaluate_generator`,

---> 100%
```

</div>

## Создание приложения с единственной моделью { #create-the-app-with-a-single-model }

Сначала мы создадим самую простую первую версию приложения с одной моделью **SQLModel**.

Позже мы улучшим его, повысив безопасность и универсальность, добавив **несколько моделей**. 🤓

### Создание моделей { #create-models }

Импортируйте `SQLModel` и создайте модель базы данных:

## 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.

///

## 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