<build>
    <plugins>
      <plugin>
        <groupId>org.codehaus.modello</groupId>
        <artifactId>modello-maven-plugin</artifactId>
        <configuration>
          <version>1.2.0</version>
          <models>
            <model>src/main/mdo/core-extensions.mdo</model>
          </models>
          <templates>
            <template>model.vm</template>
          </templates>
          <params>

      <plugin>
        <groupId>org.codehaus.modello</groupId>
        <artifactId>modello-maven-plugin</artifactId>
        <configuration>
          <version>2.0.0</version>
          <basedir>${project.basedir}/../../api/maven-api-settings</basedir>
          <velocityBasedir>${project.basedir}/../../src/mdo</velocityBasedir>
          <models>
            <model>src/main/mdo/settings.mdo</model>
          </models>
          <params>

Dans la documentation automatique, vous pouvez vérifier que le modèle d'entrée et le modèle de sortie auront chacun leur propre JSON Schema :

<img src="/img/tutorial/response-model/image01.png">

Et les deux modèles seront utilisés pour la documentation API interactive :

<img src="/img/tutorial/response-model/image02.png">

## Autres annotations de type de retour { #other-return-type-annotations }

Quando você vê a documentação automática, pode verificar se o modelo de entrada e o modelo de saída terão seus próprios esquemas JSON:

<img src="/img/tutorial/response-model/image01.png">

E ambos os modelos serão usados ​​para a documentação interativa da API:

<img src="/img/tutorial/response-model/image02.png">

## Outras anotações de tipo de retorno { #other-return-type-annotations }

      <plugin>
        <groupId>org.codehaus.modello</groupId>
        <artifactId>modello-maven-plugin</artifactId>
        <configuration>
          <version>1.1.0</version>
          <basedir>${project.basedir}/../../api/maven-api-toolchain</basedir>
          <velocityBasedir>${project.basedir}/../../src/mdo</velocityBasedir>
          <models>
            <model>src/main/mdo/toolchains.mdo</model>
          </models>
        </configuration>

     *
     * @param modelId The identifier of the desired raw model, must not be {@code null}.
     * @return The raw model or {@code null} if the specified model id does not refer to a known model.
     */
    Model getRawModel(String modelId);

    /**
     * Gets the profiles from the specified model that were active during model building. The model identifier should be

///

/// 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).

Vamos corrigir essas coisas adicionando alguns **modelos extras**. Aqui é onde o SQLModel vai brilhar. ✨

### Criar Múltiplos Modelos { #create-multiple-models }

No **SQLModel**, qualquer classe de modelo que tenha `table=True` é um **modelo de tabela**.

E qualquer classe de modelo que não tenha `table=True` é um **modelo de dados**, esses são na verdade apenas modelos Pydantic (com alguns recursos extras pequenos). 🤓

4. The daemon runs the request, sending back data such as logging output or tooling API events and intermediate models while doing so.
5. The daemon sends the result back. For some requests, this might be a simple success/failure result, and for others this might also include a more complex object, such as a tooling API model.

### Modèle pour l'entrée { #model-for-input }

Si vous utilisez ce modèle en entrée comme ici :

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:15] hl[14] *}

... alors, le champ `description` ne sera **pas requis**. Parce qu'il a une valeur par défaut de `None`.

### Modèle d'entrée dans les documents { #input-model-in-docs }