*  @author Victor Jonsson, http://victorjonsson.se
 *  @license MIT
 */

# Modèle Full Stack FastAPI { #full-stack-fastapi-template }

Les modèles, bien qu'ils soient généralement livrés avec une configuration spécifique, sont conçus pour être flexibles et personnalisables. Cela vous permet de les modifier et de les adapter aux exigences de votre projet, ce qui en fait un excellent point de départ. 🏁

It's **not supported** by Pydantic to have a model of Pydantic v2 with its own fields defined as Pydantic v1 models or 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

        <executions>
          <execution>
            <id>attach-gradle-module-metadata</id>
            <goals>
              <goal>attach-artifact</goal>
            </goals>
            <configuration>
              <artifacts>
                <artifact>
                  <file>target/publish/module.json</file>
                  <type>module</type>
                </artifact>
              </artifacts>

Use base64 apenas se realmente precisar incluir dados binários em JSON e não puder usar arquivos para isso.

## Pydantic `bytes` { #pydantic-bytes }

Você pode declarar um modelo Pydantic com campos `bytes` e então usar `val_json_bytes` na configuração do modelo para indicar que deve usar base64 para *validar* os dados JSON de entrada; como parte dessa validação, ele decodificará a string base64 em bytes.

              <goal>velocity</goal>
            </goals>
            <configuration>
              <version>4.0.0</version>
              <models>
                <model>target/mdo/maven.mdo</model>
              </models>
              <templates>
                <template>src/main/mdo/model-v3.vm</template>
                <template>src/main/mdo/merger.vm</template>
                <template>src/main/mdo/transformer.vm</template>

    }

    fun uses(platform: PlatformBuilder) {
        uses.add(platform)
    }

    fun module(platformName: String, moduleConfiguration: ArchitectureModuleBuilder.() -> Unit) {
        val module = ArchitectureModuleBuilder(platformName, settings)
        modules.add(module)
        module.moduleConfiguration()
    }

    override fun build(): Platform {

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

/// info

Keep in mind that dataclasses can't do everything Pydantic models can do.

So, you might still need to use Pydantic models.

              <velocityBasedir>${project.basedir}/../../src/mdo</velocityBasedir>
              <version>2.0.0</version>
              <models>
                <model>src/main/mdo/plugin.mdo</model>
              </models>
              <templates>
                <template>model.vm</template>
              </templates>
              <params>
                <param>packageModelV4=org.apache.maven.api.plugin.descriptor</param>

Zum Beispiel diese:

* `int`
* `float`
* `bool`
* `bytes`

{* ../../docs_src/python_types/tutorial005_py310.py hl[1] *}

### `typing`-Modul { #typing-module }