Da mesma forma, este banco de dados não receberia um modelo Pydantic (um objeto com atributos), apenas um `dict`.

Você pode usar a função `jsonable_encoder` para resolver isso.

A função recebe um objeto, como um modelo Pydantic e retorna uma versão compatível com JSON:

//// tab | Python 3.10+

```Python hl_lines="4  21"

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

## ドキュメントを見る

自動ドキュメントを見ると、入力モデルと出力モデルがそれぞれ独自のJSON Schemaを持っていることが確認できます。

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image01.png">

そして、両方のモデルは、対話型のAPIドキュメントに使用されます:

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image02.png">

## レスポンスモデルのエンコーディングパラメータ

レスポンスモデルにはデフォルト値を設定することができます:

```Python hl_lines="11 13 14"

          <basedir>${project.basedir}/../../api/maven-api-toolchain</basedir>
          <velocityBasedir>${project.basedir}/../../src/mdo</velocityBasedir>
          <models>
            <model>src/main/mdo/toolchains.mdo</model>
          </models>
        </configuration>
        <executions>
          <execution>
            <id>velocity</id>
            <goals>
              <goal>velocity</goal>
            </goals>

* **Güvenli şifreleme** .
* **JWT token** kimlik doğrulama.
* **SQLAlchemy** models (Flask dan bağımsızdır. Celery worker'ları ile kullanılabilir).
* Kullanıcılar için temel başlangıç ​​modeli (gerektiği gibi değiştirin ve kaldırın).
* **Alembic** migration.
* **CORS** (Cross Origin Resource Sharing).

          <basedir>${project.basedir}/../../api/maven-api-model</basedir>
          <velocityBasedir>${project.basedir}/../../src/mdo</velocityBasedir>
          <version>4.1.0</version>
          <models>
            <model>src/main/mdo/maven.mdo</model>
          </models>
          <params>
            <param>forcedIOModelVersion=4.0.0</param>
            <param>packageModelV3=org.apache.maven.model</param>

    - 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

```

////

/// info

In Pydantic Version 1 hieß die Methode zum Abrufen des JSON-Schemas für ein Modell `Item.schema()`, in Pydantic Version 2 heißt die Methode `Item.model_json_schema()`.

///

Obwohl wir nicht die standardmäßig integrierte Funktionalität verwenden, verwenden wir dennoch ein Pydantic-Modell, um das JSON-Schema für die Daten, die wir in YAML empfangen möchten, manuell zu generieren.

                <param>packageToolV4=org.apache.maven.plugin.lifecycle.io</param>
              </params>
              <models>
                <model>../../api/maven-api-plugin/src/main/mdo/lifecycle.mdo</model>
              </models>
              <version>2.0.0</version>
            </configuration>
          </execution>
          <execution>
            <id>velocity-plugin</id>

Let's see how that works and how to change it if you need to do that.

## Pydantic Models for Input and Output

Let's say you have a Pydantic model with default values, like this one:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:7] hl[7] *}

### Model for Input