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

И также обе модели будут использованы в интерактивной документации API:

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

## Другие аннотации типов

## 👀 ⚫️ 🩺

🕐❔ 👆 👀 🏧 🩺, 👆 💪 ✅ 👈 🔢 🏷 & 🔢 🏷 🔜 👯‍♂️ ✔️ 👫 👍 🎻 🔗:

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

&amp; 👯‍♂️ 🏷 🔜 ⚙️ 🎓 🛠️ 🧾:

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

## 🎏 📨 🆎 ✍

📤 5️⃣📆 💼 🌐❔ 👆 📨 🕳 👈 🚫 ☑ Pydantic 🏑 &amp; 👆 ✍ ⚫️ 🔢, 🕴 🤚 🐕‍🦺 🚚 🏭 (👨‍🎨, ✍, ♒️).

### 📨 📨 🔗

import org.apache.maven.api.annotations.Nonnull;
import org.apache.maven.api.model.Model;

/**
 * Provides the super POM that all models implicitly inherit from.
 *
 * @since 4.0.0
 */
@Experimental
public interface SuperPomProvider extends Service {

    /**
     * Gets the super POM for the specified model version.
     *
     * @param version The model version to retrieve the super POM for (e.g. "4.0.0"), must not be {@code null}.

/// info

Bedenken Sie, dass Datenklassen nicht alles können, was Pydantic-Modelle können.

Daher müssen Sie möglicherweise weiterhin Pydantic-Modelle verwenden.

Wenn Sie jedoch eine Menge Datenklassen herumliegen haben, ist dies ein guter Trick, um sie für eine Web-API mithilfe von FastAPI zu verwenden. 🤓

///

 *
 * <p>This factory is responsible for instantiating concrete toolchain implementations
 * based on toolchain model configurations or default settings.</p>
 *
 * @since 4.0.0
 */
@Experimental
@Consumer
public interface ToolchainFactory {
    /**
     * Creates a toolchain instance from the provided model configuration.
     *

따라서 **FastAPI**는 출력 모델에서 선언하지 않은 모든 데이터를 (Pydantic을 사용하여) 필터링합니다.

## 문서에서 보기

자동 생성 문서를 보면 입력 모델과 출력 모델이 각자의 JSON 스키마를 가지고 있음을 확인할 수 있습니다:

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

그리고 두 모델 모두 대화형 API 문서에 사용됩니다:

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

## 응답 모델 인코딩 매개변수

응답 모델은 아래와 같이 기본값을 가질 수 있습니다:

{* ../../docs_src/response_model/tutorial004.py hl[11,13:14] *}

@app.get("/items/")
async def read_items(filter_query: Annotated[FilterParams, Query()]):
    return filter_query
```

Read the new docs: [Query Parameter Models](https://fastapi.tiangolo.com/tutorial/query-param-models/).

#### `Header` Parameter Models

Use Pydantic models for `Header` parameters:

```python
from typing import Annotated

from fastapi import FastAPI, Header
from pydantic import BaseModel

specific language governing permissions and limitations
under the License.
-->

<model xmlns="http://codehaus-plexus.github.io/MODELLO/2.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://codehaus-plexus.github.io/MODELLO/2.0.0 https://codehaus-plexus.github.io/modello/xsd/modello-2.0.0.xsd"
  xml.namespace="http://maven.apache.org/LIFECYCLE/${version}"

Melhora o suporte do editor para seus modelos Pydantic com::

* completação automática
* verificação de tipos
* refatoração
* buscas
* inspeções

///

## Use o modelo

Dentro da função, você pode acessar todos os atributos do objeto do modelo diretamente:

{* ../../docs_src/body/tutorial002.py hl[21] *}

Ce qui améliore le support pour les modèles Pydantic avec :

* de l'auto-complétion
* des vérifications de type
* du "refactoring" (ou remaniement de code)
* de la recherche
* de l'inspection

///

## Utilisez le modèle

Dans la fonction, vous pouvez accéder à tous les attributs de l'objet du modèle directement :

{* ../../docs_src/body/tutorial002.py hl[21] *}