# Modelos de Parâmetros do Cabeçalho

Se você possui um grupo de **parâmetros de cabeçalho** relacionados, você pode criar um **modelo do Pydantic** para declará-los.

Isso vai lhe permitir **reusar o modelo** em **múltiplos lugares** e também declarar validações e metadadados para todos os parâmetros de uma vez. 😎

/// note | Nota

Isso é possível desde a versão `0.115.0` do FastAPI. 🤓

///

## Parâmetros do Cabeçalho com um Modelo Pydantic

              <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>

    - 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

              </params>
              <models>
                <model>../../api/maven-api-plugin/src/main/mdo/plugin.mdo</model>
              </models>
              <version>2.0.0</version>
            </configuration>
          </execution>
          <execution>
            <id>modello-site-docs</id>
            <phase>none</phase>
          </execution>
        </executions>

It improves editor support for Pydantic models, with:

* auto-completion
* type checks
* refactoring
* searching
* inspections

///

## Use the model

Inside of the function, you can access all the attributes of the model object directly:

{!> ../../docs_src/body/tutorial002_py310.py!}

## Request body + path parameters

* 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.

          <execution>
            <id>modello-v3</id>
            <goals>
              <goal>velocity</goal>
            </goals>
            <configuration>
              <version>1.1.0</version>
              <models>
                <model>../../api/maven-api-metadata/src/main/mdo/metadata.mdo</model>
              </models>
              <templates>
                <template>model-v3.vm</template>
              </templates>

 * under the License.
 */
package org.apache.maven.model.building;

import java.io.File;
import java.nio.file.Path;
import java.util.Date;
import java.util.List;
import java.util.Properties;

import org.apache.maven.model.Model;
import org.apache.maven.model.Profile;
import org.apache.maven.model.resolution.ModelResolver;
import org.apache.maven.model.resolution.WorkspaceModelResolver;

/**

## Use Case

Let's start with an example **use case** and then see how to solve it with this.

Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖

The same models are shared among requests, so, it's not one model per request, or one per user or something similar.

https://twitter.com/_nicoax link: https://blog.codecentric.de/2019/08/inbetriebnahme-eines-scikit-learn-modells-mit-onnx-und-fastapi/ title: Inbetriebnahme eines scikit-learn-Modells mit ONNX und FastAPI - author: Felix Schürmeyer author_link: https://hellocoding.de/autor/felix-schuermeyer/ link: https://hellocoding.de/blog/coding-language/python/fastapi title: REST-API Programmieren mittels Python und dem FastAPI Modul Japanese: - author: '@bee2' author_link: https://qiita.com/bee2 link: https://qiita.com/be...