# Query Parameter Models { #query-parameter-models }

If you have a group of **query parameters** that are related, you can create a **Pydantic model** to declare them.

This would allow you to **re-use the model** in **multiple places** and also to declare validations and metadata for all the parameters at once. 😎

/// note

This is supported since FastAPI version `0.115.0`. 🤓

///

# Modelos de Parâmetros de Cookie { #cookie-parameter-models }

Se você possui um grupo de **cookies** que estão relacionados, você pode criar um **modelo Pydantic** para declará-los. 🍪

Isso lhe permitiria **reutilizar o modelo** em **diversos lugares** e também declarar validações e metadata para todos os parâmetros de uma vez. 😎

/// note | Nota

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

///

/// tip | Dica

# Modelos de Parámetros Query { #query-parameter-models }

Si tienes un grupo de **parámetros query** que están relacionados, puedes crear un **modelo de Pydantic** para declararlos.

Esto te permitiría **reutilizar el modelo** en **múltiples lugares** y también declarar validaciones y metadatos para todos los parámetros de una vez. 😎

/// note | Nota

Esto es compatible desde la versión `0.115.0` de FastAPI. 🤓

///

<img src="/img/python-types/image06.png">

Notice that this means "`one_person` is an **instance** of the class `Person`".

It doesn't mean "`one_person` is the **class** called `Person`".

## Pydantic models { #pydantic-models }

<a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a> is a Python library to perform data validation.

You declare the "shape" of the data as classes with attributes.

Using the below example document:
<img width="85%" src="https://bradley.codes/static/img/jstream-levels.gif" alt="jstream"/>

we can choose to extract and act only the objects within the top-level array:
```go
f, _ := os.Open("input.json")

client = TestClient(app)


def test_get_enums_alexnet():
    response = client.get("/models/alexnet")
    assert response.status_code == 200
    assert response.json() == {"model_name": "alexnet", "message": "Deep Learning FTW!"}


def test_get_enums_lenet():
    response = client.get("/models/lenet")
    assert response.status_code == 200

 *       ImmutableSet.of("AZ", "CQ", "ZX");
 *
 *   private final ImmutableSet<String> codes;
 *
 *   public Foo(Iterable<String> codes) {
 *     this.codes = ImmutableSet.copyOf(codes);
 *     checkArgument(Collections.disjoint(this.codes, RESERVED_CODES));
 *   }
 * }
 * }
 *
 * <h3>See also</h3>
 *
 * <p>See the Guava User Guide article on <a href=

            <configuration>
              <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>

///

If `bump-pydantic` doesn't work for your use case, you can use the support for both Pydantic v1 and v2 models in the same app to do the migration to Pydantic v2 gradually.

You could fist upgrade Pydantic to use the latest version 2, and change the imports to use `pydantic.v1` for all your models.

import com.google.common.collect.testing.SampleElements.Colliders;
import java.util.List;
import org.jspecify.annotations.NullMarked;

/**
 * A generator using sample elements whose hash codes all collide badly.
 *
 * @author Kevin Bourrillion
 */
@GwtCompatible
@NullMarked
public abstract class TestCollidingSetGenerator implements TestSetGenerator<Object> {
  @Override