* Convert the copied model to something that can be stored in your DB (for example, using the `jsonable_encoder`).
    * This is comparable to using the model's `.model_dump()` method again, but it makes sure (and converts) the values to data types that can be converted to JSON, for example, `datetime` to `str`.
* Save the data to your DB.
* Return the updated model.

{* ../../docs_src/body_updates/tutorial002_py310.py hl[28:35] *}

/// tip

* Converter o modelo copiado em algo que possa ser armazenado no seu BD (por exemplo, usando o `jsonable_encoder`).
    * Isso é comparável a usar o método `.model_dump()` do modelo novamente, mas garante (e converte) os valores para tipos de dados que possam ser convertidos em JSON, por exemplo, `datetime` para `str`.
* Salvar os dados no seu BD.

 * lookups.
 *
 * <p>During construction, names are normalized in two ways:
 *
 * <ol>
 *   <li>ASCII uppercase characters are converted to lowercase.
 *   <li>Unicode dot separators other than the ASCII period ({@code '.'}) are converted to the ASCII
 *       period.
 * </ol>
 *
 * <p>The normalized values will be returned from {@link #toString()} and {@link #parts()}, and will

    }

    /**
     * Converts a TermRangeQuery to a QueryBuilder with the given boost value.
     * Handles both search fields and default field queries with proper range semantics.
     *
     * @param context the query context
     * @param termRangeQuery the term range query to convert
     * @param boost the boost value to apply
     * @return the converted QueryBuilder
     */

 */
package org.codelibs.fess.suggest.converter;

import java.util.List;

import junit.framework.TestCase;

public class KatakanaToAlphabetConverterTest extends TestCase {

    private KatakanaToAlphabetConverter converter;

    @Override
    protected void setUp() throws Exception {
        super.setUp();
        converter = new KatakanaToAlphabetConverter();
        converter.init();
    }

But when you declare them with Python types (in the example above, as `int`), they are converted to that type and validated against it.

All the same process that applied for path parameters also applies for query parameters:

* Editor support (obviously)
* Data <dfn title="converting the string that comes from an HTTP request into Python data">"parsing"</dfn>
* Data validation
* Automatic documentation

También puedes usar `dataclasses` en el parámetro `response_model`:

{* ../../docs_src/dataclasses_/tutorial002_py310.py hl[1,6:12,18] *}

El dataclass será automáticamente convertido a un dataclass de Pydantic.

De esta manera, su esquema aparecerá en la interfaz de usuario de la documentación de la API:

<img src="/img/tutorial/dataclasses/image01.png">

    }

    /**
     * Retrieves related queries for a given search term.
     * The search is performed using the current virtual host context and
     * the query term is converted to lowercase for case-insensitive matching.
     *
     * @param query the search term to find related queries for
     * @return an array of related query strings, or empty array if none found
     */

        }
    }

    @Nested
    @DisplayName("Hex Conversion Tests")
    class HexConversionTests {

        @Test
        @DisplayName("Various byte values are converted to uppercase hex")
        void testHexConversion() {
            byte[] challenge = new byte[] { (byte) 0x00, (byte) 0x0F, (byte) 0x10, (byte) 0x7F, (byte) 0x80, (byte) 0xFF };
            UniAddress dc = mock(UniAddress.class);

        "price": 42.0,
        "tax": 3.2
    },
    "user": {
        "username": "dave",
        "full_name": "Dave Grohl"
    },
    "importance": 5
}
```

Nuevamente, convertirá los tipos de datos, validará, documentará, etc.

## Múltiples parámetros de cuerpo y query { #multiple-body-params-and-query }