public WildcardQueryCommand() {
        super();
    }

    private static final Logger logger = LogManager.getLogger(WildcardQueryCommand.class);

    /**
     * Flag indicating whether wildcard terms should be converted to lowercase.
     */
    protected boolean lowercaseWildcard = true;

    @Override
    protected String getQueryClassName() {
        return WildcardQuery.class.getSimpleName();
    }

    @Override

# Codificador compatible con JSON { #json-compatible-encoder }

Hay algunos casos en los que podrías necesitar convertir un tipo de dato (como un modelo de Pydantic) a algo compatible con JSON (como un `dict`, `list`, etc).

Por ejemplo, si necesitas almacenarlo en una base de datos.

Para eso, **FastAPI** proporciona una función `jsonable_encoder()`.

## Usando el `jsonable_encoder` { #using-the-jsonable-encoder }

    }

    /**
     * Registers a value in the render data with the specified key.
     * If the value is an Entity object, it will be converted to a Map using BeanUtil.
     * If the value is a Collection containing Entity objects, each Entity will be converted to a Map.
     * For other types of values, they are registered directly.
     *
     * @param data the RenderData object to register the value in

Par exemple, vous ne pouvez pas mettre un modèle Pydantic dans une `JSONResponse` sans d'abord le convertir en un `dict` avec tous les types de données (comme `datetime`, `UUID`, etc.) convertis en types compatibles avec JSON.

Pour ces cas, vous pouvez spécifier un appel à `jsonable_encoder` pour convertir vos données avant de les passer à une réponse :

{* ../../docs_src/response_directly/tutorial001.py hl[6:7,21:22] *}

    }

    /**
     * Converts to the appropriate {@link Number}.
     *
     * @param type
     *            Target type
     * @param o
     *            Source object
     * @return {@link Number} converted to {@literal type}
     */
    public static Object convertNumber(final Class<?> type, final Object o) {
        if (type == Integer.class) {
            return IntegerConversionUtil.toInteger(o);

    this.min = min;
    this.max = max;
  }

  /**
   * Returns statistics over a dataset containing the given values.
   *
   * @param values a series of values, which will be converted to {@code double} values (this may
   *     cause loss of precision)
   */
  public static Stats of(Iterable<? extends Number> values) {
    StatsAccumulator accumulator = new StatsAccumulator();

      }

    /**
     * Sets the default timeout for complete calls. A value of 0 means no timeout, otherwise values
     * must be between 1 and [Integer.MAX_VALUE] when converted to milliseconds.
     *
     * The call timeout spans the entire call: resolving DNS, connecting, writing the request body,
     * server processing, and reading the response body. If the call requires redirects or retries

     * @param clazz the class type to convert the value to
     * @param defaultValue the default value to return if the key is not found or conversion fails
     * @return the converted value or the default value if not found
     */
    public static <T> T getValue(final Map<String, Object> doc, final String key, final Class<T> clazz, final T defaultValue) {
        final T value = getValue(doc, key, clazz);

## Convertir cualquier Dato a Compatible con JSON { #convert-any-data-to-json-compatible }

Para convertir cualquier dato a compatible con JSON, lee la documentación para [Tutorial - Codificador Compatible con JSON](../tutorial/encoder.md){.internal-link target=_blank}.

For example, you cannot put a Pydantic model in a `JSONResponse` without first converting it to a `dict` with all the data types (like `datetime`, `UUID`, etc) converted to JSON-compatible types.

For those cases, you can use the `jsonable_encoder` to convert your data before passing it to a response:

{* ../../docs_src/response_directly/tutorial001_py310.py hl[5:6,20:21] *}