import org.apache.lucene.search.TermQuery;
import org.codelibs.core.CoreLibConstants;
import org.codelibs.fess.suggest.converter.AnalyzerConverter;
import org.codelibs.fess.suggest.converter.KatakanaToAlphabetConverter;
import org.codelibs.fess.suggest.converter.ReadingConverter;
import org.codelibs.fess.suggest.converter.ReadingConverterChain;
import org.codelibs.fess.suggest.entity.SuggestItem;

- **Multi-language Support**: Built-in support for Japanese text processing with Kuromoji analyzer
- **Popular Words Analytics**: Track and analyze frequently searched terms
- **Flexible Text Processing**: Configurable converters and normalizers for text transformation
- **OpenSearch Integration**: Seamless integration with OpenSearch/Elasticsearch clusters
- **Asynchronous Operations**: Non-blocking suggestion requests with callback support

        // Test that multiple converter instances work independently
        ReadingConverter converter1 = new TestReadingConverter();
        ReadingConverter converter2 = new TestReadingConverter();

        converter1.init();
        converter2.init();

        String text = "test";
        String field = "content";

        List<String> readings1 = converter1.convert(text, field, "en");

        List<String> results = converter.convert(text, field, "en");

        assertNotNull(results);
    }

    @Test
    public void testMultipleConverterInstances() throws IOException {
        // Test that multiple converter instances work independently
        AnalyzerConverter converter1 = new AnalyzerConverter(client, settings);
        AnalyzerConverter converter2 = new AnalyzerConverter(client, settings);

Het aanroepen van dit programma leidt tot het volgende resultaat:

```
John Doe
```

De functie voert het volgende uit:

* Neem een `first_name` en een `last_name`
* Converteer de eerste letter van elk naar een hoofdletter met `title()`.
``
* <abbr title="Voegt ze samen, als één. Met de inhoud van de een na de ander.">Voeg samen</abbr> met een spatie in het midden.

Você declara a "forma" dos dados como classes com atributos.

E cada atributo tem um tipo.

Em seguida, você cria uma instância dessa classe com alguns valores e ela os validará, os converterá para o tipo apropriado (se for esse o caso) e fornecerá um objeto com todos os dados.

E você recebe todo o suporte do editor com esse objeto resultante.

Retirado dos documentos oficiais dos Pydantic:

 * possible readings.
 * </p>
 *
 * <p>
 * The class also utilizes ICU4J's {@link com.ibm.icu.text.Transliterator} to convert full-width characters
 * to half-width and to convert any characters to lowercase, ensuring consistency in the output.
 * </p>
 *
 * <p>
 * Example:
 * For the input "キャ", the converter might return a list containing "kya".

        }
        return targetList;
    }

    /**
     * Extracts a field value from OpenSearch results and converts it to the specified type.
     *
     * @param <T> The target type.
     * @param field The field value from OpenSearch.
     * @param clazz The target class.
     * @return The converted field value.
     */
    @SuppressWarnings("unchecked")

	"github.com/minio/minio/internal/logger"
	"github.com/pkg/errors"
	"github.com/prometheus/client_golang/prometheus"
)

type collectorPath string

// metricPrefix converts a collector path to a metric name prefix. The path is
// converted to snake-case (by replaced '/' and '-' with '_') and prefixed with
// `minio_`.
func (cp collectorPath) metricPrefix() string {
	s := strings.TrimPrefix(string(cp), SlashSeparator)

Declaras la "forma" de los datos como clases con atributos.

Y cada atributo tiene un tipo.

Entonces creas un instance de esa clase con algunos valores y validará los valores, los convertirá al tipo adecuado (si es el caso) y te dará un objeto con todos los datos.

Y obtienes todo el soporte del editor con ese objeto resultante.

Un ejemplo de la documentación oficial de Pydantic: