return resultList.subList(0, maxReadingNum);
        }
        return resultList;
    }

    /**
     * Adds a converter to the chain.
     * @param converter The converter to add.
     */
    public void addConverter(final ReadingConverter converter) {
        converters.add(converter);
    }

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;

├── settings/                   # Configuration management
├── entity/                     # Domain models (SuggestItem, etc.)
├── normalizer/                 # Text normalizers
├── converter/                  # Reading converters (katakana, romaji)
├── concurrent/                 # Async patterns (Deferred/Promise)
└── util/                      # Utilities
```

### Key Design Patterns

    *   `parallel_for.pfor`: add converters for Softmax, LogSoftmax, IsNaN, All,
        Any, and MatrixSetDiag.
    *   `parallel_for`: add converters for LowerTriangularSolve and Cholesky.
    *   `parallel_for`: add converters for `LogMatrixDeterminant` and
        `MatrixBandPart`.
    *   `parallel_for`: Add converter for `MatrixDiag`.
    *   `parallel_for`: Add converters for `OneHot`, `LowerBound`, `UpperBound`.

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 JSON.

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

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

  public void testSerialization_andThen() {
    Converter<String, Long> converterA = Longs.stringConverter();
    Converter<Long, String> reverseConverter = Longs.stringConverter().reverse();
    Converter<String, String> composedConverter = converterA.andThen(reverseConverter);
    SerializableTester.reserializeAndAssert(composedConverter);
  }

  public void testSerialization_from() {

 */
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();
    }

  }

  /**
   * Returns a serializable {@code Converter} that converts strings from this format to {@code
   * targetFormat}.
   *
   * @since 16.0
   */
  public Converter<String, String> converterTo(CaseFormat targetFormat) {
    return new StringConverter(this, targetFormat);
  }

  private static final class StringConverter extends Converter<String, String>
      implements Serializable {

  }

  /**
   * Returns a serializable {@code Converter} that converts strings from this format to {@code
   * targetFormat}.
   *
   * @since 16.0
   */
  public Converter<String, String> converterTo(CaseFormat targetFormat) {
    return new StringConverter(this, targetFormat);
  }

  private static final class StringConverter extends Converter<String, String>
      implements Serializable {

# Codificador Compatível com JSON { #json-compatible-encoder }

Existem alguns casos em que você pode precisar converter um tipo de dados (como um modelo Pydantic) para algo compatível com JSON (como um `dict`, `list`, etc).

Por exemplo, se você precisar armazená-lo em um banco de dados.

Para isso, **FastAPI** fornece uma função `jsonable_encoder()`.

## Usando a função `jsonable_encoder` { #using-the-jsonable-encoder }