/**
     * 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;

     * @param langFieldName The name of the field that contains language information.
     * @param readingConverter The converter to use for reading fields.
     * @param contentsReadingConverter The converter to use for reading content fields.
     * @param normalizer The normalizer to use for normalizing field values.
     * @param analyzer The analyzer to use for analyzing field values.

- **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);

        return 10;
    }

    /**
     * Initializes the converter.
     *
     * @throws IOException if an I/O error occurs during initialization.
     */
    void init() throws IOException;

    /**
     * Converts the given text into a list of readings based on the specified field and languages.
     *
     * @param text the text to be converted.
     * @param field the field to be used for conversion.

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] *}

    *   `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`.

# Codificador Compatível com JSON

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`