// Test with BooleanQuery
        BooleanQuery.Builder builder = new BooleanQuery.Builder();
        Term term1 = new Term("content", "test");
        Term term2 = new Term("title", "query");
        builder.add(new TermQuery(term1), BooleanClause.Occur.SHOULD);
        builder.add(new TermQuery(term2), BooleanClause.Occur.SHOULD);
        BooleanQuery booleanQuery = builder.build();

    /** CRUD operation mode (CREATE, EDIT, etc.) */
    @ValidateTypeFailure
    public Integer crudMode;

    /** Input terms (comma-separated) that will be mapped to the output term */
    @Required
    @Size(max = 1000)
    public String inputs;

    /** Output term that input terms will be mapped to */
    @Size(min = 1, max = 1000)
    public String output;

    /**

	</dd>
	<dt>Boost</dt>
	<dd>
		To boost a term use the "^" symbol with a boost factor (a number) at
		the end of the term you are searching.
		<pre>Fess^100</pre>
	</dd>
	<dt>Fuzzy</dt>
	<dd>
		To do a fuzzy search use the "~" symbol at the end of a single word
		term. For example to search for a term similar in spelling to "Fess"
		use the fuzzy search:
		<pre>Fess~0.5</pre>
	</dd>

                return queries;
            }
        }
        return StringUtil.EMPTY_STRINGS;
    }

    private String toLowerCase(final String term) {
        return term != null ? term.toLowerCase(Locale.ROOT) : term;
    }

        final Term[] terms = phraseQuery.getTerms();
        if (terms.length == 0) {
            throw new InvalidQueryException(messages -> messages.addErrorsInvalidQueryUnknown(UserMessages.GLOBAL_PROPERTY_KEY),
                    "Unknown phrase query: " + phraseQuery);
        }
        final FessConfig fessConfig = ComponentUtil.getFessConfig();
        final String field = terms[0].field();

        /**
         * Constructs a Field from OpenSearch Terms aggregation.
         * Decodes the field name and processes all term buckets to extract
         * field values and their document counts.
         *
         * @param termFacet the OpenSearch Terms aggregation containing field facet data
         */
        public Field(final Terms termFacet) {

 *   <li>Optionally removing duplicate terms based on a flag.</li>
 *   <li>Limiting the maximum size of alphanumeric and symbol terms.</li>
 * </ul>
 *
 * <p>The {@link TextNormalizeContext} class provides a fluent API to configure the text
 * normalization process, including setting initial buffer capacity, maximum term sizes,
 * duplicate term removal, and custom space characters.
 *
 * <p>Example usage:

    }

    private String toLowerCase(final String term) {
        return term != null ? term.toLowerCase(Locale.ROOT) : term;
    }

    /**
     * Sets the prefix used to identify regex patterns in related content terms.
     * When a term starts with this prefix, it is treated as a regular expression
     * pattern rather than an exact match term.
     *

    }

    /**
     * Converts a string to lowercase.
     *
     * @param term The string to convert.
     * @return The lowercase string.
     */
    private String toLowerCase(final String term) {
        return term != null ? term.toLowerCase(Locale.ROOT) : term;
    }

     * Gets the maximum size for alphanumeric terms from configuration.
     *
     * @return the maximum alphanumeric term size
     */
    protected int getMaxAlphanumTermSize() {
        final FessConfig fessConfig = ComponentUtil.getFessConfig();
        return fessConfig.getCrawlerDocumentMaxAlphanumTermSizeAsInteger();
    }

    /**
     * Gets the maximum size for symbol terms from configuration.
     *