testData2.add(createRelatedQuery("term2", new String[] { "query2" }, ""));
        mockBhv.setTestData(testData2);

        relatedQueryHelper.load();

        // Old term should not be found
        results = relatedQueryHelper.getRelatedQueries("term1");
        assertEquals(0, results.length);

        // New term should be found

        testData.add(createRelatedContent("term1", "content1", ""));
        testData.add(createRelatedContent("term2", "content2", "host1"));
        mockBhv.setTestData(testData);

        List<RelatedContent> result = relatedContentHelper.getAvailableRelatedContentList();
        assertEquals(2, result.size());
        assertEquals("term1", result.get(0).getTerm());
        assertEquals("term2", result.get(1).getTerm());
    }

            QueryBuilder result = fuzzyQueryCommand.convertFuzzyQuery(context, fuzzyQuery, 1.0f);
            assertNotNull(result);
            assertTrue(result.toString().contains(text));

        }
    }

    // Test with empty term text
    public void test_convertFuzzyQuery_withEmptyTerm() {
        QueryContext context = new QueryContext("test", false);
        Term term = new Term("title", "");

        // 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();

                return queries;
            }
        }
        return StringUtil.EMPTY_STRINGS;
    }

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

	</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>

    /** 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;

    /**

        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();

          Exceptions and Limitations apply to Your use, this Public
          License does not apply, and You do not need to comply with
          its terms and conditions.

       3. Term. The term of this Public License is specified in Section
          6(a).

       4. Media and formats; technical modifications allowed. The
          Licensor authorizes You to exercise the Licensed Rights in

 *   <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: