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

        }
    }

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

                return queries;
            }
        }
        return StringUtil.EMPTY_STRINGS;
    }

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

    }

    @Test
    public void test_convertPhraseQuery_multipleTerms_defaultField() {
        // Test with multiple terms in default field
        PhraseQuery.Builder builder = new PhraseQuery.Builder();
        builder.add(new Term(Constants.DEFAULT_FIELD, "hello"));
        builder.add(new Term(Constants.DEFAULT_FIELD, "world"));
        PhraseQuery phraseQuery = builder.build();

- Use the same translation for the same term throughout the document.
- If you translate a concept one way, keep it consistent across all occurrences.

### Links and references

- Never modify link syntax like `{.internal-link target=_blank}`.
- Keep markdown link structure intact: `[text](url){.internal-link}`.

### Preferred translations / glossary

    }

    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.
     *

        /**
         * 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) {

                }
                buckets.add(new StringTerms.Bucket(term, rand.nextInt(10000), subAggs, true, 0L, DocValueFormat.RAW));
            }

            Collections.sort(buckets, (a, b) -> a.compareKey(b));
            return new StringTerms(
                "terms",
                BucketOrder.key(true),
                BucketOrder.count(false),
                topNSize,

# Execute um Servidor Manualmente { #run-a-server-manually }

## Utilize o comando `fastapi run` { #use-the-fastapi-run-command }

Em resumo, utilize o comando `fastapi run` para inicializar sua aplicação FastAPI:

<div class="termy">

```console
$ <font color="#4E9A06">fastapi</font> run <u style="text-decoration-style:solid">main.py</u>

  <span style="background-color:#009485"><font color="#D3D7CF"> FastAPI </font></span>  Starting production server 🚀

        }
    }

    /**
     * Emulates an aggregation that collects from a single bucket "by accident"
     * and gets a "small" bucket ords. This can happen to a {@code terms} inside
     * of another {@code terms} when the "inner" terms only even has a single
     * bucket.
     */
    @Benchmark
    public void singleBucketIntoMultiSmall(Blackhole bh) {

    /**
     * Gets the set of highlighted query terms.
     * @return The set of highlighted query terms, or empty set if not initialized.
     */
    public Set<String> getHighlightedQuerySet() {
        return highlightedQuerySet != null ? highlightedQuerySet : new HashSet<>();
    }

    /**
     * Gets the field log map containing field names and their associated query terms.