* The search is performed using the current virtual host context and
     * the query term is converted to lowercase for case-insensitive matching.
     *
     * @param query the search term to find related queries for
     * @return an array of related query strings, or empty array if none found
     */
    public String[] getRelatedQueries(final String query) {
        final String key = ComponentUtil.getVirtualHostHelper().getVirtualHostKey();

            if (available) {
                cb.query().setAvailable_Equal(Constants.T);
            }
            if (idList != null) {
                cb.query().setId_InScope(idList);
            }
            cb.query().setName_NotEqual(ComponentUtil.getFessConfig().getFormAdminDefaultTemplateName());
            cb.query().addOrderBy_SortOrder_Asc();
            cb.query().addOrderBy_Name_Asc();

{
    "properties": {
      "userInfoId": {
        "type": "keyword"
      },
      "url": {
        "type": "keyword"
      },
      "docId": {
        "type": "keyword"
      },
      "queryId": {
        "type": "keyword"
      },
      "createdAt": {
        "type": "date",
        "format": "date_optional_time"
      }
    }

import org.opensearch.core.xcontent.XContentParser;
import org.opensearch.index.query.AbstractQueryBuilder;
import org.opensearch.index.query.QueryBuilder;
import org.opensearch.index.query.QueryShardContext;

/**
 * A query builder for a stored LTR (Learning to Rank) query.
 * This builder constructs a query that uses a pre-trained LTR model
 * to re-rank search results based on a given set of features.
 */

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(q: str | None = Query(default=None, alias="item-query")):
    results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
    if q:
        results.update({"q": q})

## Importe `Header` { #import-header }

Primeiro importe `Header`:

{* ../../docs_src/header_params/tutorial001_an_py310.py hl[3] *}

## Declare parâmetros de `Header` { #declare-header-parameters }

Então declare os paramêtros de cabeçalho usando a mesma estrutura que em `Path`, `Query` e `Cookie`.

    /** Query parameter name for main search query. */
    public static final String OPTION_QUERY_Q = "q";

    /** Query parameter name for constraint query. */
    public static final String OPTION_QUERY_CQ = "cq";

    /** Query parameter name for optional query. */
    public static final String OPTION_QUERY_OQ = "oq";

    /** Query parameter name for negative query. */

	"http://dbflute.org/meta/lastadi10.dtd">
<components>
	<component name="queryFieldConfig"
		class="org.codelibs.fess.query.QueryFieldConfig">
	</component>

	<component name="queryProcessor"
		class="org.codelibs.fess.query.QueryProcessor">
	</component>

	<component name="booleanQueryCommand"
		class="org.codelibs.fess.query.BooleanQueryCommand">
		<postConstruct name="register"></postConstruct>
	</component>

from typing import Annotated

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(
    q: Annotated[str | None, Query(title="Query string", min_length=3)] = None,
):
    results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
    if q:
        results.update({"q": q})

    /**
     * Performs a search operation using the specified query and parameters.
     *
     * @param query the search query string
     * @param params the search request parameters
     * @param userBean the optional user bean for access control
     * @return the search result containing documents and metadata
     */
    @Override