In this case you will have to ensure that each operation ID is **unique** in some other way.

For example, you could make sure that each *path operation* has a tag, and then generate the operation ID based on the **tag** and the *path operation* **name** (the function name).

### Custom Generate Unique ID Function

import org.codehaus.plexus.interpolation.PrefixedValueSourceWrapper;
import org.codehaus.plexus.interpolation.RecursionInterceptor;
import org.codehaus.plexus.interpolation.ValueSource;

/**
 * Use a regular expression search to find and resolve expressions within the POM.
 *
 * @deprecated use {@link org.apache.maven.api.services.ModelBuilder} instead
 */
@Deprecated(since = "4.0.0")

   * i, i + target.length)} contains exactly the same elements as {@code target}.
   *
   * @param array the array to search for the sequence {@code target}
   * @param target the array to search for as a sub-sequence of {@code array}
   */
  public static int indexOf(byte[] array, byte[] target) {
    checkNotNull(array, "array");
    checkNotNull(target, "target");

import io.restassured.response.Response;

public class CrawlTestBase extends ITBase {
    private static final Logger logger = LogManager.getLogger(CrawlTestBase.class);

    private static final String DOC_INDEX_NAME = "fess.search";

    protected static void createJob(final Map<String, Object> requestBody) {
        checkMethodBase(requestBody).put("/api/admin/scheduler/setting").then().body("response.created", equalTo(true))

import org.opensearch.action.bulk.BulkResponse;
import org.opensearch.action.index.IndexAction;
import org.opensearch.action.index.IndexRequestBuilder;
import org.opensearch.action.search.SearchAction;
import org.opensearch.action.search.SearchRequestBuilder;
import org.opensearch.index.query.QueryBuilder;
import org.opensearch.index.query.QueryBuilders;
import org.opensearch.index.query.TermQueryBuilder;

                }
                exitCode.set(1);
            }
        }

        if (ComponentUtil.getFessConfig().isSuggestSearchLog()) {
            logger.info("Parsing words from search logs.");
            try {
                suggestHelper.storeSearchLog();
            } catch (final Exception e) {
                if (logger.isDebugEnabled()) {

## Modèles d'apprentissage automatique avec spaCy et FastAPI - Fonctionnalités

* Intégration d'un modèle NER **spaCy**.
* Formatage de requête pour **Azure Cognitive Search**.
* Serveur Python web **prêt à utiliser en production** utilisant Uvicorn et Gunicorn.
* Déploiement CI/CD Kubernetes pour **Azure DevOps** (AKS).

                extractFile(ent.getKey(), embeddedFile, writer);
            }
        }
    }

    protected PDEmbeddedFile getEmbeddedFile(final PDComplexFileSpecification fileSpec) {
        // search for the first available alternative of the embedded file
        PDEmbeddedFile embeddedFile = null;
        if (fileSpec != null) {
            embeddedFile = fileSpec.getEmbeddedFileUnicode();

### Modelle für maschinelles Lernen mit spaCy und FastAPI – Funktionen

* **spaCy** NER-Modellintegration.
* **Azure Cognitive Search**-Anforderungsformat integriert.
* **Produktionsbereit** Python-Webserver, verwendet Uvicorn und Gunicorn.
* **Azure DevOps** Kubernetes (AKS) CI/CD-Deployment integriert.

     */
    private int checkHeaderComment(List<String> commentLines) {
        if (getHeader() == null && layout.isEmpty()) {
            // This is the first comment. Search for blank lines.
            int index = commentLines.size() - 1;
            while (index >= 0 && !commentLines.get(index).isEmpty()) {
                index--;
            }