├── app                  # „app“ ist ein Python-Package
│   ├── __init__.py      # diese Datei macht „app“ zu einem „Python-Package“
│   ├── main.py          # „main“-Modul, z. B. import app.main
│   ├── dependencies.py  # „dependencies“-Modul, z. B. import app.dependencies
│   └── routers          # „routers“ ist ein „Python-Subpackage“
│   │   ├── __init__.py  # macht „routers“ zu einem „Python-Subpackage“

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;

     */
    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--;
            }

        assertThrows(
            TimeoutException.class, () -> service.startAsync().awaitRunning(1, MILLISECONDS));
    assertThat(e)
        .hasMessageThat()
        .isEqualTo("Timed out waiting for Foo [STARTING] to reach the RUNNING state.");
  }

  private static class TestService extends AbstractIdleService {
    int startUpCalled = 0;
    int shutDownCalled = 0;
    final List<State> transitionStates = Lists.newArrayList();

### 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.

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

    /** The path of the HTML: /profile/index.jsp */
    HtmlNext path_Profile_IndexJsp = new HtmlNext("/profile/index.jsp");

    /** The path of the HTML: /search.jsp */
    HtmlNext path_SearchJsp = new HtmlNext("/search.jsp");

    /** The path of the HTML: /searchNoResult.jsp */
    HtmlNext path_SearchNoResultJsp = new HtmlNext("/searchNoResult.jsp");

    /** The path of the HTML: /searchOptions.jsp */

        return ROLE;
    }

    // ===================================================================================
    //                                                                      Search Execute
    //                                                                      ==============

    @Execute
    @Secured({ ROLE, ROLE + VIEW })
    public HtmlResponse index() {
        return asIndexHtml();

                stream(path.split(File.pathSeparator)).of(stream -> stream.map(String::trim).forEach(s -> pathList.add(s)));
            }
            if (logger.isDebugEnabled()) {
                logger.debug("search paths: {}", pathList);
            }
            available = generatorList.stream().map(s -> {
                if (s.startsWith("${path}")) {
                    for (final String p : pathList) {

Dennoch ist Uvicorn derzeit nur mit asyncio kompatibel und verwendet normalerweise <a href="https://github.com/MagicStack/uvloop" class="external-link" target="_blank">`uvloop`</a>, den leistungsstarken Drop-in-Ersatz für `asyncio`.