### 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.

* <a href="https://developers.liblab.com/tutorials/sdk-for-fastapi/?utm_source=fastapi" class="external-link" target="_blank">liblab</a>

There are also several other companies offering similar services that you can search and find online. 🤓

## Generate a TypeScript Frontend Client

Let's start with a simple FastAPI application:

//// tab | Python 3.9+

```Python hl_lines="7-9  12-13  16-17  21"

    /** 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) {

import org.lastaflute.web.ruts.process.ActionRuntime;
import org.lastaflute.web.util.LaRequestUtil;
import org.lastaflute.web.util.LaServletContextUtil;
import org.opensearch.core.common.text.Text;
import org.opensearch.search.fetch.subphase.highlight.HighlightField;

import com.github.jknack.handlebars.Context;
import com.github.jknack.handlebars.Handlebars;
import com.github.jknack.handlebars.Template;

   * given to [connectionUser] who may (for example) assign it to a [RealCall.connection].
   *
   * This confirms the returned connection is healthy before returning it. If this encounters any
   * unhealthy connections in its search, this will clean them up.
   *
   * If [routes] is non-null these are the resolved routes (ie. IP addresses) for the connection.
   * This is used to coalesce related domains to the same HTTP/2 connection, such as `square.com`

    } catch (ClassCastException e) {
      return false;
    }
  }

  @Override
  public boolean containsAll(Collection<?> targets) {
    // TODO(jlevy): For optimal performance, use a binary search when
    // targets.size() < size() / log(size())
    // TODO(kevinb): see if we can share code with OrderedIterator after it
    // graduates from labs.
    if (targets instanceof Multiset) {

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

    // GET /api/admin/stats
    @Execute
    public JsonResponse<ApiResult> index() {

```Python
{!> ../../docs_src/app_testing/app_b/test_main.py!}
```

Whenever you need the client to pass information in the request and you don't know how to, you can search (Google) how to do it in `httpx`, or even how to do it with `requests`, as HTTPX's design is based on Requests' design.

Then you just do the same in your tests.

E.g.: