/**
 * Provides tools for processing Maven dependency injection annotations at compile time.
 * <p>
 * This package contains annotation processors that generate metadata files used by
 * the Maven dependency injection system. The main component is the {@link org.apache.maven.di.tool.DiIndexProcessor},
 * which processes classes annotated with {@link org.apache.maven.api.di.Named} and creates an index file

```Dockerfile
FROM tiangolo/uvicorn-gunicorn-fastapi:python3.7

COPY ./app /app/app
```

### Raspberry Pi et autres architectures

Si vous utilisez Docker sur un Raspberry Pi (qui a un processeur ARM) ou toute autre architecture, vous pouvez créer un `Dockerfile` à partir de zéro, basé sur une image de base Python (qui est multi-architecture) et utiliser Uvicorn seul.

        includeFilter.processUrl("http://example.com/");

        assertEquals(1, includeFilter.cachedIncludeSet.size());
        assertEquals(0, includeFilter.cachedExcludeSet.size());
        assertEquals("http://example.com/.*", includeFilter.cachedIncludeSet.toArray()[0]);

        includeFilter.processUrl("https://test.com");

func (cs *callbacks) Create() *processor {
	return cs.processors["create"]
}

func (cs *callbacks) Query() *processor {
	return cs.processors["query"]
}

func (cs *callbacks) Update() *processor {
	return cs.processors["update"]
}

func (cs *callbacks) Delete() *processor {
	return cs.processors["delete"]
}

func (cs *callbacks) Row() *processor {
	return cs.processors["row"]
}

        List<InterpolationPostProcessor> processors = new ArrayList<>(2);
        if (projectDir != null) {
            processors.add(new PathTranslatingPostProcessor(
                    PROJECT_PREFIXES, TRANSLATED_PATH_EXPRESSIONS,
                    projectDir, pathTranslator));
        }
        processors.add(new UrlNormalizingPostProcessor(urlNormalizer));
        return processors;
    }

* Restarts
* **Replication (the number of processes running)**
* Memory
* Previous steps before starting

Up to this point, with all the tutorials in the docs, you have probably been running a **server program**, for example, using the `fastapi` command, that runs Uvicorn, running a **single process**.

import org.opensearch.search.aggregations.bucket.terms.Terms;

import com.google.common.io.BaseEncoding;

/**
 * Response object for faceted search results containing query counts and field facets.
 * This class processes OpenSearch aggregations to provide structured facet information
 * for search result filtering and navigation.
 */
public class FacetResponse {
    /**

     *
     * @return the ingest factory instance
     */
    protected IngestFactory getIngestFactory() {
        return ComponentUtil.getIngestFactory();
    }

    /**
     * Processes a result data object for web/file crawling.
     * Default implementation returns the target unchanged.
     *
     * @param target the result data to process
     * @param responseData the response data from crawling

Como viste en el capítulo anterior sobre [Conceptos de Despliegue](concepts.md){.internal-link target=_blank}, hay múltiples estrategias que puedes usar.

Aquí te mostraré cómo usar **Uvicorn** con **worker processes** usando el comando `fastapi` o el comando `uvicorn` directamente.

/// info | Información

import org.codelibs.fess.util.DocumentUtil;
import org.opensearch.index.query.QueryBuilder;
import org.opensearch.script.Script;

/**
 * Job class for updating label information in the search index.
 * This job processes documents and updates their label fields based on URL pattern matching.
 */
public class UpdateLabelJob {

    private static final Logger logger = LogManager.getLogger(UpdateLabelJob.class);

    /**