doc.put("content", "xyz");

        // Without a real detector, this will throw NullPointerException
        try {
            languageHelper.updateDocument(doc);
            fail("Should throw NullPointerException without detector");
        } catch (NullPointerException e) {
            // Expected since we don't have a detector
        }
    }

    @Test
    public void test_updateDocument_emptyDoc() {

	<include path="fess_llm.xml"/>

	<component name="curlHelper" class="org.codelibs.fess.helper.CurlHelper">
	</component>
	<component name="languageHelper" class="org.codelibs.fess.helper.LanguageHelper">
		<property name="detector">
			<component class="org.apache.tika.langdetect.optimaize.OptimaizeLangDetector">
				<postConstruct name="loadModels"></postConstruct>
			</component>
		</property>
	</component>

*   Android: person detection + tracking demo implementing Scalable Object
    Detection using Deep Neural Networks.
*   New (experimental)
    [Java API](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/java).
*   Add new Android image stylization demo based on "A Learned Representation
    For Artistic Style", and add YOLO object detector support.

## Breaking Changes to the API

 * governing permissions and limitations under the License.
 */
package org.codelibs.fess.llm;

/**
 * Represents the result of intent detection from user input.
 * Contains the detected intent type, Fess query, and other metadata.
 */
public class IntentDetectionResult {

    private final ChatIntent intent;
    private final String query;
    private final String documentUrl;

 *       to check for a cycle that reaches the lock to be acquired. If no cycle is detected, a new
 *       "safe" edge is created.
 *   <li>If a cycle is detected, an "unsafe" (cyclic) edge is created to represent a potential
 *       deadlock situation, and the appropriate Policy is executed.
 * </ul>
 *
 * <p>Note that detection of potential deadlock does not necessarily indicate that deadlock will

        mimeTypeHelper.init();

        // With empty config, no override is applied; content-based detection runs
        try (InputStream is = new ByteArrayInputStream(SQL_REM_CONTENT.getBytes(StandardCharsets.UTF_8))) {
            final String contentType = mimeTypeHelper.getContentType(is, "test.sql");
            // Without override, Tika detects based on content+filename
            assertNotNull(contentType);
        }
    }

    @Test

Python se quejará si pones un valor con "por defecto" antes de un valor que no tenga "por defecto".

Pero puedes reordenarlos y poner el valor sin un valor por defecto (el parámetro de query `q`) primero.

No importa para **FastAPI**. Detectará los parámetros por sus nombres, tipos y declaraciones por defecto (`Query`, `Path`, etc), no le importa el orden.

Así que puedes declarar tu función como:

            throw new LlmException("systemPrompt is not configured for " + getName());
        }
        return systemPrompt;
    }

    /**
     * Gets the intent detection prompt template.
     *
     * @return the intent detection prompt
     */
    protected String getIntentDetectionPrompt() {
        if (intentDetectionPrompt == null) {

## `fastapi run` { #fastapi-run }

Ejecutar `fastapi run` inicia FastAPI en modo de producción por defecto.

El valor por defecto del parámetro de función es el valor real por defecto, eso es más intuitivo con Python en general. 😌