///

### [Webargs](https://webargs.readthedocs.io/en/latest/) { #webargs }

Une autre grande fonctionnalité requise par les API est l’<dfn title="lecture et conversion en données Python">analyse</dfn> des données provenant des requêtes entrantes.

Webargs est un outil qui a été créé pour fournir cela par-dessus plusieurs frameworks, dont Flask.

    }

    /**
     * Analyze text.
     * @param analyzer Analyzer
     * @param field Field
     * @param text Text
     * @param lang Language
     * @return List of tokens
     */
    protected List<AnalyzeToken> analyzeText(final SuggestAnalyzer analyzer, final String field, final String text, final String lang) {
        return analyzeTextInternal(analyzer, field, text, lang, false);
    }

    /**

 *
 * <p>Usage example:</p>
 * <pre>
 * PomMemoryAnalyzer analyzer = new PomMemoryAnalyzer();
 * Model model = reader.read(Files.newInputStream(pomPath));
 * analyzer.analyzePom(model);
 * analyzer.printAnalysis();
 * </pre>
 *
 * <p>The analysis output includes:</p>
 * <ul>
 *   <li>Total memory usage per POM path</li>

        document.put("title", "タイトル");

        SuggestAnalyzer analyzer = suggester.settings().analyzer().new DefaultContentsAnalyzer();
        List<SuggestItem> items = defaultContentsParser.parseDocument(document, supportedFields, tagFieldNames, roleFieldName, "lang",
                createDefaultReadingConverter(), createDefaultReadingConverter(), createDefaultNormalizer(), analyzer);

        assertNotNull(items);

                .put("analysis.analyzer.test_reading_analyzer.type", "custom")
                .put("analysis.analyzer.test_reading_analyzer.tokenizer", "standard")
                .put("analysis.analyzer.test_reading_term_analyzer.type", "custom")
                .put("analysis.analyzer.test_reading_term_analyzer.tokenizer", "standard")
                .put("analysis.analyzer.test_reading_analyzer_ja.type", "custom")

        Model effectiveModel = result.getEffectiveModel();

        // Analyze plugins from effective model
        return analyzePluginsFromEffectiveModel(context, effectiveModel, pluginUpgrades);
    }

    /**
     * Analyzes plugins from the effective model and determines which ones need upgrades.
     */
    private Set<String> analyzePluginsFromEffectiveModel(

     * and adds them to the given map.
     *
     * @param clazz
     *            the class to analyze
     * @param type
     *            the type to analyze
     * @param map
     *            the map to which the type variables and type arguments are added
     */

```

## Optional: Search Analysis Support

If your language requires special text analysis (stemming, stop words, etc.), you may need to configure OpenSearch analyzers.

### 1. Update OpenSearch Index Template

Edit `src/main/resources/fess_indices/fess.json` to add language-specific analyzers.

**Example for Swedish:**
```json
{
  "analysis": {
    "filter": {
      "swedish_stop": {

        runner.ensureYellow();
        suggester = Suggester.builder().build(runner.client(), "DefaultContentsParserEdgeCaseTest");
        analyzer = suggester.settings().analyzer().new DefaultContentsAnalyzer();
        defaultReadingConverter = createDefaultReadingConverter();
        defaultNormalizer = createDefaultNormalizer();
    }

    @AfterClass

        operationsStale = true;
        return this;
    }

    /**
     * Sets the analyzer.
     * @param analyzer The analyzer.
     * @return This SuggestIndexer instance.
     */
    public SuggestIndexer setAnalyzer(final SuggestAnalyzer analyzer) {
        this.analyzer = analyzer;
        operationsStale = true;
        return this;
    }

    /**
     * Sets the contents parser.