labels.user_list=Elenco utenti
labels.suggest_word_list=Elenco parole suggerite
labels.storage_list=Elenco archiviazione
labels.search_log_list=Elenco log ricerche
labels.scheduledjob_list=Elenco lavori programmati
labels.role_list=Elenco ruoli
labels.request_header_list=Elenco intestazioni richiesta
labels.related_query_list=Elenco query correlate
labels.related_content_list=Elenco contenuto correlato

indexer.unprocessed.document.size=1000
# Whether to enable click count tracking in the indexer.
indexer.click.count.enabled=true
# Whether to enable favorite count tracking in the indexer.
indexer.favorite.count.enabled=true
# Commit margin time (ms) for webfs in the indexer.
indexer.webfs.commit.margin.time=5000
# Maximum number of empty lists for webfs in the indexer.

    @Resource
    protected HttpServletRequest request;

    /** Flag indicating whether search logging is enabled. */
    protected boolean searchLogSupport;

    /** Flag indicating whether favorite functionality is enabled. */
    protected boolean favoriteSupport;

    /** Flag indicating whether thumbnail generation is enabled. */
    protected boolean thumbnailSupport;

    /**

    /** Property key for user information configuration. */
    public static final String USER_INFO_PROPERTY = "user.info";

    /** Property key for user favorite functionality configuration. */
    public static final String USER_FAVORITE_PROPERTY = "user.favorite";

    /** Property key for search log configuration. */
    public static final String SEARCH_LOG_PROPERTY = "search.log";

You can also return Pydantic models (you'll see more about that later).

There are many other objects and models that will be automatically converted to JSON (including ORMs, etc). Try using your favorite ones, it's highly probable that they are already supported.

### Step 6: Deploy it { #step-6-deploy-it }

                        breakCursor = true;
                    }
                }
            });
        };
    }

    /**
     * Get the write call for favorite log ndjson.
     * @return The write call.
     */
    public static Consumer<Writer> getFavoriteLogNdjsonWriteCall() {
        final FessConfig fessConfig = ComponentUtil.getFessConfig();

    /** The key of the message: by Favorite Count (asc) */
    public static final String LABELS_search_result_sort_favorite_count_asc = "{labels.search_result_sort_favorite_count_asc}";

    /** The key of the message: by Favorite Count (desc) */

                    .addSearchLog(params, DfTypeUtil.toLocalDateTime(requestedTime), queryId, query, params.getStartPosition(),
                            params.getPageSize(), queryResponseList);
        }

        // favorite
        if (fessConfig.isUserFavorite()) {
            ComponentUtil.getUserInfoHelper().storeQueryId(queryId, documentItems);
        }

    }

    /**

Esto resolvió tener que escribir YAML (otra sintaxis) dentro de docstrings de Python.

Esta combinación de Flask, Flask-apispec con Marshmallow y Webargs fue mi stack de backend favorito hasta construir **FastAPI**.

Usarlo llevó a la creación de varios generadores de full-stack para Flask. Estos son los principales stacks que yo (y varios equipos externos) hemos estado usando hasta ahora:

Isso resolveu ter que escrever YAML (outra sintaxe) dentro das docstrings do Python.

Essa combinação de Flask, Flask-apispec com Marshmallow e Webargs foi a minha stack de backend favorita até construir o **FastAPI**.

Usá-la levou à criação de vários geradores Flask full-stack. Estas são as principais stacks que eu (e várias equipes externas) tenho utilizado até agora: