labels.dict_stopwords_file 	=	Fichier des Stopwords
labels.boost_document_rule_configuration	=	Doc Boost
labels.boost_document_rule_title_details	=	Doc Boost
labels.boost_document_rule_list_url_expr	=	Condition
labels.boost_document_rule_url_expr	=	Condition
labels.boost_document_rule_boost_expr	=	Boost Expr
labels.boost_document_rule_sort_order	=	Ordre de tri
labels.access_token_configuration	=	Access Token

                final String id = crawlingInfoHelper.generateId(dataMap);

                if (logger.isDebugEnabled()) {
                    logger.debug("Searching indexed document: {}", id);
                }
                final Map<String, Object> document = indexingHelper.getDocument(searchEngineClient, id,
                        new String[] { fessConfig.getIndexFieldId(), fessConfig.getIndexFieldLastModified(),

    @Execute
    public JsonResponse<ApiResult> docs(final SearchBody body) {
        validateApi(body, messages -> {});

        if (StringUtil.isBlank(body.q)) {
            // query matches on all documents.
            body.q = Constants.MATCHES_ALL_QUERY;
        }

        final SearchRenderData renderData = new SearchRenderData();
        body.initialize();
        try {

    # - - - - - - - - - -/

    # /- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    # o isTableDispNameUpperCase: (NotRequired - Default false)
    #   [true]
    #     Table names for display, e.g. on documents, are forcedly treated as upper case.
    #
    #; isTableDispNameUpperCase = false
    # - - - - - - - - - -/

    # /- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

!!! note
    Notice that as we communicate with the database using `await`, the *path operation function* is declared with `async`.

### Notice the `response_model=List[Note]`

It uses `typing.List`.

That documents (and validates, serializes, filters) the output data, as a `list` of `Note`s.

## Create notes

Create the *path operation function* to create notes:

```Python hl_lines="61-65"

        values = $favorite.attr("href").split("#"),
        actionUrl,
        docId;
    if (values.length === 2 && $queryId.length > 0) {
      docId = values[1];
      actionUrl = contextPath + "/api/v1/documents/" + docId + "/favorite";
      $.ajax({
        dataType: "json",
        cache: false,
        type: "post",
        timeoutNumber: 10000,
        url: actionUrl,
        data: {

        #  (so the tables are not existing in SchemaHTML and HistoryHTML and so on).
        #  But you can specify the '@gen' suffix that means generate-only except.
        #  A table with the mark can be treated as documents but no generating classes.
        #
        #; tableExceptList = list:{FOO_TABLE@gen ; prefix:FOO_@gen ; suffix:_FOO ; contain:_FOO_}

        # o tableTargetList: (NotRequired - Default list:{})

        final SystemHelper systemHelper = ComponentUtil.getSystemHelper();
        final long execTime = systemHelper.getCurrentTimeAsLong();
        if (logger.isDebugEnabled()) {
            logger.debug("Sending {} documents to a server.", docList.size());
        }
        try {
            if (fessConfig.isThumbnailCrawlerEnabled()) {
                final ThumbnailManager thumbnailManager = ComponentUtil.getThumbnailManager();

Download a file, print its headers, and print its response body as a string.

The `string()` method on response body is convenient and efficient for small documents. But if the response body is large (greater than 1 MiB), avoid `string()` because it will load the entire document into memory. In that case, prefer to process the body as a stream.

=== ":material-language-kotlin: Kotlin"
    ```kotlin
      private val client = OkHttpClient()

Fess also contains a Crawler, which can crawl documents on a [web server](https://fess.codelibs.org/14.12/admin/webconfig-guide.html), [file system](https://fess.codelibs.org/14.12/admin/fileconfig-guide.html), or [Data Store](https://fess.codelibs.org/14.12/admin/dataconfig-guide.html) (such as a CSV...