public void test_convertWithFieldCheck_addsToContext() {
        FessConfig fessConfig = ComponentUtil.getFessConfig();
        QueryContext context = new QueryContext("test", true);

        String queryText = "search term";
        queryCommand.convertWithFieldCheck(fessConfig, context, "title", queryText, 1.0f,
                (field, boost) -> QueryBuilders.matchQuery(field, queryText).boost(boost),

        List<Map<String, Object>> logs = JsonPath.from(response).getList("response.logs");

        logger.info("Filtered logs by job name: {}", logs);

        // Verify all returned logs contain the search term in job name
        for (Map<String, Object> log : logs) {
            String jobName = log.get("job_name").toString();
            assertTrue(jobName.contains(NAME_PREFIX), "Job name should contain '" + NAME_PREFIX + "'");

        List<Map<String, Object>> logs = JsonPath.from(response).getList("response.logs");

        logger.info("Filtered logs by URL: {}", logs);

        // Verify all returned logs contain the search term in URL
        for (Map<String, Object> log : logs) {
            String url = log.get("url").toString();
            assertTrue(url.contains("failure"), "URL should contain 'failure'");
        }

crawler.document.append.filename=false
# Maximum size of alphanumeric terms in documents.
crawler.document.max.alphanum.term.size=20
# Maximum size of symbol terms in documents.
crawler.document.max.symbol.term.size=10
# Whether to remove duplicate terms in documents.
crawler.document.duplicate.term.removed=false
# Unicode space characters for document parsing.

covering security practices for the entirety of the TensorFlow ecosystem.

## TensorFlow models are programs

TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).

    /** The key of the message: Label */
    public static final String LABELS_TARGET_LABEL = "{labels.targetLabel}";

    /** The key of the message: Term */
    public static final String LABELS_TERM = "{labels.term}";

    /** The key of the message: Fields */
    public static final String LABELS_FIELDS = "{labels.fields}";

    /** The key of the message: Extended Query */

For "synchronous" (contrary to "asynchronous") they commonly also use the term "sequential", because the computer / program follows all the steps in sequence before switching to a different task, even if those steps involve waiting.

### Concurrency and Burgers { #concurrency-and-burgers }

		}
	}
	return lval, nil
}

func (e *UnaryTerm) evalNode(r Record, tableAlias string) (*Value, error) {
	if e.Negated == nil {
		return e.Primary.evalNode(r, tableAlias)
	}

	v, err := e.Negated.Term.evalNode(r, tableAlias)
	if err != nil {
		return nil, err
	}

	inferTypeForArithOp(v)
	v.negate()
	if v.isNumeric() {
		return v, nil
	}
	return nil, errArithMismatchedTypes
}

		statusCode: 400,
		cause:      err,
	}
}

func errParseUnexpectedTerm(err error) *s3Error {
	return &s3Error{
		code:       "ParseUnexpectedTerm",
		message:    "The SQL expression contains an unexpected term.",
		statusCode: 400,
		cause:      err,
	}
}

func errParseUnexpectedToken(err error) *s3Error {
	return &s3Error{
		code:       "ParseUnexpectedToken",

See e.g. section `### List of English terms and their preferred German translations` in `docs/de/llm-prompt.md`.