List<String> keywords = SuggestUtil.getKeywords(query, fields);
        assertNotNull(keywords);
        assertEquals(2, keywords.size());
        assertTrue(keywords.contains("test"));
        assertTrue(keywords.contains("query"));

        // Test with OR operator
        List<String> orKeywords = SuggestUtil.getKeywords("test OR query", fields);
        assertNotNull(orKeywords);

        this.title = title;
    }

    /**
     * Gets the keywords.
     * @return the keywords
     */
    public String getKeywords() {
        return keywords;
    }

    /**
     * Sets the keywords.
     * @param keywords the keywords to set
     */
    public void setKeywords(final String keywords) {
        this.keywords = keywords;
    }

    /**
     * Gets the stock tickers.

    }

    public void test_putValues() {
        // Test putValues method
        ExtractData data = new ExtractData();

        String[] keywords = { "java", "crawler", "testing" };
        data.putValues("keywords", keywords);

        String[] retrievedKeywords = data.getValues("keywords");
        assertNotNull(retrievedKeywords);
        assertEquals(3, retrievedKeywords.length);
        assertEquals("java", retrievedKeywords[0]);

     *
     * @param q the query string to parse and extract keywords from
     * @param fields the fields to consider when extracting keywords
     * @return a list of unique keywords extracted from the query string
     */
    public static List<String> getKeywords(final String q, final String[] fields) {
        final List<String> keywords = new ArrayList<>();

So, we get a Pydantic model from the data in another Pydantic model.

#### Unpacking a `dict` and extra keywords { #unpacking-a-dict-and-extra-keywords }

And then adding the extra keyword argument `hashed_password=hashed_password`, like in:

```Python
UserInDB(**user_in.model_dump(), hashed_password=hashed_password)
```

...ends up being like:

            list = new ArrayList<>();
            fieldLogMap.put(field, list);
        }
        list.add(text);
    }

    /**
     * Gets the default field keywords from the field log.
     * @return List of keywords for the default field, or empty list if none.
     */
    public List<String> getDefaultKeyword() {
        if (fieldLogMap != null) {

        if (map != null) {
            return map;
        }
        return Collections.emptyMap();
    }

    /**
     * Builds a query for boosting documents based on the keyword list.
     *
     * @param keywordList The list of keywords.
     * @param list The list of filter function builders to add to.
     */
    public void buildQuery(final List<String> keywordList, final List<FilterFunctionBuilder> list) {

- AWS S3's [reserved keywords](https://docs.aws.amazon.com/AmazonS3/latest/dev/s3-glacier-select-sql-reference-keyword-list.html) list is not yet respected.

Así, obtenemos un modelo Pydantic a partir de los datos en otro modelo Pydantic.

#### Desempaquetando un `dict` y palabras clave adicionales { #unpacking-a-dict-and-extra-keywords }

Y luego agregando el argumento de palabra clave adicional `hashed_password=hashed_password`, como en:

```Python
UserInDB(**user_in.dict(), hashed_password=hashed_password)
```

...termina siendo como:

Auf diese Weise erhalten wir ein Pydantic-Modell aus den Daten eines anderen Pydantic-Modells.

#### Ein `dict` entpacken und zusätzliche Schlüsselwort-Argumente { #unpacking-a-dict-and-extra-keywords }

Und dann fügen wir das zusätzliche Schlüsselwort-Argument `hashed_password=hashed_password` hinzu, wie in:

```Python
UserInDB(**user_in.model_dump(), hashed_password=hashed_password)
```