defaultList.add("keyword1");
        defaultList.add("keyword2");
        fieldLogMap.put(Constants.DEFAULT_FIELD, defaultList);
        getMockRequest().setAttribute(Constants.FIELD_LOGS, fieldLogMap);

        queryContext = new QueryContext("test", true);
        List<String> keywords = queryContext.getDefaultKeyword();
        assertEquals(2, keywords.size());

        "keywords": ["Hola", "món", "recerca"]
      },
      "catalan_stemmer": {
        "type":       "stemmer",
        "language":   "catalan"
      },
      "czech_stop": {
        "type":       "stop",
        "stopwords":  "_czech_"
      },
      "czech_keywords": {
        "type":       "keyword_marker",
        "keywords": ["Haló", "svět", "vyhledávání"]
      },

        "keywords": ["Hola", "món", "recerca"]
      },
      "catalan_stemmer": {
        "type":       "stemmer",
        "language":   "catalan"
      },
      "czech_stop": {
        "type":       "stop",
        "stopwords":  "_czech_"
      },
      "czech_keywords": {
        "type":       "keyword_marker",
        "keywords": ["Haló", "svět", "vyhledávání"]
      },

            for (String keyword : query.split(" ")) {
                if (keyword.equals("AND")) {
                    continue;
                }
                assertTrue(
                        doc.entrySet()
                                .stream()
                                .anyMatch(entry -> entry.getValue().toString().toLowerCase().contains(keyword.toLowerCase())),

      },
      "english_keywords": {
        "type":       "keyword_marker",
        "keywords":   ["hello"]
      },
      "finnish_stop": {
        "type":       "stop",
        "stopwords":  "_finnish_"
      },
      "finnish_keywords": {
        "type":       "keyword_marker",
        "keywords":   ["Hei"]
      },
      "finnish_stemmer": {
        "type":       "stemmer",

        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) {

     * those keywords are replaced by, for example, the actual timestamp.
     *
     * @see ArtifactCoordinates#getVersionConstraint()
     */
    @Nonnull
    Version getVersion();

    /**
     * {@return the version or meta-version of the artifact}.
     * A meta-version is a version suffixed with the {@code SNAPSHOT} keyword.

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.dict(), hashed_password=hashed_password)
```

...ends up being like:

      "name": {
        "type": "keyword"
      },
      "password": {
        "type": "keyword"
      },
      "surname": {
        "type": "keyword"
      },
      "givenName": {
        "type": "keyword"
      },
      "employeeNumber" : {
        "type" : "keyword"
      },
      "mail" : {
        "type" : "keyword"
      },
      "telephoneNumber" : {
        "type" : "keyword"
      },
      "homePhone" : {

          },
          "groups" : {
            "type" : "keyword"
          },
          "homeDirectory" : {
            "type" : "keyword"
          },
          "homePhone" : {
            "type" : "keyword"
          },
          "homePostalAddress" : {
            "type" : "keyword"
          },
          "initials" : {
            "type" : "keyword"
          },