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.model_dump(), hashed_password=hashed_password)
```

  integrity sha512-F0SAmZ8iUtS//m8DmCTA0jlh6TDKkHQyK6xc6V4KDTyZKA9dnvX9/3sRTVQrWm79glUAZbnmmNcdYwUIHWVybw==

ajv-keywords@^3.5.2:
  version "3.5.2"
  resolved "https://registry.yarnpkg.com/ajv-keywords/-/ajv-keywords-3.5.2.tgz#31f29da5ab6e00d1c2d329acf7b5929614d5014d"
  integrity sha512-5p6WTN0DdTGVQk6VjcEju19IgaHudalcfabD7yhDGeA6bcQnmL+CpveLJq/3hvfwd1aof6L386Ougkx6RfyMIQ==

ajv@^6.12.5:

    /**
     * Detects the intent of a user message using the configured LLM client.
     *
     * @param userMessage the user's message
     * @return the detected intent with extracted keywords
     * @throws LlmException if LLM is not available
     */
    public IntentDetectionResult detectIntent(final String userMessage) {
        if (logger.isDebugEnabled()) {

  ':x-pack:plugin:ilm',
  ':x-pack:plugin:ilm:qa:multi-node',
  ':x-pack:plugin:ilm:qa:rest',
  ':x-pack:plugin:ilm:qa:with-security',
  ':x-pack:plugin:mapper-constant-keyword',
  ':x-pack:plugin:mapper-flattened',
  ':x-pack:plugin:ml',
  ':x-pack:plugin:ml:qa:basic-multi-node',
  ':x-pack:plugin:ml:qa:disabled',
  ':x-pack:plugin:ml:qa:ml-with-security',

    }

    @Test
    public void test_search_withSimpleQuery() {
        final String query = "singleKeyword";
        final IntentDetectionResult result = IntentDetectionResult.search(query, "single keyword search");

        assertEquals(ChatIntent.SEARCH, result.getIntent());
        assertEquals("singleKeyword", result.getQuery());
    }

    @Test
    public void test_search_withFessQuery() {

`.add_task()` receives as arguments:

* A task function to be run in the background (`write_notification`).
* Any sequence of arguments that should be passed to the task function in order (`email`).
* Any keyword arguments that should be passed to the task function (`message="some notification"`).

## Dependency Injection { #dependency-injection }

labels.search_result_time=({0} seconds)
labels.prev_page=Prev
labels.next_page=Next
labels.did_not_match=Your search - <b>{0}</b> - did not match any documents.
labels.did_not_match_suggestion=Try different keywords or check your spelling.
labels.search_title=Fess
labels.search_popular_word_word=Popular Words:
labels.search_related_queries=Related Words:
labels.search_result_select_sort=-  Sort  -

Ainsi, nous obtenons un modèle Pydantic à partir des données d'un autre modèle Pydantic.

#### Déballer un `dict` et ajouter des mots-clés supplémentaires { #unpacking-a-dict-and-extra-keywords }

Et en ajoutant ensuite l'argument nommé supplémentaire `hashed_password=hashed_password`, comme ici :

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

... revient à :

    })
    .analyzerSettings(analyzerSettings -> {
        analyzerSettings.setReadingAnalyzer("kuromoji_reading");
        analyzerSettings.setNormalizeAnalyzer("keyword");
    })
    .badWordSettings(badWordSettings -> {
        badWordSettings.setList(new String[]{"blocked", "terms"});
    })
    .elevateWordSettings(elevateWordSettings -> {

```

...因为 `user_in.model_dump()` 是 `dict`，在传递给 `UserInDB` 时，把 `**` 加在 `user_in.model_dump()` 前，可以让 Python 进行解包。

这样，就可以用其它 Pydantic 模型中的数据生成 Pydantic 模型。

#### 解包 `dict` 并添加额外关键字参数 { #unpacking-a-dict-and-extra-keywords }

接下来，继续添加关键字参数 `hashed_password=hashed_password`，例如：

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

...输出结果如下：

```Python
UserInDB(