assertEquals("keyword2", keywords.get(1));
    }

    // Test getDefaultKeyword with no default field
    @Test
    public void test_getDefaultKeyword_noDefaultField() {
        getMockRequest().setAttribute(Constants.FIELD_LOGS, new HashMap<String, List<String>>());

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

        },
        "arabic_keywords": {
          "type":       "keyword_marker",
          "keywords": ["مرحباا", "عالم", "بحث"]
        },
        "arabic_stemmer": {
          "type":       "stemmer",
          "language":   "arabic"
        },
        "arabic_override": {

        },
        "arabic_keywords": {
          "type":       "keyword_marker",
          "keywords": ["مرحباا", "عالم", "بحث"]
        },
        "arabic_stemmer": {
          "type":       "stemmer",
          "language":   "arabic"
        },
        "arabic_override": {

                    try {
                        final Map<String, Object> data = new HashMap<>();
                        data.put("phase", phase);
                        data.put("status", "start");
                        data.put("message", phaseMessage);
                        if (keywords != null) {
                            data.put("keywords", keywords);

     * @return the detected intent with extracted keywords
     */
    IntentDetectionResult detectIntent(String userMessage);

    /**
     * Detects the intent of a user message with conversation history context.
     *
     * @param userMessage the user's message
     * @param history the conversation history for context
     * @return the detected intent with extracted keywords
     */

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:

        } catch (Exception e) {
            // Expected due to missing dependencies in test environment
            assertTrue(true);
            return;
        }

        // Should handle various keywords including empty string
        assertNotNull(functionBuilders);
        assertEquals(0, functionBuilders.size()); // No data loaded
    }

    @Test
    public void test_buildQuery_withNullKeywordsInList() {

    /**
     * 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()) {

                    // Replace __keywords__ placeholder with actual keywords
                    if (data.keywords) {
                        phaseMessage = phaseMessage.replace('__keywords__', data.keywords);
                    }
                    showStatus('thinking', phaseMessage);
                    updateProgressMessage(phaseMessage);

UserInDB(**user_in.model_dump())
```

...因為 `user_in.model_dump()` 回傳的是 `dict`，接著在傳給 `UserInDB` 時以 `**` 前綴讓 Python 進行解包。

因此，我們可以用一個 Pydantic 模型的資料建立另一個 Pydantic 模型。

#### 解包 `dict` 並加入額外參數 { #unpacking-a-dict-and-extra-keywords }

接著加入額外的具名引數 `hashed_password=hashed_password`，如下：

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

...結果等同於：

```Python
UserInDB(