/** Log type constant for daily average request time aggregation. */
    public static final String LOG_TYPE_SEARCH_REQTIMEAVG_DAY = "search_reqtimeavg_day_agg";

    /** Log type constant for search keyword aggregation. */
    public static final String LOG_TYPE_SEARCH_KEYWORD = "search_keyword_agg";

    /** Log type constant for zero-hit search aggregation. */

Таким образом мы получаем Pydantic-модель на основе данных из другой Pydantic-модели.

#### Распаковка `dict` и дополнительные именованные аргументы { #unpacking-a-dict-and-extra-keywords }

И затем, если мы добавим дополнительный именованный аргумент `hashed_password=hashed_password` как здесь:

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

Então, obtemos um modelo Pydantic a partir dos dados em outro modelo Pydantic.

#### Desembrulhando um `dict` e palavras-chave extras { #unpacking-a-dict-and-extra-keywords }

E, então, adicionando o argumento de palavra-chave extra `hashed_password=hashed_password`, como em:

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

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)
```

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(

        context.addFieldLog(field, text);
        context.addHighlightedQuery(text);
        return buildMatchPhraseQuery(field, text).boost(boost);
    }

    /**
     * Converts a term query to a keyword-based exact term query.
     *
     * @param fessConfig the Fess configuration
     * @param context the query context
     * @param termQuery the term query to convert
     * @param boost the boost value to apply

///

/// info

OpenAPI 3.1.0 (used since FastAPI 0.99.0) added support for `examples`, which is part of the **JSON Schema** standard.

Before that, it only supported the keyword `example` with a single example. That is still supported by OpenAPI 3.1.0, but is deprecated and is not part of the JSON Schema standard. So you are encouraged to migrate `example` to `examples`. 🤓

                                .startObject()
                                .startObject("properties")
                                .startObject(FieldNames.ARRAY_KEY)
                                .field("type", "keyword")
                                .endObject()
                                .endObject()
                                .endObject())
                        .execute()

...왜냐하면 `user_in.model_dump()`는 `dict`이며, 이를 `**`로 Python이 "언팩(unpack)"하도록 하여 `UserInDB`에 전달하기 때문입니다.

따라서, 다른 Pydantic 모델의 데이터를 사용하여 새로운 Pydantic 모델을 생성할 수 있습니다.

#### `dict` 언패킹과 추가 키워드 { #unpacking-a-dict-and-extra-keywords }

그리고 다음과 같이 추가 키워드 인자 `hashed_password=hashed_password`를 추가하면:

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

다음과 같은 결과를 생성합니다:

```Python
UserInDB(

        client.setChatResponse("```json\n{\"query\": \"search keywords\", \"reasoning\": \"simplified\"}\n```");

        final String result = client.regenerateQuery("complex question", "complex query syntax", "no_results", Collections.emptyList());

        assertEquals("search keywords", result);
    }

    @Test
    public void test_regenerateQuery_promptContainsPlaceholders() {