@Override
    protected String getQueryClassName() {
        return PrefixQuery.class.getSimpleName();
    }

    @Override
    public QueryBuilder execute(final QueryContext context, final Query query, final float boost) {
        if (query instanceof final PrefixQuery prefixQuery) {
            if (logger.isDebugEnabled()) {
                logger.debug("PrefixQuery: query={}, boost={}", query, boost);
            }

另一個實用情境是當你希望鍵是其他型別（例如，`int`）時。

這正是我們在此要示範的。

在此情況下，只要是擁有 `int` 鍵且對應 `float` 值的 `dict` 都會被接受：

{* ../../docs_src/body_nested_models/tutorial009_py310.py hl[7] *}

/// tip

請記住，JSON 只支援 `str` 作為鍵。

但 Pydantic 具有自動資料轉換。

這表示即使你的 API 用戶端只能以字串作為鍵，只要這些字串是純整數，Pydantic 會自動轉換並驗證它們。

而你作為 `weights` 所接收的 `dict`，實際上會擁有 `int` 鍵與 `float` 值。

///

## 總結 { #recap }

                            || actual == Short.class
                            || actual == Byte.class)) {
                return true;
            }
            if (formal == Float.TYPE
                    && (actual == Float.class
                            || actual == Long.class
                            || actual == Integer.class
                            || actual == Short.class

        final AtomicBoolean calledFirst = new AtomicBoolean(false);
        queryProcessor.addFilter(new QueryProcessor.Filter() {
            @Override
            public QueryBuilder execute(QueryContext context, Query query, float boost, FilterChain chain) {
                calledFirst.set(true);
                assertFalse(called.get());
                QueryBuilder builder = chain.execute(context, query, boost);
                assertTrue(called.get());

{
  "fess_config.key_match" : {
    "aliases" : { },
    "mappings" : {
      "key_match" : {
        "properties" : {
          "boost" : {
            "type" : "float"
          },
          "createdBy" : {
            "type" : "keyword"
          },
          "createdTime" : {
            "type" : "long"
          },
          "maxSize" : {
            "type" : "integer"
          },
          "query" : {

  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  float value = 0.0f;
  TF_Tensor* t = TFE_TensorHandleResolve(value_handle, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  ASSERT_EQ(sizeof(float), TF_TensorByteSize(t));
  memcpy(&value, TF_TensorData(t), sizeof(float));
  TF_DeleteTensor(t);
  EXPECT_EQ(12.0, value);

  TFE_DeleteTensorHandle(var_handle);

                            <la:message key="labels.dict_stopwords_title"/>
                        </h1>
                    </div>
                    <div class="col-sm-6">
                        <ol class="breadcrumb float-sm-right">
                            <li class="breadcrumb-item"><la:link href="/admin/dict">
                                <la:message key="labels.dict_list_link"/>
                            </la:link></li>

* Data conversion
* Data validation
* Automatic documentation

## Special types and validation { #special-types-and-validation }

Apart from normal singular types like `str`, `int`, `float`, etc. you can use more complex singular types that inherit from `str`.

from fastapi import FastAPI
from fastapi.temp_pydantic_v1_params import Body
from pydantic.v1 import BaseModel


class Item(BaseModel):
    name: str
    description: str | None = None
    size: float


app = FastAPI()


@app.post("/items/")
async def create_item(item: Annotated[Item, Body(embed=True)]) -> Item:

                            <la:message key="labels.dict_stemmeroverride_title"/>
                        </h1>
                    </div>
                    <div class="col-sm-6">
                        <ol class="breadcrumb float-sm-right">
                            <li class="breadcrumb-item"><la:link href="/admin/dict">
                                <la:message key="labels.dict_list_link"/>
                            </la:link></li>