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" : {

   * number of CPUS. Table slots remain empty (null) until they are
   * needed.
   *
   * A single spinlock ("busy") is used for initializing and
   * resizing the table, as well as populating slots with new Cells.
   * There is no need for a blocking lock; when the lock is not
   * available, threads try other slots (or the base).  During these

  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>

import json
from pathlib import Path

file_path = Path("./openapi.json")
openapi_content = json.loads(file_path.read_text())

for path_data in openapi_content["paths"].values():
    for operation in path_data.values():
        tag = operation["tags"][0]
        operation_id = operation["operationId"]
        to_remove = f"{tag}-"
        new_operation_id = operation_id[len(to_remove) :]
        operation["operationId"] = new_operation_id

    "aliases" : { },
    "mappings" : {
      "data_config" : {
        "properties" : {
          "available" : {
            "type" : "boolean"
          },
          "boost" : {
            "type" : "float"
          },
          "createdBy" : {
            "type" : "keyword"
          },
          "createdTime" : {
            "type" : "long"
          },
          "description" : {
            "type" : "text"