assert response.json() == {
        "detail": [
            {
                "type": "missing",
                "loc": ["query", "q"],
                "msg": "Field required",
                "input": None,
            }
        ]
    }


def test_main_depends_q_foo():
    response = client.get("/main-depends/?q=foo")
    assert response.status_code == 200
    assert response.json() == {

				<la:form styleId="newPassword" method="post">
					<div class="input-group mb-3">
						<c:set var="ph_new_password">
							<la:message key="labels.login.placeholder_new_password" />
						</c:set>
						<la:password property="password" class="form-control"
							placeholder="${ph_new_password}" />
						<div class="input-group-append">
							<span class="input-group-text">
								<i class="fa fa-lock fa-fw" aria-hidden="true"></i>

<div class="screenshot">
<img src="/img/tutorial/separate-openapi-schemas/image03.png">
</div>

### 문서에서의 입력과 출력 모델 { #model-for-input-and-output-in-docs }

또 OpenAPI에서 사용 가능한 모든 Schemas(JSON Schemas)를 확인해 보면, `Item-Input` 하나와 `Item-Output` 하나, 이렇게 두 개가 있는 것을 볼 수 있습니다.

`Item-Input`에서는 `description`이 **필수가 아니며**, 빨간 별표가 없습니다.

하지만 `Item-Output`에서는 `description`이 **필수이며**, 빨간 별표가 있습니다.

<div class="screenshot">

    assert response.json() == {
        "detail": [
            {
                "type": "missing",
                "loc": ["query", "p"],
                "msg": "Field required",
                "input": IsOneOf(None, {}),
            }
        ]
    }


@pytest.mark.parametrize(
    "path",
    ["/required-list-str", "/model-required-list-str"],
)
def test_required_list_str(path: str):

     * requires some work.
     */

    return Collector.of(
        ImmutableTableCollectorState<R, C, V>::new,
        (state, input) ->
            state.put(
                rowFunction.apply(input),
                columnFunction.apply(input),
                valueFunction.apply(input),
                mergeFunction),
        (s1, s2) -> s1.combine(s2, mergeFunction),
        state -> state.toTable());
  }

  static <

自從 Pydantic v2 發佈後，生成的 OpenAPI 比以往更精確也更正確。😎

實際上，在某些情況下，同一個 Pydantic 模型在 OpenAPI 中會同時有兩個 JSON Schema：分別用於輸入與輸出，這取決於它是否有預設值。

來看看它如何運作，以及若需要時該如何調整。

## 作為輸入與輸出的 Pydantic 模型 { #pydantic-models-for-input-and-output }

假設你有一個帶有預設值的 Pydantic 模型，如下所示：

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:7] hl[7] *}

### 輸入用模型 { #model-for-input }

如果你把這個模型用作輸入，如下所示：

<div class="screenshot">
<img src="/img/tutorial/separate-openapi-schemas/image03.png">
</div>

### 文档中的输入/输出模型 { #model-for-input-and-output-in-docs }

如果你查看 OpenAPI 中可用的所有 Schema（JSON Schema），你会看到有两个，一个是 `Item-Input`，一个是 `Item-Output`。

对于 `Item-Input`，`description` **不是必填**，没有红色星号。

但对于 `Item-Output`，`description` 是**必填**，带有红色星号。

<div class="screenshot">

  private static final class RoundToDoubleTester {
    private final BigDecimal input;
    private final Map<RoundingMode, Double> expectedValues = new EnumMap<>(RoundingMode.class);
    private boolean unnecessaryShouldThrow = false;

    RoundToDoubleTester(BigDecimal input) {
      this.input = input;
    }

    @CanIgnoreReturnValue

  @Param({"xxxx", "xxXx", "xXxX", "XXXX"})
  String text;

  private String input;

  private static final Splitter CHAR_SPLITTER = Splitter.on('X');
  private static final Splitter STRING_SPLITTER = Splitter.on("X");

  @BeforeExperiment
  @SuppressWarnings("InlineMeInliner") // String.repeat unavailable under Java 8
  void setUp() {
    input = Strings.repeat(text, length);
  }

  @Benchmark
  int charSplitter(int reps) {

    assert response.json() == {
        "detail": [
            {
                "type": "int_parsing",
                "loc": ["body", "foo", "[key]"],
                "msg": "Input should be a valid integer, unable to parse string as an integer",
                "input": "foo",
            }
        ]
    }


def test_openapi_schema(client: TestClient):
    response = client.get("/openapi.json")