)
def test_optional_list_str_schema(path: str):
    assert app.openapi()["paths"][path]["get"]["parameters"] == [
        {
            "required": False,
            "schema": {
                "anyOf": [
                    {"items": {"type": "string"}, "type": "array"},
                    {"type": "null"},
                ],
                "title": "P",
            },

    ["/required-str", "/model-required-str"],
)
def test_required_str_schema(path: str):
    assert app.openapi()["paths"][path]["get"]["parameters"] == [
        {
            "required": True,
            "schema": {"title": "P", "type": "string"},
            "name": "p",
            "in": "query",
        }
    ]


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

Directory for DBFlute client

manage.bat(sh) => 21 (jdbc):
A execution command of JDBC task
which gets your schema info and saves it to SchemaXML
located to the "schema" directory.
This task should be executed after ReplaceSchema task
and before other tasks(e.g. Generate, Document task).

manage.bat(sh) => 22 (doc):
A execution command of Document task
which creates documents, for example, SchemaHTML, HistoryHTML
to the "output/doc" directory.

Ele também foi um dos primeiros frameworks a gerar um schema personalizado declarando a API inteira em JSON.

Ele não era baseado em um padrão como OpenAPI e JSON Schema. Então não seria simples integrá-lo com outras ferramentas, como Swagger UI. Mas novamente, era uma ideia muito inovadora.

            "/a": {
                "get": {
                    "operationId": "a_a_get",
                    "responses": {
                        "200": {
                            "content": {"application/json": {"schema": {}}},
                            "description": "Successful Response",
                        }
                    },
                    "summary": "A",
                }
            },
            "/b": {

* Générer des définitions <a href="https://json-schema.org" class="external-link" target="_blank">JSON Schema</a> pour votre modèle, qui peuvent être utilisées où vous en avez besoin dans votre projet ensuite.
* Ces schémas participeront à la constitution du schéma généré OpenAPI, et seront donc utilisés par les documentations automatiquement générées.

## Documentation automatique

* Generate <a href="https://json-schema.org" class="external-link" target="_blank">JSON Schema</a> definitions for your model, you can also use them anywhere else you like if it makes sense for your project.
* Those schemas will be part of the generated OpenAPI schema, and used by the automatic documentation <abbr title="User Interfaces">UIs</abbr>.

## Automatic docs { #automatic-docs }

                    "responses": {
                        "200": {
                            "content": {
                                "application/json": {
                                    "schema": {},
                                },
                            },
                            "description": "The GraphiQL integrated development environment.",
                        },

### Gerar o esquema OpenAPI { #generate-the-openapi-schema }

Em seguida, use a mesma função utilitária para gerar o esquema OpenAPI, dentro de uma função `custom_openapi()`:

{* ../../docs_src/extending_openapi/tutorial001_py39.py hl[2,15:21] *}

### Modificar o esquema OpenAPI { #modify-the-openapi-schema }

	keys := make([]string, 0, len(mapValue))
	for k := range mapValue {
		keys = append(keys, k)
	}
	sort.Strings(keys)

	for _, k := range keys {
		value := mapValue[k]
		if stmt.Schema != nil {
			if field := stmt.Schema.LookUpField(k); field != nil {
				k = field.DBName
			}
		}

		if v, ok := selectColumns[k]; (ok && v) || (!ok && !restricted) {
			values.Columns = append(values.Columns, clause.Column{Name: k})