/**
     * Creates a new model source backed by the specified URL.
     *
     * @param settingsUrl The settings URL, must not be {@code null}.
     */
    public UrlSettingsSource(URL settingsUrl) {
        super(settingsUrl);
    }

    /**
     * Gets the settings URL of this model source.
     *
     * @return The underlying settings URL, never {@code null}.

    class ResponseModelV1(BaseModel):
        name: str

    app = FastAPI()

    with pytest.raises(PydanticV1NotSupportedError):

        @app.get("/response-model", response_model=ResponseModelV1)
        def endpoint():  # pragma: no cover
            return {"name": "test"}


def test_raises_pydantic_v1_model_in_additional_responses_model() -> None:

	if err := DB.Create(&data).Error; err != nil {
		t.Errorf("Should got no error when creating data, but got %v", err)
	}

	if err := DB.Model(&data).Update("password", EncryptedData("xnewpass")).Error; err == nil {
		t.Errorf("Should failed to update data with invalid data")
	}

	if err := DB.Model(&data).Update("password", EncryptedData("newpass")).Error; err != nil {
		t.Errorf("Should got no error update data with valid data, but got %v", err)

## 数据过滤 - 安全性 { #filter-data-security }

为确保不返回超过需要的数据，请阅读 [教程 - 响应模型 - 返回类型](../tutorial/response-model.md) 文档。

## 优化响应性能 - 响应模型 - 返回类型 { #optimize-response-performance-response-model-return-type }

在返回 JSON 数据时优化性能，请使用返回类型或响应模型，这样 Pydantic 会在 Rust 侧处理到 JSON 的序列化，而无需经过 Python。更多内容请阅读 [教程 - 响应模型 - 返回类型](../tutorial/response-model.md) 文档。

## 文档的标签 - OpenAPI { #documentation-tags-openapi }

    }

    //
    // Taken from LegacyRepositorySystem
    //

    public static org.apache.maven.model.Repository fromSettingsRepository(
            org.apache.maven.settings.Repository settingsRepository) {
        org.apache.maven.model.Repository modelRepository = new org.apache.maven.model.Repository();
        modelRepository.setId(settingsRepository.getId());

/// info

Note that the `TestClient` receives data that can be converted to JSON, not Pydantic models.

If you have a Pydantic model in your test and you want to send its data to the application during testing, you can use the `jsonable_encoder` described in [JSON Compatible Encoder](encoder.md).

///

## Run it { #run-it }

Verwenden Sie base64 nur, wenn Sie Binärdaten unbedingt in JSON einbetten müssen und dafür keine Dateien verwenden können.

## Pydantic `bytes` { #pydantic-bytes }

import org.elasticsearch.gradle.internal.conventions.VersionPropertiesLoader
import org.apache.tools.ant.taskdefs.condition.Os
import org.gradle.plugins.ide.eclipse.model.AccessRule
import org.gradle.plugins.ide.eclipse.model.SourceFolder
import org.gradle.plugins.ide.eclipse.model.ProjectDependency

plugins {
  id 'java-gradle-plugin'
  id 'groovy-gradle-plugin'
  id 'groovy'
  id 'elasticsearch.build-tools'
  id 'elasticsearch.eclipse'

Aynı şekilde bu veritabanı bir Pydantic model'i (attribute'lara sahip bir obje) de kabul etmez; yalnızca bir `dict` kabul eder.

Bunun için `jsonable_encoder` kullanabilirsiniz.

Bir Pydantic model gibi bir obje alır ve JSON ile uyumlu bir versiyonunu döndürür:

{* ../../docs_src/encoder/tutorial001_py310.py hl[4,21] *}

Bu örnekte, Pydantic model'i bir `dict`'e, `datetime`'ı da bir `str`'e dönüştürür.

Suponha que você tem um único parâmetro de corpo `item`, a partir de um modelo Pydantic `Item`.

Por padrão, o **FastAPI** esperará que seu conteúdo venha no corpo diretamente.

Mas se você quiser que ele espere por um JSON com uma chave `item` e dentro dele os conteúdos do modelo, como ocorre ao declarar vários parâmetros de corpo, você pode usar o parâmetro especial de `Body` chamado `embed`: