contextName: {
				AuthInfo: userName,
				Cluster:  clusterName,
			},
		},
		CurrentContext: contextName,
	}

	lcfg, err := latest.Scheme.ConvertToVersion(kcfg, latest.ExternalVersion)
	if err != nil {
		return kubeconfig{}, err
	}
	// Convert to v1 schema which has proper encoding
	fullYaml, err := yaml.Marshal(lcfg)
	if err != nil {
		return kubeconfig{}, err
	}

	// Log with redaction

            }
        },
        "components": {
            "securitySchemes": {
                "HTTPBasic": {
                    "type": "http",
                    "scheme": "basic",
                    "description": "HTTPBasic scheme",
                }
            }
        },

                "get": {
                    "responses": {
                        "200": {
                            "description": "Successful Response",
                            "content": {"application/json": {"schema": {}}},
                        }
                    },
                    "summary": "Read Current User",
                    "operationId": "read_current_user_users_me_get",

#### Überprüfen Sie die `openapi.json`

Falls Sie wissen möchten, wie das rohe OpenAPI-Schema aussieht: FastAPI generiert automatisch ein JSON (Schema) mit den Beschreibungen Ihrer gesamten API.

syntax = "proto2";

package k8s.io.api.apidiscovery.v2beta1;

import "k8s.io/apimachinery/pkg/apis/meta/v1/generated.proto";
import "k8s.io/apimachinery/pkg/runtime/generated.proto";
import "k8s.io/apimachinery/pkg/runtime/schema/generated.proto";

// Package-wide variables from generator "generated".
option go_package = "k8s.io/api/apidiscovery/v2beta1";

                "get": {
                    "responses": {
                        "200": {
                            "description": "Successful Response",
                            "content": {"application/json": {"schema": {}}},
                        }
                    },
                    "summary": "Read Current User",
                    "operationId": "read_current_user_users_me_get",

                "get": {
                    "responses": {
                        "200": {
                            "description": "Successful Response",
                            "content": {"application/json": {"schema": {}}},
                        }
                    },
                    "summary": "Read Current User",
                    "operationId": "read_current_user_users_me_get",

                "get": {
                    "responses": {
                        "200": {
                            "description": "Successful Response",
                            "content": {"application/json": {"schema": {}}},
                        }
                    },
                    "summary": "Read Current User",
                    "operationId": "read_current_user_users_me_get",

			cacheStore.Store(embeddedCacheKey, true)
			if field.EmbeddedSchema, err = getOrParse(fieldValue.Interface(), cacheStore, embeddedNamer{Table: schema.Table, Namer: schema.namer}); err != nil {
				schema.err = err
			}

			for _, ef := range field.EmbeddedSchema.Fields {
				ef.Schema = schema
				ef.OwnerSchema = field.EmbeddedSchema
				ef.BindNames = append([]string{fieldStruct.Name}, ef.BindNames...)

## 创建 Pydantic 模型

现在让我们查看一下文件`sql_app/schemas.py`。

/// tip

为了避免 SQLAlchemy*模型*和 Pydantic*模型*之间的混淆，我们将有`models.py`（SQLAlchemy 模型的文件）和`schemas.py`（ Pydantic 模型的文件）。

这些 Pydantic 模型或多或少地定义了一个“schema”（一个有效的数据形状）。

因此，这将帮助我们在使用两者时避免混淆。

///

### 创建初始 Pydantic*模型*/模式

创建一个`ItemBase`和`UserBase`Pydantic*模型*（或者我们说“schema”），他们拥有创建或读取数据时具有的共同属性。