因此，**FastAPI** 将会负责过滤掉未在输出模型中声明的所有数据（使用 Pydantic）。

## 在文档中查看

当你查看自动化文档时，你可以检查输入模型和输出模型是否都具有自己的 JSON Schema：

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image01.png">

并且两种模型都将在交互式 API 文档中使用：

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image02.png">

## 响应模型编码参数

你的响应模型可以具有默认值，例如：

{* ../../docs_src/response_model/tutorial004.py hl[11,13:14] *}

Bedenken Sie, dass Datenklassen nicht alles können, was Pydantic-Modelle können.

Daher müssen Sie möglicherweise weiterhin Pydantic-Modelle verwenden.

Wenn Sie jedoch eine Menge Datenklassen herumliegen haben, ist dies ein guter Trick, um sie für eine Web-API mithilfe von FastAPI zu verwenden. 🤓

///

## Datenklassen in `response_model` { #dataclasses-in-response-model }

...viendra surement plus tard, suivant le temps que j'ai.  😅 🎉

## Modèles d'apprentissage automatique avec spaCy et FastAPI

GitHub : <a href="https://github.com/microsoft/cookiecutter-spacy-fastapi" class="external-link" target="_blank">https://github.com/microsoft/cookiecutter-spacy-fastapi</a>

## Modèles d'apprentissage automatique avec spaCy et FastAPI - Fonctionnalités

* Intégration d'un modèle NER **spaCy**.

    }

    @Test
    public void testToStringNotNonsense() {
        MailingList list = new MailingList();
        list.setName("modello-dev");

        String s = list.toString();

        assertEquals("MailingList {name=modello-dev, archive=null}", s);
    }

 */
package org.apache.maven.model.superpom;

import org.apache.maven.model.Model;

/**
 * Provides the super POM that all models implicitly inherit from.
 *
 * @deprecated use {@code org.apache.maven.api.services.ModelBuilder} instead
 */
@Deprecated(since = "4.0.0")
public interface SuperPomProvider {

    /**

    page = context.new_page()
    page.goto("http://localhost:8000/docs")
    page.get_by_role("link", name="/items/").click()
    # Manually add the screenshot
    page.screenshot(path="docs/en/docs/img/tutorial/cookie-param-models/image01.png")

    # ---------------------
    context.close()
    browser.close()


process = subprocess.Popen(
    ["fastapi", "run", "docs_src/cookie_param_models/tutorial001.py"]
)
try:

 *
 * <p>This factory is responsible for instantiating concrete toolchain implementations
 * based on toolchain model configurations or default settings.</p>
 *
 * @since 4.0.0
 */
@Experimental
@Consumer
public interface ToolchainFactory {
    /**
     * Creates a toolchain instance from the provided model configuration.
     *

    /**
     * Injects plugin executions induced by lifecycle bindings into the specified model. The model has already undergone
     * injection of plugin management so any plugins that are injected by lifecycle bindings and are not already present
     * in the model's plugin section need to be subjected to the model's plugin management.
     *
     * @param model The model into which to inject the default plugin executions for its packaging, must not be

from typing import Annotated, Optional

from annotated_doc import Doc
from fastapi.openapi.models import OpenIdConnect as OpenIdConnectModel
from fastapi.security.base import SecurityBase
from starlette.exceptions import HTTPException
from starlette.requests import Request
from starlette.status import HTTP_401_UNAUTHORIZED


class OpenIdConnect(SecurityBase):
    """
    OpenID Connect authentication class. An instance of it would be used as a

from enum import Enum

from fastapi import FastAPI


class ModelName(str, Enum):
    alexnet = "alexnet"
    resnet = "resnet"
    lenet = "lenet"


app = FastAPI()


@app.get("/models/{model_name}")
async def get_model(model_name: ModelName):
    if model_name is ModelName.alexnet:
        return {"model_name": model_name, "message": "Deep Learning FTW!"}

    if model_name.value == "lenet":