raw_body = await request.body()
    try:
        data = yaml.safe_load(raw_body)
    except yaml.YAMLError:
        raise HTTPException(status_code=422, detail="Invalid YAML")
    try:
        item = Item.parse_obj(data)
    except ValidationError as e:
        raise HTTPException(status_code=422, detail=e.errors())

    raw_body = await request.body()
    try:
        data = yaml.safe_load(raw_body)
    except yaml.YAMLError:
        raise HTTPException(status_code=422, detail="Invalid YAML")
    try:
        item = Item.model_validate(data)
    except ValidationError as e:
        raise HTTPException(status_code=422, detail=e.errors(include_url=False))

        x - x-men
        x - x-avengers
        """
    response = client.post("/items/", content=yaml_data)
    assert response.status_code == 422, response.text
    assert response.json() == {"detail": "Invalid YAML"}


def test_post_invalid(client: TestClient):
    yaml_data = """
        name: Deadpoolio
        tags:
        - x-force
        - x-men
        - x-avengers
        - sneaky: object
        """

import logging
import secrets
import subprocess
from pathlib import Path

import yaml
from github import Github
from pydantic import BaseModel, SecretStr
from pydantic_settings import BaseSettings


class Settings(BaseSettings):
    github_repository: str
    github_token: SecretStr


class Repo(BaseModel):
    name: str
    html_url: str
    stars: int
    owner_login: str
    owner_html_url: str

{* ../../docs_src/path_operation_advanced_configuration/tutorial007_py39.py hl[15:20, 22] *}

Obwohl wir nicht die standardmäßig integrierte Funktionalität verwenden, verwenden wir dennoch ein Pydantic-Modell, um das JSON-Schema für die Daten, die wir in YAML empfangen möchten, manuell zu generieren.

{* ../../docs_src/path_operation_advanced_configuration/tutorial007.py hl[17:22,24] *}

Néanmoins, bien que nous n'utilisions pas la fonctionnalité par défaut, nous utilisons toujours un modèle Pydantic pour générer manuellement le schéma JSON pour les données que nous souhaitons recevoir en YAML.

Затем мы работаем с запросом напрямую и извлекаем тело как `bytes`. Это означает, что FastAPI даже не попытается распарсить полезную нагрузку запроса как JSON.

А затем в нашем коде мы напрямую парсим этот YAML и снова используем ту же Pydantic-модель для валидации YAML-содержимого:

//// tab | Pydantic v2

{* ../../docs_src/path_operation_advanced_configuration/tutorial007_py39.py hl[15:20, 22] *}

Nevertheless, although we are not using the default integrated functionality, we are still using a Pydantic model to manually generate the JSON Schema for the data that we want to receive in YAML.

tags:
  - crawler
  - extractor
  - markdown
---

# Introduction

This is a sample Markdown document for testing the MarkdownExtractor.

## Features

The extractor should handle:

- YAML front matter extraction
- Heading structure
- **Bold text** and *italic text*
- Lists and other formatting

### Code Examples

Here is some inline `code` and a code block:

```java
public class Example {

Luego usamos el request directamente, y extraemos el cuerpo como `bytes`. Esto significa que FastAPI ni siquiera intentará parsear la carga útil del request como JSON.

Y luego en nuestro código, parseamos ese contenido YAML directamente, y nuevamente estamos usando el mismo modelo Pydantic para validar el contenido YAML:

//// tab | Pydantic v2