{* ../../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.

- Added support for a new kubectl output format, `kyaml`. KYAML was a strict subset of YAML and should be accepted by any YAML processor. The formatting of KYAML was halfway between JSON and YAML.  Because it was more explicit than the default YAML style, it was less error-prone. ([#132942](https://github.com/kubernetes/kubernetes/pull/132942), [@thockin](https://github.com/thockin)) [SIG API Machinery,...

type BatchJobRequest struct {
	ID        string               `yaml:"-" json:"name"`
	User      string               `yaml:"-" json:"user"`
	Started   time.Time            `yaml:"-" json:"started"`
	Replicate *BatchJobReplicateV1 `yaml:"replicate" json:"replicate"`
	KeyRotate *BatchJobKeyRotateV1 `yaml:"keyrotate" json:"keyrotate"`
	Expire    *BatchJobExpire      `yaml:"expire" json:"expire"`
	ctx       context.Context      `msg:"-"`
}

golang.org/x/text v0.23.0/go.mod h1:/BLNzu4aZCJ1+kcD0DNRotWKage4q2rGVAg4o22unh4=
gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
gopkg.in/yaml.v3 v3.0.1 h1:fxVm/GzAzEWqLHuvctI91KS9hhNmmWOoWu0XTYJS7CA=

/**
 * Extracts text content and metadata from Markdown files.
 * This extractor provides better structured data extraction compared to Tika's generic text extraction.
 *
 * <p>Features:
 * <ul>
 *   <li>YAML front matter metadata extraction</li>
 *   <li>Heading structure extraction</li>
 *   <li>Link URL extraction</li>
 *   <li>Code block content extraction</li>
 *   <li>Clean text conversion from Markdown</li>

import logging
import secrets
import subprocess
from collections import Counter
from datetime import datetime
from pathlib import Path
from typing import Any

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

github_graphql_url = "https://api.github.com/graphql"


prs_query = """
query Q($after: String) {

	github.com/pmezard/go-difflib v1.0.0 // indirect
	github.com/tjfoc/gmsm v1.4.1 // indirect
	golang.org/x/crypto v0.43.0 // indirect
	golang.org/x/sync v0.17.0 // indirect
	golang.org/x/text v0.30.0 // indirect
	gopkg.in/yaml.v3 v3.0.1 // indirect
)

Он работает так: вы пишете определение схемы в формате YAML внутри докстринга каждой функции, обрабатывающей маршрут.

И он генерирует схемы OpenAPI.

Так это работает во Flask, Starlette, Responder и т. д.

Но у нас снова возникает проблема: появляется микро-синтаксис внутри строки Python (большой YAML).

The way it works is that you write the definition of the schema using YAML format inside the docstring of each function handling a route.

And it generates OpenAPI schemas.

That's how it works in Flask, Starlette, Responder, etc.

But then, we have again the problem of having a micro-syntax, inside of a Python string (a big YAML).

Le principe est le suivant : vous écrivez la définition du schéma au format YAML dans la docstring de chaque fonction gérant une route.

Et il génère des schémas OpenAPI.

C'est ainsi que cela fonctionne dans Flask, Starlette, Responder, etc.

Mais alors, nous avons à nouveau le problème d'avoir une micro-syntaxe, dans une docstring Python (un gros morceau de YAML).