* **Embrace modern AI coding practices**: Leave everything up to an LLM to decide. The model knows best. Always.
* **No code reviews**: There's no code to review. No PRs to approve. No comments to address. Embrace vibe coding fully, replace the theater of approving and merging vibe coded PRs that no one looks at with full proper vibes only.

/// tip

## Top Translation Reviewers

These users are the **Top Translation Reviewers**. 🕵️

Translation reviewers have the [**power to approve translations**](contributing.md#translations) of the documentation. Without them, there wouldn't be documentation in several other languages.

<div class="user-list user-list-center">

/// tip

Check the docs about [adding a pull request review](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/about-pull-request-reviews) to approve it or request changes.

///

PRs with suggestions to the language-specific LLM prompt require approval from at least one native speaker. Your help here is very much appreciated!

#### Subscribe to Notifications for Your Language

name: Label Approved

on:
  schedule:
    - cron: "0 12 * * *"
  workflow_dispatch:

permissions:
  pull-requests: write

jobs:
  label-approved:
    if: github.repository_owner == 'fastapi'
    runs-on: ubuntu-latest
    steps:
    - name: Dump GitHub context
      env:
        GITHUB_CONTEXT: ${{ toJson(github) }}
      run: echo "$GITHUB_CONTEXT"
    - uses: actions/checkout@v6
    - name: Set up Python

Once a native speaker comes, reviews the PR, and approves it, the GitHub Action will come and remove the `awaiting-review` label, and add the `approved-1` label.

This way, we can notice when there are new translations ready, because they have the `approved-1` label.

## Merge Translation PRs

Translations are generated automatically with LLMs and scripts.

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

awaiting_label = "awaiting-review"
lang_all_label = "lang-all"
approved_label = "approved-1"


github_graphql_url = "https://api.github.com/graphql"
questions_translations_category_id = "DIC_kwDOCZduT84CT5P9"

all_discussions_query = """
query Q($category_id: ID) {

			continue
		}
		if needApproval {
			feature, approval, ok := strings.Cut(line, "#")
			if !ok {
				log.Printf("%s:%d: missing proposal approval\n", filename, i+1)
				exitCode = 1
			} else {
				_, err := strconv.Atoi(approval)
				if err != nil {
					log.Printf("%s:%d: malformed proposal approval #%s\n", filename, i+1, approval)
					exitCode = 1
				}
			}
			line = strings.TrimSpace(feature)

* **Starlette** :
    * A les secondes meilleures performances après Uvicorn. En réalité, Starlette utilise Uvicorn. De ce fait, il ne peut qu’être plus « lent » qu'Uvicorn car il requiert l'exécution de plus de code.
    * Cependant, il apporte les outils pour construire une application web simple, avec un routage basé sur des chemins, etc.
    * Si on compare Starlette, il faut le comparer à d'autres frameworks web (ou microframeworks) comme Sanic, Flask, Django, etc.

**[FastAPI Cloud](https://fastapicloud.com)** est créée par le même auteur et l'équipe à l'origine de **FastAPI**.

Elle simplifie le processus de **création**, de **déploiement** et **d'accès** à une API avec un effort minimal.

Elle apporte la même **expérience développeur** que celle de la création d'applications avec FastAPI au **déploiement** de celles-ci dans le cloud. 🎉

/// details | 🌐 Translation by AI and humans

This translation was made by AI guided by humans. 🤝

It could have mistakes of misunderstanding the original meaning, or looking unnatural, etc. 🤖

You can improve this translation by [helping us guide the AI LLM better](https://fastapi.tiangolo.com/contributing/#translations).

[English version](ENGLISH_VERSION_URL)