🔖 Release version 0.70.1

Co-authored-by: github-actions <github-actions@github.com>

.github/actions/notify-translations/Dockerfile

@@ -0,0 +1,7 @@
+FROM python:3.7
+
+RUN pip install httpx PyGithub "pydantic==1.5.1" "pyyaml>=5.3.1,<6.0.0"
+
+COPY ./app /app
+
+CMD ["python", "/app/main.py"]

.github/actions/notify-translations/action.yml

@@ -0,0 +1,10 @@
+name: "Notify Translations"
+description: "Notify in the issue for a translation when there's a new PR available"
+author: "Sebastián Ramírez <tiangolo@gmail.com>"
+inputs:
+  token:
+    description: 'Token, to read the GitHub API. Can be passed in using {{ secrets.GITHUB_TOKEN }}'
+    required: true
+runs:
+  using: 'docker'
+  image: 'Dockerfile'

.github/actions/notify-translations/app/main.py

@@ -0,0 +1,104 @@
+import logging
+import time
+from pathlib import Path
+import random
+from typing import Dict, Optional
+
+import yaml
+from github import Github
+from pydantic import BaseModel, BaseSettings, SecretStr
+
+awaiting_label = "awaiting review"
+lang_all_label = "lang-all"
+approved_label = "approved-2"
+translations_path = Path(__file__).parent / "translations.yml"
+
+
+class Settings(BaseSettings):
+    github_repository: str
+    input_token: SecretStr
+    github_event_path: Path
+    github_event_name: Optional[str] = None
+    input_debug: Optional[bool] = False
+
+
+class PartialGitHubEventIssue(BaseModel):
+    number: int
+
+
+class PartialGitHubEvent(BaseModel):
+    pull_request: PartialGitHubEventIssue
+
+
+if __name__ == "__main__":
+    settings = Settings()
+    if settings.input_debug:
+        logging.basicConfig(level=logging.DEBUG)
+    else:
+        logging.basicConfig(level=logging.INFO)
+    logging.debug(f"Using config: {settings.json()}")
+    g = Github(settings.input_token.get_secret_value())
+    repo = g.get_repo(settings.github_repository)
+    if not settings.github_event_path.is_file():
+        raise RuntimeError(
+            f"No github event file available at: {settings.github_event_path}"
+        )
+    contents = settings.github_event_path.read_text()
+    github_event = PartialGitHubEvent.parse_raw(contents)
+    translations_map: Dict[str, int] = yaml.safe_load(translations_path.read_text())
+    logging.debug(f"Using translations map: {translations_map}")
+    sleep_time = random.random() * 10  # random number between 0 and 10 seconds
+    pr = repo.get_pull(github_event.pull_request.number)
+    logging.debug(
+        f"Processing PR: {pr.number}, with anti-race condition sleep time: {sleep_time}"
+    )
+    if pr.state == "open":
+        logging.debug(f"PR is open: {pr.number}")
+        label_strs = set([label.name for label in pr.get_labels()])
+        if lang_all_label in label_strs and awaiting_label in label_strs:
+            logging.info(
+                f"This PR seems to be a language translation and awaiting reviews: {pr.number}"
+            )
+            if approved_label in label_strs:
+                message = (
+                    f"It seems this PR already has the approved label: {pr.number}"
+                )
+                logging.error(message)
+                raise RuntimeError(message)
+            langs = []
+            for label in label_strs:
+                if label.startswith("lang-") and not label == lang_all_label:
+                    langs.append(label[5:])
+            for lang in langs:
+                if lang in translations_map:
+                    num = translations_map[lang]
+                    logging.info(
+                        f"Found a translation issue for language: {lang} in issue: {num}"
+                    )
+                    issue = repo.get_issue(num)
+                    message = f"Good news everyone! 😉 There's a new translation PR to be reviewed: #{pr.number} 🎉"
+                    already_notified = False
+                    time.sleep(sleep_time)
+                    logging.info(
+                        f"Sleeping for {sleep_time} seconds to avoid race conditions and multiple comments"
+                    )
+                    logging.info(
+                        f"Checking current comments in issue: {num} to see if already notified about this PR: {pr.number}"
+                    )
+                    for comment in issue.get_comments():
+                        if message in comment.body:
+                            already_notified = True
+                    if not already_notified:
+                        logging.info(
+                            f"Writing comment in issue: {num} about PR: {pr.number}"
+                        )
+                        issue.create_comment(message)
+                    else:
+                        logging.info(
+                            f"Issue: {num} was already notified of PR: {pr.number}"
+                        )
+    else:
+        logging.info(
+            f"Changing labels in a closed PR doesn't trigger comments, PR: {pr.number}"
+        )
+    logging.info("Finished")

.github/actions/notify-translations/app/translations.yml

@@ -0,0 +1,15 @@
+pt: 1211
+es: 1218
+zh: 1228
+ru: 1362
+it: 1556
+ja: 1572
+uk: 1748
+tr: 1892
+fr: 1972
+ko: 2017
+sq: 2041
+pl: 3169
+de: 3716
+id: 3717
+az: 3994

.github/actions/people/app/main.py

@@ -476,21 +476,16 @@ if __name__ == "__main__":
     )
 
     tiers = get_individual_sponsors(settings=settings)
-    sponsors_50 = []
-    for login, sponsor in tiers[50].items():
-        sponsors_50.append(
-            {"login": login, "avatarUrl": sponsor.avatarUrl, "url": sponsor.url}
-        )
     keys = list(tiers.keys())
     keys.sort(reverse=True)
     sponsors = []
     for key in keys:
-        if key >= 50:
-            continue
+        sponsor_group = []
         for login, sponsor in tiers[key].items():
-            sponsors.append(
+            sponsor_group.append(
                 {"login": login, "avatarUrl": sponsor.avatarUrl, "url": sponsor.url}
             )
+        sponsors.append(sponsor_group)
 
     people = {
         "maintainers": maintainers,
@@ -498,16 +493,21 @@ if __name__ == "__main__":
         "last_month_active": last_month_active,
         "top_contributors": top_contributors,
         "top_reviewers": top_reviewers,
-        "sponsors_50": sponsors_50,
+    }
+    github_sponsors = {
         "sponsors": sponsors,
     }
     people_path = Path("./docs/en/data/people.yml")
+    github_sponsors_path = Path("./docs/en/data/github_sponsors.yml")
     people_old_content = people_path.read_text(encoding="utf-8")
-    new_content = yaml.dump(people, sort_keys=False, width=200, allow_unicode=True)
-    if people_old_content == new_content:
+    github_sponsors_old_content = github_sponsors_path.read_text(encoding="utf-8")
+    new_people_content = yaml.dump(people, sort_keys=False, width=200, allow_unicode=True)
+    new_github_sponsors_content = yaml.dump(github_sponsors, sort_keys=False, width=200, allow_unicode=True)
+    if people_old_content == new_people_content and github_sponsors_old_content == new_github_sponsors_content:
         logging.info("The FastAPI People data hasn't changed, finishing.")
         sys.exit(0)
-    people_path.write_text(new_content, encoding="utf-8")
+    people_path.write_text(new_people_content, encoding="utf-8")
+    github_sponsors_path.write_text(new_github_sponsors_content, encoding="utf-8")
     logging.info("Setting up GitHub Actions git user")
     subprocess.run(["git", "config", "user.name", "github-actions"], check=True)
     subprocess.run(

.github/workflows/notify-translations.yml

@@ -0,0 +1,21 @@
+name: Notify Translations
+
+on:
+  pull_request_target:
+    types:
+      - labeled
+
+jobs:
+  notify-translations:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      # Allow debugging with tmate
+      - name: Setup tmate session
+        uses: mxschmitt/action-tmate@v3
+        if: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.debug_enabled }}
+        with:
+          limit-access-to-actor: true
+      - uses: ./.github/actions/notify-translations
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/test.yml

@@ -2,6 +2,8 @@ name: Test
 
 on:
   push:
+    branches:
+      - master
   pull_request:
     types: [opened, synchronize]
 
@@ -10,9 +12,9 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.6, 3.7, 3.8]
+        python-version: [3.6, 3.7, 3.8, 3.9]
       fail-fast: false
-    
+
     steps:
       - uses: actions/checkout@v2
       - name: Set up Python
@@ -30,6 +32,9 @@ jobs:
       - name: Install Dependencies
         if: steps.cache.outputs.cache-hit != 'true'
         run: flit install --symlink
+      - name: Lint
+        if: ${{ matrix.python-version != '3.6' }}
+        run: bash scripts/lint.sh
       - name: Test
         run: bash scripts/test.sh
       - name: Upload coverage

README.md

@@ -5,8 +5,8 @@
     <em>FastAPI framework, high performance, easy to learn, fast to code, ready for production</em>
 </p>
 <p align="center">
-<a href="https://github.com/tiangolo/fastapi/actions?query=workflow%3ATest" target="_blank">
-    <img src="https://github.com/tiangolo/fastapi/workflows/Test/badge.svg" alt="Test">
+<a href="https://github.com/tiangolo/fastapi/actions?query=workflow%3ATest+event%3Apush+branch%3Amaster" target="_blank">
+    <img src="https://github.com/tiangolo/fastapi/workflows/Test/badge.svg?event=push&branch=master" alt="Test">
 </a>
 <a href="https://codecov.io/gh/tiangolo/fastapi" target="_blank">
     <img src="https://img.shields.io/codecov/c/github/tiangolo/fastapi?color=%2334D058" alt="Coverage">
@@ -14,6 +14,9 @@
 <a href="https://pypi.org/project/fastapi" target="_blank">
     <img src="https://img.shields.io/pypi/v/fastapi?color=%2334D058&label=pypi%20package" alt="Package version">
 </a>
+<a href="https://pypi.org/project/fastapi" target="_blank">
+    <img src="https://img.shields.io/pypi/pyversions/fastapi.svg?color=%2334D058" alt="Supported Python versions">
+</a>
 </p>
 
 ---
@@ -44,11 +47,14 @@ The key features are:
 
 <!-- sponsors -->
 
-<a href="https://www.deta.sh/?ref=fastapi" target="_blank" title="The launchpad for all your (team's) ideas"><img src="https://fastapi.tiangolo.com/img/sponsors/deta.svg"></a>
 <a href="https://bit.ly/2QSouzH" target="_blank" title="Jina: build neural search-as-a-service for any kind of data in just minutes."><img src="https://fastapi.tiangolo.com/img/sponsors/jina.svg"></a>
+<a href="https://cryptapi.io/" target="_blank" title="CryptAPI: Your easy to use, secure and privacy oriented payment gateway."><img src="https://fastapi.tiangolo.com/img/sponsors/cryptapi.svg"></a>
+<a href="https://www.deta.sh/?ref=fastapi" target="_blank" title="The launchpad for all your (team's) ideas"><img src="https://fastapi.tiangolo.com/img/sponsors/deta.svg"></a>
 <a href="https://www.investsuite.com/jobs" target="_blank" title="Wealthtech jobs with FastAPI"><img src="https://fastapi.tiangolo.com/img/sponsors/investsuite.svg"></a>
 <a href="https://www.vim.so/?utm_source=FastAPI" target="_blank" title="We help you master vim with interactive exercises"><img src="https://fastapi.tiangolo.com/img/sponsors/vimso.png"></a>
 <a href="https://talkpython.fm/fastapi-sponsor" target="_blank" title="FastAPI video courses on demand from people you trust"><img src="https://fastapi.tiangolo.com/img/sponsors/talkpython.png"></a>
+<a href="https://testdriven.io/courses/tdd-fastapi/" target="_blank" title="Learn to build high-quality web apps with best practices"><img src="https://fastapi.tiangolo.com/img/sponsors/testdriven.svg"></a>
+<a href="https://github.com/deepset-ai/haystack/" target="_blank" title="Build powerful search from composable, open source building blocks"><img src="https://fastapi.tiangolo.com/img/sponsors/haystack-fastapi.svg"></a>
 
 <!-- /sponsors -->
 
@@ -128,7 +134,7 @@ You will also need an ASGI server, for production such as <a href="https://www.u
 <div class="termy">
 
 ```console
-$ pip install uvicorn[standard]
+$ pip install "uvicorn[standard]"
 
 ---> 100%
 ```
@@ -314,7 +320,7 @@ And now, go to <a href="http://127.0.0.1:8000/redoc" class="external-link" targe
 
 ### Recap
 
-In summary, you declare **once** the types of parameters, body, etc. as function parameters. 
+In summary, you declare **once** the types of parameters, body, etc. as function parameters.
 
 You do that with standard modern Python types.
 
@@ -371,7 +377,7 @@ Coming back to the previous code example, **FastAPI** will:
     * As the `q` parameter is declared with `= None`, it is optional.
     * Without the `None` it would be required (as is the body in the case with `PUT`).
 * For `PUT` requests to `/items/{item_id}`, Read the body as JSON:
-    * Check that it has a required attribute `name` that should be a `str`. 
+    * Check that it has a required attribute `name` that should be a `str`.
     * Check that it has a required attribute `price` that has to be a `float`.
     * Check that it has an optional attribute `is_offer`, that should be a `bool`, if present.
     * All this would also work for deeply nested JSON objects.
@@ -416,9 +422,9 @@ For a more complete example including more features, see the <a href="https://fa
 * A very powerful and easy to use **<abbr title="also known as components, resources, providers, services, injectables">Dependency Injection</abbr>** system.
 * Security and authentication, including support for **OAuth2** with **JWT tokens** and **HTTP Basic** auth.
 * More advanced (but equally easy) techniques for declaring **deeply nested JSON models** (thanks to Pydantic).
+* **GraphQL** integration with <a href="https://strawberry.rocks" class="external-link" target="_blank">Strawberry</a> and other libraries.
 * Many extra features (thanks to Starlette) as:
     * **WebSockets**
-    * **GraphQL**
     * extremely easy tests based on `requests` and `pytest`
     * **CORS**
     * **Cookie Sessions**
@@ -440,12 +446,10 @@ Used by Pydantic:
 Used by Starlette:
 
 * <a href="https://requests.readthedocs.io" target="_blank"><code>requests</code></a> - Required if you want to use the `TestClient`.
-* <a href="https://github.com/Tinche/aiofiles" target="_blank"><code>aiofiles</code></a> - Required if you want to use `FileResponse` or `StaticFiles`.
 * <a href="https://jinja.palletsprojects.com" target="_blank"><code>jinja2</code></a> - Required if you want to use the default template configuration.
 * <a href="https://andrew-d.github.io/python-multipart/" target="_blank"><code>python-multipart</code></a> - Required if you want to support form <abbr title="converting the string that comes from an HTTP request into Python data">"parsing"</abbr>, with `request.form()`.
 * <a href="https://pythonhosted.org/itsdangerous/" target="_blank"><code>itsdangerous</code></a> - Required for `SessionMiddleware` support.
 * <a href="https://pyyaml.org/wiki/PyYAMLDocumentation" target="_blank"><code>pyyaml</code></a> - Required for Starlette's `SchemaGenerator` support (you probably don't need it with FastAPI).
-* <a href="https://graphene-python.org/" target="_blank"><code>graphene</code></a> - Required for `GraphQLApp` support.
 * <a href="https://github.com/esnme/ultrajson" target="_blank"><code>ujson</code></a> - Required if you want to use `UJSONResponse`.
 
 Used by FastAPI / Starlette:
@@ -453,7 +457,7 @@ Used by FastAPI / Starlette:
 * <a href="https://www.uvicorn.org" target="_blank"><code>uvicorn</code></a> - for the server that loads and serves your application.
 * <a href="https://github.com/ijl/orjson" target="_blank"><code>orjson</code></a> - Required if you want to use `ORJSONResponse`.
 
-You can install all of these with `pip install fastapi[all]`.
+You can install all of these with `pip install "fastapi[all]"`.
 
 ## License
 

docs/az/mkdocs.yml

@@ -0,0 +1,128 @@
+site_name: FastAPI
+site_description: FastAPI framework, high performance, easy to learn, fast to code, ready for production
+site_url: https://fastapi.tiangolo.com/az/
+theme:
+  name: material
+  custom_dir: overrides
+  palette:
+  - scheme: default
+    primary: teal
+    accent: amber
+    toggle:
+      icon: material/lightbulb
+      name: Switch to light mode
+  - scheme: slate
+    primary: teal
+    accent: amber
+    toggle:
+      icon: material/lightbulb-outline
+      name: Switch to dark mode
+  features:
+  - search.suggest
+  - search.highlight
+  - content.tabs.link
+  icon:
+    repo: fontawesome/brands/github-alt
+  logo: https://fastapi.tiangolo.com/img/icon-white.svg
+  favicon: https://fastapi.tiangolo.com/img/favicon.png
+  language: en
+repo_name: tiangolo/fastapi
+repo_url: https://github.com/tiangolo/fastapi
+edit_uri: ''
+google_analytics:
+- UA-133183413-1
+- auto
+plugins:
+- search
+- markdownextradata:
+    data: data
+nav:
+- FastAPI: index.md
+- Languages:
+  - en: /
+  - az: /az/
+  - de: /de/
+  - es: /es/
+  - fr: /fr/
+  - id: /id/
+  - it: /it/
+  - ja: /ja/
+  - ko: /ko/
+  - pl: /pl/
+  - pt: /pt/
+  - ru: /ru/
+  - sq: /sq/
+  - tr: /tr/
+  - uk: /uk/
+  - zh: /zh/
+markdown_extensions:
+- toc:
+    permalink: true
+- markdown.extensions.codehilite:
+    guess_lang: false
+- mdx_include:
+    base_path: docs
+- admonition
+- codehilite
+- extra
+- pymdownx.superfences:
+    custom_fences:
+    - name: mermaid
+      class: mermaid
+      format: !!python/name:pymdownx.superfences.fence_code_format ''
+- pymdownx.tabbed
+extra:
+  social:
+  - icon: fontawesome/brands/github-alt
+    link: https://github.com/tiangolo/fastapi
+  - icon: fontawesome/brands/discord
+    link: https://discord.gg/VQjSZaeJmf
+  - icon: fontawesome/brands/twitter
+    link: https://twitter.com/fastapi
+  - icon: fontawesome/brands/linkedin
+    link: https://www.linkedin.com/in/tiangolo
+  - icon: fontawesome/brands/dev
+    link: https://dev.to/tiangolo
+  - icon: fontawesome/brands/medium
+    link: https://medium.com/@tiangolo
+  - icon: fontawesome/solid/globe
+    link: https://tiangolo.com
+  alternate:
+  - link: /
+    name: en - English
+  - link: /az/
+    name: az
+  - link: /de/
+    name: de
+  - link: /es/
+    name: es - español
+  - link: /fr/
+    name: fr - français
+  - link: /id/
+    name: id
+  - link: /it/
+    name: it - italiano
+  - link: /ja/
+    name: ja - 日本語
+  - link: /ko/
+    name: ko - 한국어
+  - link: /pl/
+    name: pl
+  - link: /pt/
+    name: pt - português
+  - link: /ru/
+    name: ru - русский язык
+  - link: /sq/
+    name: sq - shqip
+  - link: /tr/
+    name: tr - Türkçe
+  - link: /uk/
+    name: uk - українська мова
+  - link: /zh/
+    name: zh - 汉语
+extra_css:
+- https://fastapi.tiangolo.com/css/termynal.css
+- https://fastapi.tiangolo.com/css/custom.css
+extra_javascript:
+- https://fastapi.tiangolo.com/js/termynal.js
+- https://fastapi.tiangolo.com/js/custom.js

docs/de/docs/features.md

@@ -0,0 +1,203 @@
+# Merkmale
+
+## FastAPI Merkmale
+
+**FastAPI** ermöglicht Ihnen folgendes:
+
+### Basiert auf offenen Standards
+
+* <a href="https://github.com/OAI/OpenAPI-Specification" class="external-link" target="_blank"><strong>OpenAPI</strong></a> für API-Erstellung, zusammen mit Deklarationen von <abbr title="auch genannt: Endpunkte, Routen">Pfad</abbr> <abbr title="gemeint sind: HTTP-Methoden, wie POST, GET, PUT, DELETE">Operationen</abbr>, Parameter, Nachrichtenrumpf-Anfragen (englisch: body request), Sicherheit, etc.
+* Automatische Dokumentation der Datenentitäten mit dem <a href="https://json-schema.org/" class="external-link" target="_blank"><strong>JSON Schema</strong></a> (OpenAPI basiert selber auf dem JSON Schema).
+* Entworfen auf Grundlage dieser Standards nach einer sorgfältigen Studie, statt einer nachträglichen Schicht über diesen Standards.
+* Dies ermöglicht automatische **Quellcode-Generierung auf Benutzerebene** in vielen Sprachen.
+
+### Automatische Dokumentation
+
+Mit einer interaktiven API-Dokumentation und explorativen webbasierten Benutzerschnittstellen. Da FastAPI auf OpenAPI basiert, gibt es hierzu mehrere Optionen, wobei zwei standartmäßig vorhanden sind.
+
+* <a href="https://github.com/swagger-api/swagger-ui" class="external-link" target="_blank"><strong>Swagger UI</strong></a>, bietet interaktive Exploration: testen und rufen Sie ihre API direkt vom Webbrowser auf.
+
+![Swagger UI Interaktion](https://fastapi.tiangolo.com/img/index/index-03-swagger-02.png)
+
+* Alternative API-Dokumentation mit <a href="https://github.com/Rebilly/ReDoc" class="external-link" target="_blank"><strong>ReDoc</strong></a>.
+
+![ReDoc](https://fastapi.tiangolo.com/img/index/index-06-redoc-02.png)
+
+### Nur modernes Python
+
+Alles basiert auf **Python 3.6 Typ**-Deklarationen (dank Pydantic). Es muss keine neue Syntax gelernt werden, nur standardisiertes modernes Python.
+
+ 
+
+Wenn Sie eine kurze, zweiminütige, Auffrischung in der Benutzung von Python Typ-Deklarationen benötigen (auch wenn Sie FastAPI nicht nutzen), schauen Sie sich diese kurze Einführung an (Englisch): Python Types{.internal-link target=_blank}.
+
+Sie schreiben Standard-Python mit Typ-Deklarationen:
+
+```Python
+from typing import List, Dict
+from datetime import date
+
+from pydantic import BaseModel
+
+# Deklariere eine Variable als str
+# und bekomme Editor-Unterstütung innerhalb der Funktion
+def main(user_id: str):
+    return user_id
+
+
+# Ein Pydantic model
+class User(BaseModel):
+    id: int
+    name: str
+    joined: date
+```
+
+Dies kann nun wiefolgt benutzt werden:
+
+```Python
+my_user: User = User(id=3, name="John Doe", joined="2018-07-19")
+
+second_user_data = {
+    "id": 4,
+    "name": "Mary",
+    "joined": "2018-11-30",
+}
+
+my_second_user: User = User(**second_user_data)
+```
+
+!!! info
+    `**second_user_data` bedeutet:
+
+    Übergebe die Schlüssel und die zugehörigen Werte des `second_user_data` Datenwörterbuches direkt als Schlüssel-Wert Argumente, äquivalent zu: `User(id=4, name="Mary", joined="2018-11-30")`
+
+### Editor Unterstützung
+
+FastAPI wurde so entworfen, dass es einfach und intuitiv zu benutzen ist; alle Entscheidungen wurden auf mehreren Editoren getestet (sogar vor der eigentlichen Implementierung), um so eine best mögliche Entwicklererfahrung zu gewährleisten.
+
+In der letzen Python Entwickler Umfrage stellte sich heraus, dass <a href="https://www.jetbrains.com/research/python-developers-survey-2017/#tools-and-features" class="external-link" target="_blank">die meist genutzte Funktion die "Autovervollständigung" ist</a>.
+
+Die gesamte Struktur von **FastAPI** soll dem gerecht werden. Autovervollständigung funktioniert überall.
+
+Sie müssen selten in die Dokumentation schauen.
+
+So kann ihr Editor Sie unterstützen:
+
+* in <a href="https://code.visualstudio.com/" class="external-link" target="_blank">Visual Studio Code</a>:
+
+![editor support](https://fastapi.tiangolo.com/img/vscode-completion.png)
+
+* in <a href="https://www.jetbrains.com/pycharm/" class="external-link" target="_blank">PyCharm</a>:
+
+![editor support](https://fastapi.tiangolo.com/img/pycharm-completion.png)
+
+Sie bekommen Autovervollständigung an Stellen, an denen Sie dies vorher nicht für möglich gehalten hätten. Zum Beispiel der `price` Schlüssel aus einem JSON Datensatz (dieser könnte auch verschachtelt sein) aus einer Anfrage.
+
+Hierdurch werden Sie nie wieder einen falschen Schlüsselnamen benutzen und sparen sich lästiges Suchen in der Dokumentation, um beispielsweise herauszufinden ob Sie `username` oder `user_name` als Schlüssel verwenden.
+
+### Kompakt
+
+FastAPI nutzt für alles sensible **Standard-Einstellungen**, welche optional überall konfiguriert werden können. Alle Parameter können ganz genau an Ihre Bedürfnisse angepasst werden, sodass sie genau die API definieren können, die sie brachen.
+
+Aber standartmäßig, **"funktioniert einfach"** alles.
+
+### Validierung
+
+* Validierung für die meisten (oder alle?) Python **Datentypen**, hierzu gehören:
+    * JSON Objekte (`dict`).
+    * JSON Listen (`list`), die den Typ ihrer Elemente definieren.
+    * Zeichenketten (`str`), mit definierter minimaler und maximaler Länge.
+    * Zahlen (`int`, `float`) mit minimaler und maximaler Größe, usw.
+
+* Validierung für ungewögnliche Typen, wie:
+    * URL.
+    * Email.
+    * UUID.
+    * ... und andere.
+
+Die gesamte Validierung übernimmt das etablierte und robuste **Pydantic**.
+
+### Sicherheit und Authentifizierung
+
+Sicherheit und Authentifizierung integriert. Ohne einen Kompromiss aufgrund einer Datenbank oder den Datenentitäten.
+
+Unterstützt alle von OpenAPI definierten Sicherheitsschemata, hierzu gehören:
+
+* HTTP Basis Authentifizierung.
+* **OAuth2** (auch mit **JWT Zugriffstokens**). Schauen Sie sich hierzu dieses Tutorial an: [OAuth2 mit JWT](tutorial/security/oauth2-jwt.md){.internal-link target=_blank}.
+* API Schlüssel in:
+    * Kopfzeile (HTTP Header).
+    * Anfrageparametern.
+    * Cookies, etc.
+
+Zusätzlich gibt es alle Sicherheitsfunktionen von Starlette (auch **session cookies**).
+
+Alles wurde als wiederverwendbare Werkzeuge und Komponenten geschaffen, die einfach in ihre Systeme, Datenablagen, relationale und nicht-relationale Datenbanken, ..., integriert werden können.
+
+### Einbringen von Abhängigkeiten (meist: Dependency Injection)
+
+FastAPI enthält ein extrem einfaches, aber extrem mächtiges <abbr title='oft verwendet im Zusammenhang von: Komponenten, Resourcen, Diensten, Dienstanbieter'><strong>Dependency Injection</strong></abbr> System.
+
+* Selbst Abhängigkeiten können Abhängigkeiten haben, woraus eine Hierachie oder ein **"Graph" von Abhängigkeiten** entsteht.
+* **Automatische Umsetzung** durch FastAPI.
+* Alle abhängigen Komponenten könnten Daten von Anfragen, **Erweiterungen der Pfadoperations-**Einschränkungen und der automatisierten Dokumentation benötigen.
+* **Automatische Validierung** selbst für *Pfadoperationen*-Parameter, die in den Abhängigkeiten definiert wurden.
+* Unterstütz komplexe Benutzerauthentifizierungssysteme, mit **Datenbankverbindungen**, usw.
+* **Keine Kompromisse** bei Datenbanken, Eingabemasken, usw. Sondern einfache Integration von allen.
+
+### Unbegrenzte Erweiterungen
+
+Oder mit anderen Worten, sie werden nicht benötigt. Importieren und nutzen Sie Quellcode nach Bedarf.
+
+Jede Integration wurde so entworfen, dass sie einfach zu nutzen ist (mit Abhängigkeiten), sodass Sie eine Erweiterung für Ihre Anwendung mit nur zwei Zeilen an Quellcode implementieren können. Hierbei nutzen Sie die selbe Struktur und Syntax, wie bei Pfadoperationen.
+
+### Getestet
+
+* 100% <abbr title="Die Anzahl an Code, die automatisch getestet wird">Testabdeckung</abbr>.
+* 100% <abbr title="Python Typ Annotationen, mit dennen Ihr Editor und andere exteren Werkezuge Sie besser unterstützen können">Typen annotiert</abbr>.
+* Verwendet in Produktionsanwendungen.
+
+## Starlette's Merkmale
+
+**FastAPI** ist vollkommen kompatibel (und basiert auf) <a href="https://www.starlette.io/" class="external-link" target="_blank"><strong>Starlette</strong></a>. Das bedeutet, auch ihr eigner Starlett Quellcode funktioniert.
+
+`FastAPI` ist eigentlich eine Unterklasse von `Starlette`. Wenn sie also bereits Starlette kennen oder benutzen, können Sie das meiste Ihres Wissen direkt anwenden.
+
+Mit **FastAPI** bekommen Sie viele von **Starlette**'s Funktionen (da FastAPI nur Starlette auf Steroiden ist):
+
+* Stark beeindruckende Performanz. Es ist <a href="https://github.com/encode/starlette#performance" class="external-link" target="_blank">eines der schnellsten Python frameworks, auf Augenhöhe mit **NodeJS** und **Go**</a>.
+* **WebSocket**-Unterstützung.
+* Hintergrundaufgaben im selben Prozess.
+* Ereignisse für das Starten und Herunterfahren.
+* Testclient basierend auf `requests`.
+* **CORS**, GZip, statische Dateien, Antwortfluss.
+* **Sitzungs und Cookie** Unterstützung.
+* 100% Testabdeckung.
+* 100% Typen annotiert.
+
+## Pydantic's Merkmale
+
+**FastAPI** ist vollkommen kompatibel (und basiert auf) <a href="https://pydantic-docs.helpmanual.io" class="external-link" target="_blank"><strong>Pydantic</strong></a>. Das bedeutet, auch jeder zusätzliche Pydantic Quellcode funktioniert.
+
+Verfügbar sind ebenso externe auf Pydantic basierende Bibliotheken, wie <abbr title="Object-Relational Mapper (Abbildung von Objekten auf relationale Strukturen)">ORM</abbr>s, <abbr title="Object-Document Mapper (Abbildung von Objekten auf nicht-relationale Strukturen)">ODM</abbr>s für Datenbanken.
+
+Daher können Sie in vielen Fällen das Objekt einer Anfrage **direkt zur Datenbank** schicken, weil alles automatisch validiert wird.
+
+Das selbe gilt auch für die andere Richtung: Sie können jedes Objekt aus der Datenbank  **direkt zum Klienten** schicken.
+
+Mit **FastAPI** bekommen Sie alle Funktionen von **Pydantic** (da FastAPI für die gesamte Datenverarbeitung Pydantic nutzt):
+
+* **Kein Kopfzerbrechen**:
+    * Sie müssen keine neue Schemadefinitionssprache lernen.
+    * Wenn Sie mit Python's Typisierung arbeiten können, können Sie auch mit Pydantic arbeiten.
+* Gutes Zusammenspiel mit Ihrer/Ihrem **<abbr title="Integrierten Entwicklungsumgebung, ähnlich zu (Quellcode-)Editor">IDE</abbr>/<abbr title="Ein Programm, was Fehler im Quellcode sucht">linter</abbr>/Gehirn**:
+    * Weil Datenstrukturen von Pydantic einfach nur Instanzen ihrer definierten Klassen sind, sollten Autovervollständigung, Linting, mypy und ihre Intuition einwandfrei funktionieren.
+* **Schnell**:
+    * In <a href="https://pydantic-docs.helpmanual.io/#benchmarks-tag" class="external-link" target="_blank">Vergleichen</a> ist Pydantic schneller als jede andere getestete Bibliothek.
+* Validierung von **komplexen Strukturen**:
+    * Benutzung von hierachischen Pydantic Schemata, Python `typing`’s `List` und `Dict`, etc.
+    * Validierungen erlauben klare und einfache Datenschemadefinition, überprüft und dokumentiert als JSON Schema.
+    * Sie können stark **verschachtelte JSON** Objekte haben und diese sind trotzdem validiert und annotiert.
+* **Erweiterbar**:
+    * Pydantic erlaubt die Definition von eigenen Datentypen oder sie können die Validierung mit einer `validator` dekorierten Methode erweitern..
+* 100% Testabdeckung.

docs/de/docs/index.md

@@ -425,7 +425,6 @@ For a more complete example including more features, see the <a href="https://fa
 * More advanced (but equally easy) techniques for declaring **deeply nested JSON models** (thanks to Pydantic).
 * Many extra features (thanks to Starlette) as:
     * **WebSockets**
-    * **GraphQL**
     * extremely easy tests based on `requests` and `pytest`
     * **CORS**
     * **Cookie Sessions**
@@ -452,7 +451,6 @@ Used by Starlette:
 * <a href="https://andrew-d.github.io/python-multipart/" target="_blank"><code>python-multipart</code></a> - Required if you want to support form <abbr title="converting the string that comes from an HTTP request into Python data">"parsing"</abbr>, with `request.form()`.
 * <a href="https://pythonhosted.org/itsdangerous/" target="_blank"><code>itsdangerous</code></a> - Required for `SessionMiddleware` support.
 * <a href="https://pyyaml.org/wiki/PyYAMLDocumentation" target="_blank"><code>pyyaml</code></a> - Required for Starlette's `SchemaGenerator` support (you probably don't need it with FastAPI).
-* <a href="https://graphene-python.org/" target="_blank"><code>graphene</code></a> - Required for `GraphQLApp` support.
 * <a href="https://github.com/esnme/ultrajson" target="_blank"><code>ujson</code></a> - Required if you want to use `UJSONResponse`.
 
 Used by FastAPI / Starlette:

docs/de/mkdocs.yml

@@ -9,17 +9,18 @@ theme:
     primary: teal
     accent: amber
     toggle:
-      icon: material/lightbulb-outline
+      icon: material/lightbulb
       name: Switch to light mode
   - scheme: slate
     primary: teal
     accent: amber
     toggle:
-      icon: material/lightbulb
+      icon: material/lightbulb-outline
       name: Switch to dark mode
   features:
   - search.suggest
   - search.highlight
+  - content.tabs.link
   icon:
     repo: fontawesome/brands/github-alt
   logo: https://fastapi.tiangolo.com/img/icon-white.svg
@@ -39,6 +40,7 @@ nav:
 - FastAPI: index.md
 - Languages:
   - en: /
+  - az: /az/
   - de: /de/
   - es: /es/
   - fr: /fr/
@@ -53,12 +55,13 @@ nav:
   - tr: /tr/
   - uk: /uk/
   - zh: /zh/
+- features.md
 markdown_extensions:
 - toc:
     permalink: true
 - markdown.extensions.codehilite:
     guess_lang: false
-- markdown_include.include:
+- mdx_include:
     base_path: docs
 - admonition
 - codehilite
@@ -67,7 +70,7 @@ markdown_extensions:
     custom_fences:
     - name: mermaid
       class: mermaid
-      format: !!python/name:pymdownx.superfences.fence_div_format ''
+      format: !!python/name:pymdownx.superfences.fence_code_format ''
 - pymdownx.tabbed
 extra:
   social:
@@ -88,6 +91,8 @@ extra:
   alternate:
   - link: /
     name: en - English
+  - link: /az/
+    name: az
   - link: /de/
     name: de
   - link: /es/
@@ -120,6 +125,5 @@ extra_css:
 - https://fastapi.tiangolo.com/css/termynal.css
 - https://fastapi.tiangolo.com/css/custom.css
 extra_javascript:
-- https://unpkg.com/mermaid@8.4.6/dist/mermaid.min.js
 - https://fastapi.tiangolo.com/js/termynal.js
 - https://fastapi.tiangolo.com/js/custom.js

docs/en/data/external_links.yml

@@ -1,5 +1,13 @@
 articles:
   english:
+  - author: Kaustubh Gupta
+    author_link: https://medium.com/@kaustubhgupta1828/
+    link: https://www.analyticsvidhya.com/blog/2021/06/deploying-ml-models-as-api-using-fastapi-and-heroku/
+    title: Deploying ML Models as API Using FastAPI and Heroku
+  - link: https://jarmos.netlify.app/posts/using-github-actions-to-deploy-a-fastapi-project-to-heroku/
+    title: Using GitHub Actions to Deploy a FastAPI Project to Heroku
+    author_link: https://jarmos.netlify.app/
+    author: Somraj Saha
   - author: "@pystar"
     author_link: https://pystar.substack.com/
     link: https://pystar.substack.com/p/how-to-create-a-fake-certificate

docs/en/data/github_sponsors.yml

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docs/en/data/people.yml

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-  avatarUrl: https://avatars.githubusercontent.com/u/51492342?u=2280f57134118714645e16b535c1a37adf6b369b&v=4
-  url: https://github.com/akanz1
-- login: rychardvale
-  avatarUrl: https://avatars.githubusercontent.com/u/54805553?u=3d20ab05301d05f9ac3500fb79a2bfee3842b753&v=4
-  url: https://github.com/rychardvale
-- login: athemeart
-  avatarUrl: https://avatars.githubusercontent.com/u/61623624?v=4
-  url: https://github.com/athemeart
-- login: Rhythmicc
-  avatarUrl: https://avatars.githubusercontent.com/u/29839231?u=2100781089a259707c475c4547bd7995b0fc18ee&v=4
-  url: https://github.com/Rhythmicc

docs/en/data/sponsors.yml

@@ -1,11 +1,14 @@
 gold:
-  - url: https://www.deta.sh/?ref=fastapi
-    title: The launchpad for all your (team's) ideas
-    img: https://fastapi.tiangolo.com/img/sponsors/deta.svg
   - url: https://bit.ly/2QSouzH
     title: "Jina: build neural search-as-a-service for any kind of data in just minutes."
     img: https://fastapi.tiangolo.com/img/sponsors/jina.svg
+  - url: https://cryptapi.io/
+    title: "CryptAPI: Your easy to use, secure and privacy oriented payment gateway."
+    img: https://fastapi.tiangolo.com/img/sponsors/cryptapi.svg
 silver:
+  - url: https://www.deta.sh/?ref=fastapi
+    title: The launchpad for all your (team's) ideas
+    img: https://fastapi.tiangolo.com/img/sponsors/deta.svg
   - url: https://www.investsuite.com/jobs
     title: Wealthtech jobs with FastAPI
     img: https://fastapi.tiangolo.com/img/sponsors/investsuite.svg
@@ -15,7 +18,13 @@ silver:
   - url: https://talkpython.fm/fastapi-sponsor
     title: FastAPI video courses on demand from people you trust
     img: https://fastapi.tiangolo.com/img/sponsors/talkpython.png
-bronze:
   - url: https://testdriven.io/courses/tdd-fastapi/
     title: Learn to build high-quality web apps with best practices
     img: https://fastapi.tiangolo.com/img/sponsors/testdriven.svg
+  - url: https://github.com/deepset-ai/haystack/
+    title: Build powerful search from composable, open source building blocks
+    img: https://fastapi.tiangolo.com/img/sponsors/haystack-fastapi.svg
+bronze:
+  - url: https://calmcode.io
+    title: Code. Simply. Clearly. Calmly.
+    img: https://fastapi.tiangolo.com/img/sponsors/calmcode.jpg

docs/en/data/sponsors_badge.yml

@@ -0,0 +1,9 @@
+logins:
+  - jina-ai
+  - deta
+  - investsuite
+  - vimsoHQ
+  - mikeckennedy
+  - koaning
+  - deepset-ai
+  - cryptapi

docs/en/docs/advanced/async-tests.md

@@ -6,21 +6,9 @@ Being able to use asynchronous functions in your tests could be useful, for exam
 
 Let's look at how we can make that work.
 
-## pytest-asyncio
+## pytest.mark.anyio
 
-If we want to call asynchronous functions in our tests, our test functions have to be asynchronous. Pytest provides a neat library for this, called `pytest-asyncio`, that allows us to specify that some test functions are to be called asynchronously.
-
-You can install it via:
-
-<div class="termy">
-
-```console
-$ pip install pytest-asyncio
-
----> 100%
-```
-
-</div>
+If we want to call asynchronous functions in our tests, our test functions have to be asynchronous. Anyio provides a neat plugin for this, that allows us to specify that some test functions are to be called asynchronously.
 
 ## HTTPX
 
@@ -66,7 +54,7 @@ $ pytest
 
 ## In Detail
 
-The marker `@pytest.mark.asyncio` tells pytest that this test function should be called asynchronously:
+The marker `@pytest.mark.anyio` tells pytest that this test function should be called asynchronously:
 
 ```Python hl_lines="7"
 {!../../../docs_src/async_tests/test_main.py!}
@@ -97,4 +85,4 @@ that we used to make our requests with the `TestClient`.
 As the testing function is now asynchronous, you can now also call (and `await`) other `async` functions apart from sending requests to your FastAPI application in your tests, exactly as you would call them anywhere else in your code.
 
 !!! tip
-    If you encounter a `RuntimeError: Task attached to a different loop` when integrating asynchronous function calls in your tests (e.g. when using <a href="https://stackoverflow.com/questions/41584243/runtimeerror-task-attached-to-a-different-loop" class="external-link" target="_blank">MongoDB's MotorClient</a>) check out <a href="https://github.com/pytest-dev/pytest-asyncio/issues/38#issuecomment-264418154" class="external-link" target="_blank">this issue</a> in the pytest-asyncio repository.
+    If you encounter a `RuntimeError: Task attached to a different loop` when integrating asynchronous function calls in your tests (e.g. when using <a href="https://stackoverflow.com/questions/41584243/runtimeerror-task-attached-to-a-different-loop" class="external-link" target="_blank">MongoDB's MotorClient</a>) Remember to instantiate objects that need an event loop only within async functions, e.g. an `'@app.on_event("startup")` callback.

docs/en/docs/advanced/extending-openapi.md

@@ -152,21 +152,6 @@ After that, your file structure could look like:
     └── swagger-ui.css
 ```
 
-### Install `aiofiles`
-
-Now you need to install `aiofiles`:
-
-
-<div class="termy">
-
-```console
-$ pip install aiofiles
-
----> 100%
-```
-
-</div>
-
 ### Serve the static files
 
 * Import `StaticFiles`.

docs/en/docs/advanced/graphql.md

@@ -1,44 +1,56 @@
 # GraphQL
 
-**FastAPI** has optional support for GraphQL (provided by Starlette directly), using the `graphene` library.
+As **FastAPI** is based on the **ASGI** standard, it's very easy to integrate any **GraphQL** library also compatible with ASGI.
 
 You can combine normal FastAPI *path operations* with GraphQL on the same application.
 
-## Import and use `graphene`
+!!! tip
+    **GraphQL** solves some very specific use cases.
 
-GraphQL is implemented with Graphene, you can check <a href="https://docs.graphene-python.org/en/latest/quickstart/" class="external-link" target="_blank">Graphene's docs</a> for more details.
+    It has **advantages** and **disadvantages** when compared to common **web APIs**.
 
-Import `graphene` and define your GraphQL data:
+    Make sure you evaluate if the **benefits** for your use case compensate the **drawbacks**. 🤓
 
-```Python hl_lines="1  6-10"
+## GraphQL Libraries
+
+Here are some of the **GraphQL** libraries that have **ASGI** support. You could use them with **FastAPI**:
+
+* <a href="https://strawberry.rocks/" class="external-link" target="_blank">Strawberry</a> 🍓
+    * With <a href="https://strawberry.rocks/docs/integrations/fastapi" class="external-link" target="_blank">docs for FastAPI</a>
+* <a href="https://ariadnegraphql.org/" class="external-link" target="_blank">Ariadne</a>
+    * With <a href="https://ariadnegraphql.org/docs/starlette-integration" class="external-link" target="_blank">docs for Starlette</a> (that also apply to FastAPI)
+* <a href="https://tartiflette.io/" class="external-link" target="_blank">Tartiflette</a>
+    * With <a href="https://tartiflette.github.io/tartiflette-asgi/" class="external-link" target="_blank">Tartiflette ASGI</a> to provide ASGI integration
+* <a href="https://graphene-python.org/" class="external-link" target="_blank">Graphene</a>
+    * With <a href="https://github.com/ciscorn/starlette-graphene3" class="external-link" target="_blank">starlette-graphene3</a>
+
+## GraphQL with Strawberry
+
+If you need or want to work with **GraphQL**, <a href="https://strawberry.rocks/" class="external-link" target="_blank">**Strawberry**</a> is the **recommended** library as it has the design closest to **FastAPI's** design, it's all based on **type annotations**.
+
+Depending on your use case, you might prefer to use a different library, but if you asked me, I would probably suggest you try **Strawberry**.
+
+Here's a small preview of how you could integrate Strawberry with FastAPI:
+
+```Python hl_lines="3  22  25-26"
 {!../../../docs_src/graphql/tutorial001.py!}
 ```
 
-## Add Starlette's `GraphQLApp`
+You can learn more about Strawberry in the <a href="https://strawberry.rocks/" class="external-link" target="_blank">Strawberry documentation</a>.
 
-Then import and add Starlette's `GraphQLApp`:
+And also the docs about <a href="https://strawberry.rocks/docs/integrations/fastapi" class="external-link" target="_blank">Strawberry with FastAPI</a>.
 
-```Python hl_lines="3  14"
-{!../../../docs_src/graphql/tutorial001.py!}
-```
+## Older `GraphQLApp` from Starlette
 
-!!! info
-    Here we are using `.add_route`, that is the way to add a route in Starlette (inherited by FastAPI) without declaring the specific operation (as would be with `.get()`, `.post()`, etc).
+Previous versions of Starlette included a `GraphQLApp` class to integrate with <a href="https://graphene-python.org/" class="external-link" target="_blank">Graphene</a>.
 
-## Check it
+It was deprecated from Starlette, but if you have code that used it, you can easily **migrate** to <a href="https://github.com/ciscorn/starlette-graphene3" class="external-link" target="_blank">starlette-graphene3</a>, that covers the same use case and has an **almost identical interface**.
 
-Run it with Uvicorn and open your browser at <a href="http://127.0.0.1:8000" class="external-link" target="_blank">http://127.0.0.1:8000</a>.
+!!! tip
+    If you need GraphQL, I still would recommend you check out <a href="https://strawberry.rocks/" class="external-link" target="_blank">Strawberry</a>, as it's based on type annotations instead of custom classes and types.
 
-You will see GraphiQL web user interface:
+## Learn More
 
-<img src="/img/tutorial/graphql/image01.png">
+You can learn more about **GraphQL** in the <a href="https://graphql.org/" class="external-link" target="_blank">official GraphQL documentation</a>.
 
-## More details
-
-For more details, including:
-
-* Accessing request information
-* Adding background tasks
-* Using normal or async functions
-
-check the official <a href="https://www.starlette.io/graphql/" class="external-link" target="_blank">Starlette GraphQL docs</a>.
+You can also read more about each those libraries described above in their links.

docs/en/docs/advanced/path-operation-advanced-configuration.md

@@ -77,7 +77,7 @@ This *path operation*-specific OpenAPI schema is normally generated automaticall
 !!! tip
     This is a low level extension point.
 
-    If you only need to declare additonal responses, a more convenient way to do it is with [Additional Responses in OpenAPI](./additional-responses.md){.internal-link target=_blank}.
+    If you only need to declare additional responses, a more convenient way to do it is with [Additional Responses in OpenAPI](./additional-responses.md){.internal-link target=_blank}.
 
 You can extend the OpenAPI schema for a *path operation* using the parameter `openapi_extra`.
 

docs/en/docs/advanced/templates.md

@@ -2,7 +2,7 @@
 
 You can use any template engine you want with **FastAPI**.
 
-A common election is Jinja2, the same one used by Flask and other tools.
+A common choice is Jinja2, the same one used by Flask and other tools.
 
 There are utilities to configure it easily that you can use directly in your **FastAPI** application (provided by Starlette).
 
@@ -20,18 +20,6 @@ $ pip install jinja2
 
 </div>
 
-If you need to also serve static files (as in this example), install `aiofiles`:
-
-<div class="termy">
-
-```console
-$ pip install aiofiles
-
----> 100%
-```
-
-</div>
-
 ## Using `Jinja2Templates`
 
 * Import `Jinja2Templates`.

docs/en/docs/alternatives.md

@@ -242,8 +242,6 @@ It was one of the first extremely fast Python frameworks based on `asyncio`. It
 
 Falcon is another high performance Python framework, it is designed to be minimal, and work as the foundation of other frameworks like Hug.
 
-It uses the previous standard for Python web frameworks (WSGI) which is synchronous, so it can't handle WebSockets and other use cases. Nevertheless, it also has a very good performance.
-
 It is designed to have functions that receive two parameters, one "request" and one "response". Then you "read" parts from the request, and "write" parts to the response. Because of this design, it is not possible to declare request parameters and bodies with standard Python type hints as function parameters.
 
 So, data validation, serialization, and documentation, have to be done in code, not automatically. Or they have to be implemented as a framework on top of Falcon, like Hug. This same distinction happens in other frameworks that are inspired by Falcon's design, of having one request object and one response object as parameters.
@@ -367,7 +365,6 @@ It has:
 
 * Seriously impressive performance.
 * WebSocket support.
-* GraphQL support.
 * In-process background tasks.
 * Startup and shutdown events.
 * Test client built on requests.
@@ -375,7 +372,7 @@ It has:
 * Session and Cookie support.
 * 100% test coverage.
 * 100% type annotated codebase.
-* Zero hard dependencies.
+* Few hard dependencies.
 
 Starlette is currently the fastest Python framework tested. Only surpassed by Uvicorn, which is not a framework, but a server.
 

docs/en/docs/async.md

@@ -324,7 +324,15 @@ So, about the egg and the chicken, how do you call the first `async` function?
 
 If you are working with **FastAPI** you don't have to worry about that, because that "first" function will be your *path operation function*, and FastAPI will know how to do the right thing.
 
-But if you want to use `async` / `await` without FastAPI, <a href="https://docs.python.org/3/library/asyncio-task.html#coroutine" class="external-link" target="_blank">check the official Python docs</a>.
+But if you want to use `async` / `await` without FastAPI, you can do it as well.
+
+### Write your own async code
+
+Starlette (and **FastAPI**) are based on <a href="https://anyio.readthedocs.io/en/stable/" class="external-link" target="_blank">AnyIO</a>, which makes it compatible with both Python's standard library <a href="https://docs.python.org/3/library/asyncio-task.html" class="external-link" target="_blank">asyncio</a> and <a href="https://trio.readthedocs.io/en/stable/" class="external-link" target="_blank">Trio</a>.
+
+In particular, you can directly use <a href="https://anyio.readthedocs.io/en/stable/" class="external-link" target="_blank">AnyIO</a> for your advanced concurrency use cases that require more advanced patterns in your own code.
+
+And even if you were not using FastAPI, you could also write your own async applications with <a href="https://anyio.readthedocs.io/en/stable/" class="external-link" target="_blank">AnyIO</a> to be highly compatible and get its benefits (e.g. *structured concurrency*).
 
 ### Other forms of asynchronous code
 

docs/en/docs/contributing.md

@@ -163,20 +163,6 @@ It will also auto-sort all your imports.
 
 For it to sort them correctly, you need to have FastAPI installed locally in your environment, with the command in the section above using `--symlink` (or `--pth-file` on Windows).
 
-### Format imports
-
-There is another script that formats all the imports and makes sure you don't have unused imports:
-
-<div class="termy">
-
-```console
-$ bash scripts/format-imports.sh
-```
-
-</div>
-
-As it runs one command after the other and modifies and reverts many files, it takes a bit longer to run, so it might be easier to use `scripts/format.sh` frequently and `scripts/format-imports.sh` only before committing.
-
 ## Docs
 
 First, make sure you set up your environment as described above, that will install all the requirements.
@@ -340,7 +326,7 @@ docs/es/docs/features.md
 * Now open the MkDocs config file for English at:
 
 ```
-docs/en/docs/mkdocs.yml
+docs/en/mkdocs.yml
 ```
 
 * Find the place where that `docs/features.md` is located in the config file. Somewhere like:
@@ -359,7 +345,7 @@ nav:
 * Open the MkDocs config file for the language you are editing, e.g.:
 
 ```
-docs/es/docs/mkdocs.yml
+docs/es/mkdocs.yml
 ```
 
 * Add it there at the exact same location it was for English, e.g.:

docs/en/docs/css/custom.css

@@ -1,3 +1,13 @@
+.termynal-comment {
+  color: #4a968f;
+  font-style: italic;
+  display: block;
+}
+
+.termy [data-termynal] {
+  white-space: pre-wrap;
+}
+
 a.external-link::after {
   /* \00A0 is a non-breaking space
         to make the mark be on the same line as the link
@@ -12,6 +22,10 @@ a.internal-link::after {
   content: "\00A0↪";
 }
 
+.shadow {
+  box-shadow: 5px 5px 10px #999;
+}
+
 /* Give space to lower icons so Gitter chat doesn't get on top of them */
 .md-footer-meta {
   padding-bottom: 2em;
@@ -20,6 +34,7 @@ a.internal-link::after {
 .user-list {
   display: flex;
   flex-wrap: wrap;
+  margin-bottom: 2rem;
 }
 
 .user-list-center {

docs/en/docs/css/termynal.css

@@ -17,7 +17,8 @@
     max-width: 100%;
     background: var(--color-bg);
     color: var(--color-text);
-    font-size: 18px;
+    /* font-size: 18px; */
+    font-size: 15px;
     /* font-family: 'Fira Mono', Consolas, Menlo, Monaco, 'Courier New', Courier, monospace; */
     font-family: 'Roboto Mono', 'Fira Mono', Consolas, Menlo, Monaco, 'Courier New', Courier, monospace;
     border-radius: 4px;

docs/en/docs/deployment/concepts.md

@@ -0,0 +1,311 @@
+# Deployments Concepts
+
+When deploying a **FastAPI** application, or actually, any type of web API, there are several concepts that you probably care about, and using them you can find the **most appropriate** way to **deploy your application**.
+
+Some of the important concepts are:
+
+* Security - HTTPS
+* Running on startup
+* Restarts
+* Replication (the number of processes running)
+* Memory
+* Previous steps before starting
+
+We'll see how they would affect **deployments**.
+
+In the end, the ultimate objective is to be able to **serve your API clients** in a way that is **secure**, to **avoid disruptions**, and to use the **compute resources** (for example remote servers/virtual machines) as efficiently as possible. 🚀
+
+I'll tell you a bit more about these **concepts** here, and that would hopefully give you the **intuition** you would need to decide how to deploy your API in very different environments, possibly even in **future** ones that don't exist yet.
+
+By considering these concepts, you will be able to **evaluate and design** the best way to deploy **your own APIs**.
+
+In the next chapters, I'll give you more **concrete recipes** to deploy FastAPI applications.
+
+But for now, let's check these important **conceptual ideas**. These concepts also apply to any other type of web API. 💡
+
+## Security - HTTPS
+
+In the [previous chapter about HTTPS](./https.md){.internal-link target=_blank} we learned about how HTTPS provides encryption for your API.
+
+We also saw that HTTPS is normally provided by a component **external** to your application server, a **TLS Termination Proxy**.
+
+And there has to be something in charge of **renewing the HTTPS certificates**, it could be the same component or it could be something different.
+
+### Example Tools for HTTPS
+
+Some of the tools you could use as a TLS Termination Proxy are:
+
+* Traefik
+    * Automatically handles certificates renewals ✨
+* Caddy
+    * Automatically handles certificates renewals ✨
+* Nginx
+    * With an external component like Certbot for certificate renewals
+* HAProxy
+    * With an external component like Certbot for certificate renewals
+* Kubernetes with an Ingress Controller like Nginx
+    * With an external component like cert-manager for certificate renewals
+* Handled internally by a cloud provider as part of their services (read below 👇)
+
+Another option is that you could use a **cloud service** that does more of the work including setting up HTTPS. It could have some restrictions or charge you more, etc. But in that case, you wouldn't have to set up a TLS Termination Proxy yourself.
+
+I'll show you some concrete examples in the next chapters.
+
+---
+
+Then the next concepts to consider are all about the program running your actual API (e.g. Uvicorn).
+
+## Program and Process
+
+We will talk a lot about the running "**process**", so it's useful to have clarity about what it means, and what's the difference with the word "**program**".
+
+### What is a Program
+
+The word **program** is commonly used to describe many things:
+
+* The **code** that you write, the **Python files**.
+* The **file** that can be **executed** by the operating system, for example: `python`, `python.exe` or `uvicorn`.
+* A particular program while it is **running** on the operating system, using the CPU, and storing things on memory. This is also called a **process**.
+
+### What is a Process
+
+The word **process** is normally used in a more specific way, only referring to the thing that is running in the operating system (like in the last point above):
+
+* A particular program while it is **running** on the operating system.
+    * This doesn't refer to the file, nor to the code, it refers **specifically** to the thing that is being **executed** and managed by the operating system.
+* Any program, any code, **can only do things** when it is being **executed**. So, when there's a **process running**.
+* The process can be **terminated** (or "killed") by you, or by the operating system. At that point, it stops running/being executed, and it can **no longer do things**.
+* Each application that you have running on your computer has some process behind it, each running program, each window, etc. And there are normally many processes running **at the same time** while a computer is on.
+* There can be **multiple processes** of the **same program** running at the same time.
+
+If you check out the "task manager" or "system monitor" (or similar tools) in your operating system, you will be able to see many of those processes running.
+
+And, for example, you will probably see that there are multiple processes running the same browser program (Firefox, Chrome, Edge, etc). They normally run one process per tab, plus some other extra processes.
+
+<img class="shadow" src="/img/deployment/concepts/image01.png">
+
+---
+
+Now that we know the difference between the terms **process** and **program**, let's continue talking about deployments.
+
+## Running on Startup
+
+In most cases, when you create a web API, you want it to be **always running**, uninterrupted, so that your clients can always access it. This is of course, unless you have a specific reason why you want it to run only in certain situations, but most of the time you want it constantly running and **available**.
+
+### In a Remote Server
+
+When you set up a remote server (a cloud server, a virtual machine, etc.) the simplest thing you can do is to run Uvicorn (or similar) manually, the same way you do when developing locally.
+
+And it will work and will be useful **during development**.
+
+But if your connection to the server is lost, the **running process** will probably die.
+
+And if the server is restarted (for example after updates, or migrations from the cloud provider) you probably **won't notice it**. And because of that, you won't even know that you have to restart the process manually. So, your API will just stay dead. 😱
+
+### Run Automatically on Startup
+
+In general, you will probably want the server program (e.g. Uvicorn) to be started automatically on server startup, and without needing any **human intervention**, to have a process always running with your API (e.g. Uvicorn running your FastAPI app).
+
+### Separate Program
+
+To achieve this, you will normally have a **separate program** that would make sure your application is run on startup. And in many cases, it would also make sure other components or applications are also run, for example, a database.
+
+### Example Tools to Run at Startup
+
+Some examples of the tools that can do this job are:
+
+* Docker
+* Kubernetes
+* Docker Compose
+* Docker in Swarm Mode
+* Systemd
+* Supervisor
+* Handled internally by a cloud provider as part of their services
+* Others...
+
+I'll give you more concrete examples in the next chapters.
+
+## Restarts
+
+Similar to making sure your application is run on startup, you probably also want to make sure it is **restarted** after failures.
+
+### We Make Mistakes
+
+We, as humans, make **mistakes**, all the time. Software almost *always* has **bugs** hidden in different places. 🐛
+
+And we as developers keep improving the code as we find those bugs and as we implement new features (possibly adding new bugs too 😅).
+
+### Small Errors Automatically Handled
+
+When building web APIs with FastAPI, if there's an error in our code, FastAPI will normally contain it to the single request that triggered the error. 🛡
+
+The client will get a **500 Internal Server Error** for that request, but the application will continue working for the next requests instead of just crashing completely.
+
+### Bigger Errors - Crashes
+
+Nevertheless, there might be cases where we write some code that **crashes the entire application** making Uvicorn and Python crash. 💥
+
+And still, you would probably not want the application to stay dead because there was an error in one place, you probably want it to **continue running** at least for the *path operations* that are not broken.
+
+### Restart After Crash
+
+But in those cases with really bad errors that crash the running **process**, you would want an external component that is in charge of **restarting** the process, at least a couple of times...
+
+!!! tip
+    ...Although if the whole application is just **crashing immediately** it probably doesn't make sense to keep restarting it forever. But in those cases, you will probably notice it during development, or at least right after deployment.
+
+    So let's focus on the main cases, where it could crash entirely in some particular cases **in the future**, and it still makes sense to restart it.
+
+You would probably want to have the thing in charge of restarting your application as an **external component**, because by that point, the same application with Uvicorn and Python already crashed, so there's nothing in the same code of the same app that could do anything about it.
+
+### Example Tools to Restart Automatically
+
+In most cases, the same tool that is used to **run the program on startup** is also used to handle automatic **restarts**.
+
+For example, this could be handled by:
+
+* Docker
+* Kubernetes
+* Docker Compose
+* Docker in Swarm Mode
+* Systemd
+* Supervisor
+* Handled internally by a cloud provider as part of their services
+* Others...
+
+## Replication - Processes and Memory
+
+With a FastAPI application, using a server program like Uvicorn, running it once in **one process** can serve multiple clients concurrently.
+
+But in many cases, you will want to run several worker processes at the same time.
+
+### Multiple Processes - Workers
+
+If you have more clients than what a single process can handle (for example if the virtual machine is not too big) and you have **multiple cores** in the server's CPU, then you could have **multiple processes** running with the same application at the same time, and distribute all the requests among them.
+
+When you run **multiple processes** of the same API program, they are commonly called **workers**.
+
+### Worker Processes and Ports
+
+Remember from the docs [About HTTPS](./https.md){.internal-link target=_blank} that only one process can be listening on one combination of port and IP address in a server?
+
+This is still true.
+
+So, to be able to have **multiple processes** at the same time, there has to be a **single process listening on a port** that then transmits the communication to each worker process in some way.
+
+### Memory per Process
+
+Now, when the program loads things in memory, for example, a machine learning model in a variable, or the contents of a large file in a variable, all that **consumes a bit of the memory (RAM)** of the server.
+
+And multiple processes normally **don't share any memory**. This means that each running process has its own things, variables, and memory. And if you are consuming a large amount of memory in your code, **each process** will consume an equivalent amount of memory.
+
+### Server Memory
+
+For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.
+
+And if your remote server or virtual machine only has 3 GB of RAM, trying to load more than 4 GB of RAM will cause problems. 🚨
+
+### Multiple Processes - An Example
+
+In this example, there's a **Manager Process** that starts and controls two **Worker Processes**.
+
+This Manager Process would probably be the one listening on the **port** in the IP. And it would transmit all the communication to the worker processes.
+
+Those worker processes would be the ones running your application, they would perform the main computations to receive a **request** and return a **response**, and they would load anything you put in variables in RAM.
+
+<img src="/img/deployment/concepts/process-ram.svg">
+
+And of course, the same machine would probably have **other processes** running as well, apart from your application.
+
+An interesting detail is that the percentage of the **CPU used** by each process can **vary** a lot over time, but the **memory (RAM)** normally stays more or less **stable**.
+
+If you have an API that does a comparable amount of computations every time and you have a lot of clients, then the **CPU utilization** will probably *also be stable* (instead of constantly going up and down quickly).
+
+### Examples of Replication Tools and Strategies
+
+There can be several approaches to achieve this, and I'll tell you more about specific strategies in the next chapters, for example when talking about Docker and containers.
+
+The main constraint to consider is that there has to be a **single** component handling the **port** in the **public IP**. And then it has to have a way to **transmit** the communication to the replicated **processes/workers**.
+
+Here are some possible combinations and strategies:
+
+* **Gunicorn** managing **Uvicorn workers**
+    * Gunicorn would be the **process manager** listening on the **IP** and **port**, the replication would be by having **multiple Uvicorn worker processes**
+* **Uvicorn** managing **Uvicorn workers**
+    * One Uvicorn **process manager** would listen on the **IP** and **port**, and it would start **multiple Uvicorn worker processes**
+* **Kubernetes** and other distributed **container systems**
+    * Something in the **Kubernetes** layer would listen on the **IP** and **port**. The replication would be by having **multiple containers**, each with **one Uvicorn process** running
+* **Cloud services** that handle this for your
+    * The cloud service will probably **handle replication for you**. It would possibly let you define **a process to run**, or a **container image** to use, in any case, it would most probably be **a single Uvicorn process**, and the cloud service would be in charge of replicating it.
+
+!!! tip
+    Don't worry if some of these items about **containers**, Docker, or Kubernetes don't make a lot of sense yet.
+
+    I'll tell you more about container images, Docker, Kubernetes, etc. in a future chapter: [FastAPI in Containers - Docker](./docker.md){.internal-link target=_blank}.
+
+## Previous Steps Before Starting
+
+There are many cases where you want to perform some steps **before starting** your application.
+
+For example, you might want to run **database migrations**.
+
+But in most cases, you will want to perform these steps only **once**.
+
+So, you will want to have a **single process** to perform those **previous steps**, before starting the application.
+
+And you will have to make sure that it's a single process running those previous steps *even* if afterwards, you start **multiple processes** (multiple workers) for the application itself. If those steps were run by **multiple processes**, they would **duplicate** the work by running it on **parallel**, and if the steps were something delicate like a database migration, they could cause conflicts with each other.
+
+Of course, there are some cases where there's no problem in running the previous steps multiple times, in that case, it's a lot easier to handle.
+
+!!! tip
+    Also, have in mind that depending on your setup, in some cases you **might not even need any previous steps** before starting your application.
+
+    In that case, you wouldn't have to worry about any of this. 🤷
+
+### Examples of Previous Steps Strategies
+
+This will **depend heavily** on the way you **deploy your system**, and it would probably be connected to the way you start programs, handling restarts, etc.
+
+Here are some possible ideas:
+
+* An "Init Container" in Kubernetes that runs before your app container
+* A bash script that runs the previous steps and then starts your application
+    * You would still need a way to start/restart *that* bash script, detect errors, etc.
+
+!!! tip
+    I'll give you more concrete examples for doing this with containers in a future chapter: [FastAPI in Containers - Docker](./docker.md){.internal-link target=_blank}.
+
+## Resource Utilization
+
+Your server(s) is (are) a **resource**, you can consume or **utilize**, with your programs, the computation time on the CPUs, and the RAM memory available.
+
+How much of the system resources do you want to be consuming/utilizing? It might be easy to think "not much", but in reality, you will probably want to consume **as much as possible without crashing**.
+
+If you are paying for 3 servers but you are using only a little bit of their RAM and CPU, you are probably **wasting money** 💸, and probably **wasting server electric power** 🌎, etc.
+
+In that case, it could be better to have only 2 servers and use a higher percentage of their resources (CPU, memory, disk, network bandwidth, etc).
+
+On the other hand, if you have 2 servers and you are using **100% of their CPU and RAM**, at some point one process will ask for more memory, and the server will have to use the disk as "memory" (which can be thousands of times slower), or even **crash**. Or one process might need to do some computation and would have to wait until the CPU is free again.
+
+In this case, it would be better to get **one extra server** and run some processes on it so that they all have **enough RAM and CPU time**.
+
+There's also the chance that for some reason you have a **spike** of usage of your API. Maybe it went viral, or maybe some other services or bots start using it. And you might want to have extra resources to be safe in those cases.
+
+You could put an **arbitrary number** to target, for example, something **between 50% to 90%** of resource utilization. The point is that those are probably the main things you will want to measure and use to tweak your deployments.
+
+You can use simple tools like `htop` to see the CPU and RAM used in your server or the amount used by each process. Or you can use more complex monitoring tools, which may be distributed across servers, etc.
+
+## Recap
+
+You have been reading here some of the main concepts that you would probably need to have in mind when deciding how to deploy your application:
+
+* Security - HTTPS
+* Running on startup
+* Restarts
+* Replication (the number of processes running)
+* Memory
+* Previous steps before starting
+
+Understanding these ideas and how to apply them should give you the intuition necessary to take any decisions when configuring and tweaking your deployments. 🤓
+
+In the next sections, I'll give you more concrete examples of possible strategies you can follow. 🚀

docs/en/docs/deployment/deta.md

@@ -9,7 +9,7 @@ It will take you about **10 minutes**.
 
 ## A basic **FastAPI** app
 
-* Create a directory for your app, for example `./fastapideta/` and enter in it.
+* Create a directory for your app, for example, `./fastapideta/` and enter into it.
 
 ### FastAPI code
 
@@ -213,7 +213,7 @@ Now you can share that URL with anyone and they will be able to access your API.
 
 Congrats! You deployed your FastAPI app to Deta! 🎉 🍰
 
-Also notice that Deta correctly handles HTTPS for you, so you don't have to take care of that and can be sure that your clients will have a secure encrypted connection. ✅ 🔒
+Also, notice that Deta correctly handles HTTPS for you, so you don't have to take care of that and can be sure that your clients will have a secure encrypted connection. ✅ 🔒
 
 ## Check the Visor
 
@@ -235,6 +235,24 @@ You can also edit them and re-play them.
 
 ## Learn more
 
-At some point you will probably want to store some data for your app in a way that persists through time. For that you can use <a href="https://docs.deta.sh/docs/base/py_tutorial?ref=fastapi" class="external-link" target="_blank">Deta Base</a>, it also has a generous **free tier**.
+At some point, you will probably want to store some data for your app in a way that persists through time. For that you can use <a href="https://docs.deta.sh/docs/base/py_tutorial?ref=fastapi" class="external-link" target="_blank">Deta Base</a>, it also has a generous **free tier**.
 
 You can also read more in the <a href="https://docs.deta.sh?ref=fastapi" class="external-link" target="_blank">Deta Docs</a>.
+
+## Deployment Concepts
+
+Coming back to the concepts we discussed in [Deployments Concepts](./concepts.md){.internal-link target=_blank}, here's how each of them would be handled with Deta:
+
+* **HTTPS**: Handled by Deta, they will give you a subdomain and handle HTTPS automatically.
+* **Running on startup**: Handled by Deta, as part of their service.
+* **Restarts**: Handled by Deta, as part of their service.
+* **Replication**: Handled by Deta, as part of their service.
+* **Memory**: Limit predefined by Deta, you could contact them to increase it.
+* **Previous steps before starting**: Not directly supported, you could make it work with their Cron system or additional scripts.
+
+!!! note
+    Deta is designed to make it easy (and free) to deploy simple applications quickly.
+
+    It can simplify several use cases, but at the same time, it doesn't support others, like using external databases (apart from Deta's own NoSQL database system), custom virtual machines, etc.
+
+    You can read more details in the <a href="https://docs.deta.sh/docs/micros/about/" class="external-link" target="_blank">Deta docs</a> to see if it's the right choice for you.

docs/en/docs/deployment/docker.md

@@ -1,67 +1,144 @@
-# Deploy with Docker
+# FastAPI in Containers - Docker
 
-In this section you'll see instructions and links to guides to know how to:
+When deploying FastAPI applications a common approach is to build a **Linux container image**. It's normally done using <a href="https://www.docker.com/" class="external-link" target="_blank">**Docker**</a>. You can then deploy that container image in one of a few possible ways.
 
-* Make your **FastAPI** application a Docker image/container with maximum performance. In about **5 min**.
-* (Optionally) understand what you, as a developer, need to know about HTTPS.
-* Set up a Docker Swarm mode cluster with automatic HTTPS, even on a simple $5 USD/month server. In about **20 min**.
-* Generate and deploy a full **FastAPI** application, using your Docker Swarm cluster, with HTTPS, etc. In about **10 min**.
-
-You can use <a href="https://www.docker.com/" class="external-link" target="_blank">**Docker**</a> for deployment. It has several advantages like security, replicability, development simplicity, etc.
-
-If you are using Docker, you can use the official Docker image:
-
-## <a href="https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker" class="external-link" target="_blank">tiangolo/uvicorn-gunicorn-fastapi</a>
-
-This image has an "auto-tuning" mechanism included, so that you can just add your code and get very high performance automatically. And without making sacrifices.
-
-But you can still change and update all the configurations with environment variables or configuration files.
+Using Linux containers has several advantages including **security**, **replicability**, **simplicity**, and others.
 
 !!! tip
-    To see all the configurations and options, go to the Docker image page: <a href="https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker" class="external-link" target="_blank">tiangolo/uvicorn-gunicorn-fastapi</a>.
+    In a hurry and already know this stuff? Jump to the [`Dockerfile` below 👇](#build-a-docker-image-for-fastapi).
 
-## Create a `Dockerfile`
-
-* Go to your project directory.
-* Create a `Dockerfile` with:
+<details>
+<summary>Dockerfile Preview 👀</summary>
 
 ```Dockerfile
-FROM tiangolo/uvicorn-gunicorn-fastapi:python3.7
+FROM python:3.9
 
-COPY ./app /app
-```
+WORKDIR /code
 
-### Bigger Applications
+COPY ./requirements.txt /code/requirements.txt
 
-If you followed the section about creating [Bigger Applications with Multiple Files](../tutorial/bigger-applications.md){.internal-link target=_blank}, your `Dockerfile` might instead look like:
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
 
-```Dockerfile
-FROM tiangolo/uvicorn-gunicorn-fastapi:python3.7
-
-COPY ./app /app/app
-```
-
-### Raspberry Pi and other architectures
-
-If you are running Docker in a Raspberry Pi (that has an ARM processor) or any other architecture, you can create a `Dockerfile` from scratch, based on a Python base image (that is multi-architecture) and use Uvicorn alone.
-
-In this case, your `Dockerfile` could look like:
-
-```Dockerfile
-FROM python:3.7
-
-RUN pip install fastapi uvicorn
-
-EXPOSE 80
-
-COPY ./app /app
+COPY ./app /code/app
 
 CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "80"]
+
+# If running behind a proxy like Nginx or Traefik add --proxy-headers
+# CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "80", "--proxy-headers"]
 ```
 
-## Create the **FastAPI** Code
+</details>
 
-* Create an `app` directory and enter in it.
+## What is a Container
+
+Containers (mainly Linux containers) are a very **lightweight** way to package applications including all their dependencies and necessary files while keeping them isolated from other containers (other applications or components) in the same system.
+
+Linux containers run using the same Linux kernel of the host (machine, virtual machine, cloud server, etc). This just means that they are very lightweight (compared to full virtual machines emulating an entire operating system).
+
+This way, containers consume **little resources**, an amount comparable to running the processes directly (a virtual machine would consume much more).
+
+Containers also have their own **isolated** running processes (commonly just one process), file system, and network, simplifying deployment, security, development, etc.
+
+## What is a Container Image
+
+A **container** is run from a **container image**.
+
+A container image is a **static** version of all the files, environment variables, and the default command/program that should be present in a container. **Static** here means that the container **image** is not running, it's not being executed, it's only the packaged files and metadata.
+
+In contrast to a "**container image**" that is the stored static contents, a "**container**" normally refers to the running instance, the thing that is being **executed**.
+
+When the **container** is started and running (started from a **container image**) it could create or change files, environment variables, etc. Those changes will exist only in that container, but would not persist in the underlying container image (would not be saved to disk).
+
+A container image is comparable to the **program** file and contents, e.g. `python` and some file `main.py`.
+
+And the **container** itself (in contrast to the **container image**) is the actual running instance of the image, comparable to a **process**. In fact, a container is running only when it has a **process running** (and normally it's only a single process). The container stops when there's no process running in it.
+
+## Container Images
+
+Docker has been one of the main tools to create and manage **container images** and **containers**.
+
+And there's a public <a href="https://hub.docker.com/" class="external-link" target="_blank">Docker Hub</a> with pre-made **official container images** for many tools, environments, databases, and applications.
+
+For example, there's an official <a href="https://hub.docker.com/_/python" class="external-link" target="_blank">Python Image</a>.
+
+And there are many other images for different things like databases, for example for:
+
+* <a href="https://hub.docker.com/_/postgres" class="external-link" target="_blank">PostgreSQL</a>
+* <a href="https://hub.docker.com/_/mysql" class="external-link" target="_blank">MySQL</a>
+* <a href="https://hub.docker.com/_/mongo" class="external-link" target="_blank">MongoDB</a>
+* <a href="https://hub.docker.com/_/redis" class="external-link" target="_blank">Redis</a>, etc.
+
+By using a pre-made container image it's very easy to **combine** and use different tools. For example, to try out a new database. In most cases, you can use the **official images**, and just configure them with environment variables.
+
+That way, in many cases you can learn about containers and Docker and re-use that knowledge with many different tools and components.
+
+So, you would run **multiple containers** with different things, like a database, a Python application, a web server with a React frontend application, and connect them together via their internal network.
+
+All the container management systems (like Docker or Kubernetes) have these networking features integrated into them.
+
+## Containers and Processes
+
+A **container image** normally includes in its metadata the default program or command that should be run when the **container** is started and the parameters to be passed to that program. Very similar to what would be if it was in the command line.
+
+When a **container** is started, it will run that command/program (although you can override it and make it run a different command/program).
+
+A container is running as long as the **main process** (command or program) is running.
+
+A container normally has a **single process**, but it's also possible to start subprocesses from the main process, and that way you will have **multiple processes** in the same container.
+
+But it's not possible to have a running container without **at least one running process**. If the main process stops, the container stops.
+
+## Build a Docker Image for FastAPI
+
+Okay, let's build something now! 🚀
+
+I'll show you how to build a **Docker image** for FastAPI **from scratch**, based on the **official Python** image.
+
+This is what you would want to do in **most cases**, for example:
+
+* Using **Kubernetes** or similar tools
+* When running on a **Raspberry Pi**
+* Using a cloud service that would run a container image for you, etc.
+
+### Package Requirements
+
+You would normally have the **package requirements** for your application in some file.
+
+It would depend mainly on the tool you use to **install** those requirements.
+
+The most common way to do it is to have a file `requirements.txt` with the package names and their versions, one per line.
+
+You would of course use the same ideas you read in [About FastAPI versions](./versions.md){.internal-link target=_blank} to set the ranges of versions.
+
+For example, your `requirements.txt` could look like:
+
+```
+fastapi>=0.68.0,<0.69.0
+pydantic>=1.8.0,<2.0.0
+uvicorn>=0.15.0,<0.16.0
+```
+
+And you would normally install those package dependencies with `pip`, for example:
+
+<div class="termy">
+
+```console
+$ pip install -r requirements.txt
+---> 100%
+Successfully installed fastapi pydantic uvicorn
+```
+
+</div>
+
+!!! info
+    There are other formats and tools to define and install package dependencies.
+
+    I'll show you an example using Poetry later in a section below. 👇
+
+### Create the **FastAPI** Code
+
+* Create an `app` directory and enter it.
+* Create an empty file `__init__.py`.
 * Create a `main.py` file with:
 
 ```Python
@@ -82,16 +159,126 @@ def read_item(item_id: int, q: Optional[str] = None):
     return {"item_id": item_id, "q": q}
 ```
 
-* You should now have a directory structure like:
+### Dockerfile
+
+Now in the same project directory create a file `Dockerfile` with:
+
+```{ .dockerfile .annotate }
+# (1)
+FROM python:3.9
+
+# (2)
+WORKDIR /code
+
+# (3)
+COPY ./requirements.txt /code/requirements.txt
+
+# (4)
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# (5)
+COPY ./app /code/app
+
+# (6)
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "80"]
+```
+
+1. Start from the official Python base image.
+
+2. Set the current working directory to `/code`.
+
+    This is where we'll put the `requirements.txt` file and the `app` directory.
+
+3. Copy the file with the requirements to the `/code` directory.
+
+    Copy **only** the file with the requirements first, not the rest of the code.
+
+    As this file **doesn't change often**, Docker will detect it and use the **cache** for this step, enabling the cache for the next step too.
+
+4. Install the package dependencies in the requirements file.
+
+    The `--no-cache-dir` option tells `pip` to not save the downloaded packages locally, as that is only if `pip` was going to be run again to install the same packages, but that's not the case when working with containers.
+
+    !!! note
+        The `--no-cache-dir` is only related to `pip`, it has nothing to do with Docker or containers.
+
+    The `--upgrade` option tells `pip` to upgrade the packages if they are already installed.
+
+    Because the previous step copying the file could be detected by the **Docker cache**, this step will also **use the Docker cache** when available.
+
+    Using the cache in this step will **save** you a lot of **time** when building the image again and again during development, instead of **downloading and installing** all the dependencies **every time**.
+
+5. Copy the `./app` directory inside the `/code` directory.
+
+    As this has all the code which is what **changes most frequently** the Docker **cache** won't be used for this or any **following steps** easily.
+
+    So, it's important to put this **near the end** of the `Dockerfile`, to optimize the container image build times.
+
+6. Set the **command** to run the `uvicorn` server.
+
+    `CMD` takes a list of strings, each of these strings is what you would type in the command line separated by spaces.
+
+    This command will be run from the **current working directory**, the same `/code` directory you set above with `WORKDIR /code`.
+
+    Because the program will be started at `/code` and inside of it is the directory `./app` with your code, **Uvicorn** will be able to see and **import** `app` from `app.main`.
+
+!!! tip
+    Review what each line does by clicking each number bubble in the code. 👆
+
+You should now have a directory structure like:
 
 ```
 .
 ├── app
+│   ├── __init__.py
 │   └── main.py
-└── Dockerfile
+├── Dockerfile
+└── requirements.txt
 ```
 
-## Build the Docker image
+#### Behind a TLS Termination Proxy
+
+If you are running your container behind a TLS Termination Proxy (load balancer) like Nginx or Traefik, add the option `--proxy-headers`, this will tell Uvicorn to trust the headers sent by that proxy telling it that the application is running behind HTTPS, etc.
+
+```Dockerfile
+CMD ["uvicorn", "app.main:app", "--proxy-headers", "--host", "0.0.0.0", "--port", "80"]
+```
+
+#### Docker Cache
+
+There's an important trick in this `Dockerfile`, we first copy the **file with the dependencies alone**, not the rest of the code. Let me tell you why is that.
+
+```Dockerfile
+COPY ./requirements.txt /code/requirements.txt
+```
+
+Docker and other tools **build** these container images **incrementally**, adding **one layer on top of the other**, starting from the top of the `Dockerfile` and adding any files created by each of the instructions of the `Dockerfile`.
+
+Docker and similar tools also use an **internal cache** when building the image, if a file hasn't changed since the last time building the container image, then it will **re-use the same layer** created the last time, instead of copying the file again and creating a new layer from scratch.
+
+Just avoiding the copy of files doesn't necessarily improve things too much, but because it used the cache for that step, it can **use the cache for the next step**. For example, it could use the cache for the instruction that installs dependencies with:
+
+```Dockerfile
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+```
+
+The file with the package requirements **won't change frequently**. So, by copying only that file, Docker will be able to **use the cache** for that step.
+
+And then, Docker will be able to **use the cache for the next step** that downloads and install those dependencies. And here's where we **save a lot of time**. ✨ ...and avoid boredom waiting. 😪😆
+
+Downloading and installing the package dependencies **could take minutes**, but using the **cache** would **take seconds** at most.
+
+And as you would be building the container image again and again during development to check that your code changes are working, there's a lot of accumulated time this would save.
+
+Then, near the end of the `Dockerfile`, we copy all the code. As this is what **changes most frequently**, we put it near the end, because almost always, anything after this step will not be able to use the cache.
+
+```Dockerfile
+COPY ./app /code/app
+```
+
+### Build the Docker Image
+
+Now that all the files are in place, let's build the container image.
 
 * Go to the project directory (in where your `Dockerfile` is, containing your `app` directory).
 * Build your FastAPI image:
@@ -106,7 +293,12 @@ $ docker build -t myimage .
 
 </div>
 
-## Start the Docker container
+!!! tip
+    Notice the `.` at the end, it's equivalent to `./`, it tells Docker the directory to use to build the container image.
+
+    In this case, it's the same current directory (`.`).
+
+### Start the Docker Container
 
 * Run a container based on your image:
 
@@ -118,8 +310,6 @@ $ docker run -d --name mycontainer -p 80:80 myimage
 
 </div>
 
-Now you have an optimized FastAPI server in a Docker container. Auto-tuned for your current server (and number of CPU cores).
-
 ## Check it
 
 You should be able to check it in your Docker container's URL, for example: <a href="http://192.168.99.100/items/5?q=somequery" class="external-link" target="_blank">http://192.168.99.100/items/5?q=somequery</a> or <a href="http://127.0.0.1/items/5?q=somequery" class="external-link" target="_blank">http://127.0.0.1/items/5?q=somequery</a> (or equivalent, using your Docker host).
@@ -146,34 +336,363 @@ You will see the alternative automatic documentation (provided by <a href="https
 
 ![ReDoc](https://fastapi.tiangolo.com/img/index/index-02-redoc-simple.png)
 
-## Traefik
+## Build a Docker Image with a Single-File FastAPI
 
-<a href="https://traefik.io/" class="external-link" target="_blank">Traefik</a> is a high performance reverse proxy / load balancer. It can do the "TLS Termination Proxy" job (apart from other features).
+If your FastAPI is a single file, for example, `main.py` without an `./app` directory, your file structure could look like this:
 
-It has integration with Let's Encrypt. So, it can handle all the HTTPS parts, including certificate acquisition and renewal.
+```
+.
+├── Dockerfile
+├── main.py
+└── requirements.txt
+```
 
-It also has integrations with Docker. So, you can declare your domains in each application configurations and have it read those configurations, generate the HTTPS certificates and serve HTTPS to your application automatically, without requiring any change in its configuration.
+Then you would just have to change the corresponding paths to copy the file inside the `Dockerfile`:
+
+```{ .dockerfile .annotate hl_lines="10  13" }
+FROM python:3.9  
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# (1)
+COPY ./main.py /code/
+
+# (2)
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80"]
+```
+
+1. Copy the `main.py` file to the `/code` directory directly (without any `./app` directory).
+
+2. Run Uvicorn and tell it to import the `app` object from `main` (instead of importing from `app.main`).
+
+Then adjust the Uvicorn command to use the new module `main` instead of `app.main` to import the FastAPI object `app`.
+
+## Deployment Concepts
+
+Let's talk again about some of the same [Deployment Concepts](./concepts.md){.internal-link target=_blank} in terms of containers.
+
+Containers are mainly a tool to simplify the process of **building and deploying** an application, but they don't enforce a particular approach to handle these **deployment concepts**, and there are several possible strategies.
+
+The **good news** is that with each different strategy there's a way to cover all of the deployment concepts. 🎉
+
+Let's review these **deployment concepts** in terms of containers:
+
+* HTTPS
+* Running on startup
+* Restarts
+* Replication (the number of processes running)
+* Memory
+* Previous steps before starting
+
+## HTTPS
+
+If we focus just on the **container image** for a FastAPI application (and later the running **container**), HTTPS normally would be handled **externally** by another tool.
+
+It could be another container, for example with <a href="https://traefik.io/" class="external-link" target="_blank">Traefik</a>, handling **HTTPS** and **automatic** acquisition of **certificates**.
+
+!!! tip
+    Traefik has integrations with Docker, Kubernetes, and others, so it's very easy to set up and configure HTTPS for your containers with it.
+
+Alternatively, HTTPS could be handled by a cloud provider as one of their services (while still running the application in a container).
+
+## Running on Startup and Restarts
+
+There is normally another tool in charge of **starting and running** your container.
+
+It could be **Docker** directly, **Docker Compose**, **Kubernetes**, a **cloud service**, etc.
+
+In most (or all) cases, there's a simple option to enable running the container on startup and enabling restarts on failures. For example, in Docker, it's the command line option `--restart`.
+
+Without using containers, making applications run on startup and with restarts can be cumbersome and difficult. But when **working with containers** in most cases that functionality is included by default. ✨
+
+## Replication - Number of Processes
+
+If you have a <abbr title="A group of machines that are configured to be connected and work together in some way.">cluster</abbr> of machines with **Kubernetes**, Docker Swarm Mode, Nomad, or another similar complex system to manage distributed containers on multiple machines, then you will probably want to **handle replication** at the **cluster level** instead of using a **process manager** (like Gunicorn with workers) in each container.
+
+One of those distributed container management systems like Kubernetes normally has some integrated way of handling **replication of containers** while still supporting **load balancing** for the incoming requests. All at the **cluster level**.
+
+In those cases, you would probably want to build a **Docker image from scratch** as [explained above](#dockerfile), installing your dependencies, and running **a single Uvicorn process** instead of running something like Gunicorn with Uvicorn workers.
+
+### Load Balancer
+
+When using containers, you would normally have some component **listening on the main port**. It could possibly be another container that is also a **TLS Termination Proxy** to handle **HTTPS** or some similar tool.
+
+As this component would take the **load** of requests and distribute that among the workers in a (hopefully) **balanced** way, it is also commonly called a **Load Balancer**.
+
+!!! tip
+    The same **TLS Termination Proxy** component used for HTTPS would probably also be a **Load Balancer**.
+
+And when working with containers, the same system you use to start and manage them would already have internal tools to transmit the **network communication** (e.g. HTTP requests) from that **load balancer** (that could also be a **TLS Termination Proxy**) to the container(s) with your app.
+
+### One Load Balancer - Multiple Worker Containers
+
+When working with **Kubernetes** or similar distributed container management systems, using their internal networking mechanisms would allow the single **load balancer** that is listening on the main **port** to transmit communication (requests) to possibly **multiple containers** running your app.
+
+Each of these containers running your app would normally have **just one process** (e.g. a Uvicorn process running your FastAPI application). They would all be **identical containers**, running the same thing, but each with its own process, memory, etc. That way you would take advantage of **parallelization** in **different cores** of the CPU, or even in **different machines**.
+
+And the distributed container system with the **load balancer** would **distribute the requests** to each one of the containers with your app **in turns**. So, each request could be handled by one of the multiple **replicated containers** running your app.
+
+And normally this **load balancer** would be able to handle requests that go to *other* apps in your cluster (e.g. to a different domain, or under a different URL path prefix), and would transmit that communication to the right containers for *that other* application running in your cluster.
+
+### One Process per Container
+
+In this type of scenario, you probably would want to have **a single (Uvicorn) process per container**, as you would already be handling replication at the cluster level.
+
+So, in this case, you **would not** want to have a process manager like Gunicorn with Uvicorn workers, or Uvicorn using its own Uvicorn workers. You would want to have just a **single Uvicorn process** per container (but probably multiple containers).
+
+Having another process manager inside the container (as would be with Gunicorn or Uvicorn managing Uvicorn workers) would only add **unnecessary complexity** that you are most probably already taking care of with your cluster system.
+
+### Containers with Multiple Processes and Special Cases
+
+Of course, there are **special cases** where you could want to have **a container** with a **Gunicorn process manager** starting several **Uvicorn worker processes** inside.
+
+In those cases, you can use the **official Docker image** that includes **Gunicorn** as a process manager running multiple **Uvicorn worker processes**, and some default settings to adjust the number of workers based on the current CPU cores automatically. I'll tell you more about it below in [Official Docker Image with Gunicorn - Uvicorn](#official-docker-image-with-gunicorn-uvicorn).
+
+Here are some examples of when that could make sense:
+
+#### A Simple App
+
+You could want a process manager in the container if your application is **simple enough** that you don't need (at least not yet) to fine-tune the number of processes too much, and you can just use an automated default (with the official Docker image), and you are running it on a **single server**, not a cluster.
+
+#### Docker Compose
+
+You could be deploying to a **single server** (not a cluster) with **Docker Compose**, so you wouldn't have an easy way to manage replication of containers (with Docker Compose) while preserving the shared network and **load balancing**.
+
+Then you could want to have **a single container** with a **process manager** starting **several worker processes** inside.
+
+#### Prometheus and Other Reasons
+
+You could also have **other reasons** that would make it easier to have a **single container** with **multiple processes** instead of having **multiple containers** with **a single process** in each of them.
+
+For example (depending on your setup) you could have some tool like a Prometheus exporter in the same container that should have access to **each of the requests** that come.
+
+In this case, if you had **multiple containers**, by default, when Prometheus came to **read the metrics**, it would get the ones for **a single container each time** (for the container that handled that particular request), instead of getting the **accumulated metrics** for all the replicated containers.
+
+Then, in that case, it could be simpler to have **one container** with **multiple processes**, and a local tool (e.g. a Prometheus exporter) on the same container collecting Prometheus metrics for all the internal processes and exposing those metrics on that single container.
 
 ---
 
-With this information and tools, continue with the next section to combine everything.
+The main point is, **none** of these are **rules written in stone** that you have to blindly follow. You can use these ideas to **evaluate your own use case** and decide what is the best approach for your system, checking out how to manage the concepts of:
 
-## Docker Swarm mode cluster with Traefik and HTTPS
+* Security - HTTPS
+* Running on startup
+* Restarts
+* Replication (the number of processes running)
+* Memory
+* Previous steps before starting
 
-You can have a Docker Swarm mode cluster set up in minutes (about 20 min) with a main Traefik handling HTTPS (including certificate acquisition and renewal).
+## Memory
 
-By using Docker Swarm mode, you can start with a "cluster" of a single machine (it can even be a $5 USD / month server) and then you can grow as much as you need adding more servers.
+If you run **a single process per container** you will have a more or less well-defined, stable, and limited amount of memory consumed by each of those containers (more than one if they are replicated).
 
-To set up a Docker Swarm Mode cluster with Traefik and HTTPS handling, follow this guide:
+And then you can set those same memory limits and requirements in your configurations for your container management system (for example in **Kubernetes**). That way it will be able to **replicate the containers** in the **available machines** taking into account the amount of memory needed by them, and the amount available in the machines in the cluster.
 
-### <a href="https://medium.com/@tiangolo/docker-swarm-mode-and-traefik-for-a-https-cluster-20328dba6232" class="external-link" target="_blank">Docker Swarm Mode and Traefik for an HTTPS cluster</a>
+If your application is **simple**, this will probably **not be a problem**, and you might not need to specify hard memory limits. But if you are **using a lot of memory** (for example with **machine learning** models), you should check how much memory you are consuming and adjust the **number of containers** that runs in **each machine** (and maybe add more machines to your cluster).
 
-### Deploy a FastAPI application
+If you run **multiple processes per container** (for example with the official Docker image) you will have to make sure that the number of processes started doesn't **consume more memory** than what is available.
 
-The easiest way to set everything up, would be using the [**FastAPI** Project Generators](../project-generation.md){.internal-link target=_blank}.
+## Previous Steps Before Starting and Containers
 
-It is designed to be integrated with this Docker Swarm cluster with Traefik and HTTPS described above.
+If you are using containers (e.g. Docker, Kubernetes), then there are two main approaches you can use.
 
-You can generate a project in about 2 min.
+### Multiple Containers
 
-The generated project has instructions to deploy it, doing it takes another 2 min.
+If you have **multiple containers**, probably each one running a **single process** (for example, in a **Kubernetes** cluster), then you would probably want to have a **separate container** doing the work of the **previous steps** in a single container, running a single process, **before** running the replicated worker containers.
+
+!!! info
+    If you are using Kubernetes, this would probably be an <a href="https://kubernetes.io/docs/concepts/workloads/pods/init-containers/" class="external-link" target="_blank">Init Container</a>.
+
+If in your use case there's no problem in running those previous steps **multiple times in parallel** (for example if you are not running database migrations, but just checking if the database is ready yet), then you could also just put them in each container right before starting the main process.
+
+### Single Container
+
+If you have a simple setup, with a **single container** that then starts multiple **worker processes** (or also just one process), then you could run those previous steps in the same container, right before starting the process with the app. The official Docker image supports this internally.
+
+## Official Docker Image with Gunicorn - Uvicorn
+
+There is an official Docker image that includes Gunicorn running with Uvicorn workers, as detailed in a previous chapter: [Server Workers - Gunicorn with Uvicorn](./server-workers.md){.internal-link target=_blank}.
+
+This image would be useful mainly in the situations described above in: [Containers with Multiple Processes and Special Cases](#containers-with-multiple-processes-and-special-cases).
+
+* <a href="https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker" class="external-link" target="_blank">tiangolo/uvicorn-gunicorn-fastapi</a>.
+
+!!! warning
+    There's a high chance that you **don't** need this base image or any other similar one, and would be better off by building the image from scratch as [described above in: Build a Docker Image for FastAPI](#build-a-docker-image-for-fastapi).
+
+This image has an **auto-tuning** mechanism included to set the **number of worker processes** based on the CPU cores available.
+
+It has **sensible defaults**, but you can still change and update all the configurations with **environment variables** or configuration files.
+
+It also supports running <a href="https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker#pre_start_path" class="external-link" target="_blank">**previous steps before starting**</a> with a script.
+
+!!! tip
+    To see all the configurations and options, go to the Docker image page: <a href="https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker" class="external-link" target="_blank">tiangolo/uvicorn-gunicorn-fastapi</a>.
+
+### Number of Processes on the Official Docker Image
+
+The **number of processes** on this image is **computed automatically** from the CPU **cores** available.
+
+This means that it will try to **squeeze** as much **performance** from the CPU as possible.
+
+You can also adjust it with the configurations using **environment variables**, etc.
+
+But it also means that as the number of processes depends on the CPU the container is running, the **amount of memory consumed** will also depend on that.
+
+So, if your application consumes a lot of memory (for example with machine learning models), and your server has a lot of CPU cores **but little memory**, then your container could end up trying to use more memory than what is available, and degrading performance a lot (or even crashing). 🚨
+
+### Create a `Dockerfile`
+
+Here's how you would create a `Dockerfile` based on this image:
+
+```Dockerfile
+FROM tiangolo/uvicorn-gunicorn-fastapi:python3.9
+
+COPY ./requirements.txt /app/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /app/requirements.txt
+
+COPY ./app /app
+```
+
+### Bigger Applications
+
+If you followed the section about creating [Bigger Applications with Multiple Files](../tutorial/bigger-applications.md){.internal-link target=_blank}, your `Dockerfile` might instead look like:
+
+```Dockerfile hl_lines="7"
+FROM tiangolo/uvicorn-gunicorn-fastapi:python3.9
+
+COPY ./requirements.txt /app/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /app/requirements.txt
+
+COPY ./app /app/app
+```
+
+### When to Use
+
+You should probably **not** use this official base image (or any other similar one) if you are using **Kubernetes** (or others) and you are already setting **replication** at the cluster level, with multiple **containers**. In those cases, you are better off **building an image from scratch** as described above: [Build a Docker Image for FastAPI](#build-a-docker-image-for-fastapi).
+
+This image would be useful mainly in the special cases described above in [Containers with Multiple Processes and Special Cases](#containers-with-multiple-processes-and-special-cases). For example, if your application is **simple enough** that setting a default number of processes based on the CPU works well, you don't want to bother with manually configuring the replication at the cluster level, and you are not running more than one container with your app. Or if you are deploying with **Docker Compose**, running on a single server, etc.
+
+## Deploy the Container Image
+
+After having a Container (Docker) Image there are several ways to deploy it.
+
+For example:
+
+* With **Docker Compose** in a single server
+* With a **Kubernetes** cluster
+* With a Docker Swarm Mode cluster
+* With another tool like Nomad
+* With a cloud service that takes your container image and deploys it
+
+## Docker Image with Poetry
+
+If you use <a href="https://python-poetry.org/" class="external-link" target="_blank">Poetry</a> to manage your project's dependencies, you could use Docker multi-stage building:
+
+```{ .dockerfile .annotate }
+# (1)
+FROM python:3.9 as requirements-stage
+
+# (2)
+WORKDIR /tmp
+
+# (3)
+RUN pip install poetry
+
+# (4)
+COPY ./pyproject.toml ./poetry.lock* /tmp/
+
+# (5)
+RUN poetry export -f requirements.txt --output requirements.txt --without-hashes
+
+# (6)
+FROM python:3.9
+
+# (7)
+WORKDIR /code
+
+# (8)
+COPY --from=requirements-stage /tmp/requirements.txt /code/requirements.txt
+
+# (9)
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# (10)
+COPY ./app /code/app
+
+# (11)
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "80"]
+```
+
+1. This is the first stage, it is named `requirements-stage`.
+
+2. Set `/tmp` as the current working directory.
+
+    Here's where we will generate the file `requirements.txt`
+
+3. Install Poetry in this Docker stage.
+
+4. Copy the `pyproject.toml` and `poetry.lock` files to the `/tmp` directory.
+
+    Because it uses `./poetry.lock*` (ending with a `*`), it won't crash if that file is not available yet.
+
+5. Generate the `requirements.txt` file.
+
+6. This is the final stage, anything here will be preserved in the final container image.
+
+7. Set the current working directory to `/code`.
+
+8. Copy the `requirements.txt` file to the `/code` directory.
+
+    This file only lives in the previous Docker stage, that's why we use `--from-requirements-stage` to copy it.
+
+9. Install the package dependencies in the generated `requirements.txt` file.
+
+10. Copy the `app` directory to the `/code` directory.
+
+11. Run the `uvicorn` command, telling it to use the `app` object imported from `app.main`.
+
+!!! tip
+    Click the bubble numbers to see what each line does.
+
+A **Docker stage** is a part of a `Dockerfile` that works as a **temporary container image** that is only used to generate some files to be used later.
+
+The first stage will only be used to **install Poetry** and to **generate the `requirements.txt`** with your project dependencies from Poetry's `pyproject.toml` file.
+
+This `requirements.txt` file will be used with `pip` later in the **next stage**.
+
+In the final container image **only the final stage** is preserved. The previous stage(s) will be discarded.
+
+When using Poetry, it would make sense to use **Docker multi-stage builds** because you don't really need to have Poetry and its dependencies installed in the final container image, you **only need** to have the generated `requirements.txt` file to install your project dependencies.
+
+Then in the next (and final) stage you would build the image more or less in the same way as described before.
+
+### Behind a TLS Termination Proxy - Poetry
+
+Again, if you are running your container behind a TLS Termination Proxy (load balancer) like Nginx or Traefik, add the option `--proxy-headers` to the command:
+
+```Dockerfile
+CMD ["uvicorn", "app.main:app", "--proxy-headers", "--host", "0.0.0.0", "--port", "80"]
+```
+
+## Recap
+
+Using container systems (e.g. with **Docker** and **Kubernetes**) it becomes fairly straightforward to handle all the **deployment concepts**:
+
+* HTTPS
+* Running on startup
+* Restarts
+* Replication (the number of processes running)
+* Memory
+* Previous steps before starting
+
+In most cases, you probably won't want to use any base image, and instead **build a container image from scratch** one based on the official Python Docker image.
+
+Taking care of the **order** of instructions in the `Dockerfile` and the **Docker cache** you can **minimize build times**, to maximize your productivity (and avoid boredom). 😎
+
+In certain special cases, you might want to use the official Docker image for FastAPI. 🤓

docs/en/docs/deployment/https.md

@@ -7,42 +7,184 @@ But it is way more complex than that.
 !!! tip
     If you are in a hurry or don't care, continue with the next sections for step by step instructions to set everything up with different techniques.
 
-To learn the basics of HTTPS, from a consumer perspective, check <a href="https://howhttps.works/" class="external-link" target="_blank">https://howhttps.works/</a>.
+To **learn the basics of HTTPS**, from a consumer perspective, check <a href="https://howhttps.works/" class="external-link" target="_blank">https://howhttps.works/</a>.
 
-Now, from a developer's perspective, here are several things to have in mind while thinking about HTTPS:
+Now, from a **developer's perspective**, here are several things to have in mind while thinking about HTTPS:
 
-* For HTTPS, the server needs to have "certificates" generated by a third party.
-    * Those certificates are actually acquired from the third-party, not "generated".
-* Certificates have a lifetime.
-    * They expire.
-    * And then they need to be renewed, acquired again from the third party.
-* The encryption of the connection happens at the TCP level.
-    * That's one layer below HTTP.
-    * So, the certificate and encryption handling is done before HTTP.
-* TCP doesn't know about "domains". Only about IP addresses.
-    * The information about the specific domain requested goes in the HTTP data.
-* The HTTPS certificates "certify" a certain domain, but the protocol and encryption happen at the TCP level, before knowing which domain is being dealt with.
-* By default, that would mean that you can only have one HTTPS certificate per IP address.
+* For HTTPS, **the server** needs to **have "certificates"** generated by a **third party**.
+    * Those certificates are actually **acquired** from the third party, not "generated".
+* Certificates have a **lifetime**.
+    * They **expire**.
+    * And then they need to be **renewed**, **acquired again** from the third party.
+* The encryption of the connection happens at the **TCP level**.
+    * That's one layer **below HTTP**.
+    * So, the **certificate and encryption** handling is done **before HTTP**.
+* **TCP doesn't know about "domains"**. Only about IP addresses.
+    * The information about the **specific domain** requested goes in the **HTTP data**.
+* The **HTTPS certificates** "certify" a **certain domain**, but the protocol and encryption happen at the TCP level, **before knowing** which domain is being dealt with.
+* **By default**, that would mean that you can only have **one HTTPS certificate per IP address**.
     * No matter how big your server is or how small each application you have on it might be.
-    * There is a solution to this, however.
-* There's an extension to the TLS protocol (the one handling the encryption at the TCP level, before HTTP) called <a href="https://en.wikipedia.org/wiki/Server_Name_Indication" class="external-link" target="_blank"><abbr title="Server Name Indication">SNI</abbr></a>.
-    * This SNI extension allows one single server (with a single IP address) to have several HTTPS certificates and serve multiple HTTPS domains/applications.
-    * For this to work, a single component (program) running on the server, listening on the public IP address, must have all the HTTPS certificates in the server.
-* After obtaining a secure connection, the communication protocol is still HTTP.
-    * The contents are encrypted, even though they are being sent with the HTTP protocol.
+    * There is a **solution** to this, however.
+* There's an **extension** to the **TLS** protocol (the one handling the encryption at the TCP level, before HTTP) called **<a href="https://en.wikipedia.org/wiki/Server_Name_Indication" class="external-link" target="_blank"><abbr title="Server Name Indication">SNI</abbr></a>**.
+    * This SNI extension allows one single server (with a **single IP address**) to have **several HTTPS certificates** and serve **multiple HTTPS domains/applications**.
+    * For this to work, a **single** component (program) running on the server, listening on the **public IP address**, must have **all the HTTPS certificates** in the server.
+* **After** obtaining a secure connection, the communication protocol is **still HTTP**.
+    * The contents are **encrypted**, even though they are being sent with the **HTTP protocol**.
 
-It is a common practice to have one program/HTTP server running on the server (the machine, host, etc.) and managing all the HTTPS parts : sending the decrypted HTTP requests to the actual HTTP application running in the same server (the **FastAPI** application, in this case), take the HTTP response from the application, encrypt it using the appropriate certificate and sending it back to the client using HTTPS. This server is often called a <a href="https://en.wikipedia.org/wiki/TLS_termination_proxy" class="external-link" target="_blank">TLS Termination Proxy</a>.
+It is a common practice to have **one program/HTTP server** running on the server (the machine, host, etc.) and **managing all the HTTPS parts**: receiving the **encrypted HTTPS requests**, sending the **decrypted HTTP requests** to the actual HTTP application running in the same server (the **FastAPI** application, in this case), take the **HTTP response** from the application, **encrypt it** using the appropriate **HTTPS certificate** and sending it back to the client using **HTTPS**. This server is often called a **<a href="https://en.wikipedia.org/wiki/TLS_termination_proxy" class="external-link" target="_blank">TLS Termination Proxy</a>**.
+
+Some of the options you could use as a TLS Termination Proxy are:
+
+* Traefik (that can also handle certificate renewals)
+* Caddy (that can also handle certificate renewals)
+* Nginx
+* HAProxy
 
 ## Let's Encrypt
 
-Before Let's Encrypt, these HTTPS certificates were sold by trusted third-parties.
+Before Let's Encrypt, these **HTTPS certificates** were sold by trusted third parties.
 
 The process to acquire one of these certificates used to be cumbersome, require quite some paperwork and the certificates were quite expensive.
 
-But then <a href="https://letsencrypt.org/" class="external-link" target="_blank">Let's Encrypt</a> was created.
+But then **<a href="https://letsencrypt.org/" class="external-link" target="_blank">Let's Encrypt</a>** was created.
 
-It is a project from the Linux Foundation. It provides HTTPS certificates for free. In an automated way. These certificates use all the standard cryptographic security, and are short lived (about 3 months), so the security is actually better because of their reduced lifespan.
+It is a project from the Linux Foundation. It provides **HTTPS certificates for free**, in an automated way. These certificates use all the standard cryptographic security, and are short-lived (about 3 months), so the **security is actually better** because of their reduced lifespan.
 
 The domains are securely verified and the certificates are generated automatically. This also allows automating the renewal of these certificates.
 
-The idea is to automate the acquisition and renewal of these certificates, so that you can have secure HTTPS, for free, forever.
+The idea is to automate the acquisition and renewal of these certificates so that you can have **secure HTTPS, for free, forever**.
+
+## HTTPS for Developers
+
+Here's an example of how an HTTPS API could look like, step by step, paying attention mainly to the ideas important for developers.
+
+### Domain Name
+
+It would probably all start by you **acquiring** some **domain name**. Then, you would configure it in a DNS server (possibly your same cloud provider).
+
+You would probably get a cloud server (a virtual machine) or something similar, and it would have a <abbr title="That doesn't change">fixed</abbr> **public IP address**.
+
+In the DNS server(s) you would configure a record (an "`A record`") to point **your domain** to the public **IP address of your server**.
+
+You would probably do this just once, the first time, when setting everything up.
+
+!!! tip
+    This Domain Name part is way before HTTPS, but as everything depends on the domain and the IP address, it's worth mentioning it here.
+
+### DNS
+
+Now let's focus on all the actual HTTPS parts.
+
+First, the browser would check with the **DNS servers** what is the **IP for the domain**, in this case, `someapp.example.com`.
+
+The DNS servers would tell the browser to use some specific **IP address**. That would be the public IP address used by your server, that you configured in the DNS servers.
+
+<img src="/img/deployment/https/https01.svg">
+
+### TLS Handshake Start
+
+The browser would then communicate with that IP address on **port 443** (the HTTPS port).
+
+The first part of the communication is just to establish the connection between the client and the server and to decide the cryptographic keys they will use, etc.
+
+<img src="/img/deployment/https/https02.svg">
+
+This interaction between the client and the server to establish the TLS connection is called the **TLS handshake**.
+
+### TLS with SNI Extension
+
+**Only one process** in the server can be listening on a specific **port** in a specific **IP address**. There could be other processes listening on other ports in the same IP address, but only one for each combination of IP address and port.
+
+TLS (HTTPS) uses the specific port `443` by default. So that's the port we would need.
+
+As only one process can be listening on this port, the process that would do it would be the **TLS Termination Proxy**.
+
+The TLS Termination Proxy would have access to one or more **TLS certificates** (HTTPS certificates).
+
+Using the **SNI extension** discussed above, the TLS Termination Proxy would check which of the TLS (HTTPS) certificates available it should use for this connection, using the one that matches the domain expected by the client.
+
+In this case, it would use the certificate for `someapp.example.com`.
+
+<img src="/img/deployment/https/https03.svg">
+
+The client already **trusts** the entity that generated that TLS certificate (in this case Let's Encrypt, but we'll see about that later), so it can **verify** that the certificate is valid.
+
+Then, using the certificate, the client and the TLS Termination Proxy **decide how to encrypt** the rest of the **TCP communication**. This completes the **TLS Handshake** part.
+
+After this, the client and the server have an **encrypted TCP connection**, this is what TLS provides. And then they can use that connection to start the actual **HTTP communication**.
+
+And that's what **HTTPS** is, it's just plain **HTTP** inside a **secure TLS connection** instead of a pure (unencrypted) TCP connection.
+
+!!! tip
+    Notice that the encryption of the communication happens at the **TCP level**, not at the HTTP level.
+
+### HTTPS Request
+
+Now that the client and server (specifically the browser and the TLS Termination Proxy) have an **encrypted TCP connection**, they can start the **HTTP communication**.
+
+So, the client sends an **HTTPS request**. This is just an HTTP request through an encrypted TLS connection.
+
+<img src="/img/deployment/https/https04.svg">
+
+### Decrypt the Request
+
+The TLS Termination Proxy would use the encryption agreed to **decrypt the request**, and would transmit the **plain (decrypted) HTTP request** to the process running the application (for example a process with Uvicorn running the FastAPI application).
+
+<img src="/img/deployment/https/https05.svg">
+
+### HTTP Response
+
+The application would process the request and send a **plain (unencrypted) HTTP response** to the TLS Termination Proxy.
+
+<img src="/img/deployment/https/https06.svg">
+
+### HTTPS Response
+
+The TLS Termination Proxy would then **encrypt the response** using the cryptography agreed before (that started with the certificate for `someapp.example.com`), and send it back to the browser.
+
+Next, the browser would verify that the response is valid and encrypted with the right cryptographic key, etc. It would then **decrypt the response** and process it.
+
+<img src="/img/deployment/https/https07.svg">
+
+The client (browser) will know that the response comes from the correct server because it is using the cryptography they agreed using the **HTTPS certificate** before.
+
+### Multiple Applications
+
+In the same server (or servers), there could be **multiple applications**, for example, other API programs or a database.
+
+Only one process can be handling the specific IP and port (the TLS Termination Proxy in our example) but the other applications/processes can be running on the server(s) too, as long as they don't try to use the same **combination of public IP and port**.
+
+<img src="/img/deployment/https/https08.svg">
+
+That way, the TLS Termination Proxy could handle HTTPS and certificates for **multiple domains**, for multiple applications, and then transmit the requests to the right application in each case.
+
+### Certificate Renewal
+
+At some point in the future, each certificate would **expire** (about 3 months after acquiring it).
+
+And then, there would be another program (in some cases it's another program, in some cases it could be the same TLS Termination Proxy) that would talk to Let's Encrypt, and renew the certificate(s).
+
+<img src="/img/deployment/https/https.svg">
+
+The **TLS certificates** are **associated with a domain name**, not with an IP address.
+
+So, to renew the certificates, the renewal program needs to **prove** to the authority (Let's Encrypt) that it indeed **"owns" and controls that domain**.
+
+To do that, and to accommodate different application needs, there are several ways it can do it. Some popular ways are:
+
+* **Modify some DNS records**.
+    * For this, the renewal program needs to support the APIs of the DNS provider, so, depending on the DNS provider you are using, this might or might not be an option.
+* **Run as a server** (at least during the certificate acquisition process) on the public IP address associated with the domain.
+    * As we said above, only one process can be listening on a specific IP and port.
+    * This is one of the reasons why it's very useful when the same TLS Termination Proxy also takes care of the certificate renewal process.
+    * Otherwise, you might have to stop the TLS Termination Proxy momentarily, start the renewal program to acquire the certificates, then configure them with the TLS Termination Proxy, and then restart the TLS Termination Proxy. This is not ideal, as your app(s) will not be available during the time that the TLS Termination Proxy is off.
+
+All this renewal process, while still serving the app, is one of the main reasons why you would want to have a **separate system to handle HTTPS** with a TLS Termination Proxy instead of just using the TLS certificates with the application server directly (e.g. Uvicorn).
+
+## Recap
+
+Having **HTTPS** is very important, and quite **critical** in most cases. Most of the effort you as a developer have to put around HTTPS is just about **understanding these concepts** and how they work.
+
+But once you know the basic information of **HTTPS for developers** you can easily combine and configure different tools to help you manage everything in a simple way.
+
+In some of the next chapters, I'll show you several concrete examples of how to set up **HTTPS** for **FastAPI** applications. 🔒

docs/en/docs/deployment/index.md

@@ -2,6 +2,20 @@
 
 Deploying a **FastAPI** application is relatively easy.
 
+## What Does Deployment Mean
+
+To **deploy** an application means to perform the necessary steps to make it **available to the users**.
+
+For a **web API**, it normally involves putting it in a **remote machine**, with a **server program** that provides good performance, stability, etc, so that your **users** can **access** the application efficiently and without interruptions or problems.
+
+This is in contrast to the **development** stages, where you are constantly changing the code, breaking it and fixing it, stopping and restarting the development server, etc.
+
+## Deployment Strategies
+
 There are several ways to do it depending on your specific use case and the tools that you use.
 
-You will see more details to have in mind and some of the techniques to do it in the next sections.
+You could **deploy a server** yourself using a combination of tools, you could use a **cloud service** that does part of the work for you, or other possible options.
+
+I will show you some of the main concepts you should probably have in mind when deploying a **FastAPI** application (although most of it applies to any other type of web application).
+
+You will see more details to have in mind and some of the techniques to do it in the next sections. ✨

docs/en/docs/deployment/manually.md

@@ -1,8 +1,26 @@
-# Deploy manually
+# Run a Server Manually - Uvicorn
 
-You can deploy **FastAPI** manually as well.
+The main thing you need to run a **FastAPI** application in a remote server machine is an ASGI server program like **Uvicorn**.
 
-You just need to install an ASGI compatible server like:
+There are 3 main alternatives:
+
+* <a href="https://www.uvicorn.org/" class="external-link" target="_blank">Uvicorn</a>: a high performance ASGI server.
+* <a href="https://pgjones.gitlab.io/hypercorn/" class="external-link" target="_blank">Hypercorn</a>: an ASGI server compatible with HTTP/2 and Trio among other features.
+* <a href="https://github.com/django/daphne" class="external-link" target="_blank">Daphne</a>: the ASGI server built for Django Channels.
+
+## Server Machine and Server Program
+
+There's a small detail about names to have in mind. 💡
+
+The word "**server**" is commonly used to refer to both the remote/cloud computer (the physical or virtual machine) and also the program that is running on that machine (e.g. Uvicorn).
+
+Just have that in mind when you read "server" in general, it could refer to one of those two things.
+
+When referring to the remote machine, it's common to call it **server**, but also **machine**, **VM** (virtual machine), **node**. Those all refer to some type of remote machine, normally running Linux, where you run programs.
+
+## Install the Server Program
+
+You can install an ASGI compatible server with:
 
 === "Uvicorn"
 
@@ -11,7 +29,7 @@ You just need to install an ASGI compatible server like:
     <div class="termy">
 
     ```console
-    $ pip install uvicorn[standard]
+    $ pip install "uvicorn[standard]"
 
     ---> 100%
     ```
@@ -39,7 +57,9 @@ You just need to install an ASGI compatible server like:
 
     ...or any other ASGI server.
 
-And run your application the same way you have done in the tutorials, but without the `--reload` option, e.g.:
+## Run the Server Program
+
+You can then your application the same way you have done in the tutorials, but without the `--reload` option, e.g.:
 
 === "Uvicorn"
 
@@ -65,10 +85,61 @@ And run your application the same way you have done in the tutorials, but withou
 
     </div>
 
-You might want to set up some tooling to make sure it is restarted automatically if it stops.
+!!! warning
+    Remember to remove the `--reload` option if you were using it.
 
-You might also want to install <a href="https://gunicorn.org/" class="external-link" target="_blank">Gunicorn</a> and <a href="https://www.uvicorn.org/#running-with-gunicorn" class="external-link" target="_blank">use it as a manager for Uvicorn</a>, or use Hypercorn with multiple workers.
+    The `--reload` option consumes much more resources, is more unstable, etc.
+    
+    It helps a lot during **development**, but you **shouldn't** use it in **production**.
 
-Making sure to fine-tune the number of workers, etc.
+## Hypercorn with Trio
 
-But if you are doing all that, you might just use the Docker image that does it automatically.
+Starlette and **FastAPI** are based on <a href="https://anyio.readthedocs.io/en/stable/" class="external-link" target="_blank">AnyIO</a>, which makes them compatible with both Python's standard library <a href="https://docs.python.org/3/library/asyncio-task.html" class="external-link" target="_blank">asyncio</a> and <a href="https://trio.readthedocs.io/en/stable/" class="external-link" target="_blank">Trio</a>.
+
+Nevertheless, Uvicorn is currently only compatible with asyncio, and it normally uses <a href="https://github.com/MagicStack/uvloop" class="external-link" target="_blank">`uvloop`</a>, the high-performance drop-in replacement for `asyncio`.
+
+But if you want to directly use **Trio**, then you can use **Hypercorn** as it supports it. ✨
+
+### Install Hypercorn with Trio
+
+First you need to install Hypercorn with Trio support:
+
+<div class="termy">
+
+```console
+$ pip install "hypercorn[trio]"
+---> 100%
+```
+
+</div>
+
+### Run with Trio
+
+Then you can pass the command line option `--worker-class` with the value `trio`:
+
+<div class="termy">
+
+```console
+$ hypercorn main:app --worker-class trio
+```
+
+</div>
+
+And that will start Hypercorn with your app using Trio as the backend.
+
+Now you can use Trio internally in your app. Or even better, you can use AnyIO, to keep your code compatible with both Trio and asyncio. 🎉
+
+## Deployment Concepts
+
+These examples run the server program (e.g Uvicorn), starting **a single process**, listening on all the IPs (`0.0.0.0`) on a predefined port (e.g. `80`).
+
+This is the basic idea. But you will probably want to take care of some additional things, like:
+
+* Security - HTTPS
+* Running on startup
+* Restarts
+* Replication (the number of processes running)
+* Memory
+* Previous steps before starting
+
+I'll tell you more about each of these concepts, how to think about them, and some concrete examples with strategies to handle them in the next chapters. 🚀

docs/en/docs/deployment/server-workers.md

@@ -0,0 +1,178 @@
+# Server Workers - Gunicorn with Uvicorn
+
+Let's check back those deployment concepts from before:
+
+* Security - HTTPS
+* Running on startup
+* Restarts
+* **Replication (the number of processes running)**
+* Memory
+* Previous steps before starting
+
+Up to this point, with all the tutorials in the docs, you have probably been running a **server program** like Uvicorn, running a **single process**.
+
+When deploying applications you will probably want to have some **replication of processes** to take advantage of **multiple cores** and to be able to handle more requests.
+
+As you saw in the previous chapter about [Deployment Concepts](./concepts.md){.internal-link target=_blank}, there are multiple strategies you can use.
+
+Here I'll show you how to use <a href="https://gunicorn.org/" class="external-link" target="_blank">**Gunicorn**</a> with **Uvicorn worker processes**.
+
+!!! info
+    If you are using containers, for example with Docker or Kubernetes, I'll tell you more about that in the next chapter: [FastAPI in Containers - Docker](./docker.md){.internal-link target=_blank}.
+
+    In particular, when running on **Kubernetes** you will probably **not** want to use Gunicorn and instead run **a single Uvicorn process per container**, but I'll tell you about it later in that chapter.
+
+## Gunicorn with Uvicorn Workers
+
+**Gunicorn** is mainly an application server using the **WSGI standard**. That means that Gunicorn can serve applications like Flask and Django. Gunicorn by itself is not compatible with **FastAPI**, as FastAPI uses the newest **<a href="https://asgi.readthedocs.io/en/latest/" class="external-link" target="_blank">ASGI standard</a>**.
+
+But Gunicorn supports working as a **process manager** and allowing users to tell it which specific **worker process class** to use. Then Gunicorn would start one or more **worker processes** using that class.
+
+And **Uvicorn** has a **Gunicorn-compatible worker class**.
+
+Using that combination, Gunicorn would act as a **process manager**, listening on the **port** and the **IP**. And it would **transmit** the communication to the worker processes running the **Uvicorn class**.
+
+And then the Gunicorn-compatible **Uvicorn worker** class would be in charge of converting the data sent by Gunicorn to the ASGI standard for FastAPI to use it.
+
+## Install Gunicorn and Uvicorn
+
+<div class="termy">
+
+```console
+$ pip install "uvicorn[standard]" gunicorn
+
+---> 100%
+```
+
+</div>
+
+That will install both Uvicorn with the `standard` extra packages (to get high performance) and Gunicorn.
+
+## Run Gunicorn with Uvicorn Workers
+
+Then you can run Gunicorn with:
+
+<div class="termy">
+
+```console
+$ gunicorn main:app --workers 4 --worker-class uvicorn.workers.UvicornWorker --bind 0.0.0.0:80
+
+[19499] [INFO] Starting gunicorn 20.1.0
+[19499] [INFO] Listening at: http://0.0.0.0:80 (19499)
+[19499] [INFO] Using worker: uvicorn.workers.UvicornWorker
+[19511] [INFO] Booting worker with pid: 19511
+[19513] [INFO] Booting worker with pid: 19513
+[19514] [INFO] Booting worker with pid: 19514
+[19515] [INFO] Booting worker with pid: 19515
+[19511] [INFO] Started server process [19511]
+[19511] [INFO] Waiting for application startup.
+[19511] [INFO] Application startup complete.
+[19513] [INFO] Started server process [19513]
+[19513] [INFO] Waiting for application startup.
+[19513] [INFO] Application startup complete.
+[19514] [INFO] Started server process [19514]
+[19514] [INFO] Waiting for application startup.
+[19514] [INFO] Application startup complete.
+[19515] [INFO] Started server process [19515]
+[19515] [INFO] Waiting for application startup.
+[19515] [INFO] Application startup complete.
+```
+
+</div>
+
+Let's see what each of those options mean:
+
+* `main:app`: This is the same syntax used by Uvicorn, `main` means the Python module named "`main`", so, a file `main.py`. And `app` is the name of the variable that is the **FastAPI** application.
+    * You can imagine that `main:app` is equivalent to a Python `import` statement like:
+
+        ```Python
+        from main import app
+        ```
+
+    * So, the colon in `main:app` would be equivalent to the Python `import` part in `from main import app`.
+* `--workers`: The number of worker processes to use, each will run a Uvicorn worker, in this case, 4 workers.
+* `--worker-class`: The Gunicorn-compatible worker class to use in the worker processes.
+    * Here we pass the class that Gunicorn can import and use with:
+
+        ```Python
+        import uvicorn.workers.UvicornWorker
+        ```
+
+* `--bind`: This tells Gunicorn the IP and the port to listen to, using a colon (`:`) to separate the IP and the port.
+    * If you were running Uvicorn directly, instead of `--bind 0.0.0.0:80` (the Gunicorn option) you would use `--host 0.0.0.0` and `--port 80`.
+
+In the output, you can see that it shows the **PID** (process ID) of each process (it's just a number).
+
+You can see that:
+
+* The Gunicorn **process manager** starts with PID `19499` (in your case it will be a different number).
+* Then it starts `Listening at: http://0.0.0.0:80`.
+* Then it detects that it has to use the worker class at `uvicorn.workers.UvicornWorker`.
+* And then it starts **4 workers**, each with its own PID: `19511`, `19513`, `19514`, and `19515`.
+
+Gunicorn would also take care of managing **dead processes** and **restarting** new ones if needed to keep the number of workers. So that helps in part with the **restart** concept from the list above.
+
+Nevertheless, you would probably also want to have something outside making sure to **restart Gunicorn** if necessary, and also to **run it on startup**, etc.
+
+## Uvicorn with Workers
+
+Uvicorn also has an option to start and run several **worker processes**.
+
+Nevertheless, as of now, Uvicorn's capabilities for handling worker processes are more limited than Gunicorn's. So, if you want to have a process manager at this level (at the Python level), then it might be better to try with Gunicorn as the process manager.
+
+In any case, you would run it like this:
+
+<div class="termy">
+
+```console
+$ uvicorn main:app --host 0.0.0.0 --port 8080 --workers 4
+<font color="#A6E22E">INFO</font>:     Uvicorn running on <b>http://0.0.0.0:8080</b> (Press CTRL+C to quit)
+<font color="#A6E22E">INFO</font>:     Started parent process [<font color="#A1EFE4"><b>27365</b></font>]
+<font color="#A6E22E">INFO</font>:     Started server process [<font color="#A1EFE4">27368</font>]
+<font color="#A6E22E">INFO</font>:     Waiting for application startup.
+<font color="#A6E22E">INFO</font>:     Application startup complete.
+<font color="#A6E22E">INFO</font>:     Started server process [<font color="#A1EFE4">27369</font>]
+<font color="#A6E22E">INFO</font>:     Waiting for application startup.
+<font color="#A6E22E">INFO</font>:     Application startup complete.
+<font color="#A6E22E">INFO</font>:     Started server process [<font color="#A1EFE4">27370</font>]
+<font color="#A6E22E">INFO</font>:     Waiting for application startup.
+<font color="#A6E22E">INFO</font>:     Application startup complete.
+<font color="#A6E22E">INFO</font>:     Started server process [<font color="#A1EFE4">27367</font>]
+<font color="#A6E22E">INFO</font>:     Waiting for application startup.
+<font color="#A6E22E">INFO</font>:     Application startup complete.
+```
+
+</div>
+
+The only new option here is `--workers` telling Uvicorn to start 4 worker processes.
+
+You can also see that it shows the **PID** of each process, `27365` for the parent process (this is the **process manager**) and one for each worker process: `27368`, `27369`, `27370`, and `27367`.
+
+## Deployment Concepts
+
+Here you saw how to use **Gunicorn** (or Uvicorn) managing **Uvicorn worker processes** to **parallelize** the execution of the application, take advantage of **multiple cores** in the CPU, and be able to serve **more requests**.
+
+From the list of deployment concepts from above, using workers would mainly help with the **replication** part, and a little bit with the **restarts**, but you still need to take care of the others:
+
+* **Security - HTTPS**
+* **Running on startup**
+* ***Restarts***
+* Replication (the number of processes running)
+* **Memory**
+* **Previous steps before starting**
+
+## Containers and Docker
+
+In the next chapter about [FastAPI in Containers - Docker](./docker.md){.internal-link target=_blank} I'll tell some strategies you could use to handle the other **deployment concepts**.
+
+I'll also show you the **official Docker image** that includes **Gunicorn with Uvicorn workers** and some default configurations that can be useful for simple cases.
+
+There I'll also show you how to **build your own image from scratch** to run a single Uvicorn process (without Gunicorn). It is a simple process and is probably what you would want to do when using a distributed container management system like **Kubernetes**.
+
+## Recap
+
+You can use **Gunicorn** (or also Uvicorn) as a process manager with Uvicorn workers to take advantage of **multi-core CPUs**, to run **multiple processes in parallel**.
+
+You could use these tools and ideas if you are setting up **your own deployment system** while taking care of the other deployment concepts yourself.
+
+Check out the next chapter to learn about **FastAPI** with containers (e.g. Docker and Kubernetes). You will see that those tools have simple ways to solve the other **deployment concepts** as well. ✨

docs/en/docs/fastapi-people.md

@@ -114,54 +114,55 @@ These are the **Sponsors**. 😎
 
 They are supporting my work with **FastAPI** (and others), mainly through <a href="https://github.com/sponsors/tiangolo" class="external-link" target="_blank">GitHub Sponsors</a>.
 
+{% if sponsors %}
+
+{% if sponsors.gold %}
+
 ### Gold Sponsors
 
-{% if sponsors %}
 {% for sponsor in sponsors.gold -%}
 <a href="{{ sponsor.url }}" target="_blank" title="{{ sponsor.title }}"><img src="{{ sponsor.img }}" style="border-radius:15px"></a>
 {% endfor %}
 {% endif %}
 
+{% if sponsors.silver %}
+
 ### Silver Sponsors
 
-{% if sponsors %}
 {% for sponsor in sponsors.silver -%}
 <a href="{{ sponsor.url }}" target="_blank" title="{{ sponsor.title }}"><img src="{{ sponsor.img }}" style="border-radius:15px"></a>
 {% endfor %}
 {% endif %}
 
+{% if sponsors.bronze %}
+
 ### Bronze Sponsors
 
-{% if sponsors %}
 {% for sponsor in sponsors.bronze -%}
 <a href="{{ sponsor.url }}" target="_blank" title="{{ sponsor.title }}"><img src="{{ sponsor.img }}" style="border-radius:15px"></a>
 {% endfor %}
 {% endif %}
 
+{% endif %}
+
 ### Individual Sponsors
 
-{% if people %}
-{% if people.sponsors_50 %}
+{% if github_sponsors %}
+{% for group in github_sponsors.sponsors %}
 
 <div class="user-list user-list-center">
-{% for user in people.sponsors_50 %}
+
+{% for user in group %}
+{% if user.login not in sponsors_badge.logins %}
 
 <div class="user"><a href="{{ user.url }}" target="_blank"><div class="avatar-wrapper"><img src="{{ user.avatarUrl }}"/></div><div class="title">@{{ user.login }}</div></a></div>
+
+{% endif %}
 {% endfor %}
 
 </div>
 
-{% endif %}
-{% endif %}
-
-{% if people %}
-<div class="user-list user-list-center">
-{% for user in people.sponsors %}
-
-<div class="user"><a href="{{ user.url }}" target="_blank"><div class="avatar-wrapper"><img src="{{ user.avatarUrl }}"/></div><div class="title">@{{ user.login }}</div></a></div>
 {% endfor %}
-
-</div>
 {% endif %}
 
 ## About the data - technical details

docs/en/docs/features.md

@@ -32,7 +32,6 @@ If you need a 2 minute refresher of how to use Python types (even if you don't u
 You write standard Python with types:
 
 ```Python
-from typing import List, Dict
 from datetime import date
 
 from pydantic import BaseModel
@@ -165,7 +164,6 @@ With **FastAPI** you get all of **Starlette**'s features (as FastAPI is just Sta
 
 * Seriously impressive performance. It is <a href="https://github.com/encode/starlette#performance" class="external-link" target="_blank">one of the fastest Python frameworks available, on par with **NodeJS** and **Go**</a>.
 * **WebSocket** support.
-* **GraphQL** support.
 * In-process background tasks.
 * Startup and shutdown events.
 * Test client built on `requests`.

docs/en/docs/help-fastapi.md

@@ -121,7 +121,7 @@ Have in mind that as chats allow more "free conversation", it's easy to ask ques
 
 In GitHub issues the template will guide you to write the right question so that you can more easily get a good answer, or even solve the problem yourself even before asking. And in GitHub I can make sure I always answer everything, even if it takes some time. I can't personally do that with the chat systems. 😅
 
-Conversations in the chat systems are also not as easily searchable as in GitHub, so questions and answers might get lost in the conversation. And only the ones in GitHub issues count to become a [FastAPI Expert](fastapi-people.md#experts){.internal-link target=_blank}, so you will most probably receive more attention in GitHub isssues.
+Conversations in the chat systems are also not as easily searchable as in GitHub, so questions and answers might get lost in the conversation. And only the ones in GitHub issues count to become a [FastAPI Expert](fastapi-people.md#experts){.internal-link target=_blank}, so you will most probably receive more attention in GitHub issues.
 
 On the other side, there are thousands of users in the chat systems, so there's a high chance you'll find someone to talk to there, almost all the time. 😄
 

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docs/en/docs/img/deployment/https/https01.drawio

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docs/en/docs/img/deployment/https/https02.drawio

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docs/en/docs/img/deployment/https/https03.drawio

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docs/en/docs/img/deployment/https/https04.drawio

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docs/en/docs/img/deployment/https/https05.drawio

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docs/en/docs/img/deployment/https/https06.drawio

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docs/en/docs/img/deployment/https/https08.drawio

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docs/en/docs/index.md

@@ -5,8 +5,8 @@
     <em>FastAPI framework, high performance, easy to learn, fast to code, ready for production</em>
 </p>
 <p align="center">
-<a href="https://github.com/tiangolo/fastapi/actions?query=workflow%3ATest" target="_blank">
-    <img src="https://github.com/tiangolo/fastapi/workflows/Test/badge.svg" alt="Test">
+<a href="https://github.com/tiangolo/fastapi/actions?query=workflow%3ATest+event%3Apush+branch%3Amaster" target="_blank">
+    <img src="https://github.com/tiangolo/fastapi/workflows/Test/badge.svg?event=push&branch=master" alt="Test">
 </a>
 <a href="https://codecov.io/gh/tiangolo/fastapi" target="_blank">
     <img src="https://img.shields.io/codecov/c/github/tiangolo/fastapi?color=%2334D058" alt="Coverage">
@@ -14,6 +14,9 @@
 <a href="https://pypi.org/project/fastapi" target="_blank">
     <img src="https://img.shields.io/pypi/v/fastapi?color=%2334D058&label=pypi%20package" alt="Package version">
 </a>
+<a href="https://pypi.org/project/fastapi" target="_blank">
+    <img src="https://img.shields.io/pypi/pyversions/fastapi.svg?color=%2334D058" alt="Supported Python versions">
+</a>
 </p>
 
 ---
@@ -131,7 +134,7 @@ You will also need an ASGI server, for production such as <a href="https://www.u
 <div class="termy">
 
 ```console
-$ pip install uvicorn[standard]
+$ pip install "uvicorn[standard]"
 
 ---> 100%
 ```
@@ -317,7 +320,7 @@ And now, go to <a href="http://127.0.0.1:8000/redoc" class="external-link" targe
 
 ### Recap
 
-In summary, you declare **once** the types of parameters, body, etc. as function parameters. 
+In summary, you declare **once** the types of parameters, body, etc. as function parameters.
 
 You do that with standard modern Python types.
 
@@ -374,7 +377,7 @@ Coming back to the previous code example, **FastAPI** will:
     * As the `q` parameter is declared with `= None`, it is optional.
     * Without the `None` it would be required (as is the body in the case with `PUT`).
 * For `PUT` requests to `/items/{item_id}`, Read the body as JSON:
-    * Check that it has a required attribute `name` that should be a `str`. 
+    * Check that it has a required attribute `name` that should be a `str`.
     * Check that it has a required attribute `price` that has to be a `float`.
     * Check that it has an optional attribute `is_offer`, that should be a `bool`, if present.
     * All this would also work for deeply nested JSON objects.
@@ -419,9 +422,9 @@ For a more complete example including more features, see the <a href="https://fa
 * A very powerful and easy to use **<abbr title="also known as components, resources, providers, services, injectables">Dependency Injection</abbr>** system.
 * Security and authentication, including support for **OAuth2** with **JWT tokens** and **HTTP Basic** auth.
 * More advanced (but equally easy) techniques for declaring **deeply nested JSON models** (thanks to Pydantic).
+* **GraphQL** integration with <a href="https://strawberry.rocks" class="external-link" target="_blank">Strawberry</a> and other libraries.
 * Many extra features (thanks to Starlette) as:
     * **WebSockets**
-    * **GraphQL**
     * extremely easy tests based on `requests` and `pytest`
     * **CORS**
     * **Cookie Sessions**
@@ -443,12 +446,10 @@ Used by Pydantic:
 Used by Starlette:
 
 * <a href="https://requests.readthedocs.io" target="_blank"><code>requests</code></a> - Required if you want to use the `TestClient`.
-* <a href="https://github.com/Tinche/aiofiles" target="_blank"><code>aiofiles</code></a> - Required if you want to use `FileResponse` or `StaticFiles`.
 * <a href="https://jinja.palletsprojects.com" target="_blank"><code>jinja2</code></a> - Required if you want to use the default template configuration.
 * <a href="https://andrew-d.github.io/python-multipart/" target="_blank"><code>python-multipart</code></a> - Required if you want to support form <abbr title="converting the string that comes from an HTTP request into Python data">"parsing"</abbr>, with `request.form()`.
 * <a href="https://pythonhosted.org/itsdangerous/" target="_blank"><code>itsdangerous</code></a> - Required for `SessionMiddleware` support.
 * <a href="https://pyyaml.org/wiki/PyYAMLDocumentation" target="_blank"><code>pyyaml</code></a> - Required for Starlette's `SchemaGenerator` support (you probably don't need it with FastAPI).
-* <a href="https://graphene-python.org/" target="_blank"><code>graphene</code></a> - Required for `GraphQLApp` support.
 * <a href="https://github.com/esnme/ultrajson" target="_blank"><code>ujson</code></a> - Required if you want to use `UJSONResponse`.
 
 Used by FastAPI / Starlette:
@@ -456,7 +457,7 @@ Used by FastAPI / Starlette:
 * <a href="https://www.uvicorn.org" target="_blank"><code>uvicorn</code></a> - for the server that loads and serves your application.
 * <a href="https://github.com/ijl/orjson" target="_blank"><code>orjson</code></a> - Required if you want to use `ORJSONResponse`.
 
-You can install all of these with `pip install fastapi[all]`.
+You can install all of these with `pip install "fastapi[all]"`.
 
 ## License
 

docs/en/docs/release-notes.md

@@ -2,13 +2,179 @@
 
 ## Latest Changes
 
+## 0.70.1
+
+There's nothing interesting in this particular FastAPI release. It is mainly to enable/unblock the release of the next version of Pydantic that comes packed with features and improvements. 🤩
+
+### Fixes
+
+* 🐛 Fix JSON Schema for dataclasses, supporting the fixes in Pydantic 1.9. PR [#4272](https://github.com/tiangolo/fastapi/pull/4272) by [@PrettyWood](https://github.com/PrettyWood).
+
+### Translations
+
+* 🌐 Add Korean translation for `docs/tutorial/request-forms-and-files.md`. PR [#3744](https://github.com/tiangolo/fastapi/pull/3744) by [@NinaHwang](https://github.com/NinaHwang).
+* 🌐 Add Korean translation for `docs/tutorial/request-files.md`. PR [#3743](https://github.com/tiangolo/fastapi/pull/3743) by [@NinaHwang](https://github.com/NinaHwang).
+* 🌐 Add portuguese translation for `docs/tutorial/query-params-str-validations.md`. PR [#3965](https://github.com/tiangolo/fastapi/pull/3965) by [@leandrodesouzadev](https://github.com/leandrodesouzadev).
+* 🌐 Add Korean translation for `docs/tutorial/response-status-code.md`. PR [#3742](https://github.com/tiangolo/fastapi/pull/3742) by [@NinaHwang](https://github.com/NinaHwang).
+* 🌐 Add Korean translation for Tutorial - JSON Compatible Encoder. PR [#3152](https://github.com/tiangolo/fastapi/pull/3152) by [@NEONKID](https://github.com/NEONKID).
+* 🌐 Add Korean translation for Tutorial - Path Parameters and Numeric Validations. PR [#2432](https://github.com/tiangolo/fastapi/pull/2432) by [@hard-coders](https://github.com/hard-coders).
+* 🌐 Add Korean translation for `docs/ko/docs/deployment/versions.md`. PR [#4121](https://github.com/tiangolo/fastapi/pull/4121) by [@DevDae](https://github.com/DevDae).
+* 🌐 Fix Korean translation for `docs/ko/docs/tutorial/index.md`. PR [#4193](https://github.com/tiangolo/fastapi/pull/4193) by [@kimjaeyoonn](https://github.com/kimjaeyoonn).
+* 🔧 Add CryptAPI sponsor. PR [#4264](https://github.com/tiangolo/fastapi/pull/4264) by [@tiangolo](https://github.com/tiangolo).
+* 📝 Update `docs/tutorial/dependencies/classes-as-dependencies`: Add type of query parameters in a description of `Classes as dependencies`. PR [#4015](https://github.com/tiangolo/fastapi/pull/4015) by [@0417taehyun](https://github.com/0417taehyun).
+* 🌐 Add French translation for Tutorial - First steps. PR [#3455](https://github.com/tiangolo/fastapi/pull/3455) by [@Smlep](https://github.com/Smlep).
+* 🌐 Add French translation for `docs/tutorial/path-params.md`. PR [#3548](https://github.com/tiangolo/fastapi/pull/3548) by [@Smlep](https://github.com/Smlep).
+* 🌐 Add French translation for `docs/tutorial/query-params.md`. PR [#3556](https://github.com/tiangolo/fastapi/pull/3556) by [@Smlep](https://github.com/Smlep).
+* 🌐 Add Turkish translation for `docs/python-types.md`. PR [#3926](https://github.com/tiangolo/fastapi/pull/3926) by [@BilalAlpaslan](https://github.com/BilalAlpaslan).
+
+### Internal
+
+* 👥 Update FastAPI People. PR [#4274](https://github.com/tiangolo/fastapi/pull/4274) by [@github-actions[bot]](https://github.com/apps/github-actions).
+
+## 0.70.0
+
+This release just upgrades Starlette to the latest version, `0.16.0`, which includes several bug fixes and some small breaking changes.
+
+These last **three consecutive releases** are independent so that you can **migrate gradually**:
+
+* First to FastAPI `0.68.2`, with no breaking changes, but upgrading all the sub-dependencies.
+* Next to FastAPI `0.69.0`, which upgrades Starlette to `0.15.0`, with AnyIO support, and a higher chance of having breaking changes in your code.
+* Finally to FastAPI `0.70.0`, just upgrading Starlette to the latest version `0.16.0` with additional bug fixes.
+
+This way, in case there was a breaking change for your code in one of the releases, you can still benefit from the previous upgrades. ✨
+
+### Breaking Changes - Upgrade
+
+* ⬆️ Upgrade Starlette to 0.16.0. PR [#4016](https://github.com/tiangolo/fastapi/pull/4016) by [@tiangolo](https://github.com/tiangolo).
+
+Also upgrades the ranges of optional dependencies:
+
+* `"jinja2 >=2.11.2,<4.0.0"`
+* `"itsdangerous >=1.1.0,<3.0.0"`
+
+## 0.69.0
+
+### Breaking Changes - Upgrade
+
+This release adds support for [Trio](https://trio.readthedocs.io/en/stable/). ✨
+
+It upgrades the version of Starlette to `0.15.0`, now based on [AnyIO](https://anyio.readthedocs.io/en/stable/), and the internal async components in **FastAPI** are now based on AnyIO as well, making it compatible with both **asyncio** and **Trio**.
+
+You can read the docs about running [FastAPI with Trio using Hypercorn](https://fastapi.tiangolo.com/deployment/manually/#hypercorn-with-trio).
+
+This release also removes `graphene` as an optional dependency for GraphQL. If you need to work with GraphQL, the recommended library now is [Strawberry](https://strawberry.rocks/). You can read the new [FastAPI with GraphQL docs](https://fastapi.tiangolo.com/advanced/graphql/).
+
+### Features
+
+* ✨ Add support for Trio via AnyIO, upgrading Starlette to `0.15.0`. PR [#3372](https://github.com/tiangolo/fastapi/pull/3372) by [@graingert](https://github.com/graingert).
+* ➖ Remove `graphene` as an optional dependency. PR [#4007](https://github.com/tiangolo/fastapi/pull/4007) by [@tiangolo](https://github.com/tiangolo).
+
+### Docs
+
+* 📝 Add docs for using Trio with Hypercorn. PR [#4014](https://github.com/tiangolo/fastapi/pull/4014) by [@tiangolo](https://github.com/tiangolo).
+* ✏ Fix typos in Deployment Guide. PR [#3975](https://github.com/tiangolo/fastapi/pull/3975) by [@ghandic](https://github.com/ghandic).
+* 📝 Update docs with pip install calls when using extras with brackets, use quotes for compatibility with Zsh. PR [#3131](https://github.com/tiangolo/fastapi/pull/3131) by [@tomwei7](https://github.com/tomwei7).
+* 📝 Add external link to article: Deploying ML Models as API Using FastAPI and Heroku. PR [#3904](https://github.com/tiangolo/fastapi/pull/3904) by [@kaustubhgupta](https://github.com/kaustubhgupta).
+* ✏ Fix typo in file paths in `docs/en/docs/contributing.md`. PR [#3752](https://github.com/tiangolo/fastapi/pull/3752) by [@NinaHwang](https://github.com/NinaHwang).
+* ✏ Fix a typo in `docs/en/docs/advanced/path-operation-advanced-configuration.md` and `docs/en/docs/release-notes.md`. PR [#3750](https://github.com/tiangolo/fastapi/pull/3750) by [@saintmalik](https://github.com/saintmalik).
+* ✏️ Add a missing comma in the security tutorial. PR [#3564](https://github.com/tiangolo/fastapi/pull/3564) by [@jalvaradosegura](https://github.com/jalvaradosegura).
+* ✏ Fix typo in `docs/en/docs/help-fastapi.md`. PR [#3760](https://github.com/tiangolo/fastapi/pull/3760) by [@jaystone776](https://github.com/jaystone776).
+* ✏ Fix typo about file path in `docs/en/docs/tutorial/bigger-applications.md`. PR [#3285](https://github.com/tiangolo/fastapi/pull/3285) by [@HolyDorus](https://github.com/HolyDorus).
+* ✏ Re-word to clarify test client in `docs/en/docs/tutorial/testing.md`. PR [#3382](https://github.com/tiangolo/fastapi/pull/3382) by [@Bharat123rox](https://github.com/Bharat123rox).
+* 📝  Fix incorrect highlighted code. PR [#3325](https://github.com/tiangolo/fastapi/pull/3325) by [@paxcodes](https://github.com/paxcodes).
+* 📝 Add external link to article: How-to deploy FastAPI app to Heroku. PR [#3241](https://github.com/tiangolo/fastapi/pull/3241) by [@Jarmos-san](https://github.com/Jarmos-san).
+* ✏ Fix typo (mistranslation) in `docs/en/docs/advanced/templates.md`. PR [#3211](https://github.com/tiangolo/fastapi/pull/3211) by [@oerpli](https://github.com/oerpli).
+* 📝 Remove note about (now supported) feature from Swagger UI in `docs/en/docs/tutorial/request-files.md`. PR [#2803](https://github.com/tiangolo/fastapi/pull/2803) by [@gsganden](https://github.com/gsganden).
+* ✏ Fix typo re-word in `docs/tutorial/handling-errors.md`. PR [#2700](https://github.com/tiangolo/fastapi/pull/2700) by [@graue70](https://github.com/graue70).
+
+### Translations
+
+* 🌐 Initialize Azerbaijani translations. PR [#3941](https://github.com/tiangolo/fastapi/pull/3941) by [@madatbay](https://github.com/madatbay).
+* 🌐 Add Turkish translation for `docs/fastapi-people.md`. PR [#3848](https://github.com/tiangolo/fastapi/pull/3848) by [@BilalAlpaslan](https://github.com/BilalAlpaslan).
+
+### Internal
+
+* 📝 Add supported Python versions badge. PR [#2794](https://github.com/tiangolo/fastapi/pull/2794) by [@hramezani](https://github.com/hramezani).
+* ✏ Fix link in Japanese docs for `docs/ja/docs/deployment/docker.md`. PR [#3245](https://github.com/tiangolo/fastapi/pull/3245) by [@utamori](https://github.com/utamori).
+* 🔧 Correct DeprecationWarning config and comment in pytest settings. PR [#4008](https://github.com/tiangolo/fastapi/pull/4008) by [@graingert](https://github.com/graingert).
+* 🔧 Swap light/dark theme button icon. PR [#3246](https://github.com/tiangolo/fastapi/pull/3246) by [@eddsalkield](https://github.com/eddsalkield).
+* 🔧 Lint only in Python 3.7 and above. PR [#4006](https://github.com/tiangolo/fastapi/pull/4006) by [@tiangolo](https://github.com/tiangolo).
+* 🔧 Add GitHub Action notify-translations config for Azerbaijani. PR [#3995](https://github.com/tiangolo/fastapi/pull/3995) by [@tiangolo](https://github.com/tiangolo).
+
+## 0.68.2
+
+This release has **no breaking changes**. 🎉
+
+It upgrades the version ranges of sub-dependencies to allow applications using FastAPI to easily upgrade them.
+
+Soon there will be a new FastAPI release upgrading Starlette to take advantage of recent improvements, but as that has a higher chance of having breaking changes, it will be in a separate release.
+
+### Features
+
+* ⬆Increase supported version of aiofiles to suppress warnings. PR [#2899](https://github.com/tiangolo/fastapi/pull/2899) by [@SnkSynthesis](https://github.com/SnkSynthesis).
+* ➖ Do not require backports in Python >= 3.7. PR [#1880](https://github.com/tiangolo/fastapi/pull/1880) by [@FFY00](https://github.com/FFY00).
+* ⬆ Upgrade required Python version to >= 3.6.1, needed by typing.Deque, used by Pydantic. PR [#2733](https://github.com/tiangolo/fastapi/pull/2733) by [@hukkin](https://github.com/hukkin).
+* ⬆️ Bump Uvicorn max range to 0.15.0. PR [#3345](https://github.com/tiangolo/fastapi/pull/3345) by [@Kludex](https://github.com/Kludex).
+
+### Docs
+
+* 📝 Update GraphQL docs, recommend Strawberry. PR [#3981](https://github.com/tiangolo/fastapi/pull/3981) by [@tiangolo](https://github.com/tiangolo).
+* 📝 Re-write and extend Deployment guide: Concepts, Uvicorn, Gunicorn, Docker, Containers, Kubernetes. PR [#3974](https://github.com/tiangolo/fastapi/pull/3974) by [@tiangolo](https://github.com/tiangolo).
+* 📝 Upgrade HTTPS guide with more explanations and diagrams. PR [#3950](https://github.com/tiangolo/fastapi/pull/3950) by [@tiangolo](https://github.com/tiangolo).
+
+### Translations
+
+* 🌐 Add Turkish translation for `docs/features.md`. PR [#1950](https://github.com/tiangolo/fastapi/pull/1950) by [@ycd](https://github.com/ycd).
+* 🌐 Add Turkish translation for `docs/benchmarks.md`. PR [#2729](https://github.com/tiangolo/fastapi/pull/2729) by [@Telomeraz](https://github.com/Telomeraz).
+* 🌐 Add Turkish translation for `docs/index.md`. PR [#1908](https://github.com/tiangolo/fastapi/pull/1908) by [@ycd](https://github.com/ycd).
+* 🌐 Add French translation for `docs/tutorial/body.md`. PR [#3671](https://github.com/tiangolo/fastapi/pull/3671) by [@Smlep](https://github.com/Smlep).
+* 🌐 Add French translation for `deployment/docker.md`. PR [#3694](https://github.com/tiangolo/fastapi/pull/3694) by [@rjNemo](https://github.com/rjNemo).
+* 🌐 Add Portuguese translation for `docs/tutorial/path-params.md`. PR [#3664](https://github.com/tiangolo/fastapi/pull/3664) by [@FelipeSilva93](https://github.com/FelipeSilva93).
+* 🌐 Add Portuguese translation for `docs/deployment/https.md`. PR [#3754](https://github.com/tiangolo/fastapi/pull/3754) by [@lsglucas](https://github.com/lsglucas).
+* 🌐 Add German translation for `docs/features.md`. PR [#3699](https://github.com/tiangolo/fastapi/pull/3699) by [@mawassk](https://github.com/mawassk).
+
+### Internal
+
+* ✨ Update GitHub Action: notify-translations, to avoid a race conditions. PR [#3989](https://github.com/tiangolo/fastapi/pull/3989) by [@tiangolo](https://github.com/tiangolo).
+* ⬆️ Upgrade development `autoflake`, supporting multi-line imports. PR [#3988](https://github.com/tiangolo/fastapi/pull/3988) by [@tiangolo](https://github.com/tiangolo).
+* ⬆️ Increase dependency ranges for tests and docs: pytest-cov, pytest-asyncio, black, httpx, sqlalchemy, databases, mkdocs-markdownextradata-plugin. PR [#3987](https://github.com/tiangolo/fastapi/pull/3987) by [@tiangolo](https://github.com/tiangolo).
+* 👥 Update FastAPI People. PR [#3986](https://github.com/tiangolo/fastapi/pull/3986) by [@github-actions[bot]](https://github.com/apps/github-actions).
+* 💚 Fix badges in README and main page. PR [#3979](https://github.com/tiangolo/fastapi/pull/3979) by [@ghandic](https://github.com/ghandic).
+* ⬆ Upgrade internal testing dependencies: mypy to version 0.910, add newly needed type packages. PR [#3350](https://github.com/tiangolo/fastapi/pull/3350) by [@ArcLightSlavik](https://github.com/ArcLightSlavik).
+* ✨ Add Deepset Sponsorship. PR [#3976](https://github.com/tiangolo/fastapi/pull/3976) by [@tiangolo](https://github.com/tiangolo).
+* 🎨 Tweak CSS styles for shell animations. PR [#3888](https://github.com/tiangolo/fastapi/pull/3888) by [@tiangolo](https://github.com/tiangolo).
+* 🔧 Add new Sponsor Calmcode.io. PR [#3777](https://github.com/tiangolo/fastapi/pull/3777) by [@tiangolo](https://github.com/tiangolo).
+
+## 0.68.1
+
+* ✨ Add support for `read_with_orm_mode`, to support [SQLModel](https://sqlmodel.tiangolo.com/) relationship attributes. PR [#3757](https://github.com/tiangolo/fastapi/pull/3757) by [@tiangolo](https://github.com/tiangolo).
+
+### Translations
+
+* 🌐 Add Portuguese translation of `docs/fastapi-people.md`. PR [#3461](https://github.com/tiangolo/fastapi/pull/3461) by [@ComicShrimp](https://github.com/ComicShrimp).
+* 🌐 Add Chinese translation for `docs/tutorial/dependencies/dependencies-in-path-operation-decorators.md`. PR [#3492](https://github.com/tiangolo/fastapi/pull/3492) by [@jaystone776](https://github.com/jaystone776).
+* 🔧 Add new Translation tracking issues for German and Indonesian. PR [#3718](https://github.com/tiangolo/fastapi/pull/3718) by [@tiangolo](https://github.com/tiangolo).
+* 🌐 Add Chinese translation for `docs/tutorial/dependencies/sub-dependencies.md`. PR [#3491](https://github.com/tiangolo/fastapi/pull/3491) by [@jaystone776](https://github.com/jaystone776).
+* 🌐 Add Portuguese translation for `docs/advanced/index.md`. PR [#3460](https://github.com/tiangolo/fastapi/pull/3460) by [@ComicShrimp](https://github.com/ComicShrimp).
+* 🌐 Portuguese translation of `docs/async.md`. PR [#1330](https://github.com/tiangolo/fastapi/pull/1330) by [@Serrones](https://github.com/Serrones).
+* 🌐 Add French translation for `docs/async.md`. PR [#3416](https://github.com/tiangolo/fastapi/pull/3416) by [@Smlep](https://github.com/Smlep).
+
+### Internal
+
+* ✨ Add GitHub Action: Notify Translations. PR [#3715](https://github.com/tiangolo/fastapi/pull/3715) by [@tiangolo](https://github.com/tiangolo).
+* ✨ Update computation of FastAPI People and sponsors. PR [#3714](https://github.com/tiangolo/fastapi/pull/3714) by [@tiangolo](https://github.com/tiangolo).
+* ✨ Enable recent Material for MkDocs Insiders features. PR [#3710](https://github.com/tiangolo/fastapi/pull/3710) by [@tiangolo](https://github.com/tiangolo).
+* 🔥 Remove/clean extra imports from examples in docs for features. PR [#3709](https://github.com/tiangolo/fastapi/pull/3709) by [@tiangolo](https://github.com/tiangolo).
+* ➕ Update docs library to include sources in Markdown. PR [#3648](https://github.com/tiangolo/fastapi/pull/3648) by [@tiangolo](https://github.com/tiangolo).
+* ⬆ Enable tests for Python 3.9. PR [#2298](https://github.com/tiangolo/fastapi/pull/2298) by [@Kludex](https://github.com/Kludex).
+* 👥 Update FastAPI People. PR [#3642](https://github.com/tiangolo/fastapi/pull/3642) by [@github-actions[bot]](https://github.com/apps/github-actions).
 
 ## 0.68.0
 
 ### Features
 
 * ✨ Add support for extensions and updates to the OpenAPI schema in each *path operation*. New docs: [FastAPI Path Operation Advanced Configuration - OpenAPI Extra](https://fastapi.tiangolo.com/advanced/path-operation-advanced-configuration/#openapi-extra). Initial PR [#1922](https://github.com/tiangolo/fastapi/pull/1922) by [@edouardlp](https://github.com/edouardlp).
-* ✨ Add additonal OpenAPI metadata parameters to `FastAPI` class, shown on the automatic API docs UI. New docs: [Metadata and Docs URLs](https://fastapi.tiangolo.com/tutorial/metadata/). Initial PR [#1812](https://github.com/tiangolo/fastapi/pull/1812) by [@dkreeft](https://github.com/dkreeft).
+* ✨ Add additional OpenAPI metadata parameters to `FastAPI` class, shown on the automatic API docs UI. New docs: [Metadata and Docs URLs](https://fastapi.tiangolo.com/tutorial/metadata/). Initial PR [#1812](https://github.com/tiangolo/fastapi/pull/1812) by [@dkreeft](https://github.com/dkreeft).
 * ✨ Add `description` parameter to all the security scheme classes, e.g. `APIKeyQuery(name="key", description="A very cool API key")`. PR [#1757](https://github.com/tiangolo/fastapi/pull/1757) by [@hylkepostma](https://github.com/hylkepostma).
 * ✨ Update OpenAPI models, supporting recursive models and extensions. PR [#3628](https://github.com/tiangolo/fastapi/pull/3628) by [@tiangolo](https://github.com/tiangolo).
 * ✨ Import and re-export data structures from Starlette, used by Request properties, on `fastapi.datastructures`. Initial PR [#1872](https://github.com/tiangolo/fastapi/pull/1872) by [@jamescurtin](https://github.com/jamescurtin).

docs/en/docs/tutorial/bigger-applications.md

@@ -234,7 +234,7 @@ mean:
 
 * Starting in the same package that this module (the file `app/routers/items.py`) lives in (the directory `app/routers/`)...
 * go to the parent package (the directory `app/`)...
-* and in there, find the module `dependencies` (the file at `app/routers/dependencies.py`)...
+* and in there, find the module `dependencies` (the file at `app/dependencies.py`)...
 * and from it, import the function `get_token_header`.
 
 That works correctly! 🎉
@@ -252,7 +252,7 @@ that would mean:
 * Starting in the same package that this module (the file `app/routers/items.py`) lives in (the directory `app/routers/`)...
 * go to the parent package (the directory `app/`)...
 * then go to the parent of that package (there's no parent package, `app` is the top level 😱)...
-* and in there, find the module `dependencies` (the file at `app/routers/dependencies.py`)...
+* and in there, find the module `dependencies` (the file at `app/dependencies.py`)...
 * and from it, import the function `get_token_header`.
 
 That would refer to some package above `app/`, with its own file `__init__.py`, etc. But we don't have that. So, that would throw an error in our example. 🚨

docs/en/docs/tutorial/body-multiple-params.md

@@ -109,7 +109,7 @@ q: Optional[str] = None
 
 as in:
 
-```Python hl_lines="27"
+```Python hl_lines="28"
 {!../../../docs_src/body_multiple_params/tutorial004.py!}
 ```
 

docs/en/docs/tutorial/dependencies/classes-as-dependencies.md

@@ -91,9 +91,9 @@ Those parameters are what **FastAPI** will use to "solve" the dependency.
 
 In both cases, it will have:
 
-* an optional `q` query parameter.
-* a `skip` query parameter, with a default of `0`.
-* a `limit` query parameter, with a default of `100`.
+* An optional `q` query parameter that is a `str`.
+* A `skip` query parameter that is an `int`, with a default of `0`.
+* A `limit` query parameter that is an `int`, with a default of `100`.
 
 In both cases the data will be converted, validated, documented on the OpenAPI schema, etc.
 

docs/en/docs/tutorial/dependencies/dependencies-with-yield.md

@@ -7,15 +7,6 @@ To do this, use `yield` instead of `return`, and write the extra steps after.
 !!! tip
     Make sure to use `yield` one single time.
 
-!!! info
-    For this to work, you need to use **Python 3.7** or above, or in **Python 3.6**, install the "backports":
-
-    ```
-    pip install async-exit-stack async-generator
-    ```
-
-    This installs <a href="https://github.com/sorcio/async_exit_stack" class="external-link" target="_blank">async-exit-stack</a> and <a href="https://github.com/python-trio/async_generator" class="external-link" target="_blank">async-generator</a>.
-
 !!! note "Technical Details"
     Any function that is valid to use with:
 

docs/en/docs/tutorial/handling-errors.md

@@ -260,6 +260,4 @@ You can import and re-use the default exception handlers from `fastapi.exception
 {!../../../docs_src/handling_errors/tutorial006.py!}
 ```
 
-In this example, you are just `print`ing the error with a very expressive message.
-
-But you get the idea, you can use the exception and then just re-use the default exception handlers.
+In this example you are just `print`ing the error with a very expressive message, but you get the idea. You can use the exception and then just re-use the default exception handlers.

docs/en/docs/tutorial/index.md

@@ -43,7 +43,7 @@ For the tutorial, you might want to install it with all the optional dependencie
 <div class="termy">
 
 ```console
-$ pip install fastapi[all]
+$ pip install "fastapi[all]"
 
 ---> 100%
 ```
@@ -64,7 +64,7 @@ $ pip install fastapi[all]
     Also install `uvicorn` to work as the server:
 
     ```
-    pip install uvicorn[standard]
+    pip install "uvicorn[standard]"
     ```
 
     And the same for each of the optional dependencies that you want to use.

docs/en/docs/tutorial/request-files.md

@@ -127,13 +127,6 @@ To use that, declare a `List` of `bytes` or `UploadFile`:
 
 You will receive, as declared, a `list` of `bytes` or `UploadFile`s.
 
-!!! note
-    Notice that, as of 2019-04-14, Swagger UI doesn't support multiple file uploads in the same form field. For more information, check <a href="https://github.com/swagger-api/swagger-ui/issues/4276" class="external-link" target="_blank">#4276</a> and <a href="https://github.com/swagger-api/swagger-ui/issues/3641" class="external-link" target="_blank">#3641</a>.
-
-    Nevertheless, **FastAPI** is already compatible with it, using the standard OpenAPI.
-    
-    So, whenever Swagger UI supports multi-file uploads, or any other tools that supports OpenAPI, they will be compatible with **FastAPI**.
-
 !!! note "Technical Details"
     You could also use `from starlette.responses import HTMLResponse`.
 

docs/en/docs/tutorial/security/get-current-user.md

@@ -84,7 +84,7 @@ Just use any kind of model, any kind of class, any kind of database that you nee
 
 ## Code size
 
-This example might seem verbose. Have in mind that we are mixing security, data models utility functions and *path operations* in the same file.
+This example might seem verbose. Have in mind that we are mixing security, data models, utility functions and *path operations* in the same file.
 
 But here's the key point.
 

docs/en/docs/tutorial/security/oauth2-jwt.md

@@ -33,7 +33,7 @@ We need to install `python-jose` to generate and verify the JWT tokens in Python
 <div class="termy">
 
 ```console
-$ pip install python-jose[cryptography]
+$ pip install "python-jose[cryptography]"
 
 ---> 100%
 ```
@@ -76,7 +76,7 @@ So, install PassLib with Bcrypt:
 <div class="termy">
 
 ```console
-$ pip install passlib[bcrypt]
+$ pip install "passlib[bcrypt]"
 
 ---> 100%
 ```

docs/en/docs/tutorial/sql-databases.md

@@ -441,17 +441,6 @@ You can find an example of Alembic in a FastAPI project in the templates from [P
 
 ### Create a dependency
 
-!!! info
-    For this to work, you need to use **Python 3.7** or above, or in **Python 3.6**, install the "backports":
-
-    ```console
-    $ pip install async-exit-stack async-generator
-    ```
-
-    This installs <a href="https://github.com/sorcio/async_exit_stack" class="external-link" target="_blank">async-exit-stack</a> and <a href="https://github.com/python-trio/async_generator" class="external-link" target="_blank">async-generator</a>.
-
-    You can also use the alternative method with a "middleware" explained at the end.
-
 Now use the `SessionLocal` class we created in the `sql_app/databases.py` file to create a dependency.
 
 We need to have an independent database session/connection (`SessionLocal`) per request, use the same session through all the request and then close it after the request is finished.

docs/en/docs/tutorial/static-files.md

@@ -2,20 +2,6 @@
 
 You can serve static files automatically from a directory using `StaticFiles`.
 
-## Install `aiofiles`
-
-First you need to install `aiofiles`:
-
-<div class="termy">
-
-```console
-$ pip install aiofiles
-
----> 100%
-```
-
-</div>
-
 ## Use `StaticFiles`
 
 * Import `StaticFiles`.

docs/en/docs/tutorial/testing.md

@@ -10,7 +10,7 @@ With it, you can use <a href="https://docs.pytest.org/" class="external-link" ta
 
 Import `TestClient`.
 
-Create a `TestClient` passing to it your **FastAPI**.
+Create a `TestClient` passing to it your **FastAPI** application.
 
 Create functions with a name that starts with `test_` (this is standard `pytest` conventions).
 

docs/en/mkdocs.yml

@@ -9,17 +9,18 @@ theme:
     primary: teal
     accent: amber
     toggle:
-      icon: material/lightbulb-outline
+      icon: material/lightbulb
       name: Switch to light mode
   - scheme: slate
     primary: teal
     accent: amber
     toggle:
-      icon: material/lightbulb
+      icon: material/lightbulb-outline
       name: Switch to dark mode
   features:
   - search.suggest
   - search.highlight
+  - content.tabs.link
   icon:
     repo: fontawesome/brands/github-alt
   logo: img/icon-white.svg
@@ -39,6 +40,7 @@ nav:
 - FastAPI: index.md
 - Languages:
   - en: /
+  - az: /az/
   - de: /de/
   - es: /es/
   - fr: /fr/
@@ -146,9 +148,11 @@ nav:
   - deployment/index.md
   - deployment/versions.md
   - deployment/https.md
-  - deployment/deta.md
-  - deployment/docker.md
   - deployment/manually.md
+  - deployment/concepts.md
+  - deployment/deta.md
+  - deployment/server-workers.md
+  - deployment/docker.md
 - project-generation.md
 - alternatives.md
 - history-design-future.md
@@ -162,7 +166,7 @@ markdown_extensions:
     permalink: true
 - markdown.extensions.codehilite:
     guess_lang: false
-- markdown_include.include:
+- mdx_include:
     base_path: docs
 - admonition
 - codehilite
@@ -171,7 +175,7 @@ markdown_extensions:
     custom_fences:
     - name: mermaid
       class: mermaid
-      format: !!python/name:pymdownx.superfences.fence_div_format ''
+      format: !!python/name:pymdownx.superfences.fence_code_format ''
 - pymdownx.tabbed
 extra:
   social:
@@ -192,6 +196,8 @@ extra:
   alternate:
   - link: /
     name: en - English
+  - link: /az/
+    name: az
   - link: /de/
     name: de
   - link: /es/
@@ -224,6 +230,5 @@ extra_css:
 - css/termynal.css
 - css/custom.css
 extra_javascript:
-- https://unpkg.com/mermaid@8.4.6/dist/mermaid.min.js
 - js/termynal.js
 - js/custom.js

docs/en/overrides/main.html

@@ -22,12 +22,12 @@
     </div>
   </div>
   <div id="announce-right" style="position: relative;">
-    <div class="item">
+    <!-- <div class="item">
       <a title="The launchpad for all your (team's) ideas" style="display: block; position: relative;" href="https://www.deta.sh/?ref=fastapi" target="_blank">
         <span class="sponsor-badge">sponsor</span>
         <img class="sponsor-image" src="/img/sponsors/deta-banner.svg" />
       </a>
-    </div>
+    </div> -->
     <div class="item">
       <a title="Get three courses at 10% off their current prices! Plus, we'll be donating 10% of all profits from sales of this bundle to the FastAPI team." style="display: block; position: relative;" href="https://testdriven.io/talkpython/" target="_blank">
         <span class="sponsor-badge">sponsor</span>
@@ -40,6 +40,12 @@
         <img class="sponsor-image" src="/img/sponsors/jina-banner.svg" />
       </a>
     </div>
+    <div class="item">
+      <a title="CryptAPI: Your easy to use, secure and privacy oriented payment gateway." style="display: block; position: relative;" href="https://cryptapi.io/" target="_blank">
+        <span class="sponsor-badge">sponsor</span>
+        <img class="sponsor-image" src="/img/sponsors/cryptapi-banner.svg" />
+      </a>
+    </div>
   </div>
 </div>
 {% endblock %}

docs/es/docs/features.md

@@ -32,7 +32,6 @@ Si necesitas un repaso de 2 minutos de cómo usar los tipos de Python (así no u
 Escribes Python estándar con tipos así:
 
 ```Python
-from typing import List, Dict
 from datetime import date
 
 from pydantic import BaseModel

docs/es/docs/tutorial/index.md

@@ -4,9 +4,7 @@ Este tutorial te muestra cómo usar **FastAPI** con la mayoría de sus caracter
 
 Cada sección se basa gradualmente en las anteriores, pero está estructurada en temas separados, así puedes ir directamente a cualquier tema en concreto para resolver tus necesidades específicas sobre la API.
 
-También está diseñado para funcionar como una referencia futura.
-
-Para que puedas volver y ver exactamente lo que necesitas.
+Funciona también como una referencia futura, para que puedas volver y ver exactamente lo que necesitas.
 
 ## Ejecuta el código
 

docs/es/mkdocs.yml

@@ -9,17 +9,18 @@ theme:
     primary: teal
     accent: amber
     toggle:
-      icon: material/lightbulb-outline
+      icon: material/lightbulb
       name: Switch to light mode
   - scheme: slate
     primary: teal
     accent: amber
     toggle:
-      icon: material/lightbulb
+      icon: material/lightbulb-outline
       name: Switch to dark mode
   features:
   - search.suggest
   - search.highlight
+  - content.tabs.link
   icon:
     repo: fontawesome/brands/github-alt
   logo: https://fastapi.tiangolo.com/img/icon-white.svg
@@ -39,6 +40,7 @@ nav:
 - FastAPI: index.md
 - Languages:
   - en: /
+  - az: /az/
   - de: /de/
   - es: /es/
   - fr: /fr/
@@ -68,7 +70,7 @@ markdown_extensions:
     permalink: true
 - markdown.extensions.codehilite:
     guess_lang: false
-- markdown_include.include:
+- mdx_include:
     base_path: docs
 - admonition
 - codehilite
@@ -77,7 +79,7 @@ markdown_extensions:
     custom_fences:
     - name: mermaid
       class: mermaid
-      format: !!python/name:pymdownx.superfences.fence_div_format ''
+      format: !!python/name:pymdownx.superfences.fence_code_format ''
 - pymdownx.tabbed
 extra:
   social:
@@ -98,6 +100,8 @@ extra:
   alternate:
   - link: /
     name: en - English
+  - link: /az/
+    name: az
   - link: /de/
     name: de
   - link: /es/
@@ -130,6 +134,5 @@ extra_css:
 - https://fastapi.tiangolo.com/css/termynal.css
 - https://fastapi.tiangolo.com/css/custom.css
 extra_javascript:
-- https://unpkg.com/mermaid@8.4.6/dist/mermaid.min.js
 - https://fastapi.tiangolo.com/js/termynal.js
 - https://fastapi.tiangolo.com/js/custom.js

docs/fr/docs/async.md

@@ -0,0 +1,394 @@
+# Concurrence et les mots-clés async et await
+
+Cette page vise à fournir des détails sur la syntaxe `async def` pour les *fonctions de chemins* et quelques rappels sur le code asynchrone, la concurrence et le parallélisme.
+
+## Vous êtes pressés ?
+
+<abbr title="'too long; didn't read' en anglais, ou 'trop long ; j'ai pas lu'"><strong>TL;DR :</strong></abbr>
+
+Si vous utilisez des bibliothèques tierces qui nécessitent d'être appelées avec `await`, telles que :
+
+```Python
+results = await some_library()
+```
+Alors, déclarez vos *fonctions de chemins* avec `async def` comme ceci :
+
+```Python hl_lines="2"
+@app.get('/')
+async def read_results():
+    results = await some_library()
+    return results
+```
+
+!!! note
+    Vous pouvez uniquement utiliser `await` dans les fonctions créées avec `async def`.
+
+---
+
+Si vous utilisez une bibliothèque externe qui communique avec quelque chose (une BDD, une API, un système de fichiers, etc.) et qui ne supporte pas l'utilisation d'`await` (ce qui est actuellement le cas pour la majorité des bibliothèques de BDD), alors déclarez vos *fonctions de chemin* normalement, avec le classique `def`, comme ceci :
+
+```Python hl_lines="2"
+@app.get('/')
+def results():
+    results = some_library()
+    return results
+```
+
+---
+
+Si votre application n'a pas à communiquer avec une bibliothèque externe et pas à attendre de réponse, utilisez `async def`.
+
+---
+
+Si vous ne savez pas, utilisez seulement `def` comme vous le feriez habituellement.
+
+---
+
+**Note** : vous pouvez mélanger `def` et `async def` dans vos *fonctions de chemin* autant que nécessaire, **FastAPI** saura faire ce qu'il faut avec.
+
+Au final, peu importe le cas parmi ceux ci-dessus, **FastAPI** fonctionnera de manière asynchrone et sera extrêmement rapide.
+
+Mais si vous suivez bien les instructions ci-dessus, alors **FastAPI** pourra effectuer quelques optimisations et ainsi améliorer les performances.
+
+## Détails techniques
+
+Les versions modernes de Python supportent le **code asynchrone** grâce aux **"coroutines"** avec les syntaxes **`async` et `await`**.
+
+Analysons les différentes parties de cette phrase dans les sections suivantes :
+
+* **Code asynchrone**
+* **`async` et `await`**
+* **Coroutines**
+
+## Code asynchrone
+
+Faire du code asynchrone signifie que le langage 💬 est capable de dire à l'ordinateur / au programme 🤖 qu'à un moment du code, il 🤖 devra attendre que *quelque chose d'autre* se termine autre part. Disons que ce *quelque chose d'autre* est appelé "fichier-lent" 📝.
+
+Donc, pendant ce temps, l'ordinateur pourra effectuer d'autres tâches, pendant que "fichier-lent" 📝 se termine.
+
+Ensuite l'ordinateur / le programme 🤖 reviendra à chaque fois qu'il en a la chance que ce soit parce qu'il attend à nouveau, ou car il 🤖 a fini tout le travail qu'il avait à faire. Il 🤖 regardera donc si les tâches qu'il attend ont terminé d'être effectuées.
+
+Ensuite, il 🤖 prendra la première tâche à finir (disons, notre "fichier-lent" 📝) et continuera à faire avec cette dernière ce qu'il était censé.
+
+Ce "attendre quelque chose d'autre" fait généralement référence à des opérations <abbr title="Input/Output ou Entrées et Sorties ">I/O</abbr> qui sont relativement "lentes" (comparées à la vitesse du processeur et de la mémoire RAM) telles qu'attendre que :
+
+* de la donnée soit envoyée par le client à travers le réseau
+* de la donnée envoyée depuis votre programme soit reçue par le client à travers le réseau
+* le contenu d'un fichier sur le disque soit lu par le système et passé à votre programme
+* le contenu que votre programme a passé au système soit écrit sur le disque
+* une opération effectuée à distance par une API se termine
+* une opération en BDD se termine
+* une requête à une BDD renvoie un résultat
+* etc.
+
+Le temps d'exécution étant consommé majoritairement par l'attente d'opérations <abbr title="Input/Output ou Entrées et Sorties ">I/O</abbr> on appelle ceci des opérations <a href="https://fr.wikipedia.org/wiki/I/O_bound" class="external-link" target="_blank">"I/O bound"</a>.
+
+Ce concept se nomme l'"asynchronisme" car l'ordinateur / le programme n'a pas besoin d'être "synchronisé" avec la tâche, attendant le moment exact où cette dernière se terminera en ne faisant rien, pour être capable de récupérer le résultat de la tâche et l'utiliser dans la suite des opérations.
+
+À la place, en étant "asynchrone", une fois terminée, une tâche peut légèrement attendre (quelques microsecondes) que l'ordinateur / le programme finisse ce qu'il était en train de faire, et revienne récupérer le résultat.
+
+Pour parler de tâches "synchrones" (en opposition à "asynchrones"), on utilise souvent le terme "séquentiel", car l'ordinateur / le programme va effectuer toutes les étapes d'une tâche séquentiellement avant de passer à une autre tâche, même si ces étapes impliquent de l'attente.
+
+### Concurrence et Burgers
+
+L'idée de code **asynchrone** décrite ci-dessus est parfois aussi appelée **"concurrence"**. Ce qui est différent du **"parallélisme"**.
+
+La **concurrence** et le **parallélisme** sont tous deux liés à l'idée de "différentes choses arrivant plus ou moins au même moment".
+
+Mais les détails entre la **concurrence** et le **parallélisme** diffèrent sur de nombreux points.
+
+Pour expliquer la différence, voici une histoire de burgers :
+
+#### Burgers concurrents
+
+Vous amenez votre crush 😍 dans votre fast food 🍔 favori, et faites la queue pendant que le serveur 💁 prend les commandes des personnes devant vous.
+
+Puis vient votre tour, vous commandez alors 2 magnifiques burgers 🍔 pour votre crush 😍 et vous.
+
+Vous payez 💸.
+
+Le serveur 💁 dit quelque chose à son collègue dans la cuisine 👨‍🍳 pour qu'il sache qu'il doit préparer vos burgers 🍔 (bien qu'il soit déjà en train de préparer ceux des clients précédents).
+
+Le serveur 💁 vous donne le numéro assigné à votre commande.
+
+Pendant que vous attendez, vous allez choisir une table avec votre crush 😍, vous discutez avec votre crush 😍 pendant un long moment (les burgers étant "magnifiques" ils sont très longs à préparer ✨🍔✨).
+
+Pendant que vous êtes assis à table, en attendant que les burgers 🍔 soient prêts, vous pouvez passer ce temps à admirer à quel point votre crush 😍 est géniale, mignonne et intelligente ✨😍✨.
+
+Pendant que vous discutez avec votre crush 😍, de temps en temps vous jetez un coup d'oeil au nombre affiché au-dessus du comptoir pour savoir si c'est à votre tour d'être servis.
+
+Jusqu'au moment où c'est (enfin) votre tour. Vous allez au comptoir, récupérez vos burgers 🍔 et revenez à votre table.
+
+Vous et votre crush 😍 mangez les burgers 🍔 et passez un bon moment ✨.
+
+---
+
+Imaginez que vous êtes l'ordinateur / le programme 🤖 dans cette histoire.
+
+Pendant que vous faites la queue, vous être simplement inactif 😴, attendant votre tour, ne faisant rien de "productif". Mais la queue est rapide car le serveur 💁 prend seulement les commandes (et ne les prépare pas), donc tout va bien.
+
+Ensuite, quand c'est votre tour, vous faites des actions "productives" 🤓, vous étudiez le menu, décidez ce que vous voulez, demandez à votre crush 😍 son choix, payez 💸, vérifiez que vous utilisez la bonne carte de crédit, vérifiez que le montant débité sur la carte est correct, vérifiez que la commande contient les bons produits, etc.
+
+Mais ensuite, même si vous n'avez pas encore vos burgers 🍔, votre travail avec le serveur 💁 est "en pause" ⏸, car vous devez attendre 🕙 que vos burgers soient prêts.
+
+Après vous être écarté du comptoir et vous être assis à votre table avec le numéro de votre commande, vous pouvez tourner 🔀 votre attention vers votre crush 😍, et "travailler" ⏯ 🤓 là-dessus. Vous êtes donc à nouveau en train de faire quelque chose de "productif" 🤓, vous flirtez avec votre crush 😍.
+
+Puis le serveur 💁 dit "J'ai fini de préparer les burgers" 🍔 en mettant votre numéro sur l'affichage du comptoir, mais vous ne courrez pas immédiatement au moment où votre numéro s'affiche. Vous savez que personne ne volera vos burgers 🍔 car vous avez votre numéro et les autres clients ont le leur.
+
+Vous attendez donc que votre crush 😍 finisse son histoire, souriez gentiment et dites que vous allez chercher les burgers ⏸.
+
+Pour finir vous allez au comptoir 🔀, vers la tâche initiale qui est désormais terminée ⏯, récupérez les burgers 🍔, remerciez le serveur et ramenez les burgers 🍔 à votre table. Ceci termine l'étape / la tâche d'interaction avec le comptoir ⏹. Ce qui ensuite, crée une nouvelle tâche de "manger les burgers"  🔀 ⏯, mais la précédente, "récupérer les burgers" est terminée ⏹.
+
+#### Burgers parallèles
+
+Imaginons désormais que ce ne sont pas des "burgers concurrents" mais des "burgers parallèles".
+
+Vous allez avec votre crush 😍 dans un fast food 🍔 parallélisé.
+
+Vous attendez pendant que plusieurs (disons 8) serveurs qui sont aussi des cuisiniers 👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳 prennent les commandes des personnes devant vous.
+
+Chaque personne devant vous attend 🕙 que son burger 🍔 soit prêt avant de quitter le comptoir car chacun des 8 serveurs va lui-même préparer le burger directement avant de prendre la commande suivante.
+
+Puis c'est enfin votre tour, vous commandez 2 magnifiques burgers 🍔 pour vous et votre crush 😍.
+
+Vous payez 💸.
+
+Le serveur va dans la cuisine 👨‍🍳.
+
+Vous attendez devant le comptoir afin que personne ne prenne vos burgers 🍔 avant vous, vu qu'il n'y a pas de numéro de commande.
+
+Vous et votre crush 😍 étant occupés à vérifier que personne ne passe devant vous prendre vos burgers au moment où ils arriveront 🕙, vous ne pouvez pas vous préoccuper de votre crush 😞.
+
+C'est du travail "synchrone", vous être "synchronisés" avec le serveur/cuisinier 👨‍🍳. Vous devez attendre 🕙 et être présent au moment exact où le serveur/cuisinier 👨‍🍳 finira les burgers 🍔 et vous les donnera, sinon quelqu'un risque de vous les prendre.
+
+Puis le serveur/cuisinier 👨‍🍳 revient enfin avec vos burgers 🍔, après un long moment d'attente 🕙 devant le comptoir.
+
+Vous prenez vos burgers 🍔 et allez à une table avec votre crush 😍
+
+Vous les mangez, et vous avez terminé 🍔 ⏹.
+
+Durant tout ce processus, il n'y a presque pas eu de discussions ou de flirts car la plupart de votre temps à été passé à attendre 🕙 devant le comptoir 😞.
+
+---
+
+Dans ce scénario de burgers parallèles, vous êtes un ordinateur / programme 🤖 avec deux processeurs (vous et votre crush 😍) attendant 🕙 à deux et dédiant votre attention 🕙 à "attendre devant le comptoir" pour une longue durée.
+
+Le fast-food a 8 processeurs (serveurs/cuisiniers) 👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳. Alors que le fast-food de burgers concurrents en avait 2 (un serveur et un cuisinier).
+
+Et pourtant l'expérience finale n'est pas meilleure 😞.
+
+---
+
+C'est donc l'histoire équivalente parallèle pour les burgers 🍔.
+
+Pour un exemple plus courant dans la "vie réelle", imaginez une banque.
+
+Jusqu'à récemment, la plupart des banques avaient plusieurs caisses (et banquiers) 👨‍💼👨‍💼👨‍💼👨‍💼 et une unique file d'attente 🕙🕙🕙🕙🕙🕙🕙🕙.
+
+Tous les banquiers faisaient l'intégralité du travail avec chaque client avant de passer au suivant 👨‍💼⏯.
+
+Et vous deviez attendre 🕙 dans la file pendant un long moment ou vous perdiez votre place.
+
+Vous n'auriez donc probablement pas envie d'amener votre crush 😍 avec vous à la banque 🏦.
+
+#### Conclusion
+
+Dans ce scénario des "burgers du fast-food avec votre crush", comme il y a beaucoup d'attente 🕙, il est très logique d'avoir un système concurrent ⏸🔀⏯.
+
+Et c'est le cas pour la plupart des applications web.
+
+Vous aurez de nombreux, nombreux utilisateurs, mais votre serveur attendra 🕙 que leur connexion peu performante envoie des requêtes.
+
+Puis vous attendrez 🕙 de nouveau que leurs réponses reviennent.
+
+Cette "attente" 🕙 se mesure en microsecondes, mais tout de même, en cumulé cela fait beaucoup d'attente.
+
+C'est pourquoi il est logique d'utiliser du code asynchrone ⏸🔀⏯ pour des APIs web.
+
+La plupart des frameworks Python existants (y compris Flask et Django) ont été créés avant que les nouvelles fonctionnalités asynchrones de Python n'existent. Donc, les façons dont ils peuvent être déployés supportent l'exécution parallèle  et une ancienne forme d'exécution asynchrone qui n'est pas aussi puissante que les nouvelles fonctionnalités de Python.
+
+Et cela, bien que les spécifications principales du web asynchrone en Python (ou ASGI) ont été développées chez Django, pour ajouter le support des WebSockets.
+
+Ce type d'asynchronicité est ce qui a rendu NodeJS populaire (bien que NodeJS ne soit pas parallèle) et c'est la force du Go en tant que langage de programmation.
+
+Et c'est le même niveau de performance que celui obtenu avec **FastAPI**.
+
+Et comme on peut avoir du parallélisme et de l'asynchronicité en même temps, on obtient des performances plus hautes que la plupart des frameworks NodeJS et égales à celles du Go, qui est un langage compilé plus proche du C <a href="https://www.techempower.com/benchmarks/#section=data-r17&hw=ph&test=query&l=zijmkf-1" class="external-link" target="_blank">(tout ça grâce à Starlette)</a>.
+
+### Est-ce que la concurrence est mieux que le parallélisme ?
+
+Nope ! C'est ça la morale de l'histoire.
+
+La concurrence est différente du parallélisme. C'est mieux sur des scénarios **spécifiques** qui impliquent beaucoup d'attente. À cause de ça, c'est généralement bien meilleur que le parallélisme pour le développement d'applications web. Mais pas pour tout.
+ 
+Donc pour équilibrer tout ça, imaginez l'histoire suivante :
+
+> Vous devez nettoyer une grande et sale maison.
+
+*Oui, c'est toute l'histoire*.
+
+---
+
+Il n'y a plus d'attente 🕙 nulle part, juste beaucoup de travail à effectuer, dans différentes pièces de la maison.
+
+Vous pourriez diviser en différentes sections comme avec les burgers, d'abord le salon, puis la cuisine, etc. Mais vous n'attendez 🕙 rien, vous ne faites que nettoyer et nettoyer, la séparation en sections ne changerait rien au final.
+
+Cela prendrait autant de temps pour finir avec ou sans sections (concurrence) et vous auriez effectué la même quantité de travail.
+
+Mais dans ce cas, si pouviez amener 8 ex-serveurs/cuisiniers/devenus-nettoyeurs 👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳, et que chacun d'eux (plus vous) pouvait prendre une zone de la maison pour la nettoyer, vous pourriez faire tout le travail en parallèle, et finir plus tôt.
+
+Dans ce scénario, chacun des nettoyeurs (vous y compris) serait un processeur, faisant sa partie du travail.
+
+Et comme la plupart du temps d'exécution est pris par du "vrai" travail (et non de l'attente), et que le travail dans un ordinateur est fait par un <abbr title="Central Processing Unit">CPU</abbr>, ce sont des problèmes dits "CPU bound".
+
+---
+
+Des exemples communs d'opérations "CPU bounds" sont les procédés qui requièrent des traitements mathématiques complexes.
+
+Par exemple :
+
+* Traitements d'**audio** et d'**images**.
+* La **vision par ordinateur** : une image est composée de millions de pixels, chaque pixel ayant 3 valeurs / couleurs, les traiter tous va nécessiter d'effectuer des traitements sur chaque pixel, et de préférence tous en même temps.
+* L'apprentissage automatique (ou **Machine Learning**) : cela nécessite de nombreuses multiplications de matrices et vecteurs. Imaginez une énorme feuille de calcul remplie de nombres que vous multiplierez entre eux tous au même moment.
+* L'apprentissage profond (ou **Deep Learning**) : est un sous-domaine du **Machine Learning**, donc les mêmes raisons s'appliquent. Avec la différence qu'il n'y a pas une unique feuille de calcul de nombres à multiplier, mais une énorme quantité d'entre elles, et dans de nombreux cas, on utilise un processeur spécial pour construire et / ou utiliser ces modèles.
+
+### Concurrence + Parallélisme : Web + Machine Learning
+
+Avec **FastAPI** vous pouvez bénéficier de la concurrence qui est très courante en developement web (c'est l'attrait principal de NodeJS).
+
+Mais vous pouvez aussi profiter du parallélisme et multiprocessing afin de gérer des charges **CPU bound** qui sont récurrentes dans les systèmes de *Machine Learning*.
+
+Ça, ajouté au fait que Python soit le langage le plus populaire pour la **Data Science**, le **Machine Learning** et surtout le **Deep Learning**, font de **FastAPI** un très bon choix pour les APIs et applications de **Data Science** / **Machine Learning**.
+
+Pour comprendre comment mettre en place ce parallélisme en production, allez lire la section [Déploiement](deployment/index.md){.internal-link target=_blank}.
+
+## `async` et `await`
+
+Les versions modernes de Python ont une manière très intuitive de définir le code asynchrone, tout en gardant une apparence de code "séquentiel" classique en laissant Python faire l'attente pour vous au bon moment.
+
+Pour une opération qui nécessite de l'attente avant de donner un résultat et qui supporte ces nouvelles fonctionnalités Python, vous pouvez l'utiliser comme tel :
+
+```Python
+burgers = await get_burgers(2)
+```
+
+Le mot-clé important ici est `await`. Il informe Python qu'il faut attendre ⏸ que `get_burgers(2)` finisse d'effectuer ses opérations 🕙 avant de stocker les résultats dans la variable `burgers`. Grâce à cela, Python saura qu'il peut aller effectuer d'autres opérations 🔀 ⏯ pendant ce temps (comme par exemple recevoir une autre requête).
+
+Pour que `await` fonctionne, il doit être placé dans une fonction qui supporte l'asynchronicité. Pour que ça soit le cas, il faut déclarer cette dernière avec `async def` :
+
+```Python hl_lines="1"
+async def get_burgers(number: int):
+    # Opérations asynchrones pour créer les burgers
+    return burgers
+```
+
+...et non `def` :
+
+```Python hl_lines="2"
+# Ceci n'est pas asynchrone
+def get_sequential_burgers(number: int):
+    # Opérations asynchrones pour créer les burgers
+    return burgers
+```
+
+Avec `async def`, Python sait que dans cette fonction il doit prendre en compte les expressions `await`, et qu'il peut mettre en pause ⏸ l'exécution de la fonction pour aller faire autre chose 🔀 avant de revenir.
+
+Pour appeler une fonction définie avec `async def`, vous devez utiliser `await`. Donc ceci ne marche pas : 
+
+```Python
+# Ceci ne fonctionne pas, car get_burgers a été défini avec async def
+burgers = get_burgers(2)
+```
+
+---
+
+Donc, si vous utilisez une bibliothèque qui nécessite que ses fonctions soient appelées avec `await`, vous devez définir la *fonction de chemin* en utilisant `async def` comme dans :
+
+```Python hl_lines="2-3"
+@app.get('/burgers')
+async def read_burgers():
+    burgers = await get_burgers(2)
+    return burgers
+```
+
+### Plus de détails techniques
+
+Vous avez donc compris que `await` peut seulement être utilisé dans des fonctions définies avec `async def`.
+
+Mais en même temps, les fonctions définies avec `async def` doivent être appelées avec `await` et donc dans des fonctions définies elles aussi avec `async def`.
+
+Vous avez donc remarqué ce paradoxe d'oeuf et de la poule, comment appelle-t-on la première fonction `async` ?
+
+Si vous utilisez **FastAPI**, pas besoin de vous en inquiéter, car cette "première" fonction sera votre *fonction de chemin* ; et **FastAPI** saura comment arriver au résultat attendu.
+
+Mais si vous utilisez `async` / `await` sans **FastAPI**, <a href="https://docs.python.org/3/library/asyncio-task.html#coroutine" class="external-link" target="_blank">allez jetez un coup d'oeil à la documentation officielle de Python</a>.
+
+### Autres formes de code asynchrone
+
+L'utilisation d'`async` et `await` est relativement nouvelle dans ce langage.
+
+Mais cela rend la programmation asynchrone bien plus simple.
+
+Cette même syntaxe (ou presque) était aussi incluse dans les versions modernes de Javascript (dans les versions navigateur et NodeJS).
+
+Mais avant ça, gérer du code asynchrone était bien plus complexe et difficile.
+
+Dans les versions précédentes de Python, vous auriez utilisé des *threads* ou <a href="https://www.gevent.org/" class="external-link" target="_blank">Gevent</a>.  Mais le code aurait été bien plus difficile à comprendre, débugger, et concevoir.
+
+Dans les versions précédentes de Javascript NodeJS / Navigateur, vous auriez utilisé des "callbacks". Menant potentiellement à ce que l'on appelle <a href="http://callbackhell.com/" class="external-link" target="_blank">le "callback hell"</a>.
+
+
+## Coroutines
+
+**Coroutine** est juste un terme élaboré pour désigner ce qui est retourné par une fonction définie avec `async def`. Python sait que c'est comme une fonction classique qui va démarrer à un moment et terminer à un autre, mais qu'elle peut aussi être mise en pause ⏸, du moment qu'il y a un `await` dans son contenu.
+
+Mais toutes ces fonctionnalités d'utilisation de code asynchrone avec `async` et `await` sont souvent résumées comme l'utilisation des *coroutines*. On peut comparer cela à la principale fonctionnalité clé de Go, les "Goroutines".
+
+## Conclusion
+
+Reprenons la phrase du début de la page :
+
+> Les versions modernes de Python supportent le **code asynchrone** grâce aux **"coroutines"** avec les syntaxes **`async` et `await`**.
+
+Ceci devrait être plus compréhensible désormais. ✨
+
+Tout ceci est donc ce qui donne sa force à **FastAPI** (à travers Starlette) et lui permet d'avoir des performances aussi impressionnantes.
+
+## Détails très techniques
+
+!!! warning "Attention !"
+    Vous pouvez probablement ignorer cela.
+
+    Ce sont des détails très poussés sur comment **FastAPI** fonctionne en arrière-plan.
+
+    Si vous avez de bonnes connaissances techniques (coroutines, threads, code bloquant, etc.) et êtes curieux de comment **FastAPI** gère `async def` versus le `def` classique, cette partie est faite pour vous.
+
+### Fonctions de chemin
+
+Quand vous déclarez une *fonction de chemin* avec un `def` normal et non `async def`, elle est exécutée dans un groupe de threads (threadpool) externe qui est ensuite attendu, plutôt que d'être appelée directement (car cela bloquerait le serveur).
+
+Si vous venez d'un autre framework asynchrone qui ne fonctionne pas comme de la façon décrite ci-dessus et que vous êtes habitués à définir des *fonctions de chemin* basiques avec un simple `def` pour un faible gain de performance (environ 100 nanosecondes), veuillez noter que dans **FastAPI**, l'effet serait plutôt contraire. Dans ces cas-là, il vaut mieux utiliser `async def` à moins que votre *fonction de chemin* utilise du code qui effectue des opérations <abbr title="Input/Output ou Entrées et Sorties ">I/O</abbr> bloquantes.
+
+Au final, dans les deux situations, il est fort probable que **FastAPI** soit tout de même [plus rapide](/#performance){.internal-link target=_blank} que (ou au moins de vitesse égale à) votre framework précédent.
+
+### Dépendances
+
+La même chose s'applique aux dépendances. Si une dépendance est définie avec `def` plutôt que `async def`, elle est exécutée dans la threadpool externe.
+
+### Sous-dépendances 
+
+Vous pouvez avoir de multiples dépendances et sous-dépendances dépendant les unes des autres (en tant que paramètres de la définition de la *fonction de chemin*), certaines créées avec `async def` et d'autres avec `def`. Cela fonctionnerait aussi, et celles définies avec un simple `def` seraient exécutées sur un thread externe (venant de la threadpool) plutôt que d'être "attendues".
+
+### Autres fonctions utilitaires
+
+Toute autre fonction utilitaire que vous appelez directement peut être créée avec un classique `def` ou avec `async def` et **FastAPI** n'aura pas d'impact sur la façon dont vous l'appelez.
+
+Contrairement aux fonctions que **FastAPI** appelle pour vous : les *fonctions de chemin* et dépendances.
+
+Si votre fonction utilitaire est une fonction classique définie avec `def`, elle sera appelée directement (telle qu'écrite dans votre code), pas dans une threadpool, si la fonction est définie avec `async def` alors vous devrez attendre (avec `await`) que cette fonction se termine avant de passer à la suite du code.
+
+---
+
+Encore une fois, ce sont des détails très techniques qui peuvent être utiles si vous venez ici les chercher.
+
+Sinon, les instructions de la section <a href="#vous-etes-presses">Vous êtes pressés ?</a> ci-dessus sont largement suffisantes.

docs/fr/docs/deployment/docker.md

@@ -0,0 +1,182 @@
+# Déployer avec Docker
+
+Dans cette section, vous verrez des instructions et des liens vers des guides pour savoir comment :
+
+* Faire de votre application **FastAPI** une image/conteneur Docker avec une performance maximale. En environ **5 min**.
+* (Optionnellement) comprendre ce que vous, en tant que développeur, devez savoir sur HTTPS.
+* Configurer un cluster en mode Docker Swarm avec HTTPS automatique, même sur un simple serveur à 5 dollars US/mois. En environ **20 min**.
+* Générer et déployer une application **FastAPI** complète, en utilisant votre cluster Docker Swarm, avec HTTPS, etc. En environ **10 min**.
+
+Vous pouvez utiliser <a href="https://www.docker.com/" class="external-link" target="_blank">**Docker**</a> pour le déploiement. Il présente plusieurs avantages comme la sécurité, la réplicabilité, la simplicité de développement, etc.
+
+Si vous utilisez Docker, vous pouvez utiliser l'image Docker officielle :
+
+## <a href="https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker" class="external-link" target="_blank">tiangolo/uvicorn-gunicorn-fastapi</a>
+
+Cette image est dotée d'un mécanisme d'"auto-tuning", de sorte qu'il vous suffit d'ajouter votre code pour obtenir automatiquement des performances très élevées. Et sans faire de sacrifices.
+
+Mais vous pouvez toujours changer et mettre à jour toutes les configurations avec des variables d'environnement ou des fichiers de configuration.
+
+!!! tip "Astuce"
+    Pour voir toutes les configurations et options, rendez-vous sur la page de l'image Docker : <a href="https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker" class="external-link" target="_blank">tiangolo/uvicorn-gunicorn-fastapi</a>.
+
+## Créer un `Dockerfile`
+
+* Allez dans le répertoire de votre projet.
+* Créez un `Dockerfile` avec :
+
+```Dockerfile
+FROM tiangolo/uvicorn-gunicorn-fastapi:python3.7
+
+COPY ./app /app
+```
+
+### Applications plus larges
+
+Si vous avez suivi la section sur la création d' [Applications avec plusieurs fichiers](../tutorial/bigger-applications.md){.internal-link target=_blank}, votre `Dockerfile` pourrait ressembler à ceci :
+
+```Dockerfile
+FROM tiangolo/uvicorn-gunicorn-fastapi:python3.7
+
+COPY ./app /app/app
+```
+
+### Raspberry Pi et autres architectures
+
+Si vous utilisez Docker sur un Raspberry Pi (qui a un processeur ARM) ou toute autre architecture, vous pouvez créer un `Dockerfile` à partir de zéro, basé sur une image de base Python (qui est multi-architecture) et utiliser Uvicorn seul.
+
+Dans ce cas, votre `Dockerfile` pourrait ressembler à ceci :
+
+```Dockerfile
+FROM python:3.7
+
+RUN pip install fastapi uvicorn
+
+EXPOSE 80
+
+COPY ./app /app
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "80"]
+```
+
+## Créer le code **FastAPI**.
+
+* Créer un répertoire `app` et y entrer.
+* Créez un fichier `main.py` avec :
+
+```Python
+from typing import Optional
+
+from fastapi import FastAPI
+
+app = FastAPI()
+
+
+@app.get("/")
+def read_root():
+    return {"Hello": "World"}
+
+
+@app.get("/items/{item_id}")
+def read_item(item_id: int, q: Optional[str] = None):
+    return {"item_id": item_id, "q": q}
+```
+
+* Vous devriez maintenant avoir une structure de répertoire telle que :
+
+```
+.
+├── app
+│   └── main.py
+└── Dockerfile
+```
+
+## Construire l'image Docker
+
+* Allez dans le répertoire du projet (dans lequel se trouve votre `Dockerfile`, contenant votre répertoire `app`).
+* Construisez votre image FastAPI :
+
+<div class="termy">
+
+```console
+$ docker build -t myimage .
+
+---> 100%
+```
+
+</div>
+
+## Démarrer le conteneur Docker
+
+* Exécutez un conteneur basé sur votre image :
+
+<div class="termy">
+
+```console
+$ docker run -d --name mycontainer -p 80:80 myimage
+```
+
+</div>
+
+Vous disposez maintenant d'un serveur FastAPI optimisé dans un conteneur Docker. Configuré automatiquement pour votre 
+serveur actuel (et le nombre de cœurs du CPU).
+
+## Vérifier
+
+Vous devriez pouvoir accéder à votre application via l'URL de votre conteneur Docker, par exemple : <a href="http://192.168.99.100/items/5?q=somequery" class="external-link" target="_blank">http://192.168.99.100/items/5?q=somequery</a> ou <a href="http://127.0.0.1/items/5?q=somequery" class="external-link" target="_blank">http://127.0.0.1/items/5?q=somequery</a> (ou équivalent, en utilisant votre hôte Docker).
+
+Vous verrez quelque chose comme :
+
+```JSON
+{"item_id": 5, "q": "somequery"}
+```
+
+## Documentation interactive de l'API
+
+Vous pouvez maintenant visiter <a href="http://192.168.99.100/docs" class="external-link" target="_blank">http://192.168.99.100/docs</a> ou <a href="http://127.0.0.1/docs" class="external-link" target="_blank">http://127.0.0.1/docs</a> (ou équivalent, en utilisant votre hôte Docker).
+
+Vous verrez la documentation interactive automatique de l'API (fournie par <a href="https://github.com/swagger-api/swagger-ui" class="external-link" target="_blank">Swagger UI</a>) :
+
+![Swagger UI](https://fastapi.tiangolo.com/img/index/index-01-swagger-ui-simple.png)
+
+## Documentation de l'API alternative 
+
+Et vous pouvez également aller sur <a href="http://192.168.99.100/redoc" class="external-link" target="_blank">http://192.168.99.100/redoc</a> ou <a href="http://127.0.0.1/redoc" class="external-link" target="_blank">http://127.0.0.1/redoc</a> (ou équivalent, en utilisant votre hôte Docker).
+
+Vous verrez la documentation automatique alternative (fournie par <a href="https://github.com/Rebilly/ReDoc" class="external-link" target="_blank">ReDoc</a>) :
+
+![ReDoc](https://fastapi.tiangolo.com/img/index/index-02-redoc-simple.png)
+
+## Traefik
+
+<a href="https://traefik.io/" class="external-link" target="_blank">Traefik</a> est un reverse proxy/load balancer 
+haute performance. Il peut faire office de "Proxy de terminaison TLS" (entre autres fonctionnalités).
+
+Il est intégré à Let's Encrypt. Ainsi, il peut gérer toutes les parties HTTPS, y compris l'acquisition et le renouvellement des certificats.
+
+Il est également intégré à Docker. Ainsi, vous pouvez déclarer vos domaines dans les configurations de chaque application et faire en sorte qu'elles lisent ces configurations, génèrent les certificats HTTPS et servent via HTTPS à votre application automatiquement, sans nécessiter aucune modification de leurs configurations.
+
+---
+
+Avec ces informations et ces outils, passez à la section suivante pour tout combiner.
+
+## Cluster en mode Docker Swarm avec Traefik et HTTPS
+
+Vous pouvez avoir un cluster en mode Docker Swarm configuré en quelques minutes (environ 20 min) avec un processus Traefik principal gérant HTTPS (y compris l'acquisition et le renouvellement des certificats).
+
+En utilisant le mode Docker Swarm, vous pouvez commencer par un "cluster" d'une seule machine (il peut même s'agir 
+d'un serveur à 5 USD/mois) et ensuite vous pouvez vous développer autant que vous le souhaitez en ajoutant d'autres serveurs.
+
+Pour configurer un cluster en mode Docker Swarm avec Traefik et la gestion de HTTPS, suivez ce guide :
+
+### <a href="https://medium.com/@tiangolo/docker-swarm-mode-and-traefik-for-a-https-cluster-20328dba6232" class="external-link" target="_blank">Docker Swarm Mode et Traefik pour un cluster HTTPS</a>
+
+### Déployer une application FastAPI
+
+La façon la plus simple de tout mettre en place, serait d'utiliser les [**Générateurs de projet FastAPI**](../project-generation.md){.internal-link target=_blank}.
+
+Le génerateur de projet adéquat est conçu pour être intégré à ce cluster Docker Swarm avec Traefik et HTTPS décrit ci-dessus.
+
+Vous pouvez générer un projet en 2 min environ.
+
+Le projet généré a des instructions pour le déployer et le faire prend 2 min de plus.

docs/fr/docs/fastapi-people.md

@@ -114,25 +114,28 @@ Ce sont les **Sponsors**. 😎
 
 Ils soutiennent mon travail avec **FastAPI** (et d'autres) avec <a href="https://github.com/sponsors/tiangolo" class="external-link" target="_blank">GitHub Sponsors</a>.
 
+{% if sponsors.gold %}
+
 ### Gold Sponsors
 
-{% if sponsors %}
 {% for sponsor in sponsors.gold -%}
 <a href="{{ sponsor.url }}" target="_blank" title="{{ sponsor.title }}"><img src="{{ sponsor.img }}" style="border-radius:15px"></a>
 {% endfor %}
 {% endif %}
 
+{% if sponsors.silver %}
+
 ### Silver Sponsors
 
-{% if sponsors %}
 {% for sponsor in sponsors.silver -%}
 <a href="{{ sponsor.url }}" target="_blank" title="{{ sponsor.title }}"><img src="{{ sponsor.img }}" style="border-radius:15px"></a>
 {% endfor %}
 {% endif %}
 
+{% if sponsors.bronze %}
+
 ### Bronze Sponsors
 
-{% if sponsors %}
 {% for sponsor in sponsors.bronze -%}
 <a href="{{ sponsor.url }}" target="_blank" title="{{ sponsor.title }}"><img src="{{ sponsor.img }}" style="border-radius:15px"></a>
 {% endfor %}
@@ -140,28 +143,22 @@ Ils soutiennent mon travail avec **FastAPI** (et d'autres) avec <a href="https:/
 
 ### Individual Sponsors
 
-{% if people %}
-{% if people.sponsors_50 %}
+{% if github_sponsors %}
+{% for group in github_sponsors.sponsors %}
 
 <div class="user-list user-list-center">
-{% for user in people.sponsors_50 %}
+
+{% for user in group %}
+{% if user.login not in sponsors_badge.logins %}
 
 <div class="user"><a href="{{ user.url }}" target="_blank"><div class="avatar-wrapper"><img src="{{ user.avatarUrl }}"/></div><div class="title">@{{ user.login }}</div></a></div>
+
+{% endif %}
 {% endfor %}
 
 </div>
 
-{% endif %}
-{% endif %}
-
-{% if people %}
-<div class="user-list user-list-center">
-{% for user in people.sponsors %}
-
-<div class="user"><a href="{{ user.url }}" target="_blank"><div class="avatar-wrapper"><img src="{{ user.avatarUrl }}"/></div><div class="title">@{{ user.login }}</div></a></div>
 {% endfor %}
-
-</div>
 {% endif %}
 
 ## À propos des données - détails techniques

docs/fr/docs/features.md

@@ -32,7 +32,6 @@ Si vous souhaitez un rappel de 2 minutes sur l'utilisation des types en Python (
 Vous écrivez du python standard avec des annotations de types:
 
 ```Python
-from typing import List, Dict
 from datetime import date
 
 from pydantic import BaseModel

docs/fr/docs/tutorial/body.md

@@ -0,0 +1,165 @@
+# Corps de la requête
+
+Quand vous avez besoin d'envoyer de la donnée depuis un client (comme un navigateur) vers votre API, vous l'envoyez en tant que **corps de requête**.
+
+Le corps d'une **requête** est de la donnée envoyée par le client à votre API. Le corps d'une **réponse** est la donnée envoyée par votre API au client.
+
+Votre API aura presque toujours à envoyer un corps de **réponse**. Mais un client n'a pas toujours à envoyer un corps de **requête**.
+
+Pour déclarer un corps de **requête**, on utilise les modèles de <a href="https://pydantic-docs.helpmanual.io/" class="external-link" target="_blank">Pydantic</a> en profitant de tous leurs avantages et fonctionnalités.
+
+!!! info
+    Pour envoyer de la donnée, vous devriez utiliser : `POST` (le plus populaire), `PUT`, `DELETE` ou `PATCH`.
+
+    Envoyer un corps dans une requête `GET` a un comportement non défini dans les spécifications, cela est néanmoins supporté par **FastAPI**, seulement pour des cas d'utilisation très complexes/extrêmes.
+
+    Ceci étant découragé, la documentation interactive générée par Swagger UI ne montrera pas de documentation pour le corps d'une requête `GET`, et les proxys intermédiaires risquent de ne pas le supporter.
+
+## Importez le `BaseModel` de Pydantic
+
+Commencez par importer la classe `BaseModel` du module `pydantic` :
+
+```Python hl_lines="4"
+{!../../../docs_src/body/tutorial001.py!}
+```
+
+## Créez votre modèle de données
+
+Déclarez ensuite votre modèle de données en tant que classe qui hérite de `BaseModel`.
+
+Utilisez les types Python standard pour tous les attributs :
+
+```Python hl_lines="7-11"
+{!../../../docs_src/body/tutorial001.py!}
+```
+
+Tout comme pour la déclaration de paramètres de requête, quand un attribut de modèle a une valeur par défaut, il n'est pas nécessaire. Sinon, cet attribut doit être renseigné dans le corps de la requête. Pour rendre ce champ optionnel simplement, utilisez `None` comme valeur par défaut.
+
+Par exemple, le modèle ci-dessus déclare un "objet" JSON (ou `dict` Python) tel que :
+
+```JSON
+{
+    "name": "Foo",
+    "description": "An optional description",
+    "price": 45.2,
+    "tax": 3.5
+}
+```
+
+...`description` et `tax` étant des attributs optionnels (avec `None` comme valeur par défaut), cet "objet" JSON serait aussi valide :
+
+```JSON
+{
+    "name": "Foo",
+    "price": 45.2
+}
+```
+
+## Déclarez-le comme paramètre
+
+Pour l'ajouter à votre *opération de chemin*, déclarez-le comme vous déclareriez des paramètres de chemin ou de requête :
+
+```Python hl_lines="18"
+{!../../../docs_src/body/tutorial001.py!}
+```
+
+...et déclarez que son type est le modèle que vous avez créé : `Item`.
+
+## Résultats
+
+En utilisant uniquement les déclarations de type Python, **FastAPI** réussit à :
+
+* Lire le contenu de la requête en tant que JSON.
+* Convertir les types correspondants (si nécessaire).
+* Valider la donnée.
+    * Si la donnée est invalide, une erreur propre et claire sera renvoyée, indiquant exactement où était la donnée incorrecte.
+* Passer la donnée reçue dans le paramètre `item`.
+    * Ce paramètre ayant été déclaré dans la fonction comme étant de type `Item`, vous aurez aussi tout le support offert par l'éditeur (auto-complétion, etc.) pour tous les attributs de ce paramètre et les types de ces attributs.
+* Générer des définitions <a href="https://json-schema.org" class="external-link" target="_blank">JSON Schema</a> pour votre modèle, qui peuvent être utilisées où vous en avez besoin dans votre projet ensuite.
+* Ces schémas participeront à la constitution du schéma généré OpenAPI, et seront donc utilisés par les documentations automatiquement générées.
+
+## Documentation automatique
+
+Les schémas JSON de vos modèles seront intégrés au schéma OpenAPI global de votre application, et seront donc affichés dans la documentation interactive de l'API :
+
+<img src="/img/tutorial/body/image01.png">
+
+Et seront aussi utilisés dans chaque *opération de chemin* de la documentation utilisant ces modèles :
+
+<img src="/img/tutorial/body/image02.png">
+
+## Support de l'éditeur
+
+Dans votre éditeur, vous aurez des annotations de types et de l'auto-complétion partout dans votre fonction (ce qui n'aurait pas été le cas si vous aviez utilisé un classique `dict` plutôt qu'un modèle Pydantic) :
+
+<img src="/img/tutorial/body/image03.png">
+
+Et vous obtenez aussi de la vérification d'erreur pour les opérations incorrectes de types :
+
+<img src="/img/tutorial/body/image04.png">
+
+Ce n'est pas un hasard, ce framework entier a été bati avec ce design comme objectif.
+
+Et cela a été rigoureusement testé durant la phase de design, avant toute implémentation, pour s'assurer que cela fonctionnerait avec tous les éditeurs.
+
+Des changements sur Pydantic ont même été faits pour supporter cela.
+
+Les captures d'écrans précédentes ont été prises sur <a href="https://code.visualstudio.com" class="external-link" target="_blank">Visual Studio Code</a>.
+
+Mais vous auriez le même support de l'éditeur avec <a href="https://www.jetbrains.com/pycharm/" class="external-link" target="_blank">PyCharm</a> et la majorité des autres éditeurs de code Python.
+
+<img src="/img/tutorial/body/image05.png">
+
+!!! tip "Astuce"
+    Si vous utilisez <a href="https://www.jetbrains.com/pycharm/" class="external-link" target="_blank">PyCharm</a> comme éditeur, vous pouvez utiliser le Plugin <a href="https://www.jetbrains.com/pycharm/" class="external-link" target="_blank">PyCharm</a>.
+
+    Ce qui améliore le support pour les modèles Pydantic avec :
+
+    * de l'auto-complétion
+    * des vérifications de type
+    * du "refactoring" (ou remaniement de code)
+    * de la recherche
+    * de l'inspection
+
+## Utilisez le modèle
+
+Dans la fonction, vous pouvez accéder à tous les attributs de l'objet du modèle directement :
+
+```Python hl_lines="21"
+{!../../../docs_src/body/tutorial002.py!}
+```
+
+## Corps de la requête + paramètres de chemin
+
+Vous pouvez déclarer des paramètres de chemin et un corps de requête pour la même *opération de chemin*.
+
+**FastAPI** est capable de reconnaître que les paramètres de la fonction qui correspondent aux paramètres de chemin doivent être **récupérés depuis le chemin**, et que les paramètres de fonctions déclarés comme modèles Pydantic devraient être **récupérés depuis le corps de la requête**.
+
+```Python hl_lines="17-18"
+{!../../../docs_src/body/tutorial003.py!}
+```
+
+## Corps de la requête + paramètres de chemin et de requête
+
+Vous pouvez aussi déclarer un **corps**, et des paramètres de **chemin** et de **requête** dans la même *opération de chemin*.
+
+**FastAPI** saura reconnaître chacun d'entre eux et récupérer la bonne donnée au bon endroit.
+
+```Python hl_lines="18"
+{!../../../docs_src/body/tutorial004.py!}
+```
+
+Les paramètres de la fonction seront reconnus comme tel :
+
+* Si le paramètre est aussi déclaré dans le **chemin**, il sera utilisé comme paramètre de chemin.
+* Si le paramètre est d'un **type singulier** (comme `int`, `float`, `str`, `bool`, etc.), il sera interprété comme un paramètre de **requête**.
+* Si le paramètre est déclaré comme ayant pour type un **modèle Pydantic**, il sera interprété comme faisant partie du **corps** de la requête.
+
+!!! note
+    **FastAPI** saura que la valeur de `q` n'est pas requise grâce à la valeur par défaut `=None`.
+
+    Le type `Optional` dans `Optional[str]` n'est pas utilisé par **FastAPI**, mais sera utile à votre éditeur pour améliorer le support offert par ce dernier et détecter plus facilement des erreurs de type.
+
+## Sans Pydantic
+
+Si vous ne voulez pas utiliser des modèles Pydantic, vous pouvez aussi utiliser des paramètres de **Corps**. Pour cela, allez voir la partie de la documentation sur  [Corps de la requête - Paramètres multiples](body-multiple-params.md){.internal-link target=_blank}.

docs/fr/docs/tutorial/first-steps.md

@@ -0,0 +1,334 @@
+# Démarrage
+
+Le fichier **FastAPI** le plus simple possible pourrait ressembler à cela  :
+
+```Python
+{!../../../docs_src/first_steps/tutorial001.py!}
+```
+
+Copiez ce code dans un fichier nommé `main.py`.
+
+Démarrez le serveur :
+
+<div class="termy">
+
+```console
+$ uvicorn main:app --reload
+
+<span style="color: green;">INFO</span>:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
+<span style="color: green;">INFO</span>:     Started reloader process [28720]
+<span style="color: green;">INFO</span>:     Started server process [28722]
+<span style="color: green;">INFO</span>:     Waiting for application startup.
+<span style="color: green;">INFO</span>:     Application startup complete.
+```
+
+</div>
+
+!!! note
+    La commande `uvicorn main:app` fait référence à :
+
+    * `main` : le fichier `main.py` (le module Python).
+    * `app` : l'objet créé dans `main.py` via la ligne `app = FastAPI()`.
+    * `--reload` : l'option disant à uvicorn de redémarrer le serveur à chaque changement du code. À ne pas utiliser en production !
+
+Vous devriez voir dans la console, une ligne semblable à la suivante :
+
+```hl_lines="4"
+INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
+```
+
+Cette ligne montre l'URL par laquelle l'app est actuellement accessible, sur votre machine locale.
+
+### Allez voir le résultat
+
+Ouvrez votre navigateur à l'adresse <a href="http://127.0.0.1:8000" class="external-link" target="_blank">http://127.0.0.1:8000</a>.
+
+Vous obtiendrez cette réponse JSON :
+
+```JSON
+{"message": "Hello World"}
+```
+
+### Documentation interactive de l'API
+
+Rendez-vous sur <a href="http://127.0.0.1:8000/docs" class="external-link" target="_blank">http://127.0.0.1:8000/docs</a>.
+
+Vous verrez la documentation interactive de l'API générée automatiquement (via <a href="https://github.com/swagger-api/swagger-ui" class="external-link" target="_blank">Swagger UI</a>) :
+
+![Swagger UI](https://fastapi.tiangolo.com/img/index/index-01-swagger-ui-simple.png)
+
+### Documentation alternative
+
+Ensuite, rendez-vous sur <a href="http://127.0.0.1:8000/redoc" class="external-link" target="_blank">http://127.0.0.1:8000/redoc</a>.
+
+Vous y verrez la documentation alternative (via <a href="https://github.com/Rebilly/ReDoc" class="external-link" target="_blank">ReDoc</a>) :
+
+![ReDoc](https://fastapi.tiangolo.com/img/index/index-02-redoc-simple.png)
+
+### OpenAPI
+
+**FastAPI** génère un "schéma" contenant toute votre API dans le standard de définition d'API **OpenAPI**.
+
+#### "Schéma"
+
+Un "schéma" est une définition ou une description de quelque chose. Pas le code qui l'implémente, uniquement une description abstraite.
+
+#### "Schéma" d'API
+
+Ici, <a href="https://github.com/OAI/OpenAPI-Specification" class="external-link" target="_blank">OpenAPI</a> est une spécification qui dicte comment définir le schéma de votre API.
+
+Le schéma inclut les chemins de votre API, les paramètres potentiels de chaque chemin, etc.
+
+#### "Schéma" de données
+
+Le terme "schéma" peut aussi faire référence à la forme de la donnée, comme un contenu JSON.
+
+Dans ce cas, cela signifierait les attributs JSON, ainsi que les types de ces attributs, etc.
+
+#### OpenAPI et JSON Schema
+
+**OpenAPI** définit un schéma d'API pour votre API. Il inclut des définitions (ou "schémas") de la donnée envoyée et reçue par votre API en utilisant **JSON Schema**, le standard des schémas de données JSON.
+
+#### Allez voir `openapi.json`
+
+Si vous êtes curieux d'à quoi ressemble le schéma brut **OpenAPI**, **FastAPI** génère automatiquement un (schéma) JSON avec les descriptions de toute votre API.
+
+Vous pouvez le voir directement à cette adresse : <a href="http://127.0.0.1:8000/openapi.json" class="external-link" target="_blank">http://127.0.0.1:8000/openapi.json</a>.
+
+Le schéma devrait ressembler à ceci :
+
+
+```JSON
+{
+    "openapi": "3.0.2",
+    "info": {
+        "title": "FastAPI",
+        "version": "0.1.0"
+    },
+    "paths": {
+        "/items/": {
+            "get": {
+                "responses": {
+                    "200": {
+                        "description": "Successful Response",
+                        "content": {
+                            "application/json": {
+
+
+
+...
+```
+
+#### À quoi sert OpenAPI
+
+Le schéma **OpenAPI** est ce qui alimente les deux systèmes de documentation interactive.
+
+Et il existe des dizaines d'alternatives, toutes basées sur **OpenAPI**. Vous pourriez facilement ajouter n'importe laquelle de ces alternatives à votre application **FastAPI**.
+
+Vous pourriez aussi l'utiliser pour générer du code automatiquement, pour les clients qui communiquent avec votre API. Comme par exemple, des applications frontend, mobiles ou IOT.
+
+## Récapitulatif, étape par étape
+
+### Étape 1 : import `FastAPI`
+
+```Python hl_lines="1"
+{!../../../docs_src/first_steps/tutorial001.py!}
+```
+
+`FastAPI` est une classe Python qui fournit toutes les fonctionnalités nécessaires au lancement de votre API.
+
+!!! note "Détails techniques"
+    `FastAPI` est une classe héritant directement de `Starlette`.
+
+    Vous pouvez donc aussi utiliser toutes les fonctionnalités de <a href="https://www.starlette.io/" class="external-link" target="_blank">Starlette</a> depuis `FastAPI`.
+
+### Étape 2 : créer une "instance" `FastAPI`
+
+```Python hl_lines="3"
+{!../../../docs_src/first_steps/tutorial001.py!}
+```
+
+Ici la variable `app` sera une "instance" de la classe `FastAPI`.
+
+Ce sera le point principal d'interaction pour créer toute votre API.
+
+Cette `app` est la même que celle à laquelle fait référence `uvicorn` dans la commande :
+
+<div class="termy">
+
+```console
+$ uvicorn main:app --reload
+
+<span style="color: green;">INFO</span>:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
+```
+
+</div>
+
+Si vous créez votre app avec :
+
+```Python hl_lines="3"
+{!../../../docs_src/first_steps/tutorial002.py!}
+```
+
+Et la mettez dans un fichier `main.py`, alors vous appeleriez `uvicorn` avec :
+
+<div class="termy">
+
+```console
+$ uvicorn main:my_awesome_api --reload
+
+<span style="color: green;">INFO</span>:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
+```
+
+</div>
+
+### Étape 3: créer une *opération de chemin*
+
+#### Chemin
+
+Chemin, ou "path" fait référence ici à la dernière partie de l'URL démarrant au premier `/`.
+
+Donc, dans un URL tel que :
+
+```
+https://example.com/items/foo
+```
+
+...le "path" serait :
+
+```
+/items/foo
+```
+
+!!! info
+    Un chemin, ou "path" est aussi souvent appelé route ou "endpoint".
+
+
+#### Opération
+
+"Opération" fait référence à une des "méthodes" HTTP.
+
+Une de :
+
+* `POST`
+* `GET`
+* `PUT`
+* `DELETE`
+
+...ou une des plus exotiques :
+
+* `OPTIONS`
+* `HEAD`
+* `PATCH`
+* `TRACE`
+
+Dans le protocol HTTP, vous pouvez communiquer avec chaque chemin en utilisant une (ou plus) de ces "méthodes".
+
+---
+
+En construisant des APIs, vous utilisez généralement ces méthodes HTTP spécifiques pour effectuer une action précise.
+
+Généralement vous utilisez :
+
+* `POST` : pour créer de la donnée.
+* `GET` : pour lire de la donnée.
+* `PUT` : pour mettre à jour de la donnée.
+* `DELETE` : pour supprimer de la donnée.
+
+Donc, dans **OpenAPI**, chaque méthode HTTP est appelée une "opération".
+
+Nous allons donc aussi appeler ces dernières des "**opérations**".
+
+
+#### Définir un *décorateur d'opération de chemin*
+
+```Python hl_lines="6"
+{!../../../docs_src/first_steps/tutorial001.py!}
+```
+
+Le `@app.get("/")` dit à **FastAPI** que la fonction en dessous est chargée de gérer les requêtes qui vont sur :
+
+* le chemin `/`
+* en utilisant une <abbr title="une méthode GET HTTP">opération <code>get</code></abbr>
+
+!!! info "`@décorateur` Info"
+    Cette syntaxe `@something` en Python est appelée un "décorateur".
+
+    Vous la mettez au dessus d'une fonction. Comme un joli chapeau décoratif (j'imagine que ce terme vient de là 🤷🏻‍♂).
+
+    Un "décorateur" prend la fonction en dessous et en fait quelque chose.
+
+    Dans notre cas, ce décorateur dit à **FastAPI** que la fonction en dessous correspond au **chemin** `/` avec l'**opération** `get`.
+
+    C'est le "**décorateur d'opération de chemin**".
+
+Vous pouvez aussi utiliser les autres opérations :
+
+* `@app.post()`
+* `@app.put()`
+* `@app.delete()`
+
+Tout comme celles les plus exotiques :
+
+* `@app.options()`
+* `@app.head()`
+* `@app.patch()`
+* `@app.trace()`
+
+!!! tip "Astuce"
+    Vous êtes libres d'utiliser chaque opération (méthode HTTP) comme vous le désirez.
+
+    **FastAPI** n'impose pas de sens spécifique à chacune d'elle.
+
+    Les informations qui sont présentées ici forment une directive générale, pas des obligations.  
+
+    Par exemple, quand l'on utilise **GraphQL**, toutes les actions sont effectuées en utilisant uniquement des opérations `POST`.
+
+### Étape 4 : définir la **fonction de chemin**.
+
+Voici notre "**fonction de chemin**" (ou fonction d'opération de chemin) :
+
+* **chemin** : `/`.
+* **opération** : `get`.
+* **fonction** : la fonction sous le "décorateur" (sous `@app.get("/")`).
+
+```Python hl_lines="7"
+{!../../../docs_src/first_steps/tutorial001.py!}
+```
+
+C'est une fonction Python.
+
+Elle sera appelée par **FastAPI** quand une requête sur l'URL `/` sera reçue via une opération `GET`.
+
+Ici, c'est une fonction asynchrone (définie avec `async def`).
+
+---
+
+Vous pourriez aussi la définir comme une fonction classique plutôt qu'avec `async def` :
+
+```Python hl_lines="7"
+{!../../../docs_src/first_steps/tutorial003.py!}
+```
+
+!!! note
+    Si vous ne connaissez pas la différence, allez voir la section [Concurrence : *"Vous êtes pressés ?"*](../async.md#vous-etes-presses){.internal-link target=_blank}.
+
+### Étape 5 : retourner le contenu
+
+```Python hl_lines="8"
+{!../../../docs_src/first_steps/tutorial001.py!}
+```
+
+Vous pouvez retourner un dictionnaire (`dict`), une liste (`list`), des valeurs seules comme des chaines de caractères (`str`) et des entiers (`int`), etc.
+
+Vous pouvez aussi retourner des models **Pydantic** (qui seront détaillés plus tard).
+
+Il y a de nombreux autres objets et modèles qui seront automatiquement convertis en JSON. Essayez d'utiliser vos favoris, il est fort probable qu'ils soient déjà supportés.
+
+## Récapitulatif
+
+* Importez `FastAPI`.
+* Créez une instance d'`app`.
+* Ajoutez une **décorateur d'opération de chemin** (tel que `@app.get("/")`).
+* Ajoutez une **fonction de chemin** (telle que `def root(): ...` comme ci-dessus).
+* Lancez le serveur de développement (avec `uvicorn main:app --reload`).

docs/fr/docs/tutorial/path-params.md

@@ -0,0 +1,254 @@
+# Paramètres de chemin
+
+Vous pouvez déclarer des "paramètres" ou "variables" de chemin avec la même syntaxe que celle utilisée par le
+<a href="https://docs.python.org/fr/3/library/string.html#format-string-syntax" class="external-link" target="_blank">formatage de chaîne Python</a> :
+
+
+```Python hl_lines="6-7"
+{!../../../docs_src/path_params/tutorial001.py!}
+```
+
+La valeur du paramètre `item_id` sera transmise à la fonction dans l'argument `item_id`. 
+
+Donc, si vous exécutez cet exemple et allez sur <a href="http://127.0.0.1:8000/items/foo" class="external-link" target="_blank">http://127.0.0.1:8000/items/foo</a>,
+vous verrez comme réponse :
+
+```JSON
+{"item_id":"foo"}
+```
+
+## Paramètres de chemin typés
+
+Vous pouvez déclarer le type d'un paramètre de chemin dans la fonction, en utilisant les annotations de type Python :
+
+
+```Python hl_lines="7"
+{!../../../docs_src/path_params/tutorial002.py!}
+```
+
+Ici, `item_id` est déclaré comme `int`.
+
+!!! hint "Astuce"
+    Ceci vous permettra d'obtenir des fonctionnalités de l'éditeur dans votre fonction, telles
+    que des vérifications d'erreur, de l'auto-complétion, etc.
+
+## <abbr title="aussi appelé sérialisation, ou parfois parsing ou marshalling en anglais">Conversion</abbr> de données
+
+Si vous exécutez cet exemple et allez sur <a href="http://127.0.0.1:8000/items/3" class="external-link" target="_blank">http://127.0.0.1:8000/items/3</a>, vous aurez comme réponse :
+
+```JSON
+{"item_id":3}
+```
+
+!!! hint "Astuce"
+    Comme vous l'avez remarqué, la valeur reçue par la fonction (et renvoyée ensuite) est `3`,
+    en tant qu'entier (`int`) Python, pas la chaîne de caractères (`string`) `"3"`.
+
+    Grâce aux déclarations de types, **FastAPI** fournit du 
+    <abbr title="conversion de la chaîne de caractères venant de la requête HTTP en données Python">"parsing"</abbr> automatique.
+
+## Validation de données
+
+Si vous allez sur <a href="http://127.0.0.1:8000/items/foo" class="external-link" target="_blank">http://127.0.0.1:8000/items/foo</a>, vous aurez une belle erreur HTTP :
+
+```JSON
+{
+    "detail": [
+        {
+            "loc": [
+                "path",
+                "item_id"
+            ],
+            "msg": "value is not a valid integer",
+            "type": "type_error.integer"
+        }
+    ]
+}
+```
+
+car le paramètre de chemin `item_id` possède comme valeur `"foo"`, qui ne peut pas être convertie en entier (`int`).
+
+La même erreur se produira si vous passez un nombre flottant (`float`) et non un entier, comme ici
+<a href="http://127.0.0.1:8000/items/4.2" class="external-link" target="_blank">http://127.0.0.1:8000/items/4.2</a>.
+
+
+!!! hint "Astuce"
+    Donc, avec ces mêmes déclarations de type Python, **FastAPI** vous fournit de la validation de données.
+
+    Notez que l'erreur mentionne le point exact où la validation n'a pas réussi.
+
+    Ce qui est incroyablement utile au moment de développer et débugger du code qui interagit avec votre API.
+
+## Documentation
+
+Et quand vous vous rendez sur <a href="http://127.0.0.1:8000/docs" class="external-link" target="_blank">http://127.0.0.1:8000/docs</a>, vous verrez la
+documentation générée automatiquement et interactive :
+
+<img src="/img/tutorial/path-params/image01.png">
+
+!!! info
+    À nouveau, en utilisant uniquement les déclarations de type Python, **FastAPI** vous fournit automatiquement une documentation interactive (via Swagger UI).
+
+    On voit bien dans la documentation que `item_id` est déclaré comme entier.
+
+## Les avantages d'avoir une documentation basée sur une norme, et la documentation alternative. 
+
+Le schéma généré suivant la norme <a href="https://github.com/OAI/OpenAPI-Specification/blob/master/versions/3.0.2.md" class="external-link" target="_blank">OpenAPI</a>,
+il existe de nombreux outils compatibles.
+
+Grâce à cela, **FastAPI** lui-même fournit une documentation alternative (utilisant ReDoc), qui peut être lue
+sur <a href="http://127.0.0.1:8000/redoc" class="external-link" target="_blank">http://127.0.0.1:8000/redoc</a> :
+
+<img src="/img/tutorial/path-params/image02.png">
+
+De la même façon, il existe bien d'autres outils compatibles, y compris des outils de génération de code
+pour de nombreux langages. 
+
+## Pydantic
+
+Toute la validation de données est effectué en arrière-plan avec <a href="https://pydantic-docs.helpmanual.io/" class="external-link" target="_blank">Pydantic</a>,
+dont vous bénéficierez de tous les avantages. Vous savez donc que vous êtes entre de bonnes mains.
+
+## L'ordre importe
+
+Quand vous créez des *fonctions de chemins*, vous pouvez vous retrouver dans une situation où vous avez un chemin fixe.
+
+Tel que `/users/me`, disons pour récupérer les données sur l'utilisateur actuel.
+
+Et vous avez un second chemin : `/users/{user_id}` pour récupérer de la donnée sur un utilisateur spécifique grâce à son identifiant d'utilisateur
+
+Les *fonctions de chemin* étant évaluées dans l'ordre, il faut s'assurer que la fonction correspondant à `/users/me` est déclarée avant celle de `/users/{user_id}` :
+
+```Python hl_lines="6  11"
+{!../../../docs_src/path_params/tutorial003.py!}
+```
+
+Sinon, le chemin `/users/{user_id}` correspondrait aussi à `/users/me`, la fonction "croyant" qu'elle a reçu un paramètre `user_id` avec pour valeur `"me"`.
+
+## Valeurs prédéfinies
+
+Si vous avez une *fonction de chemin* qui reçoit un *paramètre de chemin*, mais que vous voulez que les valeurs possibles des paramètres soient prédéfinies, vous pouvez utiliser les <abbr title="Enumeration">`Enum`</abbr> de Python.
+
+### Création d'un `Enum`
+
+Importez `Enum` et créez une sous-classe qui hérite de `str` et `Enum`.
+
+En héritant de `str` la documentation sera capable de savoir que les valeurs doivent être de type `string` et pourra donc afficher cette `Enum` correctement.
+
+Créez ensuite des attributs de classe avec des valeurs fixes, qui seront les valeurs autorisées pour cette énumération.
+
+```Python hl_lines="1  6-9"
+{!../../../docs_src/path_params/tutorial005.py!}
+```
+
+!!! info
+    <a href="https://docs.python.org/3/library/enum.html" class="external-link" target="_blank">Les énumérations (ou enums) sont disponibles en Python</a> depuis la version 3.4.
+
+!!! tip "Astuce"
+    Pour ceux qui se demandent, "AlexNet", "ResNet", et "LeNet" sont juste des noms de <abbr title="Techniquement, des architectures de modèles">modèles</abbr> de Machine Learning.
+
+### Déclarer un paramètre de chemin
+
+Créez ensuite un *paramètre de chemin* avec une annotation de type désignant l'énumération créée précédemment (`ModelName`) :
+
+```Python hl_lines="16"
+{!../../../docs_src/path_params/tutorial005.py!}
+```
+
+### Documentation
+
+Les valeurs disponibles pour le *paramètre de chemin* sont bien prédéfinies, la documentation les affiche correctement :
+
+<img src="/img/tutorial/path-params/image03.png">
+
+### Manipuler les *énumérations* Python
+
+La valeur du *paramètre de chemin* sera un des "membres" de l'énumération.
+
+#### Comparer les *membres d'énumération*
+
+Vous pouvez comparer ce paramètre avec les membres de votre énumération `ModelName` :
+
+```Python hl_lines="17"
+{!../../../docs_src/path_params/tutorial005.py!}
+```
+
+#### Récupérer la *valeur de l'énumération*
+
+Vous pouvez obtenir la valeur réel d'un membre (une chaîne de caractères ici), avec `model_name.value`, ou en général, `votre_membre_d'enum.value` :
+
+```Python hl_lines="20"
+{!../../../docs_src/path_params/tutorial005.py!}
+```
+
+!!! tip "Astuce"
+    Vous pouvez aussi accéder la valeur `"lenet"` avec `ModelName.lenet.value`.
+
+#### Retourner des *membres d'énumération*
+
+Vous pouvez retourner des *membres d'énumération* dans vos *fonctions de chemin*, même imbriquée dans un JSON (e.g. un `dict`).
+
+Ils seront convertis vers leurs valeurs correspondantes (chaînes de caractères ici) avant d'être transmis au client :
+
+```Python hl_lines="18  21  23"
+{!../../../docs_src/path_params/tutorial005.py!}
+```
+
+Le client recevra une réponse JSON comme celle-ci :
+
+```JSON
+{
+  "model_name": "alexnet",
+  "message": "Deep Learning FTW!"
+}
+```
+
+## Paramètres de chemin contenant des chemins
+
+Disons que vous avez une *fonction de chemin* liée au chemin `/files/{file_path}`.
+
+Mais que `file_path` lui-même doit contenir un *chemin*, comme `home/johndoe/myfile.txt` par exemple.
+
+Donc, l'URL pour ce fichier pourrait être : `/files/home/johndoe/myfile.txt`.
+
+### Support d'OpenAPI
+
+OpenAPI ne supporte pas de manière de déclarer un paramètre de chemin contenant un *chemin*, cela pouvant causer des scénarios difficiles à tester et définir.
+
+Néanmoins, cela reste faisable dans **FastAPI**, via les outils internes de Starlette.
+
+Et la documentation fonctionne quand même, bien qu'aucune section ne soit ajoutée pour dire que la paramètre devrait contenir un *chemin*.
+
+### Convertisseur de *chemin*
+
+En utilisant une option de Starlette directement, vous pouvez déclarer un *paramètre de chemin* contenant un *chemin* avec une URL comme :
+
+```
+/files/{file_path:path}
+```
+
+Dans ce cas, le nom du paramètre est `file_path`, et la dernière partie, `:path`, indique à Starlette que le paramètre devrait correspondre à un *chemin*.
+
+Vous pouvez donc l'utilisez comme tel :
+
+```Python hl_lines="6"
+{!../../../docs_src/path_params/tutorial004.py!}
+```
+
+!!! tip "Astuce"
+    Vous pourriez avoir besoin que le paramètre contienne `/home/johndoe/myfile.txt`, avec un slash au début (`/`).
+
+    Dans ce cas, l'URL serait : `/files//home/johndoe/myfile.txt`, avec un double slash (`//`) entre `files` et `home`.
+
+## Récapitulatif
+
+Avec **FastAPI**, en utilisant les déclarations de type rapides, intuitives et standards de Python, vous bénéficiez de :
+
+* Support de l'éditeur : vérification d'erreurs, auto-complétion, etc.
+* <abbr title="conversion de la chaîne de caractères venant de la requête HTTP en données Python">"Parsing"</abbr> de données.
+* Validation de données.
+* Annotations d'API et documentation automatique.
+
+Et vous n'avez besoin de le déclarer qu'une fois.
+
+C'est probablement l'avantage visible principal de **FastAPI** comparé aux autres *frameworks* (outre les performances pures).

docs/fr/docs/tutorial/query-params.md

@@ -0,0 +1,198 @@
+# Paramètres de requête
+
+Quand vous déclarez des paramètres dans votre fonction de chemin qui ne font pas partie des paramètres indiqués dans le chemin associé, ces paramètres sont automatiquement considérés comme des paramètres de "requête".
+
+```Python hl_lines="9"
+{!../../../docs_src/query_params/tutorial001.py!}
+```
+
+La partie appelée requête (ou **query**) dans une URL est l'ensemble des paires clés-valeurs placées après le `?` , séparées par des `&`. 
+
+Par exemple, dans l'URL :
+
+```
+http://127.0.0.1:8000/items/?skip=0&limit=10
+```
+
+...les paramètres de requête sont :
+
+* `skip` : avec une valeur de`0`
+* `limit` : avec une valeur de `10`
+
+Faisant partie de l'URL, ces valeurs sont des chaînes de caractères (`str`).
+
+Mais quand on les déclare avec des types Python (dans l'exemple précédent, en tant qu'`int`), elles sont converties dans les types renseignés.
+
+Toutes les fonctionnalités qui s'appliquent aux paramètres de chemin s'appliquent aussi aux paramètres de requête :
+
+* Support de l'éditeur : vérification d'erreurs, auto-complétion, etc.
+* <abbr title="conversion de la chaîne de caractères venant de la requête HTTP en données Python">"Parsing"</abbr> de données.
+* Validation de données.
+* Annotations d'API et documentation automatique.
+
+## Valeurs par défaut
+
+Les paramètres de requête ne sont pas une partie fixe d'un chemin, ils peuvent être optionnels et avoir des valeurs par défaut.
+
+Dans l'exemple ci-dessus, ils ont des valeurs par défaut qui sont `skip=0` et `limit=10`.
+
+Donc, accéder à l'URL :
+
+```
+http://127.0.0.1:8000/items/
+```
+
+serait équivalent à accéder à l'URL :
+
+```
+http://127.0.0.1:8000/items/?skip=0&limit=10
+```
+
+Mais si vous accédez à, par exemple :
+
+```
+http://127.0.0.1:8000/items/?skip=20
+```
+
+Les valeurs des paramètres de votre fonction seront :
+
+* `skip=20` : car c'est la valeur déclarée dans l'URL.
+* `limit=10` : car `limit` n'a pas été déclaré dans l'URL, et que la valeur par défaut était `10`.
+
+## Paramètres optionnels
+
+De la même façon, vous pouvez définir des paramètres de requête comme optionnels, en leur donnant comme valeur par défaut `None` :
+
+```Python hl_lines="9"
+{!../../../docs_src/query_params/tutorial002.py!}
+```
+
+Ici, le paramètre `q` sera optionnel, et aura `None` comme valeur par défaut.
+
+!!! check "Remarque"
+    On peut voir que **FastAPI** est capable de détecter que le paramètre de chemin `item_id` est un paramètre de chemin et que `q` n'en est pas un, c'est donc un paramètre de requête.
+
+!!! note
+    **FastAPI** saura que `q` est optionnel grâce au `=None`.
+
+    Le `Optional` dans `Optional[str]` n'est pas utilisé par **FastAPI** (**FastAPI** n'en utilisera que la partie `str`), mais il servira tout de même à votre editeur de texte pour détecter des erreurs dans votre code.
+
+
+## Conversion des types des paramètres de requête
+
+Vous pouvez aussi déclarer des paramètres de requête comme booléens (`bool`), **FastAPI** les convertira :
+
+```Python hl_lines="9"
+{!../../../docs_src/query_params/tutorial003.py!}
+```
+
+Avec ce code, en allant sur :
+
+```
+http://127.0.0.1:8000/items/foo?short=1
+```
+
+ou
+
+```
+http://127.0.0.1:8000/items/foo?short=True
+```
+
+ou
+
+```
+http://127.0.0.1:8000/items/foo?short=true
+```
+
+ou
+
+```
+http://127.0.0.1:8000/items/foo?short=on
+```
+
+ou
+
+```
+http://127.0.0.1:8000/items/foo?short=yes
+```
+
+ou n'importe quelle autre variation de casse (tout en majuscules, uniquement la première lettre en majuscule, etc.), votre fonction considérera le paramètre `short` comme ayant une valeur booléenne à `True`. Sinon la valeur sera à `False`.
+
+## Multiples paramètres de chemin et de requête
+
+Vous pouvez déclarer plusieurs paramètres de chemin et paramètres de requête dans la même fonction, **FastAPI** saura comment les gérer. 
+
+Et vous n'avez pas besoin de les déclarer dans un ordre spécifique.
+
+Ils seront détectés par leurs noms :
+
+```Python hl_lines="8  10"
+{!../../../docs_src/query_params/tutorial004.py!}
+```
+
+## Paramètres de requête requis
+
+Quand vous déclarez une valeur par défaut pour un paramètre qui n'est pas un paramètre de chemin (actuellement, nous n'avons vu que les paramètres de requête), alors ce paramètre n'est pas requis.
+
+Si vous ne voulez pas leur donner de valeur par défaut mais juste les rendre optionnels, utilisez `None` comme valeur par défaut.
+
+Mais si vous voulez rendre un paramètre de requête obligatoire, vous pouvez juste ne pas y affecter de valeur par défaut :
+
+```Python hl_lines="6-7"
+{!../../../docs_src/query_params/tutorial005.py!}
+```
+
+Ici le paramètre `needy` est un paramètre requis (ou obligatoire) de type `str`.
+
+Si vous ouvrez une URL comme :
+
+```
+http://127.0.0.1:8000/items/foo-item
+```
+
+...sans ajouter le paramètre requis `needy`, vous aurez une erreur :
+
+```JSON
+{
+    "detail": [
+        {
+            "loc": [
+                "query",
+                "needy"
+            ],
+            "msg": "field required",
+            "type": "value_error.missing"
+        }
+    ]
+}
+```
+
+La présence de `needy` étant nécessaire, vous auriez besoin de l'insérer dans l'URL :
+
+```
+http://127.0.0.1:8000/items/foo-item?needy=sooooneedy
+```
+
+...ce qui fonctionnerait :
+
+```JSON
+{
+    "item_id": "foo-item",
+    "needy": "sooooneedy"
+}
+```
+
+Et bien sur, vous pouvez définir certains paramètres comme requis, certains avec des valeurs par défaut et certains entièrement optionnels :
+
+```Python hl_lines="10"
+{!../../../docs_src/query_params/tutorial006.py!}
+```
+
+Ici, on a donc 3 paramètres de requête :
+
+* `needy`, requis et de type `str`.
+* `skip`, un `int` avec comme valeur par défaut `0`.
+* `limit`, un `int` optionnel.
+
+!!! tip "Astuce"
+    Vous pouvez utiliser les `Enum`s de la même façon qu'avec les [Paramètres de chemin](path-params.md#valeurs-predefinies){.internal-link target=_blank}.

docs/fr/mkdocs.yml

@@ -9,17 +9,18 @@ theme:
     primary: teal
     accent: amber
     toggle:
-      icon: material/lightbulb-outline
+      icon: material/lightbulb
       name: Switch to light mode
   - scheme: slate
     primary: teal
     accent: amber
     toggle:
-      icon: material/lightbulb
+      icon: material/lightbulb-outline
       name: Switch to dark mode
   features:
   - search.suggest
   - search.highlight
+  - content.tabs.link
   icon:
     repo: fontawesome/brands/github-alt
   logo: https://fastapi.tiangolo.com/img/icon-white.svg
@@ -39,6 +40,7 @@ nav:
 - FastAPI: index.md
 - Languages:
   - en: /
+  - az: /az/
   - de: /de/
   - es: /es/
   - fr: /fr/
@@ -57,7 +59,14 @@ nav:
 - fastapi-people.md
 - python-types.md
 - Tutoriel - Guide utilisateur:
+  - tutorial/first-steps.md
+  - tutorial/path-params.md
+  - tutorial/query-params.md
+  - tutorial/body.md
   - tutorial/background-tasks.md
+- async.md
+- Déploiement:
+  - deployment/docker.md
 - project-generation.md
 - alternatives.md
 - external-links.md
@@ -66,7 +75,7 @@ markdown_extensions:
     permalink: true
 - markdown.extensions.codehilite:
     guess_lang: false
-- markdown_include.include:
+- mdx_include:
     base_path: docs
 - admonition
 - codehilite
@@ -75,7 +84,7 @@ markdown_extensions:
     custom_fences:
     - name: mermaid
       class: mermaid
-      format: !!python/name:pymdownx.superfences.fence_div_format ''
+      format: !!python/name:pymdownx.superfences.fence_code_format ''
 - pymdownx.tabbed
 extra:
   social:
@@ -96,6 +105,8 @@ extra:
   alternate:
   - link: /
     name: en - English
+  - link: /az/
+    name: az
   - link: /de/
     name: de
   - link: /es/
@@ -128,6 +139,5 @@ extra_css:
 - https://fastapi.tiangolo.com/css/termynal.css
 - https://fastapi.tiangolo.com/css/custom.css
 extra_javascript:
-- https://unpkg.com/mermaid@8.4.6/dist/mermaid.min.js
 - https://fastapi.tiangolo.com/js/termynal.js
 - https://fastapi.tiangolo.com/js/custom.js

docs/id/mkdocs.yml

@@ -9,17 +9,18 @@ theme:
     primary: teal
     accent: amber
     toggle:
-      icon: material/lightbulb-outline
+      icon: material/lightbulb
       name: Switch to light mode
   - scheme: slate
     primary: teal
     accent: amber
     toggle:
-      icon: material/lightbulb
+      icon: material/lightbulb-outline
       name: Switch to dark mode
   features:
   - search.suggest
   - search.highlight
+  - content.tabs.link
   icon:
     repo: fontawesome/brands/github-alt
   logo: https://fastapi.tiangolo.com/img/icon-white.svg
@@ -39,6 +40,7 @@ nav:
 - FastAPI: index.md
 - Languages:
   - en: /
+  - az: /az/
   - de: /de/
   - es: /es/
   - fr: /fr/
@@ -58,7 +60,7 @@ markdown_extensions:
     permalink: true
 - markdown.extensions.codehilite:
     guess_lang: false
-- markdown_include.include:
+- mdx_include:
     base_path: docs
 - admonition
 - codehilite
@@ -67,7 +69,7 @@ markdown_extensions:
     custom_fences:
     - name: mermaid
       class: mermaid
-      format: !!python/name:pymdownx.superfences.fence_div_format ''
+      format: !!python/name:pymdownx.superfences.fence_code_format ''
 - pymdownx.tabbed
 extra:
   social:
@@ -88,6 +90,8 @@ extra:
   alternate:
   - link: /
     name: en - English
+  - link: /az/
+    name: az
   - link: /de/
     name: de
   - link: /es/
@@ -120,6 +124,5 @@ extra_css:
 - https://fastapi.tiangolo.com/css/termynal.css
 - https://fastapi.tiangolo.com/css/custom.css
 extra_javascript:
-- https://unpkg.com/mermaid@8.4.6/dist/mermaid.min.js
 - https://fastapi.tiangolo.com/js/termynal.js
 - https://fastapi.tiangolo.com/js/custom.js

docs/it/mkdocs.yml

@@ -9,17 +9,18 @@ theme:
     primary: teal
     accent: amber
     toggle:
-      icon: material/lightbulb-outline
+      icon: material/lightbulb
       name: Switch to light mode
   - scheme: slate
     primary: teal
     accent: amber
     toggle:
-      icon: material/lightbulb
+      icon: material/lightbulb-outline
       name: Switch to dark mode
   features:
   - search.suggest
   - search.highlight
+  - content.tabs.link
   icon:
     repo: fontawesome/brands/github-alt
   logo: https://fastapi.tiangolo.com/img/icon-white.svg
@@ -39,6 +40,7 @@ nav:
 - FastAPI: index.md
 - Languages:
   - en: /
+  - az: /az/
   - de: /de/
   - es: /es/
   - fr: /fr/
@@ -58,7 +60,7 @@ markdown_extensions:
     permalink: true
 - markdown.extensions.codehilite:
     guess_lang: false
-- markdown_include.include:
+- mdx_include:
     base_path: docs
 - admonition
 - codehilite
@@ -67,7 +69,7 @@ markdown_extensions:
     custom_fences:
     - name: mermaid
       class: mermaid
-      format: !!python/name:pymdownx.superfences.fence_div_format ''
+      format: !!python/name:pymdownx.superfences.fence_code_format ''
 - pymdownx.tabbed
 extra:
   social:
@@ -88,6 +90,8 @@ extra:
   alternate:
   - link: /
     name: en - English
+  - link: /az/
+    name: az
   - link: /de/
     name: de
   - link: /es/
@@ -120,6 +124,5 @@ extra_css:
 - https://fastapi.tiangolo.com/css/termynal.css
 - https://fastapi.tiangolo.com/css/custom.css
 extra_javascript:
-- https://unpkg.com/mermaid@8.4.6/dist/mermaid.min.js
 - https://fastapi.tiangolo.com/js/termynal.js
 - https://fastapi.tiangolo.com/js/custom.js

docs/ja/docs/deployment/docker.md

@@ -170,7 +170,7 @@ TraefikおよびHTTPS処理を備えたDocker Swarm Modeクラスターをセッ
 
 ### FastAPIアプリケーションのデプロイ
 
-すべてを設定するための最も簡単な方法は、[**FastAPI** Project Generators](project-generation.md){.internal-link target=_blank}を使用することでしょう。
+すべてを設定するための最も簡単な方法は、[**FastAPI** Project Generators](../project-generation.md){.internal-link target=_blank}を使用することでしょう。
 
 上述したTraefikとHTTPSを備えたDocker Swarm クラスタが統合されるように設計されています。
 

docs/ja/docs/fastapi-people.md

@@ -115,25 +115,28 @@ FastAPIには、様々なバックグラウンドの人々を歓迎する素晴
 
 彼らは、<a href="https://github.com/sponsors/tiangolo" class="external-link" target="_blank">GitHub Sponsors</a> を介して私の **FastAPI** などに関する活動を支援してくれています。
 
+{% if sponsors.gold %}
+
 ### Gold Sponsors
 
-{% if sponsors %}
 {% for sponsor in sponsors.gold -%}
 <a href="{{ sponsor.url }}" target="_blank" title="{{ sponsor.title }}"><img src="{{ sponsor.img }}" style="border-radius:15px"></a>
 {% endfor %}
 {% endif %}
 
+{% if sponsors.silver %}
+
 ### Silver Sponsors
 
-{% if sponsors %}
 {% for sponsor in sponsors.silver -%}
 <a href="{{ sponsor.url }}" target="_blank" title="{{ sponsor.title }}"><img src="{{ sponsor.img }}" style="border-radius:15px"></a>
 {% endfor %}
 {% endif %}
 
+{% if sponsors.bronze %}
+
 ### Bronze Sponsors
 
-{% if sponsors %}
 {% for sponsor in sponsors.bronze -%}
 <a href="{{ sponsor.url }}" target="_blank" title="{{ sponsor.title }}"><img src="{{ sponsor.img }}" style="border-radius:15px"></a>
 {% endfor %}
@@ -141,28 +144,22 @@ FastAPIには、様々なバックグラウンドの人々を歓迎する素晴
 
 ### Individual Sponsors
 
-{% if people %}
-{% if people.sponsors_50 %}
+{% if github_sponsors %}
+{% for group in github_sponsors.sponsors %}
 
 <div class="user-list user-list-center">
-{% for user in people.sponsors_50 %}
+
+{% for user in group %}
+{% if user.login not in sponsors_badge.logins %}
 
 <div class="user"><a href="{{ user.url }}" target="_blank"><div class="avatar-wrapper"><img src="{{ user.avatarUrl }}"/></div><div class="title">@{{ user.login }}</div></a></div>
+
+{% endif %}
 {% endfor %}
 
 </div>
 
-{% endif %}
-{% endif %}
-
-{% if people %}
-<div class="user-list user-list-center">
-{% for user in people.sponsors %}
-
-<div class="user"><a href="{{ user.url }}" target="_blank"><div class="avatar-wrapper"><img src="{{ user.avatarUrl }}"/></div><div class="title">@{{ user.login }}</div></a></div>
 {% endfor %}
-
-</div>
 {% endif %}
 
 ## データについて - 技術詳細

docs/ja/docs/features.md

@@ -31,7 +31,6 @@ FastAPIの機能はすべて、標準のPython 3.6型宣言に基づいていま
 型を使用した標準的なPythonを記述します:
 
 ```Python
-from typing import List, Dict
 from datetime import date
 
 from pydantic import BaseModel

docs/ja/mkdocs.yml

@@ -9,17 +9,18 @@ theme:
     primary: teal
     accent: amber
     toggle:
-      icon: material/lightbulb-outline
+      icon: material/lightbulb
       name: Switch to light mode
   - scheme: slate
     primary: teal
     accent: amber
     toggle:
-      icon: material/lightbulb
+      icon: material/lightbulb-outline
       name: Switch to dark mode
   features:
   - search.suggest
   - search.highlight
+  - content.tabs.link
   icon:
     repo: fontawesome/brands/github-alt
   logo: https://fastapi.tiangolo.com/img/icon-white.svg
@@ -39,6 +40,7 @@ nav:
 - FastAPI: index.md
 - Languages:
   - en: /
+  - az: /az/
   - de: /de/
   - es: /es/
   - fr: /fr/
@@ -98,7 +100,7 @@ markdown_extensions:
     permalink: true
 - markdown.extensions.codehilite:
     guess_lang: false
-- markdown_include.include:
+- mdx_include:
     base_path: docs
 - admonition
 - codehilite
@@ -107,7 +109,7 @@ markdown_extensions:
     custom_fences:
     - name: mermaid
       class: mermaid
-      format: !!python/name:pymdownx.superfences.fence_div_format ''
+      format: !!python/name:pymdownx.superfences.fence_code_format ''
 - pymdownx.tabbed
 extra:
   social:
@@ -128,6 +130,8 @@ extra:
   alternate:
   - link: /
     name: en - English
+  - link: /az/
+    name: az
   - link: /de/
     name: de
   - link: /es/
@@ -160,6 +164,5 @@ extra_css:
 - https://fastapi.tiangolo.com/css/termynal.css
 - https://fastapi.tiangolo.com/css/custom.css
 extra_javascript:
-- https://unpkg.com/mermaid@8.4.6/dist/mermaid.min.js
 - https://fastapi.tiangolo.com/js/termynal.js
 - https://fastapi.tiangolo.com/js/custom.js

docs/ko/docs/deployment/versions.md

@@ -0,0 +1,88 @@
+# FastAPI 버전들에 대하여
+
+**FastAPI** 는 이미 많은 응용 프로그램과 시스템들을 만드는데 사용되고 있습니다. 그리고 100%의 테스트 정확성을 가지고 있습니다. 하지만 이것은 아직까지도 빠르게 발전하고 있습니다.
+
+새로운 특징들이 빈번하게 추가되고, 오류들이 지속적으로 수정되고 있습니다. 그리고 코드가 계속적으로 향상되고 있습니다.
+
+이것이 아직도 최신 버전이 `0.x.x`인 이유입니다. 이것은 각각의 버전들이 잠재적으로 변할 수 있다는 것을 보여줍니다. 이는 <a href="https://semver.org/" class="external-link" target="_blank">유의적 버전</a> 관습을 따릅니다.
+
+지금 바로 **FastAPI**로 응용 프로그램을 만들 수 있습니다. 이때 (아마 지금까지 그래 왔던 것처럼), 사용하는 버전이 코드와 잘 맞는지 확인해야합니다.  
+
+## `fastapi` 버전을 표시
+
+가장 먼저 해야할 것은 응용 프로그램이 잘 작동하는 가장 최신의 구체적인 **FastAPI** 버전을 표시하는 것입니다.
+
+예를 들어, 응용 프로그램에 `0.45.0` 버전을 사용했다고 가정합니다.
+
+만약에 `requirements.txt` 파일을 사용했다면, 다음과 같이 버전을 명세할 수 있습니다:
+
+```txt
+fastapi==0.45.0
+```
+
+이것은 `0.45.0` 버전을 사용했다는 것을 의미합니다.
+
+또는 다음과 같이 표시할 수 있습니다:
+
+```txt
+fastapi>=0.45.0,<0.46.0
+```
+
+이것은 `0.45.0` 버전과 같거나 높으면서 `0.46.0` 버전 보다는 낮은 버전을 사용했다는 것을 의미합니다. 예를 들어, `0.45.2` 버전과 같은 경우는 해당 조건을 만족합니다.
+
+만약에 Poetry, Pipenv, 또는 그밖의 다양한 설치 도구를 사용한다면, 패키지에 구체적인 버전을 정의할 수 있는 방법을 가지고 있을 것입니다.
+
+## 이용가능한 버전들
+
+[Release Notes](../release-notes.md){.internal-link target=_blank}를 통해 사용할 수 있는 버전들을 확인할 수 있습니다.(예를 들어, 가장 최신의 버전을 확인할 수 있습니다.)
+
+
+## 버전들에 대해
+
+유의적 버전 관습을 따라서, `1.0.0` 이하의 모든 버전들은 잠재적으로 급변할 수 있습니다.
+
+FastAPI는 오류를 수정하고, 일반적인 변경사항을 위해 "패치"버전의 관습을 따릅니다.
+
+!!! tip "팁"
+    여기서 말하는 "패치"란 버전의 마지막 숫자로, 예를 들어 `0.2.3` 버전에서 "패치"는 `3`을 의미합니다.
+
+따라서 다음과 같이 버전을 표시할 수 있습니다: 
+
+```txt
+fastapi>=0.45.0,<0.46.0
+```
+
+수정된 사항과 새로운 요소들이 "마이너" 버전에 추가되었습니다.
+
+!!! tip "팁"
+    "마이너"란 버전 넘버의 가운데 숫자로, 예를 들어서 `0.2.3`의 "마이너" 버전은 `2`입니다.
+
+## FastAPI 버전의 업그레이드
+
+응용 프로그램을 검사해야합니다.
+
+(Starlette 덕분에), **FastAPI** 를 이용하여 굉장히 쉽게 할 수 있습니다. [Testing](../tutorial/testing.md){.internal-link target=_blank}문서를 확인해 보십시오:
+
+검사를 해보고 난 후에, **FastAPI** 버전을 더 최신으로 업그레이드 할 수 있습니다. 그리고 코드들이 테스트에 정상적으로 작동하는지 확인을 해야합니다.
+
+만약에 모든 것이 정상 작동하거나 필요한 부분을 변경하고, 모든 검사를 통과한다면, 새로운 버전의  `fastapi`를 표시할 수 있습니다.
+
+## Starlette에 대해
+
+`starlette`의 버전은 표시할 수 없습니다.
+
+서로다른 버전의 **FastAPI**가 구체적이고 새로운 버전의 Starlette을 사용할 것입니다. 
+
+그러므로 **FastAPI**가 알맞은 Starlette 버전을 사용하도록 하십시오.
+
+## Pydantic에 대해
+
+Pydantic은 **FastAPI** 를 위한 검사를 포함하고 있습니다. 따라서, 새로운 버전의 Pydantic(`1.0.0`이상)은 항상 FastAPI와 호환됩니다.
+
+작업을 하고 있는 `1.0.0` 이상의 모든 버전과 `2.0.0` 이하의 Pydantic 버전을 표시할 수 있습니다.
+
+예를 들어 다음과 같습니다:
+
+```txt
+pydantic>=1.2.0,<2.0.0
+```

docs/ko/docs/tutorial/encoder.md

@@ -0,0 +1,34 @@
+# JSON 호환 가능 인코더
+
+데이터 유형(예: Pydantic 모델)을 JSON과 호환된 형태로 반환해야 하는 경우가 있습니다. (예: `dict`, `list` 등)
+
+예를 들면, 데이터베이스에 저장해야하는 경우입니다.
+
+이를 위해, **FastAPI** 에서는 `jsonable_encoder()` 함수를 제공합니다.
+
+## `jsonable_encoder` 사용
+
+JSON 호환 가능 데이터만 수신하는 `fake_db` 데이터베이스가 존재한다고 가정하겠습니다.
+
+예를 들면, `datetime` 객체는 JSON과 호환되는 데이터가 아니므로 이 데이터는 받아들여지지 않습니다.
+
+따라서 `datetime` 객체는 <a href="https://en.wikipedia.org/wiki/ISO_8601" class="external-link" target="_blank">ISO format</a> 데이터를 포함하는 `str`로 변환되어야 합니다.
+
+같은 방식으로 이 데이터베이스는 Pydantic 모델(속성이 있는 객체)을 받지 않고, `dict` 만을 받습니다.
+
+이를 위해 `jsonable_encoder` 를 사용할 수 있습니다.
+
+Pydantic 모델과 같은 객체를 받고 JSON 호환 가능한 버전으로 반환합니다:
+
+```Python hl_lines="5  22"
+{!../../../docs_src/encoder/tutorial001.py!}
+```
+
+이 예시는 Pydantic 모델을 `dict`로, `datetime` 형식을 `str`로 변환합니다.
+
+이렇게 호출한 결과는 파이썬 표준인 <a href="https://docs.python.org/3/library/json.html#json.dumps" class="external-link" target="_blank">`json.dumps()`</a>로 인코딩 할 수 있습니다.
+
+길이가 긴 문자열 형태의 JSON 형식(문자열)의 데이터가 들어있는 상황에서는 `str`로 반환하지 않습니다. JSON과 모두 호환되는 값과 하위 값이 있는 Python 표준 데이터 구조 (예: `dict`)를 반환합니다.
+
+!!! note "참고"
+    실제로 `jsonable_encoder`는 **FastAPI** 에서 내부적으로 데이터를 변환하는 데 사용하지만, 다른 많은 곳에서도 이는 유용합니다.

docs/ko/docs/tutorial/index.md

@@ -2,11 +2,11 @@
 
 이 자습서는 **FastAPI**의 대부분의 기능을 단계별로 사용하는 방법을 보여줍니다.
 
-각 섹션은 이전 섹션을 기반해서 점진적으로 만들어 졌지만, 주제를 구분하여 구성 되었기 때문에 특정 API 요구사항을 해결하기 위해 어떤 특정 항목이던지 직접 이동할 수 있습니다.
+각 섹션은 이전 섹션을 기반해서 점진적으로 만들어 졌지만, 주제에 따라 다르게 구성되었기 때문에 특정 API 요구사항을 해결하기 위해서라면 어느 특정 항목으로던지 직접 이동할 수 있습니다.
 
 또한 향후 참조가 될 수 있도록 만들어졌습니다.
 
-그러므로 다시 돌아와서 정확히 필요한 것을 볼 수 있습니다.
+그러므로 다시 돌아와서 정확히 필요한 것을 확인할 수 있습니다.
 
 ## 코드 실행하기
 
@@ -30,7 +30,7 @@ $ uvicorn main:app --reload
 
 코드를 작성하거나 복사, 편집할 때, 로컬에서 실행하는 것을 **강력히 장려**합니다.
 
-편집기에서 이렇게 사용하면, 모든 타입 검사, 자동완성 등 작성해야 하는 코드가 얼마나 적은지 보면서 FastAPI의 장점을 실제로 확인할 수 있습니다.
+편집기에서 이렇게 사용한다면, 모든 타입 검사와 자동완성 등 작성해야 하는 코드가 얼마나 적은지 보면서 FastAPI의 장점을 실제로 확인할 수 있습니다.
 
 ---
 
@@ -77,4 +77,4 @@ $ pip install fastapi[all]
 
 하지만 (지금 읽고 있는) **자습서 - 사용자 안내서**를 먼저 읽는게 좋습니다.
 
-**자습서 - 사용자 안내서**만으로 완전한 애플리케이션을 구축한 다음, **고급 사용자 안내서**의 몇 가지 추가 아이디어를 사용하여 필요에 따라 다양한 방식으로 확장할 수 있도록 설계되었습니다.
+**자습서 - 사용자 안내서**만으로도 완전한 애플리케이션을 구축할 수 있으며, 필요에 따라 **고급 사용자 안내서**에서 제공하는 몇 가지 추가적인 기능을 사용하여 다양한 방식으로 확장할 수 있도록 설계되었습니다.