🔖 Release version 0.82.0

Co-authored-by: Sebastián Ramírez <tiangolo@gmail.com>

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -8,9 +8,9 @@ body:
         Thanks for your interest in FastAPI! 🚀
 
         Please follow these instructions, fill every question, and do every step. 🙏
-        
+
         I'm asking this because answering questions and solving problems in GitHub issues is what consumes most of the time.
-        
+
         I end up not being able to add new features, fix bugs, review pull requests, etc. as fast as I wish because I have to spend too much time handling issues.
 
         All that, on top of all the incredible help provided by a bunch of community members, the [FastAPI Experts](https://fastapi.tiangolo.com/fastapi-people/#experts), that give a lot of their time to come here and help others.
@@ -18,7 +18,7 @@ body:
         That's a lot of work they are doing, but if more FastAPI users came to help others like them just a little bit more, it would be much less effort for them (and you and me 😅).
 
         By asking questions in a structured way (following this) it will be much easier to help you.
-        
+
         And there's a high chance that you will find the solution along the way and you won't even have to submit it and wait for an answer. 😎
 
         As there are too many issues with questions, I'll have to close the incomplete ones. That will allow me (and others) to focus on helping people like you that follow the whole process and help us help you. 🤓
@@ -50,7 +50,7 @@ body:
       label: Commit to Help
       description: |
         After submitting this, I commit to one of:
-          
+
           * Read open issues with questions until I find 2 issues where I can help someone and add a comment to help there.
           * I already hit the "watch" button in this repository to receive notifications and I commit to help at least 2 people that ask questions in the future.
           * Implement a Pull Request for a confirmed bug.

.github/ISSUE_TEMPLATE/question.yml

@@ -8,9 +8,9 @@ body:
         Thanks for your interest in FastAPI! 🚀
 
         Please follow these instructions, fill every question, and do every step. 🙏
-        
+
         I'm asking this because answering questions and solving problems in GitHub issues is what consumes most of the time.
-        
+
         I end up not being able to add new features, fix bugs, review pull requests, etc. as fast as I wish because I have to spend too much time handling issues.
 
         All that, on top of all the incredible help provided by a bunch of community members, the [FastAPI Experts](https://fastapi.tiangolo.com/fastapi-people/#experts), that give a lot of their time to come here and help others.
@@ -18,7 +18,7 @@ body:
         That's a lot of work they are doing, but if more FastAPI users came to help others like them just a little bit more, it would be much less effort for them (and you and me 😅).
 
         By asking questions in a structured way (following this) it will be much easier to help you.
-        
+
         And there's a high chance that you will find the solution along the way and you won't even have to submit it and wait for an answer. 😎
 
         As there are too many issues with questions, I'll have to close the incomplete ones. That will allow me (and others) to focus on helping people like you that follow the whole process and help us help you. 🤓
@@ -50,7 +50,7 @@ body:
       label: Commit to Help
       description: |
         After submitting this, I commit to one of:
-          
+
           * Read open issues with questions until I find 2 issues where I can help someone and add a comment to help there.
           * I already hit the "watch" button in this repository to receive notifications and I commit to help at least 2 people that ask questions in the future.
           * Implement a Pull Request for a confirmed bug.

.github/actions/comment-docs-preview-in-pr/app/main.py

@@ -1,7 +1,7 @@
 import logging
 import sys
 from pathlib import Path
-from typing import Optional
+from typing import Union
 
 import httpx
 from github import Github
@@ -14,7 +14,7 @@ github_api = "https://api.github.com"
 class Settings(BaseSettings):
     github_repository: str
     github_event_path: Path
-    github_event_name: Optional[str] = None
+    github_event_name: Union[str, None] = None
     input_token: SecretStr
     input_deploy_url: str
 
@@ -42,15 +42,13 @@ if __name__ == "__main__":
     except ValidationError as e:
         logging.error(f"Error parsing event file: {e.errors()}")
         sys.exit(0)
-    use_pr: Optional[PullRequest] = None
+    use_pr: Union[PullRequest, None] = None
     for pr in repo.get_pulls():
         if pr.head.sha == event.workflow_run.head_commit.id:
             use_pr = pr
             break
     if not use_pr:
-        logging.error(
-            f"No PR found for hash: {event.workflow_run.head_commit.id}"
-        )
+        logging.error(f"No PR found for hash: {event.workflow_run.head_commit.id}")
         sys.exit(0)
     github_headers = {
         "Authorization": f"token {settings.input_token.get_secret_value()}"

.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 random
+import time
+from pathlib import Path
+from typing import Dict, Union
+
+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: Union[str, None] = None
+    input_debug: Union[bool, None] = 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 = {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 +1,19 @@
+pt: 1211
+es: 1218
+zh: 1228
+ru: 1362
+it: 1556
+ja: 1572
+uk: 1748
+tr: 1892
+fr: 1972
+ko: 2017
+fa: 2041
+pl: 3169
+de: 3716
+id: 3717
+az: 3994
+nl: 4701
+uz: 4883
+sv: 5146
+he: 5157

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

@@ -4,7 +4,7 @@ import sys
 from collections import Counter, defaultdict
 from datetime import datetime, timedelta, timezone
 from pathlib import Path
-from typing import Container, DefaultDict, Dict, List, Optional, Set
+from typing import Container, DefaultDict, Dict, List, Set, Union
 
 import httpx
 import yaml
@@ -14,7 +14,7 @@ from pydantic import BaseModel, BaseSettings, SecretStr
 github_graphql_url = "https://api.github.com/graphql"
 
 issues_query = """
-query Q($after: String) { 
+query Q($after: String) {
   repository(name: "fastapi", owner: "tiangolo") {
     issues(first: 100, after: $after) {
       edges {
@@ -47,7 +47,7 @@ query Q($after: String) {
 """
 
 prs_query = """
-query Q($after: String) { 
+query Q($after: String) {
   repository(name: "fastapi", owner: "tiangolo") {
     pullRequests(first: 100, after: $after) {
       edges {
@@ -133,7 +133,7 @@ class Author(BaseModel):
 
 class CommentsNode(BaseModel):
     createdAt: datetime
-    author: Optional[Author] = None
+    author: Union[Author, None] = None
 
 
 class Comments(BaseModel):
@@ -142,7 +142,7 @@ class Comments(BaseModel):
 
 class IssuesNode(BaseModel):
     number: int
-    author: Optional[Author] = None
+    author: Union[Author, None] = None
     title: str
     createdAt: datetime
     state: str
@@ -179,7 +179,7 @@ class Labels(BaseModel):
 
 
 class ReviewNode(BaseModel):
-    author: Optional[Author] = None
+    author: Union[Author, None] = None
     state: str
 
 
@@ -190,7 +190,7 @@ class Reviews(BaseModel):
 class PullRequestNode(BaseModel):
     number: int
     labels: Labels
-    author: Optional[Author] = None
+    author: Union[Author, None] = None
     title: str
     createdAt: datetime
     state: str
@@ -260,19 +260,21 @@ class Settings(BaseSettings):
     input_token: SecretStr
     input_standard_token: SecretStr
     github_repository: str
+    httpx_timeout: int = 30
 
 
 def get_graphql_response(
-    *, settings: Settings, query: str, after: Optional[str] = None
+    *, settings: Settings, query: str, after: Union[str, None] = None
 ):
     headers = {"Authorization": f"token {settings.input_token.get_secret_value()}"}
     variables = {"after": after}
     response = httpx.post(
         github_graphql_url,
         headers=headers,
+        timeout=settings.httpx_timeout,
         json={"query": query, "variables": variables, "operationName": "Q"},
     )
-    if not response.status_code == 200:
+    if response.status_code != 200:
         logging.error(f"Response was not 200, after: {after}")
         logging.error(response.text)
         raise RuntimeError(response.text)
@@ -280,19 +282,19 @@ def get_graphql_response(
     return data
 
 
-def get_graphql_issue_edges(*, settings: Settings, after: Optional[str] = None):
+def get_graphql_issue_edges(*, settings: Settings, after: Union[str, None] = None):
     data = get_graphql_response(settings=settings, query=issues_query, after=after)
     graphql_response = IssuesResponse.parse_obj(data)
     return graphql_response.data.repository.issues.edges
 
 
-def get_graphql_pr_edges(*, settings: Settings, after: Optional[str] = None):
+def get_graphql_pr_edges(*, settings: Settings, after: Union[str, None] = None):
     data = get_graphql_response(settings=settings, query=prs_query, after=after)
     graphql_response = PRsResponse.parse_obj(data)
     return graphql_response.data.repository.pullRequests.edges
 
 
-def get_graphql_sponsor_edges(*, settings: Settings, after: Optional[str] = None):
+def get_graphql_sponsor_edges(*, settings: Settings, after: Union[str, None] = None):
     data = get_graphql_response(settings=settings, query=sponsors_query, after=after)
     graphql_response = SponsorsResponse.parse_obj(data)
     return graphql_response.data.user.sponsorshipsAsMaintainer.edges
@@ -476,21 +478,16 @@ 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 +495,28 @@ 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(
@@ -517,7 +526,9 @@ if __name__ == "__main__":
     logging.info(f"Creating a new branch {branch_name}")
     subprocess.run(["git", "checkout", "-b", branch_name], check=True)
     logging.info("Adding updated file")
-    subprocess.run(["git", "add", str(people_path)], check=True)
+    subprocess.run(
+        ["git", "add", str(people_path), str(github_sponsors_path)], check=True
+    )
     logging.info("Committing updated file")
     message = "👥 Update FastAPI People"
     result = subprocess.run(["git", "commit", "-m", message], check=True)

.github/actions/watch-previews/app/main.py

@@ -1,7 +1,7 @@
 import logging
 from datetime import datetime
 from pathlib import Path
-from typing import List, Optional
+from typing import List, Union
 
 import httpx
 from github import Github
@@ -16,7 +16,7 @@ class Settings(BaseSettings):
     input_token: SecretStr
     github_repository: str
     github_event_path: Path
-    github_event_name: Optional[str] = None
+    github_event_name: Union[str, None] = None
 
 
 class Artifact(BaseModel):
@@ -74,7 +74,7 @@ if __name__ == "__main__":
         logging.info(f"Docs preview was notified: {notified}")
         if not notified:
             artifact_name = f"docs-zip-{commit}"
-            use_artifact: Optional[Artifact] = None
+            use_artifact: Union[Artifact, None] = None
             for artifact in artifacts_response.artifacts:
                 if artifact.name == artifact_name:
                     use_artifact = artifact

.github/dependabot.yml

@@ -0,0 +1,16 @@
+version: 2
+updates:
+  # GitHub Actions
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "daily"
+    commit-message:
+      prefix: ⬆
+  # Python
+  - package-ecosystem: "pip"
+    directory: "/"
+    schedule:
+      interval: "daily"
+    commit-message:
+      prefix: ⬆

.github/workflows/build-docs.yml

@@ -1,6 +1,8 @@
 name: Build Docs
 on:
   push:
+    branches:
+      - master
   pull_request:
     types: [opened, synchronize]
 jobs:
@@ -11,16 +13,16 @@ jobs:
         env:
           GITHUB_CONTEXT: ${{ toJson(github) }}
         run: echo "$GITHUB_CONTEXT"
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
       - name: Set up Python
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
         with:
           python-version: "3.7"
-      - uses: actions/cache@v2
+      - uses: actions/cache@v3
         id: cache
         with:
           path: ${{ env.pythonLocation }}
-          key: ${{ runner.os }}-python-${{ env.pythonLocation }}-${{ hashFiles('pyproject.toml') }}-docs
+          key: ${{ runner.os }}-python-docs-${{ env.pythonLocation }}-${{ hashFiles('pyproject.toml') }}-v03
       - name: Install Flit
         if: steps.cache.outputs.cache-hit != 'true'
         run: python3.7 -m pip install flit
@@ -28,18 +30,18 @@ jobs:
         if: steps.cache.outputs.cache-hit != 'true'
         run: python3.7 -m flit install --deps production --extras doc
       - name: Install Material for MkDocs Insiders
-        if: github.event.pull_request.head.repo.fork == false && steps.cache.outputs.cache-hit != 'true'
+        if: ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false ) && steps.cache.outputs.cache-hit != 'true'
         run: pip install git+https://${{ secrets.ACTIONS_TOKEN }}@github.com/squidfunk/mkdocs-material-insiders.git
       - name: Build Docs
         run: python3.7 ./scripts/docs.py build-all
       - name: Zip docs
         run: bash ./scripts/zip-docs.sh
-      - uses: actions/upload-artifact@v2
+      - uses: actions/upload-artifact@v3
         with:
           name: docs-zip
           path: ./docs.zip
       - name: Deploy to Netlify
-        uses: nwtgck/actions-netlify@v1.1.5
+        uses: nwtgck/actions-netlify@v1.2.3
         with:
           publish-dir: './site'
           production-branch: master

.github/workflows/latest-changes.yml

@@ -12,7 +12,7 @@ on:
         description: PR number
         required: true
       debug_enabled:
-        description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)'     
+        description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)'
         required: false
         default: false
 
@@ -20,7 +20,7 @@ jobs:
   latest-changes:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
         with:
           # To allow latest-changes to commit to master
           token: ${{ secrets.ACTIONS_TOKEN }}

.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@v3
+      # 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/people.yml

@@ -6,7 +6,7 @@ on:
   workflow_dispatch:
     inputs:
       debug_enabled:
-        description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)'     
+        description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)'
         required: false
         default: false
 
@@ -14,7 +14,7 @@ jobs:
   fastapi-people:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
       # Allow debugging with tmate
       - name: Setup tmate session
         uses: mxschmitt/action-tmate@v3

.github/workflows/preview-docs.yml

@@ -3,16 +3,16 @@ on:
   workflow_run:
     workflows:
       - Build Docs
-    types: 
+    types:
       - completed
 
 jobs:
   preview-docs:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
       - name: Download Artifact Docs
-        uses: dawidd6/action-download-artifact@v2.9.0
+        uses: dawidd6/action-download-artifact@v2.23.0
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
           workflow: build-docs.yml
@@ -25,7 +25,7 @@ jobs:
           rm -f docs.zip
       - name: Deploy to Netlify
         id: netlify
-        uses: nwtgck/actions-netlify@v1.1.5
+        uses: nwtgck/actions-netlify@v1.2.3
         with:
           publish-dir: './site'
           production-deploy: false

.github/workflows/publish.yml

@@ -13,12 +13,12 @@ jobs:
         env:
           GITHUB_CONTEXT: ${{ toJson(github) }}
         run: echo "$GITHUB_CONTEXT"
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
       - name: Set up Python
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
         with:
           python-version: "3.6"
-      - uses: actions/cache@v2
+      - uses: actions/cache@v3
         id: cache
         with:
           path: ${{ env.pythonLocation }}

.github/workflows/test.yml

@@ -2,6 +2,8 @@ name: Test
 
 on:
   push:
+    branches:
+      - master
   pull_request:
     types: [opened, synchronize]
 
@@ -10,27 +12,30 @@ 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", "3.10"]
       fail-fast: false
-    
+
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
       - name: Set up Python
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
-      - uses: actions/cache@v2
+      - uses: actions/cache@v3
         id: cache
         with:
           path: ${{ env.pythonLocation }}
-          key: ${{ runner.os }}-python-${{ env.pythonLocation }}-${{ hashFiles('pyproject.toml') }}-test
+          key: ${{ runner.os }}-python-${{ env.pythonLocation }}-${{ hashFiles('pyproject.toml') }}-test-v02
       - name: Install Flit
         if: steps.cache.outputs.cache-hit != 'true'
         run: pip install flit
       - 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
-        uses: codecov/codecov-action@v1
+        uses: codecov/codecov-action@v3

.pre-commit-config.yaml

@@ -0,0 +1,51 @@
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+    -   id: check-added-large-files
+    -   id: check-toml
+    -   id: check-yaml
+        args:
+        -   --unsafe
+    -   id: end-of-file-fixer
+    -   id: trailing-whitespace
+-   repo: https://github.com/asottile/pyupgrade
+    rev: v2.37.3
+    hooks:
+    -   id: pyupgrade
+        args:
+        - --py3-plus
+        - --keep-runtime-typing
+-   repo: https://github.com/myint/autoflake
+    rev: v1.5.1
+    hooks:
+    -   id: autoflake
+        args:
+        - --recursive
+        - --in-place
+        - --remove-all-unused-imports
+        - --remove-unused-variables
+        - --expand-star-imports
+        - --exclude
+        - __init__.py
+        - --remove-duplicate-keys
+-   repo: https://github.com/pycqa/isort
+    rev: 5.10.1
+    hooks:
+    -   id: isort
+        name: isort (python)
+    -   id: isort
+        name: isort (cython)
+        types: [cython]
+    -   id: isort
+        name: isort (pyi)
+        types: [pyi]
+-   repo: https://github.com/psf/black
+    rev: 22.6.0
+    hooks:
+    -   id: black
+ci:
+    autofix_commit_msg: 🎨 [pre-commit.ci] Auto format from pre-commit.com hooks
+    autoupdate_commit_msg: ⬆ [pre-commit.ci] pre-commit autoupdate

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>
 
 ---
@@ -29,7 +32,6 @@ FastAPI is a modern, fast (high-performance), web framework for building APIs wi
 The key features are:
 
 * **Fast**: Very high performance, on par with **NodeJS** and **Go** (thanks to Starlette and Pydantic). [One of the fastest Python frameworks available](#performance).
-
 * **Fast to code**: Increase the speed to develop features by about 200% to 300%. *
 * **Fewer bugs**: Reduce about 40% of human (developer) induced errors. *
 * **Intuitive**: Great editor support. <abbr title="also known as auto-complete, autocompletion, IntelliSense">Completion</abbr> everywhere. Less time debugging.
@@ -44,11 +46,17 @@ The key features are:
 
 <!-- sponsors -->
 
+<a href="https://bit.ly/3dmXC5S" target="_blank" title="The data structure for unstructured multimodal data"><img src="https://fastapi.tiangolo.com/img/sponsors/docarray.svg"></a>
+<a href="https://bit.ly/3JJ7y5C" target="_blank" title="Build cross-modal and multimodal applications on the cloud"><img src="https://fastapi.tiangolo.com/img/sponsors/jina2.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://doist.com/careers/9B437B1615-wa-senior-backend-engineer-python" target="_blank" title="Help us migrate doist to FastAPI"><img src="https://fastapi.tiangolo.com/img/sponsors/doist.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://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://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://training.talkpython.fm/fastapi-courses" 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>
+<a href="https://www.udemy.com/course/fastapi-rest/" target="_blank" title="Learn FastAPI by building a complete project. Extend your knowledge on advanced web development-AWS, Payments, Emails."><img src="https://fastapi.tiangolo.com/img/sponsors/ines-course.jpg"></a>
+<a href="https://careers.budget-insight.com/" target="_blank" title="Budget Insight is hiring!"><img src="https://fastapi.tiangolo.com/img/sponsors/budget-insight.svg"></a>
 
 <!-- /sponsors -->
 
@@ -123,12 +131,12 @@ $ pip install fastapi
 
 </div>
 
-You will also need an ASGI server, for production such as <a href="https://www.uvicorn.org" class="external-link" target="_blank">Uvicorn</a> or <a href="https://gitlab.com/pgjones/hypercorn" class="external-link" target="_blank">Hypercorn</a>.
+You will also need an ASGI server, for production such as <a href="https://www.uvicorn.org" class="external-link" target="_blank">Uvicorn</a> or <a href="https://github.com/pgjones/hypercorn" class="external-link" target="_blank">Hypercorn</a>.
 
 <div class="termy">
 
 ```console
-$ pip install uvicorn[standard]
+$ pip install "uvicorn[standard]"
 
 ---> 100%
 ```
@@ -142,7 +150,7 @@ $ pip install uvicorn[standard]
 * Create a file `main.py` with:
 
 ```Python
-from typing import Optional
+from typing import Union
 
 from fastapi import FastAPI
 
@@ -155,7 +163,7 @@ def read_root():
 
 
 @app.get("/items/{item_id}")
-def read_item(item_id: int, q: Optional[str] = None):
+def read_item(item_id: int, q: Union[str, None] = None):
     return {"item_id": item_id, "q": q}
 ```
 
@@ -165,7 +173,7 @@ def read_item(item_id: int, q: Optional[str] = None):
 If your code uses `async` / `await`, use `async def`:
 
 ```Python hl_lines="9  14"
-from typing import Optional
+from typing import Union
 
 from fastapi import FastAPI
 
@@ -178,7 +186,7 @@ async def read_root():
 
 
 @app.get("/items/{item_id}")
-async def read_item(item_id: int, q: Optional[str] = None):
+async def read_item(item_id: int, q: Union[str, None] = None):
     return {"item_id": item_id, "q": q}
 ```
 
@@ -257,7 +265,7 @@ Now modify the file `main.py` to receive a body from a `PUT` request.
 Declare the body using standard Python types, thanks to Pydantic.
 
 ```Python hl_lines="4  9-12  25-27"
-from typing import Optional
+from typing import Union
 
 from fastapi import FastAPI
 from pydantic import BaseModel
@@ -268,7 +276,7 @@ app = FastAPI()
 class Item(BaseModel):
     name: str
     price: float
-    is_offer: Optional[bool] = None
+    is_offer: Union[bool, None] = None
 
 
 @app.get("/")
@@ -277,7 +285,7 @@ def read_root():
 
 
 @app.get("/items/{item_id}")
-def read_item(item_id: int, q: Optional[str] = None):
+def read_item(item_id: int, q: Union[str, None] = None):
     return {"item_id": item_id, "q": q}
 
 
@@ -314,7 +322,7 @@ 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 +379,7 @@ 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 +424,9 @@ 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 +448,10 @@ 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 +459,7 @@ 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 +1,145 @@
+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:
+  - media: '(prefers-color-scheme: light)'
+    scheme: default
+    primary: teal
+    accent: amber
+    toggle:
+      icon: material/lightbulb
+      name: Switch to light mode
+  - media: '(prefers-color-scheme: dark)'
+    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: ''
+plugins:
+- search
+- markdownextradata:
+    data: data
+nav:
+- FastAPI: index.md
+- Languages:
+  - en: /
+  - az: /az/
+  - de: /de/
+  - es: /es/
+  - fa: /fa/
+  - fr: /fr/
+  - he: /he/
+  - id: /id/
+  - it: /it/
+  - ja: /ja/
+  - ko: /ko/
+  - nl: /nl/
+  - pl: /pl/
+  - pt: /pt/
+  - ru: /ru/
+  - sq: /sq/
+  - sv: /sv/
+  - 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:
+    alternate_style: true
+- attr_list
+- md_in_html
+extra:
+  analytics:
+    provider: google
+    property: UA-133183413-1
+  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: /fa/
+    name: fa
+  - link: /fr/
+    name: fr - français
+  - link: /he/
+    name: he
+  - link: /id/
+    name: id
+  - link: /it/
+    name: it - italiano
+  - link: /ja/
+    name: ja - 日本語
+  - link: /ko/
+    name: ko - 한국어
+  - link: /nl/
+    name: nl
+  - link: /pl/
+    name: pl
+  - link: /pt/
+    name: pt - português
+  - link: /ru/
+    name: ru - русский язык
+  - link: /sq/
+    name: sq - shqip
+  - link: /sv/
+    name: sv - svenska
+  - 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 standardmäßig 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 brauchen.
+
+Aber standardmäßig, **"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 ungewöhnliche Typen, wie:
+    * URL.
+    * Email.
+    * UUID.
+    * ... und andere.
+
+Die gesamte Validierung übernimmt das etablierte und robuste **Pydantic**.
+
+### Sicherheit und Authentifizierung
+
+Integrierte Sicherheit und Authentifizierung. Ohne Kompromisse bei Datenbanken oder Datenmodellen.
+
+Unterstützt werden 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.
+* Unterstützt komplexe Benutzerauthentifizierungssysteme, **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 eigener Starlette Quellcode funktioniert.
+
+`FastAPI` ist eigentlich eine Unterklasse von `Starlette`. Wenn Sie also bereits Starlette kennen oder benutzen, können Sie das meiste Ihres Wissens 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/" 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 eine 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

@@ -135,7 +135,7 @@ 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%
 ```
@@ -149,7 +149,7 @@ $ pip install uvicorn[standard]
 * Create a file `main.py` with:
 
 ```Python
-from typing import Optional
+from typing import Union
 
 from fastapi import FastAPI
 
@@ -162,7 +162,7 @@ def read_root():
 
 
 @app.get("/items/{item_id}")
-def read_item(item_id: int, q: Optional[str] = None):
+def read_item(item_id: int, q: Union[str, None] = None):
     return {"item_id": item_id, "q": q}
 ```
 
@@ -172,7 +172,7 @@ def read_item(item_id: int, q: Optional[str] = None):
 If your code uses `async` / `await`, use `async def`:
 
 ```Python hl_lines="9  14"
-from typing import Optional
+from typing import Union
 
 from fastapi import FastAPI
 
@@ -185,7 +185,7 @@ async def read_root():
 
 
 @app.get("/items/{item_id}")
-async def read_item(item_id: int, q: Optional[str] = None):
+async def read_item(item_id: int, q: Union[str, None] = None):
     return {"item_id": item_id, "q": q}
 ```
 
@@ -264,7 +264,7 @@ Now modify the file `main.py` to receive a body from a `PUT` request.
 Declare the body using standard Python types, thanks to Pydantic.
 
 ```Python hl_lines="4  9-12  25-27"
-from typing import Optional
+from typing import Union
 
 from fastapi import FastAPI
 from pydantic import BaseModel
@@ -275,7 +275,7 @@ app = FastAPI()
 class Item(BaseModel):
     name: str
     price: float
-    is_offer: Optional[bool] = None
+    is_offer: Union[bool, None] = None
 
 
 @app.get("/")
@@ -284,7 +284,7 @@ def read_root():
 
 
 @app.get("/items/{item_id}")
-def read_item(item_id: int, q: Optional[str] = None):
+def read_item(item_id: int, q: Union[str, None] = None):
     return {"item_id": item_id, "q": q}
 
 
@@ -321,7 +321,7 @@ 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.
 
@@ -378,7 +378,7 @@ 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.
@@ -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**
@@ -447,12 +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:

docs/de/mkdocs.yml

@@ -5,21 +5,24 @@ theme:
   name: material
   custom_dir: overrides
   palette:
-  - scheme: default
-    primary: teal
-    accent: amber
-    toggle:
-      icon: material/lightbulb-outline
-      name: Switch to light mode
-  - scheme: slate
+  - media: '(prefers-color-scheme: light)'
+    scheme: default
     primary: teal
     accent: amber
     toggle:
       icon: material/lightbulb
+      name: Switch to light mode
+  - media: '(prefers-color-scheme: dark)'
+    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
@@ -28,9 +31,6 @@ theme:
 repo_name: tiangolo/fastapi
 repo_url: https://github.com/tiangolo/fastapi
 edit_uri: ''
-google_analytics:
-- UA-133183413-1
-- auto
 plugins:
 - search
 - markdownextradata:
@@ -39,26 +39,32 @@ nav:
 - FastAPI: index.md
 - Languages:
   - en: /
+  - az: /az/
   - de: /de/
   - es: /es/
+  - fa: /fa/
   - fr: /fr/
+  - he: /he/
   - id: /id/
   - it: /it/
   - ja: /ja/
   - ko: /ko/
+  - nl: /nl/
   - pl: /pl/
   - pt: /pt/
   - ru: /ru/
   - sq: /sq/
+  - sv: /sv/
   - 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
@@ -67,9 +73,15 @@ markdown_extensions:
     custom_fences:
     - name: mermaid
       class: mermaid
-      format: !!python/name:pymdownx.superfences.fence_div_format ''
-- pymdownx.tabbed
+      format: !!python/name:pymdownx.superfences.fence_code_format ''
+- pymdownx.tabbed:
+    alternate_style: true
+- attr_list
+- md_in_html
 extra:
+  analytics:
+    provider: google
+    property: UA-133183413-1
   social:
   - icon: fontawesome/brands/github-alt
     link: https://github.com/tiangolo/fastapi
@@ -88,12 +100,18 @@ extra:
   alternate:
   - link: /
     name: en - English
+  - link: /az/
+    name: az
   - link: /de/
     name: de
   - link: /es/
     name: es - español
+  - link: /fa/
+    name: fa
   - link: /fr/
     name: fr - français
+  - link: /he/
+    name: he
   - link: /id/
     name: id
   - link: /it/
@@ -102,6 +120,8 @@ extra:
     name: ja - 日本語
   - link: /ko/
     name: ko - 한국어
+  - link: /nl/
+    name: nl
   - link: /pl/
     name: pl
   - link: /pt/
@@ -110,6 +130,8 @@ extra:
     name: ru - русский язык
   - link: /sq/
     name: sq - shqip
+  - link: /sv/
+    name: sv - svenska
   - link: /tr/
     name: tr - Türkçe
   - link: /uk/
@@ -120,6 +142,5 @@ 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

@@ -1,9 +1,37 @@
 articles:
   english:
+  - author: New Relic
+    author_link: https://newrelic.com
+    link: https://newrelic.com/instant-observability/fastapi/e559ec64-f765-4470-a15f-1901fcebb468
+    title: How to monitor FastAPI application performance using Python agent
+  - author: Jean-Baptiste Rocher
+    author_link: https://hashnode.com/@jibrocher
+    link: https://dev.indooroutdoor.io/series/fastapi-react-poll-app
+    title: Building the Poll App From Django Tutorial With FastAPI And React
+  - author: Silvan Melchior
+    author_link: https://github.com/silvanmelchior
+    link: https://blog.devgenius.io/seamless-fastapi-configuration-with-confz-90949c14ea12
+    title: Seamless FastAPI Configuration with ConfZ
+  - author: Kaustubh Gupta
+    author_link: https://medium.com/@kaustubhgupta1828/
+    link: https://levelup.gitconnected.com/5-advance-features-of-fastapi-you-should-try-7c0ac7eebb3e
+    title: 5 Advanced Features of FastAPI You Should Try
+  - 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
     title: How to Create A Fake Certificate Authority And Generate TLS Certs for FastAPI
+  - author: Ben Gamble
+    author_link: https://uk.linkedin.com/in/bengamble7
+    link: https://ably.com/blog/realtime-ticket-booking-solution-kafka-fastapi-ably
+    title: Building a realtime ticket booking solution with Kafka, FastAPI, and Ably
   - author: Shahriyar(Shako) Rzayev
     author_link: https://www.linkedin.com/in/shahriyar-rzayev/
     link: https://www.azepug.az/posts/fastapi/#building-simple-e-commerce-with-nuxtjs-and-fastapi-series
@@ -12,6 +40,10 @@ articles:
     author_link: https://rodrigo-arenas.medium.com/
     link: https://medium.com/analytics-vidhya/serve-a-machine-learning-model-using-sklearn-fastapi-and-docker-85aabf96729b
     title: "Serve a machine learning model using Sklearn, FastAPI and Docker"
+  - author: Yashasvi Singh
+    author_link: https://hashnode.com/@aUnicornDev
+    link: https://aunicorndev.hashnode.dev/series/supafast-api
+    title: "Building an API with FastAPI and Supabase and Deploying on Deta"
   - author: Navule Pavan Kumar Rao
     author_link: https://www.linkedin.com/in/navule/
     link: https://www.tutlinks.com/deploy-fastapi-on-ubuntu-gunicorn-caddy-2/
@@ -19,7 +51,7 @@ articles:
   - author: Patrick Ladon
     author_link: https://dev.to/factorlive
     link: https://dev.to/factorlive/python-facebook-messenger-webhook-with-fastapi-on-glitch-4n90
-    title: Python Facebook messenger webhook with FastAPI on Glitch  
+    title: Python Facebook messenger webhook with FastAPI on Glitch
   - author: Dom Patmore
     author_link: https://twitter.com/dompatmore
     link: https://dompatmore.com/blog/authenticate-your-fastapi-app-with-auth0
@@ -180,11 +212,23 @@ articles:
     author_link: https://medium.com/@williamhayes
     link: https://medium.com/@williamhayes/fastapi-starlette-debug-vs-prod-5f7561db3a59
     title: FastAPI/Starlette debug vs prod
+  - author: Mukul Mantosh
+    author_link: https://twitter.com/MantoshMukul
+    link: https://www.jetbrains.com/pycharm/guide/tutorials/fastapi-aws-kubernetes/
+    title: Developing FastAPI Application using K8s & AWS
+  - author: KrishNa
+    author_link: https://medium.com/@krishnardt365
+    link: https://medium.com/@krishnardt365/fastapi-docker-and-postgres-91943e71be92
+    title: Fastapi, Docker(Docker compose) and Postgres
   german:
   - author: Nico Axtmann
     author_link: https://twitter.com/_nicoax
     link: https://blog.codecentric.de/2019/08/inbetriebnahme-eines-scikit-learn-modells-mit-onnx-und-fastapi/
     title: Inbetriebnahme eines scikit-learn-Modells mit ONNX und FastAPI
+  - author: Felix Schürmeyer
+    author_link: https://hellocoding.de/autor/felix-schuermeyer/
+    link: https://hellocoding.de/blog/coding-language/python/fastapi
+    title: REST-API Programmieren mittels Python und dem FastAPI Modul
   japanese:
   - author: '@bee2'
     author_link: https://qiita.com/bee2

docs/en/data/github_sponsors.yml

@@ -0,0 +1,589 @@
+sponsors:
+- - login: jina-ai
+    avatarUrl: https://avatars.githubusercontent.com/u/60539444?v=4
+    url: https://github.com/jina-ai
+- - login: Doist
+    avatarUrl: https://avatars.githubusercontent.com/u/2565372?v=4
+    url: https://github.com/Doist
+  - login: cryptapi
+    avatarUrl: https://avatars.githubusercontent.com/u/44925437?u=61369138589bc7fee6c417f3fbd50fbd38286cc4&v=4
+    url: https://github.com/cryptapi
+- - login: ObliviousAI
+    avatarUrl: https://avatars.githubusercontent.com/u/65656077?v=4
+    url: https://github.com/ObliviousAI
+  - login: Lovage-Labs
+    avatarUrl: https://avatars.githubusercontent.com/u/71685552?v=4
+    url: https://github.com/Lovage-Labs
+  - login: chaserowbotham
+    avatarUrl: https://avatars.githubusercontent.com/u/97751084?v=4
+    url: https://github.com/chaserowbotham
+- - login: mikeckennedy
+    avatarUrl: https://avatars.githubusercontent.com/u/2035561?u=1bb18268bcd4d9249e1f783a063c27df9a84c05b&v=4
+    url: https://github.com/mikeckennedy
+  - login: Trivie
+    avatarUrl: https://avatars.githubusercontent.com/u/8161763?v=4
+    url: https://github.com/Trivie
+  - login: deta
+    avatarUrl: https://avatars.githubusercontent.com/u/47275976?v=4
+    url: https://github.com/deta
+  - login: deepset-ai
+    avatarUrl: https://avatars.githubusercontent.com/u/51827949?v=4
+    url: https://github.com/deepset-ai
+  - login: investsuite
+    avatarUrl: https://avatars.githubusercontent.com/u/73833632?v=4
+    url: https://github.com/investsuite
+  - login: VincentParedes
+    avatarUrl: https://avatars.githubusercontent.com/u/103889729?v=4
+    url: https://github.com/VincentParedes
+- - login: InesIvanova
+    avatarUrl: https://avatars.githubusercontent.com/u/22920417?u=409882ec1df6dbd77455788bb383a8de223dbf6f&v=4
+    url: https://github.com/InesIvanova
+- - login: SendCloud
+    avatarUrl: https://avatars.githubusercontent.com/u/7831959?v=4
+    url: https://github.com/SendCloud
+  - login: mercedes-benz
+    avatarUrl: https://avatars.githubusercontent.com/u/34240465?v=4
+    url: https://github.com/mercedes-benz
+  - login: xoflare
+    avatarUrl: https://avatars.githubusercontent.com/u/74335107?v=4
+    url: https://github.com/xoflare
+  - login: Striveworks
+    avatarUrl: https://avatars.githubusercontent.com/u/45523576?v=4
+    url: https://github.com/Striveworks
+  - login: BoostryJP
+    avatarUrl: https://avatars.githubusercontent.com/u/57932412?v=4
+    url: https://github.com/BoostryJP
+  - login: Jonathanjwp
+    avatarUrl: https://avatars.githubusercontent.com/u/110426978?u=5e6ed4984022cb8886c4fdfdc0c59f55c48a2f1d&v=4
+    url: https://github.com/Jonathanjwp
+- - login: johnadjei
+    avatarUrl: https://avatars.githubusercontent.com/u/767860?v=4
+    url: https://github.com/johnadjei
+  - login: HiredScore
+    avatarUrl: https://avatars.githubusercontent.com/u/3908850?v=4
+    url: https://github.com/HiredScore
+  - login: wdwinslow
+    avatarUrl: https://avatars.githubusercontent.com/u/11562137?u=dc01daafb354135603a263729e3d26d939c0c452&v=4
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docs/en/data/sponsors.yml

@@ -1,21 +1,42 @@
 gold:
+  - url: https://bit.ly/3dmXC5S
+    title: The data structure for unstructured multimodal data
+    img: https://fastapi.tiangolo.com/img/sponsors/docarray.svg
+  - url: https://bit.ly/3JJ7y5C
+    title: Build cross-modal and multimodal applications on the cloud
+    img: https://fastapi.tiangolo.com/img/sponsors/jina2.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
+  - url: https://doist.com/careers/9B437B1615-wa-senior-backend-engineer-python
+    title: Help us migrate doist to FastAPI
+    img: https://fastapi.tiangolo.com/img/sponsors/doist.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://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
-silver:
   - url: https://www.investsuite.com/jobs
     title: Wealthtech jobs with FastAPI
     img: https://fastapi.tiangolo.com/img/sponsors/investsuite.svg
-  - url: https://www.vim.so/?utm_source=FastAPI
-    title: We help you master vim with interactive exercises
-    img: https://fastapi.tiangolo.com/img/sponsors/vimso.png
-  - url: https://talkpython.fm/fastapi-sponsor
+  - url: https://training.talkpython.fm/fastapi-courses
     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
+  - url: https://www.udemy.com/course/fastapi-rest/
+    title: Learn FastAPI by building a complete project. Extend your knowledge on advanced web development-AWS, Payments, Emails.
+    img: https://fastapi.tiangolo.com/img/sponsors/ines-course.jpg
+  - url: https://careers.budget-insight.com/
+    title: Budget Insight is hiring!
+    img: https://fastapi.tiangolo.com/img/sponsors/budget-insight.svg
+bronze:
+  - url: https://www.exoflare.com/open-source/?utm_source=FastAPI&utm_campaign=open_source
+    title: Biosecurity risk assessments made easy.
+    img: https://fastapi.tiangolo.com/img/sponsors/exoflare.png
+  - url: https://bit.ly/3ccLCmM
+    title: https://striveworks.us/careers
+    img: https://fastapi.tiangolo.com/img/sponsors/striveworks2.png

docs/en/data/sponsors_badge.yml

@@ -0,0 +1,15 @@
+logins:
+  - jina-ai
+  - deta
+  - investsuite
+  - mikeckennedy
+  - deepset-ai
+  - cryptapi
+  - Striveworks
+  - xoflare
+  - InesIvanova
+  - DropbaseHQ
+  - VincentParedes
+  - BLUE-DEVIL1134
+  - ObliviousAI
+  - Doist

docs/en/docs/advanced/additional-responses.md

@@ -23,7 +23,7 @@ Each of those response `dict`s can have a key `model`, containing a Pydantic mod
 
 For example, to declare another response with a status code `404` and a Pydantic model `Message`, you can write:
 
-```Python hl_lines="18  23"
+```Python hl_lines="18  22"
 {!../../../docs_src/additional_responses/tutorial001.py!}
 ```
 
@@ -36,7 +36,7 @@ For example, to declare another response with a status code `404` and a Pydantic
     **FastAPI** will take the Pydantic model from there, generate the `JSON Schema`, and put it in the correct place.
 
     The correct place is:
-    
+
     * In the key `content`, that has as value another JSON object (`dict`) that contains:
         * A key with the media type, e.g. `application/json`, that contains as value another JSON object, that contains:
             * A key `schema`, that has as the value the JSON Schema from the model, here's the correct place.

docs/en/docs/advanced/additional-status-codes.md

@@ -14,7 +14,7 @@ But you also want it to accept new items. And when the items didn't exist before
 
 To achieve that, import `JSONResponse`, and return your content there directly, setting the `status_code` that you want:
 
-```Python hl_lines="4  23"
+```Python hl_lines="4  25"
 {!../../../docs_src/additional_status_codes/tutorial001.py!}
 ```
 
@@ -22,7 +22,7 @@ To achieve that, import `JSONResponse`, and return your content there directly,
     When you return a `Response` directly, like in the example above, it will be returned directly.
 
     It won't be serialized with a model, etc.
-    
+
     Make sure it has the data you want it to have, and that the values are valid JSON (if you are using `JSONResponse`).
 
 !!! note "Technical Details"

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
 
@@ -38,13 +26,23 @@ The important difference for us is that with HTTPX we are not limited to synchro
 
 ## Example
 
-For a simple example, let's consider the following `main.py` module:
+For a simple example, let's consider a file structure similar to the one described in [Bigger Applications](../tutorial/bigger-applications.md){.internal-link target=_blank} and [Testing](../tutorial/testing.md){.internal-link target=_blank}:
+
+```
+.
+├── app
+│   ├── __init__.py
+│   ├── main.py
+│   └── test_main.py
+```
+
+The file `main.py` would have:
 
 ```Python
 {!../../../docs_src/async_tests/main.py!}
 ```
 
-The `test_main.py` module that contains the tests for `main.py` could look like this now:
+The file `test_main.py` would have the tests for `main.py`, it could look like this now:
 
 ```Python
 {!../../../docs_src/async_tests/test_main.py!}
@@ -66,7 +64,7 @@ $ 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 +95,4 @@ 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/custom-response.md

@@ -21,6 +21,12 @@ For example, if you are squeezing performance, you can install and use <a href="
 
 Import the `Response` class (sub-class) you want to use and declare it in the *path operation decorator*.
 
+For large responses, returning a `Response` directly is much faster than returning a dictionary.
+
+This is because by default, FastAPI will inspect every item inside and make sure it is serializable with JSON, using the same [JSON Compatible Encoder](../tutorial/encoder.md){.internal-link target=_blank} explained in the tutorial. This is what allows you to return **arbitrary objects**, for example database models.
+
+But if you are certain that the content that you are returning is **serializable with JSON**, you can pass it directly to the response class and avoid the extra overhead that FastAPI would have by passing your return content through the `jsonable_encoder` before passing it to the response class.
+
 ```Python hl_lines="2  7"
 {!../../../docs_src/custom_response/tutorial001b.py!}
 ```
@@ -244,6 +250,36 @@ You can also use the `response_class` parameter:
 
 In this case, you can return the file path directly from your *path operation* function.
 
+## Custom response class
+
+You can create your own custom response class, inheriting from `Response` and using it.
+
+For example, let's say that you want to use <a href="https://github.com/ijl/orjson" class="external-link" target="_blank">`orjson`</a>, but with some custom settings not used in the included `ORJSONResponse` class.
+
+Let's say you want it to return indented and formatted JSON, so you want to use the orjson option `orjson.OPT_INDENT_2`.
+
+You could create a `CustomORJSONResponse`. The main thing you have to do is create a `Response.render(content)` method that returns the content as `bytes`:
+
+```Python hl_lines="9-14  17"
+{!../../../docs_src/custom_response/tutorial009c.py!}
+```
+
+Now instead of returning:
+
+```json
+{"message": "Hello World"}
+```
+
+...this response will return:
+
+```json
+{
+  "message": "Hello World"
+}
+```
+
+Of course, you will probably find much better ways to take advantage of this than formatting JSON. 😉
+
 ## Default response class
 
 When creating a **FastAPI** class instance or an `APIRouter` you can specify which response class to use by default.

docs/en/docs/advanced/dataclasses.md

@@ -8,7 +8,7 @@ But FastAPI also supports using <a href="https://docs.python.org/3/library/datac
 {!../../../docs_src/dataclasses/tutorial001.py!}
 ```
 
-This is still thanks to **Pydantic**, as it has <a href="https://pydantic-docs.helpmanual.io/usage/dataclasses/#use-of-stdlib-dataclasses-with-basemodel" class="external-link" target="_blank">internal support for `dataclasses`</a>.
+This is still supported thanks to **Pydantic**, as it has <a href="https://pydantic-docs.helpmanual.io/usage/dataclasses/#use-of-stdlib-dataclasses-with-basemodel" class="external-link" target="_blank">internal support for `dataclasses`</a>.
 
 So, even with the code above that doesn't use Pydantic explicitly, FastAPI is using Pydantic to convert those standard dataclasses to Pydantic's own flavor of dataclasses.
 

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

@@ -132,8 +132,8 @@ You can probably right-click each link and select an option similar to `Save lin
 
 **Swagger UI** uses the files:
 
-* <a href="https://cdn.jsdelivr.net/npm/swagger-ui-dist@3/swagger-ui-bundle.js" class="external-link" target="_blank">`swagger-ui-bundle.js`</a>
-* <a href="https://cdn.jsdelivr.net/npm/swagger-ui-dist@3/swagger-ui.css" class="external-link" target="_blank">`swagger-ui.css`</a>
+* <a href="https://cdn.jsdelivr.net/npm/swagger-ui-dist@4/swagger-ui-bundle.js" class="external-link" target="_blank">`swagger-ui-bundle.js`</a>
+* <a href="https://cdn.jsdelivr.net/npm/swagger-ui-dist@4/swagger-ui.css" class="external-link" target="_blank">`swagger-ui.css`</a>
 
 And **ReDoc** uses the file:
 
@@ -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`.
@@ -248,3 +233,82 @@ Now, to be able to test that everything works, create a *path operation*:
 Now, you should be able to disconnect your WiFi, go to your docs at <a href="http://127.0.0.1:8000/docs" class="external-link" target="_blank">http://127.0.0.1:8000/docs</a>, and reload the page.
 
 And even without Internet, you would be able to see the docs for your API and interact with it.
+
+## Configuring Swagger UI
+
+You can configure some extra <a href="https://swagger.io/docs/open-source-tools/swagger-ui/usage/configuration" class="external-link" target="_blank">Swagger UI parameters</a>.
+
+To configure them, pass the `swagger_ui_parameters` argument when creating the `FastAPI()` app object or to the `get_swagger_ui_html()` function.
+
+`swagger_ui_parameters` receives a dictionary with the configurations passed to Swagger UI directly.
+
+FastAPI converts the configurations to **JSON** to make them compatible with JavaScript, as that's what Swagger UI needs.
+
+### Disable Syntax Highlighting
+
+For example, you could disable syntax highlighting in Swagger UI.
+
+Without changing the settings, syntax highlighting is enabled by default:
+
+<img src="/img/tutorial/extending-openapi/image02.png">
+
+But you can disable it by setting `syntaxHighlight` to `False`:
+
+```Python hl_lines="3"
+{!../../../docs_src/extending_openapi/tutorial003.py!}
+```
+
+...and then Swagger UI won't show the syntax highlighting anymore:
+
+<img src="/img/tutorial/extending-openapi/image03.png">
+
+### Change the Theme
+
+The same way you could set the syntax highlighting theme with the key `"syntaxHighlight.theme"` (notice that it has a dot in the middle):
+
+```Python hl_lines="3"
+{!../../../docs_src/extending_openapi/tutorial004.py!}
+```
+
+That configuration would change the syntax highlighting color theme:
+
+<img src="/img/tutorial/extending-openapi/image04.png">
+
+### Change Default Swagger UI Parameters
+
+FastAPI includes some default configuration parameters appropriate for most of the use cases.
+
+It includes these default configurations:
+
+```Python
+{!../../../fastapi/openapi/docs.py[ln:7-13]!}
+```
+
+You can override any of them by setting a different value in the argument `swagger_ui_parameters`.
+
+For example, to disable `deepLinking` you could pass these settings to `swagger_ui_parameters`:
+
+```Python hl_lines="3"
+{!../../../docs_src/extending_openapi/tutorial005.py!}
+```
+
+### Other Swagger UI Parameters
+
+To see all the other possible configurations you can use, read the official <a href="https://swagger.io/docs/open-source-tools/swagger-ui/usage/configuration" class="external-link" target="_blank">docs for Swagger UI parameters</a>.
+
+### JavaScript-only settings
+
+Swagger UI also allows other configurations to be **JavaScript-only** objects (for example, JavaScript functions).
+
+FastAPI also includes these JavaScript-only `presets` settings:
+
+```JavaScript
+presets: [
+    SwaggerUIBundle.presets.apis,
+    SwaggerUIBundle.SwaggerUIStandalonePreset
+]
+```
+
+These are **JavaScript** objects, not strings, so you can't pass them from Python code directly.
+
+If you need to use JavaScript-only configurations like those, you can use one of the methods above. Override all the Swagger UI *path operation* and manually write any JavaScript you need.

docs/en/docs/advanced/generate-clients.md

@@ -0,0 +1,267 @@
+# Generate Clients
+
+As **FastAPI** is based on the OpenAPI specification, you get automatic compatibility with many tools, including the automatic API docs (provided by Swagger UI).
+
+One particular advantage that is not necessarily obvious is that you can **generate clients** (sometimes called <abbr title="Software Development Kits">**SDKs**</abbr> ) for your API, for many different **programming languages**.
+
+## OpenAPI Client Generators
+
+There are many tools to generate clients from **OpenAPI**.
+
+A common tool is <a href="https://openapi-generator.tech/" class="external-link" target="_blank">OpenAPI Generator</a>.
+
+If you are building a **frontend**, a very interesting alternative is <a href="https://github.com/ferdikoomen/openapi-typescript-codegen" class="external-link" target="_blank">openapi-typescript-codegen</a>.
+
+## Generate a TypeScript Frontend Client
+
+Let's start with a simple FastAPI application:
+
+=== "Python 3.6 and above"
+
+    ```Python hl_lines="9-11  14-15  18  19  23"
+    {!> ../../../docs_src/generate_clients/tutorial001.py!}
+    ```
+
+=== "Python 3.9 and above"
+
+    ```Python hl_lines="7-9  12-13  16-17  21"
+    {!> ../../../docs_src/generate_clients/tutorial001_py39.py!}
+    ```
+
+Notice that the *path operations* define the models they use for request payload and response payload, using the models `Item` and `ResponseMessage`.
+
+### API Docs
+
+If you go to the API docs, you will see that it has the **schemas** for the data to be sent in requests and received in responses:
+
+<img src="/img/tutorial/generate-clients/image01.png">
+
+You can see those schemas because they were declared with the models in the app.
+
+That information is available in the app's **OpenAPI schema**, and then shown in the API docs (by Swagger UI).
+
+And that same information from the models that is included in OpenAPI is what can be used to **generate the client code**.
+
+### Generate a TypeScript Client
+
+Now that we have the app with the models, we can generate the client code for the frontend.
+
+#### Install `openapi-typescript-codegen`
+
+You can install `openapi-typescript-codegen` in your frontend code with:
+
+<div class="termy">
+
+```console
+$ npm install openapi-typescript-codegen --save-dev
+
+---> 100%
+```
+
+</div>
+
+#### Generate Client Code
+
+To generate the client code you can use the command line application `openapi` that would now be installed.
+
+Because it is installed in the local project, you probably wouldn't be able to call that command directly, but you would put it on your `package.json` file.
+
+It could look like this:
+
+```JSON  hl_lines="7"
+{
+  "name": "frontend-app",
+  "version": "1.0.0",
+  "description": "",
+  "main": "index.js",
+  "scripts": {
+    "generate-client": "openapi --input http://localhost:8000/openapi.json --output ./src/client --client axios"
+  },
+  "author": "",
+  "license": "",
+  "devDependencies": {
+    "openapi-typescript-codegen": "^0.20.1",
+    "typescript": "^4.6.2"
+  }
+}
+```
+
+After having that NPM `generate-client` script there, you can run it with:
+
+<div class="termy">
+
+```console
+$ npm run generate-client
+
+frontend-app@1.0.0 generate-client /home/user/code/frontend-app
+> openapi --input http://localhost:8000/openapi.json --output ./src/client --client axios
+```
+
+</div>
+
+That command will generate code in `./src/client` and will use `axios` (the frontend HTTP library) internally.
+
+### Try Out the Client Code
+
+Now you can import and use the client code, it could look like this, notice that you get autocompletion for the methods:
+
+<img src="/img/tutorial/generate-clients/image02.png">
+
+You will also get autocompletion for the payload to send:
+
+<img src="/img/tutorial/generate-clients/image03.png">
+
+!!! tip
+    Notice the autocompletion for `name` and `price`, that was defined in the FastAPI application, in the `Item` model.
+
+You will have inline errors for the data that you send:
+
+<img src="/img/tutorial/generate-clients/image04.png">
+
+The response object will also have autocompletion:
+
+<img src="/img/tutorial/generate-clients/image05.png">
+
+## FastAPI App with Tags
+
+In many cases your FastAPI app will be bigger, and you will probably use tags to separate different groups of *path operations*.
+
+For example, you could have a section for **items** and another section for **users**, and they could be separated by tags:
+
+
+=== "Python 3.6 and above"
+
+    ```Python hl_lines="23  28  36"
+    {!> ../../../docs_src/generate_clients/tutorial002.py!}
+    ```
+
+=== "Python 3.9 and above"
+
+    ```Python hl_lines="21  26  34"
+    {!> ../../../docs_src/generate_clients/tutorial002_py39.py!}
+    ```
+
+### Generate a TypeScript Client with Tags
+
+If you generate a client for a FastAPI app using tags, it will normally also separate the client code based on the tags.
+
+This way you will be able to have things ordered and grouped correctly for the client code:
+
+<img src="/img/tutorial/generate-clients/image06.png">
+
+In this case you have:
+
+* `ItemsService`
+* `UsersService`
+
+### Client Method Names
+
+Right now the generated method names like `createItemItemsPost` don't look very clean:
+
+```TypeScript
+ItemsService.createItemItemsPost({name: "Plumbus", price: 5})
+```
+
+...that's because the client generator uses the OpenAPI internal **operation ID** for each *path operation*.
+
+OpenAPI requires that each operation ID is unique across all the *path operations*, so FastAPI uses the **function name**, the **path**, and the **HTTP method/operation** to generate that operation ID, because that way it can make sure that the operation IDs are unique.
+
+But I'll show you how to improve that next. 🤓
+
+## Custom Operation IDs and Better Method Names
+
+You can **modify** the way these operation IDs are **generated** to make them simpler and have **simpler method names** in the clients.
+
+In this case you will have to ensure that each operation ID is **unique** in some other way.
+
+For example, you could make sure that each *path operation* has a tag, and then generate the operation ID based on the **tag** and the *path operation* **name** (the function name).
+
+### Custom Generate Unique ID Function
+
+FastAPI uses a **unique ID** for each *path operation*, it is used for the **operation ID** and also for the names of any needed custom models, for requests or responses.
+
+You can customize that function. It takes an `APIRoute` and outputs a string.
+
+For example, here it is using the first tag (you will probably have only one tag) and the *path operation* name (the function name).
+
+You can then pass that custom function to **FastAPI** as the `generate_unique_id_function` parameter:
+
+=== "Python 3.6 and above"
+
+    ```Python hl_lines="8-9  12"
+    {!> ../../../docs_src/generate_clients/tutorial003.py!}
+    ```
+
+=== "Python 3.9 and above"
+
+    ```Python hl_lines="6-7  10"
+    {!> ../../../docs_src/generate_clients/tutorial003_py39.py!}
+    ```
+
+### Generate a TypeScript Client with Custom Operation IDs
+
+Now if you generate the client again, you will see that it has the improved method names:
+
+<img src="/img/tutorial/generate-clients/image07.png">
+
+As you see, the method names now have the tag and then the function name, now they don't include information from the URL path and the HTTP operation.
+
+### Preprocess the OpenAPI Specification for the Client Generator
+
+The generated code still has some **duplicated information**.
+
+We already know that this method is related to the **items** because that word is in the `ItemsService` (taken from the tag), but we still have the tag name prefixed in the method name too. 😕
+
+We will probably still want to keep it for OpenAPI in general, as that will ensure that the operation IDs are **unique**.
+
+But for the generated client we could **modify** the OpenAPI operation IDs right before generating the clients, just to make those method names nicer and **cleaner**.
+
+We could download the OpenAPI JSON to a file `openapi.json` and then we could **remove that prefixed tag** with a script like this:
+
+```Python
+{!../../../docs_src/generate_clients/tutorial004.py!}
+```
+
+With that, the operation IDs would be renamed from things like `items-get_items` to just `get_items`, that way the client generator can generate simpler method names.
+
+### Generate a TypeScript Client with the Preprocessed OpenAPI
+
+Now as the end result is in a file `openapi.json`, you would modify the `package.json` to use that local file, for example:
+
+```JSON  hl_lines="7"
+{
+  "name": "frontend-app",
+  "version": "1.0.0",
+  "description": "",
+  "main": "index.js",
+  "scripts": {
+    "generate-client": "openapi --input ./openapi.json --output ./src/client --client axios"
+  },
+  "author": "",
+  "license": "",
+  "devDependencies": {
+    "openapi-typescript-codegen": "^0.20.1",
+    "typescript": "^4.6.2"
+  }
+}
+```
+
+After generating the new client, you would now have **clean method names**, with all the **autocompletion**, **inline errors**, etc:
+
+<img src="/img/tutorial/generate-clients/image08.png">
+
+## Benefits
+
+When using the automatically generated clients you would **autocompletion** for:
+
+* Methods.
+* Request payloads in the body, query parameters, etc.
+* Response payloads.
+
+You would also have **inline errors** for everything.
+
+And whenever you update the backend code, and **regenerate** the frontend, it would have any new *path operations* available as methods, the old ones removed, and any other change would be reflected on the generated code. 🤓
+
+This also means that if something changed it will be **reflected** on the client code automatically. And if you **build** the client it will error out if you have any **mismatch** in the data used.
+
+So, you would **detect many errors** very early in the development cycle instead of having to wait for the errors to show up to your final users in production and then trying to debug where the problem is. ✨

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/openapi-callbacks.md

@@ -31,7 +31,7 @@ It will have a *path operation* that will receive an `Invoice` body, and a query
 
 This part is pretty normal, most of the code is probably already familiar to you:
 
-```Python hl_lines="10-14  37-54"
+```Python hl_lines="9-13  36-53"
 {!../../../docs_src/openapi_callbacks/tutorial001.py!}
 ```
 
@@ -83,7 +83,7 @@ So we are going to use that same knowledge to document how the *external API* sh
 
 First create a new `APIRouter` that will contain one or more callbacks.
 
-```Python hl_lines="5  26"
+```Python hl_lines="3  25"
 {!../../../docs_src/openapi_callbacks/tutorial001.py!}
 ```
 
@@ -96,7 +96,7 @@ It should look just like a normal FastAPI *path operation*:
 * It should probably have a declaration of the body it should receive, e.g. `body: InvoiceEvent`.
 * And it could also have a declaration of the response it should return, e.g. `response_model=InvoiceEventReceived`.
 
-```Python hl_lines="17-19  22-23  29-33"
+```Python hl_lines="16-18  21-22  28-32"
 {!../../../docs_src/openapi_callbacks/tutorial001.py!}
 ```
 
@@ -163,7 +163,7 @@ At this point you have the *callback path operation(s)* needed (the one(s) that
 
 Now use the parameter `callbacks` in *your API's path operation decorator* to pass the attribute `.routes` (that's actually just a `list` of routes/*path operations*) from that callback router:
 
-```Python hl_lines="36"
+```Python hl_lines="35"
 {!../../../docs_src/openapi_callbacks/tutorial001.py!}
 ```
 

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/security/http-basic-auth.md

@@ -34,13 +34,19 @@ Here's a more complete example.
 
 Use a dependency to check if the username and password are correct.
 
-For this, use the Python standard module <a href="https://docs.python.org/3/library/secrets.html" class="external-link" target="_blank">`secrets`</a> to check the username and password:
+For this, use the Python standard module <a href="https://docs.python.org/3/library/secrets.html" class="external-link" target="_blank">`secrets`</a> to check the username and password.
 
-```Python hl_lines="1  11-13"
+`secrets.compare_digest()` needs to take `bytes` or a `str` that only contains ASCII characters (the ones in English), this means it wouldn't work with characters like `á`, as in `Sebastián`.
+
+To handle that, we first convert the `username` and `password` to `bytes` encoding them with UTF-8.
+
+Then we can use `secrets.compare_digest()` to ensure that `credentials.username` is `"stanleyjobson"`, and that `credentials.password` is `"swordfish"`.
+
+```Python hl_lines="1  11-21"
 {!../../../docs_src/security/tutorial007.py!}
 ```
 
-This will ensure that `credentials.username` is `"stanleyjobson"`, and that `credentials.password` is `"swordfish"`. This would be similar to:
+This would be similar to:
 
 ```Python
 if not (credentials.username == "stanleyjobson") or not (credentials.password == "swordfish"):
@@ -102,6 +108,6 @@ That way, using `secrets.compare_digest()` in your application code, it will be
 
 After detecting that the credentials are incorrect, return an `HTTPException` with a status code 401 (the same returned when no credentials are provided) and add the header `WWW-Authenticate` to make the browser show the login prompt again:
 
-```Python hl_lines="15-19"
+```Python hl_lines="23-27"
 {!../../../docs_src/security/tutorial007.py!}
 ```

docs/en/docs/advanced/security/oauth2-scopes.md

@@ -16,11 +16,11 @@ In this section you will see how to manage authentication and authorization with
     You don't necessarily need OAuth2 scopes, and you can handle authentication and authorization however you want.
 
     But OAuth2 with scopes can be nicely integrated into your API (with OpenAPI) and your API docs.
-    
+
     Nevertheless, you still enforce those scopes, or any other security/authorization requirement, however you need, in your code.
 
     In many cases, OAuth2 with scopes can be an overkill.
-    
+
     But if you know you need it, or you are curious, keep reading.
 
 ## OAuth2 scopes and OpenAPI
@@ -47,7 +47,7 @@ They are normally used to declare specific security permissions, for example:
     In OAuth2 a "scope" is just a string that declares a specific permission required.
 
     It doesn't matter if it has other characters like `:` or if it is a URL.
-    
+
     Those details are implementation specific.
 
     For OAuth2 they are just strings.
@@ -115,7 +115,7 @@ In this case, it requires the scope `me` (it could require more than one scope).
 
 !!! note
     You don't necessarily need to add different scopes in different places.
-    
+
     We are doing it here to demonstrate how **FastAPI** handles scopes declared at different levels.
 
 ```Python hl_lines="4  139  166"

docs/en/docs/advanced/sql-databases-peewee.md

@@ -182,7 +182,7 @@ Now let's check the file `sql_app/schemas.py`.
     To avoid confusion between the Peewee *models* and the Pydantic *models*, we will have the file `models.py` with the Peewee models, and the file `schemas.py` with the Pydantic models.
 
     These Pydantic models define more or less a "schema" (a valid data shape).
-    
+
     So this will help us avoiding confusion while using both.
 
 ### Create the Pydantic *models* / schemas

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/advanced/testing-dependencies.md

@@ -28,7 +28,7 @@ To override a dependency for testing, you put as a key the original dependency (
 
 And then **FastAPI** will call that override instead of the original dependency.
 
-```Python hl_lines="26-27  30"
+```Python hl_lines="28-29  32"
 {!../../../docs_src/dependency_testing/tutorial001.py!}
 ```
 

docs/en/docs/advanced/websockets.md

@@ -2,6 +2,20 @@
 
 You can use <a href="https://developer.mozilla.org/en-US/docs/Web/API/WebSockets_API" class="external-link" target="_blank">WebSockets</a> with **FastAPI**.
 
+## Install `WebSockets`
+
+First you need to install `WebSockets`:
+
+<div class="termy">
+
+```console
+$ pip install websockets
+
+---> 100%
+```
+
+</div>
+
 ## WebSockets client
 
 ### In production

docs/en/docs/alternatives.md

@@ -235,15 +235,13 @@ It was one of the first extremely fast Python frameworks based on `asyncio`. It
 
 !!! check "Inspired **FastAPI** to"
     Find a way to have a crazy performance.
-    
+
     That's why **FastAPI** is based on Starlette, as it is the fastest framework available (tested by third-party benchmarks).
 
 ### <a href="https://falconframework.org/" class="external-link" target="_blank">Falcon</a>
 
 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.
@@ -335,7 +333,7 @@ Now APIStar is a set of tools to validate OpenAPI specifications, not a web fram
     Exist.
 
     The idea of declaring multiple things (data validation, serialization and documentation) with the same Python types, that at the same time provided great editor support, was something I considered a brilliant idea.
-    
+
     And after searching for a long time for a similar framework and testing many different alternatives, APIStar was the best option available.
 
     Then APIStar stopped to exist as a server and Starlette was created, and was a new better foundation for such a system. That was the final inspiration to build **FastAPI**.
@@ -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.
 
@@ -394,7 +391,7 @@ That's one of the main things that **FastAPI** adds on top, all based on Python
     Handle all the core web parts. Adding features on top.
 
     The class `FastAPI` itself inherits directly from the class `Starlette`.
-    
+
     So, anything that you can do with Starlette, you can do it directly with **FastAPI**, as it is basically Starlette on steroids.
 
 ### <a href="https://www.uvicorn.org/" class="external-link" target="_blank">Uvicorn</a>

docs/en/docs/async.md

@@ -26,7 +26,7 @@ async def read_results():
 
 ---
 
-If you are using a third party library that communicates with something (a database, an API, the file system, etc) and doesn't have support for using `await`, (this is currently the case for most database libraries), then declare your *path operation functions* as normally, with just `def`, like:
+If you are using a third party library that communicates with something (a database, an API, the file system, etc.) and doesn't have support for using `await`, (this is currently the case for most database libraries), then declare your *path operation functions* as normally, with just `def`, like:
 
 ```Python hl_lines="2"
 @app.get('/')
@@ -45,7 +45,7 @@ If you just don't know, use normal `def`.
 
 ---
 
-**Note**: you can mix `def` and `async def` in your *path operation functions* as much as you need and define each one using the best option for you. FastAPI will do the right thing with them.
+**Note**: You can mix `def` and `async def` in your *path operation functions* as much as you need and define each one using the best option for you. FastAPI will do the right thing with them.
 
 Anyway, in any of the cases above, FastAPI will still work asynchronously and be extremely fast.
 
@@ -102,87 +102,117 @@ To see the difference, imagine the following story about burgers:
 
 ### Concurrent Burgers
 
-<!-- The gender neutral cook emoji "🧑‍🍳" does not render well in browsers. In the meantime, I'm using a mix of male "👨‍🍳" and female "👩‍🍳" cooks. -->
+You go with your crush to get fast food, you stand in line while the cashier takes the orders from the people in front of you. 😍
 
-You go with your crush 😍 to get fast food 🍔, you stand in line while the cashier 💁 takes the orders from the people in front of you.
+<img src="/img/async/concurrent-burgers/concurrent-burgers-01.png" class="illustration">
 
-Then it's your turn, you place your order of 2 very fancy burgers 🍔 for your crush 😍 and you.
+Then it's your turn, you place your order of 2 very fancy burgers for your crush and you. 🍔🍔
 
-You pay 💸.
+<img src="/img/async/concurrent-burgers/concurrent-burgers-02.png" class="illustration">
 
-The cashier 💁 says something to the cook in the kitchen 👨‍🍳 so they know they have to prepare your burgers 🍔 (even though they are currently preparing the ones for the previous clients).
+The cashier says something to the cook in the kitchen so they know they have to prepare your burgers (even though they are currently preparing the ones for the previous clients).
 
-The cashier 💁 gives you the number of your turn.
+<img src="/img/async/concurrent-burgers/concurrent-burgers-03.png" class="illustration">
 
-While you are waiting, you go with your crush 😍 and pick a table, you sit and talk with your crush 😍 for a long time (as your burgers are very fancy and take some time to prepare ✨🍔✨).
+You pay. 💸
 
-As you are sitting on the table with your crush 😍, while you wait for the burgers 🍔, you can spend that time admiring how awesome, cute and smart your crush is ✨😍✨.
+The cashier gives you the number of your turn.
 
-While waiting and talking to your crush 😍, from time to time, you check the number displayed on the counter to see if it's your turn already.
+<img src="/img/async/concurrent-burgers/concurrent-burgers-04.png" class="illustration">
 
-Then at some point, it finally is your turn. You go to the counter, get your burgers 🍔 and come back to the table.
+While you are waiting, you go with your crush and pick a table, you sit and talk with your crush for a long time (as your burgers are very fancy and take some time to prepare).
 
-You and your crush 😍 eat the burgers 🍔 and have a nice time ✨.
+As you are sitting at the table with your crush, while you wait for the burgers, you can spend that time admiring how awesome, cute and smart your crush is ✨😍✨.
+
+<img src="/img/async/concurrent-burgers/concurrent-burgers-05.png" class="illustration">
+
+While waiting and talking to your crush, from time to time, you check the number displayed on the counter to see if it's your turn already.
+
+Then at some point, it finally is your turn. You go to the counter, get your burgers and come back to the table.
+
+<img src="/img/async/concurrent-burgers/concurrent-burgers-06.png" class="illustration">
+
+You and your crush eat the burgers and have a nice time. ✨
+
+<img src="/img/async/concurrent-burgers/concurrent-burgers-07.png" class="illustration">
+
+!!! info
+    Beautiful illustrations by <a href="https://www.instagram.com/ketrinadrawsalot" class="external-link" target="_blank">Ketrina Thompson</a>. 🎨
 
 ---
 
 Imagine you are the computer / program 🤖 in that story.
 
-While you are at the line, you are just idle 😴, waiting for your turn, not doing anything very "productive". But the line is fast because the cashier 💁 is only taking the orders (not preparing them), so that's fine.
+While you are at the line, you are just idle 😴, waiting for your turn, not doing anything very "productive". But the line is fast because the cashier is only taking the orders (not preparing them), so that's fine.
 
-Then, when it's your turn, you do actual "productive" work 🤓, you process the menu, decide what you want, get your crush's 😍 choice, pay 💸, check that you give the correct bill or card, check that you are charged correctly, check that the order has the correct items, etc.
+Then, when it's your turn, you do actual "productive" work, you process the menu, decide what you want, get your crush's choice, pay, check that you give the correct bill or card, check that you are charged correctly, check that the order has the correct items, etc.
 
-But then, even though you still don't have your burgers 🍔, your work with the cashier 💁 is "on pause" ⏸, because you have to wait 🕙 for your burgers to be ready.
+But then, even though you still don't have your burgers, your work with the cashier is "on pause" ⏸, because you have to wait 🕙 for your burgers to be ready.
 
-But as you go away from the counter and sit on the table with a number for your turn, you can switch 🔀 your attention to your crush 😍, and "work" ⏯ 🤓 on that. Then you are again doing something very "productive" 🤓, as is flirting with your crush 😍.
+But as you go away from the counter and sit at the table with a number for your turn, you can switch 🔀 your attention to your crush, and "work" ⏯ 🤓 on that. Then you are again doing something very "productive" as is flirting with your crush 😍.
 
-Then the cashier 💁 says "I'm finished with doing the burgers" 🍔 by putting your number on the counter's display, but you don't jump like crazy immediately when the displayed number changes to your turn number. You know no one will steal your burgers 🍔 because you have the number of your turn, and they have theirs.
+Then the cashier 💁 says "I'm finished with doing the burgers" by putting your number on the counter's display, but you don't jump like crazy immediately when the displayed number changes to your turn number. You know no one will steal your burgers because you have the number of your turn, and they have theirs.
 
-So you wait for your crush 😍 to finish the story (finish the current work ⏯ / task being processed 🤓), smile gently and say that you are going for the burgers ⏸.
+So you wait for your crush to finish the story (finish the current work ⏯ / task being processed 🤓), smile gently and say that you are going for the burgers ⏸.
 
-Then you go to the counter 🔀, to the initial task that is now finished ⏯, pick the burgers 🍔, say thanks and take them to the table. That finishes that step / task of interaction with the counter ⏹. That in turn, creates a new task, of "eating burgers" 🔀 ⏯, but the previous one of "getting burgers" is finished ⏹.
+Then you go to the counter 🔀, to the initial task that is now finished ⏯, pick the burgers, say thanks and take them to the table. That finishes that step / task of interaction with the counter ⏹. That in turn, creates a new task, of "eating burgers" 🔀 ⏯, but the previous one of "getting burgers" is finished ⏹.
 
 ### Parallel Burgers
 
 Now let's imagine these aren't "Concurrent Burgers", but "Parallel Burgers".
 
-You go with your crush 😍 to get parallel fast food 🍔.
+You go with your crush to get parallel fast food.
 
-You stand in line while several (let's say 8) cashiers that at the same time are cooks 👩‍🍳👨‍🍳👩‍🍳👨‍🍳👩‍🍳👨‍🍳👩‍🍳👨‍🍳 take the orders from the people in front of you.
+You stand in line while several (let's say 8) cashiers that at the same time are cooks take the orders from the people in front of you.
 
-Everyone before you is waiting 🕙 for their burgers 🍔 to be ready before leaving the counter because each of the 8 cashiers goes and prepares the burger right away before getting the next order.
+Everyone before you is waiting for their burgers to be ready before leaving the counter because each of the 8 cashiers goes and prepares the burger right away before getting the next order.
 
-Then it's finally your turn, you place your order of 2 very fancy burgers 🍔 for your crush 😍 and you.
+<img src="/img/async/parallel-burgers/parallel-burgers-01.png" class="illustration">
+
+Then it's finally your turn, you place your order of 2 very fancy burgers for your crush and you.
 
 You pay 💸.
 
-The cashier goes to the kitchen 👨‍🍳.
+<img src="/img/async/parallel-burgers/parallel-burgers-02.png" class="illustration">
 
-You wait, standing in front of the counter 🕙, so that no one else takes your burgers 🍔 before you do, as there are no numbers for turns.
+The cashier goes to the kitchen.
 
-As you and your crush 😍 are busy not letting anyone get in front of you and take your burgers whenever they arrive 🕙, you cannot pay attention to your crush 😞.
+You wait, standing in front of the counter 🕙, so that no one else takes your burgers before you do, as there are no numbers for turns.
 
-This is "synchronous" work, you are "synchronized" with the cashier/cook 👨‍🍳. You have to wait 🕙 and be there at the exact moment that the cashier/cook 👨‍🍳 finishes the burgers 🍔 and gives them to you, or otherwise, someone else might take them.
+<img src="/img/async/parallel-burgers/parallel-burgers-03.png" class="illustration">
 
-Then your cashier/cook 👨‍🍳 finally comes back with your burgers 🍔, after a long time waiting 🕙 there in front of the counter.
+As you and your crush are busy not letting anyone get in front of you and take your burgers whenever they arrive, you cannot pay attention to your crush. 😞
 
-You take your burgers 🍔 and go to the table with your crush 😍.
+This is "synchronous" work, you are "synchronized" with the cashier/cook 👨‍🍳. You have to wait 🕙 and be there at the exact moment that the cashier/cook 👨‍🍳 finishes the burgers and gives them to you, or otherwise, someone else might take them.
 
-You just eat them, and you are done 🍔 ⏹.
+<img src="/img/async/parallel-burgers/parallel-burgers-04.png" class="illustration">
 
-There was not much talk or flirting as most of the time was spent waiting 🕙 in front of the counter 😞.
+Then your cashier/cook 👨‍🍳 finally comes back with your burgers, after a long time waiting 🕙 there in front of the counter.
+
+<img src="/img/async/parallel-burgers/parallel-burgers-05.png" class="illustration">
+
+You take your burgers and go to the table with your crush.
+
+You just eat them, and you are done. ⏹
+
+<img src="/img/async/parallel-burgers/parallel-burgers-06.png" class="illustration">
+
+There was not much talk or flirting as most of the time was spent waiting 🕙 in front of the counter. 😞
+
+!!! info
+    Beautiful illustrations by <a href="https://www.instagram.com/ketrinadrawsalot" class="external-link" target="_blank">Ketrina Thompson</a>. 🎨
 
 ---
 
-In this scenario of the parallel burgers, you are a computer / program 🤖 with two processors (you and your crush 😍), both waiting 🕙 and dedicating their attention ⏯ to be "waiting on the counter" 🕙 for a long time.
+In this scenario of the parallel burgers, you are a computer / program 🤖 with two processors (you and your crush), both waiting 🕙 and dedicating their attention ⏯ to be "waiting on the counter" 🕙 for a long time.
 
-The fast food store has 8 processors (cashiers/cooks) 👩‍🍳👨‍🍳👩‍🍳👨‍🍳👩‍🍳👨‍🍳👩‍🍳👨‍🍳. While the concurrent burgers store might have had only 2 (one cashier and one cook) 💁 👨‍🍳.
+The fast food store has 8 processors (cashiers/cooks). While the concurrent burgers store might have had only 2 (one cashier and one cook).
 
-But still, the final experience is not the best 😞.
+But still, the final experience is not the best. 😞
 
 ---
 
-This would be the parallel equivalent story for burgers 🍔.
+This would be the parallel equivalent story for burgers. 🍔
 
 For a more "real life" example of this, imagine a bank.
 
@@ -208,11 +238,7 @@ This "waiting" 🕙 is measured in microseconds, but still, summing it all, it's
 
 That's why it makes a lot of sense to use asynchronous ⏸🔀⏯ code for web APIs.
 
-Most of the existing popular Python frameworks (including Flask and Django) were created before the new asynchronous features in Python existed. So, the ways they can be deployed support parallel execution and an older form of asynchronous execution that is not as powerful as the new capabilities.
-
-Even though the main specification for asynchronous web Python (ASGI) was developed at Django, to add support for WebSockets.
-
-That kind of asynchronicity is what made NodeJS popular (even though NodeJS is not parallel) and that's the strength of Go as a programming language.
+This kind of asynchronicity is what made NodeJS popular (even though NodeJS is not parallel) and that's the strength of Go as a programming language.
 
 And that's the same level of performance you get with **FastAPI**.
 
@@ -238,7 +264,7 @@ You could have turns as in the burgers example, first the living room, then the
 
 It would take the same amount of time to finish with or without turns (concurrency) and you would have done the same amount of work.
 
-But in this case, if you could bring the 8 ex-cashier/cooks/now-cleaners 👩‍🍳👨‍🍳👩‍🍳👨‍🍳👩‍🍳👨‍🍳👩‍🍳👨‍🍳, and each one of them (plus you) could take a zone of the house to clean it, you could do all the work in **parallel**, with the extra help, and finish much sooner.
+But in this case, if you could bring the 8 ex-cashier/cooks/now-cleaners, and each one of them (plus you) could take a zone of the house to clean it, you could do all the work in **parallel**, with the extra help, and finish much sooner.
 
 In this scenario, each one of the cleaners (including you) would be a processor, doing their part of the job.
 
@@ -324,7 +350,15 @@ 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
 
@@ -363,7 +397,7 @@ All that is what powers FastAPI (through Starlette) and what makes it have such
 
     These are very technical details of how **FastAPI** works underneath.
 
-    If you have quite some technical knowledge (co-routines, threads, blocking, etc) and are curious about how FastAPI handles `async def` vs normal `def`, go ahead.
+    If you have quite some technical knowledge (co-routines, threads, blocking, etc.) and are curious about how FastAPI handles `async def` vs normal `def`, go ahead.
 
 ### Path operation functions
 

docs/en/docs/contributing.md

@@ -84,7 +84,17 @@ To check it worked, use:
 
 If it shows the `pip` binary at `env/bin/pip` then it worked. 🎉
 
+Make sure you have the latest pip version on your virtual environment to avoid errors on the next steps:
 
+<div class="termy">
+
+```console
+$ python -m pip install --upgrade pip
+
+---> 100%
+```
+
+</div>
 
 !!! tip
     Every time you install a new package with `pip` under that environment, activate the environment again.
@@ -163,20 +173,6 @@ 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 +336,7 @@ 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 +355,7 @@ 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 +1,24 @@
+.termynal-comment {
+  color: #4a968f;
+  font-style: italic;
+  display: block;
+}
+
+.termy {
+  /* For right to left languages */
+  direction: ltr;
+}
+
+.termy [data-termynal] {
+  white-space: pre-wrap;
+}
+
+a.external-link {
+  /* For right to left languages */
+  direction: ltr;
+  display: inline-block;
+}
+
 a.external-link::after {
   /* \00A0 is a non-breaking space
         to make the mark be on the same line as the link
@@ -12,6 +33,10 @@ 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 +45,7 @@ a.internal-link::after {
 .user-list {
   display: flex;
   flex-wrap: wrap;
+  margin-bottom: 2rem;
 }
 
 .user-list-center {
@@ -79,7 +105,7 @@ a.announce-link:hover {
 .announce-wrapper .sponsor-badge {
   display: block;
   position: absolute;
-  top: -5px;
+  top: -10px;
   right: 0;
   font-size: 0.5rem;
   color: #999;
@@ -103,3 +129,18 @@ a.announce-link:hover {
 .twitter {
   color: #00acee;
 }
+
+/* Right to left languages */
+code {
+  direction: ltr;
+  display: inline-block;
+}
+
+.md-content__inner h1 {
+  direction: ltr !important;
+}
+
+.illustration {
+  margin-top: 2em;
+  margin-bottom: 2em;
+}

docs/en/docs/css/termynal.css

@@ -17,7 +17,8 @@
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+    /* font-size: 18px; */
+    font-size: 15px;
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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

@@ -1,71 +1,148 @@
-# 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
-from typing import Optional
+from typing import Union
 
 from fastapi import FastAPI
 
@@ -78,20 +155,130 @@ def read_root():
 
 
 @app.get("/items/{item_id}")
-def read_item(item_id: int, q: Optional[str] = None):
+def read_item(item_id: int, q: Union[str, None] = 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%
     ```
@@ -20,7 +38,7 @@ You just need to install an ASGI compatible server like:
 
     !!! tip
         By adding the `standard`, Uvicorn will install and use some recommended extra dependencies.
-        
+
         That including `uvloop`, the high-performance drop-in replacement for `asyncio`, that provides the big concurrency performance boost.
 
 === "Hypercorn"
@@ -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 run 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.
 
-Making sure to fine-tune the number of workers, etc.
+    It helps a lot during **development**, but you **shouldn't** use it in **production**.
 
-But if you are doing all that, you might just use the Docker image that does it automatically.
+## Hypercorn with Trio
+
+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`.
@@ -192,11 +190,11 @@ With **FastAPI** you get all of **Pydantic**'s features (as FastAPI is based on
 * Plays nicely with your **<abbr title="Integrated Development Environment, similar to a code editor">IDE</abbr>/<abbr title="A program that checks for code errors">linter</abbr>/brain**:
     * Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.
 * **Fast**:
-    * in <a href="https://pydantic-docs.helpmanual.io/#benchmarks-tag" class="external-link" target="_blank">benchmarks</a> Pydantic is faster than all other tested libraries.
+    * in <a href="https://pydantic-docs.helpmanual.io/benchmarks/" class="external-link" target="_blank">benchmarks</a> Pydantic is faster than all other tested libraries.
 * Validate **complex structures**:
     * Use of hierarchical Pydantic models, Python `typing`’s `List` and `Dict`, etc.
     * And validators allow complex data schemas to be clearly and easily defined, checked and documented as JSON Schema.
     * You can have deeply **nested JSON** objects and have them all validated and annotated.
-* **Extendible**:
+* **Extensible**:
     * Pydantic allows custom data types to be defined or you can extend validation with methods on a model decorated with the validator decorator.
 * 100% test coverage.

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/https02.drawio

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

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+                <mxCell id="52" value="&lt;font face=&quot;Roboto&quot; data-font-src=&quot;https://fonts.googleapis.com/css?family=Roboto&quot;&gt;&lt;span style=&quot;font-size: 24px&quot;&gt;another.example.net&lt;/span&gt;&lt;br&gt;&lt;/font&gt;" style="rounded=0;whiteSpace=wrap;html=1;strokeColor=#666666;strokeWidth=3;fillColor=#f5f5f5;fontColor=#333333;" parent="1" vertex="1">
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docs/en/docs/img/deployment/https/https04.drawio

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+<mxfile host="65bd71144e">
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+                <mxCell id="3" value="&lt;font face=&quot;Roboto&quot;&gt;&lt;span style=&quot;font-size: 24px&quot;&gt;Server(s)&lt;/span&gt;&lt;/font&gt;" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;strokeWidth=3;fontFamily=Roboto Mono, mono;FType=g;" parent="1" vertex="1">
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+                <mxCell id="9" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;strokeWidth=3;dashed=1;" parent="1" edge="1">
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+                        <mxPoint x="800" y="520" as="sourcePoint"/>
+                        <Array as="points">
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+                <mxCell id="29" value="&lt;font face=&quot;Roboto&quot; data-font-src=&quot;https://fonts.googleapis.com/css?family=Roboto&quot; style=&quot;font-size: 24px&quot;&gt;https://someapp.example.com&lt;/font&gt;" style="rounded=0;whiteSpace=wrap;html=1;fontStyle=1;strokeWidth=4;" parent="33" vertex="1">
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+                <mxCell id="7" value="&lt;font style=&quot;font-size: 24px&quot; face=&quot;Roboto&quot;&gt;TLS Termination Proxy&lt;br&gt;&lt;/font&gt;" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;strokeWidth=3;fontFamily=Roboto Mono, mono;FType=g;" parent="1" vertex="1">
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+                <mxCell id="36" value="&lt;font face=&quot;Roboto&quot; data-font-src=&quot;https://fonts.googleapis.com/css?family=Roboto&quot; style=&quot;font-size: 24px&quot;&gt;Port 443 (HTTPS)&lt;/font&gt;" style="ellipse;whiteSpace=wrap;html=1;strokeColor=#000000;strokeWidth=3;" parent="1" vertex="1">
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+                </mxCell>
+                <mxCell id="96" value="&lt;span style=&quot;font-family: &amp;#34;roboto&amp;#34; ; font-size: 24px&quot;&gt;Encrypted request for: someapp.example.com&lt;/span&gt;" style="shape=hexagon;perimeter=hexagonPerimeter2;whiteSpace=wrap;html=1;fixedSize=1;strokeColor=#82b366;strokeWidth=3;fillColor=#d5e8d4;" parent="1" vertex="1">
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+                </mxCell>
+                <mxCell id="100" value="&lt;span style=&quot;font-family: &amp;#34;roboto&amp;#34; ; font-size: 24px&quot;&gt;Who is: someapp.example.com&lt;/span&gt;" style="shape=hexagon;perimeter=hexagonPerimeter2;whiteSpace=wrap;html=1;fixedSize=1;strokeWidth=3;" parent="1" vertex="1">
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+                <mxCell id="104" value="&lt;span style=&quot;font-family: &amp;#34;roboto&amp;#34; ; font-size: 24px&quot;&gt;TLS Handshake&lt;/span&gt;" style="shape=hexagon;perimeter=hexagonPerimeter2;whiteSpace=wrap;html=1;fixedSize=1;strokeWidth=3;" parent="1" vertex="1">
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+                </mxCell>
+                <mxCell id="50" value="&lt;font style=&quot;font-size: 24px&quot; face=&quot;Roboto&quot;&gt;HTTPS certificates&lt;br&gt;&lt;/font&gt;" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;strokeWidth=3;fontFamily=Roboto Mono, mono;FType=g;" parent="1" vertex="1">
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+                <mxCell id="51" value="&lt;font face=&quot;Roboto&quot; data-font-src=&quot;https://fonts.googleapis.com/css?family=Roboto&quot;&gt;&lt;span style=&quot;font-size: 24px&quot;&gt;someapp.example.com&lt;/span&gt;&lt;br&gt;&lt;/font&gt;" style="rounded=0;whiteSpace=wrap;html=1;strokeColor=#000000;strokeWidth=3;" parent="1" vertex="1">
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+                <mxCell id="52" value="&lt;font face=&quot;Roboto&quot; data-font-src=&quot;https://fonts.googleapis.com/css?family=Roboto&quot;&gt;&lt;span style=&quot;font-size: 24px&quot;&gt;another.example.net&lt;/span&gt;&lt;br&gt;&lt;/font&gt;" style="rounded=0;whiteSpace=wrap;html=1;strokeColor=#666666;strokeWidth=3;fillColor=#f5f5f5;fontColor=#333333;" parent="1" vertex="1">
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+</mxfile>

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

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+<mxfile host="65bd71144e">
+    <diagram id="jyERGzDynktFHFRGN0ph" name="Page-1">
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docs/en/docs/img/deployment/https/https06.drawio

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docs/en/docs/img/deployment/https/https07.drawio

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+                <mxCell id="7" value="&lt;font style=&quot;font-size: 24px&quot; face=&quot;Roboto&quot;&gt;TLS Termination Proxy&lt;br&gt;&lt;/font&gt;" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;strokeWidth=3;fontFamily=Roboto Mono, mono;FType=g;" parent="1" vertex="1">
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+                <mxCell id="62" value="&lt;font face=&quot;Roboto&quot; data-font-src=&quot;https://fonts.googleapis.com/css?family=Roboto&quot; style=&quot;font-size: 24px&quot;&gt;FastAPI&lt;/font&gt;&lt;font face=&quot;Roboto&quot; data-font-src=&quot;https://fonts.googleapis.com/css?family=Roboto&quot; style=&quot;font-size: 24px ; font-weight: normal&quot;&gt; app for: someapp.example.com&lt;/font&gt;" style="rounded=0;whiteSpace=wrap;html=1;fontStyle=1;strokeWidth=4;fillColor=#dae8fc;strokeColor=#6c8ebf;" parent="1" vertex="1">
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+                <mxCell id="85" value="&lt;span style=&quot;font-family: &amp;#34;roboto&amp;#34; ; font-size: 24px&quot;&gt;Plain response from: someapp.example.com&lt;/span&gt;" style="shape=hexagon;perimeter=hexagonPerimeter2;whiteSpace=wrap;html=1;fixedSize=1;strokeColor=#9673a6;strokeWidth=3;fillColor=#e1d5e7;" parent="1" vertex="1">
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+                <mxCell id="84" value="&lt;span style=&quot;font-family: &amp;#34;roboto&amp;#34; ; font-size: 24px&quot;&gt;Decrypted request for: someapp.example.com&lt;/span&gt;" style="shape=hexagon;perimeter=hexagonPerimeter2;whiteSpace=wrap;html=1;fixedSize=1;strokeColor=#82b366;strokeWidth=3;fillColor=#d5e8d4;" parent="1" vertex="1">
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+                    <mxGeometry relative="1" as="geometry">
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+                            <mxPoint x="340" y="480"/>
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+                <mxCell id="36" value="&lt;font face=&quot;Roboto&quot; data-font-src=&quot;https://fonts.googleapis.com/css?family=Roboto&quot; style=&quot;font-size: 24px&quot;&gt;Port 443 (HTTPS)&lt;/font&gt;" style="ellipse;whiteSpace=wrap;html=1;strokeColor=#000000;strokeWidth=3;" parent="1" vertex="1">
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+                <mxCell id="96" value="&lt;span style=&quot;font-family: &amp;#34;roboto&amp;#34; ; font-size: 24px&quot;&gt;Encrypted request for: someapp.example.com&lt;/span&gt;" style="shape=hexagon;perimeter=hexagonPerimeter2;whiteSpace=wrap;html=1;fixedSize=1;strokeColor=#82b366;strokeWidth=3;fillColor=#d5e8d4;" parent="1" vertex="1">
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+                <mxCell id="100" value="&lt;span style=&quot;font-family: &amp;#34;roboto&amp;#34; ; font-size: 24px&quot;&gt;Who is: someapp.example.com&lt;/span&gt;" style="shape=hexagon;perimeter=hexagonPerimeter2;whiteSpace=wrap;html=1;fixedSize=1;strokeWidth=3;" parent="1" vertex="1">
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+                <mxCell id="50" value="&lt;font style=&quot;font-size: 24px&quot; face=&quot;Roboto&quot;&gt;HTTPS certificates&lt;br&gt;&lt;/font&gt;" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;strokeWidth=3;fontFamily=Roboto Mono, mono;FType=g;" parent="1" vertex="1">
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docs/en/docs/img/deployment/https/https08.drawio

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