🔖 Release version 0.68.2

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

.github/ISSUE_TEMPLATE/config.yml

@@ -1 +1,4 @@
 blank_issues_enabled: false
+contact_links:
+  - name: Security Contact
+    about: Please report security vulnerabilities to security@tiangolo.com

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -0,0 +1,181 @@
+name: Feature Request
+description: Suggest an idea or ask for a feature that you would like to have in FastAPI
+labels: [enhancement]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        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.
+
+        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. 🤓
+  - type: checkboxes
+    id: checks
+    attributes:
+      label: First Check
+      description: Please confirm and check all the following options.
+      options:
+        - label: I added a very descriptive title to this issue.
+          required: true
+        - label: I used the GitHub search to find a similar issue and didn't find it.
+          required: true
+        - label: I searched the FastAPI documentation, with the integrated search.
+          required: true
+        - label: I already searched in Google "How to X in FastAPI" and didn't find any information.
+          required: true
+        - label: I already read and followed all the tutorial in the docs and didn't find an answer.
+          required: true
+        - label: I already checked if it is not related to FastAPI but to [Pydantic](https://github.com/samuelcolvin/pydantic).
+          required: true
+        - label: I already checked if it is not related to FastAPI but to [Swagger UI](https://github.com/swagger-api/swagger-ui).
+          required: true
+        - label: I already checked if it is not related to FastAPI but to [ReDoc](https://github.com/Redocly/redoc).
+          required: true
+  - type: checkboxes
+    id: help
+    attributes:
+      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.
+
+      options:
+        - label: I commit to help with one of those options 👆
+          required: true
+  - type: textarea
+    id: example
+    attributes:
+      label: Example Code
+      description: |
+        Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
+
+        If I (or someone) can copy it, run it, and see it right away, there's a much higher chance I (or someone) will be able to help you.
+
+      placeholder: |
+        from fastapi import FastAPI
+
+        app = FastAPI()
+
+
+        @app.get("/")
+        def read_root():
+            return {"Hello": "World"}
+      render: python
+    validations:
+      required: true
+  - type: textarea
+    id: description
+    attributes:
+      label: Description
+      description: |
+        What is your feature request?
+
+        Write a short description telling me what you are trying to solve and what you are currently doing.
+      placeholder: |
+        * Open the browser and call the endpoint `/`.
+        * It returns a JSON with `{"Hello": "World"}`.
+        * I would like it to have an extra parameter to teleport me to the moon and back.
+    validations:
+      required: true
+  - type: textarea
+    id: wanted-solution
+    attributes:
+      label: Wanted Solution
+      description: |
+        Tell me what's the solution you would like.
+      placeholder: |
+        I would like it to have a `teleport_to_moon` parameter that defaults to `False`, and can be set to `True` to teleport me.
+    validations:
+      required: true
+  - type: textarea
+    id: wanted-code
+    attributes:
+      label: Wanted Code
+      description: Show me an example of how you would want the code to look like.
+      placeholder: |
+        from fastapi import FastAPI
+
+        app = FastAPI()
+
+
+        @app.get("/", teleport_to_moon=True)
+        def read_root():
+            return {"Hello": "World"}
+      render: python
+    validations:
+      required: true
+  - type: textarea
+    id: alternatives
+    attributes:
+      label: Alternatives
+      description: |
+        Tell me about alternatives you've considered.
+      placeholder: |
+        To wait for Space X moon travel plans to drop down long after they release them. But I would rather teleport.
+  - type: dropdown
+    id: os
+    attributes:
+      label: Operating System
+      description: What operating system are you on?
+      multiple: true
+      options:
+        - Linux
+        - Windows
+        - macOS
+        - Other
+    validations:
+      required: true
+  - type: textarea
+    id: os-details
+    attributes:
+      label: Operating System Details
+      description: You can add more details about your operating system here, in particular if you chose "Other".
+  - type: input
+    id: fastapi-version
+    attributes:
+      label: FastAPI Version
+      description: |
+        What FastAPI version are you using?
+
+        You can find the FastAPI version with:
+
+        ```bash
+        python -c "import fastapi; print(fastapi.__version__)"
+        ```
+    validations:
+      required: true
+  - type: input
+    id: python-version
+    attributes:
+      label: Python Version
+      description: |
+        What Python version are you using?
+
+        You can find the Python version with:
+
+        ```bash
+        python --version
+        ```
+    validations:
+      required: true
+  - type: textarea
+    id: context
+    attributes:
+      label: Additional Context
+      description: Add any additional context information or screenshots you think are useful.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -1,104 +0,0 @@
----
-name: Feature request
-about: Suggest an idea for this project
-title: ""
-labels: enhancement
-assignees: ''
-
----
-
-### First check
-
-* [ ] I added a very descriptive title to this issue.
-* [ ] I used the GitHub search to find a similar issue and didn't find it.
-* [ ] I searched the FastAPI documentation, with the integrated search.
-* [ ] I already searched in Google "How to X in FastAPI" and didn't find any information.
-* [ ] I already read and followed all the tutorial in the docs and didn't find an answer.
-* [ ] I already checked if it is not related to FastAPI but to [Pydantic](https://github.com/samuelcolvin/pydantic).
-* [ ] I already checked if it is not related to FastAPI but to [Swagger UI](https://github.com/swagger-api/swagger-ui).
-* [ ] I already checked if it is not related to FastAPI but to [ReDoc](https://github.com/Redocly/redoc).
-* [ ] After submitting this, I commit to:
-    * Read open issues with questions until I find 2 issues where I can help someone and add a comment to help there.
-    * Or, 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.
-
-<!-- 
-
-I'm asking all this because answering questions and solving problems in GitHub issues consumes a lot of 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 that give a lot of their time to come here and help others.
-
-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 😅).
-
--->
-
-### Example
-
-Here's a self-contained [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with my use case:
-
-<!-- Replace the code below with your own self-contained, minimal, reproducible, example -->
-
-```Python
-from fastapi import FastAPI
-
-app = FastAPI()
-
-
-@app.get("/")
-def read_root():
-    return {"Hello": "World"}
-```
-
-### Description
-
-<!-- Replace the content below with your own feature request -->
-
-* Open the browser and call the endpoint `/`.
-* It returns a JSON with `{"Hello": "World"}`.
-* I would like it to have an extra parameter to teleport me to the moon and back.
-
-### The solution you would like
-
-<!-- Replace this with your own content -->
-
-I would like it to have a `teleport_to_moon` parameter that defaults to `False`, and can be set to `True` to teleport me:
-
-```Python
-from fastapi import FastAPI
-
-app = FastAPI()
-
-
-@app.get("/", teleport_to_moon=True)
-def read_root():
-    return {"Hello": "World"}
-```
-
-### Describe alternatives you've considered
-
-<!-- Replace this with your own ideas -->
-
-To wait for Space X moon travel plans to drop down long after they release them. But I would rather teleport.
-
-### Environment
-
-* OS: [e.g. Linux / Windows / macOS]:
-* FastAPI Version [e.g. 0.3.0]:
-
-To know the FastAPI version use:
-
-```bash
-python -c "import fastapi; print(fastapi.__version__)"
-```
-
-* Python version:
-
-To know the Python version use:
-
-```bash
-python --version
-```
-
-### Additional context
-
-<!-- Add any other context or screenshots about the question here. -->

.github/ISSUE_TEMPLATE/question.md

@@ -1,81 +0,0 @@
----
-name: Question or Problem
-about: Ask a question or ask about a problem
-title: ""
-labels: question
-assignees: ""
-
----
-
-### First check
-
-* [ ] I added a very descriptive title to this issue.
-* [ ] I used the GitHub search to find a similar issue and didn't find it.
-* [ ] I searched the FastAPI documentation, with the integrated search.
-* [ ] I already searched in Google "How to X in FastAPI" and didn't find any information.
-* [ ] I already read and followed all the tutorial in the docs and didn't find an answer.
-* [ ] I already checked if it is not related to FastAPI but to [Pydantic](https://github.com/samuelcolvin/pydantic).
-* [ ] I already checked if it is not related to FastAPI but to [Swagger UI](https://github.com/swagger-api/swagger-ui).
-* [ ] I already checked if it is not related to FastAPI but to [ReDoc](https://github.com/Redocly/redoc).
-* [ ] 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.
-
-<!-- 
-
-I'm asking all this because answering questions and solving problems in GitHub issues consumes a lot of 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 that give a lot of their time to come here and help others.
-
-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 😅).
-
--->
-
-### Example
-
-Here's a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with my use case:
-
-<!-- Replace the code below with your own self-contained, minimal, reproducible, example, if I (or someone) can copy it, run it, and see it right away, there's a much higher chance I (or someone) will be able to help you -->
-
-```Python
-from fastapi import FastAPI
-
-app = FastAPI()
-
-
-@app.get("/")
-def read_root():
-    return {"Hello": "World"}
-```
-
-### Description
-
-<!-- Replace the content below with your own problem, question, or error -->
-
-* Open the browser and call the endpoint `/`.
-* It returns a JSON with `{"Hello": "World"}`.
-* But I expected it to return `{"Hello": "Sara"}`.
-
-### Environment
-
-* OS: [e.g. Linux / Windows / macOS]:
-* FastAPI Version [e.g. 0.3.0]:
-
-To know the FastAPI version use:
-
-```bash
-python -c "import fastapi; print(fastapi.__version__)"
-```
-
-* Python version:
-
-To know the Python version use:
-
-```bash
-python --version
-```
-
-### Additional context
-
-<!-- Add any other context or screenshots about the question here. -->

.github/ISSUE_TEMPLATE/question.yml

@@ -0,0 +1,146 @@
+name: Question or Problem
+description: Ask a question or ask about a problem
+labels: [question]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        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.
+
+        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. 🤓
+  - type: checkboxes
+    id: checks
+    attributes:
+      label: First Check
+      description: Please confirm and check all the following options.
+      options:
+        - label: I added a very descriptive title to this issue.
+          required: true
+        - label: I used the GitHub search to find a similar issue and didn't find it.
+          required: true
+        - label: I searched the FastAPI documentation, with the integrated search.
+          required: true
+        - label: I already searched in Google "How to X in FastAPI" and didn't find any information.
+          required: true
+        - label: I already read and followed all the tutorial in the docs and didn't find an answer.
+          required: true
+        - label: I already checked if it is not related to FastAPI but to [Pydantic](https://github.com/samuelcolvin/pydantic).
+          required: true
+        - label: I already checked if it is not related to FastAPI but to [Swagger UI](https://github.com/swagger-api/swagger-ui).
+          required: true
+        - label: I already checked if it is not related to FastAPI but to [ReDoc](https://github.com/Redocly/redoc).
+          required: true
+  - type: checkboxes
+    id: help
+    attributes:
+      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.
+
+      options:
+        - label: I commit to help with one of those options 👆
+          required: true
+  - type: textarea
+    id: example
+    attributes:
+      label: Example Code
+      description: |
+        Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
+
+        If I (or someone) can copy it, run it, and see it right away, there's a much higher chance I (or someone) will be able to help you.
+
+      placeholder: |
+        from fastapi import FastAPI
+
+        app = FastAPI()
+
+
+        @app.get("/")
+        def read_root():
+            return {"Hello": "World"}
+      render: python
+    validations:
+      required: true
+  - type: textarea
+    id: description
+    attributes:
+      label: Description
+      description: |
+        What is the problem, question, or error?
+
+        Write a short description telling me what you are doing, what you expect to happen, and what is currently happening.
+      placeholder: |
+        * Open the browser and call the endpoint `/`.
+        * It returns a JSON with `{"Hello": "World"}`.
+        * But I expected it to return `{"Hello": "Sara"}`.
+    validations:
+      required: true
+  - type: dropdown
+    id: os
+    attributes:
+      label: Operating System
+      description: What operating system are you on?
+      multiple: true
+      options:
+        - Linux
+        - Windows
+        - macOS
+        - Other
+    validations:
+      required: true
+  - type: textarea
+    id: os-details
+    attributes:
+      label: Operating System Details
+      description: You can add more details about your operating system here, in particular if you chose "Other".
+  - type: input
+    id: fastapi-version
+    attributes:
+      label: FastAPI Version
+      description: |
+        What FastAPI version are you using?
+
+        You can find the FastAPI version with:
+
+        ```bash
+        python -c "import fastapi; print(fastapi.__version__)"
+        ```
+    validations:
+      required: true
+  - type: input
+    id: python-version
+    attributes:
+      label: Python Version
+      description: |
+        What Python version are you using?
+
+        You can find the Python version with:
+
+        ```bash
+        python --version
+        ```
+    validations:
+      required: true
+  - type: textarea
+    id: context
+    attributes:
+      label: Additional Context
+      description: Add any additional context information or screenshots you think are useful.

.github/actions/notify-translations/Dockerfile

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

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

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

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

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

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

@@ -0,0 +1,14 @@
+pt: 1211
+es: 1218
+zh: 1228
+ru: 1362
+it: 1556
+ja: 1572
+uk: 1748
+tr: 1892
+fr: 1972
+ko: 2017
+sq: 2041
+pl: 3169
+de: 3716
+id: 3717

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

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

.github/workflows/build-docs.yml

@@ -4,7 +4,7 @@ on:
   pull_request:
     types: [opened, synchronize]
 jobs:
-  build:
+  build-docs:
     runs-on: ubuntu-18.04
     steps:
       - name: Dump GitHub context
@@ -13,15 +13,22 @@ jobs:
         run: echo "$GITHUB_CONTEXT"
       - uses: actions/checkout@v2
       - name: Set up Python
-        uses: actions/setup-python@v1
+        uses: actions/setup-python@v2
         with:
           python-version: "3.7"
+      - uses: actions/cache@v2
+        id: cache
+        with:
+          path: ${{ env.pythonLocation }}
+          key: ${{ runner.os }}-python-${{ env.pythonLocation }}-${{ hashFiles('pyproject.toml') }}-docs
       - name: Install Flit
+        if: steps.cache.outputs.cache-hit != 'true'
         run: python3.7 -m pip install flit
       - name: Install docs extras
-        run: python3.7 -m flit install --extras doc
+        if: steps.cache.outputs.cache-hit != 'true'
+        run: python3.7 -m flit install --deps production --extras doc
       - name: Install Material for MkDocs Insiders
-        if: github.event.pull_request.head.repo.fork == false
+        if: github.event.pull_request.head.repo.fork == false && steps.cache.outputs.cache-hit != 'true'
         run: pip install git+https://${{ secrets.ACTIONS_TOKEN }}@github.com/squidfunk/mkdocs-material-insiders.git
       - name: Build Docs
         run: python3.7 ./scripts/docs.py build-all

.github/workflows/issue-manager.yml

@@ -6,23 +6,25 @@ on:
   issue_comment:
     types:
       - created
-      - edited
   issues:
     types:
       - labeled
+  pull_request_target:
+    types:
+      - labeled
+  workflow_dispatch:
 
 jobs:
   issue-manager:
     runs-on: ubuntu-latest
     steps:
-      - uses: tiangolo/issue-manager@0.2.0
+      - uses: tiangolo/issue-manager@0.4.0
         with:
           token: ${{ secrets.GITHUB_TOKEN }}
           config: >
             {
               "answered": {
-                "users": ["tiangolo", "dmontagu"],
                 "delay": 864000,
-                "message": "Assuming the original issue was solved, it will be automatically closed now. But feel free to add more comments or create new issues."
+                "message": "Assuming the original need was handled, this will be automatically closed now. But feel free to add more comments or create new issues or PRs."
               }
             }

.github/workflows/latest-changes.yml

@@ -11,12 +11,27 @@ on:
       number:
         description: PR number
         required: true
+      debug_enabled:
+        description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)'     
+        required: false
+        default: false
 
 jobs:
   latest-changes:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v2
+        with:
+          # To allow latest-changes to commit to master
+          token: ${{ secrets.ACTIONS_TOKEN }}
+      # 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
+          token: ${{ secrets.ACTIONS_TOKEN }}
+          standard_token: ${{ secrets.GITHUB_TOKEN }}
       - uses: docker://tiangolo/latest-changes:0.0.3
         with:
           token: ${{ secrets.GITHUB_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@v2
+      # Allow debugging with tmate
+      - name: Setup tmate session
+        uses: mxschmitt/action-tmate@v3
+        if: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.debug_enabled }}
+        with:
+          limit-access-to-actor: true
+      - uses: ./.github/actions/notify-translations
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/people.yml

@@ -4,12 +4,25 @@ on:
   schedule:
     - cron: "0 14 1 * *"
   workflow_dispatch:
+    inputs:
+      debug_enabled:
+        description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)'     
+        required: false
+        default: false
 
 jobs:
   fastapi-people:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v2
+      # Allow debugging with tmate
+      - name: Setup tmate session
+        uses: mxschmitt/action-tmate@v3
+        if: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.debug_enabled }}
+        with:
+          limit-access-to-actor: true
+          token: ${{ secrets.ACTIONS_TOKEN }}
+          standard_token: ${{ secrets.GITHUB_TOKEN }}
       - uses: ./.github/actions/people
         with:
           token: ${{ secrets.ACTIONS_TOKEN }}

.github/workflows/preview-docs.yml

@@ -7,7 +7,7 @@ on:
       - completed
 
 jobs:
-  deploy:
+  preview-docs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v2

.github/workflows/publish.yml

@@ -15,12 +15,19 @@ jobs:
         run: echo "$GITHUB_CONTEXT"
       - uses: actions/checkout@v2
       - name: Set up Python
-        uses: actions/setup-python@v1
+        uses: actions/setup-python@v2
         with:
           python-version: "3.6"
+      - uses: actions/cache@v2
+        id: cache
+        with:
+          path: ${{ env.pythonLocation }}
+          key: ${{ runner.os }}-python-${{ env.pythonLocation }}-${{ hashFiles('pyproject.toml') }}-publish
       - 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: Publish
         env:

.github/workflows/test.yml

@@ -2,6 +2,8 @@ name: Test
 
 on:
   push:
+    branches:
+      - master
   pull_request:
     types: [opened, synchronize]
 
@@ -10,18 +12,25 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.6, 3.7, 3.8]
+        python-version: [3.6, 3.7, 3.8, 3.9]
       fail-fast: false
-    
+
     steps:
       - uses: actions/checkout@v2
       - name: Set up Python
-        uses: actions/setup-python@v1
+        uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
+      - uses: actions/cache@v2
+        id: cache
+        with:
+          path: ${{ env.pythonLocation }}
+          key: ${{ runner.os }}-python-${{ env.pythonLocation }}-${{ hashFiles('pyproject.toml') }}-test
       - 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: Test
         run: bash scripts/test.sh

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">
@@ -40,14 +40,17 @@ The key features are:
 
 <small>* estimation based on tests on an internal development team, building production applications.</small>
 
-## Gold Sponsors
+## Sponsors
 
 <!-- sponsors -->
 
+<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.deta.sh/?ref=fastapi" target="_blank" title="The launchpad for all your (team's) ideas"><img src="https://fastapi.tiangolo.com/img/sponsors/deta.svg"></a>
 <a href="https://www.investsuite.com/jobs" target="_blank" title="Wealthtech jobs with FastAPI"><img src="https://fastapi.tiangolo.com/img/sponsors/investsuite.svg"></a>
 <a href="https://www.vim.so/?utm_source=FastAPI" target="_blank" title="We help you master vim with interactive exercises"><img src="https://fastapi.tiangolo.com/img/sponsors/vimso.png"></a>
 <a href="https://talkpython.fm/fastapi-sponsor" target="_blank" title="FastAPI video courses on demand from people you trust"><img src="https://fastapi.tiangolo.com/img/sponsors/talkpython.png"></a>
+<a href="https://testdriven.io/courses/tdd-fastapi/" target="_blank" title="Learn to build high-quality web apps with best practices"><img src="https://fastapi.tiangolo.com/img/sponsors/testdriven.svg"></a>
+<a href="https://github.com/deepset-ai/haystack/" target="_blank" title="Build powerful search from composable, open source building blocks"><img src="https://fastapi.tiangolo.com/img/sponsors/haystack-fastapi.svg"></a>
 
 <!-- /sponsors -->
 
@@ -313,7 +316,7 @@ 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.
 
@@ -370,7 +373,7 @@ 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.
@@ -415,9 +418,9 @@ 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**
@@ -444,7 +447,6 @@ Used by Starlette:
 * <a href="https://andrew-d.github.io/python-multipart/" target="_blank"><code>python-multipart</code></a> - Required if you want to support form <abbr title="converting the string that comes from an HTTP request into Python data">"parsing"</abbr>, with `request.form()`.
 * <a href="https://pythonhosted.org/itsdangerous/" target="_blank"><code>itsdangerous</code></a> - Required for `SessionMiddleware` support.
 * <a href="https://pyyaml.org/wiki/PyYAMLDocumentation" target="_blank"><code>pyyaml</code></a> - Required for Starlette's `SchemaGenerator` support (you probably don't need it with FastAPI).
-* <a href="https://graphene-python.org/" target="_blank"><code>graphene</code></a> - Required for `GraphQLApp` support.
 * <a href="https://github.com/esnme/ultrajson" target="_blank"><code>ujson</code></a> - Required if you want to use `UJSONResponse`.
 
 Used by FastAPI / Starlette:

SECURITY.md

@@ -0,0 +1,31 @@
+# Security Policy
+
+Security is very important for FastAPI and its community. 🔒
+
+Learn more about it below. 👇
+
+## Versions
+
+The latest versions of FastAPI are supported.
+
+You are encouraged to [write tests](https://fastapi.tiangolo.com/tutorial/testing/) for your application and update your FastAPI version frequently after ensuring that your tests are passing. This way you will benefit from the latest features, bug fixes, and **security fixes**.
+
+You can learn more about [FastAPI versions and how to pin and upgrade them](https://fastapi.tiangolo.com/deployment/versions/) for your project in the docs.
+
+## Reporting a Vulnerability
+
+If you think you found a vulnerability, and even if you are not sure about it, please report it right away by sending an email to: security@tiangolo.com. Please try to be as explicit as possible, describing all the steps and example code to reproduce the security issue.
+
+I (the author, [@tiangolo](https://twitter.com/tiangolo)) will review it thoroughly and get back to you.
+
+## Public Discussions
+
+Please restrain from publicly discussing a potential security vulnerability. 🙊
+
+It's better to discuss privately and try to find a solution first, to limit the potential impact as much as possible.
+
+---
+
+Thanks for your help!
+
+The FastAPI community and I thank you for that. 🙇

docs/de/docs/features.md

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

docs/de/docs/index.md

@@ -0,0 +1,465 @@
+
+{!../../../docs/missing-translation.md!}
+
+
+<p align="center">
+  <a href="https://fastapi.tiangolo.com"><img src="https://fastapi.tiangolo.com/img/logo-margin/logo-teal.png" alt="FastAPI"></a>
+</p>
+<p align="center">
+    <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>
+<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">
+</a>
+<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>
+</p>
+
+---
+
+**Documentation**: <a href="https://fastapi.tiangolo.com" target="_blank">https://fastapi.tiangolo.com</a>
+
+**Source Code**: <a href="https://github.com/tiangolo/fastapi" target="_blank">https://github.com/tiangolo/fastapi</a>
+
+---
+
+FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints.
+
+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.
+* **Easy**: Designed to be easy to use and learn. Less time reading docs.
+* **Short**: Minimize code duplication. Multiple features from each parameter declaration. Fewer bugs.
+* **Robust**: Get production-ready code. With automatic interactive documentation.
+* **Standards-based**: Based on (and fully compatible with) the open standards for APIs: <a href="https://github.com/OAI/OpenAPI-Specification" class="external-link" target="_blank">OpenAPI</a> (previously known as Swagger) and <a href="https://json-schema.org/" class="external-link" target="_blank">JSON Schema</a>.
+
+<small>* estimation based on tests on an internal development team, building production applications.</small>
+
+## Sponsors
+
+<!-- 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 -%}
+{%- 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 %}
+
+<!-- /sponsors -->
+
+<a href="https://fastapi.tiangolo.com/fastapi-people/#sponsors" class="external-link" target="_blank">Other sponsors</a>
+
+## Opinions
+
+"_[...] I'm using **FastAPI** a ton these days. [...] I'm actually planning to use it for all of my team's **ML services at Microsoft**. Some of them are getting integrated into the core **Windows** product and some **Office** products._"
+
+<div style="text-align: right; margin-right: 10%;">Kabir Khan - <strong>Microsoft</strong> <a href="https://github.com/tiangolo/fastapi/pull/26" target="_blank"><small>(ref)</small></a></div>
+
+---
+
+"_We adopted the **FastAPI** library to spawn a **REST** server that can be queried to obtain **predictions**. [for Ludwig]_"
+
+<div style="text-align: right; margin-right: 10%;">Piero Molino, Yaroslav Dudin, and Sai Sumanth Miryala - <strong>Uber</strong> <a href="https://eng.uber.com/ludwig-v0-2/" target="_blank"><small>(ref)</small></a></div>
+
+---
+
+"_**Netflix** is pleased to announce the open-source release of our **crisis management** orchestration framework: **Dispatch**! [built with **FastAPI**]_"
+
+<div style="text-align: right; margin-right: 10%;">Kevin Glisson, Marc Vilanova, Forest Monsen - <strong>Netflix</strong> <a href="https://netflixtechblog.com/introducing-dispatch-da4b8a2a8072" target="_blank"><small>(ref)</small></a></div>
+
+---
+
+"_I’m over the moon excited about **FastAPI**. It’s so fun!_"
+
+<div style="text-align: right; margin-right: 10%;">Brian Okken - <strong><a href="https://pythonbytes.fm/episodes/show/123/time-to-right-the-py-wrongs?time_in_sec=855" target="_blank">Python Bytes</a> podcast host</strong> <a href="https://twitter.com/brianokken/status/1112220079972728832" target="_blank"><small>(ref)</small></a></div>
+
+---
+
+"_Honestly, what you've built looks super solid and polished. In many ways, it's what I wanted **Hug** to be - it's really inspiring to see someone build that._"
+
+<div style="text-align: right; margin-right: 10%;">Timothy Crosley - <strong><a href="https://www.hug.rest/" target="_blank">Hug</a> creator</strong> <a href="https://news.ycombinator.com/item?id=19455465" target="_blank"><small>(ref)</small></a></div>
+
+---
+
+"_If you're looking to learn one **modern framework** for building REST APIs, check out **FastAPI** [...] It's fast, easy to use and easy to learn [...]_"
+
+"_We've switched over to **FastAPI** for our **APIs** [...] I think you'll like it [...]_"
+
+<div style="text-align: right; margin-right: 10%;">Ines Montani - Matthew Honnibal - <strong><a href="https://explosion.ai" target="_blank">Explosion AI</a> founders - <a href="https://spacy.io" target="_blank">spaCy</a> creators</strong> <a href="https://twitter.com/_inesmontani/status/1144173225322143744" target="_blank"><small>(ref)</small></a> - <a href="https://twitter.com/honnibal/status/1144031421859655680" target="_blank"><small>(ref)</small></a></div>
+
+---
+
+## **Typer**, the FastAPI of CLIs
+
+<a href="https://typer.tiangolo.com" target="_blank"><img src="https://typer.tiangolo.com/img/logo-margin/logo-margin-vector.svg" style="width: 20%;"></a>
+
+If you are building a <abbr title="Command Line Interface">CLI</abbr> app to be used in the terminal instead of a web API, check out <a href="https://typer.tiangolo.com/" class="external-link" target="_blank">**Typer**</a>.
+
+**Typer** is FastAPI's little sibling. And it's intended to be the **FastAPI of CLIs**. ⌨️ 🚀
+
+## Requirements
+
+Python 3.6+
+
+FastAPI stands on the shoulders of giants:
+
+* <a href="https://www.starlette.io/" class="external-link" target="_blank">Starlette</a> for the web parts.
+* <a href="https://pydantic-docs.helpmanual.io/" class="external-link" target="_blank">Pydantic</a> for the data parts.
+
+## Installation
+
+<div class="termy">
+
+```console
+$ pip install fastapi
+
+---> 100%
+```
+
+</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>.
+
+<div class="termy">
+
+```console
+$ pip install uvicorn[standard]
+
+---> 100%
+```
+
+</div>
+
+## Example
+
+### Create it
+
+* Create a file `main.py` with:
+
+```Python
+from typing import Optional
+
+from fastapi import FastAPI
+
+app = FastAPI()
+
+
+@app.get("/")
+def read_root():
+    return {"Hello": "World"}
+
+
+@app.get("/items/{item_id}")
+def read_item(item_id: int, q: Optional[str] = None):
+    return {"item_id": item_id, "q": q}
+```
+
+<details markdown="1">
+<summary>Or use <code>async def</code>...</summary>
+
+If your code uses `async` / `await`, use `async def`:
+
+```Python hl_lines="9  14"
+from typing import Optional
+
+from fastapi import FastAPI
+
+app = FastAPI()
+
+
+@app.get("/")
+async def read_root():
+    return {"Hello": "World"}
+
+
+@app.get("/items/{item_id}")
+async def read_item(item_id: int, q: Optional[str] = None):
+    return {"item_id": item_id, "q": q}
+```
+
+**Note**:
+
+If you don't know, check the _"In a hurry?"_ section about <a href="https://fastapi.tiangolo.com/async/#in-a-hurry" target="_blank">`async` and `await` in the docs</a>.
+
+</details>
+
+### Run it
+
+Run the server with:
+
+<div class="termy">
+
+```console
+$ uvicorn main:app --reload
+
+INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
+INFO:     Started reloader process [28720]
+INFO:     Started server process [28722]
+INFO:     Waiting for application startup.
+INFO:     Application startup complete.
+```
+
+</div>
+
+<details markdown="1">
+<summary>About the command <code>uvicorn main:app --reload</code>...</summary>
+
+The command `uvicorn main:app` refers to:
+
+* `main`: the file `main.py` (the Python "module").
+* `app`: the object created inside of `main.py` with the line `app = FastAPI()`.
+* `--reload`: make the server restart after code changes. Only do this for development.
+
+</details>
+
+### Check it
+
+Open your browser at <a href="http://127.0.0.1:8000/items/5?q=somequery" class="external-link" target="_blank">http://127.0.0.1:8000/items/5?q=somequery</a>.
+
+You will see the JSON response as:
+
+```JSON
+{"item_id": 5, "q": "somequery"}
+```
+
+You already created an API that:
+
+* Receives HTTP requests in the _paths_ `/` and `/items/{item_id}`.
+* Both _paths_ take `GET` <em>operations</em> (also known as HTTP _methods_).
+* The _path_ `/items/{item_id}` has a _path parameter_ `item_id` that should be an `int`.
+* The _path_ `/items/{item_id}` has an optional `str` _query parameter_ `q`.
+
+### Interactive API docs
+
+Now go to <a href="http://127.0.0.1:8000/docs" class="external-link" target="_blank">http://127.0.0.1:8000/docs</a>.
+
+You will see the automatic interactive API documentation (provided by <a href="https://github.com/swagger-api/swagger-ui" class="external-link" target="_blank">Swagger UI</a>):
+
+![Swagger UI](https://fastapi.tiangolo.com/img/index/index-01-swagger-ui-simple.png)
+
+### Alternative API docs
+
+And now, go to <a href="http://127.0.0.1:8000/redoc" class="external-link" target="_blank">http://127.0.0.1:8000/redoc</a>.
+
+You will see the alternative automatic documentation (provided by <a href="https://github.com/Rebilly/ReDoc" class="external-link" target="_blank">ReDoc</a>):
+
+![ReDoc](https://fastapi.tiangolo.com/img/index/index-02-redoc-simple.png)
+
+## Example upgrade
+
+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 fastapi import FastAPI
+from pydantic import BaseModel
+
+app = FastAPI()
+
+
+class Item(BaseModel):
+    name: str
+    price: float
+    is_offer: Optional[bool] = None
+
+
+@app.get("/")
+def read_root():
+    return {"Hello": "World"}
+
+
+@app.get("/items/{item_id}")
+def read_item(item_id: int, q: Optional[str] = None):
+    return {"item_id": item_id, "q": q}
+
+
+@app.put("/items/{item_id}")
+def update_item(item_id: int, item: Item):
+    return {"item_name": item.name, "item_id": item_id}
+```
+
+The server should reload automatically (because you added `--reload` to the `uvicorn` command above).
+
+### Interactive API docs upgrade
+
+Now go to <a href="http://127.0.0.1:8000/docs" class="external-link" target="_blank">http://127.0.0.1:8000/docs</a>.
+
+* The interactive API documentation will be automatically updated, including the new body:
+
+![Swagger UI](https://fastapi.tiangolo.com/img/index/index-03-swagger-02.png)
+
+* Click on the button "Try it out", it allows you to fill the parameters and directly interact with the API:
+
+![Swagger UI interaction](https://fastapi.tiangolo.com/img/index/index-04-swagger-03.png)
+
+* Then click on the "Execute" button, the user interface will communicate with your API, send the parameters, get the results and show them on the screen:
+
+![Swagger UI interaction](https://fastapi.tiangolo.com/img/index/index-05-swagger-04.png)
+
+### Alternative API docs upgrade
+
+And now, go to <a href="http://127.0.0.1:8000/redoc" class="external-link" target="_blank">http://127.0.0.1:8000/redoc</a>.
+
+* The alternative documentation will also reflect the new query parameter and body:
+
+![ReDoc](https://fastapi.tiangolo.com/img/index/index-06-redoc-02.png)
+
+### Recap
+
+In summary, you declare **once** the types of parameters, body, etc. as function parameters. 
+
+You do that with standard modern Python types.
+
+You don't have to learn a new syntax, the methods or classes of a specific library, etc.
+
+Just standard **Python 3.6+**.
+
+For example, for an `int`:
+
+```Python
+item_id: int
+```
+
+or for a more complex `Item` model:
+
+```Python
+item: Item
+```
+
+...and with that single declaration you get:
+
+* Editor support, including:
+    * Completion.
+    * Type checks.
+* Validation of data:
+    * Automatic and clear errors when the data is invalid.
+    * Validation even for deeply nested JSON objects.
+* <abbr title="also known as: serialization, parsing, marshalling">Conversion</abbr> of input data: coming from the network to Python data and types. Reading from:
+    * JSON.
+    * Path parameters.
+    * Query parameters.
+    * Cookies.
+    * Headers.
+    * Forms.
+    * Files.
+* <abbr title="also known as: serialization, parsing, marshalling">Conversion</abbr> of output data: converting from Python data and types to network data (as JSON):
+    * Convert Python types (`str`, `int`, `float`, `bool`, `list`, etc).
+    * `datetime` objects.
+    * `UUID` objects.
+    * Database models.
+    * ...and many more.
+* Automatic interactive API documentation, including 2 alternative user interfaces:
+    * Swagger UI.
+    * ReDoc.
+
+---
+
+Coming back to the previous code example, **FastAPI** will:
+
+* Validate that there is an `item_id` in the path for `GET` and `PUT` requests.
+* Validate that the `item_id` is of type `int` for `GET` and `PUT` requests.
+    * If it is not, the client will see a useful, clear error.
+* Check if there is an optional query parameter named `q` (as in `http://127.0.0.1:8000/items/foo?q=somequery`) for `GET` requests.
+    * 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 `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.
+* Convert from and to JSON automatically.
+* Document everything with OpenAPI, that can be used by:
+    * Interactive documentation systems.
+    * Automatic client code generation systems, for many languages.
+* Provide 2 interactive documentation web interfaces directly.
+
+---
+
+We just scratched the surface, but you already get the idea of how it all works.
+
+Try changing the line with:
+
+```Python
+    return {"item_name": item.name, "item_id": item_id}
+```
+
+...from:
+
+```Python
+        ... "item_name": item.name ...
+```
+
+...to:
+
+```Python
+        ... "item_price": item.price ...
+```
+
+...and see how your editor will auto-complete the attributes and know their types:
+
+![editor support](https://fastapi.tiangolo.com/img/vscode-completion.png)
+
+For a more complete example including more features, see the <a href="https://fastapi.tiangolo.com/tutorial/">Tutorial - User Guide</a>.
+
+**Spoiler alert**: the tutorial - user guide includes:
+
+* Declaration of **parameters** from other different places as: **headers**, **cookies**, **form fields** and **files**.
+* How to set **validation constraints** as `maximum_length` or `regex`.
+* 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).
+* Many extra features (thanks to Starlette) as:
+    * **WebSockets**
+    * extremely easy tests based on `requests` and `pytest`
+    * **CORS**
+    * **Cookie Sessions**
+    * ...and more.
+
+## Performance
+
+Independent TechEmpower benchmarks show **FastAPI** applications running under Uvicorn as <a href="https://www.techempower.com/benchmarks/#section=test&runid=7464e520-0dc2-473d-bd34-dbdfd7e85911&hw=ph&test=query&l=zijzen-7" class="external-link" target="_blank">one of the fastest Python frameworks available</a>, only below Starlette and Uvicorn themselves (used internally by FastAPI). (*)
+
+To understand more about it, see the section <a href="https://fastapi.tiangolo.com/benchmarks/" class="internal-link" target="_blank">Benchmarks</a>.
+
+## Optional Dependencies
+
+Used by Pydantic:
+
+* <a href="https://github.com/esnme/ultrajson" target="_blank"><code>ujson</code></a> - for faster JSON <abbr title="converting the string that comes from an HTTP request into Python data">"parsing"</abbr>.
+* <a href="https://github.com/JoshData/python-email-validator" target="_blank"><code>email_validator</code></a> - for email validation.
+
+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://github.com/esnme/ultrajson" target="_blank"><code>ujson</code></a> - Required if you want to use `UJSONResponse`.
+
+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]`.
+
+## License
+
+This project is licensed under the terms of the MIT license.

docs/de/mkdocs.yml

@@ -0,0 +1,126 @@
+site_name: FastAPI
+site_description: FastAPI framework, high performance, easy to learn, fast to code, ready for production
+site_url: https://fastapi.tiangolo.com/de/
+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
+    primary: teal
+    accent: amber
+    toggle:
+      icon: material/lightbulb
+      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: de
+repo_name: tiangolo/fastapi
+repo_url: https://github.com/tiangolo/fastapi
+edit_uri: ''
+google_analytics:
+- UA-133183413-1
+- auto
+plugins:
+- search
+- markdownextradata:
+    data: data
+nav:
+- FastAPI: index.md
+- Languages:
+  - en: /
+  - de: /de/
+  - es: /es/
+  - fr: /fr/
+  - id: /id/
+  - it: /it/
+  - ja: /ja/
+  - ko: /ko/
+  - pl: /pl/
+  - pt: /pt/
+  - ru: /ru/
+  - sq: /sq/
+  - tr: /tr/
+  - uk: /uk/
+  - zh: /zh/
+- features.md
+markdown_extensions:
+- toc:
+    permalink: true
+- markdown.extensions.codehilite:
+    guess_lang: false
+- mdx_include:
+    base_path: docs
+- admonition
+- codehilite
+- extra
+- pymdownx.superfences:
+    custom_fences:
+    - name: mermaid
+      class: mermaid
+      format: !!python/name:pymdownx.superfences.fence_code_format ''
+- pymdownx.tabbed
+extra:
+  social:
+  - icon: fontawesome/brands/github-alt
+    link: https://github.com/tiangolo/fastapi
+  - icon: fontawesome/brands/discord
+    link: https://discord.gg/VQjSZaeJmf
+  - icon: fontawesome/brands/twitter
+    link: https://twitter.com/fastapi
+  - icon: fontawesome/brands/linkedin
+    link: https://www.linkedin.com/in/tiangolo
+  - icon: fontawesome/brands/dev
+    link: https://dev.to/tiangolo
+  - icon: fontawesome/brands/medium
+    link: https://medium.com/@tiangolo
+  - icon: fontawesome/solid/globe
+    link: https://tiangolo.com
+  alternate:
+  - link: /
+    name: en - English
+  - link: /de/
+    name: de
+  - link: /es/
+    name: es - español
+  - link: /fr/
+    name: fr - français
+  - link: /id/
+    name: id
+  - link: /it/
+    name: it - italiano
+  - link: /ja/
+    name: ja - 日本語
+  - link: /ko/
+    name: ko - 한국어
+  - link: /pl/
+    name: pl
+  - link: /pt/
+    name: pt - português
+  - link: /ru/
+    name: ru - русский язык
+  - link: /sq/
+    name: sq - shqip
+  - link: /tr/
+    name: tr - Türkçe
+  - link: /uk/
+    name: uk - українська мова
+  - link: /zh/
+    name: zh - 汉语
+extra_css:
+- https://fastapi.tiangolo.com/css/termynal.css
+- https://fastapi.tiangolo.com/css/custom.css
+extra_javascript:
+- https://fastapi.tiangolo.com/js/termynal.js
+- https://fastapi.tiangolo.com/js/custom.js

docs/en/data/external_links.yml

@@ -1,258 +1,278 @@
 articles:
   english:
-    - link: https://medium.com/@williamhayes/fastapi-starlette-debug-vs-prod-5f7561db3a59
-      title: FastAPI/Starlette debug vs prod
-      author_link: https://medium.com/@williamhayes
-      author: William Hayes
-    - link: https://medium.com/data-rebels/fastapi-google-as-an-external-authentication-provider-3a527672cf33
-      title: FastAPI — Google as an external authentication provider
-      author_link: https://medium.com/@nilsdebruin
-      author: Nils de Bruin
-    - link: https://medium.com/data-rebels/fastapi-how-to-add-basic-and-cookie-authentication-a45c85ef47d3
-      title: FastAPI — How to add basic and cookie authentication
-      author_link: https://medium.com/@nilsdebruin
-      author: Nils de Bruin
-    - link: https://dev.to/errietta/introduction-to-the-fastapi-python-framework-2n10
-      title: Introduction to the fastapi python framework
-      author_link: https://dev.to/errietta
-      author: Errieta Kostala
-    - link: https://nickc1.github.io/api,/scikit-learn/2019/01/10/scikit-fastapi.html
-      title: "FastAPI and Scikit-Learn: Easily Deploy Models"
-      author_link: https://nickc1.github.io/
-      author: Nick Cortale
-    - link: https://medium.com/data-rebels/fastapi-authentication-revisited-enabling-api-key-authentication-122dc5975680
-      title: "FastAPI authentication revisited: Enabling API key authentication"
-      author_link: https://medium.com/@nilsdebruin
-      author: Nils de Bruin
-    - link: https://medium.com/@nico.axtmann95/deploying-a-scikit-learn-model-with-onnx-und-fastapi-1af398268915
-      title: Deploying a scikit-learn model with ONNX and FastAPI
-      author_link: https://www.linkedin.com/in/nico-axtmann
-      author: Nico Axtmann
-    - link: https://geekflare.com/python-asynchronous-web-frameworks/
-      title: Top 5 Asynchronous Web Frameworks for Python
-      author_link: https://geekflare.com/author/ankush/
-      author: Ankush Thakur
-    - link: https://medium.com/@gntrm/jwt-authentication-with-fastapi-and-aws-cognito-1333f7f2729e
-      title: JWT Authentication with FastAPI and AWS Cognito
-      author_link: https://twitter.com/gntrm
-      author: Johannes Gontrum
-    - link: https://towardsdatascience.com/how-to-deploy-a-machine-learning-model-dc51200fe8cf
-      title: How to Deploy a Machine Learning Model
-      author_link: https://www.linkedin.com/in/mgrootendorst/
-      author: Maarten Grootendorst
-    - link: https://eng.uber.com/ludwig-v0-2/
-      title: "Uber: Ludwig v0.2 Adds New Features and Other Improvements to its Deep Learning Toolbox [including a FastAPI server]"
-      author_link: https://eng.uber.com
-      author: Uber Engineering
-    - link: https://gitlab.com/euri10/fastapi_cheatsheet
-      title: A FastAPI and Swagger UI visual cheatsheet
-      author_link: https://gitlab.com/euri10
-      author: "@euri10"
-    - link: https://medium.com/@mike.p.moritz/using-docker-compose-to-deploy-a-lightweight-python-rest-api-with-a-job-queue-37e6072a209b
-      title: Using Docker Compose to deploy a lightweight Python REST API with a job queue
-      author_link: https://medium.com/@mike.p.moritz
-      author: Mike Moritz
-    - link: https://robwagner.dev/tortoise-fastapi-setup/
-      title: Setting up Tortoise ORM with FastAPI
-      author_link: https://robwagner.dev/
-      author: Rob Wagner
-    - link: https://dev.to/dbanty/why-i-m-leaving-flask-3ki6
-      title: Why I'm Leaving Flask
-      author_link: https://dev.to/dbanty
-      author: Dylan Anthony
-    - link: https://medium.com/python-data/how-to-deploy-tensorflow-2-0-models-as-an-api-service-with-fastapi-docker-128b177e81f3
-      title: How To Deploy Tensorflow 2.0 Models As An API Service With FastAPI & Docker
-      author_link: https://medium.com/@bbrenyah
-      author: Bernard Brenyah
-    - link: https://testdriven.io/blog/fastapi-crud/
-      title: "TestDriven.io: Developing and Testing an Asynchronous API with FastAPI and Pytest"
-      author_link: https://testdriven.io/authors/herman
-      author: Michael Herman
-    - link: https://towardsdatascience.com/deploying-iris-classifications-with-fastapi-and-docker-7c9b83fdec3a
-      title: "Towards Data Science: Deploying Iris Classifications with FastAPI and Docker"
-      author_link: https://towardsdatascience.com/@mandygu
-      author: Mandy Gu
-    - link: https://medium.com/analytics-vidhya/deploy-machine-learning-models-with-keras-fastapi-redis-and-docker-4940df614ece
-      title: Deploy Machine Learning Models with Keras, FastAPI, Redis and Docker
-      author_link: https://medium.com/@shane.soh
-      author: Shane Soh
-    - link: https://medium.com/@arthur393/another-boilerplate-to-fastapi-azure-pipeline-ci-pytest-3c8d9a4be0bb
-      title: "Another Boilerplate to FastAPI: Azure Pipeline CI + Pytest"
-      author_link: https://twitter.com/arthurheinrique
-      author: Arthur Henrique
-    - link: https://iwpnd.pw/articles/2020-01/deploy-fastapi-to-aws-lambda
-      title: How to continuously deploy a FastAPI to AWS Lambda with AWS SAM
-      author_link: https://iwpnd.pw
-      author: Benjamin Ramser
-    - link: https://www.tutlinks.com/create-and-deploy-fastapi-app-to-heroku/
-      title: Create and Deploy FastAPI app to Heroku without using Docker
-      author_link: https://www.linkedin.com/in/navule/
-      author: Navule Pavan Kumar Rao
-    - link: https://iwpnd.pw/articles/2020-03/apache-kafka-fastapi-geostream
-      title: Apache Kafka producer and consumer with FastAPI and aiokafka
-      author_link: https://iwpnd.pw
-      author: Benjamin Ramser
-    - link: https://wuilly.com/2019/10/real-time-notifications-with-python-and-postgres/
-      title: Real-time Notifications with Python and Postgres
-      author_link: https://wuilly.com/
-      author: Guillermo Cruz
-    - link: https://dev.to/paurakhsharma/microservice-in-python-using-fastapi-24cc
-      title: Microservice in Python using FastAPI
-      author_link: https://twitter.com/PaurakhSharma
-      author: Paurakh Sharma Humagain
-    - link: https://dev.to/cuongld2/build-simple-api-service-with-python-fastapi-part-1-581o
-      title: Build simple API service with Python FastAPI — Part 1
-      author_link: https://dev.to/cuongld2
-      author: cuongld2
-    - link: https://paulsec.github.io/posts/fastapi_plus_zeit_serverless_fu/
-      title: FastAPI + Zeit.co = 🚀
-      author_link: https://twitter.com/PaulWebSec
-      author: Paul Sec
-    - link: https://dev.to/tiangolo/build-a-web-api-from-scratch-with-fastapi-the-workshop-2ehe
-      title: Build a web API from scratch with FastAPI - the workshop
-      author_link: https://twitter.com/tiangolo
-      author: Sebastián Ramírez (tiangolo)
-    - link: https://www.twilio.com/blog/build-secure-twilio-webhook-python-fastapi
-      title: Build a Secure Twilio Webhook with Python and FastAPI
-      author_link: https://www.twilio.com
-      author: Twilio
-    - link: https://www.stavros.io/posts/fastapi-with-django/
-      title: Using FastAPI with Django
-      author_link: https://twitter.com/Stavros
-      author: Stavros Korokithakis
-    - link: https://netflixtechblog.com/introducing-dispatch-da4b8a2a8072
-      title: Introducing Dispatch
-      author_link: https://netflixtechblog.com/
-      author: Netflix
-    - link: https://davidefiocco.github.io/streamlit-fastapi-ml-serving/
-      title: Machine learning model serving in Python using FastAPI and streamlit
-      author_link: https://github.com/davidefiocco
-      author: Davide Fiocco
-    - link: https://www.tutlinks.com/deploy-fastapi-on-azure/
-      title: Deploy FastAPI on Azure App Service
-      author_link: https://www.linkedin.com/in/navule/
-      author: Navule Pavan Kumar Rao
-    - link: https://towardsdatascience.com/build-and-host-fast-data-science-applications-using-fastapi-823be8a1d6a0
-      title: Build And Host Fast Data Science Applications Using FastAPI
-      author_link: https://medium.com/@farhadmalik
-      author: Farhad Malik
-    - link: https://medium.com/@gabbyprecious2000/creating-a-crud-app-with-fastapi-part-one-7c049292ad37
-      title: Creating a CRUD App with FastAPI (Part one)
-      author_link: https://medium.com/@gabbyprecious2000
-      author: Precious Ndubueze
-    - link: https://julienharbulot.com/notification-server.html
-      title: HTTP server to display desktop notifications
-      author_link: https://julienharbulot.com/
-      author: Julien Harbulot
-    - link: https://guitton.co/posts/fastapi-monitoring/
-      title: How to monitor your FastAPI service
-      author_link: https://twitter.com/louis_guitton
-      author: Louis Guitton
-    - link: https://amitness.com/2020/06/fastapi-vs-flask/
-      title: FastAPI for Flask Users
-      author_link: https://twitter.com/amitness
-      author: Amit Chaudhary
-    - link: https://valonjanuzaj.medium.com/deploy-a-dockerized-fastapi-application-to-aws-cc757830ba1b
-      title: Deploy a dockerized FastAPI application to AWS
-      author_link: https://www.linkedin.com/in/valon-januzaj-b02692187/
-      author: Valon Januzaj
-    - link: https://dompatmore.com/blog/authenticate-your-fastapi-app-with-auth0
-      title: Authenticate Your FastAPI App with auth0
-      author_link: https://twitter.com/dompatmore
-      author: Dom Patmore
-  japanese:
-    - link: https://qiita.com/mtitg/items/47770e9a562dd150631d
-      title: FastAPI｜DB接続してCRUDするPython製APIサーバーを構築
-      author_link: https://qiita.com/mtitg
-      author: "@mtitg"
-    - link: https://qiita.com/ryoryomaru/items/59958ed385b3571d50de
-      title: python製の最新APIフレームワーク FastAPI を触ってみた
-      author_link: https://qiita.com/ryoryomaru
-      author: "@ryoryomaru"
-    - link: https://qiita.com/angel_katayoku/items/0e1f5dbbe62efc612a78
-      title: FastAPIでCORSを回避
-      author_link: https://qiita.com/angel_katayoku
-      author: "@angel_katayoku"
-    - link: https://qiita.com/angel_katayoku/items/4fbc1a4e2b33fa2237d2
-      title: FastAPIをMySQLと接続してDockerで管理してみる
-      author_link: https://qiita.com/angel_katayoku
-      author: "@angel_katayoku"
-    - link: https://qiita.com/angel_katayoku/items/8a458a8952f50b73f420
-      title: FastAPIでPOSTされたJSONのレスポンスbodyを受け取る
-      author_link: https://qiita.com/angel_katayoku
-      author: "@angel_katayoku"
-    - link: https://qiita.com/hikarut/items/b178af2e2440c67c6ac4
-      title: フロントエンド開発者向けのDockerによるPython開発環境構築
-      author_link: https://qiita.com/hikarut
-      author: Hikaru Takahashi
-    - link: https://rightcode.co.jp/blog/information-technology/fastapi-tutorial-todo-apps-environment
-      title: "【第1回】FastAPIチュートリアル: ToDoアプリを作ってみよう【環境構築編】"
-      author_link: https://rightcode.co.jp/author/jun
-      author: ライトコードメディア編集部
-    - link: https://rightcode.co.jp/blog/information-technology/fastapi-tutorial-todo-apps-model-building
-      title: "【第2回】FastAPIチュートリアル: ToDoアプリを作ってみよう【モデル構築編】"
-      author_link: https://rightcode.co.jp/author/jun
-      author: ライトコードメディア編集部
-    - link: https://rightcode.co.jp/blog/information-technology/fastapi-tutorial-todo-apps-authentication-user-registration
-      title: "【第3回】FastAPIチュートリアル: toDoアプリを作ってみよう【認証・ユーザ登録編】"
-      author_link: https://rightcode.co.jp/author/jun
-      author: ライトコードメディア編集部
-    - link: https://rightcode.co.jp/blog/information-technology/fastapi-tutorial-todo-apps-admin-page-improvement
-      title: "【第4回】FastAPIチュートリアル: toDoアプリを作ってみよう【管理者ページ改良編】"
-      author_link: https://rightcode.co.jp/author/jun
-      author: ライトコードメディア編集部
-    - link: https://qiita.com/bee2/items/0ad260ab9835a2087dae
-      title: PythonのWeb frameworkのパフォーマンス比較 (Django, Flask, responder, FastAPI, japronto)
-      author_link: https://qiita.com/bee2
-      author: "@bee2"
-    - link: https://qiita.com/bee2/items/75d9c0d7ba20e7a4a0e9
-      title: "[FastAPI] Python製のASGI Web フレームワーク FastAPIに入門する"
-      author_link: https://qiita.com/bee2
-      author: "@bee2"
-  vietnamese:
-    - link: https://fullstackstation.com/fastapi-trien-khai-bang-docker/
-      title: "FASTAPI: TRIỂN KHAI BẰNG DOCKER"
-      author_link: https://fullstackstation.com/author/figonking/
-      author: Nguyễn Nhân
-  russian:
-    - link: https://habr.com/ru/post/454440/
-      title: "Мелкая питонячая радость #2: Starlette - Солидная примочка – FastAPI"
-      author_link: https://habr.com/ru/users/57uff3r/
-      author: Andrey Korchak
-    - link: https://habr.com/ru/post/478620/
-      title: Почему Вы должны попробовать FastAPI?
-      author_link: https://github.com/prostomarkeloff
-      author: prostomarkeloff
-    - link: https://trkohler.com/fast-api-introduction-to-framework
-      title: "FastAPI: знакомимся с фреймворком"
-      author_link: https://www.linkedin.com/in/trkohler/
-      author: Troy Köhler
+  - 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: 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
+    title: Building simple E-Commerce with NuxtJS and FastAPI
+  - author: Rodrigo Arenas
+    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: Navule Pavan Kumar Rao
+    author_link: https://www.linkedin.com/in/navule/
+    link: https://www.tutlinks.com/deploy-fastapi-on-ubuntu-gunicorn-caddy-2/
+    title: Deploy FastAPI on Ubuntu and Serve using Caddy 2 Web Server
+  - 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  
+  - author: Dom Patmore
+    author_link: https://twitter.com/dompatmore
+    link: https://dompatmore.com/blog/authenticate-your-fastapi-app-with-auth0
+    title: Authenticate Your FastAPI App with auth0
+  - author: Valon Januzaj
+    author_link: https://www.linkedin.com/in/valon-januzaj-b02692187/
+    link: https://valonjanuzaj.medium.com/deploy-a-dockerized-fastapi-application-to-aws-cc757830ba1b
+    title: Deploy a dockerized FastAPI application to AWS
+  - author: Amit Chaudhary
+    author_link: https://twitter.com/amitness
+    link: https://amitness.com/2020/06/fastapi-vs-flask/
+    title: FastAPI for Flask Users
+  - author: Louis Guitton
+    author_link: https://twitter.com/louis_guitton
+    link: https://guitton.co/posts/fastapi-monitoring/
+    title: How to monitor your FastAPI service
+  - author: Julien Harbulot
+    author_link: https://julienharbulot.com/
+    link: https://julienharbulot.com/notification-server.html
+    title: HTTP server to display desktop notifications
+  - author: Precious Ndubueze
+    author_link: https://medium.com/@gabbyprecious2000
+    link: https://medium.com/@gabbyprecious2000/creating-a-crud-app-with-fastapi-part-one-7c049292ad37
+    title: Creating a CRUD App with FastAPI (Part one)
+  - author: Farhad Malik
+    author_link: https://medium.com/@farhadmalik
+    link: https://towardsdatascience.com/build-and-host-fast-data-science-applications-using-fastapi-823be8a1d6a0
+    title: Build And Host Fast Data Science Applications Using FastAPI
+  - author: Navule Pavan Kumar Rao
+    author_link: https://www.linkedin.com/in/navule/
+    link: https://www.tutlinks.com/deploy-fastapi-on-azure/
+    title: Deploy FastAPI on Azure App Service
+  - author: Davide Fiocco
+    author_link: https://github.com/davidefiocco
+    link: https://davidefiocco.github.io/streamlit-fastapi-ml-serving/
+    title: Machine learning model serving in Python using FastAPI and streamlit
+  - author: Netflix
+    author_link: https://netflixtechblog.com/
+    link: https://netflixtechblog.com/introducing-dispatch-da4b8a2a8072
+    title: Introducing Dispatch
+  - author: Stavros Korokithakis
+    author_link: https://twitter.com/Stavros
+    link: https://www.stavros.io/posts/fastapi-with-django/
+    title: Using FastAPI with Django
+  - author: Twilio
+    author_link: https://www.twilio.com
+    link: https://www.twilio.com/blog/build-secure-twilio-webhook-python-fastapi
+    title: Build a Secure Twilio Webhook with Python and FastAPI
+  - author: Sebastián Ramírez (tiangolo)
+    author_link: https://twitter.com/tiangolo
+    link: https://dev.to/tiangolo/build-a-web-api-from-scratch-with-fastapi-the-workshop-2ehe
+    title: Build a web API from scratch with FastAPI - the workshop
+  - author: Paul Sec
+    author_link: https://twitter.com/PaulWebSec
+    link: https://paulsec.github.io/posts/fastapi_plus_zeit_serverless_fu/
+    title: FastAPI + Zeit.co = 🚀
+  - author: cuongld2
+    author_link: https://dev.to/cuongld2
+    link: https://dev.to/cuongld2/build-simple-api-service-with-python-fastapi-part-1-581o
+    title: Build simple API service with Python FastAPI — Part 1
+  - author: Paurakh Sharma Humagain
+    author_link: https://twitter.com/PaurakhSharma
+    link: https://dev.to/paurakhsharma/microservice-in-python-using-fastapi-24cc
+    title: Microservice in Python using FastAPI
+  - author: Guillermo Cruz
+    author_link: https://wuilly.com/
+    link: https://wuilly.com/2019/10/real-time-notifications-with-python-and-postgres/
+    title: Real-time Notifications with Python and Postgres
+  - author: Benjamin Ramser
+    author_link: https://iwpnd.pw
+    link: https://iwpnd.pw/articles/2020-03/apache-kafka-fastapi-geostream
+    title: Apache Kafka producer and consumer with FastAPI and aiokafka
+  - author: Navule Pavan Kumar Rao
+    author_link: https://www.linkedin.com/in/navule/
+    link: https://www.tutlinks.com/create-and-deploy-fastapi-app-to-heroku/
+    title: Create and Deploy FastAPI app to Heroku without using Docker
+  - author: Benjamin Ramser
+    author_link: https://iwpnd.pw
+    link: https://iwpnd.pw/articles/2020-01/deploy-fastapi-to-aws-lambda
+    title: How to continuously deploy a FastAPI to AWS Lambda with AWS SAM
+  - author: Arthur Henrique
+    author_link: https://twitter.com/arthurheinrique
+    link: https://medium.com/@arthur393/another-boilerplate-to-fastapi-azure-pipeline-ci-pytest-3c8d9a4be0bb
+    title: 'Another Boilerplate to FastAPI: Azure Pipeline CI + Pytest'
+  - author: Shane Soh
+    author_link: https://medium.com/@shane.soh
+    link: https://medium.com/analytics-vidhya/deploy-machine-learning-models-with-keras-fastapi-redis-and-docker-4940df614ece
+    title: Deploy Machine Learning Models with Keras, FastAPI, Redis and Docker
+  - author: Mandy Gu
+    author_link: https://towardsdatascience.com/@mandygu
+    link: https://towardsdatascience.com/deploying-iris-classifications-with-fastapi-and-docker-7c9b83fdec3a
+    title: 'Towards Data Science: Deploying Iris Classifications with FastAPI and Docker'
+  - author: Michael Herman
+    author_link: https://testdriven.io/authors/herman
+    link: https://testdriven.io/blog/fastapi-crud/
+    title: 'TestDriven.io: Developing and Testing an Asynchronous API with FastAPI and Pytest'
+  - author: Bernard Brenyah
+    author_link: https://medium.com/@bbrenyah
+    link: https://medium.com/python-data/how-to-deploy-tensorflow-2-0-models-as-an-api-service-with-fastapi-docker-128b177e81f3
+    title: How To Deploy Tensorflow 2.0 Models As An API Service With FastAPI & Docker
+  - author: Dylan Anthony
+    author_link: https://dev.to/dbanty
+    link: https://dev.to/dbanty/why-i-m-leaving-flask-3ki6
+    title: Why I'm Leaving Flask
+  - author: Rob Wagner
+    author_link: https://robwagner.dev/
+    link: https://robwagner.dev/tortoise-fastapi-setup/
+    title: Setting up Tortoise ORM with FastAPI
+  - author: Mike Moritz
+    author_link: https://medium.com/@mike.p.moritz
+    link: https://medium.com/@mike.p.moritz/using-docker-compose-to-deploy-a-lightweight-python-rest-api-with-a-job-queue-37e6072a209b
+    title: Using Docker Compose to deploy a lightweight Python REST API with a job queue
+  - author: '@euri10'
+    author_link: https://gitlab.com/euri10
+    link: https://gitlab.com/euri10/fastapi_cheatsheet
+    title: A FastAPI and Swagger UI visual cheatsheet
+  - author: Uber Engineering
+    author_link: https://eng.uber.com
+    link: https://eng.uber.com/ludwig-v0-2/
+    title: 'Uber: Ludwig v0.2 Adds New Features and Other Improvements to its Deep Learning Toolbox [including a FastAPI server]'
+  - author: Maarten Grootendorst
+    author_link: https://www.linkedin.com/in/mgrootendorst/
+    link: https://towardsdatascience.com/how-to-deploy-a-machine-learning-model-dc51200fe8cf
+    title: How to Deploy a Machine Learning Model
+  - author: Johannes Gontrum
+    author_link: https://twitter.com/gntrm
+    link: https://medium.com/@gntrm/jwt-authentication-with-fastapi-and-aws-cognito-1333f7f2729e
+    title: JWT Authentication with FastAPI and AWS Cognito
+  - author: Ankush Thakur
+    author_link: https://geekflare.com/author/ankush/
+    link: https://geekflare.com/python-asynchronous-web-frameworks/
+    title: Top 5 Asynchronous Web Frameworks for Python
+  - author: Nico Axtmann
+    author_link: https://www.linkedin.com/in/nico-axtmann
+    link: https://medium.com/@nico.axtmann95/deploying-a-scikit-learn-model-with-onnx-und-fastapi-1af398268915
+    title: Deploying a scikit-learn model with ONNX and FastAPI
+  - author: Nils de Bruin
+    author_link: https://medium.com/@nilsdebruin
+    link: https://medium.com/data-rebels/fastapi-authentication-revisited-enabling-api-key-authentication-122dc5975680
+    title: 'FastAPI authentication revisited: Enabling API key authentication'
+  - author: Nick Cortale
+    author_link: https://nickc1.github.io/
+    link: https://nickc1.github.io/api,/scikit-learn/2019/01/10/scikit-fastapi.html
+    title: 'FastAPI and Scikit-Learn: Easily Deploy Models'
+  - author: Errieta Kostala
+    author_link: https://dev.to/errietta
+    link: https://dev.to/errietta/introduction-to-the-fastapi-python-framework-2n10
+    title: Introduction to the fastapi python framework
+  - author: Nils de Bruin
+    author_link: https://medium.com/@nilsdebruin
+    link: https://medium.com/data-rebels/fastapi-how-to-add-basic-and-cookie-authentication-a45c85ef47d3
+    title: FastAPI — How to add basic and cookie authentication
+  - author: Nils de Bruin
+    author_link: https://medium.com/@nilsdebruin
+    link: https://medium.com/data-rebels/fastapi-google-as-an-external-authentication-provider-3a527672cf33
+    title: FastAPI — Google as an external authentication provider
+  - author: William Hayes
+    author_link: https://medium.com/@williamhayes
+    link: https://medium.com/@williamhayes/fastapi-starlette-debug-vs-prod-5f7561db3a59
+    title: FastAPI/Starlette debug vs prod
   german:
-    - 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_link: https://twitter.com/_nicoax
-      author: Nico Axtmann
+  - 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
+  japanese:
+  - author: '@bee2'
+    author_link: https://qiita.com/bee2
+    link: https://qiita.com/bee2/items/75d9c0d7ba20e7a4a0e9
+    title: '[FastAPI] Python製のASGI Web フレームワーク FastAPIに入門する'
+  - author: '@bee2'
+    author_link: https://qiita.com/bee2
+    link: https://qiita.com/bee2/items/0ad260ab9835a2087dae
+    title: PythonのWeb frameworkのパフォーマンス比較 (Django, Flask, responder, FastAPI, japronto)
+  - author: ライトコードメディア編集部
+    author_link: https://rightcode.co.jp/author/jun
+    link: https://rightcode.co.jp/blog/information-technology/fastapi-tutorial-todo-apps-admin-page-improvement
+    title: '【第4回】FastAPIチュートリアル: toDoアプリを作ってみよう【管理者ページ改良編】'
+  - author: ライトコードメディア編集部
+    author_link: https://rightcode.co.jp/author/jun
+    link: https://rightcode.co.jp/blog/information-technology/fastapi-tutorial-todo-apps-authentication-user-registration
+    title: '【第3回】FastAPIチュートリアル: toDoアプリを作ってみよう【認証・ユーザ登録編】'
+  - author: ライトコードメディア編集部
+    author_link: https://rightcode.co.jp/author/jun
+    link: https://rightcode.co.jp/blog/information-technology/fastapi-tutorial-todo-apps-model-building
+    title: '【第2回】FastAPIチュートリアル: ToDoアプリを作ってみよう【モデル構築編】'
+  - author: ライトコードメディア編集部
+    author_link: https://rightcode.co.jp/author/jun
+    link: https://rightcode.co.jp/blog/information-technology/fastapi-tutorial-todo-apps-environment
+    title: '【第1回】FastAPIチュートリアル: ToDoアプリを作ってみよう【環境構築編】'
+  - author: Hikaru Takahashi
+    author_link: https://qiita.com/hikarut
+    link: https://qiita.com/hikarut/items/b178af2e2440c67c6ac4
+    title: フロントエンド開発者向けのDockerによるPython開発環境構築
+  - author: '@angel_katayoku'
+    author_link: https://qiita.com/angel_katayoku
+    link: https://qiita.com/angel_katayoku/items/8a458a8952f50b73f420
+    title: FastAPIでPOSTされたJSONのレスポンスbodyを受け取る
+  - author: '@angel_katayoku'
+    author_link: https://qiita.com/angel_katayoku
+    link: https://qiita.com/angel_katayoku/items/4fbc1a4e2b33fa2237d2
+    title: FastAPIをMySQLと接続してDockerで管理してみる
+  - author: '@angel_katayoku'
+    author_link: https://qiita.com/angel_katayoku
+    link: https://qiita.com/angel_katayoku/items/0e1f5dbbe62efc612a78
+    title: FastAPIでCORSを回避
+  - author: '@ryoryomaru'
+    author_link: https://qiita.com/ryoryomaru
+    link: https://qiita.com/ryoryomaru/items/59958ed385b3571d50de
+    title: python製の最新APIフレームワーク FastAPI を触ってみた
+  - author: '@mtitg'
+    author_link: https://qiita.com/mtitg
+    link: https://qiita.com/mtitg/items/47770e9a562dd150631d
+    title: FastAPI｜DB接続してCRUDするPython製APIサーバーを構築
+  russian:
+  - author: Troy Köhler
+    author_link: https://www.linkedin.com/in/trkohler/
+    link: https://trkohler.com/fast-api-introduction-to-framework
+    title: 'FastAPI: знакомимся с фреймворком'
+  - author: prostomarkeloff
+    author_link: https://github.com/prostomarkeloff
+    link: https://habr.com/ru/post/478620/
+    title: Почему Вы должны попробовать FastAPI?
+  - author: Andrey Korchak
+    author_link: https://habr.com/ru/users/57uff3r/
+    link: https://habr.com/ru/post/454440/
+    title: 'Мелкая питонячая радость #2: Starlette - Солидная примочка – FastAPI'
+  vietnamese:
+  - author: Nguyễn Nhân
+    author_link: https://fullstackstation.com/author/figonking/
+    link: https://fullstackstation.com/fastapi-trien-khai-bang-docker/
+    title: 'FASTAPI: TRIỂN KHAI BẰNG DOCKER'
 podcasts:
   english:
-    - link: https://pythonbytes.fm/episodes/show/123/time-to-right-the-py-wrongs?time_in_sec=855
-      title: FastAPI on PythonBytes
-      author_link: https://pythonbytes.fm/
-      author: Python Bytes FM
-    - link: https://www.pythonpodcast.com/fastapi-web-application-framework-episode-259/
-      title: "Build The Next Generation Of Python Web Applications With FastAPI - Episode 259 - interview to Sebastían Ramírez (tiangolo)"
-      author_link: https://www.pythonpodcast.com/
-      author: Podcast.`__init__`
+  - author: Podcast.`__init__`
+    author_link: https://www.pythonpodcast.com/
+    link: https://www.pythonpodcast.com/fastapi-web-application-framework-episode-259/
+    title: Build The Next Generation Of Python Web Applications With FastAPI - Episode 259 - interview to Sebastían Ramírez (tiangolo)
+  - author: Python Bytes FM
+    author_link: https://pythonbytes.fm/
+    link: https://pythonbytes.fm/episodes/show/123/time-to-right-the-py-wrongs?time_in_sec=855
+    title: FastAPI on PythonBytes
 talks:
   english:
-    - link: https://www.youtube.com/watch?v=3DLwPcrE5mA
-      title: "PyCon UK 2019: FastAPI from the ground up"
-      author_link: https://twitter.com/chriswithers13
-      author: Chris Withers
-    - link: https://www.youtube.com/watch?v=z9K5pwb0rt8
-      title: "PyConBY 2020: Serve ML models easily with FastAPI"
-      author_link: https://twitter.com/tiangolo
-      author: "Sebastián Ramírez (tiangolo)"
-    - link: https://www.youtube.com/watch?v=PnpTY1f4k2U
-      title: "[VIRTUAL] Py.Amsterdam's flying Software Circus: Intro to FastAPI"
-      author_link: https://twitter.com/tiangolo
-      author: "Sebastián Ramírez (tiangolo)"
+  - author: Sebastián Ramírez (tiangolo)
+    author_link: https://twitter.com/tiangolo
+    link: https://www.youtube.com/watch?v=PnpTY1f4k2U
+    title: '[VIRTUAL] Py.Amsterdam''s flying Software Circus: Intro to FastAPI'
+  - author: Sebastián Ramírez (tiangolo)
+    author_link: https://twitter.com/tiangolo
+    link: https://www.youtube.com/watch?v=z9K5pwb0rt8
+    title: 'PyConBY 2020: Serve ML models easily with FastAPI'
+  - author: Chris Withers
+    author_link: https://twitter.com/chriswithers13
+    link: https://www.youtube.com/watch?v=3DLwPcrE5mA
+    title: 'PyCon UK 2019: FastAPI from the ground up'

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docs/en/data/sponsors.yml

@@ -1,4 +1,8 @@
 gold:
+  - 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.deta.sh/?ref=fastapi
     title: The launchpad for all your (team's) ideas
     img: https://fastapi.tiangolo.com/img/sponsors/deta.svg
@@ -11,7 +15,13 @@ gold:
   - url: https://talkpython.fm/fastapi-sponsor
     title: FastAPI video courses on demand from people you trust
     img: https://fastapi.tiangolo.com/img/sponsors/talkpython.png
-silver:
   - url: https://testdriven.io/courses/tdd-fastapi/
     title: Learn to build high-quality web apps with best practices
     img: https://fastapi.tiangolo.com/img/sponsors/testdriven.svg
+  - url: https://github.com/deepset-ai/haystack/
+    title: Build powerful search from composable, open source building blocks
+    img: https://fastapi.tiangolo.com/img/sponsors/haystack-fastapi.svg
+bronze:
+  - url: https://calmcode.io
+    title: Code. Simply. Clearly. Calmly.
+    img: https://fastapi.tiangolo.com/img/sponsors/calmcode.jpg

docs/en/data/sponsors_badge.yml

@@ -0,0 +1,8 @@
+logins:
+  - jina-ai
+  - deta
+  - investsuite
+  - vimsoHQ
+  - mikeckennedy
+  - koaning
+  - deepset-ai

docs/en/docs/advanced/custom-response.md

@@ -161,10 +161,33 @@ An alternative JSON response using <a href="https://github.com/ultrajson/ultrajs
 
 Returns an HTTP redirect. Uses a 307 status code (Temporary Redirect) by default.
 
+You can return a `RedirectResponse` directly:
+
 ```Python hl_lines="2  9"
 {!../../../docs_src/custom_response/tutorial006.py!}
 ```
 
+---
+
+Or you can use it in the `response_class` parameter:
+
+
+```Python hl_lines="2  7  9"
+{!../../../docs_src/custom_response/tutorial006b.py!}
+```
+
+If you do that, then you can return the URL directly from your *path operation* function.
+
+In this case, the `status_code` used will be the default one for the `RedirectResponse`, which is `307`.
+
+---
+
+You can also use the `status_code` parameter combined with the `response_class` parameter:
+
+```Python hl_lines="2  7  9"
+{!../../../docs_src/custom_response/tutorial006c.py!}
+```
+
 ### `StreamingResponse`
 
 Takes an async generator or a normal generator/iterator and streams the response body.
@@ -175,14 +198,24 @@ Takes an async generator or a normal generator/iterator and streams the response
 
 #### Using `StreamingResponse` with file-like objects
 
-If you have a file-like object (e.g. the object returned by `open()`), you can return it in a `StreamingResponse`.
+If you have a file-like object (e.g. the object returned by `open()`), you can create a generator function to iterate over that file-like object.
+
+That way, you don't have to read it all first in memory, and you can pass that generator function to the `StreamingResponse`, and return it.
 
 This includes many libraries to interact with cloud storage, video processing, and others.
 
-```Python hl_lines="2  10-11"
+```{ .python .annotate hl_lines="2  10-12  14" }
 {!../../../docs_src/custom_response/tutorial008.py!}
 ```
 
+1. This is the generator function. It's a "generator function" because it contains `yield` statements inside.
+2. By using a `with` block, we make sure that the file-like object is closed after the generator function is done. So, after it finishes sending the response.
+3. This `yield from` tells the function to iterate over that thing named `file_like`. And then, for each part iterated, yield that part as coming from this generator function.
+
+    So, it is a generator function that transfers the "generating" work to something else internally.
+
+    By doing it this way, we can put it in a `with` block, and that way, ensure that it is closed after finishing.
+
 !!! tip
     Notice that here as we are using standard `open()` that doesn't support `async` and `await`, we declare the path operation with normal `def`.
 
@@ -203,6 +236,14 @@ File responses will include appropriate `Content-Length`, `Last-Modified` and `E
 {!../../../docs_src/custom_response/tutorial009.py!}
 ```
 
+You can also use the `response_class` parameter:
+
+```Python hl_lines="2  8  10"
+{!../../../docs_src/custom_response/tutorial009b.py!}
+```
+
+In this case, you can return the file path directly from your *path operation* function.
+
 ## 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

@@ -0,0 +1,98 @@
+# Using Dataclasses
+
+FastAPI is built on top of **Pydantic**, and I have been showing you how to use Pydantic models to declare requests and responses.
+
+But FastAPI also supports using <a href="https://docs.python.org/3/library/dataclasses.html" class="external-link" target="_blank">`dataclasses`</a> the same way:
+
+```Python hl_lines="1  7-12  19-20"
+{!../../../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>.
+
+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.
+
+And of course, it supports the same:
+
+* data validation
+* data serialization
+* data documentation, etc.
+
+This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.
+
+!!! info
+    Have in mind that dataclasses can't do everything Pydantic models can do.
+
+    So, you might still need to use Pydantic models.
+
+    But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓
+
+## Dataclasses in `response_model`
+
+You can also use `dataclasses` in the `response_model` parameter:
+
+```Python hl_lines="1  7-13  19"
+{!../../../docs_src/dataclasses/tutorial002.py!}
+```
+
+The dataclass will be automatically converted to a Pydantic dataclass.
+
+This way, its schema will show up in the API docs user interface:
+
+<img src="/img/tutorial/dataclasses/image01.png">
+
+## Dataclasses in Nested Data Structures
+
+You can also combine `dataclasses` with other type annotations to make nested data structures.
+
+In some cases, you might still have to use Pydantic's version of `dataclasses`. For example, if you have errors with the automatically generated API documentation.
+
+In that case, you can simply swap the standard `dataclasses` with `pydantic.dataclasses`, which is a drop-in replacement:
+
+```{ .python .annotate hl_lines="1  5  8-11  14-17  23-25  28" }
+{!../../../docs_src/dataclasses/tutorial003.py!}
+```
+
+1. We still import `field` from standard `dataclasses`.
+
+2. `pydantic.dataclasses` is a drop-in replacement for `dataclasses`.
+
+3. The `Author` dataclass includes a list of `Item` dataclasses.
+
+4. The `Author` dataclass is used as the `response_model` parameter.
+
+5. You can use other standard type annotations with dataclasses as the request body.
+
+    In this case, it's a list of `Item` dataclasses.
+
+6. Here we are returning a dictionary that contains `items` which is a list of dataclasses.
+
+    FastAPI is still capable of <abbr title="converting the data to a format that can be transmitted">serializing</abbr> the data to JSON.
+
+7. Here the `response_model` is using a type annotation of a list of `Author` dataclasses.
+
+    Again, you can combine `dataclasses` with standard type annotations.
+
+8. Notice that this *path operation function* uses regular `def` instead of `async def`.
+
+    As always, in FastAPI you can combine `def` and `async def` as needed.
+
+    If you need a refresher about when to use which, check out the section _"In a hurry?"_ in the docs about <a href="https://fastapi.tiangolo.com/async/#in-a-hurry" target="_blank" class="internal-link">`async` and `await`</a>.
+
+9. This *path operation function* is not returning dataclasses (although it could), but a list of dictionaries with internal data.
+
+    FastAPI will use the `response_model` parameter (that includes dataclasses) to convert the response.
+
+You can combine `dataclasses` with other type annotations in many different combinations to form complex data structures.
+
+Check the in-code annotation tips above to see more specific details.
+
+## Learn More
+
+You can also combine `dataclasses` with other Pydantic models, inherit from them, include them in your own models, etc.
+
+To learn more, check the <a href="https://pydantic-docs.helpmanual.io/usage/dataclasses/" class="external-link" target="_blank">Pydantic docs about dataclasses</a>.
+
+## Version
+
+This is available since FastAPI version `0.67.0`. 🔖

docs/en/docs/advanced/graphql.md

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

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

@@ -33,7 +33,7 @@ You should do it after adding all your *path operations*.
 
 ## Exclude from OpenAPI
 
-To exclude a *path operation* from the generated OpenAPI schema (and thus, from the automatic documentation systems), use the parameter `include_in_schema` and set it to `False`;
+To exclude a *path operation* from the generated OpenAPI schema (and thus, from the automatic documentation systems), use the parameter `include_in_schema` and set it to `False`:
 
 ```Python hl_lines="6"
 {!../../../docs_src/path_operation_advanced_configuration/tutorial003.py!}
@@ -50,3 +50,121 @@ It won't show up in the documentation, but other tools (such as Sphinx) will be
 ```Python hl_lines="19-29"
 {!../../../docs_src/path_operation_advanced_configuration/tutorial004.py!}
 ```
+
+## Additional Responses
+
+You probably have seen how to declare the `response_model` and `status_code` for a *path operation*.
+
+That defines the metadata about the main response of a *path operation*.
+
+You can also declare additional responses with their models, status codes, etc.
+
+There's a whole chapter here in the documentation about it, you can read it at [Additional Responses in OpenAPI](./additional-responses.md){.internal-link target=_blank}.
+
+## OpenAPI Extra
+
+When you declare a *path operation* in your application, **FastAPI** automatically generates the relevant metadata about that *path operation* to be included in the OpenAPI schema.
+
+!!! note "Technical details"
+    In the OpenAPI specification it is called the <a href="https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.3.md#operation-object" class="external-link" target="_blank">Operation Object</a>.
+
+It has all the information about the *path operation* and is used to generate the automatic documentation.
+
+It includes the `tags`, `parameters`, `requestBody`, `responses`, etc.
+
+This *path operation*-specific OpenAPI schema is normally generated automatically by **FastAPI**, but you can also extend it.
+
+!!! 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}.
+
+You can extend the OpenAPI schema for a *path operation* using the parameter `openapi_extra`.
+
+### OpenAPI Extensions
+
+This `openapi_extra` can be helpful, for example, to declare [OpenAPI Extensions](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.3.md#specificationExtensions):
+
+```Python hl_lines="6"
+{!../../../docs_src/path_operation_advanced_configuration/tutorial005.py!}
+```
+
+If you open the automatic API docs, your extension will show up at the bottom of the specific *path operation*.
+
+<img src="/img/tutorial/path-operation-advanced-configuration/image01.png">
+
+And if you see the resulting OpenAPI (at `/openapi.json` in your API), you will see your extension as part of the specific *path operation* too:
+
+```JSON hl_lines="22"
+{
+    "openapi": "3.0.2",
+    "info": {
+        "title": "FastAPI",
+        "version": "0.1.0"
+    },
+    "paths": {
+        "/items/": {
+            "get": {
+                "summary": "Read Items",
+                "operationId": "read_items_items__get",
+                "responses": {
+                    "200": {
+                        "description": "Successful Response",
+                        "content": {
+                            "application/json": {
+                                "schema": {}
+                            }
+                        }
+                    }
+                },
+                "x-aperture-labs-portal": "blue"
+            }
+        }
+    }
+}
+```
+
+### Custom OpenAPI *path operation* schema
+
+The dictionary in `openapi_extra` will be deeply merged with the automatically generated OpenAPI schema for the *path operation*.
+
+So, you could add additional data to the automatically generated schema.
+
+For example, you could decide to read and validate the request with your own code, without using the automatic features of FastAPI with Pydantic, but you could still want to define the request in the OpenAPI schema.
+
+You could do that with `openapi_extra`:
+
+```Python hl_lines="20-37  39-40"
+{!../../../docs_src/path_operation_advanced_configuration/tutorial006.py!}
+```
+
+In this example, we didn't declare any Pydantic model. In fact, the request body is not even <abbr title="converted from some plain format, like bytes, into Python objects">parsed</abbr> as JSON, it is read directly as `bytes`, and the function `magic_data_reader()` would be in charge of parsing it in some way.
+
+Nevertheless, we can declare the expected schema for the request body.
+
+### Custom OpenAPI content type
+
+Using this same trick, you could use a Pydantic model to define the JSON Schema that is then included in the custom OpenAPI schema section for the *path operation*.
+
+And you could do this even if the data type in the request is not JSON.
+
+For example, in this application we don't use FastAPI's integrated functionality to extract the JSON Schema from Pydantic models nor the automatic validation for JSON. In fact, we are declaring the request content type as YAML, not JSON:
+
+```Python hl_lines="17-22  24"
+{!../../../docs_src/path_operation_advanced_configuration/tutorial007.py!}
+```
+
+Nevertheless, although we are not using the default integrated functionality, we are still using a Pydantic model to manually generate the JSON Schema for the data that we want to receive in YAML.
+
+Then we use the request directly, and extract the body as `bytes`. This means that FastAPI won't even try to parse the request payload as JSON.
+
+And then in our code, we parse that YAML content directly, and then we are again using the same Pydantic model to validate the YAML content:
+
+```Python hl_lines="26-33"
+{!../../../docs_src/path_operation_advanced_configuration/tutorial007.py!}
+```
+
+!!! tip
+    Here we re-use the same Pydantic model.
+
+    But the same way, we could have validated it in some other way.

docs/en/docs/advanced/settings.md

@@ -235,7 +235,7 @@ And then we can require it from the *path operation function* as a dependency an
 
 Then it would be very easy to provide a different settings object during testing by creating a dependency override for `get_settings`:
 
-```Python hl_lines="8-9  12  21"
+```Python hl_lines="9-10  13  21"
 {!../../../docs_src/settings/app02/test_main.py!}
 ```
 
@@ -288,7 +288,7 @@ Reading a file from disk is normally a costly (slow) operation, so you probably
 But every time we do:
 
 ```Python
-config.Settings()
+Settings()
 ```
 
 a new `Settings` object would be created, and at creation it would read the `.env` file again.
@@ -297,7 +297,7 @@ If the dependency function was just like:
 
 ```Python
 def get_settings():
-    return config.Settings()
+    return Settings()
 ```
 
 we would create that object for each request, and we would be reading the `.env` file for each request. ⚠️

docs/en/docs/alternatives.md

@@ -367,7 +367,6 @@ It has:
 
 * Seriously impressive performance.
 * WebSocket support.
-* GraphQL support.
 * In-process background tasks.
 * Startup and shutdown events.
 * Test client built on requests.
@@ -375,7 +374,7 @@ It has:
 * Session and Cookie support.
 * 100% test coverage.
 * 100% type annotated codebase.
-* Zero hard dependencies.
+* Few hard dependencies.
 
 Starlette is currently the fastest Python framework tested. Only surpassed by Uvicorn, which is not a framework, but a server.
 

docs/en/docs/async.md

@@ -102,13 +102,15 @@ 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.
 
 Then it's your turn, you place your order of 2 very fancy burgers 🍔 for your crush 😍 and you.
 
 You pay 💸.
 
-The cashier 💁 says something to the guy in the kitchen 👨‍🍳 so he knows he has to prepare your burgers 🍔 (even though he is 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.
 
@@ -146,9 +148,9 @@ Now let's imagine these aren't "Concurrent Burgers", but "Parallel Burgers".
 
 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 himself 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.
 
@@ -174,7 +176,7 @@ There was not much talk or flirting as most of the time was spent waiting 🕙 i
 
 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 😞.
 
@@ -236,7 +238,7 @@ 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.
 

docs/en/docs/contributing.md

@@ -163,20 +163,6 @@ It will also auto-sort all your imports.
 
 For it to sort them correctly, you need to have FastAPI installed locally in your environment, with the command in the section above using `--symlink` (or `--pth-file` on Windows).
 
-### Format imports
-
-There is another script that formats all the imports and makes sure you don't have unused imports:
-
-<div class="termy">
-
-```console
-$ bash scripts/format-imports.sh
-```
-
-</div>
-
-As it runs one command after the other and modifies and reverts many files, it takes a bit longer to run, so it might be easier to use `scripts/format.sh` frequently and `scripts/format-imports.sh` only before committing.
-
 ## Docs
 
 First, make sure you set up your environment as described above, that will install all the requirements.

docs/en/docs/css/custom.css

@@ -1,3 +1,13 @@
+.termynal-comment {
+  color: #4a968f;
+  font-style: italic;
+  display: block;
+}
+
+.termy [data-termynal] {
+  white-space: pre-wrap;
+}
+
 a.external-link::after {
   /* \00A0 is a non-breaking space
         to make the mark be on the same line as the link
@@ -12,6 +22,10 @@ a.internal-link::after {
   content: "\00A0↪";
 }
 
+.shadow {
+  box-shadow: 5px 5px 10px #999;
+}
+
 /* Give space to lower icons so Gitter chat doesn't get on top of them */
 .md-footer-meta {
   padding-bottom: 2em;
@@ -20,6 +34,7 @@ a.internal-link::after {
 .user-list {
   display: flex;
   flex-wrap: wrap;
+  margin-bottom: 2rem;
 }
 
 .user-list-center {
@@ -56,10 +71,50 @@ a.internal-link::after {
   text-align: center;
 }
 
-a.announce:link, a.announce:visited {
+a.announce-link:link,
+a.announce-link:visited {
   color: #fff;
 }
 
-a.announce:hover {
+a.announce-link:hover {
   color: var(--md-accent-fg-color);
 }
+
+.announce-wrapper {
+  display: flex;
+  justify-content: space-between;
+  flex-wrap: wrap;
+  align-items: center;
+}
+
+.announce-wrapper div.item {
+  display: none;
+}
+
+.announce-wrapper .sponsor-badge {
+  display: block;
+  position: absolute;
+  top: -5px;
+  right: 0;
+  font-size: 0.5rem;
+  color: #999;
+  background-color: #666;
+  border-radius: 10px;
+  padding: 0 10px;
+  z-index: 10;
+}
+
+.announce-wrapper .sponsor-image {
+  display: block;
+  border-radius: 20px;
+}
+
+.announce-wrapper>div {
+  min-height: 40px;
+  display: flex;
+  align-items: center;
+}
+
+.twitter {
+  color: #00acee;
+}

docs/en/docs/css/termynal.css

@@ -17,7 +17,8 @@
     max-width: 100%;
     background: var(--color-bg);
     color: var(--color-text);
-    font-size: 18px;
+    /* font-size: 18px; */
+    font-size: 15px;
     /* font-family: 'Fira Mono', Consolas, Menlo, Monaco, 'Courier New', Courier, monospace; */
     font-family: 'Roboto Mono', 'Fira Mono', Consolas, Menlo, Monaco, 'Courier New', Courier, monospace;
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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 for 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 in 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 on 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, its own variables, its own 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 or 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 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, maybe 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

@@ -238,3 +238,21 @@ You can also edit them and re-play them.
 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 a lot several use cases, but at the same time it doesn't support others, like using external databases (apart from Deta's own NoSQL database system), custom virtual machines, etc.
+
+    You can read more details in the <a href="https://docs.deta.sh/docs/micros/about/" class="external-link" target="_blank">Deta docs</a> to see if it's the right choice for you.

docs/en/docs/deployment/docker.md

@@ -1,67 +1,144 @@
-# Deploy with Docker
+# FastAPI in Containers - Docker
 
-In this section you'll see instructions and links to guides to know how to:
+When deploying FastAPI applications a common approach is to build a **Linux container image**. It's normally done using <a href="https://www.docker.com/" class="external-link" target="_blank">**Docker**</a>. And then you can deploy that container image in one of different 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>
+
+## 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 in 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 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 in it.
+* Create an empty file `__init__.py`.
 * Create a `main.py` file with:
 
 ```Python
@@ -82,16 +159,126 @@ def read_item(item_id: int, q: Optional[str] = None):
     return {"item_id": item_id, "q": q}
 ```
 
-* You should now have a directory structure like:
+### Dockerfile
+
+Now in the same project directory create a file `Dockerfile` with:
+
+```{ .dockerfile .annotate }
+# (1)
+FROM python:3.9
+
+# (2)
+WORKDIR /code
+
+# (3)
+COPY ./requirements.txt /code/requirements.txt
+
+# (4)
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# (5)
+COPY ./app /code/app
+
+# (6)
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "80"]
+```
+
+1. Start from the official Python base image.
+
+2. Set the current working directory to `/code`.
+
+    This is where we'll put the `requirements.txt` file and the `app` directory.
+
+3. Copy the file with the requirements to the `/code` directory.
+
+    Copy **only** the file with the requirements first, not the rest of the code.
+
+    As this file **doesn't change often**, Docker will detect it and use the **cache** for this step, enabling the cache for the next step too.
+
+4. Install the package dependencies in the requirements file.
+
+    The `--no-cache-dir` option tells `pip` to not save the downloaded packages locally, as that is only if `pip` was going to be run again to install the same packages, but that's not the case when working with containers.
+
+    !!! note
+        The `--no-cache-dir` is only related to `pip`, it has nothing to do with Docker or containers.
+
+    The `--upgrade` option tells `pip` to upgrade the packages if they are already installed.
+
+    Because the previous step copying the file could be detected by the **Docker cache**, this step will also **use the Docker cache** when available.
+
+    Using the cache in this step will **save** you a lot of **time** when building the image again and again during development, instead of **downloading and installing** all the dependencies **every time**.
+
+5. Copy the `./app` directory inside the `/code` directory.
+
+    As this has all the code which is what **changes most frequently** the Docker **cache** won't be used for this or any **following steps** easily.
+
+    So, it's important to put this **near the end** of the `Dockerfile`, to optimize the container image build times.
+
+6. Set the **command** to run the `uvicorn` server.
+
+    `CMD` takes a list of strings, each of this 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:
 
-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 other 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 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 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"
 
@@ -39,7 +57,9 @@ You just need to install an ASGI compatible server like:
 
     ...or any other ASGI server.
 
-And run your application the same way you have done in the tutorials, but without the `--reload` option, e.g.:
+## Run the Server Program
+
+You can then your application the same way you have done in the tutorials, but without the `--reload` option, e.g.:
 
 === "Uvicorn"
 
@@ -65,10 +85,24 @@ And run your application the same way you have done in the tutorials, but withou
 
     </div>
 
-You might want to set up some tooling to make sure it is restarted automatically if it stops.
+!!! warning
+    Remember to remove the `--reload` option if you were using it.
 
-You might also want to install <a href="https://gunicorn.org/" class="external-link" target="_blank">Gunicorn</a> and <a href="https://www.uvicorn.org/#running-with-gunicorn" class="external-link" target="_blank">use it as a manager for Uvicorn</a>, or use Hypercorn with multiple workers.
+    The `--reload` option consumes much more resources, is more unstable, etc.
+    
+    It helps a lot during **development**, but you **shouldn't** use it in **production**.
 
-Making sure to fine-tune the number of workers, etc.
+## Deployment Concepts
 
-But if you are doing all that, you might just use the Docker image that does it automatically.
+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

@@ -114,48 +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 }}"></a>
+<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 }}"></a>
+<a href="{{ sponsor.url }}" target="_blank" title="{{ sponsor.title }}"><img src="{{ sponsor.img }}" style="border-radius:15px"></a>
 {% endfor %}
 {% endif %}
 
-{% if people %}
-{% if people.sponsors_50 %}
+{% if sponsors.bronze %}
 
 ### Bronze Sponsors
 
-<div class="user-list user-list-center">
-{% for user in people.sponsors_50 %}
-
-<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>
+{% for sponsor in sponsors.bronze -%}
+<a href="{{ sponsor.url }}" target="_blank" title="{{ sponsor.title }}"><img src="{{ sponsor.img }}" style="border-radius:15px"></a>
 {% endfor %}
-
-</div>
-
 {% endif %}
+
 {% endif %}
 
 ### Individual Sponsors
 
-{% if people %}
+{% if github_sponsors %}
+{% for group in github_sponsors.sponsors %}
+
 <div class="user-list user-list-center">
-{% for user in people.sponsors %}
+
+{% 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>
+
+{% endfor %}
 {% endif %}
 
 ## About the data - technical details

docs/en/docs/features.md

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

docs/en/docs/help-fastapi.md

@@ -20,6 +20,10 @@ You can subscribe to the (infrequent) [**FastAPI and friends** newsletter](/news
 * Breaking changes 🚨
 * Tips and tricks ✅
 
+## Follow FastAPI on Twitter
+
+<a href="https://twitter.com/fastapi" class="external-link" target="_blank">Follow @fastapi on **Twitter**</a> to get the latest news about **FastAPI**. 🐦
+
 ## Star **FastAPI** in GitHub
 
 You can "star" FastAPI in GitHub (clicking the star button at the top right): <a href="https://github.com/tiangolo/fastapi" class="external-link" target="_blank">https://github.com/tiangolo/fastapi</a>. ⭐️
@@ -32,7 +36,7 @@ You can "watch" FastAPI in GitHub (clicking the "watch" button at the top right)
 
 There you can select "Releases only".
 
-Doing it, you will receive notifications (in your email) whenever there's a new release (a new version) of **FastAPI** with bug fixes and new features.
+By doing it, you will receive notifications (in your email) whenever there's a new release (a new version) of **FastAPI** with bug fixes and new features.
 
 ## Connect with the author
 
@@ -46,17 +50,18 @@ You can:
 * <a href="https://twitter.com/tiangolo" class="external-link" target="_blank">Follow me on **Twitter**</a>.
     * Tell me how you use FastAPI (I love to hear that).
     * Hear when I make announcements or release new tools.
+    * You can also <a href="https://twitter.com/fastapi" class="external-link" target="_blank">follow @fastapi on Twitter</a> (a separate account).
 * <a href="https://www.linkedin.com/in/tiangolo/" class="external-link" target="_blank">Connect with me on **Linkedin**</a>.
     * Hear when I make announcements or release new tools (although I use Twitter more often 🤷‍♂).
 * Read what I write (or follow me) on <a href="https://dev.to/tiangolo" class="external-link" target="_blank">**Dev.to**</a> or <a href="https://medium.com/@tiangolo" class="external-link" target="_blank">**Medium**</a>.
-    * Read other ideas, articles, and about tools I have created.
+    * Read other ideas, articles, and read about tools I have created.
     * Follow me to read when I publish something new.
 
 ## Tweet about **FastAPI**
 
-<a href="https://twitter.com/compose/tweet?text=I'm loving FastAPI because... https://github.com/tiangolo/fastapi cc @tiangolo" class="external-link" target="_blank">Tweet about **FastAPI**</a> and let me and others know why you like it. 🎉
+<a href="https://twitter.com/compose/tweet?text=I'm loving @fastapi because... https://github.com/tiangolo/fastapi" class="external-link" target="_blank">Tweet about **FastAPI**</a> and let me and others know why you like it. 🎉
 
-I love to hear about how **FastAPI** is being used, what have you liked in it, in which project/company are you using it, etc.
+I love to hear about how **FastAPI** is being used, what you have liked in it, in which project/company are you using it, etc.
 
 ## Vote for FastAPI
 
@@ -67,15 +72,15 @@ I love to hear about how **FastAPI** is being used, what have you liked in it, i
 
 You can see <a href="https://github.com/tiangolo/fastapi/issues" class="external-link" target="_blank">existing issues</a> and try and help others, most of the times they are questions that you might already know the answer for. 🤓
 
-If you are helping a lot of people on issues you might become an official [FastAPI Expert](fastapi-people.md#experts){.internal-link target=_blank}. 🎉
+If you are helping a lot of people with issues, you might become an official [FastAPI Expert](fastapi-people.md#experts){.internal-link target=_blank}. 🎉
 
 ## Watch the GitHub repository
 
 You can "watch" FastAPI in GitHub (clicking the "watch" button at the top right): <a href="https://github.com/tiangolo/fastapi" class="external-link" target="_blank">https://github.com/tiangolo/fastapi</a>. 👀
 
-If you select "Watching" instead of "Releases only", you will receive notifications when someone creates a new issue.
+If you select "Watching" instead of "Releases only" you will receive notifications when someone creates a new issue.
 
-Then you can try and help them solving those issues.
+Then you can try and help them solve those issues.
 
 ## Create issues
 
@@ -84,7 +89,7 @@ You can <a href="https://github.com/tiangolo/fastapi/issues/new/choose" class="e
 * Ask a **question** or ask about a **problem**.
 * Suggest a new **feature**.
 
-**Note**: if you create an issue then I'm going to ask you to also help others. 😉
+**Note**: if you create an issue, then I'm going to ask you to also help others. 😉
 
 ## Create a Pull Request
 
@@ -92,9 +97,9 @@ You can [contribute](contributing.md){.internal-link target=_blank} to the sourc
 
 * To fix a typo you found on the documentation.
 * To share an article, video, or podcast you created or found about FastAPI by <a href="https://github.com/tiangolo/fastapi/edit/master/docs/en/data/external_links.yml" class="external-link" target="_blank">editing this file</a>.
-    * Make sure you add your link to the end of the corresponding section.
+    * Make sure you add your link to the start of the corresponding section.
 * To help [translate the documentation](contributing.md#translations){.internal-link target=_blank} to your language.
-    * You can also help reviewing the translations created by others.
+    * You can also help to review the translations created by others.
 * To propose new documentation sections.
 * To fix an existing issue/bug.
 * To add a new feature.

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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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+         d="m 140.94328,116.41404 c -1.022,0 -1.68,0.504 -2.072,1.204 v -3.976 c 0,-0.126 -0.084,-0.21 -0.21,-0.21 h -1.512 c -0.126,0 -0.21,0.084 -0.21,0.21 v 9.59 c 0,0.126 0.084,0.21 0.21,0.21 h 1.512 c 0.126,0 0.21,-0.084 0.21,-0.21 v -3.556 c 0,-0.966 0.532,-1.484 1.344,-1.484 0.826,0 1.274,0.518 1.274,1.484 v 3.556 c 0,0.126 0.084,0.21 0.21,0.21 h 1.498 c 0.126,0 0.224,-0.084 0.224,-0.21 v -3.99 c 0,-1.792 -1.05,-2.828 -2.478,-2.828 z"
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+         id="path58249" />
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+         style="font-weight:bold;font-family:'Greycliff CF';-inkscape-font-specification:'Greycliff CF Bold'"
+         id="path58251" />
+    </g>
+  </g>
+</svg>

docs/en/docs/index.md

@@ -5,8 +5,8 @@
     <em>FastAPI framework, high performance, easy to learn, fast to code, ready for production</em>
 </p>
 <p align="center">
-<a href="https://github.com/tiangolo/fastapi/actions?query=workflow%3ATest" target="_blank">
-    <img src="https://github.com/tiangolo/fastapi/workflows/Test/badge.svg" alt="Test">
+<a href="https://github.com/tiangolo/fastapi/actions?query=workflow%3ATest+event%3Apush+branch%3Amaster" target="_blank">
+    <img src="https://github.com/tiangolo/fastapi/workflows/Test/badge.svg?event=push&branch=master" alt="Test">
 </a>
 <a href="https://codecov.io/gh/tiangolo/fastapi" target="_blank">
     <img src="https://img.shields.io/codecov/c/github/tiangolo/fastapi?color=%2334D058" alt="Coverage">
@@ -40,13 +40,16 @@ The key features are:
 
 <small>* estimation based on tests on an internal development team, building production applications.</small>
 
-## Gold Sponsors
+## Sponsors
 
 <!-- sponsors -->
 
 {% if sponsors %}
 {% for sponsor in sponsors.gold -%}
-<a href="{{ sponsor.url }}" target="_blank" title="{{ sponsor.title }}"><img src="{{ sponsor.img }}"></a>
+<a href="{{ sponsor.url }}" target="_blank" title="{{ sponsor.title }}"><img src="{{ sponsor.img }}" style="border-radius:15px"></a>
+{% endfor -%}
+{%- 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 %}
 
@@ -314,7 +317,7 @@ 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 +374,7 @@ 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 +419,9 @@ 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**
@@ -445,7 +448,6 @@ Used by Starlette:
 * <a href="https://andrew-d.github.io/python-multipart/" target="_blank"><code>python-multipart</code></a> - Required if you want to support form <abbr title="converting the string that comes from an HTTP request into Python data">"parsing"</abbr>, with `request.form()`.
 * <a href="https://pythonhosted.org/itsdangerous/" target="_blank"><code>itsdangerous</code></a> - Required for `SessionMiddleware` support.
 * <a href="https://pyyaml.org/wiki/PyYAMLDocumentation" target="_blank"><code>pyyaml</code></a> - Required for Starlette's `SchemaGenerator` support (you probably don't need it with FastAPI).
-* <a href="https://graphene-python.org/" target="_blank"><code>graphene</code></a> - Required for `GraphQLApp` support.
 * <a href="https://github.com/esnme/ultrajson" target="_blank"><code>ujson</code></a> - Required if you want to use `UJSONResponse`.
 
 Used by FastAPI / Starlette:

docs/en/docs/js/custom.js

@@ -128,6 +128,35 @@ function setupTermynal() {
     loadVisibleTermynals();
 }
 
+function shuffle(array) {
+    var currentIndex = array.length, temporaryValue, randomIndex;
+    while (0 !== currentIndex) {
+        randomIndex = Math.floor(Math.random() * currentIndex);
+        currentIndex -= 1;
+        temporaryValue = array[currentIndex];
+        array[currentIndex] = array[randomIndex];
+        array[randomIndex] = temporaryValue;
+    }
+    return array;
+}
+
+async function showRandomAnnouncement(groupId, timeInterval) {
+    const announceFastAPI = document.getElementById(groupId);
+    if (announceFastAPI) {
+        let children = [].slice.call(announceFastAPI.children);
+        children = shuffle(children)
+        let index = 0
+        const announceRandom = () => {
+            children.forEach((el, i) => {el.style.display = "none"});
+            children[index].style.display = "block"
+            index = (index + 1) % children.length
+        }
+        announceRandom()
+        setInterval(announceRandom, timeInterval
+        )
+    }
+}
+
 async function main() {
     if (div) {
         data = await getData()
@@ -144,6 +173,8 @@ async function main() {
     }
 
     setupTermynal();
+    showRandomAnnouncement('announce-left', 5000)
+    showRandomAnnouncement('announce-right', 10000)
 }
 
 main()

docs/en/docs/release-notes.md

@@ -2,6 +2,243 @@
 
 ## Latest Changes
 
+## 0.68.2
+
+This release has **no breaking changes**. 🎉
+
+It upgrades the version ranges of sub-dependencies to allow applications using FastAPI to easily upgrade them.
+
+Soon there will be a new FastAPI release upgrading Starlette to take advantage of recent improvements, but as that has a higher chance of having breaking changes, it will be in a separate release.
+
+### Features
+
+* ⬆Increase supported version of aiofiles to suppress warnings. PR [#2899](https://github.com/tiangolo/fastapi/pull/2899) by [@SnkSynthesis](https://github.com/SnkSynthesis).
+* ➖ Do not require backports in Python >= 3.7. PR [#1880](https://github.com/tiangolo/fastapi/pull/1880) by [@FFY00](https://github.com/FFY00).
+* ⬆ Upgrade required Python version to >= 3.6.1, needed by typing.Deque, used by Pydantic. PR [#2733](https://github.com/tiangolo/fastapi/pull/2733) by [@hukkin](https://github.com/hukkin).
+* ⬆️ Bump Uvicorn max range to 0.15.0. PR [#3345](https://github.com/tiangolo/fastapi/pull/3345) by [@Kludex](https://github.com/Kludex).
+
+### Docs
+
+* 📝 Update GraphQL docs, recommend Strawberry. PR [#3981](https://github.com/tiangolo/fastapi/pull/3981) by [@tiangolo](https://github.com/tiangolo).
+* 📝 Re-write and extend Deployment guide: Concepts, Uvicorn, Gunicorn, Docker, Containers, Kubernetes. PR [#3974](https://github.com/tiangolo/fastapi/pull/3974) by [@tiangolo](https://github.com/tiangolo).
+* 📝 Upgrade HTTPS guide with more explanations and diagrams. PR [#3950](https://github.com/tiangolo/fastapi/pull/3950) by [@tiangolo](https://github.com/tiangolo).
+
+### Translations
+
+* 🌐 Add Turkish translation for `docs/features.md`. PR [#1950](https://github.com/tiangolo/fastapi/pull/1950) by [@ycd](https://github.com/ycd).
+* 🌐 Add Turkish translation for `docs/benchmarks.md`. PR [#2729](https://github.com/tiangolo/fastapi/pull/2729) by [@Telomeraz](https://github.com/Telomeraz).
+* 🌐 Add Turkish translation for `docs/index.md`. PR [#1908](https://github.com/tiangolo/fastapi/pull/1908) by [@ycd](https://github.com/ycd).
+* 🌐 Add French translation for `docs/tutorial/body.md`. PR [#3671](https://github.com/tiangolo/fastapi/pull/3671) by [@Smlep](https://github.com/Smlep).
+* 🌐 Add French translation for `deployment/docker.md`. PR [#3694](https://github.com/tiangolo/fastapi/pull/3694) by [@rjNemo](https://github.com/rjNemo).
+* 🌐 Add Portuguese translation for `docs/tutorial/path-params.md`. PR [#3664](https://github.com/tiangolo/fastapi/pull/3664) by [@FelipeSilva93](https://github.com/FelipeSilva93).
+* 🌐 Add Portuguese translation for `docs/deployment/https.md`. PR [#3754](https://github.com/tiangolo/fastapi/pull/3754) by [@lsglucas](https://github.com/lsglucas).
+* 🌐 Add German translation for `docs/features.md`. PR [#3699](https://github.com/tiangolo/fastapi/pull/3699) by [@mawassk](https://github.com/mawassk).
+
+### Internal
+
+* ✨ Update GitHub Action: notify-translations, to avoid a race conditions. PR [#3989](https://github.com/tiangolo/fastapi/pull/3989) by [@tiangolo](https://github.com/tiangolo).
+* ⬆️ Upgrade development `autoflake`, supporting multi-line imports. PR [#3988](https://github.com/tiangolo/fastapi/pull/3988) by [@tiangolo](https://github.com/tiangolo).
+* ⬆️ Increase dependency ranges for tests and docs: pytest-cov, pytest-asyncio, black, httpx, sqlalchemy, databases, mkdocs-markdownextradata-plugin. PR [#3987](https://github.com/tiangolo/fastapi/pull/3987) by [@tiangolo](https://github.com/tiangolo).
+* 👥 Update FastAPI People. PR [#3986](https://github.com/tiangolo/fastapi/pull/3986) by [@github-actions[bot]](https://github.com/apps/github-actions).
+* 💚 Fix badges in README and main page. PR [#3979](https://github.com/tiangolo/fastapi/pull/3979) by [@ghandic](https://github.com/ghandic).
+* ⬆ Upgrade internal testing dependencies: mypy to version 0.910, add newly needed type packages. PR [#3350](https://github.com/tiangolo/fastapi/pull/3350) by [@ArcLightSlavik](https://github.com/ArcLightSlavik).
+* ✨ Add Deepset Sponsorship. PR [#3976](https://github.com/tiangolo/fastapi/pull/3976) by [@tiangolo](https://github.com/tiangolo).
+* 🎨 Tweak CSS styles for shell animations. PR [#3888](https://github.com/tiangolo/fastapi/pull/3888) by [@tiangolo](https://github.com/tiangolo).
+* 🔧 Add new Sponsor Calmcode.io. PR [#3777](https://github.com/tiangolo/fastapi/pull/3777) by [@tiangolo](https://github.com/tiangolo).
+
+## 0.68.1
+
+* ✨ Add support for `read_with_orm_mode`, to support [SQLModel](https://sqlmodel.tiangolo.com/) relationship attributes. PR [#3757](https://github.com/tiangolo/fastapi/pull/3757) by [@tiangolo](https://github.com/tiangolo).
+
+### Translations
+
+* 🌐 Add Portuguese translation of `docs/fastapi-people.md`. PR [#3461](https://github.com/tiangolo/fastapi/pull/3461) by [@ComicShrimp](https://github.com/ComicShrimp).
+* 🌐 Add Chinese translation for `docs/tutorial/dependencies/dependencies-in-path-operation-decorators.md`. PR [#3492](https://github.com/tiangolo/fastapi/pull/3492) by [@jaystone776](https://github.com/jaystone776).
+* 🔧 Add new Translation tracking issues for German and Indonesian. PR [#3718](https://github.com/tiangolo/fastapi/pull/3718) by [@tiangolo](https://github.com/tiangolo).
+* 🌐 Add Chinese translation for `docs/tutorial/dependencies/sub-dependencies.md`. PR [#3491](https://github.com/tiangolo/fastapi/pull/3491) by [@jaystone776](https://github.com/jaystone776).
+* 🌐 Add Portuguese translation for `docs/advanced/index.md`. PR [#3460](https://github.com/tiangolo/fastapi/pull/3460) by [@ComicShrimp](https://github.com/ComicShrimp).
+* 🌐 Portuguese translation of `docs/async.md`. PR [#1330](https://github.com/tiangolo/fastapi/pull/1330) by [@Serrones](https://github.com/Serrones).
+* 🌐 Add French translation for `docs/async.md`. PR [#3416](https://github.com/tiangolo/fastapi/pull/3416) by [@Smlep](https://github.com/Smlep).
+
+### Internal
+
+* ✨ Add GitHub Action: Notify Translations. PR [#3715](https://github.com/tiangolo/fastapi/pull/3715) by [@tiangolo](https://github.com/tiangolo).
+* ✨ Update computation of FastAPI People and sponsors. PR [#3714](https://github.com/tiangolo/fastapi/pull/3714) by [@tiangolo](https://github.com/tiangolo).
+* ✨ Enable recent Material for MkDocs Insiders features. PR [#3710](https://github.com/tiangolo/fastapi/pull/3710) by [@tiangolo](https://github.com/tiangolo).
+* 🔥 Remove/clean extra imports from examples in docs for features. PR [#3709](https://github.com/tiangolo/fastapi/pull/3709) by [@tiangolo](https://github.com/tiangolo).
+* ➕ Update docs library to include sources in Markdown. PR [#3648](https://github.com/tiangolo/fastapi/pull/3648) by [@tiangolo](https://github.com/tiangolo).
+* ⬆ Enable tests for Python 3.9. PR [#2298](https://github.com/tiangolo/fastapi/pull/2298) by [@Kludex](https://github.com/Kludex).
+* 👥 Update FastAPI People. PR [#3642](https://github.com/tiangolo/fastapi/pull/3642) by [@github-actions[bot]](https://github.com/apps/github-actions).
+
+## 0.68.0
+
+### Features
+
+* ✨ Add support for extensions and updates to the OpenAPI schema in each *path operation*. New docs: [FastAPI Path Operation Advanced Configuration - OpenAPI Extra](https://fastapi.tiangolo.com/advanced/path-operation-advanced-configuration/#openapi-extra). Initial PR [#1922](https://github.com/tiangolo/fastapi/pull/1922) by [@edouardlp](https://github.com/edouardlp).
+* ✨ Add additonal OpenAPI metadata parameters to `FastAPI` class, shown on the automatic API docs UI. New docs: [Metadata and Docs URLs](https://fastapi.tiangolo.com/tutorial/metadata/). Initial PR [#1812](https://github.com/tiangolo/fastapi/pull/1812) by [@dkreeft](https://github.com/dkreeft).
+* ✨ Add `description` parameter to all the security scheme classes, e.g. `APIKeyQuery(name="key", description="A very cool API key")`. PR [#1757](https://github.com/tiangolo/fastapi/pull/1757) by [@hylkepostma](https://github.com/hylkepostma).
+* ✨ Update OpenAPI models, supporting recursive models and extensions. PR [#3628](https://github.com/tiangolo/fastapi/pull/3628) by [@tiangolo](https://github.com/tiangolo).
+* ✨ Import and re-export data structures from Starlette, used by Request properties, on `fastapi.datastructures`. Initial PR [#1872](https://github.com/tiangolo/fastapi/pull/1872) by [@jamescurtin](https://github.com/jamescurtin).
+
+### Docs
+
+* 📝 Update docs about async and response-model with more gender neutral language. PR [#1869](https://github.com/tiangolo/fastapi/pull/1869) by [@Edward-Knight](https://github.com/Edward-Knight).
+
+### Translations
+
+* 🌐 Add Russian translation for `docs/python-types.md`. PR [#3039](https://github.com/tiangolo/fastapi/pull/3039) by [@dukkee](https://github.com/dukkee).
+* 🌐 Add Chinese translation for `docs/tutorial/dependencies/index.md`. PR [#3489](https://github.com/tiangolo/fastapi/pull/3489) by [@jaystone776](https://github.com/jaystone776).
+* 🌐 Add Russian translation for `docs/external-links.md`. PR [#3036](https://github.com/tiangolo/fastapi/pull/3036) by [@dukkee](https://github.com/dukkee).
+* 🌐 Add Chinese translation for `docs/tutorial/dependencies/global-dependencies.md`. PR [#3493](https://github.com/tiangolo/fastapi/pull/3493) by [@jaystone776](https://github.com/jaystone776).
+* 🌐 Add Portuguese translation for `docs/deployment/versions.md`. PR [#3618](https://github.com/tiangolo/fastapi/pull/3618) by [@lsglucas](https://github.com/lsglucas).
+* 🌐 Add Japanese translation for `docs/tutorial/security/oauth2-jwt.md`. PR [#3526](https://github.com/tiangolo/fastapi/pull/3526) by [@sattosan](https://github.com/sattosan).
+
+### Internal
+
+* ✅ Add  the `docs_src` directory to test coverage and update tests. Initial PR [#1904](https://github.com/tiangolo/fastapi/pull/1904) by [@Kludex](https://github.com/Kludex).
+* 🔧 Add new GitHub templates with forms for new issues. PR [#3612](https://github.com/tiangolo/fastapi/pull/3612) by [@tiangolo](https://github.com/tiangolo).
+* 📝 Add official FastAPI Twitter to docs: [@fastapi](https://twitter.com/fastapi). PR [#3578](https://github.com/tiangolo/fastapi/pull/3578) by [@tiangolo](https://github.com/tiangolo).
+
+## 0.67.0
+
+### Features
+
+* ✨ Add support for `dataclasses` in request bodies and `response_model`. New documentation: [Advanced User Guide - Using Dataclasses](https://fastapi.tiangolo.com/advanced/dataclasses/). PR [#3577](https://github.com/tiangolo/fastapi/pull/3577) by [@tiangolo](https://github.com/tiangolo).
+* ✨ Support `dataclasses` in responses. PR [#3576](https://github.com/tiangolo/fastapi/pull/3576) by [@tiangolo](https://github.com/tiangolo), continuation from initial PR [#2722](https://github.com/tiangolo/fastapi/pull/2722) by [@amitlissack](https://github.com/amitlissack).
+
+### Docs
+
+* 📝 Add external link: How to Create A Fake Certificate Authority And Generate TLS Certs for FastAPI. PR [#2839](https://github.com/tiangolo/fastapi/pull/2839) by [@aitoehigie](https://github.com/aitoehigie).
+* ✏ Fix code highlighted line in: `body-nested-models.md`. PR [#3463](https://github.com/tiangolo/fastapi/pull/3463) by [@jaystone776](https://github.com/jaystone776).
+* ✏ Fix typo in `body-nested-models.md`. PR [#3462](https://github.com/tiangolo/fastapi/pull/3462) by [@jaystone776](https://github.com/jaystone776).
+* ✏ Fix typo "might me" -> "might be" in `docs/en/docs/tutorial/schema-extra-example.md`. PR [#3362](https://github.com/tiangolo/fastapi/pull/3362) by [@dbrakman](https://github.com/dbrakman).
+* 📝 Add external link: Building simple E-Commerce with NuxtJS and FastAPI. PR [#3271](https://github.com/tiangolo/fastapi/pull/3271) by [@ShahriyarR](https://github.com/ShahriyarR).
+* 📝 Add external link: Serve a machine learning model using Sklearn, FastAPI and Docker. PR [#2974](https://github.com/tiangolo/fastapi/pull/2974) by [@rodrigo-arenas](https://github.com/rodrigo-arenas).
+* ✏️ Fix typo on docstring in datastructures file. PR [#2887](https://github.com/tiangolo/fastapi/pull/2887) by [@Kludex](https://github.com/Kludex).
+* 📝 Add External Link: Deploy FastAPI on Ubuntu and Serve using Caddy 2 Web Server. PR [#3572](https://github.com/tiangolo/fastapi/pull/3572) by [@tiangolo](https://github.com/tiangolo).
+* 📝 Add External Link, replaces #1898. PR [#3571](https://github.com/tiangolo/fastapi/pull/3571) by [@tiangolo](https://github.com/tiangolo).
+
+### Internal
+
+* 🎨 Improve style for sponsors, add radius border. PR [#2388](https://github.com/tiangolo/fastapi/pull/2388) by [@Kludex](https://github.com/Kludex).
+* 👷 Update GitHub Action latest-changes. PR [#3574](https://github.com/tiangolo/fastapi/pull/3574) by [@tiangolo](https://github.com/tiangolo).
+* 👷 Update GitHub Action latest-changes. PR [#3573](https://github.com/tiangolo/fastapi/pull/3573) by [@tiangolo](https://github.com/tiangolo).
+* 👷 Rename and clarify CI workflow job names. PR [#3570](https://github.com/tiangolo/fastapi/pull/3570) by [@tiangolo](https://github.com/tiangolo).
+* 👷 Update GitHub Action latest-changes, strike 2 ⚾. PR [#3575](https://github.com/tiangolo/fastapi/pull/3575) by [@tiangolo](https://github.com/tiangolo).
+* 🔧 Sort external links in docs to have the most recent at the top. PR [#3568](https://github.com/tiangolo/fastapi/pull/3568) by [@tiangolo](https://github.com/tiangolo).
+
+## 0.66.1
+
+### Translations
+
+* 🌐 Add basic setup for German translations. PR [#3522](https://github.com/tiangolo/fastapi/pull/3522) by [@0x4Dark](https://github.com/0x4Dark).
+* 🌐 Add Portuguese translation for `docs/tutorial/security/index.md`. PR [#3507](https://github.com/tiangolo/fastapi/pull/3507) by [@oandersonmagalhaes](https://github.com/oandersonmagalhaes).
+* 🌐 Add Portuguese translation for `docs/deployment/index.md`. PR [#3337](https://github.com/tiangolo/fastapi/pull/3337) by [@lsglucas](https://github.com/lsglucas).
+
+### Internal
+
+* 🔧 Configure strict pytest options and update/refactor tests. Upgrade pytest to `>=6.2.4,<7.0.0` and pytest-cov to `>=2.12.0,<3.0.0`. Initial PR [#2790](https://github.com/tiangolo/fastapi/pull/2790) by [@graingert](https://github.com/graingert).
+* ⬆️ Upgrade python-jose dependency to `>=3.3.0,<4.0.0` for tests. PR [#3468](https://github.com/tiangolo/fastapi/pull/3468) by [@tiangolo](https://github.com/tiangolo).
+
+## 0.66.0
+
+### Features
+
+* ✨ Allow setting the `response_class` to `RedirectResponse` or `FileResponse` and returning the URL from the function. New and updated docs are in the tutorial section **Custom Response - HTML, Stream, File, others**, in [RedirectResponse](https://fastapi.tiangolo.com/advanced/custom-response/#redirectresponse) and in [FileResponse](https://fastapi.tiangolo.com/advanced/custom-response/#fileresponse). PR [#3457](https://github.com/tiangolo/fastapi/pull/3457) by [@tiangolo](https://github.com/tiangolo).
+
+### Fixes
+
+* 🐛 Fix include/exclude for dicts in `jsonable_encoder`. PR [#2016](https://github.com/tiangolo/fastapi/pull/2016) by [@Rubikoid](https://github.com/Rubikoid).
+* 🐛 Support custom OpenAPI / JSON Schema fields in the generated output OpenAPI. PR [#1429](https://github.com/tiangolo/fastapi/pull/1429) by [@jmagnusson](https://github.com/jmagnusson).
+
+### Translations
+
+* 🌐 Add Spanish translation for `tutorial/query-params.md`. PR [#2243](https://github.com/tiangolo/fastapi/pull/2243) by [@mariacamilagl](https://github.com/mariacamilagl).
+* 🌐 Add Spanish translation for `advanced/response-directly.md`. PR [#1253](https://github.com/tiangolo/fastapi/pull/1253) by [@jfunez](https://github.com/jfunez).
+* 🌐 Add Spanish translation for `advanced/additional-status-codes.md`. PR [#1252](https://github.com/tiangolo/fastapi/pull/1252) by [@jfunez](https://github.com/jfunez).
+* 🌐 Add Spanish translation for `advanced/path-operation-advanced-configuration.md`. PR [#1251](https://github.com/tiangolo/fastapi/pull/1251) by [@jfunez](https://github.com/jfunez).
+
+## 0.65.3
+
+### Fixes
+
+* ♻ Assume request bodies contain JSON when no Content-Type header is provided. This fixes a breaking change introduced by [0.65.2 with PR #2118](https://github.com/tiangolo/fastapi/pull/2118). It should allow upgrading FastAPI applications with clients that send JSON data without a `Content-Type` header. And there's still protection against CSRFs. PR [#3456](https://github.com/tiangolo/fastapi/pull/3456) by [@tiangolo](https://github.com/tiangolo).
+
+### Translations
+
+* 🌐 Initialize Indonesian translations. PR [#3014](https://github.com/tiangolo/fastapi/pull/3014) by [@pace-noge](https://github.com/pace-noge).
+* 🌐 Add Spanish translation of Tutorial - Path Parameters. PR [#2219](https://github.com/tiangolo/fastapi/pull/2219) by [@mariacamilagl](https://github.com/mariacamilagl).
+* 🌐 Add Spanish translation of Tutorial - First Steps. PR [#2208](https://github.com/tiangolo/fastapi/pull/2208) by [@mariacamilagl](https://github.com/mariacamilagl).
+* 🌐 Portuguese translation of Tutorial - Body - Fields. PR [#3420](https://github.com/tiangolo/fastapi/pull/3420) by [@ComicShrimp](https://github.com/ComicShrimp).
+* 🌐 Add Chinese translation for Tutorial - Request - Forms - and - Files. PR [#3249](https://github.com/tiangolo/fastapi/pull/3249) by [@jaystone776](https://github.com/jaystone776).
+* 🌐 Add Chinese translation for Tutorial - Handling - Errors. PR [#3299](https://github.com/tiangolo/fastapi/pull/3299) by [@jaystone776](https://github.com/jaystone776).
+* 🌐 Add Chinese translation for Tutorial - Form - Data. PR [#3248](https://github.com/tiangolo/fastapi/pull/3248) by [@jaystone776](https://github.com/jaystone776).
+* 🌐 Add Chinese translation for Tutorial - Body - Updates. PR [#3237](https://github.com/tiangolo/fastapi/pull/3237) by [@jaystone776](https://github.com/jaystone776).
+* 🌐 Add Chinese translation for FastAPI People. PR [#3112](https://github.com/tiangolo/fastapi/pull/3112) by [@hareru](https://github.com/hareru).
+* 🌐 Add French translation for Project Generation. PR [#3197](https://github.com/tiangolo/fastapi/pull/3197) by [@Smlep](https://github.com/Smlep).
+* 🌐 Add French translation for Python Types Intro. PR [#3185](https://github.com/tiangolo/fastapi/pull/3185) by [@Smlep](https://github.com/Smlep).
+* 🌐 Add French translation for External Links. PR [#3103](https://github.com/tiangolo/fastapi/pull/3103) by [@Smlep](https://github.com/Smlep).
+* 🌐 Add French translation for Alternatives, Inspiration and Comparisons. PR [#3020](https://github.com/tiangolo/fastapi/pull/3020) by [@rjNemo](https://github.com/rjNemo).
+* 🌐 Fix Chinese translation code snippet mismatch in Tutorial - Python Types Intro. PR [#2573](https://github.com/tiangolo/fastapi/pull/2573) by [@BoYanZh](https://github.com/BoYanZh).
+* 🌐 Add Portuguese translation for Development Contributing. PR [#1364](https://github.com/tiangolo/fastapi/pull/1364) by [@Serrones](https://github.com/Serrones).
+* 🌐 Add Chinese translation for Tutorial - Request - Files. PR [#3244](https://github.com/tiangolo/fastapi/pull/3244) by [@jaystone776](https://github.com/jaystone776).
+
+### Internal
+
+* 👥 Update FastAPI People. PR [#3450](https://github.com/tiangolo/fastapi/pull/3450) by [@github-actions[bot]](https://github.com/apps/github-actions).
+* 👥 Update FastAPI People. PR [#3319](https://github.com/tiangolo/fastapi/pull/3319) by [@github-actions[bot]](https://github.com/apps/github-actions).
+* ⬆ Upgrade docs development dependency on `typer-cli` to >=0.0.12 to fix conflicts. PR [#3429](https://github.com/tiangolo/fastapi/pull/3429) by [@tiangolo](https://github.com/tiangolo).
+
+## 0.65.2
+
+### Security fixes
+
+* 🔒 Check Content-Type request header before assuming JSON. Initial PR [#2118](https://github.com/tiangolo/fastapi/pull/2118) by [@patrickkwang](https://github.com/patrickkwang).
+
+This change fixes a [CSRF](https://en.wikipedia.org/wiki/Cross-site_request_forgery) security vulnerability when using cookies for authentication in path operations with JSON payloads sent by browsers.
+
+In versions lower than `0.65.2`, FastAPI would try to read the request payload as JSON even if the `content-type` header sent was not set to `application/json` or a compatible JSON media type (e.g. `application/geo+json`).
+
+So, a request with a content type of `text/plain` containing JSON data would be accepted and the JSON data would be extracted.
+
+But requests with content type `text/plain` are exempt from [CORS](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS) preflights, for being considered [Simple requests](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS#simple_requests). So, the browser would execute them right away including cookies, and the text content could be a JSON string that would be parsed and accepted by the FastAPI application.
+
+See [CVE-2021-32677](https://github.com/tiangolo/fastapi/security/advisories/GHSA-8h2j-cgx8-6xv7) for more details.
+
+Thanks to [Dima Boger](https://twitter.com/b0g3r) for the security report! 🙇🔒
+
+### Internal
+
+* 🔧 Update sponsors badge, course bundle. PR [#3340](https://github.com/tiangolo/fastapi/pull/3340) by [@tiangolo](https://github.com/tiangolo).
+* 🔧 Add new gold sponsor Jina 🎉. PR [#3291](https://github.com/tiangolo/fastapi/pull/3291) by [@tiangolo](https://github.com/tiangolo).
+* 🔧 Add new banner sponsor badge for FastAPI courses bundle. PR [#3288](https://github.com/tiangolo/fastapi/pull/3288) by [@tiangolo](https://github.com/tiangolo).
+* 👷 Upgrade Issue Manager GitHub Action. PR [#3236](https://github.com/tiangolo/fastapi/pull/3236) by [@tiangolo](https://github.com/tiangolo).
+
+## 0.65.1
+
+### Security fixes
+
+* 📌 Upgrade pydantic pin, to handle security vulnerability [CVE-2021-29510](https://github.com/samuelcolvin/pydantic/security/advisories/GHSA-5jqp-qgf6-3pvh). PR [#3213](https://github.com/tiangolo/fastapi/pull/3213) by [@tiangolo](https://github.com/tiangolo).
+
+## 0.65.0
+
+### Breaking Changes - Upgrade
+
+* ⬆️  Upgrade Starlette to `0.14.2`, including internal `UJSONResponse` migrated from Starlette. This includes several bug fixes and features from Starlette. PR [#2335](https://github.com/tiangolo/fastapi/pull/2335) by [@hanneskuettner](https://github.com/hanneskuettner).
+
+### Translations
+
+* 🌐 Initialize new language Polish for translations. PR [#3170](https://github.com/tiangolo/fastapi/pull/3170) by [@neternefer](https://github.com/neternefer).
+
+### Internal
+
+* 👷 Add GitHub Action cache to speed up CI installs. PR [#3204](https://github.com/tiangolo/fastapi/pull/3204) by [@tiangolo](https://github.com/tiangolo).
+* ⬆️ Upgrade setup-python GitHub Action to v2. PR [#3203](https://github.com/tiangolo/fastapi/pull/3203) by [@tiangolo](https://github.com/tiangolo).
+* 🐛 Fix docs script to generate a new translation language with `overrides` boilerplate. PR [#3202](https://github.com/tiangolo/fastapi/pull/3202) by [@tiangolo](https://github.com/tiangolo).
+* ✨ Add new Deta banner badge with new sponsorship tier 🙇. PR [#3194](https://github.com/tiangolo/fastapi/pull/3194) by [@tiangolo](https://github.com/tiangolo).
+* 👥 Update FastAPI People. PR [#3189](https://github.com/tiangolo/fastapi/pull/3189) by [@github-actions[bot]](https://github.com/apps/github-actions).
+* 🔊 Update FastAPI People to allow better debugging. PR [#3188](https://github.com/tiangolo/fastapi/pull/3188) by [@tiangolo](https://github.com/tiangolo).
+
 ## 0.64.0
 
 ### Features
@@ -66,7 +303,6 @@
 * 🌐 Add Japanese translation for Tutorial - Body - Updates. PR [#1956](https://github.com/tiangolo/fastapi/pull/1956) by [@SwftAlpc](https://github.com/SwftAlpc).
 * 🌐 Add Japanese translation for Tutorial - Form Data. PR [#1943](https://github.com/tiangolo/fastapi/pull/1943) by [@SwftAlpc](https://github.com/SwftAlpc).
 * 🌐 Add Japanese translation for Tutorial - Cookie Parameters. PR [#1933](https://github.com/tiangolo/fastapi/pull/1933) by [@SwftAlpc](https://github.com/SwftAlpc).
-* 🔧 Update FastAPI People GitHub Sponsors order. PR [#2620](https://github.com/tiangolo/fastapi/pull/2620) by [@tiangolo](https://github.com/tiangolo).
 
 ### Internal
 
@@ -79,6 +315,7 @@
 * 🔧 Add FastAPI user survey banner. PR [#2623](https://github.com/tiangolo/fastapi/pull/2623) by [@tiangolo](https://github.com/tiangolo).
 * 🔧 Add new Bronze Sponsor(s) 🥉🎉. PR [#2622](https://github.com/tiangolo/fastapi/pull/2622) by [@tiangolo](https://github.com/tiangolo).
 * 📝 Update social links: add Discord, fix GitHub. PR [#2621](https://github.com/tiangolo/fastapi/pull/2621) by [@tiangolo](https://github.com/tiangolo).
+* 🔧 Update FastAPI People GitHub Sponsors order. PR [#2620](https://github.com/tiangolo/fastapi/pull/2620) by [@tiangolo](https://github.com/tiangolo).
 * 🔧 Update InvestSuite sponsor data. PR [#2608](https://github.com/tiangolo/fastapi/pull/2608) by [@tiangolo](https://github.com/tiangolo).
 * 👥 Update FastAPI People. PR [#2590](https://github.com/tiangolo/fastapi/pull/2590) by [@github-actions[bot]](https://github.com/apps/github-actions).
 

docs/en/docs/tutorial/body-nested-models.md

@@ -198,7 +198,7 @@ Even for items inside of lists:
 
 <img src="/img/tutorial/body-nested-models/image01.png">
 
-You couldn't get this kind of editor support if you where working directly with `dict` instead of Pydantic models.
+You couldn't get this kind of editor support if you were working directly with `dict` instead of Pydantic models.
 
 But you don't have to worry about them either, incoming dicts are converted automatically and your output is converted automatically to JSON too.
 
@@ -218,7 +218,7 @@ That's what we are going to see here.
 
 In this case, you would accept any `dict` as long as it has `int` keys with `float` values:
 
-```Python hl_lines="15"
+```Python hl_lines="9"
 {!../../../docs_src/body_nested_models/tutorial009.py!}
 ```
 

docs/en/docs/tutorial/metadata.md

@@ -2,21 +2,28 @@
 
 You can customize several metadata configurations in your **FastAPI** application.
 
-## Title, description, and version
+## Metadata for API
 
-You can set the:
+You can set the following fields that are used in the OpenAPI specification and the automatic API docs UIs:
 
-* **Title**: used as your API's title/name, in OpenAPI and the automatic API docs UIs.
-* **Description**: the description of your API, in OpenAPI and the automatic API docs UIs.
-* **Version**: the version of your API, e.g. `v2` or `2.5.0`.
-    * Useful for example if you had a previous version of the application, also using OpenAPI.
+| Parameter | Type | Description |
+|------------|------|-------------|
+| `title` | `str` | The title of the API. |
+| `description` | `str` | A short description of the API. It can use Markdown. |
+| `version` | `string` | The version of the API. This is the version of your own application, not of OpenAPI. For example `2.5.0`. |
+| `terms_of_service` | `str` | A URL to the Terms of Service for the API. If provided, this has to be a URL. |
+| `contact` | `dict` | The contact information for the exposed API. It can contain several fields. <details><summary><code>contact</code> fields</summary><table><thead><tr><th>Parameter</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td><code>name</code></td><td><code>str</code></td><td>The identifying name of the contact person/organization.</td></tr><tr><td><code>url</code></td><td><code>str</code></td><td>The URL pointing to the contact information. MUST be in the format of a URL.</td></tr><tr><td><code>email</code></td><td><code>str</code></td><td>The email address of the contact person/organization. MUST be in the format of an email address.</td></tr></tbody></table></details> |
+| `license_info` | `dict` | The license information for the exposed API. It can contain several fields. <details><summary><code>license_info</code> fields</summary><table><thead><tr><th>Parameter</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td><code>name</code></td><td><code>str</code></td><td><strong>REQUIRED</strong> (if a <code>license_info</code> is set). The license name used for the API.</td></tr><tr><td><code>url</code></td><td><code>str</code></td><td>A URL to the license used for the API. MUST be in the format of a URL.</td></tr></tbody></table></details> |
 
-To set them, use the parameters `title`, `description`, and `version`:
+You can set them as follows:
 
-```Python hl_lines="4-6"
+```Python hl_lines="3-16  19-31"
 {!../../../docs_src/metadata/tutorial001.py!}
 ```
 
+!!! tip
+    You can write Markdown in the `description` field and it will be rendered in the output.
+
 With this configuration, the automatic API docs would look like:
 
 <img src="/img/tutorial/metadata/image01.png">

docs/en/docs/tutorial/response-model.md

@@ -47,7 +47,7 @@ And we are using this model to declare our input and the same model to declare o
 
 Now, whenever a browser is creating a user with a password, the API will return the same password in the response.
 
-In this case, it might not be a problem, because the user himself is sending the password.
+In this case, it might not be a problem, because the user themself is sending the password.
 
 But if we use the same model for another *path operation*, we could be sending our user's passwords to every client.
 

docs/en/docs/tutorial/schema-extra-example.md

@@ -88,7 +88,7 @@ With `examples` added to `Body()` the `/docs` would look like:
 !!! warning
     These are very technical details about the standards **JSON Schema** and **OpenAPI**.
 
-    If the ideas above already work for you, that might me enough, and you probably don't need these details, feel free to skip them.
+    If the ideas above already work for you, that might be enough, and you probably don't need these details, feel free to skip them.
 
 When you add an example inside of a Pydantic model, using `schema_extra` or `Field(example="something")` that example is added to the **JSON Schema** for that Pydantic model.
 

docs/en/mkdocs.yml

@@ -20,6 +20,7 @@ theme:
   features:
   - search.suggest
   - search.highlight
+  - content.tabs.link
   icon:
     repo: fontawesome/brands/github-alt
   logo: img/icon-white.svg
@@ -39,11 +40,14 @@ nav:
 - FastAPI: index.md
 - Languages:
   - en: /
+  - de: /de/
   - es: /es/
   - fr: /fr/
+  - id: /id/
   - it: /it/
   - ja: /ja/
   - ko: /ko/
+  - pl: /pl/
   - pt: /pt/
   - ru: /ru/
   - sq: /sq/
@@ -116,6 +120,7 @@ nav:
     - advanced/security/oauth2-scopes.md
     - advanced/security/http-basic-auth.md
   - advanced/using-request-directly.md
+  - advanced/dataclasses.md
   - advanced/middleware.md
   - advanced/sql-databases-peewee.md
   - advanced/async-sql-databases.md
@@ -142,9 +147,11 @@ nav:
   - deployment/index.md
   - deployment/versions.md
   - deployment/https.md
-  - deployment/deta.md
-  - deployment/docker.md
   - deployment/manually.md
+  - deployment/concepts.md
+  - deployment/deta.md
+  - deployment/server-workers.md
+  - deployment/docker.md
 - project-generation.md
 - alternatives.md
 - history-design-future.md
@@ -158,7 +165,10 @@ markdown_extensions:
     permalink: true
 - markdown.extensions.codehilite:
     guess_lang: false
-- markdown_include.include:
+# Uncomment these 2 lines during development to more easily add highlights
+# - pymdownx.highlight:
+#     linenums: true
+- mdx_include:
     base_path: docs
 - admonition
 - codehilite
@@ -167,7 +177,7 @@ markdown_extensions:
     custom_fences:
     - name: mermaid
       class: mermaid
-      format: !!python/name:pymdownx.superfences.fence_div_format ''
+      format: !!python/name:pymdownx.superfences.fence_code_format ''
 - pymdownx.tabbed
 extra:
   social:
@@ -176,7 +186,7 @@ extra:
   - icon: fontawesome/brands/discord
     link: https://discord.gg/VQjSZaeJmf
   - icon: fontawesome/brands/twitter
-    link: https://twitter.com/tiangolo
+    link: https://twitter.com/fastapi
   - icon: fontawesome/brands/linkedin
     link: https://www.linkedin.com/in/tiangolo
   - icon: fontawesome/brands/dev
@@ -188,16 +198,22 @@ extra:
   alternate:
   - link: /
     name: en - English
+  - link: /de/
+    name: de
   - link: /es/
     name: es - español
   - link: /fr/
     name: fr - français
+  - link: /id/
+    name: id
   - link: /it/
     name: it - italiano
   - link: /ja/
     name: ja - 日本語
   - link: /ko/
     name: ko - 한국어
+  - link: /pl/
+    name: pl
   - link: /pt/
     name: pt - português
   - link: /ru/
@@ -214,6 +230,5 @@ extra_css:
 - css/termynal.css
 - css/custom.css
 extra_javascript:
-- https://unpkg.com/mermaid@8.4.6/dist/mermaid.min.js
 - js/termynal.js
 - js/custom.js

docs/en/overrides/main.html

@@ -1,13 +1,45 @@
 {% extends "base.html" %}
 
 {% block announce %}
-<a class="announce" href="https://fastapi.tiangolo.com/newsletter/">
-    <span class="twemoji">
-        {% include ".icons/material/email.svg" %}
-      </span> Subscribe to the <strong>FastAPI and friends</strong> newsletter 🎉
-  </a>
-  <!-- <a class="announce" href="https://tripetto.app/run/RXZ6OLDBXX?s=dc" target="_blank">
-    <span class="twemoji">
-      </span>Fill the first-ever <strong>FastAPI user survey</strong> for a chance to win official <strong>FastAPI and Typer stickers</strong> 🎁
-  </a> -->
+<div class="announce-wrapper">
+  <div id="announce-left">
+    <div class="item">
+      <a class="announce-link" href="https://twitter.com/fastapi" target="_blank">
+        <span class="twemoji twitter">
+          {% include ".icons/fontawesome/brands/twitter.svg" %}
+        </span> Follow <strong>@fastapi</strong> on <strong>Twitter</strong> to stay updated
+      </a>
+    </div>
+    <div class="item">
+      <a class="announce-link" href="https://fastapi.tiangolo.com/newsletter/">
+        <span class="twemoji">
+          {% include ".icons/material/email.svg" %}
+        </span> Subscribe to the <strong>FastAPI and friends</strong> newsletter 🎉
+      </a>
+    </div>
+    <div class="item">
+      <iframe style="display: inline-block; vertical-align: middle; border: none; margin-right: 0.5rem;" src="https://github.com/sponsors/tiangolo/button" title="Sponsor tiangolo" height="35" width="116" style="border: 0;"></iframe> <a class="announce-link" target="_blank" href="https://github.com/sponsors/tiangolo">You can now sponsor <strong>FastAPI</strong> 🍰</a>
+    </div>
+  </div>
+  <div id="announce-right" style="position: relative;">
+    <!-- <div class="item">
+      <a title="The launchpad for all your (team's) ideas" style="display: block; position: relative;" href="https://www.deta.sh/?ref=fastapi" target="_blank">
+        <span class="sponsor-badge">sponsor</span>
+        <img class="sponsor-image" src="/img/sponsors/deta-banner.svg" />
+      </a>
+    </div> -->
+    <div class="item">
+      <a title="Get three courses at 10% off their current prices! Plus, we'll be donating 10% of all profits from sales of this bundle to the FastAPI team." style="display: block; position: relative;" href="https://testdriven.io/talkpython/" target="_blank">
+        <span class="sponsor-badge">sponsor</span>
+        <img class="sponsor-image" src="/img/sponsors/fastapi-course-bundle-banner.svg" />
+      </a>
+    </div>
+    <div class="item">
+      <a title="Jina: build neural search-as-a-service for any kind of data in just minutes." style="display: block; position: relative;" href="https://bit.ly/2QSouzH" target="_blank">
+        <span class="sponsor-badge">sponsor</span>
+        <img class="sponsor-image" src="/img/sponsors/jina-banner.svg" />
+      </a>
+    </div>
+  </div>
+</div>
 {% endblock %}

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

@@ -0,0 +1,37 @@
+# Códigos de estado adicionales
+
+Por defecto, **FastAPI** devolverá las respuestas utilizando una `JSONResponse`, poniendo el contenido que devuelves en tu *operación de path* dentro de esa `JSONResponse`.
+
+Utilizará el código de estado por defecto, o el que hayas asignado en tu *operación de path*.
+
+## Códigos de estado adicionales
+
+Si quieres devolver códigos de estado adicionales además del principal, puedes hacerlo devolviendo directamente una `Response`, como una `JSONResponse`, y asignar directamente el código de estado adicional.
+
+Por ejemplo, digamos que quieres tener una *operación de path* que permita actualizar ítems y devolver códigos de estado HTTP 200 "OK" cuando sea exitosa.
+
+Pero también quieres que acepte nuevos ítems. Cuando los ítems no existan anteriormente, serán creados y devolverá un código de estado HTTP 201 "Created".
+
+Para conseguir esto importa `JSONResponse` y devuelve ahí directamente tu contenido, asignando el `status_code` que quieras:
+
+```Python hl_lines="2  19"
+{!../../../docs_src/additional_status_codes/tutorial001.py!}
+```
+
+!!! warning "Advertencia"
+    Cuando devuelves directamente una `Response`, como en los ejemplos anteriores, será devuelta directamente.
+
+    No será serializado con el modelo, etc.
+
+    Asegurate de que la respuesta tenga los datos que quieras, y que los valores sean JSON válidos (si estás usando `JSONResponse`).
+
+!!! note "Detalles Técnicos"
+    También podrías utilizar `from starlette.responses import JSONResponse`.
+
+    **FastAPI** provee las mismas `starlette.responses` que `fastapi.responses` simplemente como una convención para ti, el desarrollador. Pero la mayoría de las respuestas disponibles vienen directamente de Starlette. Lo mismo con `status`.
+
+## OpenAPI y documentación de API
+
+Si quieres devolver códigos de estado y respuestas adicionales directamente, estas no estarán incluidas en el schema de OpenAPI (documentación de API), porque FastAPI no tiene una manera de conocer de antemano lo que vas a devolver.
+
+Pero puedes documentar eso en tu código usando [Respuestas Adicionales](additional-responses.md){.internal-link target=_blank}.

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

@@ -0,0 +1,52 @@
+# Configuración avanzada de las operaciones de path
+
+## OpenAPI operationId
+
+!!! warning "Advertencia"
+    Si no eres una persona "experta" en OpenAPI, probablemente no necesitas leer esto.
+
+Puedes asignar el `operationId` de OpenAPI para ser usado en tu *operación de path* con el parámetro `operation_id`.
+
+En este caso tendrías que asegurarte de que sea único para cada operación.
+
+```Python hl_lines="6"
+{!../../../docs_src/path_operation_advanced_configuration/tutorial001.py!}
+```
+
+### Usando el nombre de la *función de la operación de path* en el operationId
+
+Si quieres usar tus nombres de funciones de API como `operationId`s, puedes iterar sobre todos ellos y sobrescribir `operation_id` de cada *operación de path* usando su `APIRoute.name`.
+
+Deberías hacerlo después de adicionar todas tus *operaciones de path*.
+
+```Python hl_lines="2 12 13 14 15 16 17 18 19 20 21 24"
+{!../../../docs_src/path_operation_advanced_configuration/tutorial002.py!}
+```
+
+!!! tip "Consejo"
+    Si llamas manualmente a `app.openapi()`, debes actualizar el `operationId`s antes de hacerlo.
+
+!!! warning "Advertencia"
+    Si haces esto, debes asegurarte de que cada una de tus *funciones de las operaciones de path* tenga un nombre único.
+
+    Incluso si están en diferentes módulos (archivos Python).
+
+## Excluir de OpenAPI
+
+Para excluir una *operación de path* del esquema OpenAPI generado (y por tanto del la documentación generada automáticamente), usa el parámetro `include_in_schema` y asigna el valor como `False`;
+
+```Python hl_lines="6"
+{!../../../docs_src/path_operation_advanced_configuration/tutorial003.py!}
+```
+
+## Descripción avanzada desde el docstring
+
+Puedes limitar las líneas usadas desde el docstring de una *operación de path* para OpenAPI.
+
+Agregar un `\f` (un carácter de "form feed" escapado) hace que **FastAPI** trunque el output utilizada para OpenAPI en ese punto.
+
+No será mostrado en la documentación, pero otras herramientas (como Sphinx) serán capaces de usar el resto.
+
+```Python hl_lines="19 20 21 22 23 24 25 26 27 28 29"
+{!../../../docs_src/path_operation_advanced_configuration/tutorial004.py!}
+```

docs/es/docs/advanced/response-directly.md

@@ -0,0 +1,63 @@
+# Devolver una respuesta directamente
+
+Cuando creas una *operación de path* normalmente puedes devolver cualquier dato: un `dict`, una `list`, un modelo Pydantic, un modelo de base de datos, etc.
+
+Por defecto, **FastAPI** convertiría automáticamente ese valor devuelto a JSON usando el `jsonable_encoder` explicado en [Codificador Compatible JSON](../tutorial/encoder.md){.internal-link target=_blank}.
+
+Luego, tras bastidores, pondría esos datos compatibles con JSON (por ejemplo, un `dict`) dentro de una `JSONResponse` que se usaría para enviar la respuesta al cliente.
+
+Pero puedes devolver una `JSONResponse` directamente de tu *operación de path*.
+
+Esto puede ser útil, por ejemplo, para devolver cookies o headers personalizados.
+
+## Devolver una `Response`
+
+De hecho, puedes devolver cualquier `Response` o cualquier subclase de la misma.
+
+!!! tip "Consejo"
+    `JSONResponse` en sí misma es una subclase de `Response`.
+
+Y cuando devuelves una `Response`, **FastAPI** la pasará directamente.
+
+No hará ninguna conversión de datos con modelos Pydantic, no convertirá el contenido a ningún tipo, etc.
+
+Esto te da mucha flexibilidad. Puedes devolver cualquier tipo de dato, sobrescribir cualquer declaración de datos o validación, etc.
+
+## Usando el `jsonable_encoder` en una `Response`
+
+Como **FastAPI** no realiza ningún cambio en la `Response` que devuelves, debes asegurarte de que el contenido está listo.
+
+Por ejemplo, no puedes poner un modelo Pydantic en una `JSONResponse` sin primero convertirlo a un `dict` con todos los tipos de datos (como `datetime`, `UUID`, etc) convertidos a tipos compatibles con JSON.
+
+Para esos casos, puedes usar el `jsonable_encoder` para convertir tus datos antes de pasarlos a la respuesta:
+
+```Python hl_lines="4 6 20 21"
+{!../../../docs_src/response_directly/tutorial001.py!}
+```
+
+!!! note "Detalles Técnicos"
+    También puedes usar `from starlette.responses import JSONResponse`.
+
+    **FastAPI** provee `starlette.responses` como `fastapi.responses`, simplemente como una conveniencia para ti, el desarrollador. Pero la mayoría de las respuestas disponibles vienen directamente de Starlette.
+
+## Devolviendo una `Response` personalizada
+
+El ejemplo anterior muestra las partes que necesitas, pero no es muy útil todavía, dado que podrías simplemente devolver el `item` directamente, y **FastAPI** lo pondría en una `JSONResponse` por ti, convirtiéndolo en un `dict`, etc. Todo esto por defecto.
+
+Ahora, veamos cómo puedes usarlo para devolver una respuesta personalizada.
+
+Digamos que quieres devolver una respuesta <a href="https://en.wikipedia.org/wiki/XML" class="external-link" target="_blank">XML</a>.
+
+Podrías poner tu contenido XML en un string, ponerlo en una `Response` y devolverlo:
+
+```Python hl_lines="1  18"
+{!../../../docs_src/response_directly/tutorial002.py!}
+```
+
+## Notas
+
+Cuando devuelves una `Response` directamente, los datos no son validados, convertidos (serializados), ni documentados automáticamente.
+
+Pero todavía es posible documentarlo como es descrito en [Respuestas adicionales en OpenAPI](additional-responses.md){.internal-link target=_blank}.
+
+Puedes ver en secciones posteriores como usar/declarar esas `Response`s personalizadas aún teniendo conversión automática de datos, documentación, etc.

docs/es/docs/features.md

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

docs/es/docs/index.md

@@ -39,13 +39,16 @@ Sus características principales son:
 
 <small>* Esta estimación está basada en pruebas con un equipo de desarrollo interno contruyendo aplicaciones listas para producción.</small>
 
-## Gold Sponsors
+## Sponsors
 
 <!-- sponsors -->
 
 {% if sponsors %}
 {% for sponsor in sponsors.gold -%}
-<a href="{{ sponsor.url }}" target="_blank" title="{{ sponsor.title }}"><img src="{{ sponsor.img }}"></a>
+<a href="{{ sponsor.url }}" target="_blank" title="{{ sponsor.title }}"><img src="{{ sponsor.img }}" style="border-radius:15px"></a>
+{% endfor -%}
+{%- 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 %}
 

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

@@ -0,0 +1,333 @@
+# Primeros pasos
+
+Un archivo muy simple de FastAPI podría verse así:
+
+```Python
+{!../../../docs_src/first_steps/tutorial001.py!}
+```
+
+Copia eso a un archivo `main.py`.
+
+Corre el servidor en vivo:
+
+<div class="termy">
+
+```console
+$ uvicorn main:app --reload
+
+<span style="color: green;">INFO</span>:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
+<span style="color: green;">INFO</span>:     Started reloader process [28720]
+<span style="color: green;">INFO</span>:     Started server process [28722]
+<span style="color: green;">INFO</span>:     Waiting for application startup.
+<span style="color: green;">INFO</span>:     Application startup complete.
+```
+
+</div>
+
+!!! note "Nota"
+    El comando `uvicorn main:app` se refiere a:
+
+    * `main`: el archivo `main.py` (el "módulo" de Python).
+    * `app`: el objeto creado dentro de `main.py` con la línea `app = FastAPI()`.
+    * `--reload`: hace que el servidor se reinicie cada vez que cambia el código. Úsalo únicamente para desarrollo.
+
+En el output, hay una línea que dice más o menos:
+
+```hl_lines="4"
+INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
+```
+
+Esa línea muestra la URL dónde se está sirviendo tu app en tu maquina local.
+
+### Revísalo
+
+Abre tu navegador en <a href="http://127.0.0.1:8000" class="external-link" target="_blank">http://127.0.0.1:8000</a>.
+
+Verás la respuesta en JSON:
+
+```JSON
+{"message": "Hello World"}
+```
+
+### Documentación interactiva de la API
+
+Ahora dirígete a <a href="http://127.0.0.1:8000/docs" class="external-link" target="_blank">http://127.0.0.1:8000/docs</a>.
+
+Ahí verás la documentación automática e interactiva de la API (proveída por <a href="https://github.com/swagger-api/swagger-ui" class="external-link" target="_blank">Swagger UI</a>):
+
+![Swagger UI](https://fastapi.tiangolo.com/img/index/index-01-swagger-ui-simple.png)
+
+### Documentación alternativa de la API
+
+Ahora, dirígete a <a href="http://127.0.0.1:8000/redoc" class="external-link" target="_blank">http://127.0.0.1:8000/redoc</a>.
+
+Aquí verás la documentación automática alternativa (proveída por <a href="https://github.com/Rebilly/ReDoc" class="external-link" target="_blank">ReDoc</a>):
+
+![ReDoc](https://fastapi.tiangolo.com/img/index/index-02-redoc-simple.png)
+
+### OpenAPI
+
+**FastAPI** genera un "schema" con toda tu API usando el estándar para definir APIs, **OpenAPI**.
+
+#### "Schema"
+
+Un "schema" es una definición o descripción de algo. No es el código que la implementa, sino solo una descripción abstracta.
+
+#### "Schema" de la API
+
+En este caso, <a href="https://github.com/OAI/OpenAPI-Specification" class="external-link" target="_blank">OpenAPI</a> es una especificación que dicta como se debe definir el schema de tu API.
+
+La definición del schema incluye los paths de tu API, los parámetros que podría recibir, etc.
+
+#### "Schema" de datos
+
+El concepto "schema" también se puede referir a la forma de algunos datos, como un contenido en formato JSON.
+
+En ese caso haría referencia a los atributos del JSON, los tipos de datos que tiene, etc.
+
+#### OpenAPI y JSON Schema
+
+OpenAPI define un schema de API para tu API. Ese schema incluye definiciones (o "schemas") de los datos enviados y recibidos por tu API usando **JSON Schema**, el estándar para los schemas de datos en JSON.
+
+#### Revisa el `openapi.json`
+
+Si sientes curiosidad por saber cómo se ve el schema de OpenAPI en bruto, FastAPI genera automáticamente un (schema) JSON con la descripción de todo tu API.
+
+Lo puedes ver directamente en: <a href="http://127.0.0.1:8000/openapi.json" class="external-link" target="_blank">http://127.0.0.1:8000/openapi.json</a>.
+
+Esto te mostrará un JSON que comienza con algo como:
+
+```JSON
+{
+    "openapi": "3.0.2",
+    "info": {
+        "title": "FastAPI",
+        "version": "0.1.0"
+    },
+    "paths": {
+        "/items/": {
+            "get": {
+                "responses": {
+                    "200": {
+                        "description": "Successful Response",
+                        "content": {
+                            "application/json": {
+
+
+
+...
+```
+
+#### ¿Para qué se usa OpenAPI?
+
+El schema de OpenAPI es lo que alimenta a los dos sistemas de documentación interactiva incluidos.
+
+También hay docenas de alternativas, todas basadas en OpenAPI. Podrías añadir fácilmente cualquiera de esas alternativas a tu aplicación construida con **FastAPI**.
+
+También podrías usarlo para generar código automáticamente, para los clientes que se comunican con tu API. Por ejemplo, frontend, móvil o aplicaciones de IoT.
+
+## Repaso, paso a paso
+
+### Paso 1: importa `FastAPI`
+
+```Python hl_lines="1"
+{!../../../docs_src/first_steps/tutorial001.py!}
+```
+
+`FastAPI` es una clase de Python que provee toda la funcionalidad para tu API.
+
+!!! note "Detalles Técnicos"
+    `FastAPI` es una clase que hereda directamente de `Starlette`.
+
+    También puedes usar toda la funcionalidad de <a href="https://www.starlette.io/" class="external-link" target="_blank">Starlette</a>.
+
+### Paso 2: crea un "instance" de `FastAPI`
+
+```Python hl_lines="3"
+{!../../../docs_src/first_steps/tutorial001.py!}
+```
+
+Aquí la variable `app` será un instance de la clase `FastAPI`.
+
+Este será el punto de interacción principal para crear todo tu API.
+
+Esta `app` es la misma a la que nos referimos cuando usamos el comando de `uvicorn`:
+
+<div class="termy">
+
+```console
+$ uvicorn main:app --reload
+
+<span style="color: green;">INFO</span>:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
+```
+
+</div>
+
+Si creas un app como:
+
+```Python hl_lines="3"
+{!../../../docs_src/first_steps/tutorial002.py!}
+```
+
+y lo guardas en un archivo `main.py`, entonces ejecutarías `uvicorn` así:
+
+<div class="termy">
+
+```console
+$ uvicorn main:my_awesome_api --reload
+
+<span style="color: green;">INFO</span>:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
+```
+
+</div>
+
+### Paso 3: crea un *operación de path*
+
+#### Path
+
+"Path" aquí se refiere a la última parte de una URL comenzando desde el primer `/`.
+
+Entonces, en una URL como:
+
+```
+https://example.com/items/foo
+```
+
+...el path sería:
+
+```
+/items/foo
+```
+
+!!! info "Información"
+    Un "path" también se conoce habitualmente como "endpoint", "route" o "ruta".
+
+Cuando construyes una API, el "path" es la manera principal de separar los <abbr title="en inglés: separation of concerns">"intereses"</abbr> y los "recursos".
+
+#### Operación
+
+"Operación" aquí se refiere a uno de los "métodos" de HTTP.
+
+Uno como:
+
+* `POST`
+* `GET`
+* `PUT`
+* `DELETE`
+
+...y los más exóticos:
+
+* `OPTIONS`
+* `HEAD`
+* `PATCH`
+* `TRACE`
+
+En el protocolo de HTTP, te puedes comunicar con cada path usando uno (o más) de estos "métodos".
+
+---
+
+Cuando construyes APIs, normalmente usas uno de estos métodos específicos de HTTP para realizar una acción específica.
+
+Normalmente usas:
+
+* `POST`: para crear datos.
+* `GET`: para leer datos.
+* `PUT`: para actualizar datos.
+* `DELETE`: para borrar datos.
+
+Así que en OpenAPI, cada uno de estos métodos de HTTP es referido como una "operación".
+
+Nosotros también los llamaremos "**operación**".
+
+#### Define un *decorador de operaciones de path*
+
+```Python hl_lines="6"
+{!../../../docs_src/first_steps/tutorial001.py!}
+```
+
+El `@app.get("/")` le dice a **FastAPI** que la función que tiene justo debajo está a cargo de manejar los requests que van a:
+
+* el path `/`
+* usando una <abbr title="an HTTP GET method">operación <code>get</code></abbr>
+
+!!! info "Información sobre `@decorator`"
+    Esa sintaxis `@algo` se llama un "decorador" en Python.
+
+    Lo pones encima de una función. Es como un lindo sombrero decorado (creo que de ahí salió el concepto).
+    
+    Un "decorador" toma la función que tiene debajo y hace algo con ella.
+
+    En nuestro caso, este decorador le dice a **FastAPI** que la función que está debajo corresponde al **path** `/` con una **operación** `get`.
+
+    Es el "**decorador de operaciones de path**".
+
+También puedes usar las otras operaciones:
+
+* `@app.post()`
+* `@app.put()`
+* `@app.delete()`
+
+y las más exóticas:
+
+* `@app.options()`
+* `@app.head()`
+* `@app.patch()`
+* `@app.trace()`
+
+!!! tip "Consejo"
+    Tienes la libertad de usar cada operación (método de HTTP) como quieras.
+
+    **FastAPI** no impone ningún significado específico.
+
+    La información que está presentada aquí es una guía, no un requerimiento.
+
+    Por ejemplo, cuando usas GraphQL normalmente realizas todas las acciones usando únicamente operaciones `POST`.
+
+### Paso 4: define la **función de la operación de path**
+
+Esta es nuestra  "**función de la operación de path**":
+
+* **path**: es `/`.
+* **operación**: es `get`.
+* **función**: es la función debajo del "decorador" (debajo de `@app.get("/")`).
+
+```Python hl_lines="7"
+{!../../../docs_src/first_steps/tutorial001.py!}
+```
+
+Esto es una función de Python.
+
+Esta función será llamada por **FastAPI** cada vez que reciba un request en la URL "`/`" usando una operación `GET`.
+
+En este caso es una función `async`.
+
+---
+
+También podrías definirla como una función normal, en vez de `async def`:
+
+```Python hl_lines="7"
+{!../../../docs_src/first_steps/tutorial003.py!}
+```
+
+!!! note "Nota"
+    Si no sabes la diferencia, revisa el [Async: *"¿Tienes prisa?"*](../async.md#in-a-hurry){.internal-link target=_blank}.
+
+### Paso 5: devuelve el contenido
+
+```Python hl_lines="8"
+{!../../../docs_src/first_steps/tutorial001.py!}
+```
+
+Puedes devolver `dict`, `list`, valores singulares como un `str`, `int`, etc.
+
+También puedes devolver modelos de Pydantic (ya verás más sobre esto más adelante).
+
+Hay muchos objetos y modelos que pueden ser convertidos automáticamente a JSON (incluyendo ORMs, etc.). Intenta usar tus favoritos, es muy probable que ya tengan soporte.
+
+## Repaso
+
+* Importa `FastAPI`.
+* Crea un instance de `app`.
+* Escribe un **decorador de operación de path** (como `@app.get("/")`).
+* Escribe una **función de la operación de path** (como `def root(): ...` arriba).
+* Corre el servidor de desarrollo (como `uvicorn main:app --reload`).

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

@@ -0,0 +1,244 @@
+# Parámetros de path
+
+Puedes declarar los "parámetros" o "variables" con la misma sintaxis que usan los format strings de Python:
+
+```Python hl_lines="6-7"
+{!../../../docs_src/path_params/tutorial001.py!}
+```
+
+El valor del parámetro de path `item_id` será pasado a tu función como el argumento `item_id`.
+
+Entonces, si corres este ejemplo y vas a <a href="http://127.0.0.1:8000/items/foo" class="external-link" target="_blank">http://127.0.0.1:8000/items/foo</a>, verás una respuesta de:
+
+```JSON
+{"item_id":"foo"}
+```
+
+## Parámetros de path con tipos
+
+Puedes declarar el tipo de un parámetro de path en la función usando las anotaciones de tipos estándar de Python:
+
+```Python hl_lines="7"
+{!../../../docs_src/path_params/tutorial002.py!}
+```
+
+En este caso, `item_id` es declarado como un `int`.
+
+!!! check "Revisa"
+    Esto te dará soporte en el editor dentro de tu función, con chequeos de errores, autocompletado, etc.
+
+## <abbr title="también conocido en inglés como: serialization, parsing, marshalling">Conversión</abbr> de datos
+
+Si corres este ejemplo y abres tu navegador en <a href="http://127.0.0.1:8000/items/3" class="external-link" target="_blank">http://127.0.0.1:8000/items/3</a> verás una respuesta de:
+
+```JSON
+{"item_id":3}
+```
+
+!!! check "Revisa"
+    Observa que el valor que recibió (y devolvió) tu función es `3`, como un Python `int`, y no un string `"3"`.
+
+    Entonces, con esa declaración de tipos **FastAPI** te da <abbr title="convertir el string que viene de un HTTP request a datos de Python">"parsing"</abbr> automático del request.
+
+## Validación de datos
+
+Pero si abres tu navegador en <a href="http://127.0.0.1:8000/items/foo" class="external-link" target="_blank">http://127.0.0.1:8000/items/foo</a> verás este lindo error de HTTP:
+
+```JSON
+{
+    "detail": [
+        {
+            "loc": [
+                "path",
+                "item_id"
+            ],
+            "msg": "value is not a valid integer",
+            "type": "type_error.integer"
+        }
+    ]
+}
+```
+
+debido a que el parámetro de path `item_id` tenía el valor `"foo"`, que no es un `int`.
+
+El mismo error aparecería si pasaras un `float` en vez de un `int` como en: <a href="http://127.0.0.1:8000/items/4.2" class="external-link" target="_blank">http://127.0.0.1:8000/items/4.2</a>
+
+!!! check "Revisa"
+    Así, con la misma declaración de tipo de Python, **FastAPI** te da validación de datos.
+
+    Observa que el error también muestra claramente el punto exacto en el que no pasó la validación.
+
+    Esto es increíblemente útil cuando estás desarrollando y debugging código que interactúa con tu API.
+
+## Documentación
+
+Cuando abras tu navegador en <a href="http://127.0.0.1:8000/docs" class="external-link" target="_blank">http://127.0.0.1:8000/docs</a> verás la documentación automática e interactiva del API como:
+
+<img src="/img/tutorial/path-params/image01.png">
+
+!!! check "Revisa"
+    Nuevamente, con la misma declaración de tipo de Python, **FastAPI** te da documentación automática e interactiva (integrándose con Swagger UI)
+
+    Observa que el parámetro de path está declarado como un integer.
+
+## Beneficios basados en estándares, documentación alternativa
+
+Debido a que el schema generado es del estándar <a href="https://github.com/OAI/OpenAPI-Specification/blob/master/versions/3.0.2.md" class="external-link" target="_blank">OpenAPI</a> hay muchas herramientas compatibles.
+
+Es por esto que **FastAPI** mismo provee una documentación alternativa de la API (usando ReDoc), a la que puedes acceder en <a href="http://127.0.0.1:8000/redoc" class="external-link" target="_blank">http://127.0.0.1:8000/redoc</a>:
+
+<img src="/img/tutorial/path-params/image02.png">
+
+De la misma manera hay muchas herramientas compatibles. Incluyendo herramientas de generación de código para muchos lenguajes.
+
+## Pydantic
+
+Toda la validación de datos es realizada tras bastidores por <a href="https://pydantic-docs.helpmanual.io/" class="external-link" target="_blank">Pydantic</a>, así que obtienes todos sus beneficios. Así sabes que estás en buenas manos.
+
+Puedes usar las mismas declaraciones de tipos con `str`, `float`, `bool` y otros tipos de datos más complejos.
+
+Exploraremos varios de estos tipos en los próximos capítulos del tutorial.
+
+## El orden importa
+
+Cuando creas *operaciones de path* puedes encontrarte con situaciones en las que tengas un path fijo.
+
+Digamos algo como `/users/me` que sea para obtener datos del usuario actual.
+
+... y luego puedes tener el path `/users/{user_id}` para obtener los datos sobre un usuario específico asociados a un ID de usuario.
+
+Porque las *operaciones de path* son evaluadas en orden, tienes que asegurarte de que el path para `/users/me` sea declarado antes que el path para `/users/{user_id}`:
+
+```Python hl_lines="6  11"
+{!../../../docs_src/path_params/tutorial003.py!}
+```
+
+De otra manera el path para `/users/{user_id}` coincidiría también con `/users/me` "pensando" que está recibiendo el parámetro `user_id` con el valor `"me"`.
+
+## Valores predefinidos
+
+Si tienes una *operación de path* que recibe un *parámetro de path* pero quieres que los valores posibles del *parámetro de path* sean predefinidos puedes usar un <abbr title="Enumeration">`Enum`</abbr> estándar de Python.
+
+### Crea una clase `Enum`
+
+Importa `Enum` y crea una sub-clase que herede desde `str` y desde `Enum`.
+
+Al heredar desde `str` la documentación de la API podrá saber que los valores deben ser de tipo `string` y podrá mostrarlos correctamente.
+
+Luego crea atributos de clase con valores fijos, que serán los valores disponibles válidos:
+
+```Python hl_lines="1  6-9"
+{!../../../docs_src/path_params/tutorial005.py!}
+```
+
+!!! info "Información"
+    Las <a href="https://docs.python.org/3/library/enum.html" class="external-link" target="_blank">Enumerations (o enums) están disponibles en Python</a> desde la versión 3.4.
+
+!!! tip "Consejo"
+    Si lo estás dudando, "AlexNet", "ResNet", y "LeNet" son solo nombres de <abbr title="Tecnicamente, arquitecturas de modelos de Deep Learning">modelos</abbr> de Machine Learning.
+
+### Declara un *parámetro de path*
+
+Luego, crea un *parámetro de path* con anotaciones de tipos usando la clase enum que creaste (`ModelName`):
+
+```Python hl_lines="16"
+{!../../../docs_src/path_params/tutorial005.py!}
+```
+
+### Revisa la documentación
+
+Debido a que los valores disponibles para el *parámetro de path* están predefinidos, la documentación interactiva los puede mostrar bien:
+
+<img src="/img/tutorial/path-params/image03.png">
+
+### Trabajando con los *enumerations* de Python
+
+El valor del *parámetro de path* será un *enumeration member*.
+
+#### Compara *enumeration members*
+
+Puedes compararlo con el *enumeration member* en el enum (`ModelName`) que creaste:
+
+```Python hl_lines="17"
+{!../../../docs_src/path_params/tutorial005.py!}
+```
+
+#### Obtén el *enumeration value*
+
+Puedes obtener el valor exacto (un `str` en este caso) usando `model_name.value`, o en general, `your_enum_member.value`:
+
+```Python hl_lines="20"
+{!../../../docs_src/path_params/tutorial005.py!}
+```
+
+!!! tip "Consejo"
+    También podrías obtener el valor `"lenet"` con `ModelName.lenet.value`.
+
+#### Devuelve *enumeration members*
+
+Puedes devolver *enum members* desde tu *operación de path* inclusive en un body de JSON anidado (por ejemplo, un `dict`).
+
+Ellos serán convertidos a sus valores correspondientes (strings en este caso) antes de devolverlos al cliente:
+
+```Python hl_lines="18  21  23"
+{!../../../docs_src/path_params/tutorial005.py!}
+```
+
+En tu cliente obtendrás una respuesta en JSON como:
+
+```JSON
+{
+  "model_name": "alexnet",
+  "message": "Deep Learning FTW!"
+}
+```
+
+## Parámetros de path parameters que contienen paths
+
+Digamos que tienes una *operación de path* con un path `/files/{file_path}`.
+
+Pero necesitas que el mismo `file_path` contenga un path como `home/johndoe/myfile.txt`.
+
+Entonces, la URL para ese archivo sería algo como: `/files/home/johndoe/myfile.txt`.
+
+### Soporte de OpenAPI
+
+OpenAPI no soporta una manera de declarar un *parámetro de path* que contenga un path, dado que esto podría llevar a escenarios que son difíciles de probar y definir.
+
+Sin embargo, lo puedes hacer en **FastAPI** usando una de las herramientas internas de Starlette.
+
+La documentación seguirá funcionando, aunque no añadirá ninguna información diciendo que el parámetro debería contener un path.
+
+### Convertidor de path
+
+Usando una opción directamente desde Starlette puedes declarar un *parámetro de path* que contenga un path usando una URL como:
+
+```
+/files/{file_path:path}
+```
+
+En este caso el nombre del parámetro es `file_path` y la última parte, `:path`, le dice que el parámetro debería coincidir con cualquier path.
+
+Entonces lo puedes usar con:
+
+```Python hl_lines="6"
+{!../../../docs_src/path_params/tutorial004.py!}
+```
+
+!!! tip "Consejo"
+    Podrías necesitar que el parámetro contenga `/home/johndoe/myfile.txt` con un slash inicial (`/`).
+
+    En este caso la URL sería `/files//home/johndoe/myfile.txt` con un slash doble (`//`) entre `files` y `home`.
+
+## Repaso
+
+Con **FastAPI**, usando declaraciones de tipo de Python intuitivas y estándares, obtienes:
+
+* Soporte en el editor: chequeos de errores, auto-completado, etc.
+* "<abbr title="convertir el string que viene de un HTTP request a datos de Python">Parsing</abbr>" de datos
+* Validación de datos
+* Anotación de la API y documentación automática
+
+Solo tienes que declararlos una vez.
+
+Esa es probablemente la principal ventaja visible de **FastAPI**  sobre otros frameworks alternativos (aparte del rendimiento puro).

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

@@ -0,0 +1,197 @@
+# Parámetros de query
+
+Cuando declaras otros parámetros de la función que no hacen parte de los parámetros de path estos se interpretan automáticamente como parámetros de "query".
+
+```Python hl_lines="9"
+{!../../../docs_src/query_params/tutorial001.py!}
+```
+
+El query es el conjunto de pares de key-value que van después del `?` en la URL, separados por caracteres `&`.
+
+Por ejemplo, en la URL:
+
+```
+http://127.0.0.1:8000/items/?skip=0&limit=10
+```
+
+...los parámetros de query son:
+
+* `skip`: con un valor de `0`
+* `limit`: con un valor de `10`
+
+Dado que son parte de la URL son strings "naturalmente".
+
+Pero cuando los declaras con tipos de Python (en el ejemplo arriba, como `int`) son convertidos a ese tipo y son validados con él.
+
+Todo el proceso que aplicaba a los parámetros de path también aplica a los parámetros de query:
+
+* Soporte del editor (obviamente)
+* <abbr title="convertir el string que viene de un HTTP request a datos de Python">"Parsing"</abbr> de datos
+* Validación de datos
+* Documentación automática
+
+## Configuraciones por defecto
+
+Como los parámetros de query no están fijos en una parte del path pueden ser opcionales y pueden tener valores por defecto.
+
+El ejemplo arriba tiene `skip=0` y `limit=10` como los valores por defecto.
+
+Entonces, si vas a la URL:
+
+```
+http://127.0.0.1:8000/items/
+```
+
+Sería lo mismo que ir a:
+
+```
+http://127.0.0.1:8000/items/?skip=0&limit=10
+```
+
+Pero, si por ejemplo vas a:
+
+```
+http://127.0.0.1:8000/items/?skip=20
+```
+
+Los valores de los parámetros en tu función serán:
+
+* `skip=20`: porque lo definiste en la URL
+* `limit=10`: porque era el valor por defecto
+
+## Parámetros opcionales
+
+Del mismo modo puedes declarar parámetros de query opcionales definiendo el valor por defecto como `None`:
+
+```Python hl_lines="9"
+{!../../../docs_src/query_params/tutorial002.py!}
+```
+
+En este caso el parámetro de la función `q` será opcional y será `None` por defecto.
+
+!!! check "Revisa"
+    También puedes notar que **FastAPI** es lo suficientemente inteligente para darse cuenta de que el parámetro de path `item_id` es un parámetro de path y que `q` no lo es, y por lo tanto es un parámetro de query.
+
+!!! note "Nota"
+    FastAPI sabrá que `q` es opcional por el `= None`.
+
+    El `Optional` en `Optional[str]` no es usado por FastAPI (FastAPI solo usará la parte `str`), pero el `Optional[str]` le permitirá a tu editor ayudarte a encontrar errores en tu código.
+
+## Conversión de tipos de parámetros de query
+
+También puedes declarar tipos `bool` y serán convertidos:
+
+```Python hl_lines="9"
+{!../../../docs_src/query_params/tutorial003.py!}
+```
+
+En este caso, si vas a:
+
+```
+http://127.0.0.1:8000/items/foo?short=1
+```
+
+o
+
+```
+http://127.0.0.1:8000/items/foo?short=True
+```
+
+o
+
+```
+http://127.0.0.1:8000/items/foo?short=true
+```
+
+o
+
+```
+http://127.0.0.1:8000/items/foo?short=on
+```
+
+o
+
+```
+http://127.0.0.1:8000/items/foo?short=yes
+```
+
+o cualquier otra variación (mayúsculas, primera letra en mayúscula, etc.) tu función verá el parámetro `short` con un valor `bool` de `True`. Si no, lo verá como `False`.
+
+## Múltiples parámetros de path y query
+
+Puedes declarar múltiples parámetros de path y parámetros de query al mismo tiempo. **FastAPI** sabe cuál es cuál.
+
+No los tienes que declarar en un orden específico.
+
+Serán detectados por nombre:
+
+```Python hl_lines="8  10"
+{!../../../docs_src/query_params/tutorial004.py!}
+```
+
+## Parámetros de query requeridos
+
+Cuando declaras un valor por defecto para los parámetros que no son de path (por ahora solo hemos visto parámetros de query), entonces no es requerido.
+
+Si no quieres añadir un valor específico sino solo hacerlo opcional, pon el valor por defecto como `None`.
+
+Pero cuando quieres hacer que un parámetro de query sea requerido, puedes simplemente no declararle un valor por defecto:
+
+```Python hl_lines="6-7"
+{!../../../docs_src/query_params/tutorial005.py!}
+```
+
+Aquí el parámetro de query `needy` es un parámetro de query requerido, del tipo `str`.
+
+Si abres tu navegador en una URL como:
+
+```
+http://127.0.0.1:8000/items/foo-item
+```
+
+...sin añadir el parámetro `needy` requerido, verás un error como:
+
+```JSON
+{
+    "detail": [
+        {
+            "loc": [
+                "query",
+                "needy"
+            ],
+            "msg": "field required",
+            "type": "value_error.missing"
+        }
+    ]
+}
+```
+
+Dado que `needy` es un parámetro requerido necesitarías declararlo en la URL:
+
+```
+http://127.0.0.1:8000/items/foo-item?needy=sooooneedy
+```
+
+...esto funcionaría:
+
+```JSON
+{
+    "item_id": "foo-item",
+    "needy": "sooooneedy"
+}
+```
+
+Por supuesto que también puedes definir algunos parámetros como requeridos, con un valor por defecto y otros completamente opcionales:
+
+```Python hl_lines="10"
+{!../../../docs_src/query_params/tutorial006.py!}
+```
+
+En este caso hay 3 parámetros de query:
+
+* `needy`, un `str` requerido.
+* `skip`, un `int` con un valor por defecto de `0`.
+* `limit`, un `int` opcional.
+
+!!! tip "Consejo"
+    También podrías usar los `Enum`s de la misma manera que con los [Parámetros de path](path-params.md#predefined-values){.internal-link target=_blank}.

docs/es/mkdocs.yml

@@ -20,6 +20,7 @@ theme:
   features:
   - search.suggest
   - search.highlight
+  - content.tabs.link
   icon:
     repo: fontawesome/brands/github-alt
   logo: https://fastapi.tiangolo.com/img/icon-white.svg
@@ -39,11 +40,14 @@ nav:
 - FastAPI: index.md
 - Languages:
   - en: /
+  - de: /de/
   - es: /es/
   - fr: /fr/
+  - id: /id/
   - it: /it/
   - ja: /ja/
   - ko: /ko/
+  - pl: /pl/
   - pt: /pt/
   - ru: /ru/
   - sq: /sq/
@@ -54,6 +58,9 @@ nav:
 - python-types.md
 - Tutorial - Guía de Usuario:
   - tutorial/index.md
+  - tutorial/first-steps.md
+  - tutorial/path-params.md
+  - tutorial/query-params.md
 - Guía de Usuario Avanzada:
   - advanced/index.md
 - async.md
@@ -62,7 +69,7 @@ markdown_extensions:
     permalink: true
 - markdown.extensions.codehilite:
     guess_lang: false
-- markdown_include.include:
+- mdx_include:
     base_path: docs
 - admonition
 - codehilite
@@ -71,7 +78,7 @@ markdown_extensions:
     custom_fences:
     - name: mermaid
       class: mermaid
-      format: !!python/name:pymdownx.superfences.fence_div_format ''
+      format: !!python/name:pymdownx.superfences.fence_code_format ''
 - pymdownx.tabbed
 extra:
   social:
@@ -80,7 +87,7 @@ extra:
   - icon: fontawesome/brands/discord
     link: https://discord.gg/VQjSZaeJmf
   - icon: fontawesome/brands/twitter
-    link: https://twitter.com/tiangolo
+    link: https://twitter.com/fastapi
   - icon: fontawesome/brands/linkedin
     link: https://www.linkedin.com/in/tiangolo
   - icon: fontawesome/brands/dev
@@ -92,16 +99,22 @@ extra:
   alternate:
   - link: /
     name: en - English
+  - link: /de/
+    name: de
   - link: /es/
     name: es - español
   - link: /fr/
     name: fr - français
+  - link: /id/
+    name: id
   - link: /it/
     name: it - italiano
   - link: /ja/
     name: ja - 日本語
   - link: /ko/
     name: ko - 한국어
+  - link: /pl/
+    name: pl
   - link: /pt/
     name: pt - português
   - link: /ru/
@@ -118,6 +131,5 @@ extra_css:
 - https://fastapi.tiangolo.com/css/termynal.css
 - https://fastapi.tiangolo.com/css/custom.css
 extra_javascript:
-- https://unpkg.com/mermaid@8.4.6/dist/mermaid.min.js
 - https://fastapi.tiangolo.com/js/termynal.js
 - https://fastapi.tiangolo.com/js/custom.js

docs/fr/docs/alternatives.md

@@ -0,0 +1,446 @@
+# Alternatives, inspiration et comparaisons
+
+Ce qui a inspiré **FastAPI**, comment il se compare à d'autres solutions et ce qu'il en a appris.
+
+## Intro
+
+**FastAPI** n'existerait pas sans les précédentes contributions d'autres projets.
+
+De nombreux outils ont été créés auparavant et ont contribué à inspirer sa création.
+
+J'ai évité la création d'un nouveau framework pendant plusieurs années. J'ai d'abord essayé de combler toutes les
+fonctionnalités couvertes par **FastAPI** en utilisant de nombreux frameworks, plug-ins et outils différents.
+
+Mais à un moment donné il n'y avait pas d'autre option que de créer quelque chose qui offrait toutes ces
+fonctionnalités, en reprenant et en combinant de la meilleure façon possible les meilleures idées des outils
+précédents, en utilisant des fonctionnalités du langage qui n'étaient même pas disponibles auparavant (type hints depuis Python 3.6+).
+
+## Outils précédents
+
+### <a href="https://www.djangoproject.com/" class="external-link" target="_blank">Django</a>
+
+C'est le framework Python le plus populaire et il bénéficie d'une grande confiance. Il est utilisé pour construire
+des systèmes tel qu'Instagram.
+
+Il est relativement fortement couplé aux bases de données relationnelles (comme MySQL ou PostgreSQL), de sorte qu'il
+n'est pas très facile d'utiliser une base de données NoSQL (comme Couchbase, MongoDB, Cassandra, etc.) comme principal moyen de
+stockage.
+
+Il a été créé pour générer le HTML en backend, pas pour créer des API consommées par un frontend moderne (comme React, Vue.js et Angular) ou par d'autres systèmes (comme les appareils <abbr title="Internet of Things">IoT</abbr>) communiquant avec lui.
+
+### <a href="https://www.django-rest-framework.org/" class="external-link" target="_blank">Django REST Framework</a>
+
+Django REST framework a été conçu comme une boîte à outils flexible permettant de construire des API Web à partir de Django, afin d'améliorer ses capacités en matière d'API.
+
+Il est utilisé par de nombreuses entreprises, dont Mozilla, Red Hat et Eventbrite.
+
+Il s'agissait de l'un des premiers exemples de **documentation automatique pour API**, et c'est précisément l'une des
+premières idées qui a inspiré "la recherche de" **FastAPI**.
+
+!!! note
+Django REST framework a été créé par Tom Christie. Le créateur de Starlette et Uvicorn, sur lesquels **FastAPI** est basé.
+
+!!! check "A inspiré **FastAPI** à"
+Avoir une interface de documentation automatique de l'API.
+
+### <a href="https://flask.palletsprojects.com" class="external-link" target="_blank">Flask</a>
+
+Flask est un "micro-framework", il ne comprend pas d'intégrations de bases de données ni beaucoup de choses qui sont fournies par défaut dans Django.
+
+Cette simplicité et cette flexibilité permettent d'utiliser des bases de données NoSQL comme principal système de stockage de données.
+
+Comme il est très simple, son apprentissage est relativement intuitif, bien que la documentation soit quelque peu
+technique par moments.
+
+Il est aussi couramment utilisé pour d'autres applications qui n'ont pas nécessairement besoin d'une base de données, de gestion des utilisateurs ou de l'une des nombreuses fonctionnalités préinstallées dans Django. Bien que beaucoup de ces fonctionnalités puissent être ajoutées avec des plug-ins.
+
+Ce découplage des parties, et le fait d'être un "micro-framework" qui puisse être étendu pour couvrir exactement ce
+qui est nécessaire, était une caractéristique clé que je voulais conserver.
+
+Compte tenu de la simplicité de Flask, il semblait bien adapté à la création d'API. La prochaine chose à trouver était un "Django REST Framework" pour Flask.
+
+!!! check "A inspiré **FastAPI** à"
+Être un micro-framework. Il est donc facile de combiner les outils et les pièces nécessaires.
+
+Proposer un système de routage simple et facile à utiliser.
+
+### <a href="https://requests.readthedocs.io" class="external-link" target="_blank">Requests</a>
+
+**FastAPI** n'est pas réellement une alternative à **Requests**. Leur cadre est très différent.
+
+Il serait en fait plus courant d'utiliser Requests _à l'intérieur_ d'une application FastAPI.
+
+Mais quand même, FastAPI s'est inspiré de Requests.
+
+**Requests** est une bibliothèque pour _interagir_ avec les API (en tant que client), tandis que **FastAPI** est une bibliothèque pour _créer_ des API (en tant que serveur).
+
+Ils sont, plus ou moins, aux extrémités opposées, se complétant l'un l'autre.
+
+Requests a un design très simple et intuitif, il est très facile à utiliser, avec des valeurs par défaut raisonnables, tout en étant très puissant et personnalisable.
+
+C'est pourquoi, comme le dit le site officiel :
+
+> Requests est l'un des packages Python les plus téléchargés de tous les temps
+
+La façon dont vous l'utilisez est très simple. Par exemple, pour faire une requête `GET`, vous devez écrire :
+
+```Python
+response = requests.get("http://example.com/some/url")
+```
+
+En contrepartie l'API _des opérations de chemin_ de FastAPI pourrait ressembler à ceci :
+
+```Python hl_lines="1"
+@app.get("/some/url")
+def read_url():
+    return {"message": "Hello World"}
+```
+
+Notez les similitudes entre `requests.get(...)` et `@app.get(...)`.
+
+!!! check "A inspiré **FastAPI** à"
+_ Avoir une API simple et intuitive.
+_ Utiliser les noms de méthodes HTTP (opérations) directement, de manière simple et intuitive. \* Avoir des valeurs par défaut raisonnables, mais des personnalisations puissantes.
+
+### <a href="https://swagger.io/" class="external-link" target="_blank">Swagger</a> / <a href="https://github.com/OAI/OpenAPI-Specification/" class="external-link" target="_blank">OpenAPI</a>
+
+La principale fonctionnalité que j'ai emprunté à Django REST Framework était la documentation automatique des API.
+
+Puis j'ai découvert qu'il existait une norme pour documenter les API, en utilisant JSON (ou YAML, une extension de JSON) appelée Swagger.
+
+Il existait déjà une interface utilisateur Web pour les API Swagger. Donc, être capable de générer une documentation
+Swagger pour une API permettrait d'utiliser cette interface utilisateur web automatiquement.
+
+À un moment donné, Swagger a été cédé à la Fondation Linux, puis a été rebaptisé OpenAPI.
+
+C'est pourquoi, lorsqu'on parle de la version 2.0, il est courant de dire "Swagger", et pour la version 3+ "OpenAPI".
+
+!!! check "A inspiré **FastAPI** à"
+Adopter et utiliser une norme ouverte pour les spécifications des API, au lieu d'un schéma personnalisé.
+
+    Intégrer des outils d'interface utilisateur basés sur des normes :
+
+    * <a href="https://github.com/swagger-api/swagger-ui" class="external-link" target="_blank">Swagger UI</a>
+    * <a href="https://github.com/Rebilly/ReDoc" class="external-link" target="_blank">ReDoc</a>
+
+    Ces deux-là ont été choisis parce qu'ils sont populaires et stables, mais en faisant une recherche rapide, vous pourriez trouver des dizaines d'alternatives supplémentaires pour OpenAPI (que vous pouvez utiliser avec **FastAPI**).
+
+### Frameworks REST pour Flask
+
+Il y a plusieurs frameworks REST pour Flask, mais après avoir investi du temps et du travail pour les étudier, j'ai
+découvert que le développement de beaucoup d'entre eux sont suspendus ou abandonnés, avec plusieurs problèmes
+permanents qui les rendent inadaptés.
+
+### <a href="https://marshmallow.readthedocs.io/en/3.0/" class="external-link" target="_blank">Marshmallow</a>
+
+L'une des principales fonctionnalités nécessaires aux systèmes API est la "<abbr title="également appelée 
+marshalling, conversion">sérialisation</abbr>" des données, qui consiste à prendre les données du code (Python) et à
+les convertir en quelque chose qui peut être envoyé sur le réseau. Par exemple, convertir un objet contenant des
+données provenant d'une base de données en un objet JSON. Convertir des objets `datetime` en strings, etc.
+
+La validation des données est une autre fonctionnalité importante dont ont besoin les API. Elle permet de s'assurer
+que les données sont valides, compte tenu de certains paramètres. Par exemple, qu'un champ est un `int`, et non un
+string.
+Ceci est particulièrement utile pour les données entrantes.
+
+Sans un système de validation des données, vous devriez effectuer toutes les vérifications à la main, dans le code.
+
+Ces fonctionnalités sont ce pourquoi Marshmallow a été construit. C'est une excellente bibliothèque, et je l'ai déjà beaucoup utilisée.
+
+Mais elle a été créée avant que les type hints n'existent en Python. Ainsi, pour définir chaque <abbr title="la définition de 
+la façon dont les données doivent être formées">schéma</abbr>, vous devez utiliser des utilitaires et des classes spécifiques fournies par Marshmallow.
+
+!!! check "A inspiré **FastAPI** à"
+Utilisez du code pour définir des "schémas" qui fournissent automatiquement les types de données et la validation.
+
+### <a href="https://webargs.readthedocs.io/en/latest/" class="external-link" target="_blank">Webargs</a>
+
+Une autre grande fonctionnalité requise par les API est le <abbr title="la lecture et la conversion en données 
+Python">parsing</abbr> des données provenant des requêtes entrantes.
+
+Webargs est un outil qui a été créé pour fournir cela par-dessus plusieurs frameworks, dont Flask.
+
+Il utilise Marshmallow pour effectuer la validation des données. Et il a été créé par les mêmes développeurs.
+
+C'est un outil formidable et je l'ai beaucoup utilisé aussi, avant d'avoir **FastAPI**.
+
+!!! info
+Webargs a été créé par les développeurs de Marshmallow.
+
+!!! check "A inspiré **FastAPI** à"
+Disposer d'une validation automatique des données des requêtes entrantes.
+
+### <a href="https://apispec.readthedocs.io/en/stable/" class="external-link" target="_blank">APISpec</a>
+
+Marshmallow et Webargs fournissent la validation, l'analyse et la sérialisation en tant que plug-ins.
+
+Mais la documentation fait toujours défaut. C'est alors qu'APISpec a été créé.
+
+Il s'agit d'un plug-in pour de nombreux frameworks (et il existe également un plug-in pour Starlette).
+
+Le principe est le suivant : vous écrivez la définition du schéma au format YAML dans la docstring de chaque fonction gérant une route.
+
+Et il génère des schémas OpenAPI.
+
+C'est ainsi que cela fonctionne dans Flask, Starlette, Responder, etc.
+
+Mais alors, nous avons à nouveau le problème d'avoir une micro-syntaxe, dans une docstring Python (un gros morceau de YAML).
+
+L'éditeur ne peut guère aider en la matière. Et si nous modifions les paramètres ou les schémas Marshmallow et que nous oublions de modifier également cette docstring YAML, le schéma généré deviendrait obsolète.
+
+!!! info
+APISpec a été créé par les développeurs de Marshmallow.
+
+!!! check "A inspiré **FastAPI** à"
+Supporter la norme ouverte pour les API, OpenAPI.
+
+### <a href="https://flask-apispec.readthedocs.io/en/latest/" class="external-link" target="_blank">Flask-apispec</a>
+
+C'est un plug-in pour Flask, qui relie Webargs, Marshmallow et APISpec.
+
+Il utilise les informations de Webargs et Marshmallow pour générer automatiquement des schémas OpenAPI, en utilisant APISpec.
+
+C'est un excellent outil, très sous-estimé. Il devrait être beaucoup plus populaire que de nombreux plug-ins Flask. C'est peut-être dû au fait que sa documentation est trop concise et abstraite.
+
+Cela a permis de ne pas avoir à écrire YAML (une autre syntaxe) à l'intérieur des docstrings Python.
+
+Cette combinaison de Flask, Flask-apispec avec Marshmallow et Webargs était ma stack backend préférée jusqu'à la création de **FastAPI**.
+
+Son utilisation a conduit à la création de plusieurs générateurs Flask full-stack. Ce sont les principales stacks que
+j'ai (ainsi que plusieurs équipes externes) utilisées jusqu'à présent :
+
+- <a href="https://github.com/tiangolo/full-stack" class="external-link" target="_blank">https://github.com/tiangolo/full-stack</a>
+- <a href="https://github.com/tiangolo/full-stack-flask-couchbase" class="external-link" target="_blank">https://github.com/tiangolo/full-stack-flask-couchbase</a>
+- <a href="https://github.com/tiangolo/full-stack-flask-couchdb" class="external-link" target="_blank">https://github.com/tiangolo/full-stack-flask-couchdb</a>
+
+Ces mêmes générateurs full-stack ont servi de base aux [Générateurs de projets pour **FastAPI**](project-generation.md){.internal-link target=\_blank}.
+
+!!! info
+Flask-apispec a été créé par les développeurs de Marshmallow.
+
+!!! check "A inspiré **FastAPI** à"
+Générer le schéma OpenAPI automatiquement, à partir du même code qui définit la sérialisation et la validation.
+
+### <a href="https://nestjs.com/" class="external-link" target="_blank">NestJS</a> (et <a href="https://angular.io/" class="external-link" target="_blank">Angular</a>)
+
+Ce n'est même pas du Python, NestJS est un framework JavaScript (TypeScript) NodeJS inspiré d'Angular.
+
+Il réalise quelque chose de similaire à ce qui peut être fait avec Flask-apispec.
+
+Il possède un système d'injection de dépendances intégré, inspiré d'Angular 2. Il nécessite de pré-enregistrer les "injectables" (comme tous les autres systèmes d'injection de dépendances que je connais), donc, cela ajoute à la verbosité et à la répétition du code.
+
+Comme les paramètres sont décrits avec des types TypeScript (similaires aux type hints de Python), la prise en charge
+par l'éditeur est assez bonne.
+
+Mais comme les données TypeScript ne sont pas préservées après la compilation en JavaScript, il ne peut pas compter sur les types pour définir la validation, la sérialisation et la documentation en même temps. En raison de cela et de certaines décisions de conception, pour obtenir la validation, la sérialisation et la génération automatique de schémas, il est nécessaire d'ajouter des décorateurs à de nombreux endroits. Cela devient donc assez verbeux.
+
+Il ne peut pas très bien gérer les modèles imbriqués. Ainsi, si le corps JSON de la requête est un objet JSON comportant des champs internes qui sont à leur tour des objets JSON imbriqués, il ne peut pas être correctement documenté et validé.
+
+!!! check "A inspiré **FastAPI** à"
+Utiliser les types Python pour bénéficier d'un excellent support de l'éditeur.
+
+    Disposer d'un puissant système d'injection de dépendances. Trouver un moyen de minimiser la répétition du code.
+
+### <a href="https://sanic.readthedocs.io/en/latest/" class="external-link" target="_blank">Sanic</a>
+
+C'était l'un des premiers frameworks Python extrêmement rapides basés sur `asyncio`. Il a été conçu pour être très similaire à Flask.
+
+!!! note "Détails techniques"
+Il utilisait <a href="https://github.com/MagicStack/uvloop" class="external-link" target="_blank">`uvloop`</a> au lieu du système par défaut de Python `asyncio`. C'est ce qui l'a rendu si rapide.
+
+    Il a clairement inspiré Uvicorn et Starlette, qui sont actuellement plus rapides que Sanic dans les benchmarks.
+
+!!! check "A inspiré **FastAPI** à"
+Trouvez un moyen d'avoir une performance folle.
+
+    C'est pourquoi **FastAPI** est basé sur Starlette, car il s'agit du framework le plus rapide disponible (testé par des benchmarks tiers).
+
+### <a href="https://falconframework.org/" class="external-link" target="_blank">Falcon</a>
+
+Falcon est un autre framework Python haute performance, il est conçu pour être minimal, et est utilisé comme fondation pour d'autres frameworks comme Hug.
+
+Il utilise le standard précédent pour les frameworks web Python (WSGI) qui est synchrone, donc il ne peut pas gérer les WebSockets et d'autres cas d'utilisation. Néanmoins, il offre de très bonnes performances.
+
+Il est conçu pour avoir des fonctions qui reçoivent deux paramètres, une "requête" et une "réponse". Ensuite, vous
+"lisez" des parties de la requête et "écrivez" des parties dans la réponse. En raison de cette conception, il n'est
+pas possible de déclarer des paramètres de requête et des corps avec des indications de type Python standard comme paramètres de fonction.
+
+Ainsi, la validation, la sérialisation et la documentation des données doivent être effectuées dans le code, et non pas automatiquement. Ou bien elles doivent être implémentées comme un framework au-dessus de Falcon, comme Hug. Cette même distinction se retrouve dans d'autres frameworks qui s'inspirent de la conception de Falcon, qui consiste à avoir un objet de requête et un objet de réponse comme paramètres.
+
+!!! check "A inspiré **FastAPI** à"
+Trouver des moyens d'obtenir de bonnes performances.
+
+    Avec Hug (puisque Hug est basé sur Falcon), **FastAPI** a inspiré la déclaration d'un paramètre `response` dans les fonctions.
+
+    Bien que dans FastAPI, il est facultatif, et est utilisé principalement pour définir les en-têtes, les cookies, et les codes de statut alternatifs.
+
+### <a href="https://moltenframework.com/" class="external-link" target="_blank">Molten</a>
+
+J'ai découvert Molten lors des premières étapes de développement de **FastAPI**. Et il a des idées assez similaires :
+
+- Basé sur les type hints Python.
+- Validation et documentation via ces types.
+- Système d'injection de dépendances.
+
+Il n'utilise pas une librairie tiers de validation, sérialisation et de documentation tel que Pydantic, il utilise son propre système. Ainsi, ces définitions de types de données ne sont pas réutilisables aussi facilement.
+
+Il nécessite une configuration un peu plus verbeuse. Et comme il est basé sur WSGI (au lieu dASGI), il n'est pas
+conçu pour profiter des hautes performances fournies par des outils comme Uvicorn, Starlette et Sanic.
+
+Le système d'injection de dépendances exige le pré-enregistrement des dépendances et les dépendances sont résolues sur la base des types déclarés. Ainsi, il n'est pas possible de déclarer plus d'un "composant" qui fournit un certain type.
+
+Les routes sont déclarées à un seul endroit, en utilisant des fonctions déclarées à d'autres endroits (au lieu
+d'utiliser des décorateurs qui peuvent être placés juste au-dessus de la fonction qui gère l'endpoint). Cette
+méthode est plus proche de celle de Django que de celle de Flask (et Starlette). Il sépare dans le code des choses
+qui sont relativement fortement couplées.
+
+!!! check "A inspiré **FastAPI** à"
+Définir des validations supplémentaires pour les types de données utilisant la valeur "par défaut" des attributs du modèle. Ceci améliore le support de l'éditeur, et n'était pas disponible dans Pydantic auparavant.
+
+    Cela a en fait inspiré la mise à jour de certaines parties de Pydantic, afin de supporter le même style de déclaration de validation (toute cette fonctionnalité est maintenant déjà disponible dans Pydantic).
+
+### <a href="https://www.hug.rest/" class="external-link" target="_blank">Hug</a>
+
+Hug a été l'un des premiers frameworks à implémenter la déclaration des types de paramètres d'API en utilisant les type hints Python. C'était une excellente idée qui a inspiré d'autres outils à faire de même.
+
+Il utilisait des types personnalisés dans ses déclarations au lieu des types Python standard, mais c'était tout de même un énorme pas en avant.
+
+Il a également été l'un des premiers frameworks à générer un schéma personnalisé déclarant l'ensemble de l'API en JSON.
+
+Il n'était pas basé sur une norme comme OpenAPI et JSON Schema. Il ne serait donc pas simple de l'intégrer à d'autres outils, comme Swagger UI. Mais encore une fois, c'était une idée très innovante.
+
+Il présente une caractéristique intéressante et peu commune : à l'aide du même framework, il est possible de créer des
+API et des CLI.
+
+Comme il est basé sur l'ancienne norme pour les frameworks web Python synchrones (WSGI), il ne peut pas gérer les Websockets et autres, bien qu'il soit également très performant.
+
+!!! info
+Hug a été créé par Timothy Crosley, le créateur de <a href="https://github.com/timothycrosley/isort" class="external-link" target="_blank">`isort`</a>, un excellent outil pour trier automatiquement les imports dans les fichiers Python.
+
+!!! check "A inspiré **FastAPI** à"
+Hug a inspiré certaines parties d'APIStar, et était l'un des outils que je trouvais les plus prometteurs, à côté d'APIStar.
+
+    Hug a contribué à inspirer **FastAPI** pour utiliser les type hints Python
+    pour déclarer les paramètres, et pour générer automatiquement un schéma définissant l'API.
+
+    Hug a inspiré **FastAPI** pour déclarer un paramètre `response` dans les fonctions pour définir les en-têtes et les cookies.
+
+### <a href="https://github.com/encode/apistar" class="external-link" target="_blank">APIStar</a> (<= 0.5)
+
+Juste avant de décider de développer **FastAPI**, j'ai trouvé le serveur **APIStar**. Il contenait presque tout ce
+que je recherchais et avait un beau design.
+
+C'était l'une des premières implémentations d'un framework utilisant les type hints Python pour déclarer les paramètres
+et les requêtes que j'ai vues (avant NestJS et Molten). Je l'ai trouvé plus ou moins en même temps que Hug. Mais APIStar utilisait le standard OpenAPI.
+
+Il disposait de la validation automatique, sérialisation des données et d'une génération de schéma OpenAPI basée sur les mêmes type hints à plusieurs endroits.
+
+La définition du schéma de corps de requête n'utilisait pas les mêmes type hints Python que Pydantic, il était un peu plus proche de Marshmallow, donc le support de l'éditeur n'était pas aussi bon, mais APIStar était quand même la meilleure option disponible.
+
+Il avait les meilleures performances d'après les benchmarks de l'époque (seulement surpassé par Starlette).
+
+Au départ, il ne disposait pas d'une interface web de documentation automatique de l'API, mais je savais que je pouvais lui ajouter une interface Swagger.
+
+Il avait un système d'injection de dépendances. Il nécessitait un pré-enregistrement des composants, comme d'autres outils discutés ci-dessus. Mais c'était quand même une excellente fonctionnalité.
+
+Je n'ai jamais pu l'utiliser dans un projet complet, car il n'avait pas d'intégration de sécurité, et je ne pouvais donc pas remplacer toutes les fonctionnalités que j'avais avec les générateurs complets basés sur Flask-apispec. J'avais dans mon backlog de projets de créer une pull request pour ajouter cette fonctionnalité.
+
+Mais ensuite, le projet a changé d'orientation.
+
+Il ne s'agissait plus d'un framework web API, le créateur devant se concentrer sur Starlette.
+
+Maintenant, APIStar est un ensemble d'outils pour valider les spécifications OpenAPI, et non un framework web.
+
+!!! info
+APIStar a été créé par Tom Christie. Le même gars qui a créé :
+
+    * Django REST Framework
+    * Starlette (sur lequel **FastAPI** est basé)
+    * Uvicorn (utilisé par Starlette et **FastAPI**)
+
+!!! check "A inspiré **FastAPI** à"
+Exister.
+
+    L'idée de déclarer plusieurs choses (validation des données, sérialisation et documentation) avec les mêmes types Python, tout en offrant un excellent support pour les éditeurs, était pour moi une idée brillante.
+
+    Et après avoir longtemps cherché un framework similaire et testé de nombreuses alternatives, APIStar était la meilleure option disponible.
+
+    Puis APIStar a cessé d'exister en tant que serveur et Starlette a été créé, et a constitué une meilleure base pour un tel système. Ce fut l'inspiration finale pour construire **FastAPI**.
+
+    Je considère **FastAPI** comme un "successeur spirituel" d'APIStar, tout en améliorant et en augmentant les fonctionnalités, le système de typage et d'autres parties, sur la base des enseignements tirés de tous ces outils précédents.
+
+## Utilisés par **FastAPI**
+
+### <a href="https://pydantic-docs.helpmanual.io/" class="external-link" target="_blank">Pydantic</a>
+
+Pydantic est une bibliothèque permettant de définir la validation, la sérialisation et la documentation des données (à l'aide de JSON Schema) en se basant sur les Python type hints.
+
+Cela le rend extrêmement intuitif.
+
+Il est comparable à Marshmallow. Bien qu'il soit plus rapide que Marshmallow dans les benchmarks. Et comme il est
+basé sur les mêmes type hints Python, le support de l'éditeur est grand.
+
+!!! check "**FastAPI** l'utilise pour"
+Gérer toute la validation des données, leur sérialisation et la documentation automatique du modèle (basée sur le schéma JSON).
+
+    **FastAPI** prend ensuite ces données JSON Schema et les place dans OpenAPI, en plus de toutes les autres choses qu'il fait.
+
+### <a href="https://www.starlette.io/" class="external-link" target="_blank">Starlette</a>
+
+Starlette est un framework/toolkit léger <abbr title="Le nouveau standard pour construire des applications web assynchrones en Python">ASGI</abbr>, qui est idéal pour construire des services asyncio performants.
+
+Il est très simple et intuitif. Il est conçu pour être facilement extensible et avoir des composants modulaires.
+
+Il offre :
+
+- Des performances vraiment impressionnantes.
+- Le support des WebSockets.
+- Le support de GraphQL.
+- Les tâches d'arrière-plan.
+- Les événements de démarrage et d'arrêt.
+- Un client de test basé sur request.
+- CORS, GZip, fichiers statiques, streaming des réponses.
+- Le support des sessions et des cookies.
+- Une couverture de test à 100 %.
+- 100 % de la base de code avec des annotations de type.
+- Zéro forte dépendance à d'autres packages.
+
+Starlette est actuellement le framework Python le plus rapide testé. Seulement dépassé par Uvicorn, qui n'est pas un framework, mais un serveur.
+
+Starlette fournit toutes les fonctionnalités de base d'un micro-framework web.
+
+Mais il ne fournit pas de validation automatique des données, de sérialisation ou de documentation.
+
+C'est l'une des principales choses que **FastAPI** ajoute par-dessus, le tout basé sur les type hints Python (en utilisant Pydantic). Cela, plus le système d'injection de dépendances, les utilitaires de sécurité, la génération de schémas OpenAPI, etc.
+
+!!! note "Détails techniques"
+ASGI est une nouvelle "norme" développée par les membres de l'équipe principale de Django. Il ne s'agit pas encore d'une "norme Python" (un PEP), bien qu'ils soient en train de le faire.
+
+    Néanmoins, il est déjà utilisé comme "standard" par plusieurs outils. Cela améliore grandement l'interopérabilité, puisque vous pouvez remplacer Uvicorn par n'importe quel autre serveur ASGI (comme Daphne ou Hypercorn), ou vous pouvez ajouter des outils compatibles ASGI, comme `python-socketio`.
+
+!!! check "**FastAPI** l'utilise pour"
+Gérer toutes les parties web de base. Ajouter des fonctionnalités par-dessus.
+
+    La classe `FastAPI` elle-même hérite directement de la classe `Starlette`.
+
+    Ainsi, tout ce que vous pouvez faire avec Starlette, vous pouvez le faire directement avec **FastAPI**, car il s'agit en fait de Starlette sous stéroïdes.
+
+### <a href="https://www.uvicorn.org/" class="external-link" target="_blank">Uvicorn</a>
+
+Uvicorn est un serveur ASGI rapide comme l'éclair, basé sur uvloop et httptools.
+
+Il ne s'agit pas d'un framework web, mais d'un serveur. Par exemple, il ne fournit pas d'outils pour le routing. C'est
+quelque chose qu'un framework comme Starlette (ou **FastAPI**) fournirait par-dessus.
+
+C'est le serveur recommandé pour Starlette et **FastAPI**.
+
+!!! check "**FastAPI** le recommande comme"
+Le serveur web principal pour exécuter les applications **FastAPI**.
+
+    Vous pouvez le combiner avec Gunicorn, pour avoir un serveur multi-processus asynchrone.
+
+    Pour plus de détails, consultez la section [Déploiement](deployment/index.md){.internal-link target=_blank}.
+
+## Benchmarks et vitesse
+
+Pour comprendre, comparer et voir la différence entre Uvicorn, Starlette et FastAPI, consultez la section sur les [Benchmarks](benchmarks.md){.internal-link target=\_blank}.

docs/fr/docs/async.md

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