* However, when a Markdown document is translated, the `#hash-parts` in links to its headings may change. Update these links if possible.
    * Search for such links in the translated document using the regex `#[^# ]`.
    * Search in all documents already translated into your language for `your-translated-document.md`. For example VS Code has an option "Edit" -> "Find in Files".

It is used by many companies including Mozilla, Red Hat and Eventbrite.

It was one of the first examples of **automatic API documentation**, and this was specifically one of the first ideas that inspired "the search for" **FastAPI**.

!!! note
    Django REST Framework was created by Tom Christie. The same creator of Starlette and Uvicorn, on which **FastAPI** is based.


!!! check "Inspired **FastAPI** to"

There are also several other companies offering similar services that you can search and find online. 🤓

## Generate a TypeScript Frontend Client

Let's start with a simple FastAPI application:

=== "Python 3.9+"

    ```Python hl_lines="7-9  12-13  16-17  21"

    {!> ../../../docs_src/query_params_str_validations/tutorial003_py310.py!}
    ```

## 🚮 🥔 🧬

👆 💪 🔬 <abbr title="A regular expression, regex or regexp is a sequence of characters that define a search pattern for strings.">🥔 🧬</abbr> 👈 🔢 🔜 🏏:

=== "🐍 3️⃣.6️⃣ &amp; 🔛"

    ```Python hl_lines="11"
    {!> ../../../docs_src/query_params_str_validations/tutorial004.py!}
    ```

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.

* Aide votre **<abbr title="Integrated Development Environment, il s'agit de votre éditeur de code">IDE</abbr>/<abbr title="Programme qui analyse le code à la recherche d'erreurs">linter</abbr>/cerveau**:
    * Parce que les structures de données de pydantic consistent seulement en une instance de classe que vous définissez; l'auto-complétion, le linting, mypy et votre intuition devrait être largement suffisante pour valider vos données.

    {!> ../../../docs_src/query_params_str_validations/tutorial003.py!}
    ```

## Add regular expressions

You can define a <abbr title="A regular expression, regex or regexp is a sequence of characters that define a search pattern for strings.">regular expression</abbr> `pattern` that the parameter should match:

=== "Python 3.10+"

    ```Python hl_lines="11"
    {!> ../../../docs_src/query_params_str_validations/tutorial004_an_py310.py!}

            if (/code|token|error/.test(window.location.hash)) {
                qp = window.location.hash.substring(1).replace('?', '&');
            } else {
                qp = location.search.substring(1);
            }

            arr = qp.split("&");
            arr.forEach(function (v,i,_arr) { _arr[i] = '"' + v.replace('=', '":"') + '"';});
            qp = qp ? JSON.parse('{' + arr.join() + '}',

<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>

"""


def generate_readme_content() -> str:
    en_index = en_docs_path / "docs" / "index.md"
    content = en_index.read_text("utf-8")
    match_pre = re.search(r"</style>\n\n", content)
    match_start = re.search(r"<!-- sponsors -->", content)
    match_end = re.search(r"<!-- /sponsors -->", content)
    sponsors_data_path = en_docs_path / "data" / "sponsors.yml"