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  1. docs/fr/docs/history-design-future.md

    Mais à un moment donné, il n'y avait pas d'autre option que de créer quelque chose qui offre toutes ces fonctionnalités, en prenant les meilleures idées des outils précédents, et en les combinant de la meilleure façon possible, en utilisant des fonctionnalités du langage qui n'étaient même pas disponibles auparavant (annotations de type pour Python 3.6+).
    
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  2. docs/en/docs/alternatives.md

    It was created to generate the HTML in the backend, not to create APIs used by a modern frontend (like React, Vue.js and Angular) or by other systems (like <abbr title="Internet of Things">IoT</abbr> devices) communicating with it.
    
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  3. docs/ru/docs/deployment/docker.md

    Так, вы можете запустить одновременно **множество контейнеров** с базой данных, Python-приложением, веб-сервером, React-приложением и соединить их вместе через внутреннюю сеть.
    
    Все системы управления контейнерами (такие, как Docker или Kubernetes) имеют встроенные возможности для организации такого сетевого взаимодействия.
    
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  4. docs/de/docs/advanced/websockets.md

    ```console
    $ pip install websockets
    
    ---> 100%
    ```
    
    </div>
    
    ## WebSockets-Client
    
    ### In Produktion
    
    In Ihrem Produktionssystem haben Sie wahrscheinlich ein Frontend, das mit einem modernen Framework wie React, Vue.js oder Angular erstellt wurde.
    
    Und um über WebSockets mit Ihrem Backend zu kommunizieren, würden Sie wahrscheinlich die Werkzeuge Ihres Frontends verwenden.
    
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  5. docs/en/docs/tutorial/security/first-steps.md

    <img src="/img/tutorial/security/image02.png">
    
    !!! note
        It doesn't matter what you type in the form, it won't work yet. But we'll get there.
    
    This is of course not the frontend for the final users, but it's a great automatic tool to document interactively all your API.
    
    It can be used by the frontend team (that can also be yourself).
    
    It can be used by third party applications and systems.
    
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  6. docs/en/docs/tutorial/security/oauth2-jwt.md

    So, the thief won't be able to try to use that password in another system (as many users use the same password everywhere, this would be dangerous).
    
    ## Install `passlib`
    
    PassLib is a great Python package to handle password hashes.
    
    It supports many secure hashing algorithms and utilities to work with them.
    
    The recommended algorithm is "Bcrypt".
    
    So, install PassLib with Bcrypt:
    
    <div class="termy">
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  7. docs/ja/docs/alternatives.md

    Pythonのフレームワークの中で最もポピュラーで、広く信頼されています。Instagramのようなシステムの構築に使われています。
    
    リレーショナルデータベース (MySQLやPostgreSQLなど) と比較的強固に結合されているので、NoSQLデータベース (Couchbase、MongoDB、Cassandraなど) をメインに利用することは簡単ではありません。
    
    バックエンドでHTMLを生成するために作られたものであり、現代的なフロントエンド (ReactやVue.js、Angularなど) や、他のシステム (IoTデバイスなど) と通信するAPIを構築するために作られたものではありません。
    
    ### <a href="https://www.django-rest-framework.org/" class="external-link" target="_blank">Django REST Framework</a>
    
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  8. docs/en/docs/release-notes.md

        yield
        # Clean up the ML models and release the resources
        ml_models.clear()
    
    
    app = FastAPI(lifespan=lifespan)
    
    
    @app.get("/predict")
    async def predict(x: float):
        result = ml_models["answer_to_everything"](x)
        return {"result": result}
    ```
    
    **Note**: This is the recommended way going forward, instead of using `startup` and `shutdown` events.
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  9. docs/en/docs/project-generation.md

        - 🔍 [Pydantic](https://docs.pydantic.dev), used by FastAPI, for the data validation and settings management.
        - 💾 [PostgreSQL](https://www.postgresql.org) as the SQL database.
    - 🚀 [React](https://react.dev) for the frontend.
        - 💃 Using TypeScript, hooks, Vite, and other parts of a modern frontend stack.
        - 🎨 [Chakra UI](https://chakra-ui.com) for the frontend components.
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  10. docs_src/events/tutorial003.py

        yield
        # Clean up the ML models and release the resources
        ml_models.clear()
    
    
    app = FastAPI(lifespan=lifespan)
    
    
    @app.get("/predict")
    async def predict(x: float):
        result = ml_models["answer_to_everything"](x)
    Python
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