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  1. fastapi/security/http.py

        For example, in an HTTP Bearer token scheme, the client will send a header
        like:
    
        ```
        Authorization: Bearer deadbeef12346
        ```
    
        In this case:
    
        * `scheme` will have the value `"Bearer"`
        * `credentials` will have the value `"deadbeef12346"`
        """
    
        scheme: Annotated[
            str,
            Doc(
                """
    Python
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  2. docs/en/docs/tutorial/response-model.md

        ```
    
    FastAPI will use this return type to:
    
    * **Validate** the returned data.
        * If the data is invalid (e.g. you are missing a field), it means that *your* app code is broken, not returning what it should, and it will return a server error instead of returning incorrect data. This way you and your clients can be certain that they will receive the data and the data shape expected.
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  3. docs/en/docs/advanced/generate-clients.md

    You will have inline errors for the data that you send:
    
    <img src="/img/tutorial/generate-clients/image04.png">
    
    The response object will also have autocompletion:
    
    <img src="/img/tutorial/generate-clients/image05.png">
    
    ## FastAPI App with Tags
    
    In many cases your FastAPI app will be bigger, and you will probably use tags to separate different groups of *path operations*.
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  4. docs/en/docs/contributing.md

    <div class="termy">
    
    ```console
    $ python -m venv env
    ```
    
    </div>
    
    That will create a directory `./env/` with the Python binaries, and then you will be able to install packages for that local environment.
    
    ### Activate the environment
    
    Activate the new environment with:
    
    === "Linux, macOS"
    
        <div class="termy">
    
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  5. fastapi/security/oauth2.py

                    Security scheme name.
    
                    It will be included in the generated OpenAPI (e.g. visible at `/docs`).
                    """
                ),
            ] = None,
            description: Annotated[
                Optional[str],
                Doc(
                    """
                    Security scheme description.
    
                    It will be included in the generated OpenAPI (e.g. visible at `/docs`).
    Python
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  6. docs/en/docs/advanced/behind-a-proxy.md

    ```JSON
    {
        "message": "Hello World",
        "root_path": "/api/v1"
    }
    ```
    
    So, it won't expect to be accessed at `http://127.0.0.1:8000/api/v1/app`.
    
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  7. docs/en/docs/tutorial/first-steps.md

    You will see the JSON response as:
    
    ```JSON
    {"message": "Hello World"}
    ```
    
    ### 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>.
    
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  8. docs/em/docs/contributing.md

    ๐Ÿšฅ ๐Ÿ‘† โŽ ๐ŸŽ โœณ, ๐Ÿ‘† ๐Ÿ’ช โŽ ๐Ÿ› ๏ธ โฎ๏ธ:
    
    <div class="termy">
    
    ```console
    $ typer --install-completion
    
    zsh completion installed in /home/user/.bashrc.
    Completion will take effect once you restart the terminal.
    ```
    
    </div>
    
    ### ๐Ÿ“ฑ &amp; ๐Ÿฉบ ๐ŸŽ ๐Ÿ•ฐ
    
    ๐Ÿšฅ ๐Ÿ‘† ๐Ÿƒ ๐Ÿ–ผ โฎ๏ธ, โœ…:
    
    <div class="termy">
    
    ```console
    $ uvicorn tutorial001:app --reload
    
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  9. docs/ja/docs/deployment/concepts.md

    * OSไธŠใง**ๅฎŸ่กŒ**ใ—ใฆใ„ใ‚‹็‰นๅฎšใฎใƒ—ใƒญใ‚ฐใƒฉใƒ 
        * ใ“ใ‚Œใฏใƒ•ใ‚กใ‚คใƒซใ‚„ใ‚ณใƒผใƒ‰ใ‚’ๆŒ‡ใ™ใฎใงใฏใชใใ€OSใซใ‚ˆใฃใฆ**ๅฎŸ่กŒ**ใ•ใ‚Œใ€็ฎก็†ใ•ใ‚Œใฆใ„ใ‚‹ใ‚‚ใฎใ‚’ๆŒ‡ใ—ใพใ™ใ€‚
    * ใฉใ‚“ใชใƒ—ใƒญใ‚ฐใƒฉใƒ ใ‚„ใ‚ณใƒผใƒ‰ใ‚‚ใ€ใใ‚ŒใŒ**ๅฎŸ่กŒใ•ใ‚Œใฆใ„ใ‚‹ใจใใซใ ใ‘ๆฉŸ่ƒฝ**ใ—ใพใ™ใ€‚ใคใพใ‚Šใ€**ใƒ—ใƒญใ‚ปใ‚นใจใ—ใฆๅฎŸ่กŒใ•ใ‚Œใฆใ„ใ‚‹ใจใใ ใ‘**ใงใ™ใ€‚
    * ใƒ—ใƒญใ‚ปใ‚นใฏใ€ใƒฆใƒผใ‚ถใƒผใซใ‚ใ‚‹ใ„ใฏOSใซใ‚ˆใฃใฆใ€ **็ต‚ไบ†**๏ผˆใ‚ใ‚‹ใ„ใฏ "kill"๏ผ‰ใ•ใ›ใ‚‹ใ“ใจใŒใงใใพใ™ใ€‚ใใฎๆ™‚็‚นใงใ€ใƒ—ใƒญใ‚ปใ‚นใฏๅฎŸ่กŒ/ๅฎŸ่กŒใ•ใ‚Œใ‚‹ใ“ใจใ‚’ๅœๆญขใ—ใ€ใใ‚Œไปฅ้™ใฏ**ไฝ•ใ‚‚ใงใใชใใชใ‚Šใพใ™**ใ€‚
    * ใ‚ณใƒณใƒ”ใƒฅใƒผใ‚ฟใงๅฎŸ่กŒใ•ใ‚Œใฆใ„ใ‚‹ๅ„ใ‚ขใƒ—ใƒชใ‚ฑใƒผใ‚ทใƒงใƒณใฏใ€ๅฎŸ่กŒไธญใฎใƒ—ใƒญใ‚ฐใƒฉใƒ ใ‚„ๅ„ใ‚ฆใ‚ฃใƒณใƒ‰ใ‚ฆใชใฉใ€ใใฎ่ƒŒๅพŒใซใ„ใใคใ‹ใฎใƒ—ใƒญใ‚ปใ‚นใ‚’ๆŒใฃใฆใ„ใพใ™ใ€‚ใใ—ใฆ้€šๅธธใ€ใ‚ณใƒณใƒ”ใƒฅใƒผใ‚ฟใŒ่ตทๅ‹•ใ—ใฆใ„ใ‚‹้–“ใ€**ๅคšใใฎใƒ—ใƒญใ‚ปใ‚นใŒ**ๅŒๆ™‚ใซๅฎŸ่กŒใ•ใ‚Œใฆใ„ใพใ™ใ€‚
    * **ๅŒใ˜ใƒ—ใƒญใ‚ฐใƒฉใƒ **ใฎ**่ค‡ๆ•ฐใฎใƒ—ใƒญใ‚ปใ‚น**ใŒๅŒๆ™‚ใซๅฎŸ่กŒใ•ใ‚Œใฆใ„ใ‚‹ใ“ใจใŒใ‚ใ‚Šใพใ™ใ€‚
    
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  10. docs/en/docs/deployment/concepts.md

    ### Server Memory
    
    For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.
    
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