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  1. docs/en/docs/advanced/behind-a-proxy.md

    The `root_path` is used to handle these specific cases.
    
    And it's also used internally when mounting sub-applications.
    
    ## Proxy with a stripped path prefix
    
    Having a proxy with a stripped path prefix, in this case, means that you could declare a path at `/app` in your code, but then, you add a layer on top (the proxy) that would put your **FastAPI** application under a path like `/api/v1`.
    
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  2. docs/en/docs/tutorial/first-steps.md

    This schema definition includes your API paths, the possible parameters they take, etc.
    
    #### Data "schema"
    
    The term "schema" might also refer to the shape of some data, like a JSON content.
    
    In that case, it would mean the JSON attributes, and data types they have, etc.
    
    #### OpenAPI and JSON Schema
    
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  3. docs/en/docs/deployment/concepts.md

    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**.
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  4. docs/en/docs/release-notes.md

    This means that now, if you set a value in a context variable before `yield`, the value would still be available after `yield` (as you would intuitively expect). And it also means that you can reset the context variable with a token afterwards.
    
    For example, this works correctly now:
    
    ```Python
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  5. docs/en/docs/advanced/openapi-webhooks.md

    # OpenAPI Webhooks
    
    There are cases where you want to tell your API **users** that your app could call *their* app (sending a request) with some data, normally to **notify** of some type of **event**.
    
    This means that instead of the normal process of your users sending requests to your API, it's **your API** (or your app) that could **send requests to their system** (to their API, their app).
    
    This is normally called a **webhook**.
    
    ## Webhooks steps
    
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  6. docs/en/docs/features.md

    second_user_data = {
        "id": 4,
        "name": "Mary",
        "joined": "2018-11-30",
    }
    
    my_second_user: User = User(**second_user_data)
    ```
    
    !!! info
        `**second_user_data` means:
    
        Pass the keys and values of the `second_user_data` dict directly as key-value arguments, equivalent to: `User(id=4, name="Mary", joined="2018-11-30")`
    
    ### Editor support
    
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  7. docs/en/docs/fastapi-cli.md

    ## `fastapi run`
    
    When you run `fastapi run`, it will run on production mode by default.
    
    It will have **auto-reload disabled** by default.
    
    It will listen on the IP address `0.0.0.0`, which means all the available IP addresses, this way it will be publicly accessible to anyone that can communicate with the machine. This is how you would normally run it in production, for example, in a container.
    
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  8. README.md

    <a href="https://training.talkpython.fm/fastapi-courses" target="_blank" title="FastAPI video courses on demand from people you trust"><img src="https://fastapi.tiangolo.com/img/sponsors/talkpython-v2.jpg"></a>
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  9. docs/en/docs/advanced/settings.md

    These environment variables can only handle text strings, as they are external to Python and have to be compatible with other programs and the rest of the system (and even with different operating systems, as Linux, Windows, macOS).
    
    That means that any value read in Python from an environment variable will be a `str`, and any conversion to a different type or validation has to be done in code.
    
    ## Pydantic `Settings`
    
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  10. docs/en/docs/deployment/docker.md

    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.
    
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