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

    And the proxy would be **"stripping"** the **path prefix** on the fly before transmitting the request to the app server (probably Uvicorn via FastAPI CLI), keeping your application convinced that it is being served at `/app`, so that you don't have to update all your code to include the prefix `/api/v1`.
    
    Up to here, everything would work as normally.
    
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  2. docs/en/docs/environment-variables.md

    Hello Wade Wilson
    ```
    
    </div>
    
    ////
    
    ## Read env vars in Python
    
    You could also create environment variables **outside** of Python, in the terminal (or with any other method), and then **read them in Python**.
    
    For example you could have a file `main.py` with:
    
    ```Python hl_lines="3"
    import os
    
    name = os.getenv("MY_NAME", "World")
    print(f"Hello {name} from Python")
    ```
    
    /// tip
    
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  3. docs/en/docs/deployment/concepts.md

    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.
    
    Registered: Sun Nov 03 07:19:11 UTC 2024
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  4. docs/en/docs/deployment/docker.md

    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.
    
    Registered: Sun Nov 03 07:19:11 UTC 2024
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  5. docs/en/docs/advanced/generate-clients.md

    </div>
    
    #### Generate Client Code
    
    To generate the client code you can use the command line application `openapi-ts` that would now be installed.
    
    Because it is installed in the local project, you probably wouldn't be able to call that command directly, but you would put it on your `package.json` file.
    
    It could look like this:
    
    ```JSON  hl_lines="7"
    {
      "name": "frontend-app",
      "version": "1.0.0",
      "description": "",
    Registered: Sun Nov 03 07:19:11 UTC 2024
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  6. docs/en/docs/virtual-environments.md

    After that, you would need to **install** FastAPI and any other **packages** you want to use.
    
    To install packages you would normally use the `pip` command that comes with Python (or similar alternatives).
    
    Nevertheless, if you just use `pip` directly, the packages would be installed in your **global Python environment** (the global installation of Python).
    
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  7. docs/en/docs/alternatives.md

    The way you use it is very simple. For example, to do a `GET` request, you would write:
    
    ```Python
    response = requests.get("http://example.com/some/url")
    ```
    
    The FastAPI counterpart API *path operation* could look like:
    
    ```Python hl_lines="1"
    @app.get("/some/url")
    def read_url():
        return {"message": "Hello World"}
    ```
    
    See the similarities in `requests.get(...)` and `@app.get(...)`.
    
    Registered: Sun Nov 03 07:19:11 UTC 2024
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  8. impl/maven-core/plugin-manager.txt

    h4. Working with POMs during development
    
    Registered: Sun Nov 03 03:35:11 UTC 2024
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  9. docs/en/docs/async.md

    You could have turns as in the burgers example, first the living room, then the kitchen, but as you are not waiting 🕙 for anything, just cleaning and cleaning, the turns wouldn't affect anything.
    
    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.
    
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  10. docs/en/docs/tutorial/response-model.md

    For example, you could want to **return a dictionary** or a database object, but **declare it as a Pydantic model**. This way the Pydantic model would do all the data documentation, validation, etc. for the object that you returned (e.g. a dictionary or database object).
    
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