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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.
Registered: Sun Sep 07 07:19:17 UTC 2025 - Last Modified: Sun Aug 31 19:34:08 UTC 2025 - 16K bytes - Viewed (0) -
docs/en/docs/environment-variables.md
</div> //// ## Read env vars in Python { #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
Registered: Sun Sep 07 07:19:17 UTC 2025 - Last Modified: Sun Aug 31 09:15:41 UTC 2025 - 8.1K bytes - Viewed (0) -
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(...)`.
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docs/en/docs/deployment/https.md
Here's an example of how an HTTPS API could look like, step by step, paying attention mainly to the ideas important for developers. ### Domain Name { #domain-name } It would probably all start by you **acquiring** some **domain name**. Then, you would configure it in a DNS server (possibly your same cloud provider).
Registered: Sun Sep 07 07:19:17 UTC 2025 - Last Modified: Sun Aug 31 19:34:08 UTC 2025 - 14.3K bytes - Viewed (0) -
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 { #package-requirements } You would normally have the **package requirements** for your application in some file.
Registered: Sun Sep 07 07:19:17 UTC 2025 - Last Modified: Sun Aug 31 09:15:41 UTC 2025 - 29.5K bytes - Viewed (1) -
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.
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docs/en/docs/tutorial/query-params-str-validations.md
Instead, use the actual default value of the function parameter. Otherwise, it would be inconsistent. For example, this is not allowed: ```Python q: Annotated[str, Query(default="rick")] = "morty" ``` ...because it's not clear if the default value should be `"rick"` or `"morty"`. So, you would use (preferably): ```Python q: Annotated[str, Query()] = "rick" ```
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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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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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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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