llama_parse VS kamal

I'm part of the team that build LlamaParse. It's net improvement compare to other PDF->Structured Text extractors (I build several in the past, includig https://github.com/axa-group/Parsr).

For character extraction, LlamaParse use a mixture of OCR / character extraction from the PDF (it's the only parser I'm aware of that address some of the buggy PDF font issues, check the 'text' mode to see raw document before reconstruction), use a mixture of heuristic and Machine learning models to reconstruct the document.

Once plug with a Recursive retrieval strategy, allow you to get Sota result on question answering over complexe text (see notebook: https://github.com/run-llama/llama_parse/blob/main/examples/...).

AMA

Guessing you're talking about https://kamal-deploy.org/ which looks interesting, though I tend to like reconciliation logic based systems ... but often only fired off imperatively with a plan/apply separation. So I shall be having a poke around anyway :)

Navigating the deployment landscape can be a daunting task for any developer, particularly those going it alone. PopaDex uses Kamal as its deployment solution, due to its straightforward and efficient nature. This tool is designed to facilitate the quick deployment of Rails applications across diverse environments, streamlining what can often be a complicated process. Kamal's appeal lies in its ability to demystify deployment, removing the hurdles commonly associated with Docker and app deployment in general. A couple of the main benefits of Kamal:

Or you can use [https://kamal-deploy.org/] and GitHub actions for automating at least deploys, but there are still will be issues that you will need to solve.

For homelab, Docker compose should be enough

For something more production oriented https://github.com/basecamp/kamal

Kamal is a wonderfully simple way to deploy your applications anywhere. It will also be included by default in Rails 8. Kamal is trivial, but I don’t recommend using it on your development machine.