I worked on an in-house version of this feature for my employer (turning files into LLM-friendly text). After reading the source code, I can say this is a pretty reasonable implementation of this type of thing. But I would avoid using it for images, since the LLM providers let you just pass images directly, and I would also avoid using it for spreadsheets, since LLMs are very bad at interpreting Markdown tables.
There are a lot of random startups and open source projects who try to make this space sound fancy, but I really hope the end state is a simple project like this, easy to understand and easy to deploy.
I do wish it had a knob to turn for "how much processing do you want me to do." For PDF specifically, you either have to get a crappy version of the plain text using heuristics in a way that is very sensitive to how the PDF is exported, or you have to go full OCR, and it's annoying when a project locks you into one or the other. I'm also not sure I'd want to use the speech-to-text features here since they might have very different performance characteristics than the text-to-text stuff.
There are a lot of random startups and open source projects who try to make this space sound fancy, but I really hope the end state is a simple project like this, easy to understand and easy to deploy.
I do wish it had a knob to turn for "how much processing do you want me to do." For PDF specifically, you either have to get a crappy version of the plain text using heuristics in a way that is very sensitive to how the PDF is exported, or you have to go full OCR, and it's annoying when a project locks you into one or the other. I'm also not sure I'd want to use the speech-to-text features here since they might have very different performance characteristics than the text-to-text stuff.