unstructured VS geppetto

Be careful with unstructured:

https://github.com/Unstructured-IO/unstructured/blob/d11c70c...

from: https://github.com/open-webui/open-webui/issues/687

I’ve been looking at this

https://freeling-user-manual.readthedocs.io/en/v4.2/modules/...

at the freeling library in general, also spaCy and NLTK. The chunking algorithms being used in the likes of LangChain are remarkably bad surprisingly.

There is also

https://github.com/Unstructured-IO/unstructured

But I don’t like it, can’t explain why yet.

My intuition is that 1st step is clean sentences and paragraphs and titles/labels/headers. Then probably an LLM can handle outlining and table of contents generation using a stripped down list of objects in the text.

BRIO/BERT summarization could also have a role of some type.

Those are my ideas so far.

Unstructured is the best one I’ve used so far: https://www.unstructured.io

https://www.unstructured.io/ can parse PDFs, then you can feed all of them to Claude, which has a 100k context window.

If the copy & pase method does not work: One approach is to use the functionality of Unstructured to parse the PDF. If need be, it can do OCR on the PDF too if you have Detectron2 installed. After conversion you would still have to save it as an excel file though.

I would check out https://github.com/Unstructured-IO/unstructured (what lang chain uses) or https://github.com/axa-group/Parsr (probably what unstructured copied to get their startup off the ground lol)

I'm heavily using https://github.com/go-go-golems/geppetto for my work, which has a CLI mode and TUI chat mode. It exposes prompt templates as command line verbs, which it can load from multiple "repositories".

I maintain a set of prompts for each repository I am working in (alongside custom "prompto" https://github.com/go-go-golems/prompto scripts that generate dynamic prompting context, i made quite a few for thirdparty libraries for example: https://github.com/go-go-golems/promptos ).

Here's some of the public prompts I use: https://github.com/go-go-golems/geppetto/tree/main/cmd/pinoc...

I am currently working on a declarative agent framework.

(author here): I am currently writing a book about programming with LLMs, I have absolutely put my money where my mouth is over the last year, and there is not doubt in my mind that we will see incredible tools in 2024.

Already the emergent tools and frameworks are impressive, and the fact that you can make them yours by adding a couple of prompting lines and really tailor them to your codebase is the killer factor.

My tooling ( https://github.com/go-go-golems/geppetto ) sucks ass UI wise, yet I get an incredible value out of it. It's hard to quantify as a 10X, because my code architecture has changed to accomodate the models.

In some ways, the trick to coding with LLMs is to... not have them produce code, but intermediate DSL representations. There's much more to it, thus the book.

All of these examples were built in a couple of hours altogether. By the end of the article, you will be able to build them too, with no code involved.

I don't bother manually writing any of this data munching / API wrapping / result validating code anymore. I had to build a server-to-server integration with Google Tag Manager recently. I literally copy pasted the webpage into a simple 3 line prompt and can now generate PHP classes, typescript interfaces, event log parsers, SQL serialization with a simple shell command.