OpenLLM VS paperai

OpenLLM is a powerful platform that empowers developers to leverage the potential of open-source large language models (LLMs). It is like a Swiss Army knife for LLMs. It's a set of tools that helps developers overcome these deployment hurdles.

13. OpenLLM by BentoML | Github | tutorial

for those who care: i am developing a cljs frontend for OpenLLM right now: https://github.com/bentoml/OpenLLM/pull/89

Run inference on any LLM using OpenLLM.

... thought I had seen this but that was paper-ai; https://news.ycombinator.com/item?id=39363115 : gh topic: pdfgpt

neuml/paperai has a YAML report definition schema: https://github.com/neuml/paperai : > Semantic search and workflows for medical/scientific paper :

  python -m paperai.report report.yml 50 md 

Nice project!

I've spent quite a lot of time in the medical/scientific literature space. With regards to LLMs, specifically RAG, how the data is chunked is quite important. With that, I have a couple projects that might be beneficial additions.

paperetl (https://github.com/neuml/paperetl) - supports parsing arXiv, PubMed and integrates with GROBID to handle parsing metadata and text from arbitrary papers.

paperai (https://github.com/neuml/paperai) - builds embeddings databases of medical/scientific papers. Supports LLM prompting, semantic workflows and vector search. Built with txtai (https://github.com/neuml/txtai).

While arbitrary chunking/splitting can work, I've found that integrating parsing that has knowledge of medical/scientific paper structure increases the overall accuracy and experience of downstream applications.

https://github.com/neuml/paperai

Disclaimer: I am the author of both

paperai | paperetl