vault-ai VS paper-qa

There's this GitHub repo for Pinecone Vector with custom knowledge base: VaultAI. But I'm sure the costs would be exorbitant at scale. Basically trains it on specific files, but the API is expensive as expected. Edit: I didn't read and thought you were talking about training your own, sorry. But I'll leave the second paragraph up anyways lol. Someone mentioned LLaMA and another Falcon, the latter of which I hadn't heard of but which looks good too.

Check out the instructions readme here! You may need a little bit of command line know-how but chatgpt can help guide you if you provide it the contents of the readme

Yes, this use-case is a perfect fit actually – This deals very well with any type of manual with lots of human readable text (as opposed to charts or code). It is also better at answering more specific questions, so the example you gave regarding diagnosing engine issues is a really good match for what this is capable of. If you want to try it out you can check out the deployed version of the code here: https://vault.pash.city

Have you tried Vault AI? Saw it pop up on a couple of other Reddits!

OP Vault ChatGPT: Give ChatGPT long-term memory using the OP Stack (https://github.com/pashpashpash/vault-ai)

https://github.com/Future-House/paper-qa?tab=readme-ov-file#... :

> PaperQA2 is engineered to be the best agentic RAG model for working with scientific papers.

> [ Semantic Scholar, CrossRef, ]

paperqa-zotero: https://github.com/lejacobroy/paperqa-zotero

The Oracle of Zotero is a fork of paperqa-zotero fork FAISS and langchain:

Great article, I’ve had similar findings! LLM based “document-chunk” ranking is a core feature of PaperQA2 (https://github.com/Future-House/paper-qa) and part of why it works so well for scientific Q&A compared to traditional embedding-ranking based RAG systems.

Love the name!

OCR was discussed here lately several times (https://news.ycombinator.com/item?id=42952605 and https://news.ycombinator.com/item?id=42871143), and some cool projects like https://github.com/Future-House/paper-qa?tab=readme-ov-file#... are using PyMuPDF. My experience with Tesseract is pretty sad, it's usually not good enough and modern LLMs are better.

This is awesome! If you’re interested, you could add a search tool client for your backend in paper-qa (https://github.com/Future-House/paper-qa). Then paper-qa users would be able to use your semantic search as part of its workflow.

We used an open-source AI RAG library, PaperQA2 (https://github.com/Future-House/paper-qa), to generate well cited articles for every gene in the human genome, ~15k of which had no existing prior articles. In terms of factuality, we tested our generated claims against the same gene's human written Wikipedia article in a blinded study evaluated by PhD biologists. Our system's articles were more precise on average than cited claims from existing articles. (https://paper.wikicrow.ai)

The system is scalable in that we can comfortably generate all 19.2k gene articles once per week, building a repository of cited articles that automatically syncs with all published literature.

We're sharing some experiments in designing RAG systems via the open source PaperQA2 system (https://github.com/Future-House/paper-qa). PaperQA2's design is interesting because it isn't concerned with cost, so it uses expensive operations like agentic tool calling and LLM based re-ranking and contextual summarization for each query.

Even though the costs are higher, we see that the RAG accuracy gains (in question-answering tasks) are worth it. Including LLM chunk re-ranking and contextual summaries in your RAG flow also makes the system robust to changes in chunk sizes, parsing oddities and embedding model shortcomings. It's one of the largest drivers of performance we could find.