Show HN: Talk to any ArXiv paper just by changing the URL (github.com)

I spent forever looking at various PDF parsing solutions like Unstructured, and eventually stumbled across GROBID, which was an absolute perfect fit since it's entirely made for scientific papers and has header/section level segmentation capabilities (splitting the paper into Abstract, Introduction, References, etc.) It's lightweight and fast too!

I've spent the last couple weeks diving into various PDF parsing solutions for scientific documents. GROBID is pretty cool, but it made some mistakes when trying to parse (I think arxiv) papers which removed some of the text.

Even though it gave a lot of great structured options, missing even a single sentence was unforgivable to me. I went with Nougat instead, for arxiv papers.

(Also check out Marker (mentioned on hn in the last month) for pretty high fidelity paper conversion to markdown. Does reasonable job with equations too.)

I wonder if they knew that they could get html versions of the paper by just changing the link from ...arxiv.. to ar5iv..

Yes! I'll set up talk2biorxiv.org very soon as it would be simple to port over. I also plan on making the underlying research PDF RAG framework available as an independent module

Thank you :). I updated the README to have some more explanation of the steps.

The chunking algorithm chunks by logical section (intro, abstract, authors, etc.) and also utilizes recursive subdivision chunking (chunk at 512 characters, then 256, then 128...). It is quite naive still but it works OK for now. An improvement would perhaps involve more advanced techniques like knowledge graph precomputation.

Reranking works by instead of embedding each text chunk as a vector and performing cosine similarity nearest neighbor search, you use a Cross-Encoder model that compares two texts and outputs a similarity score. Specifically, I chose Cohere's Reranker that specializes in comparing Query and Answer chunk pairs.

CERN circa 1994.

It bugs me cause it's kinda true but kinda misleading, I don't know if casual web users realize it's a whole different domain. Sometimes it's not important, sometimes it is.

The oldest instance of it that I know is putting something like download before or after the youtube domain. This must have been 2008±2. I very much doubt that's the first instance ever but I wasn't around online in the 90s (aside from on my grandma's machine who didn't know her computer had a web browser, but that wasn't very conscious, just a neutral net (young me) clicking buttons to see the effect)

It works well with bookmarklets. This swizzles between arxiv.org <-> www.talk2arxiv.org. I've now added it to my Favorites bar, next to arxiv.org/abs <-> arxiv.org/pdf and twitter.org <-> nitter.net. Thanks for the service!

javascript:((u,a,b,c)=%3Ewindow.location.href=u.match(a)?u.replace(a,b):u.startsWith(b)?u.replace(b,c):u)(window.location.href,/https:\/\/arxiv\.org\/(abs|pdf)\//,'https://www.talk2arxiv.org/pdf/','https://arxiv.org/pdf/')

nyud.net (coral cdn) was my first experience with this pattern... back from the slashdot days in the 00s.

I use this on mitta.us/ to save pages. Got the idea from saved.io/, which does something similar.

Thank you! Thanks for pointing that out, since the underlying RAG is rather naive (simple embedding cosine similarity lookup, as opposed to knowledge graph / advanced techniques), I opted to embed both "small" (512 character and below) chunks as well as entire section chunks (embedding the entire introduction) in order to support questions such as "Please summarize the introduction". Since I also use 5 chunks for each context, I suspect this can add up to a massive amount on papers with huge sections.

Oops sorry for the miscommunication, actually you don't need to enter an API key for now. Feel free to just try it out!

Definitely I'll move to the LaTeX source code instead of a PDF backend since that allows better support for non textual data that gets poorly scraped by GROBID. That is a really cool development I didn't know about, also theres https://ar5iv.labs.arxiv.org/ which already has most arXiv papers as HTML documents. I chose GROBID because they not only parse the PDF but organize the text into logical sections for me (Intro, abstract, references) which I didn't want to manually do with heuristics that I'd have to devise.

Interesting thought. I then wondered if perhaps we could introduce some kind of mechanism like a token that is authorized to spend a certain amount, and then wondered if we could take that concept to the extreme and have one key with some money loaded that users could put into websites. it was at this point I realised I'd just invented the credit card.

ideally we do something like Oauth eventually.

All while calling the solution open source.

Probably with https://github.com/BerriAI/litellm

There is a cache! You hit a new PDF but at least you will not have to wait for that one again ;)

The questions would come from reading a paper in particular. Have a question? Ask away. That's how I'd use it personally.

"What's the gist of this thing?"

"I'm building xyz app, how could this be applicable?"

"I want to know if x or y, what does this paper say, or does it even apply?"

"Implement the pseudocode algorithm in python"

"Can you help me understand section X, the wording is tricky"

"Doesn't this suffer from {flaw}?"

"How does the paper address x?"

No, an AI will not give 100% perfect answers all of the time. You know who else? Humans. You already have mechanisms to deal with unreliability, so please save yourself time and use AI to be more efficient.

Presumably you're reading the paper for a reason. Take why you're reading the paper and form it into a question.

I struggled with the same problem. What's interesting with LLMs, is that they will augment the data in the paper with their general knowledge. So you can talk to them like you would with a colleague or mentor (but without the shame of asking dumb questions).

If you want a generalized version of something similar, try this: https://github.com/MittaAI/mitta-community/tree/main/cookboo...

The query pipeline isn't that sophisticated, but it could be altered to do page reference and use keyterms first to filter, instead of doing the vector similarity on all data.

One thing with MittaAI is that it doesn't do UI interfaces. It expects you to handle those bits.

Thank you so much, yes I will have that up soon as well

Ah, I figured the dimension was 1024. Assuming cosine for the metric.