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Amazing!

True, but similarly if your embeddings are any good they'll capture interesting associations between authors, topics and your search query. If you find any interesting author overlap results I'd be very interested!

One chunk embedded together

Exactly! A near property of the matryoshka embeddings is that you can compute a low dimension embedding similarity really fast and then refine afterwards.

That's a nice idea! Might take a look this weekend!

Thanks!

Looks cool! You can input either a search query or a paper URL on arxiv xplorer. You can even combine paper URLs to search for combinations of ideas by putting + or - before the URL, like `+ 2501.12948 + 1712.01815`

Sadly I couldn't find a public API for chemrxiv, but would be happy to be proven wrong!

Do they have a public API/dataset?

Sure! I first used openai embeddings on all the paper titles, abstracts and authors. When a user submits a search query, I embed the query, find the closest matching papers and return those results. Nothing too fancy involved!

I'm also maintaining a dataset of all the embeddings on kaggle if you want to use them yourself: https://www.kaggle.com/datasets/tomtum/openai-arxiv-embeddin...

Tensorflow also created Eager - their dynamic environment

Yeah I think it's important to be completely open about it since browser mining has a bad rep. Everyone trying it should know what happens as much as possible before just clicking 'mine'

Hey HN!

Would love to hear any feedback on this project! Positive or negative