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document.getElementsByTagName('main')[0].style.margin = '0 auto';You can use pgvector for the vector lookup and paradedb for bm25.
In this case, this was a project at $EMPLOYER in an existing codebase with colleagues who have never seen Haskell code, using Haskell would've been a major error in judgement.
Admittedly, INI is quite a simple format, hence I mention this as an anecdote.
[0] https://www.freedesktop.org/software/systemd/man/latest/syst...
[1] https://www.freedesktop.org/software/systemd/man/latest/syst...
[2] https://www.freedesktop.org/software/systemd/man/latest/syst...
[3] https://www.freedesktop.org/software/systemd/man/latest/syst...
IIRC, next thing on my TODO list was to add vectorization. Also (like OP) it uses log probabilities to avoid floating-point underflow.
A while back I wrote this https://mohamed.computer/posts/python-internals-cpython-byte..., perhaps it's interesting for people who use `dir` and wonder what some of the weird things that show up are.
SOTA for what? Isn't it just a vector store?
Moreover I am curious why you guys use bm25 over SPLADE?
Unfortunately, it seems like it's not working properly anymore. I just messaged Bret Victor and maybe he can get it back in working order or can reach out to someone.
> vision-based search for comprehensive document understanding
but it's not clear to me what this means, is it just vector embeddings for each image in every document via a CLIP-like model?
In addition, I'd be curious what's the rationale behind using the plethora of databases, given the docs on running it in production spins them all up, I assume they're all required, for instance I'd be curious on the trade-offs between using postgres with something like pg_search (for bm25 support, which vanilla postgres FTS doesn't have) vs using both postgres and ElasticSearch.
The docs are also very minimal, I'd have loved to see at least 1 example of usage.
[0] http://pllab.is.ocha.ac.jp/~asai/Black/
[1] https://www.lirmm.fr/~dony/enseig/MR/notes-etudes/Reflective...