usearch VS jvector

Might have a look at this library:

https://github.com/unum-cloud/usearch

It does HNSW and there is a SQLite related project, though not quite the same thing.

Another worth mentioning in this thread is usearch, though not a separate algorithm, based on HNSW with a bunch of optimizations https://github.com/unum-cloud/usearch

I've used usearch successfully for a small project: https://github.com/unum-cloud/usearch/

The hardest (still missing) part of efficient cosine computation distance computation is picking a good epsilon for the `sqrt` calculation and avoiding "division by zero" problems.

We have an open issue about it in USearch and a related one in SimSIMD itself, so if you have any suggestions, please share your insights - they would impact millions of devices using the library (directly on servers and mobile, and through projects like ClickHouse and some of the Google repos): https://github.com/unum-cloud/usearch/issues/320

As you scale, you may benefit from these two projects I maintain, and the Big Tech uses :)

https://github.com/unum-cloud/usearch - for faster search

https://github.com/unum-cloud/uform - for cheaper multi-lingual multi-modal embeddings

> It doesn't appear to query CPUID

Yes, I'm actually looking for a good way to do it for other projects as well. I've looked into a couple more libs, and here is the best I've come up with so far: https://github.com/unum-cloud/usearch/blob/f942b6f334b31716f...

> Your substring routines have multiplicative worst case

Yes, that is true. It's a very simple stupid trick, just happens to work well for me :)

> It seems quite likely that your confirmation step

We have a different library internally at Unum, that avoids this shortcoming. It has a few thousand lines of C++ templates with SIMD intrinsics... and it's definitely more efficient, but the margins aren't always high. So I kept the pure C version with inlined functions as minimal and simple as possible.

> It would actually be possible to hook Stringzilla up to `memchr`'s benchmark suite if you were interested. :-)

Yes, that would be a fun thing to do! I haven't had time to look into `memchr` yet, but would expect great perf from your lib as well. For me the State of the Art is Intel HyperScan. Probably the most advanced SIMD library overall, not just for strings. I was very impressed with their perf ~5 years ago. But the repo is 200 K LOC... So get ready to invest a weekend :)

That said, I'm a bit slammed with work right now, including open-source. Hoping to ship a new major release in UCall this week, and a minor one in USearch :)

JVector builds on the ideas in DiskANN to provide state-of-the-art vector search for Java applications. I’ve used the JVector Bench driver to visualize how recall (search accuracy) degrades when searching for the top 100 neighbors in data sets created by different embedding models against a small sample of chunked Wikipedia articles. (The data sets are built using the open source Neighborhood Watch tool.) Perfect accuracy would be a recall of 1.0.

Nice to see people care about index construction time.

JVector scales linearly to at least 32 cores and may be the only graph-based vector index designed around nonblocking data structures (as opposed to fine-grained locks): https://github.com/jbellis/jvector/

JVector indexes the Sift1M dataset in under 19s on a 32 core aws box (m6i.16xl), compared to 50s for Lantern in the article.