NumKong

Okay, bummer. No support for quantized datatypes yet and from the docs I cannot see anything that mentions fast brute force search. I personally don't need an index. But I see that https://github.com/unum-cloud/usearch which is used by duckdb-vss in turn uses https://github.com/ashvardanian/simsimd which should make a really fast exact vector similarity search possible. Am I missing something here?

Here's an explanation from one of my repos: <https://github.com/ashvardanian/simsimd?tab=readme-ov-file#d...>

You can always get more accuracy by expanding those 3 lines to handle more of the Taylor components… but it’s important to remember that this is still educational material. You can find more complete examples in my SimSIMD (https://github.com/ashvardanian/SimSIMD), but they also often assume that at a certain part of a kernel, a floating point number is guaranteed to be in a certain range. This can greatly simplify the implementation for kernels like Atan2. For general-purpose inputs, go to SLEEF (https://sleef.org). Just remember that every large, complicated optimization starts with a small example.

… except for byte-level processing, variable-length codecs, or mixed-precision numerics. That never works with autovectorization and can’t be solved with general-purpose SIMD wrappers. For me the solution was to implement those manually, and even at a scale of just 2 libraries I’ve eneded up with somewhat different project layouts & dispatch mechanisms: https://github.com/ashvardanian/SimSIMD , https://github.com/ashvardanian/StringZilla One big family not covered there, is sparse data-strictures and related algorithms. I’ve only started integrating scatter/gather in AVX-512 and SVE, and on synthetic benchmarks both look promising: https://github.com/ashvardanian/less_slow.cpp/releases/tag/v... Those should probably unlock a much wider set of applications for SIMD, but designing libraries for those may benefit from yet another project str

It’s a nice post, but “using array methods” probably shouldn’t be placed in the “Efficient Implementation” section. As often happens with high-level languages, a single plain old loop is faster than three array methods. Similarly, if you plan to query those vectors in search, you should consider continuous `TypedArray` types and smaller scalars than the double precision `number`. I know very little about JS, but some of the amazing HackerNews community members have previously helped port SimSIMD to JavaScript (https://github.com/ashvardanian/SimSIMD), and I wrote a blog post covering some of those JS/TS-specifics, NumJS, and MathJS in 2023 (https://ashvardanian.com/posts/javascript-ai-vector-search/). Hopefully, it should help unlock another 10-100x in performance.

USearch author here :) The engine supports arbitrary predicates for C, C++, and Rust users. In higher level languages it’s hard to combine callbacks and concurrent state management. In terms of scalability and efficiency, the only tool I’ve seen coming close is Nvidia’s cuVS if you have GPUs available. FAISS HNSW implementation can easily be 10x slower and most commercial & venture-backed alternatives are even slower: https://www.unum.cloud/blog/2023-11-07-scaling-vector-search... In this use-case, I believe SimSIMD raw kernels may be a better choice. Just replace NumPy and enjoy speedups. It provides hundreds of hand-written SIMD kernels for all kinds of vector-vector operations for AVX, AVX-512, NEON, and SVE across F64, F32, BF16, F16, I8, and binary vectors, mostly operating in mixed precision to avoid overflow and instability: https: