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Up to 200x Faster Inner Products and Vector Similarity — for Python, JavaScript, Rust, C, and Swift, supporting f64, f32, f16 real & complex, i8, and binary vectors using SIMD for both x86 AVX2 & AVX-...

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I mostly took inspiration from the Rust integration because Swift and Rust are similar

Recently we've been implementing some spacial distance functions and benchmarking them against some existing libraries, when testing with high dimensional data (1024 dims) we observe `simsimd` taking on average `619ns`...

invalid

All existing metrics imply dense vector representations. Dealing with very high-dimensional vectors, sparse representations may provide huge space-efficiency gains. The only operation that needs to be implemented for Jaccard, Hamming,...

Covariance isn't a distance function, as it can be negative. It however, is often used for similarity search over time-series and should be implemented in SimSIMD.

good first issue

any optimization on SIMD plan for BFloat16 datatype ? thanks

help wanted
good first issue

The changes move the inline C functions from simsimd.go to a new file, simsimd.c. This separation enhances code organization and readability. It also allows for better management of the C...

Initial [implementation](https://github.com/ashvardanian/SimSIMD/commit/7461e8acd2c71afefd6054b7034a4d21709db13f) is very slow, due to the costs of cGo calls.

low priority

Both Intel and Apple now have specialized AMX tiled matrix multiplication extensions. Both are tricky to use, but may result in substantial performance improvements. Potentially even for single vector dot-products...

Hello @ashvardanian ! Been meaning to use `simsimd.cdist` hamming distance with `np.uint8` arrays, but I'm having execution time issues (long execution times) as well as huge memory consumption. I'm trying...

It would be nice to have a `.pyi` integrated to the python package that would expose the types for IDEs auto-completion and type checkers.