Unum: Vector Search engine in a single file

• usearch

  21 1,756 9.8 C++

  Fast Open-Source Search & Clustering engine × for Vectors & 🔜 Strings × in C++, C, Python, JavaScript, Rust, Java, Objective-C, Swift, C#, GoLang, and Wolfram 🔍

  

We don't use BLAS. Why? BLAS helps with matrix-matrix multiplications, if you feel lazy and don't want to write the matrix tiling code manually.

They bring essentially nothing of value in vector-vector operations, as compilers can properly auto-vectorize simple dot products... Moreover, they generally only target single and double precision, while we often prefer half or quarter precision. All in all, meaningless dependency.

What do we use? I wrote a tiny package called SimSIMD. It's idea is to utilize less common SIMD instructions, especially in mixed-typed computations, that are hard for compilers to optimize. It was also a fun exercise to evaluate the performance of new SVE instruction on recent Arm CPUs, like the Graviton 3. You can find the code, the benchmarks, and the results in the repo: https://github.com/ashvardanian/simsimd

Still, even without SimSIMD, USearch seems to be one of the faster implementations of vector search. You can find the benchmarks in the first table here: https://github.com/unum-cloud/usearch#memory-efficiency-down...

• SimSIMD

  15 746 9.6 C

  Up to 200x Faster Inner Products and Vector Similarity — for Python, JavaScript, Rust, and C, supporting f64, f32, f16 real & complex, i8, and binary vectors using SIMD for both x86 AVX2 & AVX-512 and Arm NEON & SVE 📐

We don't use BLAS. Why? BLAS helps with matrix-matrix multiplications, if you feel lazy and don't want to write the matrix tiling code manually.

They bring essentially nothing of value in vector-vector operations, as compilers can properly auto-vectorize simple dot products... Moreover, they generally only target single and double precision, while we often prefer half or quarter precision. All in all, meaningless dependency.

What do we use? I wrote a tiny package called SimSIMD. It's idea is to utilize less common SIMD instructions, especially in mixed-typed computations, that are hard for compilers to optimize. It was also a fun exercise to evaluate the performance of new SVE instruction on recent Arm CPUs, like the Graviton 3. You can find the code, the benchmarks, and the results in the repo: https://github.com/ashvardanian/simsimd

Still, even without SimSIMD, USearch seems to be one of the faster implementations of vector search. You can find the benchmarks in the first table here: https://github.com/unum-cloud/usearch#memory-efficiency-down...

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• uform

  8 911 9.2 Python

  Pocket-Sized Multimodal AI for content understanding and generation across multilingual texts, images, and 🔜 video, up to 5x faster than OpenAI CLIP and LLaVA 🖼️ & 🖋️

  

Ouch! That’s fat! Which model is that?

We have built a few video-search system by now, using USearch and UForm for embedding. They are only 256 dims and you can concatenate a few from different parts of the video. Any chance it would help?

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

• ann-benchmarks

  51 4,658 7.7 Python

  Benchmarks of approximate nearest neighbor libraries in Python

• s2geometry

  26 2,199 5.8 C++

  Computational geometry and spatial indexing on the sphere

  

• faiss

  71 28,528 9.4 C++

  A library for efficient similarity search and clustering of dense vectors.

  

But FAISS has their own version ("FastScan") https://github.com/facebookresearch/faiss/wiki/Fast-accumula...

• kuzu

  11 1,068 9.9 C++

  Embeddable property graph database management system built for query speed and scalability. Implements Cypher.

  

• SaaSHub

  www.saashub.com featured

  SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

  

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