AI: fast embeddings search with SIMD (JS, Python, ...)

usearch - npm
Smaller & Faster Single-File
Similarity Search & Clustering Engine for Vectors


C++ 11 • Python 3 • JavaScript • Java • Rust • C 99 • Objective-C • Swift • C# • GoLang • Wolfram
Linux • MacOS • Windows • iOS • Android • WebAssembly • SQLite3


✅ 10x faster HNSW implementation than FAISS.
✅ Simple and extensible single C++11 header library.
✅ Trusted by giants like Google and DBs like ClickHouse & DuckDB.
✅ SIMD-optimized and user-defined metrics with JIT compilation.
✅ Hardware-agnostic f16 & i8 - half-precision & quarter-precision support.

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

Unum · USearch 2.16.2 documentation @GitHub

usearch/javascript at main · unum-cloud/usearch

USearch is a high-performance library for building and querying vector search indexes, optimized for Node.js and WASM environments.

Alternative

@sroussey/simsimd - npm

Hardware-Accelerated Similarity Metrics and Distance Functions

• Zero-dependency header-only C 99 library with bindings for Python and JavaScript.
• Targets ARM NEON, SVE, x86 AVX2, AVX-512 (VNNI, FP16) hardware backends.
• Zero-copy compatible with NumPy, PyTorch, TensorFlow, and other tensors.
• Handles f64 double-, f32 single-, and f16 half-precision, i8 integral, and binary vectors.
• Up to 200x faster than scipy.spatial.distance and numpy.inner.
• Used in USearch and several DBMS products.

ashvardanian/SimSIMD: Up to 200x Faster Dot Products & Similarity Metrics — for Python, Rust, C, JS, and Swift, supporting f64, f32, f16 real & complex, i8, and bit vectors using SIMD for both AVX2, AVX-512, NEON, SVE, & SVE2 📐 @GitHub

JavaScript

npm i @sroussey/simsimd

import { dot } from '@sroussey/simsimd';

const vector1 = Float32Array.from([1, 2, 3]);

const vector2 = Float32Array.from([4, 5, 6]);

const dotProduct = dot(vector1, vector2); 

// Calculate cosine similarity using the dot product

const cosineSimilarity = dotProduct / (magnitude(vector1) * magnitude(vector2));

Python

@sroussey/simsimd - npm

pip install simsimd

import simsimd
import numpy as np
vec1 = np.random.randn(1536).astype(np.float32)
vec2 = np.random.randn(1536).astype(np.float32)
dist = simsimd.cosine(vec1, vec2)