usearch-2-24-0

name	usearch-2-24-0
description	High-performance single-file similarity search engine for vectors using HNSW with user-defined metrics, quantization, and multi-language bindings (Python, C++, Rust, JavaScript, Go, and more). Use when building vector search applications, implementing ANN search, performing semantic search, or requiring faster alternatives to FAISS.
license	MIT
author	Tangled <[e-mail]>
version	0.1.0
tags	["vector-search","hnsw","similarity-search","approximate-nearest-neighbors","embedding-search","faiss-alternative","quantization","clustering","semantic-search"]
category	machine-learning
external_references	["https://github.com/unum-cloud/usearch"]

USearch 2.24

Overview

USearch is a smaller and faster single-file similarity search and clustering engine for vectors. It implements the HNSW (Hierarchical Navigable Small World) algorithm with SIMD-optimized distance kernels from NumKong, supporting over 100 distance functions across x86 and ARM architectures.

Key characteristics:

• Single-header C++11 library — ~3 K SLOC vs FAISS's 84 K SLOC
• 10x faster indexing than FAISS on large datasets
• 10 language bindings — Python, C++, Rust, JavaScript, Java, Go, C, Swift, C#, Objective-C, Wolfram
• No required dependencies — unlike FAISS which needs BLAS and OpenMP
• User-defined metrics with JIT compilation support
• Hardware-agnostic quantization — f64, f32, bf16, f16, e5m2, e4m3, e3m2, e2m3, u8, i8, b1
• Disk-based serving — view large indexes from disk without loading into RAM (up to 20x cost reduction on AWS)
• 40-bit key support — address 4B+ vectors with 37.5% less memory than 64-bit keys
• Thread-safe concurrent index construction and search

Trusted by ClickHouse, DuckDB, ScyllaDB, TiDB, YugaByte, MemGraph, Google (UniSim), LangChain, Microsoft Semantic Kernel, and others.

When to Use

• Building semantic search applications with embedding vectors
• Implementing approximate nearest neighbors (ANN) search at scale
• Replacing FAISS with a lighter, faster alternative
• Needing custom distance functions beyond cosine and L2
• Performing molecular similarity matching with binary fingerprints (Tanimoto/Sorensen)
• Geospatial indexing with Haversine or Vincenty distance
• Clustering large vector datasets without standalone libraries
• Memory-constrained deployments requiring quantization (bf16, i8, b1)
• Serving indexes from disk to reduce RAM costs

Core Concepts

HNSW Algorithm

USearch uses Hierarchical Navigable Small World graphs for approximate nearest neighbor search. The graph has multiple layers — higher layers enable long-range navigation, lower layers provide fine-grained local search. Key parameters control the trade-off between index quality and speed: