| 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 <noreply@tangledgroup.com> |
| 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:
