unum-cloud/USearch

Features

• Approximate Nearest Neighbor Search - Ships a proximity-graph based indexing system for fast approximate nearest neighbor searches across massive datasets.
• Graph-Based Indexing - Organizes high-dimensional vectors into a graph structure to enable fast approximate nearest neighbor search via traversal.
• Vector Similarity Search - Implements a high-performance engine for finding nearest neighbors in high-dimensional datasets using SIMD-accelerated distance metrics.
• Proximity Graph Indexes - Uses a proximity-graph indexing system to enable fast retrieval across billions of vectors.
• Index Construction - Provides thread-safe concurrent insertion and pre-allocated memory management for constructing vector indices.
• Memory-Mapped Indexing - Maps binary index files directly into the virtual address space to enable fast loading and low RAM overhead.
• Serializable Indexes - Writes the proximity graph and metadata to a binary file and reads it back without requiring a full memory load.
• Similarity Search - Implements both approximate graph-based and exact SIMD-optimized similarity search methods.
• Similarity Search Engines - Provides a computational engine optimized for identifying nearest neighbors in high-dimensional spaces using advanced indexing.
• Vector Databases - Functions as a low-level storage and retrieval system for dense embeddings with quantization and memory-mapping.
• Vector Indexing - Provides tools for creating and managing high-dimensional vector indexes with support for binary serialization and memory-mapped storage.
• Vector Quantization - Supports storing vectors in various bit-depths from double precision to 1-bit binary to balance accuracy and memory usage.
• High-Dimensional Vector Compressors - Reduces the memory footprint of vector collections through quantization and precision casting.
• SIMD-Accelerated Arithmetic - Computes vector similarities using hardware-level SIMD instructions for high-throughput numerical processing.
• Vector Distance Kernels - Ships hardware-level SIMD acceleration for Euclidean, cosine, and Hamming distance kernels.
• Vector Distance Metrics - Provides SIMD-accelerated mathematical primitives for calculating distances between high-dimensional vectors.
• Thread-Safe Index Construction - Utilizes a thread-safe processing engine to run multiple additions and searches across different threads simultaneously.
• Billion-Scale Vector Search - Scales similarity search to billions of vectors using custom integer types and optimized memory management.
• Batch Processing - Allows adding multiple concatenated vectors in a single operation to increase indexing throughput.
• Brute-Force Search Algorithms - Performs an exact search by calculating distances across every vector in a dataset using parallel priority queues.
• Semantic Joins - Creates mappings between two large vector datasets using approximate similarity to perform fuzzy or semantic joins.
• Geospatial Search - Calculates real-world distances between spherical coordinates using Haversine metrics for location-based retrieval.
• Quantized Optimizers - Implements floating-point and integer quantization to balance search accuracy and memory consumption.
• Parallel Index Lookups - Supports simultaneous searching across multiple indices to handle workloads containing trillions of vectors.
• User-Defined Functions - Allows the integration of custom compiled functions or assembly code to implement specialized distance calculations.
• Vector Index Compression - Reduces memory footprint by storing vectors as half-precision floats, 8-bit integers, or packed binary formats.
• Batch Vector Queries - Enables simultaneous querying of multiple vectors using tensors to increase throughput for bulk workloads.
• Brute Force Linear Scans - Provides exact vector search via brute-force linear scans for guaranteed accuracy on smaller datasets.
• User-Defined Distance Metrics - Supports the integration of custom compiled functions or assembly code to implement specialized distance calculations.
• Vectorized Data Processing - Processes multiple query vectors simultaneously using flattened arrays to maximize throughput for bulk similarity searches.
• Performance Benchmarks - Includes tools for tracking search efficiency and operations per second to analyze index performance.
• Concurrent Index Construction - Uses a multi-threaded execution engine to distribute vector additions and index construction across multiple CPU cores.
• Search Accuracy Evaluation - Measures index quality by comparing approximate results against exact ground-truth searches using recall metrics.
• Machine Learning - Fast search and clustering for vectors.
• Database Systems - Similarity search engine for vectors and strings.

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