Kannolo: Sweet And Smooth Approximate K-nearest Neighbors Search

Abstract

Approximate Nearest Neighbors (ANN) search is a crucial task in several applications like recommender systems and information retrieval. Current state-of-the-art ANN libraries, although being performance-oriented, often lack modularity and ease of use. This translates into them not being fully suitable for easy prototyping and testing of research ideas, an important feature to enable. We address these limitations by introducing kANNolo, a novel research-oriented ANN library written in Rust and explicitly designed to combine usability with performance effectively. kANNolo is the first ANN library that supports dense and sparse vector representations made available on top of different similarity measures, e.g., euclidean distance and inner product. Moreover, it also supports vector quantization techniques, e.g., Product Quantization, on top of the indexing strategies implemented. These functionalities are managed through Rust traits, allowing shared behaviors to be handled abstractly. Th

Stats

• citations4
• S2 citations—
• github stars55
• HF likes0
• heat score8.74
• arxiv keydelfino2025kannolo

Related papers

• Lorann: Low-rank Matrix Factorization For Approximate Nearest Neighbor Search (2024)2.26
• Approximate Nearest Neighbour Search On Dynamic Datasets: An Investigation (2024)0.00
• Ann-benchmarks: A Benchmarking Tool For Approximate Nearest Neighbor Algorithms (2018)14.73
• EFANNA : An Extremely Fast Approximate Nearest Neighbor Search Algorithm Based On Knn Graph (2016)0.00
• Parlayann: Scalable And Deterministic Parallel Graph-based Approximate Nearest Neighbor Search Algorithms (2023)10.35
• Automating Nearest Neighbor Search Configuration With Constrained Optimization (2023)0.00
• DEANN: Speeding Up Kernel-density Estimation Using Approximate Nearest Neighbor Search (2021)0.00
• A Memory-efficient Distributed Algorithm For Approximate Nearest Neighbour Search With Arbitrary Distances (2024)0.00