PM-LSH — A Fast and Accurate In-memory Framework for High-Dimensional ANNS

ICDE 2023 and TKDE 2023 papers introducing DB-LSH, a locality-sensitive hashing approach with query-based dynamic bucketing for efficient approximate nearest neighbor search.

SIGIR 2025 paper proposing a locality-sensitive indexing approach for graph-based approximate nearest neighbor search, combining LSH principles with graph structure for improved search accuracy.

VLDB 2021 paper introducing MP-RW-LSH, an efficient multi-probe locality-sensitive hashing solution for A1-norm (Manhattan distance) approximate nearest neighbor search.

VLDB 2015 paper introducing QALSH, a query-aware locality-sensitive hashing scheme that improves retrieval accuracy by dynamically adjusting hash functions based on query characteristics.

VLDB 2023 paper proposing LSH-APG, a method combining locality-sensitive hashing with adaptive proximity graphs for efficient index construction and approximate nearest neighbor search in high-dimensional spaces.

An in-memory, open-source, and free analytical database that speaks SQL, heavily based on vectorization. It can store and process vector embeddings using Array and List data types to enable vector search, bridging the gap between data engineering and AI workflows with fast response times.