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A scalable system for approximate nearest neighbor search at web-scale, relevant for implementing and understanding vector database infrastructure for high-dimensional data.
This paper introduces the HNSW algorithm, which is widely adopted in vector databases and search engines for its efficient and robust performance on high-dimensional data. HNSW is foundational in powering modern vector search systems.
An influential paper analyzing and improving approximate nearest neighbor search methods for high-dimensional data, highly relevant for developing and understanding vector databases.
BANG is a billion-scale approximate nearest neighbor search system optimized for single GPU execution, enabling high-performance vector search in vector database environments at massive scale.
A unified system designed for efficient multi-index vector search, directly addressing large-scale vector database performance and scalability challenges.
Product Quantization (PQ) is a technique for compressing high-dimensional vectors into compact codes, enabling efficient approximate nearest neighbor (ANN) search in vector databases. PQ reduces memory footprint and search time, making it a foundational algorithm for large-scale vector search systems.
A curated GitHub repository of research papers and technical blogs focused on vector search, approximate nearest neighbor search (ANN Search), and vector databases. This resource serves as a comprehensive directory for foundational and cutting-edge research, making it highly relevant for anyone building or exploring vector database technologies.
#LANNS: a web-scale approximate nearest neighbor lookup system
A scalable system for approximate nearest neighbor search at web-scale, relevant for implementing and understanding vector database infrastructure for high-dimensional data.