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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.
A curated collection of papers and technical blogs focused on vector databases, semantic-based vector search, and approximate nearest neighbor search (ANN Search). These resources are essential for understanding and building large-scale information retrieval systems and vector databases.
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 scalable system for approximate nearest neighbor search at web-scale, relevant for implementing and understanding vector database infrastructure for high-dimensional data.
A curated GitHub repository collecting research papers and technical blogs on vector search, approximate nearest neighbor search (ANN Search), and vector databases. This resource is designed as a comprehensive directory for foundational and advanced research in vector search technologies, relevant for information retrieval, RAG (retrieval-augmented generation), recommendation systems, and more.
Curated Resource Lists
vector-search, research, papers, ann, vector-databases
https://github.com/matchyc/vector-search-papers