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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.
An overview of the architectural components common to Vector Database Management Systems (VDBMS), which are designed to efficiently store, index, and query high-dimensional vector embeddings. This provides foundational knowledge for anyone interested in the internal workings of vector databases.
A research paper that proposes the first structured roadmap for testing Vector Database Management Systems (VDBMS), analyzing bugs, vulnerabilities, and test challenges unique to vector databases. It provides insights and future directions for improving the reliability and robustness of vector databases.
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.
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