A comprehensive academic survey that explores the architecture, storage, retrieval techniques, and challenges associated with vector databases. It categorizes algorithmic approaches to approximate nearest neighbor search (ANNS) and discusses how vector databases can be integrated with large language models, offering valuable insights and foundational knowledge for understanding and building vector database systems.

An influential paper analyzing and improving approximate nearest neighbor search methods for high-dimensional data, highly relevant for developing and understanding 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 research group focused on advancing the theory and practice of vector databases, providing resources, publications, and tools related to vector database technology.

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.

Cottontail DB is an open-source vector database for storing and searching high-dimensional data, with features geared towards research and production environments.