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 comprehensive research roadmap addressing the unique challenges of testing vector database management systems (VDBMS), including approaches for test input generation, oracle definition, and test evaluation tailored to vector databases. The work highlights the complexities of high-dimensional vector data, approximate search semantics, and integration with AI/LLM pipelines, making it a valuable resource for advancing reliability and trustworthiness in 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.

This survey paper provides an overview of the landscape, technologies, and applications of vector databases, making it a valuable resource for understanding the field.

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.

This ACL 2023 tutorial reviews retrieval-based language models, which often rely on vector databases and vector search systems to retrieve relevant context. The tutorial covers methods and applications central to the use of vector databases in modern NLP systems.