Benchmark results and tools by MyScale aimed at measuring the performance of vector databases in various search and retrieval tasks.

Maze is a web-scale video deduplication system that relies on large-scale approximate nearest neighbor vector search over video embeddings to detect and remove duplicate or near-duplicate videos efficiently. While not a general-purpose vector database, it represents a specialized, production-scale application of vector search infrastructure for multimedia content management.

A research group focused on advancing the theory and practice of vector databases, providing resources, publications, and tools related to vector database technology.

An academic paper providing a comprehensive overview of the architecture, empirical defects, and future research roadmap for Vector Database Management Systems (VDBMS). This resource is directly relevant for understanding the current state and challenges in building and testing reliable 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.