A Brief Survey of Vector Databases

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.

A comprehensive research paper outlining the fundamental concepts, practical use-cases, and current challenges in the field of vector database management systems.

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.