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

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

A curated directory of resources, tools, tutorials, and libraries dedicated to vector databases, focusing on efficient data retrieval, similarity search, and machine learning applications.

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 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.