Graph-based Methods

AiSAQ is an all-in-storage approximate nearest neighbor search system that uses product quantization to enable DRAM-free vector similarity search, serving as a specialized vector search/indexing approach for large-scale information retrieval.

Adanns is a framework for adaptive semantic search, focusing on efficient and scalable similarity search in high-dimensional vector spaces. Its relevance to 'Awesome Vector Databases' lies in its support for advanced vector search techniques suitable for AI and machine learning applications.

Reconfigurable Inverted Index (Rii) is a research project and open-source library for approximate nearest neighbor and similarity search over high-dimensional vectors. It focuses on flexible, reconfigurable inverted index structures that support efficient vector search, making it directly relevant as a vector-search engine component for AI and multimedia retrieval applications.

A database that incorporates neuro-symbolic AI and offers a managed service (AllegroGraph Cloud) for neuro-symbolic AI knowledge graphs, indicating its relevance to advanced AI applications, likely including vector capabilities.

Denser Retriever is a vector-based retrieval system designed for efficient similarity search and information access in AI and ML workloads.

HVS is a graph-based index structure leveraging Voronoi diagrams for approximate nearest neighbor search in high-dimensional vector spaces. It is directly relevant to vector databases as it provides efficient similarity search capabilities for large-scale vector data.