iRangeGraph is an ANN indexing approach and accompanying implementation for range-filtering nearest neighbor search. It provides a specialized graph-based index that supports vector similarity search under range constraints, making it directly useful as a component or reference implementation for advanced vector database indexing and retrieval.

NSG is an approximate nearest neighbor search algorithm based on a sparse navigable graph structure designed for high-dimensional vector similarity search. The reference implementation provides a graph-based ANN index that can be integrated into custom vector retrieval systems.

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.

RTNN is a research prototype system and codebase that accelerates high-dimensional nearest neighbor search using hardware ray tracing units on modern GPUs. It targets vector similarity search workloads common in AI applications, exploring ray-tracing hardware as an alternative acceleration path to traditional CPU- or CUDA-based ANN indexes.

KGraph is an open-source library for fast approximate nearest neighbor search in high-dimensional vector spaces, applicable to vector database solutions.

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.