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Vector Database

HVS is an open-source, self-hosted graph-based ANN index using Voronoi diagrams for high-dimensional vector similarity search. Key features include hierarchical graphs, efficient large-scale queries, and free use. Suited for RAG embedding storage/search prototypes, cost-free self-hosting vs Pinecone.

A 2026 research paper presenting graph-based algorithms for diverse similarity search, where results must be both similar to the query and diverse from each other. This addresses the common problem of redundant results in traditional similarity search.

A 2026 research paper on streaming approximate nearest neighbor search with in-place graph index updates. The approach enables real-time index modifications without expensive rebuilds, crucial for dynamic datasets.

Joint Attribute Graphs for Filtered Nearest Neighbor Search, a research paper that addresses the challenge of combining vector similarity search with attribute filtering. JAG presents a novel index structure that efficiently handles filtered ANN queries common in real-world applications.

Research on using Monte Carlo Tree Search algorithms for optimizing vector index construction and search strategies. Explores adaptive decision-making during graph building and query routing.

An ultra-scalable graph-based nearest neighbor indexing algorithm that builds state-of-the-art indexes up to 11.6× faster than Vamana (DiskANN) and 12.9× faster than HNSW. PiPNN uses HashPrune, a novel online pruning algorithm that enables efficient billion-scale index construction on a single machine.

Research on maintaining balanced, high-quality graph indexes while streaming data arrives continuously. Addresses the challenge of index degradation over time with incremental updates.

Graph-based approximate nearest neighbor search library built on LMDB key-value storage. The successor to Arroy, Hannoy combines graph-based ANN algorithms with production-ready persistent storage for vector databases.

A graph-based approximate nearest neighbor search algorithm that uses both long-range and short-range links to achieve poly-logarithmic search complexity. Foundation for the more advanced HNSW algorithm.

Fast and accurate graph-based ANN index for streaming similarity search, enabling real-time updates on billion-point indexes using a single machine with real-time freshness.

Leading graph-based ANN library optimized for approximate nearest neighbor search, offering competitive performance especially at lower recall levels across diverse datasets.

Performant and predicate-agnostic search algorithm for vector embeddings with structured data. Uses two-hop graph expansion to maintain high recall under selective filters in Weaviate.

Graph-based similarity search algorithm achieving 0.99 recall, building indexes 3-8x faster than competitors with 40% less memory. Answers 1-NN queries up to 10x faster than serial scan.

Hierarchical Clustering-based Nearest Neighbor Graph using MST to connect dataset points through multiple hierarchical clusters. Performs efficient guided search instead of traditional greedy routing.

Graph-based algorithm for approximate nearest neighbor search where vertices represent vectors and edges are constructed heuristically. Foundation for HNSW with (poly/)logarithmic search complexity using greedy routing.

Graph-based indexing algorithm powering Microsoft's DiskANN. Uses flat graph structure with minimized search diameter for efficient disk-based nearest neighbor search with 40x GPU speedup available via NVIDIA cuVS.