The Excellence
Directory Platform Template

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.

DET-LSH is a locality-sensitive hashing scheme that introduces a dynamic encoding tree structure to accelerate approximate nearest neighbor (ANN) search in high-dimensional spaces. While it is a research algorithm rather than a production database, it directly targets the core operation behind vector databases—efficient ANN search over vector embeddings—and is relevant for designing or optimizing vector indexing components within vector database systems.

GTS is a GPU-based tree index for fast similarity search over high-dimensional vector data, providing an efficient ANN index structure that can be integrated into or used to build high-performance vector database 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.

SOAR is a set of improved algorithms on top of ScaNN that accelerate vector search by introducing controlled redundancy and multi-cluster assignment, enabling faster approximate nearest neighbor retrieval with smaller indexes in large‑scale vector databases and search systems.

This work by Jingfan Meng is a comprehensive research thesis on efficient locality-sensitive hashing (LSH), covering algorithmic solutions, core primitives, and applications for approximate nearest neighbor search. It is relevant to vector databases because LSH-based indexing is a foundational technique for scalable similarity search over high-dimensional vectors, informing the design of vector indexes, retrieval engines, and similarity search modules in modern vector database systems.

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.

BANG is a billion-scale approximate nearest neighbor search system optimized for single GPU execution, enabling high-performance vector search in vector database environments at massive scale.

Cagra provides highly parallel graph construction and approximate nearest neighbor search for GPUs, supporting large-scale vector database operations and efficient similarity search.

ACORN is a performant and predicate-agnostic search system for vector embeddings and structured data, enhancing the capability of vector databases to handle complex queries over high-dimensional data efficiently.

A category of vector database solutions and algorithms leveraging graph-based approaches for efficient similarity search and vector indexing, which are core to many vector database implementations in AI applications.

Starling is an I/O-efficient, disk-resident graph index framework tailored for high-dimensional vector similarity search on large data segments, supporting the scalable storage and retrieval needs of vector databases.