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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.

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.

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.

A scalable system for approximate nearest neighbor search at web-scale, relevant for implementing and understanding vector database infrastructure for high-dimensional data.

A unified system designed for efficient multi-index vector search, directly addressing large-scale vector database performance and scalability challenges.

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.

A research group focused on advancing the theory and practice of vector databases, providing resources, publications, and tools related to vector database technology.