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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.
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.
Neo4j is a graph database that has added vector search capabilities, providing unique and effective approaches for retrieval augmented generation (RAG) and other AI applications.
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.
QdrantCloud is the managed cloud version of Qdrant, a vector database tailored for AI-powered similarity search and matching.
Category: Research Papers & Surveys
Description: Graph-based Methods refer to a set of vector database solutions and algorithms that utilize graph structures for efficient similarity search and vector indexing. These methods are fundamental to many vector database implementations, especially in AI applications where finding similar items in large, high-dimensional datasets is required.
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Tags: graph-database, similarity-search, vector-indexing, ai