9 papers
E2E: Efficient Filtered AKNN Search via Adaptive Termination
Wenxuan Xia, Mingyu Yang, Wentao Li +1
Approximate k-Nearest Neighbor (AKNN) search is widely used in vector databases. When vectors carry additional attributes (e.g., labels or numerical values), filtered AKNN search r…
BubbleRAG: Evidence-Driven Retrieval-Augmented Generation for Black-Box Knowledge Graphs
Duyi Pan, Tianao Lou, Xin Li +5
Large Language Models (LLMs) exhibit hallucinations in knowledge-intensive tasks. Graph-based retrieval augmented generation (RAG) has emerged as a promising solution, yet existing…
Multiple Index Merge for Approximate Nearest Neighbor Search
Liuchang Jing, Mingyu Yang, Lei Li +2
Approximate nearest neighbor (AKNN) search in high-dimensional space is a foundational problem in vector databases with widespread applications. Among the numerous AKNN indexes…
Quantization Meets Projection: A Happy Marriage for Approximate k-Nearest Neighbor Search
Mingyu Yang, Liuchang Jing, Wentao Li +1
Approximate -nearest neighbor (AKNN) search is a fundamental problem with wide applications. To reduce memory and accelerate search, vector quantization is widely adopted. Howev…
Elastic Index Selection for Label-Hybrid AKNN Search
Mingyu Yang, Wenxuan Xia, Wentao Li +2
Real-world vector embeddings are usually associated with extra labels, such as attributes and keywords. Many applications require the nearest neighbor search that contains specific…
VSAG: An Optimized Search Framework for Graph-based Approximate Nearest Neighbor Search
Xiaoyao Zhong, Haotian Li, Jiabao Jin +11
Approximate nearest neighbor search (ANNS) is a fundamental problem in vector databases and AI infrastructures. Recent graph-based ANNS algorithms have achieved high search accurac…