DGAI decouples vector storage from graph topology in on-disk ANN indexes and adds similarity-aware dynamic layout plus hierarchical PQ two-stage querying to achieve 8x faster insertions/deletions and 67% lower peak query latency under mixed workloads.
When large language models meet vector databases: A survey
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
MINT defines multi-vector search index tuning and provides algorithms that achieve 2.1X to 8.3X latency speedup over baselines under storage and recall constraints.
RAGe is a modular evaluation framework that correlates retrieval and generation quality with hardware constraints to recommend optimal RAG components for specific datasets.
citing papers explorer
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DGAI: Decoupled On-Disk Graph-Based ANN Index for Efficient Updates and Queries
DGAI decouples vector storage from graph topology in on-disk ANN indexes and adds similarity-aware dynamic layout plus hierarchical PQ two-stage querying to achieve 8x faster insertions/deletions and 67% lower peak query latency under mixed workloads.
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MINT: Multi-Vector Search Index Tuning
MINT defines multi-vector search index tuning and provides algorithms that achieve 2.1X to 8.3X latency speedup over baselines under storage and recall constraints.
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RAGe: A Retrieval-Augmented Generation Evaluation Framework
RAGe is a modular evaluation framework that correlates retrieval and generation quality with hardware constraints to recommend optimal RAG components for specific datasets.