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A Comprehensive Survey on Vector Database:Storage and Retrieval Techniques 认领 引用
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作者 MALe ZHANG Ran +16 位作者 HAN Yikun YU Shirui WANG Zaitian NING Zhiyuan ZHANG Jinghan XU Ping LI Pengjiang QIAO Ziyue JUWei CHEN Chong WANG Dongjie LIU Kunpeng WANG Pengyang WANG Pengfei FU Yanjie LIU Chunjiang LYU Changtian 《数据与计算发展前沿（中英文）》 CSCD 2026年第4期42-71,共30页
[Objective]As high-dimensional vector data increasingly surpass the processing capabilities of traditional database management systems,Vector Databases(VDBs)have emerged and become tightly integrated with large langua... [Objective]As high-dimensional vector data increasingly surpass the processing capabilities of traditional database management systems,Vector Databases(VDBs)have emerged and become tightly integrated with large language models,being widely applied in modern artificial intelligence systems.However,existing research has primarily focused on underlying technologies such as approximate nearest neighbor search,with relatively few studies providing a systematic architectural-level review of VDBs or analyzing how these core technologies collectively support the overall capacity of VDBs.This survey aims to offer a comprehensive overview of the core designs and algorithms of VDBs,establishing a holistic understanding of this rapidly evolving field.[Methods]First,we systematically review the key technologies and design principles of VDBs from the two core dimensions of storage and retrieval,tracing their technological evolution.Next,we conduct an in-depth comparison of several mainstream VDB architectures,summarizing their strengths,limitations,and typical application scenarios.Finally,we explore emerging directions for integrating VDBs with large language models,including open research challenges and trends such as novel indexing strategies.[Conclusions]This survey serves as a systematic reference guide for researchers and practitioners,helping readers quickly grasp the technological landscape and development trends in the field of vector databases,and promoting further innovation in both theoretical and applied aspects. 展开更多
关键词 vector database similarity search indexing techniques machine learning embeddings large language models AI infrastructure
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Toward a Large Language Model-Driven Medical Knowledge Retrieval and QA System:Framework Design and Evaluation 认领 引用 被引量：4
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作者 Yuyang Liu Xiaoying Li +6 位作者 Yan Luo Jinhua Du Ying Zhang Tingyu Lv Hao Yin Xiaoli Tang Hui Liu 《Engineering》 SCIE EI CSCD 2025年第7期270-282,共13页
Recent advancements in large language models(LLMs)have driven remarkable progress in text process-ing,opening new avenues for medical knowledge discovery.In this study,we present ERQA,a mEdical knowledge Retrieval and... Recent advancements in large language models(LLMs)have driven remarkable progress in text process-ing,opening new avenues for medical knowledge discovery.In this study,we present ERQA,a mEdical knowledge Retrieval and Question-Answering framework powered by an enhanced LLM that integrates a semantic vector database and a curated literature repository.The ERQA framework leverages domain-specific incremental pretraining and conducts supervised fine-tuning on medical literature,enabling retrieval and question-answering(QA)tasks to be completed with high precision.Performance evaluations implemented on the coronavirus disease 2019(COVID-19)and TripClick data-sets demonstrate the robust capabilities of ERQA across multiple tasks.On the COVID-19 dataset,ERQA-13B achieves state-of-the-art retrieval metrics,with normalized discounted cumulative gain at top 10(NDCG@10)0.297,recall values at top 10(Recall@10)0.347,and mean reciprocal rank(MRR)=0.370;it also attains strong abstract summarization performance,with a recall-oriented understudy for gisting evaluation(ROUGE)-1 score of 0.434,and QA performance,with a bilingual evaluation understudy(BLEU)-1 score of 7.851.The comparable performance achieved on the TripClick dataset further under-scores the adaptability of ERQA across diverse medical topics.These findings suggest that ERQA repre-sents a significant step toward efficient biomedical knowledge retrieval and QA. 展开更多
关键词 Large language models Medical knowledge Information retrieval Vector database
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