Vector Database Optimizations

Survey papers on Vector Databases

Review papers on vector databases:

• James Jie Pan, Jianguo Wang, Guoliang Li, 21 Oct 2023, Survey of Vector Database Management Systems, https://arxiv.org/abs/2310.14021 https://link.springer.com/article/10.1007/s00778-024-00864-x
• Zhi Jing, Yongye Su, Yikun Han, Bo Yuan, Haiyun Xu, Chunjiang Liu, Kehai Chen, Min Zhang, 6 Feb 2024 (v2), When Large Language Models Meet Vector Databases: A Survey, https://arxiv.org/abs/2402.01763
• Yikun Han, Chunjiang Liu, Pengfei Wang, 18 Oct 2023, A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge, https://arxiv.org/abs/2310.11703
• Toni Taipalus, 2024, Vector database management systems: Fundamental concepts, use-cases, and current challenges, Cognitive Systems Research, Volume 85, 101216, ISSN 1389-0417, https://doi.org/10.1016/j.cogsys.2024.101216 https://www.sciencedirect.com/science/article/pii/S1389041724000093

Research on Vector Databases

Research papers on vector databases:

• Sebastian Bruch, Jan 2024, Foundations of Vector Retrieval, https://arxiv.org/abs/2401.09350 (Extensive 200+ pages review of vector lookup data structures such as LSH and clustering.)
• Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang, 1 Jul 2024, Searching for Best Practices in Retrieval-Augmented Generation, https://arxiv.org/abs/2407.01219 Project: https://github.com/FudanDNN-NLP/RAG (Attempts to optimize the entire RAG system, including the various options for different RAG modules in the RAG pipeline, such as optimal methods for chunking, retrieval, embedding models, vector databases, prompt compression, reranking, repacking, summarizers, and other components.)
• Dr. Ashish Bamania, Jun 18, 2024, Google’s New Algorithms Just Made Searching Vector Databases Faster Than Ever: A Deep Dive into how Google’s ScaNN and SOAR Search algorithms supercharge the performance of Vector Databases, https://levelup.gitconnected.com/googles-new-algorithms-just-made-searching-vector-databases-faster-than-ever-36073618d078
• Chips Ahoy Capital, Jul 02, 2024, Evolution of Databases in the World of AI Apps, https://chipsahoycapital.substack.com/p/evolution-of-databases-in-the-world
• Donald Farmer, 08 Aug 2024, 10 top vector database options for similarity searches, https://www.techtarget.com/searchdatamanagement/tip/Top-vector-database-options-for-similarity-searches
• Pere Martra, Aug 2024 (accessed), Implementing semantic cache to improve a RAG system with FAISS, https://huggingface.co/learn/cookbook/semantic_cache_chroma_vector_database
• Richmond Alake, Apoorva Joshi, Aug 14, 2024, Adding Semantic Caching and Memory to Your RAG Application Using MongoDB and LangChain, MongoDB, https://www.mongodb.com/developer/products/atlas/advanced-rag-langchain-mongodb/
• James Jie Pan, Jianguo Wang, Guoliang Li, 21 Oct 2023, Survey of Vector Database Management Systems, https://arxiv.org/abs/2310.14021 https://link.springer.com/article/10.1007/s00778-024-00864-x
• Zhi Jing, Yongye Su, Yikun Han, Bo Yuan, Haiyun Xu, Chunjiang Liu, Kehai Chen, Min Zhang, 6 Feb 2024 (v2), When Large Language Models Meet Vector Databases: A Survey, https://arxiv.org/abs/2402.01763
• Yikun Han, Chunjiang Liu, Pengfei Wang, 18 Oct 2023, A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge, https://arxiv.org/abs/2310.11703
• Toni Taipalus, 2024, Vector database management systems: Fundamental concepts, use-cases, and current challenges, Cognitive Systems Research, Volume 85, 101216, ISSN 1389-0417, https://doi.org/10.1016/j.cogsys.2024.101216 https://www.sciencedirect.com/science/article/pii/S1389041724000093
• Zhi Yao, Zhiqing Tang, Jiong Lou, Ping Shen, Weijia Jia, 19 Jun 2024, VELO: A Vector Database-Assisted Cloud-Edge Collaborative LLM QoS Optimization Framework, https://arxiv.org/abs/2406.13399
• David Spuler, March 2024, Vector Databases, in Generative AI in C++, https://www.aussieai.com/book/ch6-vector-databases
• David Spuler, March 2024, Semantic Caching and Vector Databases, in Generative AI in C++, https://www.aussieai.com/book/ch29-semantic-caching-vector-databases
• Chirag Agrawal, Sep 20, 2024, Unlocking the Power of Efficient Vector Search in RAG Applications, https://pub.towardsai.net/unlocking-the-power-of-efficient-vector-search-in-rag-applications-c2e3a0c551d5
• F Sundh, Oct 2024, Evaluating the efficacy of modality conversion in vector databases, Bachelor's Thesis, Computer Science and Engineering, Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, https://www.diva-portal.org/smash/get/diva2:1905628/FULLTEXT01.pdf
• Tolga Şakar and Hakan Emekci, 30 October 2024, Maximizing RAG efficiency: A comparative analysis of RAG methods, Natural Language Processing. doi:10.1017/nlp.2024.53, https://www.cambridge.org/core/journals/natural-language-processing/article/maximizing-rag-efficiency-a-comparative-analysis-of-rag-methods/D7B259BCD35586E04358DF06006E0A85 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/D7B259BCD35586E04358DF06006E0A85/S2977042424000530a.pdf/div-class-title-maximizing-rag-efficiency-a-comparative-analysis-of-rag-methods-div.pdf
• Sonal Prabhune, Donald J. Berndt, 7 Nov 2024, Deploying Large Language Models With Retrieval Augmented Generation, https://arxiv.org/abs/2411.11895
• Matvey Arye, Avthar Sewrathan, 29 Oct 2024, Vector Databases Are the Wrong Abstraction, https://www.timescale.com/blog/vector-databases-are-the-wrong-abstraction/
• Michael Shen, Muhammad Umar, Kiwan Maeng, G. Edward Suh, Udit Gupta, 16 Dec 2024, Towards Understanding Systems Trade-offs in Retrieval-Augmented Generation Model Inference, https://arxiv.org/abs/2412.11854
• Derrick Quinn, Mohammad Nouri, Neel Patel, John Salihu, Alireza Salemi, Sukhan Lee, Hamed Zamani, Mohammad Alian, 14 Dec 2024, Accelerating Retrieval-Augmented Generation, https://arxiv.org/abs/2412.15246 (Speeding up vector databases using either approximate or exact nearest neighbor search.)
• Harvey Bower, 2024, Debugging RAG Pipelines: Best Practices for High-Performance LLMs, https://www.amazon.com/dp/B0DNWN5RB1
• Shige Liu, Zhifang Zeng, Li Chen, Adil Ainihaer, Arun Ramasami, Songting Chen, Yu Xu, Mingxi Wu, Jianguo Wang, 20 Jan 2025, TigerVector: Supporting Vector Search in Graph Databases for Advanced RAGs, https://arxiv.org/abs/2501.11216
• Bharani Subramaniam, 13 February 2025, Emerging Patterns in Building GenAI Products, https://martinfowler.com/articles/gen-ai-patterns/
• Vasilis Mageirakos, Bowen Wu, Gustavo Alonso, 3 Mar 2025, Cracking Vector Search Indexes, https://arxiv.org/abs/2503.01823
• Nitish Upreti, Krishnan Sundaram, Hari Sudan Sundar, Samer Boshra, Balachandar Perumalswamy, Shivam Atri, Martin Chisholm, Revti Raman Singh, Greg Yang, Subramanyam Pattipaka, Tamara Hass, Nitesh Dudhey, James Codella, Mark Hildebrand, Magdalen Manohar, Jack Moffitt, Haiyang Xu, Naren Datha, Suryansh Gupta, Ravishankar Krishnaswamy, Prashant Gupta, Abhishek Sahu, Ritika Mor, Santosh Kulkarni, Hemeswari Varada, Sudhanshu Barthwal, Amar Sagare, Dinesh Billa, Zishan Fu, Neil Deshpande, Shaun Cooper, Kevin Pilch, Simon Moreno, Aayush Kataria, Vipul Vishal, Harsha Vardhan Simhadri, 9 May 2025, Cost-Effective, Low Latency Vector Search with Azure Cosmos DB, https://arxiv.org/abs/2505.05885
• Adel Ammar, Anis Koubaa, Omer Nacar, Wadii Boulila, 13 May 2025, Optimizing Retrieval-Augmented Generation: Analysis of Hyperparameter Impact on Performance and Efficiency, https://arxiv.org/abs/2505.08445
• Leonardo Kuffo, Peter Boncz, 12 May 2025, Bang for the Buck: Vector Search on Cloud CPUs, https://arxiv.org/abs/2505.07621
• Yaoqi Chen, Jinkai Zhang, Baotong Lu, Qianxi Zhang, Chengruidong Zhang, Jingjia Luo, Di Liu, Huiqiang Jiang, Qi Chen, Jing Liu, Bailu Ding, Xiao Yan, Jiawei Jiang, Chen Chen, Mingxing Zhang, Yuqing Yang, Fan Yang, Mao Yang, 5 May 2025, RetroInfer: A Vector-Storage Approach for Scalable Long-Context LLM Inference, https://arxiv.org/abs/2505.02922

Vector Database Optimizations

Research papers on vector databases:

• Dr. Ashish Bamania, Jun 18, 2024, Google’s New Algorithms Just Made Searching Vector Databases Faster Than Ever: A Deep Dive into how Google’s ScaNN and SOAR Search algorithms supercharge the performance of Vector Databases, https://levelup.gitconnected.com/googles-new-algorithms-just-made-searching-vector-databases-faster-than-ever-36073618d078
• James Jie Pan, Jianguo Wang, Guoliang Li, 21 Oct 2023, Survey of Vector Database Management Systems, https://arxiv.org/abs/2310.14021 https://link.springer.com/article/10.1007/s00778-024-00864-x
• Michael Shen, Muhammad Umar, Kiwan Maeng, G. Edward Suh, Udit Gupta, 16 Dec 2024, Towards Understanding Systems Trade-offs in Retrieval-Augmented Generation Model Inference, https://arxiv.org/abs/2412.11854
• Derrick Quinn, Mohammad Nouri, Neel Patel, John Salihu, Alireza Salemi, Sukhan Lee, Hamed Zamani, Mohammad Alian, 14 Dec 2024, Accelerating Retrieval-Augmented Generation, https://arxiv.org/abs/2412.15246 (Speeding up vector databases using either approximate or exact nearest neighbor search.)
• Harvey Bower, 2024, Debugging RAG Pipelines: Best Practices for High-Performance LLMs, https://www.amazon.com/dp/B0DNWN5RB1
• Shige Liu, Zhifang Zeng, Li Chen, Adil Ainihaer, Arun Ramasami, Songting Chen, Yu Xu, Mingxi Wu, Jianguo Wang, 20 Jan 2025, TigerVector: Supporting Vector Search in Graph Databases for Advanced RAGs, https://arxiv.org/abs/2501.11216
• Vasilis Mageirakos, Bowen Wu, Gustavo Alonso, 3 Mar 2025, Cracking Vector Search Indexes, https://arxiv.org/abs/2503.01823
• Nitish Upreti, Krishnan Sundaram, Hari Sudan Sundar, Samer Boshra, Balachandar Perumalswamy, Shivam Atri, Martin Chisholm, Revti Raman Singh, Greg Yang, Subramanyam Pattipaka, Tamara Hass, Nitesh Dudhey, James Codella, Mark Hildebrand, Magdalen Manohar, Jack Moffitt, Haiyang Xu, Naren Datha, Suryansh Gupta, Ravishankar Krishnaswamy, Prashant Gupta, Abhishek Sahu, Ritika Mor, Santosh Kulkarni, Hemeswari Varada, Sudhanshu Barthwal, Amar Sagare, Dinesh Billa, Zishan Fu, Neil Deshpande, Shaun Cooper, Kevin Pilch, Simon Moreno, Aayush Kataria, Vipul Vishal, Harsha Vardhan Simhadri, 9 May 2025, Cost-Effective, Low Latency Vector Search with Azure Cosmos DB, https://arxiv.org/abs/2505.05885
• Adel Ammar, Anis Koubaa, Omer Nacar, Wadii Boulila, 13 May 2025, Optimizing Retrieval-Augmented Generation: Analysis of Hyperparameter Impact on Performance and Efficiency, https://arxiv.org/abs/2505.08445
• Leonardo Kuffo, Peter Boncz, 12 May 2025, Bang for the Buck: Vector Search on Cloud CPUs, https://arxiv.org/abs/2505.07621
• Yaoqi Chen, Jinkai Zhang, Baotong Lu, Qianxi Zhang, Chengruidong Zhang, Jingjia Luo, Di Liu, Huiqiang Jiang, Qi Chen, Jing Liu, Bailu Ding, Xiao Yan, Jiawei Jiang, Chen Chen, Mingxing Zhang, Yuqing Yang, Fan Yang, Mao Yang, 5 May 2025, RetroInfer: A Vector-Storage Approach for Scalable Long-Context LLM Inference, https://arxiv.org/abs/2505.02922

Vector Database Caching

Research papers on the use of caching to optimize vector databases:

• Yikun Han, Chunjiang Liu, Pengfei Wang, 18 Oct 2023, A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge, https://arxiv.org/abs/2310.11703

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