dangkhoasdc/awesome-vector-database

A curated list of awesome works related to high dimensional structure/vector search & database

🔍 Awesome Vector Database

A curated list of awesome works related to high dimensional structure/vector search & database

Services

• Google Vector Search (Vertex AI)
• Pinecone
• Weaviate [Beginner Guide]
• Vespa
• txtai
• marqo
• vectara
• Epsilla
• algolia
• nucliadb
• OpenSearch
• MyScale
• QdrantCloud
• zilliz
• OpenSearch's AlibabaCloud
• Typesense's Cloud
• MongoDB Atlas Vector Search
• SuperDuperDB

Comparisons

• From Vespa
• Vector DB Comparison by VectorHub

Libraries & Engines

Multidimensional data / Vectors

• ⭐ 🥇 Vector DB Feature Matrix
• ⭐ Faiss Paper
• Typesense
• Qdrant
  • Video tutorial, Notebook
• annoy
• NGT
• pgvector
• Chroma
• LlamaIndex
• Epsilla
• jvector
• RAFT
• Vald
• Voyager
• tinyvector
• USearch
• vearch
• MRPT
• milvus
• infinity
• havenask
• chromem-go
• OasysDB [Notebook]

Texts

• PISA
• Tantivy
• sonic

Others

• SimSIMD: Efficient Alternative to scipy.spatial.distance and numpy.inner

Benchmarks & Databases

• ANN Benchmarks [Paper].
• Billion-scale ANNS Benchmarks
  • 2021 Result
• BEIR
• VectorDBBench - A Vector Database Benchmark Tool
• Qdrant's Vector Database Benchmarks
• MyScale's Vector Database Benchmark
• Li, Wen, et al. "Approximate nearest neighbor search on high dimensional data—experiments, analyses, and improvement." IEEE Transactions on Knowledge and Data Engineering 32.8 (2019): 1475-1488.

📚 Books

• Foundations of Multidimensional and Metric Data Structures
• Introduction to Information Retrieval
• Deep Learning for Search
• Foundations of Vector Retrieval

Conferences & Workshops

• ⭐ VLDB
  • Tutorial:
    • New Trends in High-D Vector Similarity Search [slides, video, paper]
• ⭐ Image Retrieval in the Wild (CVPR20) [Video]
• Haystack
• Neural Search In Action
• ACM MM 2020: Effective and Efficient: Toward Open-world Instance Re-identification
  • Billion-scale Approximate Nearest Neighbor Search: [Slides, Video]
  • Is instance search a solved problem? [Slides, Video]
• Retrieval Augmented Generation and Vespa [Slides]
• SISAP Indexing Challenge
  • 2023 Competition
  • 2024 Competition

Courses

• Long Term Memory in AI - Vector Search and Databases (COS 495 - Princeton) [Class Notes]
• Freiburg Information Retrieval WS 2022-2023 [Website, Video Lectures]

Publications

Survey

• ⭐ Pan, James Jie, Jianguo Wang, and Guoliang Li. "Survey of Vector Database Management Systems." arXiv preprint arXiv:2310.14021 (2023). [Paper]
• Aumüller, Martin, and Matteo Ceccarello. "Recent Approaches and Trends in Approximate Nearest Neighbor Search." {IEEE} Data Engineering Bulletin (2023).
• Nearest neighbor search: the old, the new, and the impossible. Andoni, Alexandr. [Paper]

Quantization

Source: A survey of product quantization.

• ⭐ PQ: Product quantization for nearest neighbor search. Jegou, Herve, Matthijs Douze, and Cordelia Schmid. [Paper, Code, Julia Code, nanopq]
• ⭐ k-selection on GPU: Billion-scale similarity search with gpus. Johnson, Jeff, Matthijs Douze, and Hervé Jégou [Paper, Code]
• ⭐ A survey of product quantization. Matsui, Yusuke, Yusuke Uchida, Hervé Jégou, and Shin'ichi Satoh [Paper]
• OPQ: Optimized Product Quantization. Ge, Tiezheng, Kaiming He, Qifa Ke, and Jian Sun [Homepage, Paper, Code, nanopq]
• Quicker adc: Unlocking the hidden potential of product quantization with simd. André, Fabien, Anne-Marie Kermarrec, and Nicolas Le Scouarnec [Paper, Code]
  • Accelerated nearest neighbor search with quick adc. André, Fabien, Anne-Marie Kermarrec, and Nicolas Le Scouarnec [Paper].
  • Cache locality is not enough: High-performance nearest neighbor search with product quantization fast scan. Fabien André, Anne-Marie Kermarrec, Nicolas Le Scouarnec [Paper]
• ScaNN: Accelerating Large-Scale Inference with Anisotropic Vector Quantization. Guo, Ruiqi, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, and Sanjiv Kumar [Paper, Python/C++ Inference, Julia Training/Inference]
• The inverted multi-index. Babenko, Artem, and Victor Lempitsky [Paper, Code]
• Are We There Yet? Product Quantization and its Hardware Acceleration. Fernandez-Marques, Javier, Ahmed F. AbouElhamayed, Nicholas D. Lane, and Mohamed S. Abdelfattah. [Paper]
• LibVQ: A Toolkit for Optimizing Vector Quantization and Efficient Neural Retrieval. Li, Chaofan, Zheng Liu, Shitao Xiao, Yingxia Shao, Defu Lian, and Zhao Cao. [Paper, Code]
• Matsui, Yusuke, Ryota Hinami, and Shin'ichi Satoh. "Reconfigurable Inverted Index." Proceedings of the 26th ACM international conference on Multimedia. 2018. [Paper, Project, Code]
• Aguerrebere, Cecilia, et al. "Similarity search in the blink of an eye with compressed indices." arXiv preprint arXiv:2304.04759 (2023).
• Huijben, Iris, et al. "Residual Quantization with Implicit Neural Codebooks." arXiv preprint arXiv:2401.14732 (2024). [Code]
• Rege, Aniket, et al. "Adanns: A framework for adaptive semantic search." Advances in Neural Information Processing Systems 36 (2024).
• Amara, Kenza, et al. "Nearest neighbor search with compact codes: A decoder perspective." Proceedings of the 2022 International Conference on Multimedia Retrieval. 2022.
• Krishnan, Aditya, and Edo Liberty. "Projective Clustering Product Quantization." arXiv preprint arXiv:2112.02179 (2021).
• Noh, Haechan, Taeho Kim, and Jae-Pil Heo. "Product quantizer aware inverted index for scalable nearest neighbor search." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021.
• Zhan, Jingtao, et al. "Jointly optimizing query encoder and product quantization to improve retrieval performance." Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021.
• Wang, Runhui, and Dong Deng. "DeltaPQ: lossless product quantization code compression for high dimensional similarity search." Proceedings of the VLDB Endowment 13.13 (2020): 3603-3616.
• Jang, Young Kyun, and Nam Ik Cho. "Generalized product quantization network for semi-supervised image retrieval." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.
• Chen, Ting, Lala Li, and Yizhou Sun. "Differentiable product quantization for end-to-end embedding compression." International Conference on Machine Learning. PMLR, 2020.
• Huang, Rong, et al. "Learning Discrete Document Representations in Web Search." Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2023.
• Nardini, Franco Maria, Cosimo Rulli, and Rossano Venturini. "Efficient Multi-vector Dense Retrieval with Bit Vectors." European Conference on Information Retrieval. Cham: Springer Nature Switzerland, 2024. [Code]

Graph-based Methods

• ⭐ Wang, Zeyu, et al. "Graph-and Tree-based Indexes for High-dimensional Vector Similarity Search: Analyses, Comparisons, and Future Directions." Data Engineering (2023): 3-21.
• ⭐ A comprehensive survey and experimental comparison of graph-based approximate nearest neighbor search. Wang, Mengzhao, Xiaoliang Xu, Qiang Yue, and Yuxiang Wang. [Paper, Code]
• Lin, Peng-Cheng, and Wan-Lei Zhao. "Graph based nearest neighbor search: Promises and failures." arXiv preprint arXiv:1904.02077 (2019).
• ⭐ HNSW: Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. Malkov, Yu A., and Dmitry A. Yashunin. [Paper, Code], Rust Version
• Scaling Graph-Based ANNS Algorithms to Billion-Size Datasets: A Comparative Analysis. Dobson, Magdalen, Zheqi Shen, Guy E. Blelloch, Laxman Dhulipala, Yan Gu, Harsha Vardhan Simhadri, and Yihan Sun. [Paper]
• FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search. Chen, Patrick, Wei-Cheng Chang, Jyun-Yu Jiang, Hsiang-Fu Yu, Inderjit Dhillon, and Cho-Jui Hsieh [Paper, Video]
• NSG : Navigating Spread-out Graph For Approximate Nearest Neighbor Search. Fu, Cong, Chao Xiang, Changxu Wang, and Deng Cai. [Paper, Code]
• EFANNA : Extremely Fast Approximate Nearest Neighbor Search Algorithm Based on kNN Graph. Cong Fu, Deng Cai. [Paper, Code]
• Khan, Saim, et al. "BANG: Billion-Scale Approximate Nearest Neighbor Search using a Single GPU." arXiv preprint arXiv:2401.11324 (2024).
• Ootomo, Hiroyuki, et al. "Cagra: Highly parallel graph construction and approximate nearest neighbor search for gpus." arXiv preprint arXiv:2308.15136 (2023).
• Oguri, Yutaro, and Yusuke Matsui. "Theoretical and Empirical Analysis of Adaptive Entry Point Selection for Graph-based Approximate Nearest Neighbor Search." arXiv preprint arXiv:2402.04713 (2024).
• Oguri, Yutaro, and Yusuke Matsui. "General and practical tuning method for off-the-shelf graph-based index: Sisap indexing challenge report by team utokyo." International Conference on Similarity Search and Applications. Cham: Springer Nature Switzerland, 2023.
• Wang, Mengzhao, et al. "Starling: An I/O-Efficient Disk-Resident Graph Index Framework for High-Dimensional Vector Similarity Search on Data Segment." arXiv preprint arXiv:2401.02116 (2024).
• Manohar, Magdalen Dobson, et al. "ParlayANN: Scalable and Deterministic Parallel Graph-Based Approximate Nearest Neighbor Search Algorithms." Proceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming. 2024. [Code]
• Wang, Mengzhao, et al. "An Efficient and Robust Framework for Approximate Nearest Neighbor Search with Attribute Constraint." Advances in Neural Information Processing Systems 36 (2024).
• Yu, Shangdi, et al. "Pecann: Parallel efficient clustering with graph-based approximate nearest neighbor search." arXiv preprint arXiv:2312.03940 (2023).
• Azizi, Ilias, Karima Echihabi, and Themis Palpanas. "ELPIS: Graph-Based Similarity Search for Scalable Data Science." Proceedings of the VLDB Endowment 16.6 (2023): 1548-1559.
• Indyk, Piotr, and Haike Xu. "Worst-case performance of popular approximate nearest neighbor search implementations: Guarantees and limitations." Advances in Neural Information Processing Systems 36 (2024).
• Liu, Jun, et al. "Optimizing Graph-based Approximate Nearest Neighbor Search: Stronger and Smarter." 2022 23rd IEEE International Conference on Mobile Data Management (MDM). IEEE, 2022.
• Wang, Hui, Yong Wang, and Wan-Lei Zhao. "Graph-based Approximate NN Search: A Revisit." arXiv preprint arXiv:2204.00824 (2022).
• Peng, Zhen, et al. "Speed-ANN: Low-Latency and High-Accuracy Nearest Neighbor Search via Intra-Query Parallelism." arXiv preprint arXiv:2201.13007 (2022).
• Lu, Kejing, et al. "HVS: hierarchical graph structure based on voronoi diagrams for solving approximate nearest neighbor search." Proceedings of the VLDB Endowment 15.2 (2021): 246-258. [Code]
• Yingfan, Liu, Cheng Hong, and Cui Jiangtao. "Revisiting $ k $-Nearest Neighbor Graph Construction on High-Dimensional Data: Experiments and Analyses." arXiv preprint arXiv:2112.02234 (2021).
• Zhu, Dantong, and Minjia Zhang. "Understanding and Generalizing Monotonic Proximity Graphs for Approximate Nearest Neighbor Search." arXiv preprint arXiv:2107.13052 (2021).
• Gottesbüren, Lars, et al. "Unleashing Graph Partitioning for Large-Scale Nearest Neighbor Search." arXiv preprint arXiv:2403.01797 (2024).
• Singh, Aditi, et al. "Freshdiskann: A fast and accurate graph-based ann index for streaming similarity search." arXiv preprint arXiv:2105.09613 (2021).
• Wang, Hui, Wan-Lei Zhao, and Xiangxiang Zeng. "Large-Scale Approximate k-NN Graph Construction on GPU." arXiv preprint arXiv:2103.15386 (2021).
• Patel, Liana, et al. "ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data." arXiv preprint arXiv:2403.04871 (2024).
• Zuo, Chaoji, et al. "SeRF: Segment Graph for Range-Filtering Approximate Nearest Neighbor Search." Proceedings of the ACM on Management of Data 2.1 (2024): 1-26.

Tree-based Methods

• Jayaram Subramanya, Suhas, et al. "Diskann: Fast accurate billion-point nearest neighbor search on a single node." Advances in Neural Information Processing Systems 32 (2019). [Code]
• Li, Haitao, et al. "Constructing Tree-based Index for Efficient and Effective Dense Retrieval." arXiv preprint arXiv:2304.11943 (2023).
• Engels, Joshua, et al. "Approximate Nearest Neighbor Search with Window Filters." arXiv preprint arXiv:2402.00943 (2024).
• Song, Yang, et al. "ProMIPS: Efficient high-dimensional C-approximate maximum inner product search with a lightweight index." 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021.
• Zhu, Yifan, et al. "GTS: GPU-based Tree Index for Fast Similarity Search." arXiv preprint arXiv:2404.00966 (2024).
• Tatsuno, Kento, et al. "AiSAQ: All-in-Storage ANNS with Product Quantization for DRAM-free Information Retrieval." arXiv preprint arXiv:2404.06004 (2024).

Hashing

• ⭐ Awesome Papers on Learning to Hash
• ⭐ A survey on learning to hash. Wang, Jingdong, Ting Zhang, Nicu Sebe, and Heng Tao Shen [Paper]
• ⭐ A survey on deep hashing methods. Luo, Xiao, Haixin Wang, Daqing Wu, Chong Chen, Minghua Deng, Jianqiang Huang, and Xian-Sheng Hua. [Paper]
• ⭐ Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval. Gong, Yunchao, Svetlana Lazebnik, Albert Gordo, and Florent Perronnin [Paper, Python code, Matlab code]
• Gan, Yukang, et al. "Binary Embedding-based Retrieval at Tencent." arXiv preprint arXiv:2302.08714 (2023).
• Yan, Bencheng, et al. "Binary code based hash embedding for web-scale applications." Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021.
• Weng, Zhenyu, and Yuesheng Zhu. "Unsupervised Online Hashing with Multi-Bit Quantization." Proceedings of the Asian Conference on Computer Vision. 2022.
• Huang, Qiang, Yifan Lei, and Anthony KH Tung. "Point-to-hyperplane nearest neighbor search beyond the unit hypersphere." Proceedings of the 2021 International Conference on Management of Data. 2021.
• Weng, Zhenyu, Yuesheng Zhu, and Ruixin Liu. "Fast Search on Binary Codes by Weighted Hamming Distance." arXiv preprint arXiv:2009.08591 (2020).
• Jian, Xiaozheng, et al. "Fast top-K cosine similarity search through XOR-friendly binary quantization on GPUs." arXiv preprint arXiv:2008.02002 (2020).
• Zheng, Bolong, et al. "PM-LSH: A fast and accurate LSH framework for high-dimensional approximate NN search." Proceedings of the VLDB Endowment 13.5 (2020): 643-655.
• Eghbali, Sepehr. "Scalable Nearest Neighbor Search with Compact Codes." (2019).
• Lei, Yifan, et al. "Locality-sensitive hashing scheme based on longest circular co-substring." Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. 2020.

Other Approaches

• Chen, Qi, et al. "Spann: Highly-efficient billion-scale approximate nearest neighbor search." arXiv preprint arXiv:2111.08566 (2021). [Code]
• Li, Yuliang, et al. "Index-based, high-dimensional, cosine threshold querying with optimality guarantees." Theory of Computing Systems 65 (2021): 42-83.
• Chen, Yewang, et al. "Semi-convex hull tree: Fast nearest neighbor queries for large scale data on GPUs." 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 2018.
• Engels, Joshua, Benjamin Coleman, and Anshumali Shrivastava. "Practical near neighbor search via group testing." Advances in Neural Information Processing Systems 34 (2021): 9950-9962. [Supplement]
• Gong, Long, et al. "iDEC: indexable distance estimating codes for approximate nearest neighbor search." Proceedings of the VLDB Endowment 13.9 (2020).
• Lu, Kejing, et al. "VHP: approximate nearest neighbor search via virtual hypersphere partitioning." Proceedings of the VLDB Endowment 13.9 (2020): 1443-1455.

Systems

• Qin, An, et al. "Maze: A Cost-Efficient Video Deduplication System at Web-scale." Proceedings of the 30th ACM International Conference on Multimedia. 2022.
• Doshi, Ishita, et al. "LANNS: a web-scale approximate nearest neighbor lookup system." arXiv preprint arXiv:2010.09426 (2020).

Others

• Search Optimization with Query Likelihood Boosting and Two-Level Approximate Search for Edge Devices
• Gao, Jianyang, and Cheng Long. "High-Dimensional Approximate Nearest Neighbor Search: with Reliable and Efficient Distance Comparison Operations." Proceedings of the ACM on Management of Data 1.2 (2023): 1-27.
• Approximate Nearest Neighbor Search in Recommender Systems. Yury Malkov.
• Accelerating vector search on the GPU with RAPIDS RAFT. Corey Nolet
• Gupta, Gaurav, et al. "CAPS: A Practical Partition Index for Filtered Similarity Search." arXiv preprint arXiv:2308.15014 (2023).
• Zhu, Yuhao. "RTNN: accelerating neighbor search using hardware ray tracing." Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. 2022. [Code]
• Levi, Asaf, et al. "Physical vs. Logical Indexing with {IDEA}: Inverted {Deduplication-Aware} Index." 22nd USENIX Conference on File and Storage Technologies (FAST 24). 2024. [Code]
• Carra, Damiano, and Giovanni Neglia. "Taking two Birds with one k-NN Cache." 2021 IEEE Global Communications Conference (GLOBECOM). IEEE, 2021.
• Salem, Tareq Si, Giovanni Neglia, and Damiano Carra. "Ascent Similarity Caching With Approximate Indexes." IEEE/ACM Transactions on Networking (2022).
• Li, Conglong, et al. "Improving approximate nearest neighbor search through learned adaptive early termination." Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. 2020.
• Karppa, Matti, Martin Aumüller, and Rasmus Pagh. "Deann: Speeding up kernel-density estimation using approximate nearest neighbor search." International Conference on Artificial Intelligence and Statistics. PMLR, 2022.
• Wang, Zeyu, et al. "Distance Comparison Operators for Approximate Nearest Neighbor Search: Exploration and Benchmark." arXiv preprint arXiv:2403.13491 (2024).
• Szilvasy, Gergely, Pierre-Emmanuel Mazaré, and Matthijs Douze. "Vector search with small radiuses." arXiv preprint arXiv:2403.10746 (2024).
• Han, Changhun, Suji Kim, and Ha-Myung Park. "Efficient Proximity Search in Time-accumulating High-dimensional Data using Multi-level Block Indexing." (2024).
• Tepper, Mariano, et al. "LeanVec: Search your vectors faster by making them fit." arXiv preprint arXiv:2312.16335 (2023).
• Harwood, Ben, et al. "Approximate Nearest Neighbour Search on Dynamic Datasets: An Investigation." arXiv preprint arXiv:2404.19284 (2024).

📈 Evaluation & Metrics

• Which BM25 do you mean? A large-scale reproducibility study of scoring variants. Kamphuis, Chris, Arjen P. de Vries, Leonid Boytsov, and Jimmy Lin [Paper]

📰 Articles & Talks

• What is a Vector Database?
• Vector databases (Part 1): What makes each one different?
• eBay’s Blazingly Fast Billion-Scale Vector Similarity Engine
• Computer Vision Meetup: Computer Vision Applications at Scale with Vector Databases
• How to choose your vector database in 2023?
• Do we really need a specialized vector database?
• Vector database is not a separate database category
• Vector Databases: A First-Principles Approach
• Vector Search RAG Tutorial – Combine Your Data with LLMs with Advanced Search
• Efficient Vector Similarity Search in Recommender Workflows Using Milvus with NVIDIA Merlin
• Vector Databases: A Beginner’s Guide!
• Vector Database and Spring IA
• How to handle a Million Vector Embeddings in the RAG Applications
• How Meilisearch Updates a Millions Vector Embeddings Database in Under a Minute
• Common Pitfalls To Avoid When Using Vector Databases

Related Lists

• Awesome Vector Search Engine

awesome-vector-database's Issues

Add AI-native database Infinity

Hi~

Could I ask you to add Infinity: https://github.com/infiniflow/infinity to your vector database list (Definition here: https://medium.com/@infiniflowai/ai-native-database-powering-the-next-gen-rag-for-llm-da70cabcac1a)?

Regards,
Uno

A session with code samples and example projects would very useful.

A session with code samples and example projects would very useful for those that want to develop for the various types of vector databases....

add Chroma

can chroma be added? probably in the Multidimensional data / Vectors section makes the most sense - though chroma will have a hosted service as well

better paper reference format

as titled. The current format seems to verbose ...

service comparisons

services / engine categorization is confusing ...
will have a table showing whether it's SaaS, engine (including other components such as query analyzer, inference, post prcessing) or only libraries (imple 1/more vector search algos)

Which vector database should be chosen for a mobile phone?

For mobile scenario, which vector database should be selected？