Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.
Awesome Lists | Featured Topics | Projects
https://github.com/dangkhoasdc/awesome-vector-database

A curated list of awesome works related to high dimensional structure/vector search & database
https://github.com/dangkhoasdc/awesome-vector-database
List: awesome-vector-database
approximate-nearest-neighbor-search embedding-similarity embeddings-similarity nearest-neighbor-search search-engine similarity-search vector-database vector-search vector-search-engine
Last synced: about 1 month ago
JSON representation
A curated list of awesome works related to high dimensional structure/vector search & database
Host: GitHub
URL: https://github.com/dangkhoasdc/awesome-vector-database
Owner: dangkhoasdc
License: cc0-1.0
Created: 2023-03-21T05:22:35.000Z (almost 2 years ago)
Default Branch: main
Last Pushed: 2024-05-17T03:43:46.000Z (7 months ago)
Last Synced: 2024-05-22T23:00:50.601Z (7 months ago)
Topics: approximate-nearest-neighbor-search, embedding-similarity, embeddings-similarity, nearest-neighbor-search, search-engine, similarity-search, vector-database, vector-search, vector-search-engine
Homepage:
Size: 309 KB
Stars: 142
Watchers: 9
Forks: 7
Open Issues: 3
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

awesome-rag - awesome-vector-database
ultimate-awesome - awesome-vector-database - A curated list of awesome works related to high dimensional structure/vector search & database. (Other Lists / Monkey C Lists)
README

        # :mag: Awesome Vector Database [![Awesome](https://cdn.jsdelivr.net/gh/sindresorhus/awesome@d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

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

# Services

- [Google Vector Search (Vertex AI)](https://cloud.google.com/vertex-ai/docs/vector-search/overview)

- [Pinecone](https://www.pinecone.io/)

- [Weaviate](https://github.com/weaviate/weaviate) [[Beginner Guide](https://towardsdatascience.com/getting-started-with-weaviate-a-beginners-guide-to-search-with-vector-databases-14bbb9285839)]

- [Vespa](https://vespa.ai/)

- [txtai](https://github.com/neuml/txtai)

- [marqo](https://github.com/marqo-ai/marqo)

- [vectara](https://vectara.com)

- [Epsilla](https://epsilla.com/)

- [algolia](https://www.algolia.com/)

- [Meilisearch](https://www.meilisearch.com/solutions/vector-search)

- [nucliadb](https://nuclia.com/vector-database/)

- [OpenSearch](https://opensearch.org/)

- [MyScale](https://myscale.com)

- [QdrantCloud](https://cloud.qdrant.io/)

- [zilliz](https://cloud.zilliz.com/signup)

- [OpenSearch's AlibabaCloud](https://www.alibabacloud.com/product/opensearch)

- [Typesense's Cloud](https://cloud.typesense.org)

- [MongoDB Atlas Vector Search](https://www.mongodb.com/products/platform/atlas-vector-search)

- [SuperDuperDB](https://github.com/SuperDuperDB/superduperdb)

- [KBD.AI](https://kdb.ai/)

## Comparisons

- [From Vespa](https://cloud.vespa.ai/feature-comparison.html)

- [Vector DB Comparison by VectorHub](https://vdbs.superlinked.com/)

- [MyScale Vector Database Benchmark 🚀](https://github.com/myscale/vector-db-benchmark)

  

# Libraries & Engines

## Multidimensional data / Vectors

- :star: 🥇 [Vector DB Feature Matrix](https://docs.google.com/spreadsheets/d/170HErOyOkLDjQfy3TJ6a3XXXM1rHvw_779Sit-KT7uc/edit#gid=0)

- :star: [Faiss](https://faiss.ai/) [Paper](https://arxiv.org/pdf/2401.08281.pdf)

- [Typesense](https://typesense.org/)

- [Qdrant](https://qdrant.tech/)

  - [Video tutorial](https://youtu.be/LRcZ9pbGnno), [Notebook](https://github.com/qdrant/examples/blob/master/qdrant_101_getting_started/getting_started.ipynb)

- [annoy](https://github.com/spotify/annoy)

- [NGT](https://github.com/yahoojapan/NGT)

- [pgvector](https://github.com/pgvector/pgvector)

- [Chroma](https://github.com/chroma-core/chroma)

- [LlamaIndex](https://github.com/jerryjliu/llama_index)

- [Epsilla](https://github.com/epsilla-cloud/vectordb/tree/main)

- [jvector](https://github.com/jbellis/jvector)

- [RAFT](https://github.com/rapidsai/raft)

- [Vald](https://vald.vdaas.org/)

- [Voyager](https://github.com/spotify/voyager)

- [tinyvector](https://github.com/0hq/tinyvector)

- [USearch](https://github.com/unum-cloud/usearch)

- [vearch](https://vearch.github.io/)

- [MRPT](https://github.com/vioshyvo/mrpt)

- [milvus](https://milvus.io/)

- [infinity](https://github.com/infiniflow/infinity)

- [havenask](https://github.com/alibaba/havenask)

- [chromem-go](https://github.com/philippgille/chromem-go)

- [OasysDB](https://github.com/oasysai/oasysdb) [[Notebook](https://colab.research.google.com/drive/15_1hH7jGKzMeQ6IfnScjsc-iJRL5XyL7?usp=sharing)]

- [Meilisearch](https://github.com/meilisearch) - Search engine API for Semantic (vectors), full-text & hybrid search

- [arroy](https://github.com/meilisearch/arroy) - Approximate Nearest Neighbors Rust library

- [bleve](https://github.com/blevesearch/bleve)

- [cuVS](https://github.com/rapidsai/cuvs)

- [vsag](https://github.com/alipay/vsag)

- [sqlite-vec](https://github.com/asg017/sqlite-vec)

- [MyScaleDB](https://github.com/myscale/MyScaleDB)

- [hora](https://github.com/hora-search/hora)

- [arroy](https://github.com/meilisearch/arroy)

- [KGraph](https://github.com/aaalgo/kgraph)

- [NearestNeighbors.jl](https://github.com/KristofferC/NearestNeighbors.jl)

- [MuopDB](https://github.com/hicder/muopdb)

## Texts

- [PISA](https://github.com/pisa-engine/pisa)

- [Tantivy](https://github.com/quickwit-oss/tantivy)

- [sonic](https://github.com/valeriansaliou/sonic)

## Others

- [SimSIMD](https://github.com/ashvardanian/SimSIMD): Efficient Alternative to `scipy.spatial.distance` and `numpy.inner`

# Benchmarks & Databases

- [ANN Benchmarks](http://ann-benchmarks.com/) [[Paper](https://arxiv.org/pdf/1807.05614.pdf)].

- [Billion-scale ANNS Benchmarks](https://big-ann-benchmarks.com)

    - [2021 Result](https://proceedings.mlr.press/v176/simhadri22a/simhadri22a.pdf)

    - Simhadri, Harsha Vardhan, et al. "[Results of the Big ANN: NeurIPS'23 competition.](https://arxiv.org/pdf/2409.17424)" arXiv preprint arXiv:2409.17424 (2024).

- [BEIR](https://github.com/beir-cellar/beir)

- [VectorDBBench - A Vector Database Benchmark Tool](https://zilliz.com/vector-database-benchmark-tool) , [[Github](https://github.com/zilliztech/VectorDBBench)]

- [Qdrant's Vector Database Benchmarks](https://qdrant.tech/benchmarks/)

- [MyScale's Vector Database Benchmark](https://myscale.github.io/benchmark/#/benchmark)

- Li, Wen, et al. "[Approximate nearest neighbor search on high dimensional data—experiments, analyses, and improvement](https://arxiv.org/pdf/1610.02455.pdf)." IEEE Transactions on Knowledge and Data Engineering 32.8 (2019): 1475-1488.

- Zeng, Xianzhi, et al. "[CANDY: A Benchmark for Continuous Approximate Nearest Neighbor Search with Dynamic Data Ingestion.](https://arxiv.org/pdf/2406.19651)" arXiv preprint arXiv:2406.19651 (2024).

# 📚 Books 

- [Foundations of Multidimensional and Metric Data Structures](https://www.amazon.com/Foundations-Multidimensional-Structures-Kaufmann-Computer/dp/0123694469/)

- [Introduction to Information Retrieval](https://nlp.stanford.edu/IR-book/information-retrieval-book.html)

- [Deep Learning for Search](https://www.manning.com/books/deep-learning-for-search)

- [Foundations of Vector Retrieval](https://arxiv.org/abs/2401.09350)

# Conferences & Workshops

- :star: [VLDB](https://vldb.org)

  - Tutorial:

    - New Trends in High-D Vector Similarity Search [[slides](https://vldb.org/2021/files/slides/tutorial/tutorial5.pdf), [video](https://www.youtube.com/watch?v=TFsrFwF0bC4&ab_channel=VLDB2021), [paper](https://echihabi.com/publications/tutorials/vldb2021-tutorial-summary.pdf)]

- :star: [Image Retrieval in the Wild (CVPR20)](https://matsui528.github.io/cvpr2020_tutorial_retrieval/) [[Video](https://www.youtube.com/watch?v=SKrHs03i08Q)]

- [Haystack](https://haystackconf.com)

- [Neural Search In Action](https://matsui528.github.io/cvpr2023_tutorial_neural_search/)

- ACM MM 2020: [Effective and Efficient: Toward Open-world Instance Re-identification](https://wangzwhu.github.io/home/acmmm2020_tutorial_reid.html)

  - Billion-scale Approximate Nearest Neighbor Search: [[Slides](https://wangzwhu.github.io/home/file/acmmm-t-part3-ann.pdf), [Video](https://www.youtube.com/watch?v=iI8e3kU11eU)]

  - Is instance search a solved problem? [[Slides](https://wangzwhu.github.io/home/file/acmmm-t-part4-ins.pdf), [Video](https://www.youtube.com/watch?v=cH256Zqt5Ms)]

- Retrieval Augmented Generation and Vespa [[Slides](https://docs.google.com/presentation/d/1LRAQfdT4UH69pgojNi_EMspSgsHn9YJVac_bbnhy038/edit#slide=id.p1)]

- [SISAP Indexing Challenge](https://sisap-challenges.github.io/#sisap_indexing_challenge)

  - [2023 Competition](https://sisap-challenges.github.io/2023/)

  - [2024 Competition](https://sisap-challenges.github.io/2024/)

 

## Courses

- Long Term Memory in AI - Vector Search and Databases (COS 495 - Princeton) [[Class Notes](https://github.com/edoliberty/vector-search-class-notes)]

- Freiburg Information Retrieval WS 2022-2023 [[Website](https://ad-wiki.informatik.uni-freiburg.de/teaching/InformationRetrievalWS2223), [Video Lectures](https://www.youtube.com/playlist?list=PL682UO4IMem9rQdlqJZZGQYD_CTRpTJZU)]

- Vector Similarity Search and Faiss Course [[Youtube Playlist](https://www.youtube.com/playlist?list=PLIUOU7oqGTLhlWpTz4NnuT3FekouIVlqc)]

## Others

- [VectorHub](https://github.com/superlinked/VectorHub): a free, open-source learning website for people (software developers to senior ML architects) interested in adding vector retrieval to their ML stack.

# Publications

## Survey

- :star: Pan, James Jie, Jianguo Wang, and Guoliang Li. "Survey of Vector Database Management Systems." arXiv preprint arXiv:2310.14021 (2023). [[Paper](https://arxiv.org/abs/2310.14021)]

- Aumüller, Martin, and Matteo Ceccarello. "[Recent Approaches and Trends in Approximate Nearest Neighbor Search](http://sites.computer.org/debull/A23sept/p89.pdf)." {IEEE} Data Engineering Bulletin (2023).

- Nearest neighbor search: the old, the new, and the impossible. Andoni, Alexandr. [[Paper](https://dspace.mit.edu/bitstream/handle/1721.1/55090/587638612-MIT.pdf?sequence=2)]

## Quantization

![](https://raw.githubusercontent.com/wiki/facebookresearch/faiss/PQ_variants_Faiss_annotated.png)

Source: A survey of product quantization.

- :star: PQ: Product quantization for nearest neighbor search. Jegou, Herve, Matthijs Douze, and Cordelia Schmid. [[Paper](https://lear.inrialpes.fr/pubs/2011/JDS11/jegou_searching_with_quantization.pdf), [Code](https://github.com/facebookresearch/faiss), [Julia Code](https://github.com/una-dinosauria/Rayuela.jl), [nanopq](https://github.com/matsui528/nanopq)]

- :star: k-selection on GPU: Billion-scale similarity search with gpus. Johnson, Jeff, Matthijs Douze, and Hervé Jégou [[Paper](https://arxiv.org/pdf/1702.08734.pdf), [Code](https://github.com/facebookresearch/faiss)]

- :star: A survey of product quantization. Matsui, Yusuke, Yusuke Uchida, Hervé Jégou, and Shin'ichi Satoh [[Paper](https://www.jstage.jst.go.jp/article/mta/6/1/6_2/_pdf)]

- OPQ: Optimized Product Quantization. Ge, Tiezheng, Kaiming He, Qifa Ke, and Jian Sun [[Homepage](https://kaiminghe.github.io/cvpr13/index.html), [Paper](https://www.microsoft.com/en-us/research/wp-content/uploads/2013/11/pami13opq.pdf), [Code](https://kaiminghe.github.io/cvpr13/matlab_OPQ_release_v1.1.rar), [nanopq](https://github.com/matsui528/nanopq)]

- Quicker adc: Unlocking the hidden potential of product quantization with simd. André, Fabien, Anne-Marie Kermarrec, and Nicolas Le Scouarnec [[Paper](https://arxiv.org/pdf/1812.09162), [Code](https://github.com/technicolor-research/faiss-quickeradc)]

  - Accelerated nearest neighbor search with quick adc. André, Fabien, Anne-Marie Kermarrec, and Nicolas Le Scouarnec [[Paper](https://arxiv.org/pdf/1704.07355.pdf)].

  - Cache locality is not enough: High-performance nearest neighbor search with product quantization fast scan. Fabien André, Anne-Marie Kermarrec, Nicolas Le Scouarnec [[Paper](https://hal.inria.fr/hal-01239055/document)]

- 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](http://proceedings.mlr.press/v119/guo20h/guo20h.pdf), [Python/C++ Inference](https://github.com/google-research/google-research/tree/master/scann), [Julia Training/Inference](https://github.com/AxelvL/AHPQ.jl)]

- The inverted multi-index. Babenko, Artem, and Victor Lempitsky [[Paper](https://cmp.felk.cvut.cz/~toliageo/rg/papers/BabenkoLempitsky_PAMI2014_The%20Inverted%20Multi-Index.pdf), [Code](https://github.com/jatin7gupta/Product-Quantization)]

- Are We There Yet? Product Quantization and its Hardware Acceleration. Fernandez-Marques, Javier, Ahmed F. AbouElhamayed, Nicholas D. Lane, and Mohamed S. Abdelfattah. [[Paper](https://arxiv.org/pdf/2305.18334.pdf)]

- 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](https://dl.acm.org/doi/10.1145/3539618.3591799), [Code](https://github.com/staoxiao/LibVQ/tree/demo)]

- Matsui, Yusuke, Ryota Hinami, and Shin'ichi Satoh. "Reconfigurable Inverted Index." Proceedings of the 26th ACM international conference on Multimedia. 2018. [[Paper](https://dl.acm.org/ft_gateway.cfm?id=3240630), [Project](https://yusukematsui.me/project/rii/), [Code](https://github.com/matsui528/rii)]

- Aguerrebere, Cecilia, et al. "[Similarity search in the blink of an eye with compressed indices.](https://arxiv.org/pdf/2304.04759.pdf)" arXiv preprint arXiv:2304.04759 (2023).

- Huijben, Iris, et al. "[Residual Quantization with Implicit Neural Codebooks](https://arxiv.org/pdf/2401.14732.pdf)." arXiv preprint arXiv:2401.14732 (2024). [[Code](https://github.com/facebookresearch/Qinco)]

- Rege, Aniket, et al. "[Adanns: A framework for adaptive semantic search](https://proceedings.neurips.cc/paper_files/paper/2023/file/f062da1973ac9ac61fc6d44dd7fa309f-Paper-Conference.pdf)." Advances in Neural Information Processing Systems 36 (2024).

- Amara, Kenza, et al. "[Nearest neighbor search with compact codes: A decoder perspective](https://arxiv.org/pdf/2112.09568)." Proceedings of the 2022 International Conference on Multimedia Retrieval. 2022.

- Krishnan, Aditya, and Edo Liberty. "[Projective Clustering Product Quantization](https://arxiv.org/pdf/2112.02179.pdf)." arXiv preprint arXiv:2112.02179 (2021).

- Noh, Haechan, Taeho Kim, and Jae-Pil Heo. "[Product quantizer aware inverted index for scalable nearest neighbor search](https://openaccess.thecvf.com/content/ICCV2021/papers/Noh_Product_Quantizer_Aware_Inverted_Index_for_Scalable_Nearest_Neighbor_Search_ICCV_2021_paper.pdf)." 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](https://arxiv.org/pdf/2108.00644)." 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](http://vldb.org/pvldb/vol13/p3603-wang.pdf)." 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](https://openaccess.thecvf.com/content_CVPR_2020/papers/Jang_Generalized_Product_Quantization_Network_for_Semi-Supervised_Image_Retrieval_CVPR_2020_paper.pdf)." 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](http://proceedings.mlr.press/v119/chen20l/chen20l.pdf)." International Conference on Machine Learning. PMLR, 2020.

- Huang, Rong, et al. "[Learning Discrete Document Representations in Web Search](https://dl.acm.org/doi/pdf/10.1145/3580305.3599854)." 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](https://arxiv.org/pdf/2404.02805.pdf)." European Conference on Information Retrieval. Cham: Springer Nature Switzerland, 2024. [[Code](https://github.com/CosimoRulli/emvb)]

- Gao, Jianyang, and Cheng Long. "[RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search.](https://arxiv.org/pdf/2405.12497)" arXiv preprint arXiv:2405.12497 (2024). [(Code)[https://github.com/gaoj0017/RaBitQ]]

- Gao, Jianyang, et al. "[Practical and Asymptotically Optimal Quantization of High-Dimensional Vectors in Euclidean Space for Approximate Nearest Neighbor Search.](https://arxiv.org/pdf/2409.09913)" arXiv preprint arXiv:2409.09913 (2024).

- Mohoney, Jason, et al. "[Incremental IVF Index Maintenance for Streaming Vector Search.](https://arxiv.org/pdf/2411.00970)" arXiv preprint arXiv:2411.00970 (2024).

## Graph-based Methods

- :star: Wang, Zeyu, et al. "[Graph-and Tree-based Indexes for High-dimensional Vector Similarity Search: Analyses, Comparisons, and Future Directions](https://helios2.mi.parisdescartes.fr/~themisp/publications/bulletin23.pdf)." Data Engineering (2023): 3-21.

- :star: A comprehensive survey and experimental comparison of graph-based approximate nearest neighbor search. Wang, Mengzhao, Xiaoliang Xu, Qiang Yue, and Yuxiang Wang. [[Paper](https://arxiv.org/pdf/2101.12631.pdf), [Code](https://github.com/Lsyhprum/WEAVESS)]

- Lin, Peng-Cheng, and Wan-Lei Zhao. "[Graph based nearest neighbor search: Promises and failures](https://arxiv.org/pdf/1904.02077)." arXiv preprint arXiv:1904.02077 (2019).

- :star: HNSW: Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. Malkov, Yu A., and Dmitry A. Yashunin. [[Paper](https://arxiv.org/pdf/1603.09320.pdf), [Code](https://github.com/nmslib/hnswlib), [Rust Version](https://github.com/rust-cv/hnsw), [Go Version](https://github.com/coder/hnsw)]

- 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](https://arxiv.org/pdf/2305.04359.pdf)]

- 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](https://dl.acm.org/doi/pdf/10.1145/3543507.3583318), [Video](https://www.youtube.com/watch?v=OsxZG2XfcZA)]

- NSG : Navigating Spread-out Graph For Approximate Nearest Neighbor Search. Fu, Cong, Chao Xiang, Changxu Wang, and Deng Cai. [[Paper](https://www.vldb.org/pvldb/vol12/p461-fu.pdf), [Code](https://github.com/ZJULearning/nsg)]

- EFANNA : Extremely Fast Approximate Nearest Neighbor Search Algorithm Based on kNN Graph. Cong Fu, Deng Cai. [[Paper](https://arxiv.org/abs/1609.07228), [Code](https://github.com/ZJULearning/efanna/tree/master)]

- Khan, Saim, et al. "[BANG: Billion-Scale Approximate Nearest Neighbor Search using a Single GPU.](https://arxiv.org/pdf/2401.11324.pdf)" arXiv preprint arXiv:2401.11324 (2024).

- Ootomo, Hiroyuki, et al. "[Cagra: Highly parallel graph construction and approximate nearest neighbor search for gpus.](https://arxiv.org/pdf/2308.15136.pdf)" 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.](https://arxiv.org/pdf/2402.04713.pdf)" 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.](https://arxiv.org/pdf/2309.00472.pdf)" 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](https://arxiv.org/pdf/2401.02116.pdf)." arXiv preprint arXiv:2401.02116 (2024). [[Code](https://github.com/zilliztech/starling)]

- Manohar, Magdalen Dobson, et al. "[ParlayANN: Scalable and Deterministic Parallel Graph-Based Approximate Nearest Neighbor Search Algorithms](https://dl.acm.org/doi/pdf/10.1145/3627535.3638475)." Proceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming. 2024. [[Code](https://github.com/cmuparlay/ParlayANN)]

- Wang, Mengzhao, et al. "[An Efficient and Robust Framework for Approximate Nearest Neighbor Search with Attribute Constraint](https://proceedings.neurips.cc/paper_files/paper/2023/file/32e41d6b0a51a63a9a90697da19d235d-Paper-Conference.pdf)." Advances in Neural Information Processing Systems 36 (2024).

- Yu, Shangdi, et al. "[Pecann: Parallel efficient clustering with graph-based approximate nearest neighbor search](https://arxiv.org/pdf/2312.03940.pdf)." arXiv preprint arXiv:2312.03940 (2023).

- Azizi, Ilias, Karima Echihabi, and Themis Palpanas. "[ELPIS: Graph-Based Similarity Search for Scalable Data Science](https://www.vldb.org/pvldb/vol16/p1548-azizi.pdf)." 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](https://proceedings.neurips.cc/paper_files/paper/2023/file/d0ac28b79816b51124fcc804b2496a36-Paper-Conference.pdf)." Advances in Neural Information Processing Systems 36 (2024).

- Liu, Jun, et al. "[Optimizing Graph-based Approximate Nearest Neighbor Search: Stronger and Smarter.](https://nicsefc.ee.tsinghua.edu.cn/nics_file/pdf/dacb55cd-fe0a-4b00-9fa0-8f32e3243930.pdf)" 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](https://arxiv.org/pdf/2204.00824.pdf)." arXiv preprint arXiv:2204.00824 (2022).

- Peng, Zhen, et al. "[Speed-ANN: Low-Latency and High-Accuracy Nearest Neighbor Search via Intra-Query Parallelism](https://arxiv.org/pdf/2201.13007.pdf)." arXiv preprint arXiv:2201.13007 (2022).

- Lu, Kejing, et al. "[HVS: hierarchical graph structure based on voronoi diagrams for solving approximate nearest neighbor search](https://www.vldb.org/pvldb/vol15/p246-lu.pdf)." Proceedings of the VLDB Endowment 15.2 (2021): 246-258. [[Code](https://github.com/chuanxiao1983/HVS)]

- Yingfan, Liu, Cheng Hong, and Cui Jiangtao. "[Revisiting $ k $-Nearest Neighbor Graph Construction on High-Dimensional Data: Experiments and Analyses](https://arxiv.org/pdf/2112.02234)." arXiv preprint arXiv:2112.02234 (2021).

- Zhu, Dantong, and Minjia Zhang. "[Understanding and Generalizing Monotonic Proximity Graphs for Approximate Nearest Neighbor Search](https://arxiv.org/pdf/2107.13052)." arXiv preprint arXiv:2107.13052 (2021).

- Gottesbüren, Lars, et al. "[Unleashing Graph Partitioning for Large-Scale Nearest Neighbor Search](https://arxiv.org/pdf/2403.01797v1.pdf)." arXiv preprint arXiv:2403.01797 (2024).

- Singh, Aditi, et al. "[Freshdiskann: A fast and accurate graph-based ann index for streaming similarity search](https://arxiv.org/pdf/2105.09613.pdf)." arXiv preprint arXiv:2105.09613 (2021).

- Wang, Hui, Wan-Lei Zhao, and Xiangxiang Zeng. "[Large-Scale Approximate k-NN Graph Construction on GPU](https://arxiv.org/pdf/2103.15386)." arXiv preprint arXiv:2103.15386 (2021).

- Patel, Liana, et al. "[ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data](https://arxiv.org/pdf/2403.04871v1.pdf)." arXiv preprint arXiv:2403.04871 (2024).

- Zuo, Chaoji, et al. "[SeRF: Segment Graph for Range-Filtering Approximate Nearest Neighbor Search](https://dl.acm.org/doi/pdf/10.1145/3639324)." Proceedings of the ACM on Management of Data 2.1 (2024): 1-26.

- Hezel, Nico, et al. "[An Exploration Graph with Continuous Refinement for Efficient Multimedia Retrieval](https://dl.acm.org/doi/pdf/10.1145/3652583.3658117)." Proceedings of the 2024 International Conference on Multimedia Retrieval. 2024.

- Xiao, Wentao, et al. "[Enhancing HNSW Index for Real-Time Updates: Addressing Unreachable Points and Performance Degradation](https://arxiv.org/pdf/2407.07871)." arXiv preprint arXiv:2407.07871 (2024).

- Yang, Shuo, et al. "[Revisiting the Index Construction of Proximity Graph-Based Approximate Nearest Neighbor Search.](https://arxiv.org/pdf/2410.01231)" arXiv preprint arXiv:2410.01231 (2024).

## 🎄Tree-based Methods

- Jayaram Subramanya, Suhas, et al. "[Diskann: Fast accurate billion-point nearest neighbor search on a single node.](https://proceedings.neurips.cc/paper_files/paper/2019/file/09853c7fb1d3f8ee67a61b6bf4a7f8e6-Paper.pdf)" Advances in Neural Information Processing Systems 32 (2019). [[Code](https://github.com/microsoft/DiskANN)]

- Li, Haitao, et al. "[Constructing Tree-based Index for Efficient and Effective Dense Retrieval.](https://arxiv.org/pdf/2304.11943.pdf)" arXiv preprint arXiv:2304.11943 (2023).

- Engels, Joshua, et al. "[Approximate Nearest Neighbor Search with Window Filters](https://arxiv.org/html/2402.00943v1)." arXiv preprint arXiv:2402.00943 (2024).

- Song, Yang, et al. "[ProMIPS: Efficient high-dimensional C-approximate maximum inner product search with a lightweight index](https://arxiv.org/pdf/2104.04406)." 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021.

- Zhu, Yifan, et al. "[GTS: GPU-based Tree Index for Fast Similarity Search](https://arxiv.org/html/2404.00966v1)." arXiv preprint arXiv:2404.00966 (2024).

- Tatsuno, Kento, et al. "[AiSAQ: All-in-Storage ANNS with Product Quantization for DRAM-free Information Retrieval](https://arxiv.org/pdf/2404.06004.pdf)." arXiv preprint arXiv:2404.06004 (2024).

## Hashing

- :star: [Awesome Papers on Learning to Hash](https://learning2hash.github.io)

- :star: A survey on learning to hash. Wang, Jingdong, Ting Zhang, Nicu Sebe, and Heng Tao Shen [[Paper](https://arxiv.org/pdf/1606.00185.pdf)]

- :star: A survey on deep hashing methods. Luo, Xiao, Haixin Wang, Daqing Wu, Chong Chen, Minghua Deng, Jianqiang Huang, and Xian-Sheng Hua. [[Paper](https://dl.acm.org/doi/full/10.1145/3532624)]

- :star: Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval. Gong, Yunchao, Svetlana Lazebnik, Albert Gordo, and Florent Perronnin [[Paper](https://slazebni.cs.illinois.edu/publications/ITQ.pdf), [Python code](https://github.com/twistedcubic/learn-to-hash/blob/master/itq.py), [Matlab code](https://github.com/dangkhoasdc/sah/tree/master/itq)]

- Gan, Yukang, et al. "[Binary Embedding-based Retrieval at Tencent](https://arxiv.org/pdf/2302.08714)." arXiv preprint arXiv:2302.08714 (2023).

- Yan, Bencheng, et al. "[Binary code based hash embedding for web-scale applications](https://arxiv.org/pdf/2109.02471)." Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021.

- Weng, Zhenyu, and Yuesheng Zhu. "[Unsupervised Online Hashing with Multi-Bit Quantization](https://openaccess.thecvf.com/content/ACCV2022/papers/Weng_Unsupervised_Online_Hashing_with_Multi-Bit_Quantization_ACCV_2022_paper.pdf)." 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](https://dl.acm.org/doi/pdf/10.1145/3448016.3457240)." 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](https://arxiv.org/pdf/2009.08591)." arXiv preprint arXiv:2009.08591 (2020).

- Jian, Xiaozheng, et al. "[Fast top-K cosine similarity search through XOR-friendly binary quantization on GPUs](https://arxiv.org/pdf/2008.02002)." arXiv preprint arXiv:2008.02002 (2020).

- Zheng, Bolong, et al. "[PM-LSH: A fast and accurate LSH framework for high-dimensional approximate NN search](https://vbn.aau.dk/files/391642966/p643_zheng_1_.pdf)." Proceedings of the VLDB Endowment 13.5 (2020): 643-655.

- Eghbali, Sepehr. "[Scalable Nearest Neighbor Search with Compact Codes](https://uwspace.uwaterloo.ca/bitstream/handle/10012/15355/Eghbali_Sepehr.pdf?sequence=3&isAllowed=y)." (2019).

- Lei, Yifan, et al. "[Locality-sensitive hashing scheme based on longest circular co-substring](https://arxiv.org/pdf/2004.05345)." Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. 2020.

- Wei, Jiuqi, et al. "[DET-LSH: A Locality-Sensitive Hashing Scheme with Dynamic Encoding Tree for Approximate Nearest Neighbor Search](https://arxiv.org/pdf/2406.10938)." arXiv preprint arXiv:2406.10938 (2024).

## Other Approaches

- Chen, Qi, et al. "[Spann: Highly-efficient billion-scale approximate nearest neighbor search](https://papers.nips.cc/paper/2021/file/299dc35e747eb77177d9cea10a802da2-Paper.pdf)." arXiv preprint arXiv:2111.08566 (2021). [[Code](https://github.com/microsoft/SPTAG)]

- Li, Yuliang, et al. "[Index-based, high-dimensional, cosine threshold querying with optimality guarantees](https://arxiv.org/pdf/1812.07695.pdf)." 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](https://www.researchgate.net/profile/Yewang-Chen/publication/330028721_Semi-Convex_Hull_Tree_Fast_Nearest_Neighbor_Queries_for_Large_Scale_Data_on_GPUs/links/5c316845299bf12be3b1ca36/Semi-Convex-Hull-Tree-Fast-Nearest-Neighbor-Queries-for-Large-Scale-Data-on-GPUs.pdf)." 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 2018.

- Engels, Joshua, Benjamin Coleman, and Anshumali Shrivastava. "[Practical near neighbor search via group testing](https://arxiv.org/pdf/2106.11565.pdf)." Advances in Neural Information Processing Systems 34 (2021): 9950-9962. [[Supplement](https://proceedings.neurips.cc/paper_files/paper/2021/file/5248e5118c84beea359b6ea385393661-Supplemental.pdf)]

- Gong, Long, et al. "[iDEC: indexable distance estimating codes for approximate nearest neighbor search](https://par.nsf.gov/servlets/purl/10167953)." Proceedings of the VLDB Endowment 13.9 (2020).

- Lu, Kejing, et al. "[VHP: approximate nearest neighbor search via virtual hypersphere partitioning](https://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/79717/1/3397230.3397240.pdf)." Proceedings of the VLDB Endowment 13.9 (2020): 1443-1455.

- Bing Tian, , Haikun Liu, Yuhang Tang, Shihai Xiao, Zhuohui Duan, Xiaofei Liao, Xuecang Zhang, Junhua Zhu, Yu Zhang. "[FusionANNS: An Efficient CPU/GPU Cooperative Processing Architecture for Billion-scale Approximate Nearest Neighbor Search.](https://arxiv.org/pdf/2409.16576)" (2024).

- Chen, Zhonghan, et al. "[Exploring the Meaningfulness of Nearest Neighbor Search in High-Dimensional Space.](https://arxiv.org/pdf/2410.05752)" arXiv preprint arXiv:2410.05752 (2024).

- Tepper, Mariano, et al. "[GleanVec: Accelerating vector search with minimalist nonlinear dimensionality reduction.](https://arxiv.org/pdf/2410.22347)" arXiv preprint arXiv:2410.22347 (2024).

- Li, Jingyu, et al. "[PANTHER: Private Approximate Nearest Neighbor Search in the Single Server Setting.](https://eprint.iacr.org/2024/1774.pdf)" Cryptology ePrint Archive (2024).

## Systems

- Qin, An, et al. "[Maze: A Cost-Efficient Video Deduplication System at Web-scale](https://dl.acm.org/doi/pdf/10.1145/3503161.3548145)." Proceedings of the 30th ACM International Conference on Multimedia. 2022.

- Doshi, Ishita, et al. "[LANNS: a web-scale approximate nearest neighbor lookup system](https://arxiv.org/pdf/2010.09426.pdf)." arXiv preprint arXiv:2010.09426 (2020).

- Chen, Yaoqi, et al. "[OneSparse: A Unified System for Multi-index Vector Search](https://dl.acm.org/doi/pdf/10.1145/3589335.3648338)." Companion Proceedings of the ACM on Web Conference 2024. 2024.

## Others

- [Search Optimization with Query Likelihood Boosting and Two-Level Approximate Search for Edge Devices](https://arxiv.org/abs/2312.07517)

- Gao, Jianyang, and Cheng Long. "[High-Dimensional Approximate Nearest Neighbor Search: with Reliable and Efficient Distance Comparison Operations.](https://dl.acm.org/doi/pdf/10.1145/3589282)" Proceedings of the ACM on Management of Data 1.2 (2023): 1-27.

- [Approximate Nearest Neighbor Search in Recommender Systems](https://big-ann-benchmarks.com/neurips23_slides/ANNS_for_recommendation_systems_Yury.pdf). Yury Malkov.

- [Accelerating vector search on the GPU with RAPIDS RAFT](https://big-ann-benchmarks.com/neurips23_slides/NVIDIA_Corey.pdf). Corey Nolet

- Gupta, Gaurav, et al. "[CAPS: A Practical Partition Index for Filtered Similarity Search](https://arxiv.org/pdf/2308.15014.pdf)." arXiv preprint arXiv:2308.15014 (2023).

- Zhu, Yuhao. "[RTNN: accelerating neighbor search using hardware ray tracing](https://arxiv.org/pdf/2201.01366.pdf)." Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. 2022. [[Code](https://github.com/horizon-research/rtnn)]

- Levi, Asaf, et al. "[Physical vs. Logical Indexing with {IDEA}: Inverted {Deduplication-Aware} Index](https://www.usenix.org/system/files/fast24-levi.pdf)." 22nd USENIX Conference on File and Storage Technologies (FAST 24). 2024. [[Code](https://github.com/asaflevi0812/IDEA)]

- Carra, Damiano, and Giovanni Neglia. "[Taking two Birds with one k-NN Cache](http://profs.sci.univr.it/~carra/downloads/Carra_Globecom_21.pdf)." 2021 IEEE Global Communications Conference (GLOBECOM). IEEE, 2021.

- Salem, Tareq Si, Giovanni Neglia, and Damiano Carra. "[Ascent Similarity Caching With Approximate Indexes](https://arxiv.org/pdf/2107.00957.pdf)." IEEE/ACM Transactions on Networking (2022).

- Li, Conglong, et al. "[Improving approximate nearest neighbor search through learned adaptive early termination](https://pdl.cmu.edu/PDL-FTP/BigLearning/mod0246-liA.pdf)." 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](https://proceedings.mlr.press/v151/karppa22a/karppa22a.pdf)." International Conference on Artificial Intelligence and Statistics. PMLR, 2022.

- Wang, Zeyu, et al. "[Distance Comparison Operators for Approximate Nearest Neighbor Search: Exploration and Benchmark](https://arxiv.org/pdf/2403.13491.pdf)." arXiv preprint arXiv:2403.13491 (2024).

- Szilvasy, Gergely, Pierre-Emmanuel Mazaré, and Matthijs Douze. "[Vector search with small radiuses](https://arxiv.org/pdf/2403.10746)." 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](https://openproceedings.org/2024/conf/edbt/paper-154.pdf)." (2024).

- Tepper, Mariano, et al. "[LeanVec: Search your vectors faster by making them fit.](https://arxiv.org/pdf/2312.16335v1)" arXiv preprint arXiv:2312.16335 (2023).

- Harwood, Ben, et al. "[Approximate Nearest Neighbour Search on Dynamic Datasets: An Investigation](https://arxiv.org/pdf/2404.19284)." arXiv preprint arXiv:2404.19284 (2024).

- [Characterizing the Dilemma of Performance and Index Size in Billion-Scale Vector Search and Breaking It with Second-Tier Memory](https://arxiv.org/pdf/2405.03267)

- Xu, Haike. [Worst-case Performance of Popular Approximate Nearest Neighbor Search Implementations: Guarantees and Limitations](https://dspace.mit.edu/bitstream/handle/1721.1/156284/xu-haikexu-sm-eecs-2024-thesis.pdf?sequence=1&isAllowed=y). Diss. Massachusetts Institute of Technology, 2024.

- Lin, Jimmy. "[Operational Advice for Dense and Sparse Retrievers: HNSW, Flat, or Inverted Indexes?.](https://arxiv.org/pdf/2409.06464)" arXiv preprint arXiv:2409.06464 (2024).

- Zhou, Mingxun, Elaine Shi, and Giulia Fanti. "[Pacmann: Efficient Private Approximate Nearest Neighbor Search.](https://eprint.iacr.org/2024/1600.pdf)" Cryptology ePrint Archive (2024).

## :chart_with_upwards_trend: 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](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148026/)]

## 📰 Articles & Talks

- [What is a Vector Database?](https://www.pinecone.io/learn/vector-database/)

- Vector databases (Part 1): [What makes each one different?](https://thedataquarry.com/posts/vector-db-1/)

- [eBay’s Blazingly Fast Billion-Scale Vector Similarity Engine](https://tech.ebayinc.com/engineering/ebays-blazingly-fast-billion-scale-vector-similarity-engine/)

- [Computer Vision Meetup: Computer Vision Applications at Scale with Vector Databases](https://www.youtube.com/watch?v=YTIDj7jeRbs)

- [How to choose your vector database in 2023?](https://www.sicara.fr/blog-technique/how-to-choose-your-vector-database-in-2023)

- [Do we really need a specialized vector database?](https://modelz.ai/blog/pgvector)

- [Vector database is not a separate database category](https://nextword.substack.com/p/vector-database-is-not-a-separate)

- [Vector Databases: A First-Principles Approach](https://docs.google.com/presentation/d/1qRv2nGVHjbFHXyUeUKK7bbvboj7Yal8UYcu_POEfWOQ/edit#slide=id.p)

- [Vector Search RAG Tutorial – Combine Your Data with LLMs with Advanced Search](https://www.youtube.com/watch?v=JEBDfGqrAUA&ab_channel=freeCodeCamp.org)

- [Efficient Vector Similarity Search in Recommender Workflows Using Milvus with NVIDIA Merlin](https://milvus.io/blog/efficient-vector-similarity-search-recommender-workflows-using-milvus-nvidia-merlin.md)

- [Vector Databases: A Beginner’s Guide!](https://medium.com/data-and-beyond/vector-databases-a-beginners-guide-b050cbbe9ca0)

- [Vector Database and Spring IA](https://dev.to/lucasnscr/vector-database-and-spring-ia-4dll)

- [How to handle a Million Vector Embeddings in the RAG Applications](https://medium.datadriveninvestor.com/how-to-handle-a-million-embedding-vectors-in-the-rag-application-d10b875a0218)

- [How Meilisearch Updates a Millions Vector Embeddings Database in Under a Minute](https://blog.kerollmops.com/how-meilisearch-updates-a-millions-vector-embeddings-database-in-under-a-minute)

- [Common Pitfalls To Avoid When Using Vector Databases](https://dagshub.com/blog/common-pitfalls-to-avoid-when-using-vector-databases/)

- [Getting Started With Vector Databases](https://dzone.com/refcardz/getting-started-with-vector-databases)

- [Choosing the best model for semantic search](https://blog.meilisearch.com/choosing-the-best-model-for-semantic-search/)

## Related Lists

- [Awesome Vector Search Engine](https://github.com/currentslab/awesome-vector-search)