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Amazon OpenSearch's k-NN plugin enables scalable, efficient vector search using ANN algorithms (IVF, HNSW) directly within a managed OpenSearch cluster. It is directly relevant for building, querying, and scaling vector databases on AWS.
ANN-Benchmarks is a benchmarking platform specifically for evaluating the performance of approximate nearest neighbor (ANN) search algorithms, which are foundational to vector database evaluation and comparison.
An open-source library for approximate nearest neighbor search in high-dimensional spaces, often used as a backend for vector databases and search engines.
A curated collection of papers and technical blogs focused on vector databases, semantic-based vector search, and approximate nearest neighbor search (ANN Search). These resources are essential for understanding and building large-scale information retrieval systems and vector databases.
DiskANN is a graph-based approximate nearest neighbor search (ANNS) system optimized for fast and accurate billion-point nearest neighbor search on a single node, leveraging SSD storage. It is highly relevant for large-scale vector database applications requiring efficient vector search at scale.
This paper introduces the HNSW algorithm, which is widely adopted in vector databases and search engines for its efficient and robust performance on high-dimensional data. HNSW is foundational in powering modern vector search systems.
FAISS (Facebook AI Similarity Search) is a popular open-source library for efficient similarity search and clustering of dense vectors. Developed by Facebook/Meta, it supports billions of vectors and is widely used to power vector search engines and databases, especially where raw speed and scalability are needed.
HNSWLIB is a C++ library with Python bindings for fast approximate nearest neighbor search using Hierarchical Navigable Small World (HNSW) graphs, commonly used in vector database backends.
Hora is an efficient, open-source library for approximate nearest neighbor search, written in Rust. It offers high-performance vector search capabilities for AI and machine learning applications.