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Datastax offers a vector search solution integrated with its database platform, enabling approximate similarity search and hybrid queries for enterprise use cases.
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.
K-means Tree is a clustering-based data structure that organizes high-dimensional vectors for fast similarity search and retrieval. It is used as an indexing method in some vector databases to optimize performance for vector search operations.
Locality-Sensitive Hashing (LSH) is an algorithmic technique for approximate nearest neighbor search in high-dimensional vector spaces, commonly used in vector databases to speed up similarity search while reducing memory footprint.
NMSLIB is an efficient similarity search library and toolkit for high-dimensional vector spaces, supporting a variety of indexing algorithms for vector database use cases.
PostgreSQL supports vector indexing and similarity search via the PGVector extension, allowing relational databases to manage and retrieve vector embeddings efficiently.
Qdrant is a dedicated vector database and similarity search engine supporting advanced filtering and efficient retrieval, suitable for faceted search and retrieval-augmented generation. It offers self-hosted and cloud deployment options, making it highly relevant for vector search applications.
RediSearch is a Redis module that provides high-performance vector search and similarity search capabilities on top of Redis, enabling advanced search and retrieval features for AI and data applications.
Spectral Hashing is a method for approximate nearest neighbor search that uses spectral graph theory to generate compact binary codes, often applied in vector databases to enhance retrieval efficiency on large-scale, high-dimensional data.