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PostgreSQL supports vector indexing and similarity search via the PGVector extension, allowing relational databases to manage and retrieve vector embeddings efficiently.
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.
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.
Supabase Vector extends the Supabase platform by providing vector database functionalities, making it easy to add vector search capabilities to applications with PostgreSQL backend.
Arroy is an open-source library for efficient similarity search and management of vector embeddings, useful in vector database systems.
KGraph is an open-source library for fast approximate nearest neighbor search in high-dimensional vector spaces, applicable to vector database solutions.
PGVector is an open-source PostgreSQL extension that enables efficient storage, indexing, and similarity search for vector embeddings directly within a Postgres database. It supports both exact and approximate nearest neighbor search, and a variety of vector types and distance metrics.
pg_stat_statements.PGVector is open-source and free to use.