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Faiss is a library developed by Meta's Fundamental AI Research (FAIR) group, designed for efficient similarity search and clustering of dense vectors. It offers a comprehensive suite of algorithms capable of handling vector sets of any size, including those that exceed RAM capacity. With complete wrappers for Python/numpy and GPU implementations, Faiss provides robust solutions for various vector comparison tasks.
