Faiss
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About
Faiss, developed by Meta's AI research team, is a library designed for efficient similarity search and clustering of dense vectors. It provides a variety of algorithms capable of searching through extensive sets of vectors, facilitating operations even for datasets too large to fit in RAM. Faiss supports both CPU and GPU implementations and includes wrappers for Python, making it user-friendly. Key features of Faiss include the ability to return multiple nearest neighbors, batch processing capabilities, adjusting precision for speed, and conducting searches based on various distance metrics. Additionally, it allows for storage of indices on disk and handling of binary vectors. Faiss is built on substantial research foundations, implementing advanced techniques for efficient indexing and searching within large datasets.
Platform
Features
• return multiple nearest neighbors
• evaluate and tune parameters
• index both floating-point and binary vectors
• store indices on disk
• ability to trade precision for speed
• batch processing for quicker searches
• support for gpu algorithms
• efficient searching and clustering of dense vectors
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