FAISS (Facebook AI Similarity Search) is an open-source library developed by Facebook for efficient similarity search and clustering of high-dimensional data, especially for vector-based data like embeddings. It is optimized for fast retrieval and searching in large datasets, often used in machine learning applications such as recommendation systems, image search, and natural language processing. FAISS provides several algorithms for indexing and searching, and it supports both CPU and GPU-based computation for increased performance. Its ability to perform efficient nearest neighbor searches makes it an essential tool in areas like information retrieval, computer vision, and machine learning.