Faiss (Facebook AI Similarity Search) is an open-source library developed by Facebook to perform efficient similarity search and clustering of dense vectors. It is optimized for high-dimensional data, making it ideal for use in applications that require fast similarity searches on large datasets, such as semantic search, recommendation systems, and image search.
Faiss helps enhance IR by enabling the storage and retrieval of high-dimensional vector embeddings generated by deep learning models. By using advanced algorithms like approximate nearest neighbor (ANN) search and vector quantization, Faiss significantly reduces the time required to find similar vectors, even when working with billions of items.
Faiss is highly efficient and can scale across multiple GPUs, making it suitable for large-scale IR tasks. It is widely used in machine learning and AI applications where speed and accuracy are crucial, such as in personalized search engines, image or document retrieval, and audio or video content recommendations. Faiss provides both CPU and GPU implementations, offering flexibility for different use cases.