In the world of machine learning and artificial intelligence, similarity search plays a pivotal role in numerous applications, ranging from recommendation systems to content retrieval and clustering. However, as the dimensionality and volume of data continue to grow exponentially, traditional brute-force approaches for similarity search become computationally expensive and inefficient. This is where FAISS (Facebook AI Similarity Search) comes into play, offering a powerful and efficient solution for similarity search and clustering of high-dimensional vector data.
What Is FAISS?
FAISS is an open-source library developed by Facebook AI Research for efficient similarity search and clustering of dense vector embeddings. It provides a collection of algorithms and data structures optimized for various types of similarity search, allowing for fast and accurate retrieval of nearest neighbors in high-dimensional spaces.