FAISS (Facebook AI Similarity Search) is a library developed by Facebook AI for efficient similarity search and clustering of dense vectors, particularly useful for large-scale datasets. It is optimized to handle embeddings (vector representations) and enables fast nearest neighbor search, allowing you to retrieve similar items from a large collection of vectors based on distance or similarity metrics like cosine similarity or Euclidean distance. FAISS is widely used in applications such as image and text retrieval, recommendation systems, and large-scale search systems where embeddings are used to represent items. It offers several indexing methods and can scale to billions of vectors, making it a powerful tool for handling real-time, large-scale similarity search problems efficiently.
Visit the following resources to learn more: