Faiss: A library for efficient similarity search

This month, we released Facebook AI Similarity Search (Faiss), a library that allows us to quickly search for multimedia documents that are similar to each other -- a challenge where traditional query search engines fall short. We've built nearest-neighbor search implementations for billion-scale data sets that are some 8.5x faster than the previous reported state-of-the-art, along with the fastest k-selection algorithm on the GPU known in the literature. This lets us break some records, including the first k-nearest-neighbor graph constructed on 1 billion high-dimensional vectors. Traditional databases are made up of structured tables containing symbolic information. For example, an image collection would be represented as a table with one row per indexed photo.