Faiss: A library for efficient similarity search

This post introduces Faiss, a new library for efficient similarity search on billion-scale datasets. It details the challenges of similarity search with high-dimensional vectors generated by AI models, compares Faiss to existing software packages, and outlines its advantages in speed, memory usage, and GPU implementation. The post also discusses evaluation metrics (speed, memory, accuracy) and presents performance benchmarks on billion-scale datasets using both CPU and GPU implementations, including the construction of k-nearest-neighbor graphs.