FAISS (Facebook AI Similarity Search) is a library that allows developers to quickly search for embeddings of multimedia documents that are similar to each other. It solves limitations of traditional query search engines that are optimized for hash-based searches, and provides more scalable similarity search functions.
With FAISS, developers can search multimedia documents in ways that are inefficient or impossible with standard database engines (SQL). It includes nearest-neighbor search implementations for million-to-billion-scale datasets that optimize the memory-speed-accuracy tradeoff. FAISS aims to offer state-of-the-art performance for all operating points.
FAISS contains algorithms that search in sets of vectors of any size, and also contains supporting code for evaluation and parameter tuning. Some if its most useful algorithms are implemented on the GPU. FAISS is implemented in C++, with an optional Python interface and GPU support via CUDA.
Review documentation and tutorials to famliarize yourself with how FAISS works and its capabilities.
Experiment with building indexes and searching using FAISS.