Natural language processing and computer vision methods generate high-dimensional vectors that represent text and images, yet traditional databases that can be queried like SQL are not adapted to these new representations. Given enough text and media, this information can quickly encompass billions of vectors. To find similar entries means that we must find similar high-dimensional vectors which is inefficient and likely impossible with standard query languages. Similarity search fills this void by searching for similar vectors; those nearby in Euclidean space. We can leverage similarity search algorithms once our vectors are generated by deep learning algorithms. In this post, we will use Faiss – Facebook AI Similarity Search.