I was impressed by the visualizations in this article:
Inside FAISS: Billion-Scale Similarity Search
I'm a big fan of semantic embeddings models -- I've used them extensively on various projects for everything from hardware issue diagnosis (asymmetric embeddings-model for converting user symptoms into fix resolutions) to image recognition (I've written extensively about CollectorVision in the past).
CollectorVision currently doesn't use FAISS for fast similarity-search -- it may seem surprising, but brute-force linear search over the entire catalog of 100k+ cards in Magic: The Gathering actually doesn't take that long -- even with 128 dimensions.
Even though I don't use optimization techniques for fast indexes like FAISS, the explanation in this blog post is still top-notch, and if it's something you'd like to know more about, then at least scrolling through the post and looking at the pretty pictures and animations is well worth your time. :)
I was impressed by the visualizations in this article:
Inside FAISS: Billion-Scale Similarity Search
I'm a big fan of semantic embeddings models -- I've used them extensively on various projects for everything from hardware issue diagnosis (asymmetric embeddings-model for converting user symptoms into fix resolutions) to image recognition (I've written extensively about CollectorVision in the past).
CollectorVision currently doesn't use FAISS for fast similarity-search -- it may seem surprising, but brute-force linear search over the entire catalog of 100k+ cards in Magic: The Gathering actually doesn't take that long -- even with 128 dimensions.
Even though I don't use optimization techniques for fast indexes like FAISS, the explanation in this blog post is still top-notch, and if it's something you'd like to know more about, then at least scrolling through the post and looking at the pretty pictures and animations is well worth your time. :)