I had the same initial thought based on the title. Unfortunately, the answer is no.
The article discusses a low-dimensional KNN problem. The curse of dimensionality guides intuition that the methods here likely will not apply to extremely high-dimensional problems.
faiss actually comes with a lot of excellent documentation that describes the problems unique to KNN on embedding vectors. In particular, for extremely large datasets, most of the tractable methods are approximations that make use of clustering, quantization, and centriod-difference tricks to make computation efficient.
The article discusses a low-dimensional KNN problem. The curse of dimensionality guides intuition that the methods here likely will not apply to extremely high-dimensional problems.
faiss actually comes with a lot of excellent documentation that describes the problems unique to KNN on embedding vectors. In particular, for extremely large datasets, most of the tractable methods are approximations that make use of clustering, quantization, and centriod-difference tricks to make computation efficient.
See https://github.com/facebookresearch/faiss/wiki/Faiss-indexes and related links for more information.