Solid work OpenAI, though I'd definitely like to see some more benchmarks on a wider variety of datasets in addition to the ones listed in the post. Regardless,

Solid work OpenAI, though I'd definitely like to see some more benchmarks on a wider variety of datasets in addition to the ones listed in the post. Regardless, it's good to see embeddings becoming more and more mainstream and easier to leverage out-of-box. We tried image embeddings many moons ago (2015) with AlexNet trained across a custom dataset, but we still had to add quite a few custom roles post-inference. A large selling point for ada-002 embeddings seems to be the reduced dimensionality. While lower-dimensional embeddings definitely help performance, I would say it's still highly dependent on the index that's being used. Graph- and tree-based indexes will benefit less than ones based on IVF (https://zilliz.com/blog/vector-index https://zilliz.com/blog/vector-index), as they do fewer overall distance computations during query time, but the speedup should still be significant. Still been meaning to try semantic search across Wikipedia via text embeddings. Will definitely play around with OpenAI + Milvus (https://github.com/milvus-io/milvus https://github.com/milvus-io/milvus).