I should clarify that OCI is a distribution method - the deployed system won’t run in a container, we’re booting a real Linux.
Many people reading this will call bullshit on the performance improvement metrics, and honestly, fair. I too thought the agents would stumble in hilarious ways trying, but they did not. To demonstrate that I am not bullshitting, I also decided to release a more simple Rust-with-Python-bindings project today: nndex, an in-memory vector “store” that is designed to retrieve the exact nearest neighbors as fast as possible (and has fast approximate NN too), and is now available open-sourced on GitHub. This leverages the dot product which is one of the simplest matrix ops and is therefore heavily optimized by existing libraries such as Python’s numpy…and yet after a few optimization passes, it tied numpy even though numpy leverages BLAS libraries for maximum mathematical performance. Naturally, I instructed Opus to also add support for BLAS with more optimization passes and it now is 1-5x numpy’s speed in the single-query case and much faster with batch prediction. 3 It’s so fast that even though I also added GPU support for testing, it’s mostly ineffective below 100k rows due to the GPU dispatch overhead being greater than the actual retrieval speed.，推荐阅读heLLoword翻译官方下载获取更多信息
，这一点在safew官方版本下载中也有详细论述
interior_style:。safew官方版本下载对此有专业解读
In his Matching Soulmates paper in the journal of Public Economic Theory, everyone is in a computer simulated dating pool, where thousands of digitally created daters rank each other. His algorithm picks "first‑order soulmates": pairs who choose each other in a stable matching. It removes them, and runs it again with those left, and you get second‑order soulmates, and so on.