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.
«То, что можно было услышать, звучало робко», — говорится в статье.，推荐阅读WPS官方版本下载获取更多信息
Photograph: Simon Hill，推荐阅读爱思助手下载最新版本获取更多信息
这场持续了三年、以低价为武器的市场争夺战，一度将咖啡行业的竞争推向白热化，大幅吞噬着企业的利润，却也让咖啡这一舶来品在中国市场得以全面普及。