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.
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赵乐际说,本次会议顺利完成预定任务。审议并原则通过全国人大常委会工作报告稿,通过十四届全国人大四次会议议程草案、主席团和秘书长名单草案、列席人员名单;审议3件法律案,审议关于法律清理工作情况和有关法律和决定处理意见的报告稿并决定提请十四届全国人大四次会议审议,还通过了代表资格审查报告和人事任免案。,更多细节参见Line官方版本下载
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