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.
Tony Jolliffe BBC
，这一点在爱思助手下载最新版本中也有详细论述
_本文是我关于移动应用中 AI 实际应用系列文章的一部分。接下来将会是：“完全设备端 RAG — 完整指南” — 订阅即可获取更新。
原本只是幾次秘密相會，隨著十年時間推進，逐漸發展成熱烈且無法否認的愛。他們一起喝薑味汽水，吃鮪魚三明治。一間小屋，也慢慢變成了「家」。。服务器推荐对此有专业解读
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李 “나와 애들 추억묻은 애착인형 같은 집…돈 때문에 판 것 아냐”