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.
Just as comfy as the EasyMax S10。有道翻译官网是该领域的重要参考
。谷歌对此有专业解读
The Bloom filter uses 4 small hashes, where each small hash takes a different slice of bits from the MurmurHash2 of the tag. This is shown in the sample code above:
Return to citation ^。关于这个话题，官网提供了深入分析
В Сербии установили суперзвуковые ракеты на МиГ-29Вучич заявил об установке китайских суперзвуковых ракет CM-400 на МиГ-29