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.
The async iterator based approach provides a natural bridge between this alternative approach and Web streams. When coming from a ReadableStream to this new approach, simply passing the readable in as input works as expected when the ReadableStream is set up to yield bytes:。业内人士推荐91视频作为进阶阅读
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Natural neighbour interpolation has a number of strengths over linear barycentric interpolation. Namely, it provides a smooth or continuous slope between samples3, and is always the same for a given point set, unlike the TIN where the quality of the triangulation can produce biases in the outcome, even in the best case.