instead of fetching a list that could be
hard; almost everyone gets it wrong, and you probably will too."
I’ll give you an example of what this looks like, which I went through myself: a couple years ago I was working at PlanetScale and we shipped a MySQL extension for vector similarity search. We had some very specific goals for the implementation; it was very different from everything else out there because it was fully transactional, and the vector data was stored on disk, managed by MySQL’s buffer pools. This is in contrast to simpler approaches such as pgvector, that use HNSW and require the similarity graph to fit in memory. It was a very different product, with very different trade-offs. And it was immensely alluring to take an EC2 instance with 32GB of RAM and throw in 64GB of vector data into our database. Then do the same with a Postgres instance and pgvector. It’s the exact same machine, exact same dataset! It’s doing the same queries! But PlanetScale is doing tens of thousands per second and pgvector takes more than 3 seconds to finish a single query because the HNSW graph keeps being paged back and forth from disk.，详情可参考新收录的资料
在和印度企业家Varun Mayya的对谈中，言语间他明示，在调整了数月之后，Meta即将进入加速期，交付振奋人心的成果。
，更多细节参见新收录的资料
Что думаешь? Оцени!。新收录的资料是该领域的重要参考
-O https://mat.tepper.cmu.edu/COLOR/instances/myciel6.col \