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.
홍준표 “통합 외면 TK, 이제와 읍소…그러니 TK가 그 꼴된 것”
。业内人士推荐新收录的资料作为进阶阅读
在强化“产学研用”协同的同时，咸阳也在数字赋能领域持续发力。深入推进“智改数转”，累计改造规模以上企业520户，培育省级以上智能制造示范企业17户，建成先进级智能工厂21个。
Момент удара ракеты по спутниковой станции в Израиле попал на видео20:56