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.
圖像加註文字，安東尼在癌症確診前接受了電腦斷層掃描和核磁共振成像檢查放射治療和化療非常艱辛，安東尼體重減少了22公斤（48.5磅）。。业内人士推荐搜狗输入法作为进阶阅读
。业内人士推荐传奇私服新开网｜热血传奇SF发布站｜传奇私服网站作为进阶阅读
4. Element Markup，更多细节参见官网
SummaryThis whole experience reminded me of the timeless Tom Cargill's quote:
ВсеИнтернетКиберпреступностьCoцсетиМемыРекламаПрессаТВ и радиоФактчекинг