I do, and it's very very rough around the edges to be honest. Lots of things broken, things are even breaking between releases suddenly in unexpected places. Or at least, I'm used to working with more robust data stores.
If my work was more high stakes, I'd have already advocated for moving our vector search to something more robust. Thankfully it's not and I can just maintain what we're making with not too much stress, and enjoy seeing this OS project grow from a user perspective (haven't seen a data store go through this very initial phase in my career yet).
Support from the team is great however, and congrats to them for this round!
We are for a few projects. We've been using them for over a year and have been impressed. We have 10s millions of items in there with lots of daily inserts/deletions etc. There's been a couple of gotchas but generally it is quite predictable and scalable.
We use 768 dimensional vectors for our items with several other payload filters (e.g. language). Performance has been good and I think the qdrant team focus on the right features without creeping into other areas.
I built a little proof of concept that uses it in the RAG pipeline, it's been proving quite useful, so we're just starting the move to production.
It's probably going to stay, but I'm also evaluating databricks new vector store as we're using databricks for all the analytics parts of the app already, and having them all on the same infrastructureis appealing.
I should have been clearer in my question: It would be great to hear directly from people who are using them about their successes and what their experience has been like
Support from the team is great however, and congrats to them for this round!