https://www.pinecone.io/learn/hybrid-search/ any chance we could get smth similar to this in qDrant? Would be hugely valuable for semantic search use cases, they really nailed it

Hi I have various of vector if the number of data increase I will face the problem of ram how to solve it? Can we search in two collection for search for one input?

seeing more of this broken pipe and socket timeout errors which we had noticed before , https://discord.com/channels/907569970500743200/928649303898075196/1059893176082645043 the only thing which we are having diff is the efs , and not storing data in ebs. ( is anyone else has noticed this errors) now i am seeing at a retrofit calls as well at read time , and happening at read time makes it critical. sharing the stacktrace at readtime

Out of file descriptors

Hi We have an application which makes a lot of use of vector representations of images and text. As part of our development aims I am evaluating vector databases. We have a slightly different need - where we have a vector we are not so much interested in the most similar vector, we need to know the most *disimilar* - so it's furthest neighbour rather than nearest neighbour - how would qdrant cope with this ? thanks !

Replicas? Can you expand a bit on that? thanks

Hi, is there a way to delete the previous snapshots? It seems I created snapshots too often and it takes up too much of space. I wonder if I can delete some of them.

if you don't store any valuable data in your storage, please try to clear /mnt/disks/sda/qdrant. Qdrant supports storage compatibility between consecutive versions

Hello Qdrant team! We have a philosophical question about Qdrant. We are planning to store 1B objects in a qdrant collection, with vectors and a small payload comprised of our objects' labels as we already discussed. The use-case is of course to do similarity search between the objects and constraint this search using the labels. But we would also like to have a reference database for our objects, to query them (without similarity search), and have more contextual data attached (creation dates, user ids, nested data and lot more). We were wondering if you think Qdrant could be suitable as a database + a vector search engine, and is this a use-case for which you will build features in the future (more payload types, filtering performance optimizations...). Or would you advice to use a secondary database and focus on using Qdrant as a vector search engine in sync with a primary storage?

Hello @andrey.vasnetsov , We can save embedded text in qdrant and then search from it... but is there any way to save embedded list of strings in our database and then do the search part ?