Hi, guys, any plans to support vector clustering? It looks like this could be done pretty easy given the index architecture

@andrey.vasnetsov What is the server HTTP timeout value?

Is DiskANN/Vamana considered as an alternative to HNSW in the future? If so, how far away could that be?

Hi all! We made some experiments with mmap and summarized in in the article "Minimal RAM you need to serve a million vectors" https://qdrant.tech/articles/memory-consumption/ We found that using `htop` for checking memory consumption is wrong. So we did it in a hard way. As a result, we could serve 1+ million vectors using just 135Mb of RAM! But spoiler: ||you would need fast disks for that||

Simple and easy way to expose Qdrant from your Kubernetes cluster, and use Authentication. https://bichuetti.net/qdrant-authentication

there is a CLI arguments to recover from snapshots https://qdrant.tech/documentation/snapshots/#restore-snapshot

Hi, I'm a big fan of qdrant and it solves so many headaches I would have needed to solve myself. I started to include it in my project and encountered an issue when adding many records to the index. I reproduced it on v0.11.6 and 0.11.5 x64 system I'm using docker available from dockerhub Steps: * start qdrant service with default config * add points with payload to index via python client library (prefer grpc = true) * After a while I get the errors: [2022-12-29T14:44:55.289Z ERROR collection::update_handler] Failed to flush: Service runtime error: Failed to flush id_tracker versions: Service runtime error: RocksDB flush_cf error: IO error: While open a file for appending: ./storage/collections/t19_texts_2022-12-26 13:10:48+00:00/0/segments/6f52a205-b449-4693-aafc-7b39074c8a07/000068.sst: Too many open files [2022-12-29T14:45:28.858Z ERROR collection::collection_manager::collection_updater] Update operation failed: Not recovered from previous error: Service runtime error: RocksDB put_cf error: IO error: While open a file for appending: ./storage/collections/t19_texts_2022-12-26 13:10:48+00:00/0/segments/452a8371-15c2-4afb-84ab-e43fa0b9e1a8/000179.sst: Too many open files and many more like that

Hi people. We have implemented an Ingress with Basic Authentication in front of Qdrant service. Using requests and httpx directly works fine (setting auth). But sending the auth parameter to qdrant-client is not being handled correctly (client = QdrantClient(host="domain-to-svc", port=6333, https=True, auth=('ferrajoli', '1234567890'))). Looking at client implementation, our understanding was that this keyword argument would be sent to httpx.Client on the chain of kwargs. Any clues on implementing basic auth? https://github.com/qdrant/qdrant_client/blob/c33c57d58f853c1b1dcce9d1c58e938c5001575b/qdrant_client/http/api_client.py#L51

Hey all, happy holidays. Complete newb here but how do I make vectors to upload with my documents? I feel like there is a fundamental gap in my knowledge. I’m trying to make a product search engine.

This question is not more related to qdrant , but more related to how memmap works and how like if we configure the memmap thwn how its not using the heap memory , but what memory will it use . This ss is from my qdrant box where i had done the indexing for 700k somthing vector and my buffer/ cache is around 6.3 gb .so wanted to know about how this memmap files are stored at os level . As i would think it may be related with virtual memory or it may be using some disc space and index for the memmap is only stored in heap. If u guys have some idea pls let me know