Show HN: MinDB – an extremely memory-efficient vector database(github.com)
github.com
Show HN: MinDB – an extremely memory-efficient vector database
https://github.com/D-Star-AI/minDB
5 comments
We've only done full benchmarking with the FIQA dataset, comparing minDB with Chroma. We're going to try it with Qdrant and Weaviate soon too, since they both have support for quantization, which will be a more apples-to-apples comparison with our approach.
We did test uploading and querying a Wikipedia dump, which was ~35M vectors. Query latency was around 150ms and peak memory usage was 1.5GB. We couldn't test recall, though, because we didn't have queries with ground truths.
We did test uploading and querying a Wikipedia dump, which was ~35M vectors. Query latency was around 150ms and peak memory usage was 1.5GB. We couldn't test recall, though, because we didn't have queries with ground truths.
Cool! What's next in the roadmap?
The main thing we need to add is metadata filtering, as that's required for a lot of use cases. We're also thinking about adding hybrid search support and multi-factor ranking.
extremely efficient: python.
Thank you, will keep an eye