Show HN: MinDB – an extremely memory-efficient vector database
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Re: Show HN: MinDB – an extremely memory-efficient vector database
#2That's looks exciting. Do you guys have more detailed benchmarks (doesn't have to be polished article), pastebin welcome ?
Thank you, will keep an eye
Re: Show HN: MinDB – an extremely memory-efficient vector database
#3That's looks exciting. Do you guys have more detailed benchmarks (doesn't have to be polished article), pastebin welcome ? Thank you, will keep an eye
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.
Re: Show HN: MinDB – an extremely memory-efficient vector database
#4Cool! What's next in the roadmap?
Re: Show HN: MinDB – an extremely memory-efficient vector database
#5Cool! 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.
Re: Show HN: MinDB – an extremely memory-efficient vector database
#6extremely efficient: python.