zilliztech / zilliztech/VectorDBBench
Questions regarding custom datasets and recall calculation
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- Python
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Description
Thank you for your work on this project — it's a valuable tool for evaluating vector databases.
I have a couple of technical questions:
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I’m interested in benchmarking with a custom scale/dimensions dataset (e.g., 100M vectors with 1024 dimensions). In this case, is it recommended that I generate a custom dataset manually, or is there an existing mechanism within VectorDBBench to support such configurations?
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Regarding the 15 built-in datasets, could you please provide more details on how the ground truth is constructed? Specifically, I’d like to understand the methodology used to compute recall — for example, how the nearest neighbors are determined and what metrics or tools are used to establish the ground truth.
I would greatly appreciate any clarification or documentation you could provide.
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First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
No files, tests, or entry points are named. Review the project’s dataset configuration and recall or ground-truth documentation, then document how custom scale and dimensions are supported and how the 15 built-in datasets establish nearest neighbors and recall.
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Assessment
- Tech stack
- python
- Domain
- databases, search
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100