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Show HN: Pyversity – Fast Result Diversification for Retrieval and RAG

Show HN: Pyversity – Fast Result Diversification for Retrieval and RAG

2 comments

·October 19, 2025

Hey HN! I’ve recently open-sourced Pyversity, a lightweight library for diversifying retrieval results. Most retrieval systems optimize only for relevance, which can lead to top-k results that look almost identical. Pyversity efficiently re-ranks results to balance relevance and diversity, surfacing items that remain relevant but are less redundant. This helps with improving retrieval, recommendation, and RAG pipelines without adding latency or complexity.

Main features:

- Unified API: one function (diversify) supporting several well-known strategies: MMR, MSD, DPP, and COVER (with more to come)

- Lightweight: the only dependency is NumPy, keeping the package small and easy to install

- Fast: efficient implementations for all supported strategies; diversify results in milliseconds

Re-ranking with cross-encoders is very popular right now, but also very expensive. From my experience, you can usually improve retrieval results with simpler and faster methods, such as the ones implemented in this package. This helps retrieval, recommendation, and RAG systems present richer, more informative results by ensuring each new item adds new information.

Code and docs: github.com/pringled/pyversity

Let me know if you have any feedback, or suggestions for other diversification strategies to support!

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leobg

Might also be useful for dataset curation, or even just prompt engineering. For example when training a classification task and picking a diverse set of examples for training or evaluation.

Tananon

True, I think that's also a great usecase! Though these algorithms likely won't scale to very large datasets (e.g. millions of samples), but for smaller datasets, like fine-tuning sets, I think this would work very well. I've worked on something similar in the past that works for larger datasets (semantic deduplication: https://github.com/MinishLab/semhash).