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docs: fix typos and improve clarity in quantization/turboQuant docs (#2371)

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Mohamed Arbi
2026-05-26 09:36:46 +02:00
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qdrant-landing/content/articles/binary-quantization.md
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@@ -181,7 +181,7 @@ If you have lower accuracy requirements you can even try doing a small oversampl
We retrieved some early results on the relationship between limit and oversampling using the the DBPedia OpenAI 1M vector dataset. We ran all these experiments on a Qdrant instance where 100K vectors were indexed and used 100 random queries.
We varied the 3 parameters that will affect query time and accuracy: limit, rescore and oversampling. We offer these as an initial exploration of this new feature. You are highly encouraged to reproduce these experiments with your data sets.
We varied the 3 parameters that will affect query time and accuracy: limit, rescore and oversampling. We offer these as an initial exploration of this new feature. You are highly encouraged to reproduce these experiments with your datasets.
> Aside: Since this is a new innovation in vector databases, we are keen to hear feedback and results. [Join our Discord server](https://discord.gg/Qy6HCJK9Dc) for further discussion!
@@ -231,6 +231,6 @@ If you determine that binary quantization is appropriate for your datasets and q
Binary quantization is exceptional if you need to work with large volumes of data under high recall expectations. You can try this feature either by spinning up a [Qdrant container image](https://hub.docker.com/r/qdrant/qdrant) locally or, having us create one for you through a [free account](https://cloud.qdrant.io/signup) in our cloud hosted service.
The article gives examples of data sets and configuration you can use to get going. Our documentation covers [adding large datasets to Qdrant](/documentation/tutorials-develop/bulk-upload/) to your Qdrant instance as well as [more quantization methods](/documentation/manage-data/quantization/).
The article gives examples of datasets and configuration you can use to get going. Our documentation covers [adding large datasets to Qdrant](/documentation/tutorials-develop/bulk-upload/) to your Qdrant instance as well as [more quantization methods](/documentation/manage-data/quantization/).
If you have any feedback, drop us a note on Twitter or LinkedIn to tell us about your results. [Join our lively Discord Server](https://discord.gg/Qy6HCJK9Dc) if you want to discuss BQ with like-minded people!