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upd faq (#1530)

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Andrey Vasnetsov
2025-03-25 23:00:08 +01:00
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qdrant-landing/content/documentation/faq/qdrant-fundamentals.md
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@@ -22,10 +22,6 @@ There is no inherent limitation on metadata size, but it should be [optimized fo
Yes, due to differences in hardware configurations and parallel processing, results may vary slightly.
### What to do with documents with small chunks using a fixed chunk strategy?
For documents with small chunks, consider merging chunks or using variable chunk sizes to optimize vector representation and search performance.
### How do I choose the right vector embeddings for my use case?
This depends on the nature of your data and the specific application. Consider factors like dimensionality, domain-specific models, and the performance characteristics of different embeddings.
@@ -38,6 +34,18 @@ Qdrant natively [supports multiple vectors per data point](/documentation/concep
Yes, Qdrant supports migration of embeddings from other vector stores, facilitating easy transitions and adoption of Qdrant’s features.
### Why the amount of indexed vectors doesn't match the amount of vectors in the collection?
Qdrant doesn't always need to index all vectors in the collection.
It stores data is segments, and if the segment is small enough, it is more efficient to perform a full-scan search on it.
Make sure to check that the collection status is `green` and that the number of unindexed vectors smaller than indexing threshold.
### Why collection info shows inaccurate number of points?
Collection info API in Qdrant returns an approximate number of points in the collection.
If you need an exact number, you can use the [count](/documentation/concepts/points/#counting-points) API.
## Search
### How does Qdrant handle real-time data updates and search?
@@ -121,5 +129,5 @@ We only guarantee compatibility if you update between consecutive versions. You
### Is it possible to scale down a Qdrant Cloud cluster?
It is possible to vertically scale down a Qdrant Cloud cluster, as long as the disk size is not reduced. Horizontal downscaling is currently not possible, but on our roadmap.
But in some cases, we might be able to help you with that manually. Please open a support ticket, so that we can assist.
Yes, it is possible to both vertically and horizontally scale down a Qdrant Cloud cluster.
Note, that during the vertical scaling down, the disk size cannot be reduced.