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davidmyriel
2024-10-05 08:40:13 -07:00
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qdrant-landing/content/blog/qdrant-1.12.x.md
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@@ -39,6 +39,10 @@ In data exploration, tasks like [**clustering**](https://en.wikipedia.org/wiki/D
You can use this API to compute a **sparse matrix of distances** that is optimized for large datasets. Then, you can filter through the retrieved data to find the exact vector relationships that matter. You can use this API to compute a **sparse matrix of distances** that is optimized for large datasets. Then, you can filter through the retrieved data to find the exact vector relationships that matter.
In terms of endpoints, we offer two different formats to show results:
- **Pairs** are simple, intutitive and ideal for graph representation.
- **Offsets** are more comples, but also native when defining CSR sparse matrices.
### Configuration - Pairs ### Configuration - Pairs
Use the `pairs` endpoint to compare 10 random point pairs from your dataset: Use the `pairs` endpoint to compare 10 random point pairs from your dataset:
@@ -81,7 +85,7 @@ Qdrant will list a sparse matrix of distances **between the closest pairs**:
### Configuration - Offsets ### Configuration - Offsets
The `offsets` endpoint is another method of calculating the distance between points: The `offsets` endpoint offer another format of showing the distance between points:
```http ```http
POST /collections/{collection_name}/points/search/matrix/offsets POST /collections/{collection_name}/points/search/matrix/offsets
qdrant-landing/static/blog/qdrant-1.12.x/distance-matrix.png
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