krzysztof/landing_page
1
0
Fork 0
mirror of https://github.com/qdrant/landing_page.git synced 2026-09-28 23:48:31 +02:00
Code Issues Packages Projects Releases Wiki Activity

Add dbsf description (#1075)

Browse Source
This commit is contained in:
Luis Cossío
2024-08-12 20:15:52 -04:00
committed by GitHub
parent 6f139caada
commit eb82953931
1 changed files with 17 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files
qdrant-landing/content/documentation/concepts/hybrid-queries.md
+17 -3
View File
@@ -32,11 +32,25 @@ One of the most common problems when you have different representations of the s
For example, in text search, it is often useful to combine dense and sparse vectors get the best of semantics,
plus the best of matching specific words.
There are many ways to fuse the results. One versatile method is <a href=https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf target="_blank">Reciprocal Rank Fusion (RRF)</a>,
which considers the positions of each of points in the results, and boosts the ones that appear closer to the top in multiple queries.
Qdrant currently has two ways of combining the results from different queries:
- `rrf` -
<a href=https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf target="_blank">
Reciprocal Rank Fusion
</a>
Here is an example of RRF for a query containing two prefetches against different named vectors configured to respectively hold sparse and dense vectors.
Considers the positions of results within each query, and boosts the ones that appear closer to the top in multiple of them.
- `dbsf` -
<a href=https://medium.com/plain-simple-software/distribution-based-score-fusion-dbsf-a-new-approach-to-vector-search-ranking-f87c37488b18 target="_blank">
Distribution-Based Score Fusion
</a> *(available as of v1.11.0)*
Normalizes the scores of the points in each query, using the mean +/- the 3rd standard deviation as limits, and then sums the scores of the same point across different queries.
<aside role="status"><code>dbsf</code> is stateless and calculates the normalization limits only based on the results of each query, not on all the scores that it has seen.</aside>
Here is an example of Reciprocal Rank Fusion for a query containing two prefetches against different named vectors configured to respectively hold sparse and dense vectors.
```http
POST /collections/{collection_name}/points/query