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specify that offset is not propagated into prefetches

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Luis Cossío
2025-04-11 10:41:31 -04:00
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commit 157e4a4c21
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qdrant-landing/content/documentation/concepts/hybrid-queries.md
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---
title: Hybrid Queries #required
weight: 57 # This is the order of the page in the sidebar. The lower the number, the higher the page will be in the sidebar.
aliases:
aliases:
- ../hybrid-queries
hideInSidebar: false # Optional. If true, the page will not be shown in the sidebar. It can be used in regular documentation pages and in documentation section pages (_index.md).
---
@@ -10,7 +10,7 @@ hideInSidebar: false # Optional. If true, the page will not be shown in the side
*Available as of v1.10.0*
With the introduction of [many named vectors per point](/documentation/concepts/vectors/#named-vectors), there are use-cases when the best search is obtained by combining multiple queries,
With the introduction of [many named vectors per point](/documentation/concepts/vectors/#named-vectors), there are use-cases when the best search is obtained by combining multiple queries,
or by performing the search in more than one stage.
Qdrant has a flexible and universal interface to make this possible, called `Query API` ([API reference](https://api.qdrant.tech/api-reference/search/query-points)).
@@ -23,6 +23,8 @@ Specifically, whenever a query has at least one prefetch, Qdrant will:
Additionally, prefetches can have prefetches themselves, so you can have nested prefetches.
<aside role="status">Using <code>offset</code> parameter only affects the main query. This means that the prefetches must have a <code>limit</code> of at least <code>limit + offset</code> of the main query, otherwise you can get an empty result.</aside>
## Hybrid Search
One of the most common problems when you have different representations of the same data is to combine the queried points for each representation into a single result.
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Qdrant currently has two ways of combining the results from different queries:
- `rrf` -
- `rrf` -
<a href=https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf target="_blank">
Reciprocal Rank Fusion
</a>
Considers the positions of results within each query, and boosts the ones that appear closer to the top in multiple of them.
- `dbsf` -
- `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.
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.
{{< code-snippet path="/documentation/headless/snippets/query-points/hybrid-basic/" >}}