krzysztof/landing_page
1
0
Fork 0
mirror of https://github.com/qdrant/landing_page.git synced 2026-10-07 11:58:31 +02:00
Code Issues Packages Projects Releases Wiki Activity

repo to PyPi

Browse Source
This commit is contained in:
Evgeniya Sukhodolskaya
2026-02-20 16:54:55 +01:00
parent 7781b95dfb
commit 66e132b02c
2 changed files with 3 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files
qdrant-landing/content/documentation/concepts/search-relevance.md
+1 -1
View File
@@ -193,7 +193,7 @@ To leverage the feedback in search across the entire collection, Qdrant provides
Internally, Qdrant combines the feedback list into pairs, based on the relevance scores, and then uses these pairs in a formula that modifies vector space traversal during retrieval (changes the strategy of retrieval). This relevance feedback-based retrieval considers not only the similarity of candidates to the query but also to each feedback pair. For a more detailed description of how it works, refer to the article [Relevance Feedback in Qdrant](/articles/relevance-feedback).
The `a`, `b`, and `c` parameters of the [`naive` strategy](#naive-strategy) need to be customized for each triplet of retriever, feedback model, and collection. To get these 3 weights adapted to your setup, use [our open source Python package](https://github.com/qdrant/relevance-feedback).
The `a`, `b`, and `c` parameters of the [`naive` strategy](#naive-strategy) need to be customized for each triplet of retriever, feedback model, and collection. To get these 3 weights adapted to your setup, use [our open source Python package](https://pypi.org/project/qdrant-relevance-feedback/).
<aside role="alert">When using point IDs for <code>target</code> or <code>example</code>, these points are excluded from the search results. To include them, convert them to raw vectors first and use the raw vectors in the query.</aside>