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https://issues.apache.org/jira/browse/SOLR-17319?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17995716#comment-17995716
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Sonu Sharma edited comment on SOLR-17319 at 7/4/25 9:28 AM:
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Hello folks,
This feature seems quite promising and unlocks many opportunities in future. In
order to make it happen, I have created [another
PR|https://github.com/apache/solr/pull/3418] extending the idea of existing
one. It also help resolve the issues being discussed w.r.t the [previous
PR|https://github.com/apache/solr/pull/2489], mainly around across shard
documents merging. It's a way forward towards ultimately enabling Hybrid Search
in a pure way solving the shortcomings. Would appreciate your feedback &
reviews towards a fruitful outcome.
was (Author: ercsonu):
Hello folks,
This feature seems quite promising and unlocks many opportunities in future. In
order to make it happen, I have created[ another
PR|https://github.com/apache/solr/pull/3418] extending the idea of existing
one. It also help resolve the issues being discussed w.r.t the [previous
PR|https://github.com/apache/solr/pull/2489], mainly around across shard
documents merging. It's a way forward towards ultimately enabling Hybrid Search
in a pure way solving the shortcomings. Would appreciate your feedback &
reviews towards a fruitful outcome.
> Introduce support for Reciprocal Rank Fusion (combining queries)
> ----------------------------------------------------------------
>
> Key: SOLR-17319
> URL: https://issues.apache.org/jira/browse/SOLR-17319
> Project: Solr
> Issue Type: New Feature
> Components: query
> Affects Versions: 9.6.1
> Reporter: Alessandro Benedetti
> Assignee: Alessandro Benedetti
> Priority: Major
> Labels: pull-request-available
> Time Spent: 0.5h
> Remaining Estimate: 0h
>
> Reciprocal Rank Fusion (RRF) is an algorithm that takes in input multiple
> ranked lists to produce a unified result set.
> Examples of use cases where RRF can be used include hybrid search and
> multiple Knn vector queries executed concurrently.
> RRF is based on the concept of reciprocal rank, which is the inverse of the
> rank of a document in a ranked list of search results.
> The combination of search results happens taking into account the position of
> the items in the original rankings, and giving higher score to items that
> are ranked higher in multiple lists. RRF was introduced the first time by
> Cormack et al. in [1].
> The syntax proposed:
> JSON Request
> {code:json}
> {
> "queries": {
> "lexical1": {
> "lucene": {
> "query": "id:(10^=2 OR 2^=1 OR 4^=0.5)"
> }
> },
> "lexical2": {
> "lucene": {
> "query": "id:(2^=2 OR 4^=1 OR 3^=0.5)"
> }
> }
> },
> "limit": 10,
> "fields": "[id,score]",
> "params": {
> "combiner": true,
> "combiner.upTo": 5,
> "facet": true,
> "facet.field": "id",
> "facet.mincount": 1
> }
> }
> {code}
> [1] Cormack, Gordon V. et al. “Reciprocal rank fusion outperforms condorcet
> and individual rank learning methods.” Proceedings of the 32nd international
> ACM SIGIR conference on Research and development in information retrieval
> (2009)
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