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https://issues.apache.org/jira/browse/SOLR-17319?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=18022156#comment-18022156
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Ufuk Yilmaz edited comment on SOLR-17319 at 9/23/25 4:32 PM:
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[~ercsonu] If I'm understanding correctly, what you said means that the way
OpenSearch implemented RRF is not completely accurate too, is that right?
[https://opensearch.org/blog/introducing-reciprocal-rank-fusion-hybrid-search/]
under "How RRF works" ? Is it the equivalent of Way 2?
--- The rest is just my thoughts, which might be completely uninformed. ---
I feel like such features shouldn't need to be aware of sharding. There's
already an established method for combining results from multiple shards for
regular queries, so new features like this shouldn't have to think about that
again. A generalized "post-result processor pipeline" to handle logic across
multiple query results might offer a cleaner way to implement such new
features, as more of them will probably arise (like different algorithms for
hybrid search or anything that processes multiple query results). That said,
speaking only as a user, as far as I know such a place for that kind of logic
doesn't exist. Streaming expressions do offer this functionality, but they are
not known or adopted by many users, even though I think they're great.
---
was (Author: JIRAUSER310899):
[~ercsonu] If I'm understanding correctly, what you said means that the way
OpenSearch implemented RRF is not completely accurate too, is that right?
[https://opensearch.org/blog/introducing-reciprocal-rank-fusion-hybrid-search/]
under "How RRF works" ? Is it the equivalent of Way 2?
> 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: vector-search
> Affects Versions: 9.6.1
> Reporter: Alessandro Benedetti
> Assignee: Alessandro Benedetti
> Priority: Major
> Labels: pull-request-available
> Time Spent: 27h 20m
> 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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