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Currently if a user has access to a field in their document or in a search response we would like to be able to re-rank by that metric. Currently the ReRank process supports ML_OpenSearch as a way to re rank. We would like to provide users with a way to perform a 2nd level re ranking, We would like to make it a ByFieldRerankProcessor
What solution would you like?
The rescoring logic (i.e update the _score field to reflect a new score) is already provided in the RescoringReRankProcessor. All that would be required is to implement ByFieldRerankProcessor to use the scores provided by the document or a previous search response.
Ideally the interface would look like this (My implementation is in the Neural Search repo)
It was discussed that previous scores should be kept as an option as this may hinder the expectation of the user, thus we added a field called keep_previous_score to allow this. We also want to give an option to be able delete the field (via remove_target_field) provided to perform the re ranking as this is redundant data.
What alternatives have you considered?
Creating a separate response processor, in open search core, that replaced and sorted the response. This was initially proposed in OpenSearch Core opensearch-project/OpenSearch#15631. But after offline discussion we decided that this functionality could be transferred to a processor that already does re ranking, in the Neural Search repo.
Do you have any additional context?
This functionality was brought up as a necessity to enhance the ML Inference Processor in the ML-Commons codebase
Is your feature request related to a problem?
Currently if a user has access to a field in their document or in a search response we would like to be able to re-rank by that metric. Currently the ReRank process supports ML_OpenSearch as a way to re rank. We would like to provide users with a way to perform a 2nd level re ranking, We would like to make it a
ByFieldRerankProcessor
What solution would you like?
The rescoring logic (i.e update the
_score
field to reflect a new score) is already provided in the RescoringReRankProcessor. All that would be required is to implement ByFieldRerankProcessor to use the scores provided by the document or a previous search response.Ideally the interface would look like this (My implementation is in the Neural Search repo)
It was discussed that previous scores should be kept as an option as this may hinder the expectation of the user, thus we added a field called
keep_previous_score
to allow this. We also want to give an option to be able delete the field (viaremove_target_field
) provided to perform the re ranking as this is redundant data.What alternatives have you considered?
Creating a separate response processor, in open search core, that replaced and sorted the response. This was initially proposed in OpenSearch Core opensearch-project/OpenSearch#15631. But after offline discussion we decided that this functionality could be transferred to a processor that already does re ranking, in the Neural Search repo.
Do you have any additional context?
This functionality was brought up as a necessity to enhance the ML Inference Processor in the ML-Commons codebase
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