Hi, we are working on contributing the possibility of having vector-similarity features, in Apache Solr Learning To Rank. We are starting from the Lucene contribution of related function queries, which we are close to merging. Then we'll do the Solr part.
What you are trying to do has not been tested, it may work but there's no dedicated design for that so it may be quite clunky and expensive. And by the way, Images are not visible in the mailing list. Cheers -------------------------- *Alessandro Benedetti* Director @ Sease Ltd. *Apache Lucene/Solr Committer* *Apache Solr PMC Member* e-mail: a.benede...@sease.io *Sease* - Information Retrieval Applied Consulting | Training | Open Source Website: Sease.io <http://sease.io/> LinkedIn <https://linkedin.com/company/sease-ltd> | Twitter <https://twitter.com/seaseltd> | Youtube <https://www.youtube.com/channel/UCDx86ZKLYNpI3gzMercM7BQ> | Github <https://github.com/seaseltd> On Wed, 31 May 2023 at 16:05, rajani m <rajinima...@gmail.com> wrote: > Validating the expression to begin with, it doesn't work. Vector math > supports reading from an array of values so I tried the following > expression. > > dotProduct(array(search(v9, > q="id:1", > fl="numeric_field_dfd", > sort="numeric_field_dfd asc", > qt="/export")),array(2)) > > > where numeric_field_dfd - single valued dynamic field double type. I > tried the multivalued double type assuming that it converts to an array of > values but it didn't work, so tried the single value to start with. > > output of the expression is an exception - > > [image: image.png] > > > The value is not null as seen below, so am I wrong in terms of expression > syntax then, any suggestions? > > > [image: image.png] > > > > On Tue, May 30, 2023 at 4:47 PM rajani m <rajinima...@gmail.com> wrote: > >> Hi Solr Users, >> >> Does LTR Solr Feature >> <https://solr.apache.org/guide/8_7/learning-to-rank.html#feature-engineering> >> support >> streaming expressions? Steaming expr supports vector math >> <https://solr.apache.org/guide/7_5/vector-math.html#dot-product-and-cosine-similarity>, >> I am trying to configure stream apis vector math as a solr feature which >> would fetch a vector from a document field and another from query param and >> compute cosine or dot product. >> >> For example, a LTR feature definition that would look like below, is this >> supported? Does LTR solr feature support parsing streaming api requests and >> its somewhat unique response that is not same as standard solr response? >> >> >> { >> "name": "vector_sim_score", >> "class": "org.apache.solr.ltr.feature.SolrFeature", >> "params": { >> "q": >> "expr=dotProduct(search(collection_name,q="id:$uniq_id",fl="doc_vector", >> sort="from asc", qt="/export"), ${query_vector})" >> }, >> "store": "v1_feature_store" >> } >> >>
