The select function is documented here: https://solr.apache.org/guide/solr/latest/query-guide/transform.html
Joel Bernstein http://joelsolr.blogspot.com/ On Wed, May 31, 2023 at 1:51 PM Joel Bernstein <joels...@gmail.com> wrote: > The array function doesn't operate in the way its being used here. Here > are the docs on arrays: > > > https://solr.apache.org/guide/solr/latest/query-guide/vector-math.html#arrays > > Using a multi-valued field you could do something like this where test_fs > is a multi-valued float field. The select function evaluates arrays > automatically so they can be operated on by math expressions in a streaming > context. > > select(search(jdata, fl=test_fs), > dotProduct(test_fs, test_fs) as p) > > Which returns: > > { "result-set": { "docs": [ { "p": 1.9350813535659377 }, { "p": > 2.2449532856850816 }, { "p": 1.7212359783803421 }, { "p": 2.761290822044021 > }, > > > > > Joel Bernstein > http://joelsolr.blogspot.com/ > > > On Wed, May 31, 2023 at 12:59 PM Alessandro Benedetti < > a.benede...@sease.io> wrote: > >> 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" >> >> } >> >> >> >> >> >
