In this blog post, we show in practice how you use Apache Solr to index and search vectors and then run a full end-to-end neural search.
This blog post will analyze the impact of large stored fields on Apache Solr query performance.
The Apache Solr Birds of a Feather from ApacheCon NA 2022 in New Orleans taught us we can improve Solr in many ways… This is the TOP-10!
In this blog post we present the available learning to rank Apache Solr features with a focus on categorical features and how to manage them.
This blog is meant to explain how QueryResultCache and FilterCache are used during the basic query processing in Apache Solr 8.11.0. This blog does not explain how these caches are used during the execution of more advanced components like faceting. Solr caches are associated with a specific instance of an Index Searcher. By default, elements…
How the FeatureLogger works? When the Feature Vector Cache is used in Solr? Is the cache speeding up the rerank process?
Neural Search in Apache Solr has been contributed by Sease thanks to Alessandro Benedetti, Apache Lucene/Solr committer, and Elia Porciani.
We are recently working on contributing knn search in Solr leveraging on the latest Lucene developments. This blog post goal is to give some numbers about the benchmark mesaures gathered during the development process. Setup and collection To benchmark our solution we setup our solr instances using dockerized solr in a t3.large aws machine (2…
How a learning to rank query works in Solr? How we can obtain the required features extraction time from the Solr qTime parameter?
How eDismax sow parameter works The sow(split on whitespace) is an eDismax query parser parameter  that regulates aspects of query time text analysis that impact how the user query is parsed and the internal Lucene query is built.It is particularly relevant in multi-term and multi-field search.If sow=true : first the user query text is…