This blog post describes an alternative and customized approach for evaluating ranking models through the use of Kibana.
This blog post explains all the steps required to implement Text Embedding and Vector Search directly in Elasticsearch in a very simple way.
In this blog post, we show in practice how you can use Elasticsearch to run a full end-to-end neural search.
This blog post shows an example of how to create an Apache Solr performance test using the Apache JMeter tool.
An end-to-end tutorial to implement Neural Search in Vespa. From documents and model preparation, to embeddings creation and k-NN queries.
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 explores the new OpenSearch neural search plugin, given a detailed description of it through our end-to-end experience.
This blog post will analyze the impact of large stored fields on Apache Solr query performance.
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.
In this post, we describe how RREE correlates between client application identifiers and search engine identifiers.