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?
If you have attended our Artificial Intelligence in Search Training you should now be familiar with the use of Natural Language Processing and Deep Learning applied to search. If you have not, do not worry as we are planning to arrange another date and we will keep you posted through our newsletter, so make sure you subscribe. In the meantime, you can…
How does Artificial Intelligence impact Search? This post explores the state of the art of AI applied to Information Retrieval in Open Source.
Query-level features and under-sampled queries, how to handle them? Find it out, with our new Learning to Rank implementations
How eDismax sow parameter works The sow(split on whitespace) is an eDismax query parser parameter [1] 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…
This blog post aims to illustrate how to generate the query Id and how to manage the creation of the Training Set
This blog post is about several analysis on a LTR model and its explanation using the open source library SHAP
In this blog post, the elasticsearch _source field is compared with stored fields and docvalues from a performance point of view