An end-to-end tutorial to implement Neural Search in Vespa. From documents and model preparation, to embeddings creation and k-NN queries.
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In the previous blogpost of this series, we looked at how to use BERT to improve search relevance by performing document re-ranking. The assumption of this approach is that the set of documents that need to be re-ranked, also known as candidates, contains the largest number of documents relevant to the query. We say that…
Neural Search in Apache Solr has been contributed by Sease thanks to Alessandro Benedetti, Apache Lucene/Solr committer, and Elia Porciani.
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.
This blog is a quick summary of our experience at the ECIR 2018, the European Conference on Information Retrieval.