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// we contribute back to the community
Open Source Contributions
Sease strongly believes in Open Source as a way to build a sustainable model for human progress.
If you are curious about Sease Open Source projects you find them under our company Github account.
Our team is actively supporting the public mailing lists and continuously contributing code back to the community.
This is a list of some of our biggest works :
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Apache Lucene/Solr
NEURAL SEARCH
We brought Neural Search to Apache Solr 9.0!
Through the implementation of the k-nearest neighbor search for vectors in Apache Solr, we have enabled the possibility of indexing and searching numerical vectors. You can generate the vectors using deep neural networks models such BERT(or through any other technique that encodes an information need/corpus in numerical format).
It leverages the Navigable Small Graph World Lucene internal implementation.
PARTICIPATE IN LEARNING TO RANK PLUGIN BY BLOOMBERG
With the Learning To Rank (or LTR for short) module, you can configure and run machine-learned ranking models in Apache Solr.
We joined the original developments (lead by Bloomberg), offering various insights and bug fixes and then, over the years, we continued supporting this component through many code contributions, conferences, and blog posts.
LEARNING TO RANK INTERLEAVING
The Learning To Rank interleaving capability in Apache Solr can be used to mix up the results of different rankers to leverage the users’ implicit feedback and estimate the best ranking function.
We designed and developed the functionality, available from Apache Solr 8.8.
WEIGHTED SYNONYMS
The weighted synonyms contribution makes possible to assign a different weight to each synonym for a word and leverage the configuration to improve the search relevance of your search engine.
We designed and developed the functionality in Apache Lucene and Solr, available from 8.5.
DOCUMENT CLASSIFICATION
Document classification in Apache Lucene and Solr leverages the internal implementation of text classification to assign tags and classes to entire documents, unsupervised.
We implemented it on top of Lucene text classification and integrated it in Apache Solr, available from 6.1.
MORE LIKE THIS
The More Like This allows returning similar documents to an input document.
We have worked extensively on the feature for many years, from the Lucene and Solr sides, contributing many improvements and bug fixes.
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Rated Ranking Evaluator
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RankLib (Learning to Rank)
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