Sease Ltd. explores whether modern databases like PostgreSQL and MongoDB can replace dedicated search engines such as Apache Solr and OpenSearch. While databases have improved their search capabilities, they still lack the advanced features, scalability, and performance of search engines for complex applications. The choice depends on the scale and architecture of the project.
This blog post explores the possible unexpected behaviors that can be encountered with Apache Solr’s lexicographical ordering.
This blogpost explores the performance impact of DocValues vs. Inverted Index for Apache Solr facets done through JSON facet API.
Discover late interaction in Apache Solr: how to implement ColBERT-style neural reranking to boost search accuracy.
Discover late interaction in Apache Solr: how to implement ColBERT-style neural reranking to boost search accuracy.
This blog summarises the main new features introduced in Apache Solr 10.0.0, focusing on Vector Search and Learning to Rank (LTR).
A comparison between Solr’s current LTR cache and a new implementation that works not only for logging features, but also for reranking.
In this blog we explores our recent contribution to Apache Solr that introduces support for SeededKnnVectorQuery.
This blog post explores binary vector quantization and the recent Solr contribution which adds support for this technique.
This blog post explores scalar vector quantization and the recent Solr contribution which adds support for this technique.
We are Sease, an Information Retrieval Company based in London, focused on providing R&D project guidance and implementation, Search consulting services, Training, and Search solutions using open source software like Apache Lucene/Solr, Elasticsearch, OpenSearch and Vespa.
