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.
Is it possible to integrate eDisMax-like boosting in Approximate Nearest Neighbor search for Solr and Lucene?
This blogpost introduces Approximate Search Evaluator: a tool to measure vector search performance, addressing the accuracy/speed trade-off.
Dataset Generator automates the creation of relevance datasets for search evaluation, generating queries and relevance ratings with LLMs.
Vespa implements several useful features for customizing and improving Vector Search. Here, we will go into detail of each of them.
This blogpost explores the performance impact of DocValues vs. Inverted Index for Apache Solr facets done through JSON facet API.
This blog summarises the main new features introduced in Apache Solr 10.0.0, focusing on Vector Search and Learning to Rank (LTR).
This blog post explores the Combined Query Feature using a custom algorithm in Apache Solr with a hands-on approach.
We explore the Combined Query Feature using Reciprocal Rank Fusion in Apache Solr with a hands-on approach.
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.
