As companies rush to embed generative AI into search, support, and decision-making, the real competitive edge won’t come from scale alone, but from how responsibly
In this blog we explores an early termination strategy designed to make approximate k-NN search faster in Apache Solr.
In this blog post, we provide a comprehensive overview of the features based on large language models (LLM) currently supported by OpenSearch.
Sease will talk at the upcoming conference Search Solutions and Tutorial 2025, hosted in London by the BCS group.
In this blog post, we examine the ColBERT paper, which adapts deep learning models, in particular, BERT, for efficient retrieval.
Let’s announce the twenty-fifth edition of the London Information Retrieval & AI Meetup, a hybrid event about Information Retrieval.
This blog post explores GLiNER as a viable alternative to large language models (LLMs) for query parsing tasks.
It explores an AI-powered Filter Assistant to improve User eXperience in navigating search results efficiently and effectively.
Learn all the secrets of the new semantic search in Apache Solr 9.8 that uses LLMs to vectorise text and support natural language queries.
This blog post focuses on limitations in OpenSearch v2.17 during the implementation of search features and proposes practical workarounds.
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.
