2026-06-08 –, Maschinenhaus
With Apache Solr 10 out, there are plenty of goodies coming up for vector-search aficionados.
From scalar and binary quantization to speed up your search and reduce the memory footprint, to early termination and hybrid approaches to navigate the HNSW graph.
Join us if you want to learn about the big steps forward of Apache Solr vector search!
Apache Solr 10 introduces many advancements in the realm of vector search, making many interesting Lucene features surface.
Starting from scalar and binary quantization, this feature helps users in reducing both the query time and memory footprint at the cost of some accuracy and disk space: a welcome trade-off for those using Solr on massive amounts of vectors.
Early termination introduces the ability of speeding up certain queries that saturate a configurable threshold, and Seeded KNN gives the ability to start the HNSW graph exploration from a lexical result set, rather than random entry documents (core mechanism of the Solr vector search implementation).
ACORN filtering improves the way pre-filtering happens when you mix traditional keyword searches with knn queries, and the query combiner finally offers a comprehensive strategy to mix up query results, opening the door to a more flexible hybrid search.
To conclude with a cherry on top of the cake, we'll go through many bug fixes and minor improvements, still worth mentioning.
The audience is expected to get an overview of all the new interesting vector search features coming with Solr 10 and learn how to use them and benefit from them in their use cases.
Alessandro Benedetti is an Apache Lucene/Solr committer and Solr chair of the PMC, Director at Sease Ltd.
He believes in Open Source as a way to build a bridge between Academia and Industry and facilitate the progress of applied research.
Alessandro is a passionate R&D software engineer, continuously applying the latest trends in Information Retrieval and AI to solve search problems.
He’s been working on Learning To Rank for years and more recently he’s been exploring Generative AI techs like Large Language Models and Retrieval Augmented Generation.
When he isn't on clients' projects, he contributes to the open-source community and presents at meet-ups and conferences such as ECIR, Search Solutions, Community Over Code, Haystack and Berlin Buzzwords.
Data Scientist with a strong focus on integrating Machine Learning and Deep Learning into information retrieval systems. She has also worked on Search Quality Evaluation across multiple projects. She loves exploring new technologies, applying state-of-the-art solutions in Search and giving back to the community through technical talks and open-source contributions, particularly to Apache Solr.
I’m a Research & Development Software Engineer and Search Consultant at Sease, where I help companies design and improve intelligent search solutions. I work with the most well-known search engines such as Apache Solr, Elasticsearch, OpenSearch and Vespa. I operate closely with clients to tackle complex search challenges, from relevance tuning and learning to rank to neural search and NLP integration. I enjoy diving into real-world problems, experimenting with new approaches, and finding the right balance between research and production-ready solutions. I also share insights with the search community through talks and collaborations.