Personalize Search Results with OpenSearch Agentic Memory
Improving search relevance typically requires complex personalization pipelines — recommendation engines, feature stores, ML models. This session shows a simpler path: multiple lightweight agents that collaborate through OpenSearch’s agentic memory to understand and enrich queries in real time. Same query, different results for different users.
Tensor arithmetics in search and ranking for Ecommerce.
Small, domain-specific vision models can dramatically enhance the buyer search experience by delivering more relevant visual understanding. But the real opportunity comes from controllable image embeddings: by fusing base search embeddings with additional control vectors, representing features such as color, shape, and style,
Relevance Feedback Inside the Search Engine
How does searching for new information often look? Loops: query, review results for relevance, rewrite the query, repeat… Until success, or until the user churns / the token budget burns.
This talk introduces a new instrument for search pipeline builders: propagating query-results relevance right inside the search algorithm of a search engine.
AI is here – time to throw away our search engines?
Why do we even need traditional search when AI can do everything? Or is it foolish to ignore simple, proven techniques for delivering great results? What’s the best way to combine old and new? Join our panel of experts for a fun and provocative debate!
Search is Back: Solving the “Context Crisis” for AI Agents
Why do smart agents make dumb mistakes? The culprit is context, an old problem with new solutions. Let’s fast-forward through 20 years of search evolution to fix the missing link in today’s Agentic AI.
We’ll demonstrate how to combine Knowledge Graphs and Vector Search to build reliable, context-aware applications using open-source tools.
OSS Security: Lessons from 10+ Years at Apache Solr
How are security decisions big and small made in a distributed open source community? Come find out at this session where users will gain insights and examples (both good and bad) to take back to their own projects.
Real-Time ML Pipelines: Feature Chaining with Chronon
Modern ML applications demand features computed in near real-time with sub-100ms latencies. This talk dives into Chronon, an OSS feature platform bridging streaming data infrastructure and production ML. Using a two-tower search pipeline example, we’ll show how we can chain embeddings with tabular features while minimizing hot-path computation.
Constant-Time Aggregations with Star-Tree in OpenSearch
Discover how OpenSearch breaks linear scaling. Inspired by Apache Pinot, the Star-Tree index moves performance dependency from document count to field cardinality. Learn how we extended Lucene’s DocValues to build multi-dimensional materialized views that deliver sub-second analytics on billion-scale datasets for observability workloads.
When better retrieval makes agents worse
Agentic systems can break not because information is missing, but because persuasively wrong context gets promoted into action. We examine a recurring pattern: retrieval metrics improve while agent behavior degrades as distractors enter multi-step loops. We show why relevance, reliability, and security are tightly connected in agentic retrieval.
Apache Solr 10: What’s Coming up for Vector Search
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!