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Search has evolved from matching tokens to understanding intent, but the new challenge is doing it at scale, with high precision and low latency.
In this session, we define the "State of the Art" for OpenSearch 3.x by diving into its most advanced retrieval and optimization capabilities.
We will move beyond basic k-NN to showcase specific ranking techniques, including Radial Search for precise vector scoping and Maximal Marginal Relevance (MMR) to solve the "diversity problem" in AI-generated results.
Crucially, we will look under the hood at Memory Optimized Search to demonstrate how to reduce infrastructure footprint without sacrificing accuracy. We will explain how techniques like Asymmetric Distance Computation and Random Rotation drive the latest quantization engines, allowing you to run billion-scale vector workloads efficiently.
Finally, we will show how to operationalize these complex architectures using the new OpenSearch Workflow Builder. Attendees will see how to chain these advanced processors into reusable AI search pipelines using a visual interface, proving that next-generation semantic search can be both powerful and accessible.
Italian, adopted by France not long ago, I am a constant learner, dedicated to computer science and discovery—whether uncovering solutions or gaining insights.
Lucian Precup is the CTO of [all.site](https://all.site/) - the collaborative search engine developed at [Station F](http://stationf.co) in Paris. With his colleagues at [Adelean](http://adelean.com), Lucian develops solutions for indexing, searching and analyzing data. Lucian regularly... Read More →
