Скачать или смотреть AI-Powered Search Results Navigation with LLMs & JSON Schema

More: https://2025.berlinbuzzwords.de/sessi...

Speaker: Ilaria Petreti, Anna Ruggero, Edward Lambe

Struggling to identify relevant filters among too many facets and frustrating results navigation? We explore an AI Filter Assistant for statistical data (SDMX) showing how LLMs can be leveraged to suggest the best filters for your natural language query, helping you refine the results in Apache Solr. We share wins, fails, and lessons learned.

In this talk, we explore an AI-powered Filter Assistant, designed for the Statistical Data and Metadata eXchange (SDMX) to improve User eXperience in navigating search results efficiently and effectively.
We discuss how LLMs enhance filter suggestions by analyzing both user queries and indexed data.

On the architecture side, we break down:
1) Data retrieval – how we collected and processed the input SDMX data to build taxonomies used by the model to reconcile the concepts in the natural language query
2) API structure – a deep dive into our endpoints, what they do, and the responses they return.
3) Model choice – the process of identifying the best LLM for the task, including our motivations and studies
4) Structured output & JSON Schema – key benefits, limitations, and lessons learned from extensive testing. We showcase different test results and insights on what works best.
5) Solr query optimization – how to integrate the assistant’s output into a search query, using different boolean strategies to handle the refinement of both too-many and zero-result scenarios.

Expect real-world insights, practical takeaways, and a discussion on the future of AI-driven filtering!

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