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The key concept is to enable users to express their search queries in natural language, which the LLM then enriches, interprets, and translates into structured queries based on the Solr index’s metadata.
This approach leverages the LLM’s ability to understand the nuances of natural language and the structure of documents within Apache Solr.
The LLM acts as an intermediary agent, offering a transparent experience to users automatically and potentially uncovering relevant documents that conventional search methods might overlook. The presentation will include the results of this experimental work, lessons learned, best practices, and the scope of future work that should improve the approach and make it production-ready.
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"NATO Hackathon Winner: AI-Powered Drug Search", Taras Kloba
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Harnessing the Power of NLP and Knowledge Graphs for Opioid Research
Northern Engraving | Nameplate Manufacturing Process - 2024
Northern Engraving | Nameplate Manufacturing Process - 2024
Introducing BoxLang : A new JVM language for productivity and modularity!
Introducing BoxLang : A new JVM language for productivity and modularity!
Leveraging the Graph for Clinical Trials and Standards
Leveraging the Graph for Clinical Trials and Standards