New RAG architecture enables real-time AI integration in urban environments

Researchers have developed a novel real-time spatial Retrieval Augmented Generation (RAG) architecture designed to integrate generative AI into urban environments. This approach addresses the limitations of traditional large-language models by enabling them to access and process dynamic, real-time data specific to cities. The proposed system leverages temporal and spatial filtering through linked data, with a practical demonstration using FIWARE for a tourism assistant application in Madrid. AI

IMPACT This research could enable more dynamic and responsive AI applications in smart city initiatives.

RANK_REASON The cluster contains a research paper detailing a new technical approach for AI integration. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• CatalyzeX Code Finder for Papers
• DagsHub
• FIWARE
• Generative Artificial Ingelligence
• Gotit.pub
• Hugging Face
• Javier Conde
• large-language models
• Madrid
• retrieval-augmented generation
• ScienceCast
• Urban Foundation Models: A Survey

• paper
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• infra

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