Real-time Spatial Retrieval Augmented Generation for Urban Environments

TL;DR

This paper introduces a real-time spatial Retrieval Augmented Generation architecture tailored for urban environments, enabling dynamic, context-aware AI applications in cities by leveraging linked data and FIWARE components.

Contribution

It presents a novel spatial RAG architecture that integrates temporal and spatial filtering for urban AI applications, demonstrated through a Madrid tourism assistant use case.

Findings

Successful implementation using FIWARE components

Effective integration of Foundation Models with spatial data

Validated architecture through a practical city use case

Abstract

The proliferation of Generative Artificial Ingelligence (AI), especially Large Language Models, presents transformative opportunities for urban applications through Urban Foundation Models. However, base models face limitations, as they only contain the knowledge available at the time of training, and updating them is both time-consuming and costly. Retrieval Augmented Generation (RAG) has emerged in the literature as the preferred approach for injecting contextual information into Foundation Models. It prevails over techniques such as fine-tuning, which are less effective in dynamic, real-time scenarios like those found in urban environments. However, traditional RAG architectures, based on semantic databases, knowledge graphs, structured data, or AI-powered web searches, do not fully meet the demands of urban contexts. Urban environments are complex systems characterized by large volumes of interconnected data, frequent updates, real-time processing requirements, security needs, and strong links to the physical world. This work proposes a real-time spatial RAG architecture that defines the necessary components for the effective integration of generative AI into cities, leveraging temporal and spatial filtering capabilities through linked data. The proposed architecture is implemented using FIWARE, an ecosystem of software components to develop smart city solutions and digital twins. The design and implementation are demonstrated through the use case of a tourism assistant in the city of Madrid. The use case serves to validate the correct integration of Foundation Models through the proposed RAG architecture.