Retrieval-Augmented OLAP: Generative AI Architecture for Smart Systems & Equipment

Abstract

The integration of Large Language Models (LLMs) within a Retrieval-Augmented Generation (RAG) framework, combined with Symbolic Logic (SL), holds significant potential as a reasoning engine for complex agricultural decision-making to ensure reliability, and as a centralized repository of domain-specific agricultural knowledge. However, since the smart farming domain also deals with Data Mediated by the Process (DMP), which is predominantly structured, LLMs often struggle to generate and run SQL queries for a significant portion of these data interactions. In such cases, the RAG framework resorts to fetching structured data directly from the relevant database, which can lead to performance bottlenecks and increased resource consumption, particularly when Big Data characteristics (Volume, Variety, Velocity) are prominent and traditional Online Analytical Processing (OLAP) frameworks prove ineffective. In this paper, we propose a novel Retrieval-Augmented OLAP (RA-OLAP) framework that leverages NoSQL databases to store OLAP cubes as cuboids with pre-calculated measures, employing a dictionary-based encoding strategy. Simultaneously, the same NoSQL database is utilized for vector search operations within the RAG architecture. This hybrid AI framework addresses three critical agricultural challenges: drought classification, crop production prediction, and equipment maintenance. By integrating these components, our approach aims to enhance the scalability, efficiency, and accuracy of data-driven decision-making.