Multi-Agent RAG Chatbot Architecture for Decision Support in Net-Zero Emission Energy Systems
Gihan Gamage, Nishan Mills, Daswin De Silva, Milos Manic, Harsha Moraliyage, Andrew Jennings, Damminda Alahakoon
Abstract
Modern energy platforms are increasingly leveraging Artificial Intelligence (AI) for effective decision-making and efficient operations. This has led to the development of expansive data spaces that comprise both structured and unstructured energy data in various modalities. Conversational agents with the most recent advancements in Large Language Models (LLM) are primed to facilitate the efficient retrieval of this diverse information for decision support. In this paper, we propose a multi-agent chatbot architecture for decision support in net-zero emissions energy systems, leveraging LLMs and Retrieval-Augmented Generation (RAG). This architecture consists of a Chatbot User Interface (UI), an advanced Natural Language Understanding (NLU) module for precise entity and intent recognition, a robust Chatbot Core with four specialized agents: Observer, Knowledge Retriever, Behavior Analyzer, and Visualizer and Response Construction Module. These components work together to address diverse decision support needs in energy environments, specifically for net zero carbon emissions initiatives that need to consider diverse parameters and large volumes of data. We showcase the chatbot's successful integration and evaluation for decision support in the net-zero emissions energy system of a large tertiary education institution.