ChatDC: A Multi-Expert RAG Enhanced LLM for Data Center Operations

Data centers (DC), as the computational core, play a pivotal role in driving the development of various industries. However, their 24/7 operational workload places immense pressure on operations staff, and traditional AI for IT Operations technologies due to their inability to communicate with operations personnel through language, are unable to effectively alleviate this pressure. In contrast, Retrieval-Augmented Generation (RAG), by integrating external knowledge bases with Large Language Models, enables effective communication with operations teams. Nevertheless, the direct application of RAG in DC currently faces various challenges, such as limited text representation, context length limitations, and poor scalability for large-scale databases. In this paper, we propose multi-modal multi-agent framework for DC facility operations. This framework is primarily composed of a manager and multiple specialized experts, which supports providing intuitive multi-modal information to assist in responses and optimizes response quality in terms of both depth and breadth, delivering more comprehensive and precise answers to offer an innovative solution for enhancing DC operations. Finally, we build a DC operations question-answering dataset and conduct comparative testing of the proposed framework using multiple evaluation metrics.