Augmenting LLMs to Securely Retrieve Information for Construction and Facility Management

In the past few years, generative AI has seen remarkable progress. The emergence of the transformer architecture has facilitated the creation of highly advanced language models that generate text, summarize content, and translate languages with impressive accuracy. Our study introduces a retrieval-augmented generation system tailored to the dynamic needs of facility management. The proposed system aims to provide instant, accurate access to essential information by integrating advanced techniques from natural language processing and information retrieval paradigms. The implementation leverages the Mixtral 8x7B model for multilingual text processing and the Milvus vector database for efficient document storage and retrieval. The dataset used includes documents such as images, operation manuals, inspection results, blueprints, and technical drawings, in various file formats. This diverse dataset reflects the variety of information encountered in construction and facility management. The evaluation involved generating question–answer pairs pertinent to facility management tasks and assessing the system’s performance using metrics such as ROUGE, BLEU, and semantic similarity. The findings suggest that retrieval-augmented generation systems can significantly enhance operational efficiency by reducing the time and effort required to access information while maintaining high security and data privacy standards.