Bilingual Knowledge Management System for Information Retrieval from Military Policies

Charith Gunasekara, Zachary Hamel, Rohan Ben Joseph, Feng Du

Abstract: Navigating through the extensive and bilingual military policies of the Canadian Armed Forces (CAF) can be a complex task. To address the need for streamlined access to these documents, this paper presents the implementation of containerized artificial intelligence (AI) models to develop a bilingual knowledge management system. This system is designed to enhance information retrieval from military policies by leveraging AI, metadata tagging, text embeddings, and vector databases. Our approach involves creating a comprehensive data processing pipeline to store and retrieve military policies and documents. We utilize a question-answering pipeline that incorporates language detection, semantic search, and large language models (LLM) to process queries in both English and French. Preliminary evaluations demonstrate an accuracy rate of 87.78% for English and 73.33% for French queries. This paper builds upon our previous work on semantic search for military policies by integrating a generative AI component, thus extending beyond traditional retrieval-based methods to provide more precise and contextually relevant responses.