Authors : Ruben Kanku; Hervé Kinkete; Pathy Nkayilu Wabaluku; Bruno Luwa Muanda; Michel Kabeya Kadima; Pontien Katukumbanyi Katukumbanyi

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/mrytkwwr

Scribd : https://tinyurl.com/2r4emj5d

DOI : https://doi.org/10.38124/ijisrt/26mar1798

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This paper presents the design and evaluation of a local Retrieval-Augmented Generation (RAG)-based system for legal information retrieval and question answering in the Democratic Republic of Congo (DRC). In this context, legal texts are scattered across multiple sources and are difficult to access and interpret, creating challenges for both citizens and local officials. To address this problem, we developed an AI-based system that integrates a vector database with a small language model to retrieve relevant legal provisions and generate grounded explanations. The proposed system is designed to run fully on local hardware (e.g., a personal computer using Ollama) while also supporting deployment on a server through a web interface. The prototype indexes 28 PDF documents covering 11 major domains of Congolese law and allows users to submit natural language queries in French. For each query, the system retrieves relevant legal articles and produces structured explanations, explicitly citing the source documents. A scenario-based evaluation was conducted using realistic legal questions, combined with manual expert review. Results show that the system is able to retrieve and explain key legal provisions in most cases, behaves cautiously when no relevant information is found, and maintains acceptable response times on standard local hardware. These findings demonstrate that local, deployable RAG-based systems can provide an effective technical solution for legal information access in low-resource environments. The study also highlights the importance of structured legal data, system transparency, multilingual support, and appropriate governance frameworks for AI-based legal systems.

Keywords : Retrieval-Augmented Generation (RAG); Information Retrieval; AI System Design; Vector Database; Small Language Models (SLMs); Legal Question Answering; Democratic Republic of Congo (DRC).

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