A Chatbot for the Legal Sector of Mauritius Using the Retrieval-Augmented Generation AI Framework

Mauritius is known to have a hybrid legal system as the logical consequence of being both a former French and English colony. From its independence in 1968 to date, the legal environment has changed to reflect the constant need to provide a framework to address the country’s diverse needs. With over 1200 pieces of legislation available for consultation, including those which are no longer in force, it is very difficult to know all of them. Yet, there is a legal maxim that says, “nemo censetur ignorare legem”. In other words, ignorance of the law is no excuse. This study aims to provide a solution for professionals and non-professionals to have better access to the law through the development of a chatbot. A Retrieval Augmented Generation (RAG) chatbot system has been developed to achieve this objective. A RAG system is one that leverages the use of Large Language Models (LLM) to process a query and generate a response, while ensuring accuracy by performing similarity searches against documents stored in a vector database. A sample of 46 legal documents (acts and regulations) were retrieved from the website of the Supreme Court of Mauritius. They were broken down into chunks and stored as vectors in Chroma, a vector database. The chatbot combines and processes the queries with a text prompt, searches the relevant legal texts, and generates an appropriate response using OpenAI GPT-4o-mini or MistralAI Open-Mixtral-8x22B. Since most legal texts are in English, a translation layer is included for queries in French. Sources for the answers are also displayed for easy cross-validation. This chatbot will undoubtedly be a useful tool for the Mauritian people.

@article{Mohamed2025,
  title     = {A Chatbot for the Legal Sector of Mauritius Using the Retrieval-Augmented Generation AI Framework},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Taariq Noor Mohamed and Sameerchand Pudaruth and Ivan Coste-Manière},
  doi       = {10.14569/IJACSA.2025.0160212},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160212}
}