AI-enabled Digital Research Assistant for the Legislación Mexicana Corpus

Abstract

Large-scale historical corpora present persistent challenges for research, particularly in relation to the navigation, interpretation, and extraction of meaningful information across extensive and heterogeneous collections. The Legislación Mexicana corpus, a 42-volume compilation of legal dispositions spanning more than two centuries, exemplifies these challenges due to its size, structural complexity, and evolving terminology. Traditional approaches to working with such corpora have relied on close reading and the use of indexes. While effective within defined limits, these methods constrain the scope of inquiry and require significant time and expertise to produce meaningful results. This article presents the development of LegMexIA, an AI-enabled research assistant designed to extend interaction with the corpus beyond conventional retrieval methods. The system integrates traditional text retrieval and retrieval-augmented generation via a coordinated architecture that distinguishes between different types of user queries. Structured queries are processed using Elasticsearch and BM25 ranking, while exploratory and interpretive queries are routed through a retrieval-augmented generation workflow, where relevant text fragments are retrieved using vector similarity and assembled into contextual inputs for a language model. A key aspect of the system is the agentic decision layer that analyzes user intent and dynamically selects the appropriate processing strategy, in alignment with established reference practices in academic libraries and in a way that maintains the user’s role in verification. The article also addresses key considerations related to prompt design, technological sustainability, institutional constraints, and the implications of public deployment.