MultiRAG: A Fuzzy Logic-Driven Multi-Granularity Framework for Legal Document Generation

Legal document retrieval poses unique challenges due to the structural complexity and domain-specific language of statutes and case law. This paper presents MultiRAG, a multi-layered Retrieval-Augmented Generation (RAG) framework designed for intelligent legal information systems. The framework segments legal texts—specifically from Title 18 of the U.S. Code—into sections, pages, and chapters, and encodes them using two embedding models: a general-purpose model (Google-text-embedding-005) and a legal-domain model (VoyageLaw-2). A fuzzy logic-based query analysis module evaluates linguistic and semantic features of the input query to compute a legal intensity score, which guides dynamic weighting between the embedding outputs. Retrieved content across granularities is fused using a weight-aware deduplication and scoring strategy, and passed to a generative model (Gemini-2.0-flash) for structured and contextually faithful response generation. Evaluation on $\text{2, 0 0 0}$ queries from four user groups—ranging from legal experts to the general public—demonstrates a 97.75 % success rate in generating relevant responses, with average response times under six seconds. These results show that MultiRAG offers a scalable, interpretable, and adaptable solution for legal document analysis and generation.