LEXIRAG – INTELLIGENT LEGAL ASSISTANT USING RETRIEVALAUGMENTED GENERATION AND LANGCHAIN

Abstract

The rapid growth of legal data and digital documentation has created significant challenges for legal professionals in retrieving accurate and relevant information efficiently. Traditional legal research methods are often time-consuming, labor-intensive, and prone to human error. To address these limitations, this paper presents LexiRAG (Legal Intelligent Retrieval-Augmented Generation), an advanced Artificial Intelligence (AI)-powered legal assistant developed using Retrieval-Augmented Generation (RAG) and LangChain frameworks. The proposed system integrates Natural Language Processing (NLP), Machine Learning (ML), and generative AI techniques to provide context-aware and accurate responses to legal queries. LexiRAG retrieves relevant legal documents, statutes, and case laws from a structured knowledge base and generates coherent responses grounded in factual legal information. The architecture includes data ingestion, preprocessing, document embedding, semantic retrieval, and response generation modules. Legal datasets are processed using tools such as Pandas and NumPy, while LangChain is employed for workflow orchestration and large language model integration. A Random Forest classification algorithm is incorporated to enhance document categorization and retrieval relevance. The system enables users to interact through natural language queries without requiring extensive legal or technical expertise, making it beneficial for legal practitioners, researchers, and students. Experimental analysis demonstrates that LexiRAG improves legal information retrieval efficiency, response accuracy, and user accessibility compared to traditional legal research methods. The proposed framework highlights the potential of AI-driven intelligent systems in transforming modern legal research and decision-making processes.