{"id":627189,"date":"2026-06-15T13:27:40","date_gmt":"2026-06-15T11:27:40","guid":{"rendered":"https:\/\/aivancity.ai\/blog\/?p=627189"},"modified":"2026-06-15T17:22:54","modified_gmt":"2026-06-15T15:22:54","slug":"graphrags-for-legal-chatbots-technology-ethics-and-student-innovation-in-intelligent-legal-systems","status":"publish","type":"post","link":"https:\/\/aivancity.ai\/blog\/graphrags-for-legal-chatbots-technology-ethics-and-student-innovation-in-intelligent-legal-systems\/","title":{"rendered":"GraphRAGs for Legal Chatbots: Technology, Ethics, and Student Innovation in Intelligent Legal Systems"},"content":{"rendered":"\n

Dr. Anuradha KAR<\/strong><\/strong>, Associate Professor of AI & Robotics at aivancity<\/a><\/p>\n\n\n\n

Student research highlight:<\/h2>\n\n\n\n

Final year Master (Programme Grande Ecole year 5) students at aivancity recently conducted an impactful research on intelligent legal assistants, demonstrating how Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) pipelines can bring transformation in the field of law and justice with AI while maintaining ethical responsibility. The work, \u201cBuilding a Legal Chatbot With LLMs and RAG,\u201d authored by aivancity students Jessica Mbounkap, Habiba Djigo, Tasnim Masheh, Aymrick Nzooh, and Anuradha Kar, was published in the book AI Transformations in Global Legal Systems<\/em> by the IGI Global publication house in March 2026.<\/p>\n\n\n\n

Link to the paper: <\/strong><\/p>\n\n\n\n

https:\/\/www.igi-global.com\/chapter\/technical-and-ethical-perspectives-on-building-intelligent-legal-assistants-with-llm-and-rag-pipelines\/406384<\/a><\/p>\n\n\n\n

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The recent publication of aivancity students on GraphRag based legal AI assistant<\/strong><\/figcaption><\/figure>\n\n\n\n

The research emphasizes the significance and practical value of a recent advancement in generative AI known as GraphRAG or Graph Retrieval-Augmented Generation, particularly for applications in the legal domain. Unlike conventional retrieval systems, GraphRAG captures and models the relationships among statutes, judicial precedents, and legal doctrines, allowing systems to reason across multiple legal sources, identify relevant analogies, trace the evolution of legal doctrines, and generate transparent, explainable responses for users. Such explainability and traceability are especially critical in legal contexts, where users need to understand not only the outcome but also the reasoning process behind it.<\/p>\n\n\n\n

As part of this work, the students developed a modular legal GraphRAG pipeline based on verified legal data sources, including statutes, court judgments, and official legal commentaries. They designed and implemented an autonomous legal document chatbot powered by GraphRAG for advanced document analysis. In contrast to traditional RAG systems that depend primarily on vector similarity, the GraphRAG based system developed in this work builds knowledge graphs from legal documents, enabling the system to comprehend complex relationships among entities such as parties, clauses, obligations, and legal concepts. The solution further incorporates an agentic architecture that goes beyond simple question-answering.<\/p>\n\n\n\n

Users can configure the chatbot to run on OpenAI’s GPT-4o, Anthropic’s Claude, or Google’s Gemini switching providers through the interface without any changes to the underlying codebase. This flexibility is important in enterprise legal contexts where data governance policies, contractual obligations, or regional compliance requirements may dictate which AI services an organisation is permitted to use.<\/p>\n\n\n\n

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Concept of our agentic graph rag architecture for the legal chatbot<\/strong><\/figcaption><\/figure>\n\n\n\n
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Example of a knowledge graph created from a single legal document by (Civil Procedure Act 1997) our legal AI assistant<\/strong>.<\/figcaption><\/figure>\n\n\n\n

The Knowledge Graph \u2014 What You See<\/strong><\/p>\n\n\n\n

The indexing pipeline of the chatbot produces an interactive knowledge graph where each node represents an extracted legal entity (e.g., laws, organisations, people, procedures, or legal concepts). The graph provides a structured representation of legal knowledge, exposing interactions between actors, rules, institutions, and procedures. This structured representation enables the chatbot to answer contextual and relationship-oriented legal queries, beyond traditional keyword-based retrieval.<\/p>\n\n\n\n

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  • Node size reflects graph degree: highly connected entities appear larger, highlighting structurally important concepts.<\/li>\n\n\n\n
  • Edges represent relationships between entities, labeled with descriptions such as \u201cgrants authority to\u201d<\/em> or \u201cis enforced by.\u201d<\/em><\/li>\n\n\n\n
  • Relationships are extracted directly by the language model from the source documents,<\/li>\n\n\n\n
  • Nodes are colour-coded by entity type for easier interpretation (e.g., organisations, laws, people, concepts).<\/li>\n\n\n\n
  • Selecting a node reveals its description, relationship count, and graph community membership.<\/li>\n<\/ul>\n\n\n\n

    An overview of our user Interface :<\/strong><\/p>\n\n\n\n

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    The legal chatbot interface showing the detailed functionalities<\/strong><\/figcaption><\/figure>\n\n\n\n
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    Steps to use our legal chatbot<\/strong><\/figcaption><\/figure>\n\n\n\n

    Sample answers by our legal ChatBot :<\/strong><\/p>\n\n\n\n

    For testing our chatbot we uploaded a legal document having legal regulations from the United Kingdom (any document can be used but documents with scanned text or images should be avoided) and asked it some questions. Below you can see some sample answers given by Chatgpt (a generic chatbot) and our specialized Agentic GraphRAG chatbot. It can be seen that our GraphRAG enabled richer legal reasoning by retrieving interconnected legal concepts, cases, and provisions through graph traversal, producing contextually deeper and constitutionally nuanced answers beyond standard LLM or keyword-based retrieval systems.<\/p>\n\n\n\n