LawPal : A Retrieval Augmented Generation Based System for Enhanced Legal Accessibility in India
Dnyanesh Panchal, Aaryan Gole, Vaibhav Narute, Raunak Joshi
TL;DR
LawPal presents a Retrieval-Augmented Generation-based legal chatbot for India, leveraging FAISS for fast, domain-aware retrieval and DeepSeek-R1:5B for grounded answer generation. The system ingests diverse legal texts, preprocesses and chunks data, and uses hierarchical FAISS indexing to support accurate, contextually relevant responses. Experimental results show high legal accuracy (>90%), low latency (10–50 ms retrieval, 800–1500 ms generation), and strong user satisfaction, with comparative advantages over Chroma-based retrieval. Limitations include multi-jurisdictional and long-context handling, and multilingual support, which guide future work toward broader accessibility, jurisdictional adaptability, and workflow automation integration.
Abstract
Access to legal knowledge in India is often hindered by a lack of awareness, misinformation and limited accessibility to judicial resources. Many individuals struggle to navigate complex legal frameworks, leading to the frequent misuse of laws and inadequate legal protection. To address these issues, we propose a Retrieval-Augmented Generation (RAG)-based legal chatbot powered by vectorstore oriented FAISS for efficient and accurate legal information retrieval. Unlike traditional chatbots, our model is trained using an extensive dataset comprising legal books, official documentation and the Indian Constitution, ensuring accurate responses to even the most complex or misleading legal queries. The chatbot leverages FAISS for rapid vector-based search, significantly improving retrieval speed and accuracy. It is also prompt-engineered to handle twisted or ambiguous legal questions, reducing the chances of incorrect interpretations. Apart from its core functionality of answering legal queries, the platform includes additional features such as real-time legal news updates, legal blogs, and access to law-related books, making it a comprehensive resource for users. By integrating advanced AI techniques with an optimized retrieval system, our chatbot aims to democratize legal knowledge, enhance legal literacy, and prevent the spread of misinformation. The study demonstrates that our approach effectively improves legal accessibility while maintaining high accuracy and efficiency, thereby contributing to a more informed and empowered society.