LawPal : A Retrieval Augmented Generation Based System for Enhanced Legal Accessibility in India
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913703401619456 |
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| author | Panchal, Dnyanesh Gole, Aaryan Narute, Vaibhav Joshi, Raunak |
| author_facet | Panchal, Dnyanesh Gole, Aaryan Narute, Vaibhav Joshi, Raunak |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16573 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LawPal : A Retrieval Augmented Generation Based System for Enhanced Legal Accessibility in India Panchal, Dnyanesh Gole, Aaryan Narute, Vaibhav Joshi, Raunak Artificial Intelligence Machine Learning 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. |
| title | LawPal : A Retrieval Augmented Generation Based System for Enhanced Legal Accessibility in India |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2502.16573 |