Current Volume 9
LawMate : A Locally Deployable RAG System for Accessible Indian Legal Assistance Using TinyLlama and FAISS
Abstract
Access to Indian legal information remains limited for non-lawyers due to complex statutory language, fragmented resources, and high consultation costs. This paper presents LawMate, a lightweight Retrieval-Augmented Generation (RAG)–based legal assistant designed to provide citation-grounded responses for everyday legal queries. The system integrates TinyLlama (1.1B parameters) with FAISS-based se-mantic retrieval over official Indian legal documents, ensuring responses are generated strictly from retrieved context to reduce hallucination. A FastAPI backend and React-based frontend enable efficient local deployment on resource-constrained devices. Experimental evaluation demonstrates high retrieval relevance, low response latency, and practical usability for citizen-facing applications. The proposed architecture provides a scalable, low-resource solution for transparent and accessible legal assistance in the Indian context.
Keywords
Legal AI, Retrieval-Augmented Generation, TinyLlama, FAISS, Indian Law, Offline Chatbot, Semantic Search, FastAPI.
Citations
IRE Journals:
Shreya Patil, Pratik Nakashe, Aditya Medhekar, Sourabh Sadake, Jyoti Bansode "LawMate : A Locally Deployable RAG System for Accessible Indian Legal Assistance Using TinyLlama and FAISS" Iconic Research And Engineering Journals Volume 9 Issue 12 2026 Page 391-400 https://doi.org/10.64388/IREV9I12-1718611
IEEE:
Shreya Patil, Pratik Nakashe, Aditya Medhekar, Sourabh Sadake, Jyoti Bansode
"LawMate : A Locally Deployable RAG System for Accessible Indian Legal Assistance Using TinyLlama and FAISS" Iconic Research And Engineering Journals, 9(12) https://doi.org/10.64388/IREV9I12-1718611
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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