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The convergence of large language models and advanced legal information retrieval is transforming legal research. This paper introduces a Retrieval-Augmented Generation (RAG)-based legal chatbot that utilizes the Llama-2-7b-chat-hf model to generate contextually accurate responses. By integrating Langchain for task orchestration and a custom Question Answering (QA) pipeline, the system efficiently retrieves and synthesizes legal information from vector databases, enhancing the speed and precision of research. Built with a user-friendly frontend using HTML, CSS, and JavaScript, and a robust Flask-based backend, the chatbot caters to various legal domains and custom documents. Extensive testing underscores its potential as a transformative tool for legal professionals and the public, making legal information more accessible and alleviating the cognitive load of traditional research. This modular architecture ensures adaptability and real-time data handling, marking a significant advancement in democratizing legal knowledge and promoting legal literacy.

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Democratizing Legal Research: A RAG-Based Legal Chatbot Using Llama-2 and Advanced Information Retrieval

  • 会议论文

  • 发表日期：

    2025年

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  • Varad Patil,
  • Saket Patil,
  • Prathmesh Thorat,
  • Digvijay Dawar,
  • Sachin S. Patil

摘要

The convergence of large language models and advanced legal information retrieval is transforming legal research. This paper introduces a Retrieval-Augmented Generation (RAG)-based legal chatbot that utilizes the Llama-2-7b-chat-hf model to generate contextually accurate responses. By integrating Langchain for task orchestration and a custom Question Answering (QA) pipeline, the system efficiently retrieves and synthesizes legal information from vector databases, enhancing the speed and precision of research. Built with a user-friendly frontend using HTML, CSS, and JavaScript, and a robust Flask-based backend, the chatbot caters to various legal domains and custom documents. Extensive testing underscores its potential as a transformative tool for legal professionals and the public, making legal information more accessible and alleviating the cognitive load of traditional research. This modular architecture ensures adaptability and real-time data handling, marking a significant advancement in democratizing legal knowledge and promoting legal literacy.