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The rapid growth of legal information and documentation has made it increasingly difficult for individuals and organizations to access accurate and relevant legal insights efficiently. This project presents the development of an AI-Based Legal Assistant using Retrieval-Augmented Generation (RAG) and LangChain, designed to provide intelligent, context-aware responses to legal queries. The system integrates large language models with a document retrieval mechanism to ensure that answers are generated based on reliable legal sources rather than generic knowledge. The architecture utilizes LangChain to orchestrate the workflow between user queries, vector databases, and language models. Legal documents are ingested, processed, and stored as embeddings in a vector database, enabling efficient semantic search. When a user submits a query, the system retrieves relevant legal information and augments the response generation process, ensuring accuracy, relevance, and explainability.
M.Perachiselvi et al. (Wed,) studied this question.