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Legal documents such as contracts, rental agreements, policies, and rules and regulations often contain complex language that is difficult for ordinary users to understand. This paper presents a Legal Document Analyzer, an AI-powered web application designed to simplify legal document analysis through semantic search and Retrieval-Augmented Generation (RAG). The system extracts text from uploaded PDF documents and generates semantic embeddings using the intfloat/e5-base-v2 model. These embeddings are stored in ChromaDB, enabling efficient vector-based retrieval of relevant information. When a user submits a query, the system identifies and retrieves the most relevant clauses from the indexed documents and uses the retrieved context to generate accurate, context-aware responses. The application supports multiple document uploads, clause-level retrieval, and semantic search across different legal files. By providing relevant information in response to natural language queries, the proposed system reduces the effort required to search and understand lengthy legal documents. The system aims to improve the accessibility, efficiency, and transparency of legal information retrieval while preserving the original meaning and context of the source documents.
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S et al. (2026) studied this question.