Document Analysis, Chat and Comparison Portal Using Advanced Retrieval-Augmented Generation and LLMOps Framework

Manisha Bhaskarrao Jadhav1, Swati S. Hinge2
Student, Department of Artificial Intelligence and Machine Learning, Sanghavi College of Engineering, Nashik

HOD, Department of Artificial Intelligence and Machine Learning, Sanghavi College of Engineering, Nashik

Abstract - The rapid growth of unstructured digital documents has created a significant demand for intelligent document under-standing systems capable of semantic retrieval,                            conversational      interaction,     and      automated comparison. This research presents a professional Document Analysis, Chat and Comparison Portal powered by Retrieval-Augmented Generation (RAG), semantic search, vector embeddings, and LLMOps deployment practices. The proposed system integrates document ingestion, contextual retrieval, intelligent chat, and side-by-side comparison into a unified architecture. The platform utilizes FastAPI, Streamlit, FAISS vector databases, transformer embeddings, and local Large Language Models (LLMs) for optimized inference and low latency. Advanced features including multi-document retrieval, session memory, reranking, quantized inference, and Cache Augmented Generation (CAG) significantly improve retrieval accuracy and response efficiency. Continuous Integration and Continuous Deployment (CI/CD) pipelines with Docker, GitHub Actions, AWS Fargate, and SonarQube ensure production-grade deployment. Experimental evaluation demonstrates high   retrieval precision, scalability,            and contextual    response    generation    suitable for     enterprise document intelligence applications including legal analysis, academic   research,          policy       review,  and         organizational knowledge management.

Key Words: Retrieval-Augmented Generation, RAG, LLMOps, Semantic Search, Document Intelligence, Conversational AI, FastAPI, Streamlit, Vector Database, FAISS, CacheAugmented Generation, Large Language Models.