AI POWERED MULTIMODAL ASSISTANT: A Graph-Based Multi-Agent Framework Integrating Retrieval-Augmented Generation for Intelligent Document Processing and Content Generation

The paper presents the design and development of an AI Powered Multimodal Assistant that integrates multiple intelligent functionalities into a single platform. The system is developed using modern technologies such as LangGraph, LangChain, Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs). It follows a modular and graph-based workflow where tasks are divided into smaller steps for efficient processing. The system supports multiple use cases such as chatbot interaction, PDF-based question answering, web search retrieves relevant data before generating responses, which improves accuracy and reduces incorrect outputs. The Streamlit-based interface provides a simple and interactive user experience. The proposed system improves efficiency, scalability, and usability compared to traditional AI systems.