eXact-RAG is an advanced multimodal model designed for Retrieval-Augmented Generation (RAG), integrating text, visual, and audio data to enhance content understanding and generation capabilities. It combines the principles of RAG, which involve retrieving relevant data to provide context for large language models, with multimodal processing to handle diverse data types. eXact-RAG utilizes LangChain and Ollama for backend support, FastAPI for REST API service, and offers flexibility through data storage options such as ChromaDB or Elasticsearch. The user interface, built on Streamlit, allows users to interact with the model across various modalities. This system is applicable in a range of domains, including conversational agents, content recommendation, and information retrieval, offering personalized and efficient solutions. With its robust infrastructure and user-friendly design, eXact-RAG is positioned to significantly impact the field of natural language processing and multimodal learning.