A Multimodal and Multilingual Offline Retrieval-Augmented Generation System Using Local Large Language Models

Abstract

Most AI systems today rely on cloud servers, which raises problems such as privacy risks, delay, and internet dependency.  A Multimodal and Multilingual Offline Retrieval-Augmented Generation System Using Local Large Language Models is a fully offline Retrieval-Augmented Generation system that allows users to ask questions from their personal or organizational documents while keeping all data inside the device.The system supports multiple formats like text, PDFs, images, and audio. After extracting the content, it detects the language, cleans the data, splits it into smaller parts, and creates embedding using local models. A hybrid search method that combines meaning-based matching and keyword search helps improve accuracy and reduce wrong answers.A Multimodal and Multilingual Offline Retrieval-Augmented Generation System Using Local Large Language Models also enhances offline RAG by adding support for Indian languages, voice input and output, smart re-ranking of results, and answers based on document evidence. Built with FastAPI, React, ChromaDB, and Ollama, the system is modular, scalable, and suitable for secure use in research, legal, medical, and enterprise settings.