Скачать или смотреть Design and build a multimodel Retrieval-Augmented Generation (RAG) system leveraging a LLM

Presenting our solution for SIH 2025 Problem Statement 25231 - a revolutionary offline Retrieval-Augmented Generation (RAG) system that transforms how organizations handle diverse data formats!

🎯 The Challenge:
Traditional search tools struggle with cross-format understanding, isolating text, images, and audio searches. Organizations need a unified solution to query documents, images, screenshots, and voice recordings seamlessly.

💡 Our Solution:
A fully offline multimodal RAG system that brings together:
✅ Unified semantic search across DOC, PDF, images, and audio files
✅ Natural language query interface with voice support
✅ Cross-format linking (connect audio transcripts to PDFs and images)
✅ Citation transparency with source verification
✅ Complete privacy through air-gapped, offline operation

🔧 Technical Stack:
Frontend: React, TailwindCSS, Electron
Backend: Python, FastAPI, Ollama
Database: MySQL, FAISS Vector DB
AI Models: Mistral, Gemma (Open Source LLMs)
Dependencies: Langchain, Huggingface, Whisper

🌟 Key Features:
Shared vector space for seamless cross-modal retrieval
Context-aware responses grounded in retrieved sources
Secure, offline operation for sensitive environments
Domain adaptability with custom embeddings
Cost-effective using open-source tools

🎓 Developed for:
Smart India Hackathon 2025
Theme: Smart Automation
Organization: National Technical Research Organisation (NTRO)
Team ID: 76783

🔒 Perfect for: Government agencies, defense organizations, healthcare institutions, legal firms, and any organization handling sensitive multimodal data requiring offline accessibility.
📊 Impact:
⚡ Faster access to multimodal information
🎯 Enhanced decision-making capabilities
⏱️ Significant time and effort savings
🔐 Complete data security and privacy
💰 Cost-effective with scalable architecture