AI-Enhanced Visualization: Multimodal RAG System for Text-Image Data Integration

Abstract

This award-winning project developed a multimodal AI system for connecting text and image data using pre-trained models. The system implements a RAG (Retrieval-Augmented Generation) architecture with vector database integration for enhanced query processing, demonstrating exceptional learning ability in AI field and winning jury prize among 15 competing teams.

Key Contributions

• Multimodal Integration: Connected text and image data using pre-trained models
• RAG Implementation: Developed Retrieval-Augmented Generation system with vector database
• Enhanced Queries: Implemented advanced query processing capabilities
• Award Recognition: Won jury prize among 15 competing teams at Google Hackathon

Technical Architecture

• Multimodal AI: Text-image data integration
• RAG System: Retrieval-Augmented Generation architecture
• Vector Database: Enhanced query processing and retrieval
• Pre-trained Models: Leveraged existing AI models for rapid development

Impact

This project demonstrates exceptional ability to quickly learn and implement cutting-edge AI technologies, resulting in award-winning recognition and practical application of multimodal AI systems.

Recommended citation: Mkhitaryan, D. (2024). AI-Enhanced Visualization: Multimodal RAG System for Text-Image Data Integration. Google Hackathon, Jury Prize Winner.