## Project Overview
An award-winning project developed during Google Hackathon that created 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.
## Technical Architecture
- **Multimodal AI**: Text-image data integration using pre-trained models
- **RAG System**: Retrieval-Augmented Generation with vector database
- **Enhanced Queries**: Advanced query processing capabilities
- **Vector Database**: Efficient data retrieval and processing
- **Pre-trained Models**: Leveraged existing AI models for rapid development
## Key Achievements
- **Award Recognition**: Won jury prize among 15 competing teams at Google Hackathon
- **Technical Innovation**: Implemented cutting-edge multimodal AI architecture
- **RAG Implementation**: Successfully developed Retrieval-Augmented Generation system
- **Learning Ability**: Demonstrated exceptional ability to quickly learn and implement new AI technologies
## Impact
This project demonstrates exceptional learning ability in the AI field and showcases practical implementation of cutting-edge AI technologies. The award recognition validates technical competence and innovation in multimodal AI systems.
## Skills Demonstrated
- Multimodal AI Development
- RAG System Implementation
- Vector Database Integration
- Pre-trained Model Utilization
- Hackathon Project Management
- Technical Presentation
- Rapid Prototyping
