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EV-GPT - AI-Powered Electric Vehicle Expert
2025Live DemoFaster expert answers from complex EV documentation

EV-GPT - AI-Powered Electric Vehicle Expert

PythonStreamlitGoogle GeminiLangChainChromaDBAWSDockerGit

An intelligent web application that provides AI-powered insights about electric vehicles using Google's Gemini 1.5 Flash model and advanced document retrieval capabilities.

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🔬 Project Overview

An intelligent AI-powered assistant for Electric Vehicle information, leveraging Google's Generative AI and advanced document retrieval to provide detailed insights about electric vehicles.

🎯 Problem: Limited access to comprehensive, up-to-date information about electric vehicles and difficulty in finding specific answers from technical documentation.

✨ Key Features

🤖 AI-Powered Intelligence - Leverages Google Gemini 1.5 Flash for expert-level automotive analysis
📄 Smart Document Processing - Handles PDF and TXT files with intelligent text chunking
🔍 Advanced Search - Vector-based semantic search using ChromaDB for precise document retrieval
🎨 Modern Interface - Clean, responsive Streamlit web interface
🏢 Enterprise Ready - AWS integration, Docker support, and production-grade security

🛠️ Technical Implementation

Frontend: Streamlit, Python 3.11+ - Interactive web interface with real-time updates
Backend: Python, LangChain - Robust RAG implementation with modular design
AI/ML: Google Gemini 1.5, Vector Embeddings - Advanced language processing with context-aware responses
Database: ChromaDB, SQLite - Efficient vector storage and retrieval with persistent data

🚀 Performance Metrics

| Metric | Achievement | |--------|-------------| | Response Time | < 2 seconds average | | Document Processing | Multi-threaded for optimal speed | | Search Accuracy | Context-aware responses with source tracking | | System Scalability | Efficient handling of large document sets |

🔧 Technical Challenges & Solutions

📄 Document Processing
Challenge: Efficiently processing and chunking large technical documents
Solution: Implemented multi-threaded processing with configurable chunking parameters

⚡ AI Integration
Challenge: Maintaining context while providing accurate responses
Solution: Leveraged Google's Gemini 1.5 with vector embeddings for context-aware responses

🏗️ System Architecture
Challenge: Building a scalable and maintainable system
Solution: Modular design with clear separation of concerns using LangChain

🎨 User Experience Journey

  1. 🏠 Landing → Clear interface with document upload capabilities
  2. 📤 Upload → Simple file upload for PDF and TXT documents
  3. 🔄 Processing → Real-time document processing with progress indicators
  4. 💬 Query → Natural language questions about electric vehicles
  5. 📋 Results → Context-aware responses with source attribution

🔮 Future Development Roadmap

Phase 1 (Short-term)

  • Integration with real-time EV data sources
  • Advanced analytics dashboard with vehicle comparisons
  • Multi-language support for global accessibility

Phase 2 (Long-term)

  • API endpoints for third-party integration
  • Mobile application for iOS and Android
  • Advanced AI features with image recognition for vehicle identification

🏆 Project Impact

Knowledge Accessibility → Making EV information more accessible and searchable
Technical Innovation → Advancing practical applications of RAG in automotive domain
User Experience → Simplifying complex technical information retrieval

🔗 Project Links

📁 GitHub Repository - Complete source code and documentation
🌐 Live Demo - Try the application yourself

⚠️ Note: This project requires a Google API key for Generative AI services (Gemini and Embeddings) to function properly.