A Retrieval-Augmented Generation (RAG) system that provides intelligent answers about automotive topics using Wikipedia data and Google Gemini AI.

• AI-Powered Responses: Uses Google Gemini AI for intelligent automotive Q&A
• Wikipedia Integration: Retrieves real-time information from Wikipedia
• Automotive Focus: Specialized for car brands, models, and automotive technology
• Interactive Chat: Streamlit-based conversational interface
• Smart Caching: Optimized performance with intelligent caching
• Query Normalization: Handles various automotive company aliases

pip install -r requirements.txt

Create a .env file in the project root:

# .env
GEMINI_API_KEY=your_gemini_api_key_here

Get your Gemini API key:

1. Visit Google AI Studio
2. Create a new API key
3. Copy the key to your .env file

streamlit run app.py

The application will be available at http://localhost:8501

• "When was Tesla founded?"
• "Tell me about Toyota Prius"
• "What is the Ford Mustang history?"
• "BMW M3 specifications"

The system recognizes common aliases for major automotive brands:

• Tesla, BMW, Mercedes-Benz, Audi, Volkswagen
• Toyota, Honda, Ford, Chevrolet, Nissan
• Hyundai, Kia, Mazda, Subaru, Lexus
• And many more...

1. app.py: Main Streamlit application with UI
2. logic/wiki_utils.py: Wikipedia data retrieval and parsing
3. logic/gemini_utils.py: Google Gemini AI integration
4. logic/__init__.py: Module exports

Required:

• GEMINI_API_KEY: Your Google Gemini API key

RAG_miniproj/
├── app.py                 # Main Streamlit application
├── logic/
│   ├── __init__.py       # Module exports
│   ├── wiki_utils.py     # Wikipedia integration
│   └── gemini_utils.py   # Gemini AI integration
├── requirements.txt      # Python dependencies
├── Dockerfile           # Docker configuration
└── README.md           # This file