ShivYatri - AI Tourism Assistant

Local RAG-powered chatbot for Indian travel destinations using Ollama + ChromaDB.

Prerequisites

• Python 3.10+
• Ollama installed

Build & Run

1. Clone & Install

git clone https://github.com/your-repo/shivYatra.git
cd shivYatra
pip install -r app/requirements.txt

2. Setup Ollama

# Install Ollama (Linux)
curl -fsSL https://ollama.ai/install.sh | sh

# Pull the model
ollama pull qwen2.5:1.5b

# Start Ollama (keep running)
ollama serve

3. Run Application

python app/run.py

Open http://localhost:5000 in your browser.

Project Structure

shivYatra/
├── app/
│   ├── api/server.py        # Flask server
│   ├── core/rag_engine.py   # RAG pipeline
│   ├── config/              # Configuration
│   ├── web/templates/       # Chat UI
│   └── run.py               # Entry point
├── data/                    # Tourism data
├── database/                # ChromaDB vectors
└── notebooks/               # Data processing

Configuration

Edit app/config/rag_config.py:

OLLAMA_CONFIG = {
    "model": "qwen2.5:1.5b",    # Change LLM model
    "temperature": 0.7,
    "max_tokens": 1000
}

Troubleshooting

Issue	Fix
Ollama not running	Run ollama serve in terminal
Model not found	Run ollama pull qwen2.5:1.5b
Port 5000 in use	Kill process: lsof -ti:5000 | xargs kill

Tech Stack

• LLM: Ollama (qwen2.5:1.5b)
• Vector DB: ChromaDB
• Embeddings: all-MiniLM-L6-v2
• Backend: Flask
• Frontend: HTML/CSS/JS

License

MIT