git clone https://github.com/muhajirdev/quran-labs.git
cd quran-knowledge-graph

# Create a virtual environment
python -m venv venv

# Activate the virtual environment
# On Windows
venv\Scripts\activate
# On macOS/Linux
source venv/bin/activate

pip install -r requirements.txt

# Database
DATABASE_URL=kuzu://localhost:5432/quran_graph

# API
API_PORT=8000
DEBUG=True
SECRET_KEY=your_secret_key_here

# Vector Embeddings
EMBEDDING_MODEL=bert-base-multilingual-cased

cd backend
uvicorn app.main:app --reload

cd frontend
npm install

NEXT_PUBLIC_API_URL=http://localhost:8000

npm run dev

# Build and start all services
docker-compose up -d

# View logs
docker-compose logs -f

quran-knowledge-graph/
├── backend/                # Python backend API
│   ├── app/                # Main application code
│   │   ├── api/            # API endpoints
│   │   ├── core/           # Core functionality
│   │   ├── db/             # Database models and queries
│   │   ├── embeddings/     # Vector embedding logic
│   │   └── main.py         # Application entry point
│   ├── tests/              # Backend tests
│   └── requirements.txt    # Python dependencies
├── frontend/               # Next.js frontend
│   ├── components/         # React components
│   ├── pages/              # Next.js pages
│   ├── public/             # Static assets
│   ├── styles/             # CSS styles
│   └── package.json        # Node.js dependencies
├── data/                   # Data processing scripts
│   ├── sources/            # Raw data sources
│   ├── processed/          # Processed data
│   └── scripts/            # Data processing scripts
├── docs/                   # Documentation
└── docker-compose.yml      # Docker configuration

git checkout -b feature/your-feature-name

# Backend tests
cd backend
pytest

# Frontend tests
cd frontend
npm test

from kuzu import Connection

# Connect to the database
conn = Connection("path/to/database")

# Execute a Cypher query
result = conn.execute("MATCH (v:Verse) WHERE v.surah_number = 1 RETURN v")

# Process results
for row in result:
    print(row["v"])

MATCH (v:Verse {verse_key: '1:1'})
RETURN v

MATCH (t:Topic {name: 'Patience'})<-[r:ADDRESSES_TOPIC]-(v:Verse)
RETURN v.verse_key, v.text_simple, r.relevance
ORDER BY r.relevance DESC
LIMIT 10

MATCH (w:Word)-[:HAS_ROOT]->(r:RootWord {text_arabic: 'ر-ح-م'})
RETURN w.text_arabic, w.verse_key

from app.embeddings.model import EmbeddingModel

# Initialize the embedding model
model = EmbeddingModel()

# Generate an embedding for text
text = "What does the Quran say about patience?"
embedding = model.generate_embedding(text)

# Use the embedding for similarity search
similar_verses = model.find_similar_verses(embedding, limit=5)

// Find verses similar to a query embedding
MATCH (v:Verse)
WHERE v.embedding VECTOR_DISTANCE($query_embedding) < 0.2
RETURN v.verse_key, v.text_simple
ORDER BY v.embedding VECTOR_DISTANCE($query_embedding)
LIMIT 5