​

System Architecture

This page provides a technical overview of the Quran Knowledge Graph architecture for developers who want to understand the system design or contribute to the project.

​

Architecture Overview

The Quran Knowledge Graph is built on a hybrid architecture that combines:

1. Graph Database - For storing and querying structured relationships
2. Vector Database - For semantic similarity search
3. API Layer - For unified access to both graph and vector capabilities
4. Application Layer - For user-facing interfaces

​

Core Components

​

1. Data Storage Layer

​

Graph Database (Kuzu)

We use Kuzu, a high-performance graph database with OpenCypher support, to store and query the complex relationships between Quranic entities. Key features:

• OpenCypher query language compatibility
• Native vector support
• High-performance query execution
• Flexible schema design

​

Vector Storage

Vector embeddings are stored directly in Kuzu, which supports:

• 768-dimensional embeddings (from BERT-based models)
• Approximate nearest neighbor (ANN) search
• Cosine similarity calculations

​

2. Processing Layer

​

Data Processing Pipeline

The data processing pipeline handles:

• Text normalization and cleaning
• Entity extraction and relationship mapping
• Vector embedding generation
• Graph construction

Technologies used:

• Python for data processing
• Hugging Face Transformers for embedding generation
• Custom NLP pipelines for Arabic text processing

​

Query Processing Engine

The query processing engine:

• Parses natural language queries
• Converts queries to graph patterns and vector searches
• Executes hybrid search operations
• Ranks and aggregates results

​

3. API Layer

The API layer provides:

• RESTful endpoints for common operations
• GraphQL interface for flexible querying
• Authentication and rate limiting
• Caching for performance optimization

Technologies used:

• FastAPI for REST endpoints
• Strawberry for GraphQL
• Redis for caching
• JWT for authentication

​

4. Application Layer

The application layer includes:

• Web interface for end users
• Admin dashboard for content management
• Analytics dashboard for usage insights

Technologies used:

• Next.js for the web interface
• React for UI components
• TailwindCSS for styling
• Vercel for hosting

​

Data Flow

​

1. Data Ingestion Flow

​

2. Query Processing Flow

​

Technology Stack

​

Backend

• Language: Python 3.9+
• Graph Database: Kuzu
• API Framework: FastAPI
• ML/AI: PyTorch, Hugging Face Transformers
• Caching: Redis
• Authentication: JWT, Auth0

​

Frontend

• Framework: Next.js
• UI Library: React
• Styling: TailwindCSS
• State Management: React Query, Zustand
• Visualization: D3.js, React Flow

​

DevOps

• Containerization: Docker
• CI/CD: GitHub Actions
• Deployment: Simple containerized deployment

​

Performance Considerations

​

Query Optimization

• Indexing Strategy: Indexes on frequently queried properties
• Query Caching: Redis-based caching for common queries
• Query Planning: Optimized Cypher query generation

​

Scaling Strategy

• Horizontal Scaling: Stateless API servers can scale horizontally
• Database Scaling: Kuzu supports vertical scaling for increased load
• Caching Tiers: Multi-level caching for different query patterns

​

Security Architecture

• Authentication: JWT-based authentication with Auth0 integration
• Authorization: Role-based access control for API endpoints
• Data Protection: Encryption at rest and in transit
• API Security: Rate limiting, CORS protection, input validation

​

Development Environment

To set up a development environment, see the Getting Started for Developers guide.

​

Further Technical Documentation

For more detailed technical information, see:

• Graph Database Implementation
• Vector Embeddings
• Query Optimization
• API Reference