> ## Documentation Index
> Fetch the complete documentation index at: https://docs.superquran.com/llms.txt
> Use this file to discover all available pages before exploring further.

# System Architecture

> Technical overview of the Quran Knowledge Graph architecture for developers

# 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

<img className="block dark:hidden" src="https://mintlify.s3.us-west-1.amazonaws.com/quranlabs/images/architecture-light.png" alt="Architecture diagram" />

<img className="hidden dark:block" src="https://mintlify.s3.us-west-1.amazonaws.com/quranlabs/images/architecture-dark.png" alt="Architecture diagram" />

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](https://kuzudb.com/), 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

```mermaid theme={null}
graph TD
    A[Quranic Text Sources] --> B[Data Extraction]
    C[Tafsir Collections] --> B
    D[Scholarly Resources] --> B
    B --> E[Data Cleaning & Normalization]
    E --> F[Entity Recognition]
    F --> G[Relationship Extraction]
    F --> H[Embedding Generation]
    G --> I[Graph Construction]
    H --> I
    I --> J[Kuzu Graph Database]
```

### 2. Query Processing Flow

```mermaid theme={null}
graph TD
    A[User Query] --> B[Query Understanding]
    B --> C{Query Type}
    C -->|Exact Match| D[Graph Pattern Matching]
    C -->|Semantic Search| E[Vector Similarity Search]
    C -->|Hybrid| F[Combined Search]
    D --> G[Result Ranking]
    E --> G
    F --> G
    G --> H[Result Aggregation]
    H --> I[Response Generation]
    I --> J[User Response]
```

## 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](/developers/getting-started) guide.

## Further Technical Documentation

For more detailed technical information, see:

* [Graph Database Implementation](/technical/graph-database)
* [Vector Embeddings](/technical/vector-embeddings)
* [Query Optimization](/technical/query-optimization)
* [API Reference](/api-reference/introduction)