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System Architecture

Overview

The knowledge graph system is built using a modern stack with the following key components:

Backend Components

  • FastAPI Backend - Provides REST API endpoints and WebSocket support
  • Semantic Clustering Engine - Handles intelligent node grouping and relationship detection
  • Graph Manager - Manages graph operations and state
  • Storage Layer - Handles data persistence using PostgreSQL

Frontend Components

  • React Frontend - Interactive user interface
  • Graph Visualization - Uses Cytoscape.js for graph rendering
  • Real-time Updates - WebSocket-based live updates
  • Semantic Clustering UI - Visual representation of node clusters

Data Flow

  1. User interactions trigger graph expansion or updates
  2. Backend processes changes through the Graph Manager
  3. Semantic Clustering Engine analyzes relationships
  4. Updates are broadcast via WebSocket
  5. Frontend renders changes in real-time

Technical Architecture

Graph Data Model

// Node represents a vertex in the knowledge graph
interface Node {
  id: number;
  label: string;
  type: string;
  metadata: Record<string, any>;
}

// Edge represents a connection between nodes
interface Edge {
  id: number;
  sourceId: number;
  targetId: number;
  label: string;
  weight: number;
}

// Cluster represents a group of semantically related nodes
interface ClusterMetadata {
  centroidNode: string;
  semanticTheme: string;
  coherenceScore: number;
}

Key Algorithms

  1. Semantic Clustering

    • Node similarity calculation
    • Cluster coherence scoring
    • Centroid selection

  2. Graph Analysis

    • Betweenness centrality
    • Eigenvector centrality
    • Connected components analysis

Performance Considerations

  • Efficient graph traversal algorithms
  • Optimized cluster calculations
  • WebSocket-based real-time updates
  • Lazy loading for large graphs

Security

  • API authentication and authorization
  • Input validation and sanitization
  • Rate limiting on API endpoints
  • Secure WebSocket connections