archive/task_management/task_kgvisualizer.md

📊 KGVisualizer Custom Component Development

📋 Task Overview

Task ID: Task-2.1.1
Phase: Phase 2 - Platform Innovation
Priority: High
Duration: 6 weeks
Owner: Frontend + Full Stack Team

🎯 Objective

Build a custom Gradio component for interactive knowledge graph visualization using Svelte frontend + Vis.js, creating compelling visual capabilities for agent plan exploration and KG introspection.

📊 Current Status

• ✅ Solid Foundation: Dynamic Gradio UI with smart prompt handling
• ✅ Rich Data Model: MCPTool, MCPPrompt, PlannedStep objects ready for visualization
• ✅ Graph Data: InMemoryKG with comprehensive relationships and metadata
• ❌ Complete Gap: No visualization component exists - greenfield development
• ⚠️ Innovation Opportunity: Custom component for Gradio ecosystem

📋 Requirements

• Custom Gradio component framework setup
• Svelte frontend with TypeScript
• Vis.js integration for graph rendering
• Python backend data processing
• Interactive node/edge features (hover, click, selection)
• Dynamic layout algorithms
• Responsive design for multiple screen sizes
• Integration with existing KG data structures

💻 Implementation Details

// Svelte Frontend Component
<script lang="ts">
  export let graphData: GraphData;
  export let onNodeClick: (nodeId: string) => void;
  export let onEdgeClick: (edgeId: string) => void;
  
  import { Network } from 'vis-network';
  
  let network: Network;
  let container: HTMLElement;
  
  const options = {
    physics: { enabled: true, stabilization: true },
    interaction: { hover: true, selectConnectedEdges: false },
    nodes: { borderWidth: 2, shadow: true },
    edges: { arrows: { to: true }, smooth: true }
  };
  
  onMount(() => {
    network = new Network(container, graphData, options);
    network.on("click", handleNodeClick);
  });
</script>

<div bind:this={container} class="kg-visualizer">
  <!-- Interactive graph visualization -->
</div>

# Python Backend Component
class KGVisualizer(gr.Component):
    """Custom Gradio component for knowledge graph visualization."""
    
    def __init__(self, kg: InMemoryKG, **kwargs):
        super().__init__(**kwargs)
        self.kg = kg
        
    def process_graph_data(self, filters: dict = None) -> GraphData:
        """Convert KG data to vis.js format."""
        
    def get_node_details(self, node_id: str) -> NodeDetails:
        """Get detailed information for node interaction."""
        
    def format_for_visualization(self, data: dict) -> VisualizationData:
        """Format data for frontend consumption."""

✅ Acceptance Criteria

• Custom Gradio component functional and installable
• Svelte frontend renders KG data with Vis.js
• Interactive features working (hover, click, selection)
• Python backend integrates with existing InMemoryKG
• Multiple layout algorithms available
• Responsive design works on desktop and mobile
• Component integrates seamlessly with existing Gradio UI
• Performance handles 1000+ nodes smoothly
• Documentation and examples provided

🔗 Dependencies

• Builds on: Existing InMemoryKG data structures
• Integrates with: Current Gradio UI architecture
• Uses: MCPTool, MCPPrompt, PlannedStep objects

📈 Success Metrics

• Visualization renders <2 seconds for 1000+ nodes
• Interactive features responsive (<100ms)
• Component reusable in other Gradio applications
• User engagement with visualization features
• Integration seamless with existing UI

🏷️ Tags

custom-component, visualization, svelte, gradio, interactive-ui, innovation

📂 Development Structure

frontend/
  kgvisualizer/
    package.json
    rollup.config.js
    src/
      KGVisualizer.svelte
      types.ts
      utils.ts
    dist/
python/
  gradio_kgvisualizer/
    __init__.py
    kgvisualizer.py
    templates/
tests/
  frontend/
    test_component.ts
  python/
    test_kgvisualizer.py

📋 Development Phases

Week 1-2: Custom Component Framework Setup

• Gradio custom component boilerplate
• Development environment configuration
• Basic Svelte component structure

Week 3-4: Vis.js Integration & Core Features

• Graph rendering with Vis.js
• Basic node/edge visualization
• Layout algorithm integration

Week 5: Interactive Features & Polish

• Hover effects and tooltips
• Click handling and selection
• Responsive design implementation

Week 6: Integration & Testing

• Integration with existing Gradio UI
• Performance optimization
• Documentation and examples

🔄 Integration Points

• Uses existing KG data from InMemoryKG
• Integrates with current Gradio interface layout
• Leverages existing MCPTool and MCPPrompt metadata
• Connects with agent planning visualization

💡 Innovation Value

• First custom visualization component for KGraph-MCP
• Reusable for broader Gradio ecosystem
• Interactive graph exploration capabilities
• Foundation for advanced visual analytics