# 📊 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** ```typescript // Svelte Frontend Component
``` ```python # 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