# 📊 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