🚀 Terraphim Autocomplete in Novel Editor - Demonstration

Overview

This document demonstrates how autocomplete functionality has been integrated into the Novel editor within the Terraphim desktop application. The autocomplete system provides intelligent suggestions based on your local knowledge graph and document content.

✨ Features Demonstrated

1. Local Autocomplete Service

• MCP Server Integration: Connects to local terraphim_mcp_server on port 8001
• Knowledge Graph Based: Suggestions come from your local KG files in docs/src/kg/
• Role-Based: Adapts suggestions based on selected role (Terraphim Engineer, Default, etc.)
• No External Dependencies: Works completely offline using local data

2. Autocomplete Functions Available

• autocomplete_terms - Basic term suggestions
• autocomplete_with_snippets - Suggestions with context snippets
• build_autocomplete_index - Build/rebuild the autocomplete index
• fuzzy_autocomplete_search_jaro_winkler - Fuzzy search with Jaro-Winkler algorithm
• find_matches - Find matching terms in text
• replace_matches - Replace terms with links
• extract_paragraphs_from_automata - Extract relevant paragraphs

3. Novel Editor Integration

• Seamless Integration: Works with Novel's existing autocomplete system
• Real-time Suggestions: Provides suggestions as you type
• Context-Aware: Understands document context and cursor position
• Performance Optimized: Fast response times with local data

🎯 How It Works

Architecture

Novel Editor → NovelAutocompleteService → MCP Server → terraphim_automata → Local KG

Data Flow

1. User types in Novel editor
2. Editor triggers autocomplete after 2+ characters
3. Service calls MCP server with query
4. MCP server searches local knowledge graph
5. Results returned as structured suggestions
6. Editor displays suggestions to user

🧪 Testing the Autocomplete

1. Start the MCP Server

cd crates/terraphim_mcp_server
cargo run -- --sse --bind 127.0.0.1:8001 --verbose

2. Test API Endpoints

# List available tools
curl -X POST "http://localhost:8001/message?sessionId=test123" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}'

# Test autocomplete
curl -X POST "http://localhost:8001/message?sessionId=test123" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"autocomplete_terms","arguments":{"query":"terraphim","limit":5}}}'

3. Use the Test Script

cd desktop
bun test-autocomplete.js

🔧 Configuration

MCP Server Settings

• Port: 8001 (configurable)
• Transport: SSE (Server-Sent Events)
• Profile: Desktop (Terraphim Engineer role with local KG)
• Knowledge Graph: Built from docs/src/kg/ markdown files

Novel Editor Settings

• Autocomplete: Enabled by default
• Snippets: Optional context information
• Delay: 300ms before triggering
• Min Length: 2 characters
• Max Suggestions: 10 results

📊 Sample Autocomplete Results

Basic Suggestions

Query: "terraphim"
Results:
• terraphim-graph
• terraphim-automata
• terraphim-service
• terraphim-types
• terraphim-config

With Snippets

Query: "graph"
Results:
• terraphim-graph — Knowledge graph implementation for document ranking
• knowledge-graph — Graph-based knowledge representation system
• role-graph — Role-based graph traversal and analysis

🎨 UI Components

Status Panel

• Real-time Status: Shows connection and index status
• Test Button: Manually test autocomplete functionality
• Rebuild Index: Refresh the autocomplete index
• Demo Button: Insert sample content for testing

Mock Mode

When MCP server is unavailable, the system gracefully falls back to:

• Mock Suggestions: Pre-defined relevant terms
• Demo Content: Sample text for testing
• Error Handling: Clear status messages

🚀 Future Enhancements

Planned Features

1. Real-time Updates: Live KG updates reflected in suggestions
2. Context Learning: Suggestions improve based on user behavior
3. Multi-language Support: Internationalization for suggestions
4. Advanced Filtering: Role and permission-based filtering
5. Performance Metrics: Autocomplete response time monitoring

Integration Opportunities

• VS Code Extension: Extend to VS Code editor
• Web Clipper: Browser extension integration
• Mobile Apps: React Native integration
• API Gateway: External service integration

🔍 Troubleshooting

Common Issues

1. MCP Server Not Responding

   • Check if server is running: ps aux | grep terraphim_mcp_server
   • Verify port availability: lsof -i :8001
   • Check logs: tail -f /tmp/terraphim-logs/terraphim-mcp-server.log.*

2. Autocomplete Not Working

   • Verify service status in UI
   • Check browser console for errors
   • Ensure knowledge graph files exist in docs/src/kg/

3. Performance Issues

   • Rebuild autocomplete index
   • Check knowledge graph size
   • Monitor server resource usage

📈 Performance Metrics

Benchmarks

• Index Build Time: ~2-5 seconds for typical KG
• Query Response: <100ms for most queries
• Memory Usage: ~50-100MB for autocomplete index
• Storage: ~10-50MB for serialized index

Optimization Tips

• Regular Index Rebuilds: Keep suggestions fresh
• Efficient KG Structure: Well-organized markdown files
• Role-Specific Configs: Tailor to specific use cases
• Caching: Leverage browser and server caching

🎉 Conclusion

The Terraphim autocomplete integration with Novel editor provides:

✅ Local, Fast Autocomplete - No external API calls needed ✅ Knowledge Graph Integration - Context-aware suggestions ✅ Role-Based Intelligence - Adapts to user context ✅ Graceful Degradation - Works even when server unavailable ✅ Extensible Architecture - Easy to add new features

This creates a powerful, intelligent editing experience that leverages your local knowledge base for enhanced productivity and content creation.

For technical details, see the source code in:

• desktop/src/lib/services/novelAutocompleteService.ts
• desktop/src/lib/Editor/NovelWrapper.svelte
• crates/terraphim_mcp_server/src/lib.rs