insightflow_todo.md

# InsightFlow AI Implementation Checklist

```todo
[0-00:0-15] Project Structure Setup
- [x] 1. Create directory structure (mkdir -p persona_configs data_sources utils/persona)
- [x] 2. Create __init__.py files in utils and utils/persona directories

[0-00:0-15] Persona Configurations
- [x] 3. Copy/create analytical.json in persona_configs
- [x] 4. Copy/create scientific.json in persona_configs
- [x] 5. Copy/create philosophical.json in persona_configs
- [x] 6. Copy/create factual.json in persona_configs
- [x] 7. Copy/create metaphorical.json in persona_configs
- [x] 8. Copy/create futuristic.json in persona_configs
- [x] 9. Copy/create holmes.json in persona_configs
- [x] 10. Copy/create feynman.json in persona_configs
- [x] 11. Copy/create fry.json in persona_configs

[0-15:0-35] Base Persona System
- [x] 12. Create utils/persona/base.py with PersonaReasoning abstract class
- [x] 13. Implement PersonaFactory class in base.py
- [x] 14. Create utils/persona/__init__.py to expose classes

[0-35:0-50] Persona Implementations
- [x] 15. Create utils/persona/impl.py with LLMPersonaReasoning base class
- [x] 16. Implement all persona type reasoning classes (Analytical, Scientific, etc.)
- [x] 17. Implement all personality reasoning classes (Holmes, Feynman, Fry)

[0-50:1-00] Data Acquisition
- [x] 18. Create download_data.py script
- [x] 19. Implement directory creation for each persona
- [x] 20. Add download logic for Holmes data
- [x] 21. Add download logic for Feynman data
- [x] 22. Add download logic for philosophical data
- [x] 23. Add download logic for Hannah Fry data
- [x] 24. Add download logic for remaining persona types
- [x] 25. Run download_data.py script

[1-00:1-05] State Management
- [x] 26. Create insight_state.py with InsightFlowState class

[1-05:1-20] LangGraph Implementation
- [x] 27. Add run_planner_agent function to app.py
- [x] 28. Add execute_persona_tasks function to app.py
- [x] 29. Add synthesize_responses function to app.py
- [x] 30. Add present_results function to app.py
- [x] 31. Set up LangGraph nodes and connections

[1-20:1-30] Chainlit Integration
- [x] 32. Update on_chat_start handler in app.py
- [x] 33. Implement on_action handler for persona selection
- [x] 34. Update on_message handler for query processing
- [x] 35. Final testing and debugging

[1:30-2:00] UI Improvements and Performance Optimization
- [x] 36. Fix persona selection UI in Chainlit
- [x] 37. Implement command-based interface for persona selection
- [x] 38. Add progress tracking during processing
- [x] 39. Implement timeout handling for API calls
- [x] 40. Add direct mode for bypassing multi-persona system
- [x] 41. Add quick mode with fewer personas for faster response
- [x] 42. Update help and documentation system
- [x] 43. Improve error handling and fallbacks

[2:00-2:30] Visualization System
- [x] 44. Implement basic Mermaid diagram generation
- [x] 45. Fix diagram rendering in Chainlit
- [x] 46. Add DALL-E integration for hand-drawn visualizations
- [x] 47. Implement visual-only mode
- [x] 48. Create toggle commands for visualization features
- [x] 49. Update documentation with visualization details
- [x] 50. Fix image rendering for compatibility

[2:30-3:00] Future Enhancements
- [ ] 51. Add user profile and preferences storage
- [ ] 52. Implement session persistence between interactions
- [x] 53. Add exportable PDF/markdown reports of insights
- [ ] 54. Implement data source integration (web search, documents)
- [ ] 55. Create voice input/output interface
- [ ] 56. Add multilingual support
- [ ] 57. Develop mobile-responsive interface
- [ ] 58. Implement collaborative session sharing
- [ ] 59. Add advanced visualization options (interactive charts)
- [ ] 60. Create an API endpoint for external applications

[3:00-3:30] Testing and Refinement
- [ ] 61. Conduct user testing with diverse personas
- [ ] 62. Optimize performance for large multi-perspective analyses  
- [ ] 63. Implement A/B testing for different visualization styles
- [ ] 64. Create comprehensive test suite
- [ ] 65. Perform security and privacy audit

[3:30-4:00] RAG Implementation
- [ ] 66. Execute source acquisition commands for all six perspective types
- [ ] 67. Implement perspective-specific chunking functions
- [ ] 68. Create vector databases for each perspective
- [ ] 69. Implement retrieval integration with LangGraph
- [ ] 70. Test RAG-enhanced perspectives against baseline

[4:00-4:30] Embedding Fine-Tuning
- [ ] 71. Implement 1-hour quick embedding fine-tuning for philosophical perspective
- [ ] 72. Evaluate embedding model performance
- [ ] 73. Extend fine-tuning to other perspectives if beneficial
- [ ] 74. Integrate fine-tuned embeddings with vector databases
- [ ] 75. Publish fine-tuned models to Hugging Face

[4:30-5:00] RAGAS Evaluation Framework
- [ ] 76. Create test datasets for each perspective type
- [ ] 77. Implement perspective-specific evaluation functions
- [ ] 78. Create synthesis evaluation metrics
- [ ] 79. Generate performance comparison reports
- [ ] 80. Identify and address performance bottlenecks

[5:00-5:30] Deployment Preparation
- [ ] 81. Set up Hugging Face Spaces for deployment
- [ ] 82. Create production-ready Docker container
- [ ] 83. Configure environment variables and secrets management
- [ ] 84. Implement proper logging and monitoring
- [ ] 85. Create deployment documentation

[5:30-6:00] User Documentation and Marketing
- [ ] 86. Create comprehensive user guide with command reference
- [ ] 87. Record demonstration video
- [ ] 88. Write blog post explaining the multi-perspective approach
- [ ] 89. Create visual tutorial for first-time users
- [ ] 90. Develop quick reference card for commands
```

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"Update todo items 51-53 as completed" or "Mark todo item 57 as done"