Atomizer/DEVELOPMENT.md

Atomizer Development Status

Tactical development tracking - What's done, what's next, what needs work

Last Updated: 2025-11-17 Current Phase: Phase 3.2 - Integration Sprint Status: 🟢 Phase 1 Complete | ✅ Phases 2.5-3.1 Built (85%) | 🎯 Phase 3.2 Integration TOP PRIORITY

📘 Strategic Direction: See DEVELOPMENT_GUIDANCE.md for comprehensive status, priorities, and development strategy.

📘 Long-Term Vision: See DEVELOPMENT_ROADMAP.md for the complete roadmap.

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Table of Contents

1. Current Phase
2. Completed Features
3. Active Development
4. Known Issues
5. Testing Status
6. Phase-by-Phase Progress

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Current Phase

Phase 3.2: Integration Sprint (🎯 TOP PRIORITY)

Goal: Connect LLM intelligence components to production workflow

Timeline: 2-4 weeks (Started 2025-11-17)

Status: LLM components built and tested individually (85% complete). Need to wire them into production runner.

📋 Detailed Plan: docs/PHASE_3_2_INTEGRATION_PLAN.md

Critical Path:

Week 1: Make LLM Mode Accessible (16 hours)

• 1.1 Create unified entry point optimization_engine/run_optimization.py (4h)

  • Add --llm flag for natural language mode
  • Add --request parameter for natural language input
  • Support both LLM and traditional JSON modes
  • Preserve backward compatibility

• 1.2 Wire LLMOptimizationRunner to production (8h)

  • Connect LLMWorkflowAnalyzer to entry point
  • Bridge LLMOptimizationRunner → OptimizationRunner
  • Pass model updater and simulation runner callables
  • Integrate with existing hook system

• 1.3 Create minimal example (2h)

  • Create examples/llm_mode_demo.py
  • Show natural language → optimization results
  • Compare traditional (100 lines) vs LLM (3 lines)

• 1.4 End-to-end integration test (2h)

  • Test with simple_beam_optimization study
  • Verify extractors generated correctly
  • Validate output matches manual mode

Week 2: Robustness & Safety (16 hours)

• 2.1 Code validation pipeline (6h)

  • Create optimization_engine/code_validator.py
  • Implement syntax validation (ast.parse)
  • Implement security scanning (whitelist imports)
  • Implement test execution on example OP2
  • Add retry with LLM feedback on failure

• 2.2 Graceful fallback mechanisms (4h)

  • Wrap all LLM calls in try/except
  • Provide clear error messages
  • Offer fallback to manual mode
  • Never crash on LLM failure

• 2.3 LLM audit trail (3h)

  • Create optimization_engine/llm_audit.py
  • Log all LLM requests and responses
  • Log generated code with prompts
  • Create llm_audit.json in study output

• 2.4 Failure scenario testing (3h)

  • Test invalid natural language request
  • Test LLM unavailable
  • Test generated code syntax errors
  • Test validation failures

Week 3: Learning System (12 hours)

• 3.1 Knowledge base implementation (4h)

  • Create optimization_engine/knowledge_base.py
  • Implement save_session() - Save successful workflows
  • Implement search_templates() - Find similar patterns
  • Add confidence scoring

• 3.2 Template extraction (4h)

  • Extract reusable patterns from generated code
  • Parameterize variable parts
  • Save templates with usage examples
  • Implement template application to new requests

• 3.3 ResearchAgent integration (4h)

  • Complete ResearchAgent implementation
  • Integrate into ExtractorOrchestrator error handling
  • Add user example collection workflow
  • Save learned knowledge to knowledge base

Week 4: Documentation & Discoverability (8 hours)

• 4.1 Update README (2h)

  • Add "🤖 LLM-Powered Mode" section
  • Show example command with natural language
  • Link to detailed docs

• 4.2 Create LLM mode documentation (3h)

  • Create docs/LLM_MODE.md
  • Explain how LLM mode works
  • Provide usage examples
  • Add troubleshooting guide

• 4.3 Create demo video/GIF (1h)

  • Record terminal session
  • Show before/after (100 lines → 3 lines)
  • Create animated GIF for README

• 4.4 Update all planning docs (2h)

  • Update DEVELOPMENT.md status
  • Update DEVELOPMENT_GUIDANCE.md (80-90% → 90-95%)
  • Mark Phase 3.2 as ✅ Complete

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Completed Features

✅ Phase 1: Plugin System & Infrastructure (Completed 2025-01-16)

Core Architecture

• Hook Manager (optimization_engine/plugins/hook_manager.py)

  • Hook registration with priority-based execution
  • Auto-discovery from plugin directories
  • Context passing to all hooks
  • Execution history tracking

• Lifecycle Hooks

  • pre_solve: Execute before solver launch
  • post_solve: Execute after solve, before extraction
  • post_extraction: Execute after result extraction

Logging Infrastructure

• Detailed Trial Logs (detailed_logger.py)

  • Per-trial log files in optimization_results/trial_logs/
  • Complete iteration trace with timestamps
  • Design variables, configuration, timeline
  • Extracted results and constraint evaluations

• High-Level Optimization Log (optimization_logger.py)

  • optimization.log file tracking overall progress
  • Configuration summary header
  • Compact START/COMPLETE entries per trial
  • Easy to scan format for monitoring

• Result Appenders

  • log_solve_complete.py - Appends solve completion to trial logs
  • log_results.py - Appends extracted results to trial logs
  • optimization_logger_results.py - Appends results to optimization.log

Project Organization

• Studies Structure (studies/)

  • Standardized folder layout with model/, optimization_results/, analysis/
  • Comprehensive documentation in studies/README.md
  • Example study: bracket_stress_minimization/
  • Template structure for future studies

• Path Resolution (atomizer_paths.py)

  • Intelligent project root detection using marker files
  • Helper functions: root(), optimization_engine(), studies(), tests()
  • ensure_imports() for robust module imports
  • Works regardless of script location

Testing

• Hook Validation Test (test_hooks_with_bracket.py)

  • Verifies hook loading and execution
  • Tests 3 trials with dummy data
  • Checks hook execution history

• Integration Tests

  • run_5trial_test.py - Quick 5-trial optimization
  • test_journal_optimization.py - Full optimization test

Runner Enhancements

• Context Passing (runner.py:332,365,412)
  • output_dir passed to all hook contexts
  • Trial number, design variables, extracted results
  • Configuration dictionary available to hooks

✅ Core Engine (Pre-Phase 1)

• Optuna integration with TPE sampler
• Multi-objective optimization support
• NX journal execution (nx_solver.py)
• Expression updates (nx_updater.py)
• OP2 result extraction (stress, displacement)
• Study management with resume capability
• Web dashboard (real-time monitoring)
• Precision control (4-decimal rounding)

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Active Development

In Progress

• Feature registry creation (Phase 2, Week 1)
• Claude skill definition (Phase 2, Week 1)

Up Next (Phase 2, Week 2)

• Natural language parser
• Intent classification system
• Entity extraction for optimization parameters
• Conversational workflow manager

Backlog (Phase 3+)

• Custom function generator (RSS, weighted objectives)
• Journal script generator
• Code validation pipeline
• Result analyzer with statistical analysis
• Surrogate quality checker
• HTML/PDF report generator

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Known Issues

Critical

• None currently

Minor

• .claude/settings.local.json modified during development (contains user-specific settings)
• Some old bash background processes still running from previous tests

Documentation

• Need to add examples of custom hooks to studies/README.md
• Missing API documentation for hook_manager methods
• No developer guide for creating new plugins

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Testing Status

Automated Tests

• ✅ Hook system - test_hooks_with_bracket.py passing
• ✅ 5-trial integration - run_5trial_test.py working
• ✅ Full optimization - test_journal_optimization.py functional
• ⏳ Unit tests - Need to create for individual modules
• ⏳ CI/CD pipeline - Not yet set up

Manual Testing

• ✅ Bracket optimization (50 trials)
• ✅ Log file generation in correct locations
• ✅ Hook execution at all lifecycle points
• ✅ Path resolution across different script locations
• ⏳ Resume functionality with config validation
• ⏳ Dashboard integration with new plugin system

Test Coverage

• Hook manager: ~80% (core functionality tested)
• Logging plugins: 100% (tested via integration tests)
• Path resolution: 100% (tested in all scripts)
• Result extractors: ~70% (basic tests exist)
• Overall: ~60% estimated

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Phase-by-Phase Progress

Phase 1: Plugin System ✅ (100% Complete)

Completed (2025-01-16):

• Hook system for optimization lifecycle
• Plugin auto-discovery and registration
• Hook manager with priority-based execution
• Detailed per-trial logs (trial_logs/)
• High-level optimization log (optimization.log)
• Context passing system for hooks
• Studies folder structure
• Comprehensive studies documentation
• Model file organization (model/ folder)
• Intelligent path resolution
• Test suite for hook system

Deferred to Future Phases:

• Feature registry → Phase 2 (with LLM interface)
• pre_mesh and post_mesh hooks → Future (not needed for current workflow)
• Custom objective/constraint registration → Phase 3 (Code Generation)

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Phase 2: LLM Integration 🟡 (0% Complete)

Target: 2 weeks (Started 2025-01-16)

Week 1 Todos (Feature Registry & Claude Skill)

• Create optimization_engine/feature_registry.json
• Extract all current capabilities
• Draft .claude/skills/atomizer.md
• Test LLM's ability to navigate codebase

Week 2 Todos (Natural Language Interface)

• Implement intent classifier
• Build entity extractor
• Create workflow manager
• Test end-to-end: "Create a stress minimization study"

Success Criteria:

• LLM can create optimization from natural language in <5 turns
• 90% of user requests understood correctly
• Zero manual JSON editing required

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Phase 3: Code Generation ⏳ (Not Started)

Target: 3 weeks

Key Deliverables:

• Custom function generator
  • RSS (Root Sum Square) template
  • Weighted objectives template
  • Custom constraints template
• Journal script generator
• Code validation pipeline
• Safe execution environment

Success Criteria:

• LLM generates 10+ custom functions with zero errors
• All generated code passes safety validation
• Users save 50% time vs. manual coding

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Phase 4: Analysis & Decision Support ⏳ (Not Started)

Target: 3 weeks

Key Deliverables:

• Result analyzer (convergence, sensitivity, outliers)
• Surrogate model quality checker (R², CV score, confidence intervals)
• Decision assistant (trade-offs, what-if analysis, recommendations)

Success Criteria:

• Surrogate quality detection 95% accurate
• Recommendations lead to 30% faster convergence
• Users report higher confidence in results

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Phase 5: Automated Reporting ⏳ (Not Started)

Target: 2 weeks

Key Deliverables:

• Report generator with Jinja2 templates
• Multi-format export (HTML, PDF, Markdown, JSON)
• LLM-written narrative explanations

Success Criteria:

• Reports generated in <30 seconds
• Narrative quality rated 4/5 by engineers
• 80% of reports used without manual editing

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Phase 6: NX MCP Enhancement ⏳ (Not Started)

Target: 4 weeks

Key Deliverables:

• NX documentation MCP server
• Advanced NX operations library
• Feature bank with 50+ pre-built operations

Success Criteria:

• NX MCP answers 95% of API questions correctly
• Feature bank covers 80% of common workflows
• Users write 50% less manual journal code

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Phase 7: Self-Improving System ⏳ (Not Started)

Target: 4 weeks

Key Deliverables:

• Feature learning system
• Best practices database
• Continuous documentation generation

Success Criteria:

• 20+ user-contributed features in library
• Pattern recognition identifies 10+ best practices
• Documentation auto-updates with zero manual effort

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Development Commands

Running Tests

# Hook validation (3 trials, fast)
python tests/test_hooks_with_bracket.py

# Quick integration test (5 trials)
python tests/run_5trial_test.py

# Full optimization test
python tests/test_journal_optimization.py

Code Quality

# Run linter (when available)
# pylint optimization_engine/

# Run type checker (when available)
# mypy optimization_engine/

# Run all tests (when test suite is complete)
# pytest tests/

Git Workflow

# Stage all changes
git add .

# Commit with conventional commits format
git commit -m "feat: description"  # New feature
git commit -m "fix: description"   # Bug fix
git commit -m "docs: description"  # Documentation
git commit -m "test: description"  # Tests
git commit -m "refactor: description"  # Code refactoring

# Push to GitHub
git push origin main

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Documentation

For Developers

• DEVELOPMENT_ROADMAP.md - Strategic vision and phases
• studies/README.md - Studies folder organization
• CHANGELOG.md - Version history

For Users

• README.md - Project overview and quick start
• docs/ - Additional documentation

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Notes

Architecture Decisions

• Hook system: Chose priority-based execution to allow precise control of plugin order
• Path resolution: Used marker files instead of environment variables for simplicity
• Logging: Two-tier system (detailed trial logs + high-level optimization.log) for different use cases

Performance Considerations

• Hook execution adds <1s overhead per trial (acceptable for FEA simulations)
• Path resolution caching could improve startup time (future optimization)
• Log file sizes grow linearly with trials (~10KB per trial)

Future Considerations

• Consider moving to structured logging (JSON) for easier parsing
• May need database for storing hook execution history (currently in-memory)
• Dashboard integration will require WebSocket for real-time log streaming

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Last Updated: 2025-01-16 Maintained by: Antoine Polvé (antoine@atomaste.com) Repository: GitHub - Atomizer