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feat: Complete Phase 2.5-2.7 - Intelligent LLM-Powered Workflow Analysis This commit implements three major architectural improvements to transform Atomizer from static pattern matching to intelligent AI-powered analysis. ## Phase 2.5: Intelligent Codebase-Aware Gap Detection ✅ Created intelligent system that understands existing capabilities before requesting examples: **New Files:** - optimization_engine/codebase_analyzer.py (379 lines) Scans Atomizer codebase for existing FEA/CAE capabilities - optimization_engine/workflow_decomposer.py (507 lines, v0.2.0) Breaks user requests into atomic workflow steps Complete rewrite with multi-objective, constraints, subcase targeting - optimization_engine/capability_matcher.py (312 lines) Matches workflow steps to existing code implementations - optimization_engine/targeted_research_planner.py (259 lines) Creates focused research plans for only missing capabilities **Results:** - 80-90% coverage on complex optimization requests - 87-93% confidence in capability matching - Fixed expression reading misclassification (geometry vs result_extraction) ## Phase 2.6: Intelligent Step Classification ✅ Distinguishes engineering features from simple math operations: **New Files:** - optimization_engine/step_classifier.py (335 lines) **Classification Types:** 1. Engineering Features - Complex FEA/CAE needing research 2. Inline Calculations - Simple math to auto-generate 3. Post-Processing Hooks - Middleware between FEA steps ## Phase 2.7: LLM-Powered Workflow Intelligence ✅ Replaces static regex patterns with Claude AI analysis: **New Files:** - optimization_engine/llm_workflow_analyzer.py (395 lines) Uses Claude API for intelligent request analysis Supports both Claude Code (dev) and API (production) modes - .claude/skills/analyze-workflow.md Skill template for LLM workflow analysis integration **Key Breakthrough:** - Detects ALL intermediate steps (avg, min, normalization, etc.) - Understands engineering context (CBUSH vs CBAR, directions, metrics) - Distinguishes OP2 extraction from part expression reading - Expected 95%+ accuracy with full nuance detection ## Test Coverage **New Test Files:** - tests/test_phase_2_5_intelligent_gap_detection.py (335 lines) - tests/test_complex_multiobj_request.py (130 lines) - tests/test_cbush_optimization.py (130 lines) - tests/test_cbar_genetic_algorithm.py (150 lines) - tests/test_step_classifier.py (140 lines) - tests/test_llm_complex_request.py (387 lines) All tests include: - UTF-8 encoding for Windows console - atomizer environment (not test_env) - Comprehensive validation checks ## Documentation **New Documentation:** - docs/PHASE_2_5_INTELLIGENT_GAP_DETECTION.md (254 lines) - docs/PHASE_2_7_LLM_INTEGRATION.md (227 lines) - docs/SESSION_SUMMARY_PHASE_2_5_TO_2_7.md (252 lines) **Updated:** - README.md - Added Phase 2.5-2.7 completion status - DEVELOPMENT_ROADMAP.md - Updated phase progress ## Critical Fixes 1. **Expression Reading Misclassification** (lines cited in session summary) - Updated codebase_analyzer.py pattern detection - Fixed workflow_decomposer.py domain classification - Added capability_matcher.py read_expression mapping 2. **Environment Standardization** - All code now uses 'atomizer' conda environment - Removed test_env references throughout 3. **Multi-Objective Support** - WorkflowDecomposer v0.2.0 handles multiple objectives - Constraint extraction and validation - Subcase and direction targeting ## Architecture Evolution **Before (Static & Dumb):** User Request → Regex Patterns → Hardcoded Rules → Missed Steps ❌ **After (LLM-Powered & Intelligent):** User Request → Claude AI Analysis → Structured JSON → ├─ Engineering (research needed) ├─ Inline (auto-generate Python) ├─ Hooks (middleware scripts) └─ Optimization (config) ✅ ## LLM Integration Strategy **Development Mode (Current):** - Use Claude Code directly for interactive analysis - No API consumption or costs - Perfect for iterative development **Production Mode (Future):** - Optional Anthropic API integration - Falls back to heuristics if no API key - For standalone batch processing ## Next Steps - Phase 2.8: Inline Code Generation - Phase 2.9: Post-Processing Hook Generation - Phase 3: MCP Integration for automated documentation research 🚀 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-16 13:35:41 -05:00
# Changelog
All notable changes to Atomizer will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
## [Unreleased]
feat: Add robust NX expression import system for all expression types Major Enhancement: - Implemented .exp file-based expression updates via NX journal scripts - Fixes critical issue with feature-linked expressions (e.g., hole_count) - Supports ALL NX expression types including binary-stored ones - Full 4D design space validation completed successfully New Components: 1. import_expressions.py - NX journal for .exp file import - Uses NXOpen.ExpressionCollection.ImportFromFile() - Replace mode overwrites existing values - Automatic model update and save - Comprehensive error handling 2. export_expressions.py - NX journal for .exp file export - Exports all expressions to text format - Used for unit detection and verification 3. Enhanced nx_updater.py - New update_expressions_via_import() method - Automatic unit detection from .exp export - Creates study-variable-only .exp files - Replaces fragile binary .prt editing Technical Details: - .exp Format: [Units]name=value (e.g., [MilliMeter]beam_length=5000) - Unitless expressions: name=value (e.g., hole_count=10) - Robustness: Native NX functionality, no regex failures - Performance: < 1 second per update operation Validation: - Simple Beam Optimization study (4D design space) * beam_half_core_thickness: 10-40 mm * beam_face_thickness: 10-40 mm * holes_diameter: 150-450 mm * hole_count: 5-15 (integer) Results: ✅ 3-trial validation completed successfully ✅ All 4 variables update correctly in all trials ✅ Mesh adaptation verified (hole_count: 6, 15, 11 → different mesh sizes) ✅ Trial 0: 5373 CQUAD4 elements (6 holes) ✅ Trial 1: 5158 CQUAD4 + 1 CTRIA3 (15 holes) ✅ Trial 2: 5318 CQUAD4 (11 holes) Problem Solved: - hole_count expression was not updating with binary .prt editing - Expression stored in feature parameter, not accessible via text regex - Binary format prevented reliable text-based updates Solution: - Use NX native expression import/export - Works for ALL expressions (text and binary-stored) - Automatic unit handling - Model update integrated in journal Documentation: - New: docs/NX_EXPRESSION_IMPORT_SYSTEM.md (comprehensive guide) - Updated: CHANGELOG.md with Phase 3.2 progress - Study: studies/simple_beam_optimization/ (complete example) Files Added: - optimization_engine/import_expressions.py - optimization_engine/export_expressions.py - docs/NX_EXPRESSION_IMPORT_SYSTEM.md - studies/simple_beam_optimization/ (full study) Files Modified: - optimization_engine/nx_updater.py - CHANGELOG.md Compatibility: - NX 2412 tested and verified - Python 3.10+ - Works with all NX expression types 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-17 12:34:06 -05:00
### Phase 3.2 - Integration & NX Enhancements (In Progress)
#### Added
- **NX Expression Import System** (2025-11-17)
- Robust .exp file-based expression updates via NX journal scripts
- Supports ALL NX expression types including binary-stored ones
- Automatic unit detection and formatting
- Fixes issue with `hole_count` and other feature-linked expressions
- Documentation: [NX_EXPRESSION_IMPORT_SYSTEM.md](docs/NX_EXPRESSION_IMPORT_SYSTEM.md)
- New journal: `import_expressions.py` for .exp file import
- Enhanced `nx_updater.py` with `update_expressions_via_import()` method
- **4D Design Space Validation** (2025-11-17)
- Validated full 4-variable optimization (beam_half_core_thickness, beam_face_thickness, holes_diameter, hole_count)
- All variables now updating correctly in optimization loop
- Mesh adaptation verified across different hole_count values
### Phase 2 - LLM Integration (Completed 85%)
feat: Complete Phase 2.5-2.7 - Intelligent LLM-Powered Workflow Analysis This commit implements three major architectural improvements to transform Atomizer from static pattern matching to intelligent AI-powered analysis. ## Phase 2.5: Intelligent Codebase-Aware Gap Detection ✅ Created intelligent system that understands existing capabilities before requesting examples: **New Files:** - optimization_engine/codebase_analyzer.py (379 lines) Scans Atomizer codebase for existing FEA/CAE capabilities - optimization_engine/workflow_decomposer.py (507 lines, v0.2.0) Breaks user requests into atomic workflow steps Complete rewrite with multi-objective, constraints, subcase targeting - optimization_engine/capability_matcher.py (312 lines) Matches workflow steps to existing code implementations - optimization_engine/targeted_research_planner.py (259 lines) Creates focused research plans for only missing capabilities **Results:** - 80-90% coverage on complex optimization requests - 87-93% confidence in capability matching - Fixed expression reading misclassification (geometry vs result_extraction) ## Phase 2.6: Intelligent Step Classification ✅ Distinguishes engineering features from simple math operations: **New Files:** - optimization_engine/step_classifier.py (335 lines) **Classification Types:** 1. Engineering Features - Complex FEA/CAE needing research 2. Inline Calculations - Simple math to auto-generate 3. Post-Processing Hooks - Middleware between FEA steps ## Phase 2.7: LLM-Powered Workflow Intelligence ✅ Replaces static regex patterns with Claude AI analysis: **New Files:** - optimization_engine/llm_workflow_analyzer.py (395 lines) Uses Claude API for intelligent request analysis Supports both Claude Code (dev) and API (production) modes - .claude/skills/analyze-workflow.md Skill template for LLM workflow analysis integration **Key Breakthrough:** - Detects ALL intermediate steps (avg, min, normalization, etc.) - Understands engineering context (CBUSH vs CBAR, directions, metrics) - Distinguishes OP2 extraction from part expression reading - Expected 95%+ accuracy with full nuance detection ## Test Coverage **New Test Files:** - tests/test_phase_2_5_intelligent_gap_detection.py (335 lines) - tests/test_complex_multiobj_request.py (130 lines) - tests/test_cbush_optimization.py (130 lines) - tests/test_cbar_genetic_algorithm.py (150 lines) - tests/test_step_classifier.py (140 lines) - tests/test_llm_complex_request.py (387 lines) All tests include: - UTF-8 encoding for Windows console - atomizer environment (not test_env) - Comprehensive validation checks ## Documentation **New Documentation:** - docs/PHASE_2_5_INTELLIGENT_GAP_DETECTION.md (254 lines) - docs/PHASE_2_7_LLM_INTEGRATION.md (227 lines) - docs/SESSION_SUMMARY_PHASE_2_5_TO_2_7.md (252 lines) **Updated:** - README.md - Added Phase 2.5-2.7 completion status - DEVELOPMENT_ROADMAP.md - Updated phase progress ## Critical Fixes 1. **Expression Reading Misclassification** (lines cited in session summary) - Updated codebase_analyzer.py pattern detection - Fixed workflow_decomposer.py domain classification - Added capability_matcher.py read_expression mapping 2. **Environment Standardization** - All code now uses 'atomizer' conda environment - Removed test_env references throughout 3. **Multi-Objective Support** - WorkflowDecomposer v0.2.0 handles multiple objectives - Constraint extraction and validation - Subcase and direction targeting ## Architecture Evolution **Before (Static & Dumb):** User Request → Regex Patterns → Hardcoded Rules → Missed Steps ❌ **After (LLM-Powered & Intelligent):** User Request → Claude AI Analysis → Structured JSON → ├─ Engineering (research needed) ├─ Inline (auto-generate Python) ├─ Hooks (middleware scripts) └─ Optimization (config) ✅ ## LLM Integration Strategy **Development Mode (Current):** - Use Claude Code directly for interactive analysis - No API consumption or costs - Perfect for iterative development **Production Mode (Future):** - Optional Anthropic API integration - Falls back to heuristics if no API key - For standalone batch processing ## Next Steps - Phase 2.8: Inline Code Generation - Phase 2.9: Post-Processing Hook Generation - Phase 3: MCP Integration for automated documentation research 🚀 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-16 13:35:41 -05:00
- Natural language interface for optimization configuration
- Feature registry with capability catalog
- Claude skill for Atomizer navigation
feat: Add robust NX expression import system for all expression types Major Enhancement: - Implemented .exp file-based expression updates via NX journal scripts - Fixes critical issue with feature-linked expressions (e.g., hole_count) - Supports ALL NX expression types including binary-stored ones - Full 4D design space validation completed successfully New Components: 1. import_expressions.py - NX journal for .exp file import - Uses NXOpen.ExpressionCollection.ImportFromFile() - Replace mode overwrites existing values - Automatic model update and save - Comprehensive error handling 2. export_expressions.py - NX journal for .exp file export - Exports all expressions to text format - Used for unit detection and verification 3. Enhanced nx_updater.py - New update_expressions_via_import() method - Automatic unit detection from .exp export - Creates study-variable-only .exp files - Replaces fragile binary .prt editing Technical Details: - .exp Format: [Units]name=value (e.g., [MilliMeter]beam_length=5000) - Unitless expressions: name=value (e.g., hole_count=10) - Robustness: Native NX functionality, no regex failures - Performance: < 1 second per update operation Validation: - Simple Beam Optimization study (4D design space) * beam_half_core_thickness: 10-40 mm * beam_face_thickness: 10-40 mm * holes_diameter: 150-450 mm * hole_count: 5-15 (integer) Results: ✅ 3-trial validation completed successfully ✅ All 4 variables update correctly in all trials ✅ Mesh adaptation verified (hole_count: 6, 15, 11 → different mesh sizes) ✅ Trial 0: 5373 CQUAD4 elements (6 holes) ✅ Trial 1: 5158 CQUAD4 + 1 CTRIA3 (15 holes) ✅ Trial 2: 5318 CQUAD4 (11 holes) Problem Solved: - hole_count expression was not updating with binary .prt editing - Expression stored in feature parameter, not accessible via text regex - Binary format prevented reliable text-based updates Solution: - Use NX native expression import/export - Works for ALL expressions (text and binary-stored) - Automatic unit handling - Model update integrated in journal Documentation: - New: docs/NX_EXPRESSION_IMPORT_SYSTEM.md (comprehensive guide) - Updated: CHANGELOG.md with Phase 3.2 progress - Study: studies/simple_beam_optimization/ (complete example) Files Added: - optimization_engine/import_expressions.py - optimization_engine/export_expressions.py - docs/NX_EXPRESSION_IMPORT_SYSTEM.md - studies/simple_beam_optimization/ (full study) Files Modified: - optimization_engine/nx_updater.py - CHANGELOG.md Compatibility: - NX 2412 tested and verified - Python 3.10+ - Works with all NX expression types 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-17 12:34:06 -05:00
- LLM workflow analyzer and extractor orchestration
- Dynamic code generation for hooks and extractors
feat: Complete Phase 2.5-2.7 - Intelligent LLM-Powered Workflow Analysis This commit implements three major architectural improvements to transform Atomizer from static pattern matching to intelligent AI-powered analysis. ## Phase 2.5: Intelligent Codebase-Aware Gap Detection ✅ Created intelligent system that understands existing capabilities before requesting examples: **New Files:** - optimization_engine/codebase_analyzer.py (379 lines) Scans Atomizer codebase for existing FEA/CAE capabilities - optimization_engine/workflow_decomposer.py (507 lines, v0.2.0) Breaks user requests into atomic workflow steps Complete rewrite with multi-objective, constraints, subcase targeting - optimization_engine/capability_matcher.py (312 lines) Matches workflow steps to existing code implementations - optimization_engine/targeted_research_planner.py (259 lines) Creates focused research plans for only missing capabilities **Results:** - 80-90% coverage on complex optimization requests - 87-93% confidence in capability matching - Fixed expression reading misclassification (geometry vs result_extraction) ## Phase 2.6: Intelligent Step Classification ✅ Distinguishes engineering features from simple math operations: **New Files:** - optimization_engine/step_classifier.py (335 lines) **Classification Types:** 1. Engineering Features - Complex FEA/CAE needing research 2. Inline Calculations - Simple math to auto-generate 3. Post-Processing Hooks - Middleware between FEA steps ## Phase 2.7: LLM-Powered Workflow Intelligence ✅ Replaces static regex patterns with Claude AI analysis: **New Files:** - optimization_engine/llm_workflow_analyzer.py (395 lines) Uses Claude API for intelligent request analysis Supports both Claude Code (dev) and API (production) modes - .claude/skills/analyze-workflow.md Skill template for LLM workflow analysis integration **Key Breakthrough:** - Detects ALL intermediate steps (avg, min, normalization, etc.) - Understands engineering context (CBUSH vs CBAR, directions, metrics) - Distinguishes OP2 extraction from part expression reading - Expected 95%+ accuracy with full nuance detection ## Test Coverage **New Test Files:** - tests/test_phase_2_5_intelligent_gap_detection.py (335 lines) - tests/test_complex_multiobj_request.py (130 lines) - tests/test_cbush_optimization.py (130 lines) - tests/test_cbar_genetic_algorithm.py (150 lines) - tests/test_step_classifier.py (140 lines) - tests/test_llm_complex_request.py (387 lines) All tests include: - UTF-8 encoding for Windows console - atomizer environment (not test_env) - Comprehensive validation checks ## Documentation **New Documentation:** - docs/PHASE_2_5_INTELLIGENT_GAP_DETECTION.md (254 lines) - docs/PHASE_2_7_LLM_INTEGRATION.md (227 lines) - docs/SESSION_SUMMARY_PHASE_2_5_TO_2_7.md (252 lines) **Updated:** - README.md - Added Phase 2.5-2.7 completion status - DEVELOPMENT_ROADMAP.md - Updated phase progress ## Critical Fixes 1. **Expression Reading Misclassification** (lines cited in session summary) - Updated codebase_analyzer.py pattern detection - Fixed workflow_decomposer.py domain classification - Added capability_matcher.py read_expression mapping 2. **Environment Standardization** - All code now uses 'atomizer' conda environment - Removed test_env references throughout 3. **Multi-Objective Support** - WorkflowDecomposer v0.2.0 handles multiple objectives - Constraint extraction and validation - Subcase and direction targeting ## Architecture Evolution **Before (Static & Dumb):** User Request → Regex Patterns → Hardcoded Rules → Missed Steps ❌ **After (LLM-Powered & Intelligent):** User Request → Claude AI Analysis → Structured JSON → ├─ Engineering (research needed) ├─ Inline (auto-generate Python) ├─ Hooks (middleware scripts) └─ Optimization (config) ✅ ## LLM Integration Strategy **Development Mode (Current):** - Use Claude Code directly for interactive analysis - No API consumption or costs - Perfect for iterative development **Production Mode (Future):** - Optional Anthropic API integration - Falls back to heuristics if no API key - For standalone batch processing ## Next Steps - Phase 2.8: Inline Code Generation - Phase 2.9: Post-Processing Hook Generation - Phase 3: MCP Integration for automated documentation research 🚀 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-16 13:35:41 -05:00
---
## [0.2.0] - 2025-01-16
### Phase 1 - Plugin System & Infrastructure ✅
#### Added
- **Plugin Architecture**
- Hook manager with lifecycle execution at `pre_solve`, `post_solve`, and `post_extraction` points
- Plugin auto-discovery from `optimization_engine/plugins/` directory
- Priority-based hook execution
- Context passing system for hooks (output_dir, trial_number, design_variables, results)
- **Logging Infrastructure**
- Detailed per-trial logs in `optimization_results/trial_logs/`
- Complete iteration trace with timestamps
- Design variables, configuration, execution timeline
- Extracted results and constraint evaluations
- High-level optimization progress log (`optimization.log`)
- Configuration summary header
- Trial START and COMPLETE entries (one line per trial)
- Compact format for easy progress monitoring
- **Logging Plugins**
- `detailed_logger.py` - Creates detailed trial logs
- `optimization_logger.py` - Creates high-level optimization.log
- `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 folder structure with standardized layout
- Comprehensive studies documentation ([studies/README.md](studies/README.md))
- Model files organized in `model/` subdirectory (`.prt`, `.sim`, `.fem`)
- Intelligent path resolution system (`atomizer_paths.py`)
- Marker-based project root detection
- **Test Suite**
- `test_hooks_with_bracket.py` - Hook validation test (3 trials)
- `run_5trial_test.py` - Quick integration test (5 trials)
- `test_journal_optimization.py` - Full optimization test
#### Changed
- Renamed `examples/` folder to `studies/`
- Moved bracket example to `studies/bracket_stress_minimization/`
- Consolidated FEA files into `model/` subfolder
- Updated all test scripts to use `atomizer_paths` for imports
- Runner now passes `output_dir` to all hook contexts
#### Removed
- Obsolete test scripts from examples/ (14 files deleted)
- `optimization_logs/` and `optimization_results/` from root directory
#### Fixed
- Log files now correctly generated in study-specific `optimization_results/` folder
- Path resolution works regardless of script location
- Hooks properly registered with `register_hooks()` function
---
## [0.1.0] - 2025-01-10
### Initial Release
#### Core Features
- Optuna integration with TPE sampler
- NX journal integration for expression updates and simulation execution
- OP2 result extraction (stress, displacement)
- Study management with folder-based isolation
- Web dashboard for real-time monitoring
- Precision control (4-decimal rounding for mm/degrees/MPa)
- Crash recovery and optimization resumption
---
## Development Timeline
- **Phase 1** (✅ Completed 2025-01-16): Plugin system & hooks
- **Phase 2** (🟡 Starting): LLM interface with natural language configuration
- **Phase 3** (Planned): Dynamic code generation for custom objectives
- **Phase 4** (Planned): Intelligent analysis and surrogate quality assessment
- **Phase 5** (Planned): Automated HTML/PDF report generation
- **Phase 6** (Planned): NX MCP server with full API documentation
- **Phase 7** (Planned): Self-improving feature registry
---
**Maintainer**: Antoine Polvé (antoine@atomaste.com)
**License**: Proprietary - Atomaste © 2025