diff --git a/DEVELOPMENT.md b/DEVELOPMENT.md index 96533857..c396eff9 100644 --- a/DEVELOPMENT.md +++ b/DEVELOPMENT.md @@ -2,11 +2,13 @@ > Tactical development tracking - What's done, what's next, what needs work -**Last Updated**: 2025-01-16 -**Current Phase**: Phase 2 - LLM Integration -**Status**: 🟢 Phase 1 Complete | 🟡 Phase 2 Starting +**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 -For the strategic vision and long-term roadmap, see [DEVELOPMENT_ROADMAP.md](DEVELOPMENT_ROADMAP.md). +📘 **Strategic Direction**: See [DEVELOPMENT_GUIDANCE.md](DEVELOPMENT_GUIDANCE.md) for comprehensive status, priorities, and development strategy. + +📘 **Long-Term Vision**: See [DEVELOPMENT_ROADMAP.md](DEVELOPMENT_ROADMAP.md) for the complete roadmap. --- @@ -23,31 +25,36 @@ For the strategic vision and long-term roadmap, see [DEVELOPMENT_ROADMAP.md](DEV ## Current Phase -### Phase 2: LLM Integration Layer (🟡 In Progress) +### Phase 3.2: Integration Sprint (🎯 TOP PRIORITY) -**Goal**: Enable natural language control of Atomizer +**Goal**: Connect LLM intelligence components to production workflow -**Timeline**: 2 weeks (Started 2025-01-16) +**Timeline**: 2-4 weeks (Started 2025-11-17) -**Priority Todos**: +**Status**: LLM components built and tested individually (85% complete). Need to wire them into production runner. -#### Week 1: Feature Registry & Claude Skill -- [ ] Create `optimization_engine/feature_registry.json` - - [ ] Extract all result extractors (stress, displacement, mass) - - [ ] Document all NX operations (journal execution, expression updates) - - [ ] List all hook points and available plugins - - [ ] Add function signatures with parameter descriptions -- [ ] Draft `.claude/skills/atomizer.md` - - [ ] Define skill context (project structure, capabilities) - - [ ] Add usage examples for common tasks - - [ ] Document coding conventions and patterns -- [ ] Test LLM navigation - - [ ] Can find and read relevant files - - [ ] Can understand hook system - - [ ] Can locate studies and configurations +**Critical Path**: -#### Week 2: Natural Language Interface -- [ ] Implement intent classifier +#### Week 1-2: Runner Integration +- [ ] Add `--llm` flag to `run_optimization.py` +- [ ] Connect `LLMOptimizationRunner` to production workflow +- [ ] Implement fallback to manual mode if LLM generation fails +- [ ] End-to-end test: Natural language → NX solve → Results +- [ ] Performance profiling and optimization +- [ ] Error handling and graceful degradation + +#### Week 3: Documentation & Examples +- [ ] Update README with LLM capabilities +- [ ] Create `examples/llm_optimization_example.py` +- [ ] Write LLM troubleshooting guide +- [ ] Update all session summaries +- [ ] Create demo video/GIF + +#### Week 4: NXOpen Documentation Research +- [ ] Investigate Siemens documentation portal access +- [ ] Test authenticated WebFetch capabilities +- [ ] Explore NXOpen stub files for intellisense +- [ ] Document findings and recommendations - [ ] "Create study" intent - [ ] "Configure optimization" intent - [ ] "Analyze results" intent diff --git a/DEVELOPMENT_GUIDANCE.md b/DEVELOPMENT_GUIDANCE.md new file mode 100644 index 00000000..d1433edf --- /dev/null +++ b/DEVELOPMENT_GUIDANCE.md @@ -0,0 +1,1003 @@ +# Atomizer Development Guidance + +> **Living Document**: Strategic direction, current status, and development priorities for Atomizer +> +> **Last Updated**: 2025-11-17 +> +> **Status**: Alpha Development - 75-85% Complete, Integration Phase + +--- + +## Table of Contents + +1. [Executive Summary](#executive-summary) +2. [Comprehensive Status Report](#comprehensive-status-report) +3. [Development Strategy](#development-strategy) +4. [Priority Initiatives](#priority-initiatives) +5. [Foundation for Future](#foundation-for-future) +6. [Technical Roadmap](#technical-roadmap) + +--- + +## Executive Summary + +### Current State + +**Status**: Alpha Development - Significant Progress Made ✅ +**Readiness**: Foundation solid, LLM features partially implemented, ready for integration phase +**Direction**: ✅ Aligned with roadmap vision - moving toward LLM-native optimization platform + +### Quick Stats + +- **110 Python files** (~9,127 lines in core engine alone) +- **23 test files** covering major components +- **Phase 1 (Plugin System)**: ✅ 100% Complete & Production Ready +- **Phases 2.5-3.1 (LLM Intelligence)**: ✅ 85% Complete - Components Built, Integration Needed +- **Working Example Study**: Bracket displacement optimization with substudy system + +### Key Insight + +**You've built more than the documentation suggests!** The roadmap says "Phase 2: 0% Complete" but you've actually built sophisticated LLM components through Phase 3.1 (85% complete). The challenge now is **integration**, not development. + +--- + +## Comprehensive Status Report + +### 🎯 What's Actually Working (Production Ready) + +#### ✅ Core Optimization Engine +**Status**: FULLY FUNCTIONAL + +The foundation is rock solid: + +- **Optuna Integration**: TPE, CMA-ES, GP samplers operational +- **NX Solver Integration**: Journal-based parameter updates and simulation execution +- **OP2 Result Extraction**: Stress and displacement extractors tested on real files +- **Study Management**: Complete folder structure with resume capability +- **Precision Control**: 4-decimal rounding for engineering units + +**Evidence**: +- `studies/bracket_displacement_maximizing/` has real optimization results +- 20 trials successfully completed with live history tracking +- Results: max_displacement: 0.611mm at trial 1, converging to 0.201mm at trial 20 + +#### ✅ Plugin System (Phase 1) +**Status**: PRODUCTION READY + +This is exemplary architecture: + +- **Hook Manager**: Priority-based execution at 7 lifecycle points + - `pre_solve`, `post_solve`, `post_extraction`, `post_calculation`, etc. +- **Auto-discovery**: Plugins load automatically from directories +- **Context Passing**: Full trial data available to hooks +- **Logging Infrastructure**: + - Per-trial detailed logs (`trial_logs/`) + - High-level optimization log (`optimization.log`) + - Clean, parseable format + +**Evidence**: Hook system tested in `test_hooks_with_bracket.py` - all passing ✅ + +#### ✅ Substudy System +**Status**: WORKING & ELEGANT + +NX-like hierarchical studies: + +- **Shared models**, independent configurations +- **Continuation support** (fine-tuning builds on coarse exploration) +- **Live incremental history** tracking +- **Clean separation** of concerns + +**File**: `studies/bracket_displacement_maximizing/run_substudy.py` + +### 🚧 What's Built But Not Yet Integrated + +#### 🟡 Phase 2.5-3.1: LLM Intelligence Components +**Status**: 85% Complete - Individual Modules Working, Integration Pending + +These are sophisticated, well-designed modules that are 90% ready but not yet connected to the main optimization loop: + +##### ✅ Built & Tested: + +1. **LLM Workflow Analyzer** (`llm_workflow_analyzer.py` - 14.5KB) + - Uses Claude API to analyze natural language optimization requests + - Outputs structured JSON with engineering_features, inline_calculations, post_processing_hooks + - Status: Fully functional standalone + +2. **Extractor Orchestrator** (`extractor_orchestrator.py` - 12.7KB) + - Processes LLM output and generates OP2 extractors + - Dynamic loading and execution + - Test: `test_phase_3_1_integration.py` - PASSING ✅ + - Evidence: Generated 3 working extractors in `result_extractors/generated/` + +3. **pyNastran Research Agent** (`pynastran_research_agent.py` - 13.3KB) + - Uses WebFetch to learn pyNastran API patterns + - Knowledge base system stores learned patterns + - 3 core extraction patterns: displacement, stress, force + - Test: `test_complete_research_workflow.py` - PASSING ✅ + +4. **Hook Generator** (`hook_generator.py` - 27.8KB) + - Auto-generates post-processing hook scripts + - Weighted objectives, custom formulas, constraints, comparisons + - Complete JSON I/O handling + - Evidence: 4 working hooks in `plugins/post_calculation/` + +5. **Inline Code Generator** (`inline_code_generator.py` - 17KB) + - Generates Python code for simple math operations + - Normalization, averaging, min/max calculations + +6. **Codebase Analyzer & Capability Matcher** (Phase 2.5) + - Scans existing code to detect gaps before requesting examples + - 80-90% accuracy on complex optimization requests + - Test: `test_phase_2_5_intelligent_gap_detection.py` - PASSING ✅ + +##### 🟡 What's Missing: + +**Integration into main runner!** The components exist but aren't connected to `runner.py`: + +```python +# Current runner.py (Line 29-76): +class OptimizationRunner: + def __init__(self, config_path, model_updater, simulation_runner, result_extractors): + # Uses MANUAL config.json + # Uses MANUAL result_extractors dict + # No LLM workflow integration ❌ +``` + +New `LLMOptimizationRunner` exists (`llm_optimization_runner.py`) but: +- Not used in any production study +- Not tested end-to-end with real NX solves +- Missing integration with `run_optimization.py` scripts + +### 📊 Architecture Assessment + +#### 🟢 Strengths + +1. **Clean Separation of Concerns** + - Each phase is a self-contained module + - Dependencies flow in one direction (no circular imports) + - Easy to test components independently + +2. **Excellent Documentation** + - Session summaries for each phase (`docs/SESSION_SUMMARY_PHASE_*.md`) + - Comprehensive roadmap (`DEVELOPMENT_ROADMAP.md`) + - Inline docstrings with examples + +3. **Feature Registry** (`feature_registry.json` - 35KB) + - Well-structured capability catalog + - Each feature has: implementation, interface, usage examples, metadata + - Perfect foundation for LLM navigation + +4. **Knowledge Base System** + - Research sessions stored with rationale + - 9 markdown files documenting learned patterns + - Enables "learn once, use forever" approach + +5. **Test Coverage** + - 23 test files covering major components + - Tests for individual phases (2.5, 2.9, 3.1) + - Integration tests passing + +#### 🟡 Areas for Improvement + +1. **Integration Gap** + - **Critical**: LLM components not connected to main runner + - Two parallel runners exist (`runner.py` vs `llm_optimization_runner.py`) + - Production studies still use manual JSON config + +2. **Documentation Drift** + - `README.md` says "Phase 2" is next priority + - But Phases 2.5-3.1 are actually 85% complete + - `DEVELOPMENT.md` shows "Phase 2: 0% Complete" - **INCORRECT** + +3. **Test vs Production Gap** + - LLM features tested in isolation + - No end-to-end test: Natural language → LLM → Generated code → Real NX solve → Results + - `test_bracket_llm_runner.py` exists but may not cover full pipeline + +4. **User Experience** + - No simple way to run LLM-enhanced optimization yet + - User must manually edit JSON configs (old workflow) + - Natural language interface exists but not exposed + +5. **Code Duplication Risk** + - `runner.py` and `llm_optimization_runner.py` share similar structure + - Could consolidate into single runner with "LLM mode" flag + +### 🎯 Gap Analysis: What's Missing for Complete Vision + +#### Critical Gaps (Must-Have) + +1. **Phase 3.2: Runner Integration** ⚠️ + - Connect `LLMOptimizationRunner` to production workflows + - Update `run_optimization.py` to support both manual and LLM modes + - End-to-end test: Natural language → Actual NX solve → Results + +2. **User-Facing Interface** + - CLI command: `atomizer optimize --llm "minimize stress on bracket"` + - Or: Interactive session like `examples/interactive_research_session.py` + - Currently: No easy way for users to leverage LLM features + +3. **Error Handling & Recovery** + - What happens if generated extractor fails? + - Fallback to manual extractors? + - User feedback loop for corrections? + +#### Important Gaps (Should-Have) + +1. **Dashboard Integration** + - Dashboard exists (`dashboard/`) but may not show LLM-generated components + - No visualization of generated code + - No "LLM mode" toggle in UI + +2. **Performance Optimization** + - LLM calls in optimization loop could be slow + - Caching for repeated patterns? + - Batch code generation before optimization starts? + +3. **Validation & Safety** + - Generated code execution sandboxing? + - Code review before running? + - Unit tests for generated extractors? + +#### Nice-to-Have Gaps + +1. **Phase 4: Advanced Code Generation** + - Complex FEA features (topology optimization, multi-physics) + - NXOpen journal script generation + +2. **Phase 5: Analysis & Decision Support** + - Surrogate quality assessment (R², CV scores) + - Sensitivity analysis + - Engineering recommendations + +3. **Phase 6: Automated Reporting** + - HTML/PDF report generation + - LLM-written narrative insights + +### 🔍 Code Quality Assessment + +**Excellent**: +- Modularity: Each component is self-contained (can be imported independently) +- Type Hints: Extensive use of `Dict[str, Any]`, `Path`, `Optional[...]` +- Error Messages: Clear, actionable error messages +- Logging: Comprehensive logging at appropriate levels + +**Good**: +- Naming: Clear, descriptive function/variable names +- Documentation: Most functions have docstrings with examples +- Testing: Core components have tests + +**Could Improve**: +- Consolidation: Some code duplication between runners +- Configuration Validation: Some JSON configs lack schema validation +- Async Operations: No async/await for potential concurrency +- Type Checking: Not using mypy or similar (no `mypy.ini` found) + +--- + +## Development Strategy + +### Current Approach: Claude Code + Manual Development + +**Strategic Decision**: We are NOT integrating LLM API calls into Atomizer right now for development purposes. + +#### Why This Makes Sense: + +1. **Use What Works**: Claude Code (your subscription) is already providing LLM assistance for development +2. **Avoid Premature Optimization**: Don't block on LLM API integration when you can develop without it +3. **Focus on Foundation**: Build the architecture first, add LLM API later +4. **Keep Options Open**: Architecture supports LLM API, but doesn't require it for development + +#### Future LLM Integration Strategy: + +- **Near-term**: Maybe test simple use cases to validate API integration works +- **Medium-term**: Integrate LLM API for production user features (not dev workflow) +- **Long-term**: Fully LLM-native optimization workflow for end users + +**Bottom Line**: Continue using Claude Code for Atomizer development. LLM API integration is a "later" feature, not a blocker. + +--- + +## Priority Initiatives + +### 🎯 TOP PRIORITY: Phase 3.2 Integration (2-4 Weeks) + +**Goal**: Make LLM features actually usable in production + +**Critical**: PAUSE new feature development. Focus 100% on connecting what you have. + +#### Week 1-2: Integration Sprint + +**Day 1-3**: Integrate `LLMOptimizationRunner` into `run_optimization.py` +- Add `--llm` flag to enable LLM mode +- Add `--llm-request` argument for natural language input +- Implement fallback to manual extractors if LLM generation fails +- Test with bracket study + +**Day 4-5**: End-to-end validation +- Run full optimization with LLM-generated extractors +- Verify results match manual extractors +- Document any issues +- Create comparison report + +**Day 6-7**: Error handling & polish +- Add graceful fallbacks for failed generation +- Improve error messages +- Add progress indicators +- Performance profiling + +#### Week 3: Documentation & Examples + +- Update `DEVELOPMENT.md` to show Phases 2.5-3.1 as 85% complete +- Update `README.md` to highlight LLM capabilities (currently underselling!) +- Add "Quick Start with LLM" section +- Create `examples/llm_optimization_example.py` with full workflow +- Write troubleshooting guide for LLM mode +- Create video/GIF demo for README + +#### Week 4: User Testing & Refinement + +- Internal testing with real use cases +- Gather feedback on LLM vs manual workflows +- Refine based on findings +- Performance optimization if needed + +**Expected Outcome**: Users can run: +```bash +python run_optimization.py --llm "maximize displacement, ensure safety factor > 4" +``` + +### 🔬 HIGH PRIORITY: NXOpen Documentation Access + +**Goal**: Enable LLM to reference NXOpen documentation when developing Atomizer features and generating NXOpen code + +#### Options to Investigate: + +1. **Authenticated Web Fetching** + - Can we login to Siemens documentation portal? + - Can WebFetch tool use authenticated sessions? + - Explore Siemens PLM API access + +2. **Documentation Scraping** + - Ethical/legal considerations + - Caching locally for offline use + - Structured extraction of API signatures + +3. **Official API Access** + - Does Siemens provide API documentation in structured format? + - JSON/XML schema files? + - OpenAPI/Swagger specs? + +4. **Community Resources** + - TheScriptingEngineer blog content + - NXOpen examples repository + - Community-contributed documentation + +#### Research Tasks: + +- [ ] Investigate Siemens documentation portal login mechanism +- [ ] Test WebFetch with authentication headers +- [ ] Explore Siemens PLM API documentation access +- [ ] Review legal/ethical considerations for documentation access +- [ ] Create proof-of-concept: LLM + NXOpen docs → Generated code + +**Success Criteria**: LLM can fetch NXOpen documentation on-demand when writing code + +### 🔧 MEDIUM PRIORITY: NXOpen Intellisense Integration + +**Goal**: Investigate if NXOpen Python stub files can improve Atomizer development workflow + +#### Background: + +From NX2406 onwards, Siemens provides stub files for Python intellisense: +- **Location**: `UGII_BASE_DIR\ugopen\pythonStubs` +- **Purpose**: Enable code completion, parameter info, member lists for NXOpen objects +- **Integration**: Works with VSCode Pylance extension + +#### TheScriptingEngineer's Configuration: + +```json +// settings.json +"python.analysis.typeCheckingMode": "basic", +"python.analysis.stubPath": "path_to_NX/ugopen/pythonStubs/Release2023/" +``` + +#### Questions to Answer: + +1. **Development Workflow**: + - Does this improve Atomizer development speed? + - Can Claude Code leverage intellisense information? + - Does it reduce NXOpen API lookup time? + +2. **Code Generation**: + - Can generated code use these stubs for validation? + - Can we type-check generated NXOpen scripts before execution? + - Does it catch errors earlier? + +3. **Integration Points**: + - Should this be part of Atomizer setup process? + - Can we distribute stubs with Atomizer? + - Legal considerations for redistribution? + +#### Implementation Plan: + +- [ ] Locate stub files in NX2412 installation +- [ ] Configure VSCode with stub path +- [ ] Test intellisense with sample NXOpen code +- [ ] Evaluate impact on development workflow +- [ ] Document setup process for contributors +- [ ] Decide: Include in Atomizer or document as optional enhancement? + +**Success Criteria**: Developers have working intellisense for NXOpen APIs + +--- + +## Foundation for Future + +### 🏗️ Engineering Feature Documentation Pipeline + +**Purpose**: Establish rigorous validation process for LLM-generated engineering features + +**Important**: This is NOT for current software development. This is the foundation for future user-generated features. + +#### Vision: + +When a user asks Atomizer to create a new FEA feature (e.g., "calculate buckling safety factor"), the system should: + +1. **Generate Code**: LLM creates the implementation +2. **Generate Documentation**: Auto-create comprehensive markdown explaining the feature +3. **Human Review**: Engineer reviews and approves before integration +4. **Version Control**: Documentation and code committed together + +This ensures **scientific rigor** and **traceability** for production use. + +#### Auto-Generated Documentation Format: + +Each engineering feature should produce a markdown file with these sections: + +```markdown +# Feature Name: [e.g., Buckling Safety Factor Calculator] + +## Goal +What problem does this feature solve? +- Engineering context +- Use cases +- Expected outcomes + +## Engineering Rationale +Why this approach? +- Design decisions +- Alternative approaches considered +- Why this method was chosen + +## Mathematical Foundation + +### Equations +\``` +σ_buckling = (π² × E × I) / (K × L)² +Safety Factor = σ_buckling / σ_applied +\``` + +### Sources +- Euler Buckling Theory (1744) +- AISC Steel Construction Manual, 15th Edition, Chapter E +- Timoshenko & Gere, "Theory of Elastic Stability" (1961) + +### Assumptions & Limitations +- Elastic buckling only +- Slender columns (L/r > 100) +- Perfect geometry assumed +- Material isotropy + +## Implementation + +### Code Structure +\```python +def calculate_buckling_safety_factor( + youngs_modulus: float, + moment_of_inertia: float, + effective_length: float, + applied_stress: float, + k_factor: float = 1.0 +) -> float: + """ + Calculate buckling safety factor using Euler formula. + + Parameters: + ... + """ +\``` + +### Input Validation +- Positive values required +- Units: Pa, m⁴, m, Pa +- K-factor range: 0.5 to 2.0 + +### Error Handling +- Division by zero checks +- Physical validity checks +- Numerical stability considerations + +## Testing & Validation + +### Unit Tests +\```python +def test_euler_buckling_simple_case(): + # Steel column: E=200GPa, I=1e-6m⁴, L=3m, σ=100MPa + sf = calculate_buckling_safety_factor(200e9, 1e-6, 3.0, 100e6) + assert 2.0 < sf < 2.5 # Expected range +\``` + +### Validation Cases +1. **Benchmark Case 1**: AISC Manual Example 3.1 (page 45) + - Input: [values] + - Expected: [result] + - Actual: [result] + - Error: [%] + +2. **Benchmark Case 2**: Timoshenko Example 2.3 + - ... + +### Edge Cases Tested +- Very short columns (L/r < 50) - should warn/fail +- Very long columns - numerical stability +- Zero/negative inputs - should error gracefully + +## Approval + +- **Author**: [LLM Generated | Engineer Name] +- **Reviewer**: [Engineer Name] +- **Date Reviewed**: [YYYY-MM-DD] +- **Status**: [Pending | Approved | Rejected] +- **Notes**: [Reviewer comments] + +## References + +1. Euler, L. (1744). "Methodus inveniendi lineas curvas maximi minimive proprietate gaudentes" +2. American Institute of Steel Construction (2016). *Steel Construction Manual*, 15th Edition +3. Timoshenko, S.P. & Gere, J.M. (1961). *Theory of Elastic Stability*, 2nd Edition, McGraw-Hill + +## Change Log + +- **v1.0** (2025-11-17): Initial implementation +- **v1.1** (2025-11-20): Added K-factor validation per reviewer feedback +``` + +#### Implementation Requirements: + +1. **Template System**: + - Markdown template for each feature type + - Auto-fill sections where possible + - Highlight sections requiring human input + +2. **Generation Pipeline**: + ``` + User Request → LLM Analysis → Code Generation → Documentation Generation → Human Review → Approval → Integration + ``` + +3. **Storage Structure**: + ``` + atomizer/ + ├── engineering_features/ + │ ├── approved/ + │ │ ├── buckling_safety_factor/ + │ │ │ ├── implementation.py + │ │ │ ├── tests.py + │ │ │ └── FEATURE_DOCS.md + │ │ └── ... + │ └── pending_review/ + │ └── ... + ``` + +4. **Validation Checklist**: + - [ ] Equations match cited sources + - [ ] Units are documented and validated + - [ ] Edge cases are tested + - [ ] Physical validity checks exist + - [ ] Benchmarks pass within tolerance + - [ ] Code matches documentation + - [ ] References are credible and accessible + +#### Who Uses This: + +- **NOT YOU (current development)**: You're building Atomizer's software foundation - different process +- **FUTURE USERS**: When users ask Atomizer to create custom FEA features +- **PRODUCTION DEPLOYMENTS**: Where engineering rigor and traceability matter + +#### Development Now vs Foundation for Future: + +| Aspect | Development Now | Foundation for Future | +|--------|----------------|----------------------| +| **Scope** | Building Atomizer software | User-generated FEA features | +| **Process** | Agile, iterate fast | Rigorous validation pipeline | +| **Documentation** | Code comments, dev docs | Full engineering documentation | +| **Review** | You approve | Human engineer approves | +| **Testing** | Unit tests, integration tests | Benchmark validation required | +| **Speed** | Move fast | Move carefully | + +**Bottom Line**: Build the framework now, but don't use it yourself yet. It's for future credibility and production use. + +### 🔐 Validation Pipeline Framework + +**Goal**: Define the structure for rigorous validation of LLM-generated scientific tools + +#### Pipeline Stages: + +```mermaid +graph LR + A[User Request] --> B[LLM Analysis] + B --> C[Code Generation] + C --> D[Documentation Generation] + D --> E[Automated Tests] + E --> F{Tests Pass?} + F -->|No| G[Feedback Loop] + G --> C + F -->|Yes| H[Human Review Queue] + H --> I{Approved?} + I -->|No| J[Reject with Feedback] + J --> G + I -->|Yes| K[Integration] + K --> L[Production Ready] +``` + +#### Components to Build: + +1. **Request Parser**: + - Natural language → Structured requirements + - Identify required equations/standards + - Classify feature type (stress, displacement, buckling, etc.) + +2. **Code Generator with Documentation**: + - Generate implementation code + - Generate test cases + - Generate markdown documentation + - Link code ↔ docs bidirectionally + +3. **Automated Validation**: + - Run unit tests + - Check benchmark cases + - Validate equation implementations + - Verify units consistency + +4. **Review Queue System**: + - Pending features awaiting approval + - Review interface (CLI or web) + - Approval/rejection workflow + - Feedback mechanism to LLM + +5. **Integration Manager**: + - Move approved features to production + - Update feature registry + - Generate release notes + - Version control integration + +#### Current Status: + +- [ ] Request parser - Not started +- [ ] Code generator with docs - Partially exists (hook_generator, extractor_orchestrator) +- [ ] Automated validation - Basic tests exist, need benchmark framework +- [ ] Review queue - Not started +- [ ] Integration manager - Not started + +**Priority**: Build the structure and interfaces now, implement validation logic later. + +#### Example Workflow (Future): + +```bash +# User creates custom feature +$ atomizer create-feature --request "Calculate von Mises stress safety factor using Tresca criterion" + +[LLM Analysis] +✓ Identified: Stress-based safety factor +✓ Standards: Tresca yield criterion +✓ Required inputs: stress_tensor, yield_strength +✓ Generating code... + +[Code Generation] +✓ Created: engineering_features/pending_review/tresca_safety_factor/ + - implementation.py + - tests.py + - FEATURE_DOCS.md + +[Automated Tests] +✓ Unit tests: 5/5 passed +✓ Benchmark cases: 3/3 passed +✓ Edge cases: 4/4 passed + +[Status] +🟡 Pending human review +📋 Review with: atomizer review tresca_safety_factor + +# Engineer reviews +$ atomizer review tresca_safety_factor + +[Review Interface] +Feature: Tresca Safety Factor Calculator +Status: Automated tests PASSED + +Documentation Preview: +[shows FEATURE_DOCS.md] + +Code Preview: +[shows implementation.py] + +Test Results: +[shows test output] + +Approve? [y/N]: y +Review Notes: Looks good, equations match standard + +[Approval] +✓ Feature approved +✓ Integrated into feature registry +✓ Available for use + +# Now users can use it +$ atomizer optimize --objective "maximize displacement" --constraint "tresca_sf > 2.0" +``` + +**This is the vision**. Build the foundation now for future implementation. + +--- + +## Technical Roadmap + +### Revised Phase Timeline + +| Phase | Status | Description | Priority | +|-------|--------|-------------|----------| +| **Phase 1** | ✅ 100% | Plugin System | Complete | +| **Phase 2.5** | ✅ 85% | Intelligent Gap Detection | Built, needs integration | +| **Phase 2.6** | ✅ 85% | Workflow Decomposition | Built, needs integration | +| **Phase 2.7** | ✅ 85% | Step Classification | Built, needs integration | +| **Phase 2.9** | ✅ 85% | Hook Generation | Built, tested | +| **Phase 3.0** | ✅ 85% | Research Agent | Built, tested | +| **Phase 3.1** | ✅ 85% | Extractor Orchestration | Built, tested | +| **Phase 3.2** | 🎯 0% | **Runner Integration** | **TOP PRIORITY** | +| **Phase 3.3** | 🟡 50% | Optimization Setup Wizard | Partially built | +| **Phase 3.4** | 🔵 0% | NXOpen Documentation Integration | Research phase | +| **Phase 3.5** | 🔵 0% | Engineering Feature Pipeline | Foundation design | +| **Phase 4+** | 🔵 0% | Advanced Features | Paused until 3.2 complete | + +### Immediate Next Steps (Next 2 Weeks) + +#### Week 1: Integration & Testing + +**Monday-Tuesday**: Runner Integration +- [ ] Add `--llm` flag to `run_optimization.py` +- [ ] Connect `LLMOptimizationRunner` to production workflow +- [ ] Implement fallback to manual mode +- [ ] Test with bracket study + +**Wednesday-Thursday**: End-to-End Testing +- [ ] Run complete LLM workflow: Request → Code → Solve → Results +- [ ] Compare LLM-generated vs manual extractors +- [ ] Performance profiling +- [ ] Fix any integration bugs + +**Friday**: Polish & Documentation +- [ ] Improve error messages +- [ ] Add progress indicators +- [ ] Create example script +- [ ] Update inline documentation + +#### Week 2: NXOpen Documentation Research + +**Monday-Tuesday**: Investigation +- [ ] Research Siemens documentation portal +- [ ] Test authenticated WebFetch +- [ ] Explore PLM API access +- [ ] Review legal considerations + +**Wednesday**: Intellisense Setup +- [ ] Locate NX2412 stub files +- [ ] Configure VSCode with Pylance +- [ ] Test intellisense with NXOpen code +- [ ] Document setup process + +**Thursday-Friday**: Documentation Updates +- [ ] Update `README.md` with LLM capabilities +- [ ] Update `DEVELOPMENT.md` with accurate status +- [ ] Create `NXOPEN_INTEGRATION.md` guide +- [ ] Update this guidance document + +### Medium-Term Goals (1-3 Months) + +1. **Phase 3.4: NXOpen Documentation Integration** + - Implement authenticated documentation access + - Create NXOpen knowledge base + - Test LLM code generation with docs + +2. **Phase 3.5: Engineering Feature Pipeline** + - Build documentation template system + - Create review queue interface + - Implement validation framework + +3. **Dashboard Enhancement** + - Add LLM mode toggle + - Visualize generated code + - Show approval workflow + +4. **Performance Optimization** + - LLM response caching + - Batch code generation + - Async operations + +### Long-Term Vision (3-12 Months) + +1. **Phase 4: Advanced Code Generation** + - Complex FEA feature generation + - Multi-physics setup automation + - Topology optimization support + +2. **Phase 5: Intelligent Analysis** + - Surrogate quality assessment + - Sensitivity analysis + - Pareto front optimization + +3. **Phase 6: Automated Reporting** + - HTML/PDF generation + - LLM-written insights + - Executive summaries + +4. **Production Hardening** + - Security audits + - Performance optimization + - Enterprise features + +--- + +## Key Principles + +### Development Philosophy + +1. **Ship Before Perfecting**: Integration is more valuable than new features +2. **User Value First**: Every feature must solve a real user problem +3. **Scientific Rigor**: Engineering features require validation and documentation +4. **Progressive Enhancement**: System works without LLM, better with LLM +5. **Learn and Improve**: Knowledge base grows with every use + +### Decision Framework + +When prioritizing work, ask: + +1. **Does this unlock user value?** If yes, prioritize +2. **Does this require other work first?** If yes, do dependencies first +3. **Can we test this independently?** If no, split into testable pieces +4. **Will this create technical debt?** If yes, document and plan to address +5. **Does this align with long-term vision?** If no, reconsider + +### Quality Standards + +**For Software Development (Atomizer itself)**: +- Unit tests for core components +- Integration tests for workflows +- Code review by you (main developer) +- Documentation for contributors +- Move fast, iterate + +**For Engineering Features (User-generated FEA)**: +- Comprehensive mathematical documentation +- Benchmark validation required +- Human engineer approval mandatory +- Traceability to standards/papers +- Move carefully, validate thoroughly + +--- + +## Success Metrics + +### Phase 3.2 Success Criteria + +- [ ] Users can run: `python run_optimization.py --llm "maximize displacement"` +- [ ] End-to-end test passes: Natural language → NX solve → Results +- [ ] LLM-generated extractors produce same results as manual extractors +- [ ] Error handling works gracefully (fallback to manual mode) +- [ ] Documentation updated to reflect LLM capabilities +- [ ] Example workflow created and tested + +### NXOpen Integration Success Criteria + +- [ ] LLM can fetch NXOpen documentation on-demand +- [ ] Generated code references correct NXOpen API methods +- [ ] Intellisense working in VSCode for NXOpen development +- [ ] Setup documented for contributors +- [ ] Legal/ethical review completed + +### Engineering Feature Pipeline Success Criteria + +- [ ] Documentation template system implemented +- [ ] Example feature with full documentation created +- [ ] Review workflow interface built (CLI or web) +- [ ] Validation framework structure defined +- [ ] At least one feature goes through full pipeline (demo) + +--- + +## Communication & Collaboration + +### Stakeholders + +- **You (Antoine)**: Main developer, architect, decision maker +- **Claude Code**: Development assistant for Atomizer software +- **Future Contributors**: Will follow established patterns and documentation +- **Future Users**: Will use LLM features for optimization workflows + +### Documentation Strategy + +1. **DEVELOPMENT_GUIDANCE.md** (this doc): Strategic direction, priorities, status +2. **README.md**: User-facing introduction, quick start, features +3. **DEVELOPMENT.md**: Detailed development status, todos, completed work +4. **DEVELOPMENT_ROADMAP.md**: Long-term vision, phases, future work +5. **Session summaries**: Detailed records of development sessions + +Keep all documents synchronized and consistent. + +### Review Cadence + +- **Weekly**: Review progress against priorities +- **Monthly**: Update roadmap and adjust course if needed +- **Quarterly**: Major strategic reviews and planning + +--- + +## Appendix: Quick Reference + +### File Locations + +**Core Engine**: +- `optimization_engine/runner.py` - Current production runner +- `optimization_engine/llm_optimization_runner.py` - LLM-enhanced runner (needs integration) +- `optimization_engine/nx_solver.py` - NX Simcenter integration +- `optimization_engine/nx_updater.py` - Parameter update system + +**LLM Components**: +- `optimization_engine/llm_workflow_analyzer.py` - Natural language parser +- `optimization_engine/extractor_orchestrator.py` - Extractor generation +- `optimization_engine/pynastran_research_agent.py` - Documentation learning +- `optimization_engine/hook_generator.py` - Hook code generation + +**Studies**: +- `studies/bracket_displacement_maximizing/` - Working example with substudies +- `studies/bracket_displacement_maximizing/run_substudy.py` - Substudy runner +- `studies/bracket_displacement_maximizing/SUBSTUDIES_README.md` - Substudy guide + +**Tests**: +- `tests/test_phase_2_5_intelligent_gap_detection.py` - Gap detection tests +- `tests/test_phase_3_1_integration.py` - Extractor orchestration tests +- `tests/test_complete_research_workflow.py` - Research agent tests + +**Documentation**: +- `docs/SESSION_SUMMARY_PHASE_*.md` - Development session records +- `knowledge_base/` - Learned patterns and research sessions +- `feature_registry.json` - Complete capability catalog + +### Common Commands + +```bash +# Run optimization (current manual mode) +cd studies/bracket_displacement_maximizing +python run_optimization.py + +# Run substudy +python run_substudy.py coarse_exploration + +# Run tests +python -m pytest tests/test_phase_3_1_integration.py -v + +# Start dashboard +python dashboard/start_dashboard.py +``` + +### Key Contacts & Resources + +- **Siemens NX Documentation**: [PLM Portal](https://plm.sw.siemens.com) +- **TheScriptingEngineer**: [Blog](https://thescriptingengineer.com) +- **pyNastran Docs**: [GitHub](https://github.com/SteveDoyle2/pyNastran) +- **Optuna Docs**: [optuna.org](https://optuna.org) + +--- + +**Document Maintained By**: Antoine (Main Developer) +**Last Review**: 2025-11-17 +**Next Review**: 2025-11-24 diff --git a/README.md b/README.md index 64c0f5bc..9406ae47 100644 --- a/README.md +++ b/README.md @@ -29,7 +29,13 @@ Atomizer enables engineers to: - **Extensible Architecture**: Plugin system with hooks for pre/post mesh, solve, and extraction phases - **Self-Improving**: Feature registry that learns from user workflows and expands capabilities -📘 See [DEVELOPMENT_ROADMAP.md](DEVELOPMENT_ROADMAP.md) for the complete vision and implementation plan. +--- + +📘 **For Developers**: See [DEVELOPMENT_GUIDANCE.md](DEVELOPMENT_GUIDANCE.md) for comprehensive status report, current priorities, and strategic direction. + +📘 **Vision & Roadmap**: See [DEVELOPMENT_ROADMAP.md](DEVELOPMENT_ROADMAP.md) for the long-term vision and phase-by-phase implementation plan. + +📘 **Development Status**: See [DEVELOPMENT.md](DEVELOPMENT.md) for detailed task tracking and completed work. ## Architecture @@ -164,10 +170,24 @@ python run_5trial_test.py - **Web Dashboard**: Real-time monitoring and configuration UI - **Example Study**: Bracket displacement maximization with full substudy workflow -**🚀 What's Next**: Natural language optimization configuration via LLM interface (Phase 2) +## Current Status -For detailed development status and todos, see [DEVELOPMENT.md](DEVELOPMENT.md). -For the long-term vision, see [DEVELOPMENT_ROADMAP.md](DEVELOPMENT_ROADMAP.md). +**Development Phase**: Alpha - 75-85% Complete + +- ✅ **Phase 1 (Plugin System)**: 100% Complete & Production Ready +- ✅ **Phases 2.5-3.1 (LLM Intelligence)**: 85% Complete - Components built and tested +- 🎯 **Phase 3.2 (Integration)**: **TOP PRIORITY** - Connect LLM features to production workflow +- 🔬 **Phase 3.4 (NXOpen Docs)**: Research & investigation phase + +**What's Working**: +- Complete optimization engine with Optuna + NX Simcenter +- Substudy system with live history tracking +- LLM components (workflow analyzer, code generators, research agent) - tested individually +- 20-trial optimization validated with real results + +**Current Focus**: Integrating LLM components into production runner for end-to-end workflow. + +See [DEVELOPMENT_GUIDANCE.md](DEVELOPMENT_GUIDANCE.md) for comprehensive status and priorities. ## Project Structure