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docs: Major documentation overhaul - restructure folders, update tagline, add Getting Started guide

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- Restructure docs/ folder (remove numeric prefixes):
  - 04_USER_GUIDES -> guides/
  - 05_API_REFERENCE -> api/
  - 06_PHYSICS -> physics/
  - 07_DEVELOPMENT -> development/
  - 08_ARCHIVE -> archive/
  - 09_DIAGRAMS -> diagrams/

- Replace tagline 'Talk, don't click' with 'LLM-driven optimization framework' in 9 files

- Create comprehensive docs/GETTING_STARTED.md:
  - Prerequisites and quick setup
  - Project structure overview
  - First study tutorial (Claude or manual)
  - Dashboard usage guide
  - Neural acceleration introduction

- Rewrite docs/00_INDEX.md with correct paths and modern structure

- Archive obsolete files:
  - 01_PROTOCOLS.md -> archive/historical/01_PROTOCOLS_legacy.md
  - 03_GETTING_STARTED.md -> archive/historical/
  - ATOMIZER_PODCAST_BRIEFING.md -> archive/marketing/

- Update timestamps to 2026-01-20 across all key files

- Update .gitignore to exclude docs/generated/

- Version bump: ATOMIZER_CONTEXT v1.8 -> v2.0
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# Session Summary: Phase 3.1 - Extractor Orchestration & Integration
**Date**: 2025-01-16
**Phase**: 3.1 - Complete End-to-End Automation Pipeline
**Status**: ✅ Complete
## Overview
Phase 3.1 completes the **LLM-enhanced automation pipeline** by integrating:
- **Phase 2.7**: LLM workflow analysis
- **Phase 3.0**: pyNastran research agent
- **Phase 2.8**: Inline code generation
- **Phase 2.9**: Post-processing hook generation
The result: Users can describe optimization goals in natural language and choose to leverage automatic code generation, manual coding, or a hybrid approach!
## Objectives Achieved
### ✅ LLM-Enhanced Automation Pipeline
**From User Request to Execution - Flexible LLM-Assisted Workflow:**
```
User Natural Language Request
↓
Phase 2.7 LLM Analysis
↓
Structured Engineering Features
↓
Phase 3.1 Extractor Orchestrator
↓
Phase 3.0 Research Agent (auto OP2 code generation)
↓
Generated Extractor Modules
↓
Dynamic Loading & Execution on OP2
↓
Phase 2.8 Inline Calculations
↓
Phase 2.9 Post-Processing Hooks
↓
Final Objective Value → Optuna
```
### ✅ Core Capabilities
1. **Extractor Orchestrator**
- Takes Phase 2.7 LLM output
- Generates extractors using Phase 3 research agent
- Manages extractor registry
- Provides dynamic loading and execution
2. **Dynamic Code Generation**
- Automatic extractor generation from LLM requests
- Saved to `result_extractors/generated/`
- Smart parameter filtering per pattern type
- Executable on real OP2 files
3. **Multi-Extractor Support**
- Generate multiple extractors in one workflow
- Mix displacement, stress, force extractors
- Each extractor gets appropriate pattern
4. **End-to-End Testing**
- Successfully tested on real bracket OP2 file
- Extracted displacement: 0.361783mm
- Calculated normalized objective: 0.072357
- Complete pipeline verified!
## Architecture
### ExtractorOrchestrator
Core module: [optimization_engine/extractor_orchestrator.py](../optimization_engine/extractor_orchestrator.py)
```python
class ExtractorOrchestrator:
"""
Orchestrates automatic extractor generation from LLM workflow analysis.
Bridges Phase 2.7 (LLM analysis) and Phase 3 (pyNastran research)
to create complete end-to-end automation pipeline.
"""
def __init__(self, extractors_dir=None, knowledge_base_path=None):
"""Initialize with Phase 3 research agent."""
self.research_agent = PyNastranResearchAgent(knowledge_base_path)
self.extractors: Dict[str, GeneratedExtractor] = {}
def process_llm_workflow(self, llm_output: Dict) -> List[GeneratedExtractor]:
"""
Process Phase 2.7 LLM output and generate all required extractors.
Args:
llm_output: Dict with engineering_features, inline_calculations, etc.
Returns:
List of GeneratedExtractor objects
"""
# Process each extraction feature
# Generate extractor code using Phase 3 agent
# Save to files
# Register in session
def load_extractor(self, extractor_name: str) -> Callable:
"""Dynamically load a generated extractor module."""
# Dynamic import using importlib
# Return the extractor function
def execute_extractor(self, extractor_name: str, op2_file: Path, **kwargs) -> Dict:
"""Load and execute an extractor on OP2 file."""
# Load extractor function
# Filter parameters by pattern type
# Execute and return results
```
### GeneratedExtractor Dataclass
```python
@dataclass
class GeneratedExtractor:
"""Represents a generated extractor module."""
name: str # Action name from LLM
file_path: Path # Where code is saved
function_name: str # Extracted from generated code
extraction_pattern: ExtractionPattern # From Phase 3 research agent
params: Dict[str, Any] # Parameters from LLM
```
### Directory Structure
```
optimization_engine/
├── extractor_orchestrator.py # Phase 3.1: NEW
├── pynastran_research_agent.py # Phase 3.0
├── hook_generator.py # Phase 2.9
├── inline_code_generator.py # Phase 2.8
└── result_extractors/
├── extractors.py # Manual extractors (legacy)
└── generated/ # Auto-generated extractors (NEW!)
├── extract_displacement.py
├── extract_1d_element_forces.py
└── extract_solid_stress.py
```
## Complete Workflow Example
### User Request (Natural Language)
> "Extract displacement from OP2, normalize by 5mm maximum allowed, and minimize"
### Phase 2.7: LLM Analysis
```json
{
"engineering_features": [
{
"action": "extract_displacement",
"domain": "result_extraction",
"description": "Extract displacement results from OP2 file",
"params": {
"result_type": "displacement"
}
}
],
"inline_calculations": [
{
"action": "find_maximum",
"params": {"input": "max_displacement"}
},
{
"action": "normalize",
"params": {
"input": "max_displacement",
"reference": "max_allowed_disp",
"value": 5.0
}
}
],
"post_processing_hooks": [
{
"action": "weighted_objective",
"params": {
"inputs": ["norm_disp"],
"weights": [1.0],
"objective": "minimize"
}
}
]
}
```
### Phase 3.1: Orchestrator Processing
```python
# Initialize orchestrator
orchestrator = ExtractorOrchestrator()
# Process LLM output
extractors = orchestrator.process_llm_workflow(llm_output)
# Result: extract_displacement.py generated
```
### Phase 3.0: Generated Extractor Code
**File**: `result_extractors/generated/extract_displacement.py`
```python
"""
Extract displacement results from OP2 file
Auto-generated by Atomizer Phase 3 - pyNastran Research Agent
Pattern: displacement
Result Type: displacement
API: model.displacements[subcase]
"""
from pathlib import Path
from typing import Dict, Any
import numpy as np
from pyNastran.op2.op2 import OP2
def extract_displacement(op2_file: Path, subcase: int = 1):
"""Extract displacement results from OP2 file."""
model = OP2()
model.read_op2(str(op2_file))
disp = model.displacements[subcase]
itime = 0 # static case
# Extract translation components
txyz = disp.data[itime, :, :3]
total_disp = np.linalg.norm(txyz, axis=1)
max_disp = np.max(total_disp)
node_ids = [nid for (nid, grid_type) in disp.node_gridtype]
max_disp_node = node_ids[np.argmax(total_disp)]
return {
'max_displacement': float(max_disp),
'max_disp_node': int(max_disp_node),
'max_disp_x': float(np.max(np.abs(txyz[:, 0]))),
'max_disp_y': float(np.max(np.abs(txyz[:, 1]))),
'max_disp_z': float(np.max(np.abs(txyz[:, 2])))
}
```
### Execution on Real OP2
```python
# Execute on bracket OP2
result = orchestrator.execute_extractor(
'extract_displacement',
Path('tests/bracket_sim1-solution_1.op2'),
subcase=1
)
# Result:
# {
# 'max_displacement': 0.361783,
# 'max_disp_node': 91,
# 'max_disp_x': 0.002917,
# 'max_disp_y': 0.074244,
# 'max_disp_z': 0.354083
# }
```
### Phase 2.8: Inline Calculations (Auto-Generated)
```python
# Auto-generated by Phase 2.8
max_disp = result['max_displacement'] # 0.361783
max_allowed_disp = 5.0
norm_disp = max_disp / max_allowed_disp # 0.072357
```
### Phase 2.9: Post-Processing Hook (Auto-Generated)
```python
# Auto-generated hook in plugins/post_calculation/
def weighted_objective_hook(context):
calculations = context.get('calculations', {})
norm_disp = calculations.get('norm_disp')
objective = 1.0 * norm_disp
return {'weighted_objective': objective}
# Result: weighted_objective = 0.072357
```
### Final Result → Optuna
```
Trial N completed
Objective value: 0.072357
```
**LLM-enhanced workflow with optional automation from user request to Optuna trial!** 🚀
## Key Integration Points
### 1. LLM → Orchestrator
**Input** (Phase 2.7 output):
```json
{
"engineering_features": [
{
"action": "extract_1d_element_forces",
"domain": "result_extraction",
"params": {
"element_types": ["CBAR"],
"direction": "Z"
}
}
]
}
```
**Processing**:
```python
for feature in llm_output['engineering_features']:
if feature['domain'] == 'result_extraction':
extractor = orchestrator.generate_extractor_from_feature(feature)
```
### 2. Orchestrator → Research Agent
**Request to Phase 3**:
```python
research_request = {
'action': 'extract_1d_element_forces',
'domain': 'result_extraction',
'description': 'Extract element forces from CBAR in Z direction',
'params': {
'element_types': ['CBAR'],
'direction': 'Z'
}
}
pattern = research_agent.research_extraction(research_request)
code = research_agent.generate_extractor_code(research_request)
```
**Response**:
- `pattern`: ExtractionPattern(name='cbar_force', ...)
- `code`: Complete Python module string
### 3. Generated Code → Execution
**Dynamic Loading**:
```python
# Import the generated module
spec = importlib.util.spec_from_file_location(name, file_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
# Get the function
extractor_func = getattr(module, function_name)
# Execute
result = extractor_func(op2_file, **params)
```
### 4. Smart Parameter Filtering
Different extraction patterns need different parameters:
```python
if pattern_name == 'displacement':
# Only pass subcase (no direction, element_type, etc.)
params = {k: v for k, v in kwargs.items() if k in ['subcase']}
elif pattern_name == 'cbar_force':
# Pass direction and subcase
params = {k: v for k, v in kwargs.items() if k in ['direction', 'subcase']}
elif pattern_name == 'solid_stress':
# Pass element_type and subcase
params = {k: v for k, v in kwargs.items() if k in ['element_type', 'subcase']}
```
This prevents errors from passing irrelevant parameters!
## Testing
### Test File: [tests/test_phase_3_1_integration.py](../tests/test_phase_3_1_integration.py)
**Test 1: End-to-End Workflow**
```
STEP 1: Phase 2.7 LLM Analysis
- 1 engineering feature
- 2 inline calculations
- 1 post-processing hook
STEP 2: Phase 3.1 Orchestrator
- Generated 1 extractor (extract_displacement)
STEP 3: Execution on Real OP2
- OP2 File: bracket_sim1-solution_1.op2
- Result: max_displacement = 0.361783mm at node 91
STEP 4: Inline Calculations
- norm_disp = 0.361783 / 5.0 = 0.072357
STEP 5: Post-Processing Hook
- weighted_objective = 0.072357
Result: PASSED!
```
**Test 2: Multiple Extractors**
```
LLM Output:
- extract_displacement
- extract_solid_stress
Result: Generated 2 extractors
- extract_displacement (displacement pattern)
- extract_solid_stress (solid_stress pattern)
Result: PASSED!
```
## Benefits
### 1. LLM-Enhanced Flexibility
**Traditional Manual Workflow**:
```
1. User describes optimization
2. Engineer manually writes OP2 extractor
3. Engineer manually writes calculations
4. Engineer manually writes objective function
5. Engineer integrates with optimization runner
Time: Hours to days
```
**LLM-Enhanced Workflow**:
```
1. User describes optimization in natural language
2. System offers to generate code automatically OR user writes custom code
3. Hybrid approach: mix automated and manual components as needed
Time: Seconds to minutes (user choice)
```
### 2. Reduced Learning Curve
LLM assistance helps users who are unfamiliar with:
- pyNastran API (can still write custom extractors if desired)
- OP2 file structure (LLM provides templates)
- Python coding best practices (LLM generates examples)
- Optimization framework patterns (LLM suggests approaches)
Users can describe goals in natural language and choose their preferred level of automation!
### 3. Quality LLM-Generated Code
When using automated generation, code uses:
- ✅ Proven extraction patterns from research agent
- ✅ Correct API paths from documentation
- ✅ Proper data structure access
- ✅ Error handling and validation
Users can review, modify, or replace generated code as needed!
### 4. Extensible
Adding new extraction patterns:
1. Research agent learns from pyNastran docs
2. Stores pattern in knowledge base
3. Available immediately for all future requests
## Future Enhancements
### Phase 3.2: Optimization Runner Integration
**Next Step**: Integrate orchestrator with optimization runner for complete automation:
```python
class OptimizationRunner:
def __init__(self, llm_output: Dict):
# Process LLM output
self.orchestrator = ExtractorOrchestrator()
self.extractors = self.orchestrator.process_llm_workflow(llm_output)
# Generate inline calculations (Phase 2.8)
self.calculator = InlineCodeGenerator()
self.calculations = self.calculator.generate(llm_output)
# Generate hooks (Phase 2.9)
self.hook_gen = HookGenerator()
self.hooks = self.hook_gen.generate_lifecycle_hooks(llm_output)
def run_trial(self, trial_number, design_variables):
# Run NX solve
op2_file = self.nx_solver.run(...)
# Extract results using generated extractors
results = {}
for extractor_name in self.extractors:
results.update(
self.orchestrator.execute_extractor(extractor_name, op2_file)
)
# Execute inline calculations
calculations = self.calculator.execute(results)
# Execute hooks
hook_results = self.hook_manager.execute_hooks('post_calculation', {
'results': results,
'calculations': calculations
})
# Return objective
return hook_results.get('objective')
```
### Phase 3.3: Error Recovery
- Detect extraction failures
- Attempt pattern variations
- Fallback to generic extractors
- Log failures for pattern learning
### Phase 3.4: Performance Optimization
- Cache OP2 reading for multiple extractions
- Parallel extraction for multiple result types
- Reuse loaded models across trials
### Phase 3.5: Pattern Expansion
- Learn patterns for more element types
- Composite stress/strain
- Eigenvectors/eigenvalues
- F06 result extraction
- XDB database extraction
## Files Created/Modified
### New Files
1. **optimization_engine/extractor_orchestrator.py** (380+ lines)
- ExtractorOrchestrator class
- GeneratedExtractor dataclass
- Dynamic loading and execution
- Parameter filtering logic
2. **tests/test_phase_3_1_integration.py** (200+ lines)
- End-to-end workflow test
- Multiple extractors test
- Complete pipeline validation
3. **optimization_engine/result_extractors/generated/** (directory)
- extract_displacement.py (auto-generated)
- extract_1d_element_forces.py (auto-generated)
- extract_solid_stress.py (auto-generated)
4. **docs/SESSION_SUMMARY_PHASE_3_1.md** (this file)
- Complete Phase 3.1 documentation
### Modified Files
None - Phase 3.1 is purely additive!
## Summary
Phase 3.1 successfully completes the **LLM-enhanced automation pipeline**:
- ✅ Orchestrator integrates Phase 2.7 + Phase 3.0
- ✅ Optional automatic extractor generation from LLM output
- ✅ Dynamic loading and execution on real OP2 files
- ✅ Smart parameter filtering per pattern type
- ✅ Multi-extractor support
- ✅ Complete end-to-end test passed
- ✅ Extraction successful: max_disp=0.361783mm
- ✅ Normalized objective calculated: 0.072357
**LLM-Enhanced Workflow Verified:**
```
Natural Language Request
↓
Phase 2.7 LLM → Engineering Features
↓
Phase 3.1 Orchestrator → Generated Extractors (or manual extractors)
↓
Phase 3.0 Research Agent → OP2 Extraction Code (optional)
↓
Execution on Real OP2 → Results
↓
Phase 2.8 Inline Calc → Calculations (optional)
↓
Phase 2.9 Hooks → Objective Value (optional)
↓
Optuna Trial Complete
LLM-ENHANCED WITH USER FLEXIBILITY! 🚀
```
Users can describe optimization goals in natural language and choose to leverage automated code generation, write custom code, or use a hybrid approach as needed!
## Related Documentation
- [SESSION_SUMMARY_PHASE_3.md](SESSION_SUMMARY_PHASE_3.md) - Phase 3.0 pyNastran research
- [SESSION_SUMMARY_PHASE_2_9.md](SESSION_SUMMARY_PHASE_2_9.md) - Hook generation
- [SESSION_SUMMARY_PHASE_2_8.md](SESSION_SUMMARY_PHASE_2_8.md) - Inline calculations
- [PHASE_2_7_LLM_INTEGRATION.md](PHASE_2_7_LLM_INTEGRATION.md) - LLM workflow analysis
- [HOOK_ARCHITECTURE.md](HOOK_ARCHITECTURE.md) - Unified lifecycle hooks