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feat: Complete Phase 2.5-2.7 - Intelligent LLM-Powered Workflow Analysis

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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>
This commit is contained in:
Anto01
2025-11-16 13:35:41 -05:00
parent 986285d9cf
commit 0a7cca9c6a
94 changed files with 12761 additions and 10670 deletions
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183
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"""
Quick Interactive Demo of Research Agent
This demo shows the Research Agent learning from a material XML example
and documenting the research session.
Run this to see Phase 2 in action!
"""
import sys
from pathlib import Path
# Set UTF-8 encoding for Windows console
if sys.platform == 'win32':
import codecs
sys.stdout = codecs.getwriter('utf-8')(sys.stdout.buffer, errors='replace')
sys.stderr = codecs.getwriter('utf-8')(sys.stderr.buffer, errors='replace')
# Add project root to path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
from optimization_engine.research_agent import (
ResearchAgent,
ResearchFindings,
KnowledgeGap,
CONFIDENCE_LEVELS
)
def main():
print("\n" + "="*70)
print(" RESEARCH AGENT DEMO - Phase 2 Self-Learning System")
print("="*70)
# Initialize agent
agent = ResearchAgent()
print("\n[1] Research Agent initialized")
print(f" Feature registry loaded: {agent.feature_registry_path}")
print(f" Knowledge base: {agent.knowledge_base_path}")
# Test 1: Detect knowledge gap
print("\n" + "-"*70)
print("[2] Testing Knowledge Gap Detection")
print("-"*70)
request = "Create NX material XML for titanium Ti-6Al-4V"
print(f"\nUser request: \"{request}\"")
gap = agent.identify_knowledge_gap(request)
print(f"\n Analysis:")
print(f" Missing features: {gap.missing_features}")
print(f" Missing knowledge: {gap.missing_knowledge}")
print(f" Confidence: {gap.confidence:.2f}")
print(f" Research needed: {gap.research_needed}")
# Test 2: Learn from example
print("\n" + "-"*70)
print("[3] Learning from User Example")
print("-"*70)
# Simulated user provides this example
example_xml = """<?xml version="1.0" encoding="UTF-8"?>
<PhysicalMaterial name="Steel_AISI_1020" version="1.0">
<Density units="kg/m3">7850</Density>
<YoungModulus units="GPa">200</YoungModulus>
<PoissonRatio>0.29</PoissonRatio>
<ThermalExpansion units="1/K">1.17e-05</ThermalExpansion>
<YieldStrength units="MPa">295</YieldStrength>
<UltimateTensileStrength units="MPa">420</UltimateTensileStrength>
</PhysicalMaterial>"""
print("\nUser provides example: steel_material.xml")
print(" (Simulating user uploading a file)")
# Create research findings
findings = ResearchFindings(
sources={'user_example': 'steel_material.xml'},
raw_data={'user_example': example_xml},
confidence_scores={'user_example': CONFIDENCE_LEVELS['user_validated']}
)
print(f"\n Source: user_example")
print(f" Confidence: {CONFIDENCE_LEVELS['user_validated']:.2f} (user-validated)")
# Test 3: Synthesize knowledge
print("\n" + "-"*70)
print("[4] Synthesizing Knowledge")
print("-"*70)
knowledge = agent.synthesize_knowledge(findings)
print(f"\n {knowledge.synthesis_notes}")
if knowledge.schema and 'xml_structure' in knowledge.schema:
xml_schema = knowledge.schema['xml_structure']
print(f"\n Learned Schema:")
print(f" Root element: {xml_schema['root_element']}")
print(f" Required fields: {len(xml_schema['required_fields'])}")
for field in xml_schema['required_fields'][:3]:
print(f" - {field}")
if len(xml_schema['required_fields']) > 3:
print(f" ... and {len(xml_schema['required_fields']) - 3} more")
# Test 4: Document session
print("\n" + "-"*70)
print("[5] Documenting Research Session")
print("-"*70)
session_path = agent.document_session(
topic='nx_materials_demo',
knowledge_gap=gap,
findings=findings,
knowledge=knowledge,
generated_files=[
'optimization_engine/custom_functions/nx_material_generator.py',
'knowledge_base/templates/material_xml_template.py'
]
)
print(f"\n Session saved to:")
print(f" {session_path}")
print(f"\n Files created:")
for file in ['user_question.txt', 'sources_consulted.txt', 'findings.md', 'decision_rationale.md']:
file_path = session_path / file
if file_path.exists():
print(f" [OK] {file}")
else:
print(f" [MISSING] {file}")
# Show content of findings
print("\n Preview of findings.md:")
findings_path = session_path / 'findings.md'
if findings_path.exists():
content = findings_path.read_text(encoding='utf-8')
for i, line in enumerate(content.split('\n')[:12]):
print(f" {line}")
print(" ...")
# Test 5: Now agent can generate materials
print("\n" + "-"*70)
print("[6] Agent is Now Ready to Generate Materials!")
print("-"*70)
print("\n Next time you request a material XML, the agent will:")
print(" 1. Search knowledge base and find this research session")
print(" 2. Retrieve the learned schema")
print(" 3. Generate new material XML following the pattern")
print(" 4. Confidence: HIGH (based on user-validated example)")
print("\n Example usage:")
print(' User: "Create aluminum alloy 6061-T6 material XML"')
print(' Agent: "I know how to do this! Using learned schema..."')
print(' [Generates XML with Al 6061-T6 properties]')
# Summary
print("\n" + "="*70)
print(" DEMO COMPLETE - Research Agent Successfully Learned!")
print("="*70)
print("\n What was accomplished:")
print(" [OK] Detected knowledge gap (material XML generation)")
print(" [OK] Learned XML schema from user example")
print(" [OK] Extracted reusable patterns")
print(" [OK] Documented research session for future reference")
print(" [OK] Ready to generate similar features autonomously")
print("\n Knowledge persisted in:")
print(f" {session_path}")
print("\n This demonstrates Phase 2: Self-Extending Research System")
print(" The agent can now learn ANY new capability from examples!\n")
if __name__ == '__main__':
try:
main()
except Exception as e:
print(f"\n[ERROR] {e}")
import traceback
traceback.print_exc()
sys.exit(1)

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"""
Test Phase 2.6 with CBAR Element Genetic Algorithm Optimization
Tests intelligent step classification with:
- 1D element force extraction
- Minimum value calculation (not maximum)
- CBAR element (not CBUSH)
- Genetic algorithm (not Optuna TPE)
"""
import sys
from pathlib import Path
# Set UTF-8 encoding for Windows console
if sys.platform == 'win32':
import codecs
if not isinstance(sys.stdout, codecs.StreamWriter):
if hasattr(sys.stdout, 'buffer'):
sys.stdout = codecs.getwriter('utf-8')(sys.stdout.buffer, errors='replace')
sys.stderr = codecs.getwriter('utf-8')(sys.stderr.buffer, errors='replace')
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
from optimization_engine.workflow_decomposer import WorkflowDecomposer
from optimization_engine.step_classifier import StepClassifier
from optimization_engine.codebase_analyzer import CodebaseCapabilityAnalyzer
from optimization_engine.capability_matcher import CapabilityMatcher
def main():
user_request = """I want to extract forces in direction Z of all the 1D elements and find the average of it, then find the minimum value and compere it to the average, then assign it to a objective metric that needs to be minimized.
I want to iterate on the FEA properties of the Cbar element stiffness in X to make the objective function minimized.
I want to use genetic algorithm to iterate and optimize this"""
print('=' * 80)
print('PHASE 2.6 TEST: CBAR Genetic Algorithm Optimization')
print('=' * 80)
print()
print('User Request:')
print(user_request)
print()
print('=' * 80)
print()
# Initialize all Phase 2.5 + 2.6 components
decomposer = WorkflowDecomposer()
classifier = StepClassifier()
analyzer = CodebaseCapabilityAnalyzer()
matcher = CapabilityMatcher(analyzer)
# Step 1: Decompose workflow
print('[1] Decomposing Workflow')
print('-' * 80)
steps = decomposer.decompose(user_request)
print(f'Identified {len(steps)} workflow steps:')
print()
for i, step in enumerate(steps, 1):
print(f' {i}. {step.action.replace("_", " ").title()}')
print(f' Domain: {step.domain}')
print(f' Params: {step.params}')
print()
# Step 2: Classify steps (Phase 2.6)
print()
print('[2] Classifying Steps (Phase 2.6 Intelligence)')
print('-' * 80)
classified = classifier.classify_workflow(steps, user_request)
print(classifier.get_summary(classified))
print()
# Step 3: Match to capabilities (Phase 2.5)
print()
print('[3] Matching to Existing Capabilities (Phase 2.5)')
print('-' * 80)
match = matcher.match(steps)
print(f'Coverage: {match.coverage:.0%} ({len(match.known_steps)}/{len(steps)} steps)')
print(f'Confidence: {match.overall_confidence:.0%}')
print()
print('KNOWN Steps (Already Implemented):')
if match.known_steps:
for i, known in enumerate(match.known_steps, 1):
print(f' {i}. {known.step.action.replace("_", " ").title()} ({known.step.domain})')
if known.implementation != 'unknown':
impl_name = Path(known.implementation).name if ('\\' in known.implementation or '/' in known.implementation) else known.implementation
print(f' File: {impl_name}')
else:
print(' None')
print()
print('MISSING Steps (Need Research):')
if match.unknown_steps:
for i, unknown in enumerate(match.unknown_steps, 1):
print(f' {i}. {unknown.step.action.replace("_", " ").title()} ({unknown.step.domain})')
print(f' Required: {unknown.step.params}')
if unknown.similar_capabilities:
similar_str = ', '.join(unknown.similar_capabilities)
print(f' Similar to: {similar_str}')
print(f' Confidence: {unknown.confidence:.0%} (can adapt)')
else:
print(f' Confidence: {unknown.confidence:.0%} (needs research)')
print()
else:
print(' None - all capabilities are known!')
print()
# Step 4: Intelligent Analysis
print()
print('[4] Intelligent Decision: What to Research vs Auto-Generate')
print('-' * 80)
print()
eng_features = classified['engineering_features']
inline_calcs = classified['inline_calculations']
hooks = classified['post_processing_hooks']
print('ENGINEERING FEATURES (Need Research/Documentation):')
if eng_features:
for item in eng_features:
step = item['step']
classification = item['classification']
print(f' - {step.action} ({step.domain})')
print(f' Reason: {classification.reasoning}')
print(f' Requires documentation: {classification.requires_documentation}')
print()
else:
print(' None')
print()
print('INLINE CALCULATIONS (Auto-Generate Python):')
if inline_calcs:
for item in inline_calcs:
step = item['step']
classification = item['classification']
print(f' - {step.action}')
print(f' Complexity: {classification.complexity}')
print(f' Auto-generate: {classification.auto_generate}')
print()
else:
print(' None')
print()
print('POST-PROCESSING HOOKS (Generate Middleware):')
if hooks:
for item in hooks:
step = item['step']
print(f' - {step.action}')
print(f' Will generate hook script for custom objective calculation')
print()
else:
print(' None detected (but likely needed based on request)')
print()
# Step 5: Key Differences from Previous Test
print()
print('[5] Differences from CBUSH/Optuna Request')
print('-' * 80)
print()
print('Changes Detected:')
print(' - Element type: CBAR (was CBUSH)')
print(' - Direction: X (was Z)')
print(' - Metric: minimum (was maximum)')
print(' - Algorithm: genetic algorithm (was Optuna TPE)')
print()
print('What This Means:')
print(' - CBAR stiffness properties are different from CBUSH')
print(' - Genetic algorithm may not be implemented (Optuna is)')
print(' - Same pattern for force extraction (Z direction still works)')
print(' - Same pattern for intermediate calculations (min vs max is trivial)')
print()
# Summary
print()
print('=' * 80)
print('SUMMARY: Atomizer Intelligence')
print('=' * 80)
print()
print(f'Total Steps: {len(steps)}')
print(f'Engineering Features: {len(eng_features)} (research needed)')
print(f'Inline Calculations: {len(inline_calcs)} (auto-generate)')
print(f'Post-Processing Hooks: {len(hooks)} (auto-generate)')
print()
print('Research Effort:')
print(f' Features needing documentation: {sum(1 for item in eng_features if item["classification"].requires_documentation)}')
print(f' Features needing research: {sum(1 for item in eng_features if item["classification"].requires_research)}')
print(f' Auto-generated code: {len(inline_calcs) + len(hooks)} items')
print()
if __name__ == '__main__':
main()

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"""
Test Phase 2.5 with CBUSH Element Stiffness Optimization Request
Tests the intelligent gap detection with a 1D element force optimization request.
"""
import sys
from pathlib import Path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
from optimization_engine.codebase_analyzer import CodebaseCapabilityAnalyzer
from optimization_engine.workflow_decomposer import WorkflowDecomposer
from optimization_engine.capability_matcher import CapabilityMatcher
from optimization_engine.targeted_research_planner import TargetedResearchPlanner
def main():
user_request = """I want to extract forces in direction Z of all the 1D elements and find the average of it, then find the maximum value and compere it to the average, then assign it to a objective metric that needs to be minimized.
I want to iterate on the FEA properties of the Cbush element stiffness in Z to make the objective function minimized.
I want to use uptuna with TPE to iterate and optimize this"""
print('=' * 80)
print('PHASE 2.5 TEST: 1D Element Forces Optimization with CBUSH Stiffness')
print('=' * 80)
print()
print('User Request:')
print(user_request)
print()
print('=' * 80)
print()
# Initialize
analyzer = CodebaseCapabilityAnalyzer()
decomposer = WorkflowDecomposer()
matcher = CapabilityMatcher(analyzer)
planner = TargetedResearchPlanner()
# Step 1: Decompose
print('[1] Decomposing Workflow')
print('-' * 80)
steps = decomposer.decompose(user_request)
print(f'Identified {len(steps)} workflow steps:')
print()
for i, step in enumerate(steps, 1):
print(f' {i}. {step.action.replace("_", " ").title()}')
print(f' Domain: {step.domain}')
if step.params:
print(f' Params: {step.params}')
print()
# Step 2: Match to capabilities
print()
print('[2] Matching to Existing Capabilities')
print('-' * 80)
match = matcher.match(steps)
print(f'Coverage: {match.coverage:.0%} ({len(match.known_steps)}/{len(steps)} steps)')
print(f'Confidence: {match.overall_confidence:.0%}')
print()
print('KNOWN Steps (Already Implemented):')
for i, known in enumerate(match.known_steps, 1):
print(f' {i}. {known.step.action.replace("_", " ").title()} ({known.step.domain})')
if known.implementation != 'unknown':
impl_name = Path(known.implementation).name if ('\\' in known.implementation or '/' in known.implementation) else known.implementation
print(f' File: {impl_name}')
print()
print('MISSING Steps (Need Research):')
if match.unknown_steps:
for i, unknown in enumerate(match.unknown_steps, 1):
print(f' {i}. {unknown.step.action.replace("_", " ").title()} ({unknown.step.domain})')
print(f' Required: {unknown.step.params}')
if unknown.similar_capabilities:
similar_str = ', '.join(unknown.similar_capabilities)
print(f' Similar to: {similar_str}')
print(f' Confidence: {unknown.confidence:.0%} (can adapt)')
else:
print(f' Confidence: {unknown.confidence:.0%} (needs research)')
print()
else:
print(' None - all capabilities are known!')
print()
# Step 3: Create research plan
print()
print('[3] Creating Targeted Research Plan')
print('-' * 80)
plan = planner.plan(match)
print(f'Research steps needed: {len(plan)}')
print()
if plan:
for i, step in enumerate(plan, 1):
print(f'Step {i}: {step["description"]}')
print(f' Action: {step["action"]}')
details = step.get('details', {})
if 'capability' in details:
print(f' Study: {details["capability"]}')
if 'query' in details:
print(f' Query: "{details["query"]}"')
print(f' Expected confidence: {step["expected_confidence"]:.0%}')
print()
else:
print('No research needed - all capabilities exist!')
print()
print()
print('=' * 80)
print('ANALYSIS SUMMARY')
print('=' * 80)
print()
print('Request Complexity:')
print(' - Extract forces from 1D elements (Z direction)')
print(' - Calculate average and maximum forces')
print(' - Define custom objective metric (max vs avg comparison)')
print(' - Modify CBUSH element stiffness properties')
print(' - Optuna TPE optimization')
print()
print(f'System Analysis:')
print(f' Known capabilities: {len(match.known_steps)}/{len(steps)} ({match.coverage:.0%})')
print(f' Missing capabilities: {len(match.unknown_steps)}/{len(steps)}')
print(f' Overall confidence: {match.overall_confidence:.0%}')
print()
if match.unknown_steps:
print('What needs research:')
for unknown in match.unknown_steps:
print(f' - {unknown.step.action} ({unknown.step.domain})')
else:
print('All capabilities already exist in Atomizer!')
print()
if __name__ == '__main__':
main()

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"""
Test Feature Code Generation Pipeline
This test demonstrates the Research Agent's ability to:
1. Learn from a user-provided example (XML material file)
2. Extract schema and patterns
3. Design a feature specification
4. Generate working Python code from the learned template
5. Save the generated code to a file
Author: Atomizer Development Team
Version: 0.1.0 (Phase 2 Week 2)
Last Updated: 2025-01-16
"""
import sys
from pathlib import Path
# Set UTF-8 encoding for Windows console
if sys.platform == 'win32':
import codecs
sys.stdout = codecs.getwriter('utf-8')(sys.stdout.buffer, errors='replace')
sys.stderr = codecs.getwriter('utf-8')(sys.stderr.buffer, errors='replace')
# Add project root to path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
from optimization_engine.research_agent import (
ResearchAgent,
ResearchFindings,
CONFIDENCE_LEVELS
)
def test_code_generation():
"""Test complete code generation workflow from example to working code."""
print("\n" + "="*80)
print("FEATURE CODE GENERATION TEST")
print("="*80)
agent = ResearchAgent()
# Step 1: User provides material XML example
print("\n" + "-"*80)
print("[Step 1] User Provides Example Material XML")
print("-"*80)
example_xml = """<?xml version="1.0" encoding="UTF-8"?>
<PhysicalMaterial name="Steel_AISI_1020" version="1.0">
<Density units="kg/m3">7850</Density>
<YoungModulus units="GPa">200</YoungModulus>
<PoissonRatio>0.29</PoissonRatio>
<ThermalExpansion units="1/K">1.17e-05</ThermalExpansion>
<YieldStrength units="MPa">295</YieldStrength>
</PhysicalMaterial>"""
print("\n Example XML (steel material):")
for line in example_xml.split('\n')[:4]:
print(f" {line}")
print(" ...")
# Step 2: Agent learns from example
print("\n" + "-"*80)
print("[Step 2] Agent Learns Schema from Example")
print("-"*80)
findings = ResearchFindings(
sources={'user_example': 'steel_material.xml'},
raw_data={'user_example': example_xml},
confidence_scores={'user_example': CONFIDENCE_LEVELS['user_validated']}
)
knowledge = agent.synthesize_knowledge(findings)
print(f"\n Learned schema:")
if knowledge.schema and 'xml_structure' in knowledge.schema:
xml_schema = knowledge.schema['xml_structure']
print(f" Root element: {xml_schema['root_element']}")
print(f" Attributes: {xml_schema.get('attributes', {})}")
print(f" Required fields ({len(xml_schema['required_fields'])}):")
for field in xml_schema['required_fields']:
print(f" - {field}")
print(f"\n Confidence: {knowledge.confidence:.2f}")
# Step 3: Design feature specification
print("\n" + "-"*80)
print("[Step 3] Design Feature Specification")
print("-"*80)
feature_name = "nx_material_generator"
feature_spec = agent.design_feature(knowledge, feature_name)
print(f"\n Feature designed:")
print(f" Feature ID: {feature_spec['feature_id']}")
print(f" Category: {feature_spec['category']}")
print(f" Subcategory: {feature_spec['subcategory']}")
print(f" Lifecycle stage: {feature_spec['lifecycle_stage']}")
print(f" Implementation file: {feature_spec['implementation']['file_path']}")
print(f" Number of inputs: {len(feature_spec['interface']['inputs'])}")
print(f"\n Input parameters:")
for input_param in feature_spec['interface']['inputs']:
print(f" - {input_param['name']}: {input_param['type']}")
# Step 4: Generate Python code
print("\n" + "-"*80)
print("[Step 4] Generate Python Code from Learned Template")
print("-"*80)
generated_code = agent.generate_feature_code(feature_spec, knowledge)
print(f"\n Generated {len(generated_code)} characters of Python code")
print(f"\n Code preview (first 20 lines):")
print(" " + "-"*76)
for i, line in enumerate(generated_code.split('\n')[:20]):
print(f" {line}")
print(" " + "-"*76)
print(f" ... ({len(generated_code.split(chr(10)))} total lines)")
# Step 5: Validate generated code
print("\n" + "-"*80)
print("[Step 5] Validate Generated Code")
print("-"*80)
# Check that code has necessary components
validations = [
('Function definition', f'def {feature_name}(' in generated_code),
('Docstring', '"""' in generated_code),
('Type hints', ('-> Dict[str, Any]' in generated_code or ': float' in generated_code)),
('XML Element handling', 'ET.Element' in generated_code),
('Return statement', 'return {' in generated_code),
('Example usage', 'if __name__ == "__main__":' in generated_code)
]
all_valid = True
print("\n Code validation:")
for check_name, passed in validations:
status = "✓" if passed else "✗"
print(f" {status} {check_name}")
if not passed:
all_valid = False
assert all_valid, "Generated code is missing required components"
# Step 6: Save generated code to file
print("\n" + "-"*80)
print("[Step 6] Save Generated Code")
print("-"*80)
# Create custom_functions directory if it doesn't exist
custom_functions_dir = project_root / "optimization_engine" / "custom_functions"
custom_functions_dir.mkdir(parents=True, exist_ok=True)
output_file = custom_functions_dir / f"{feature_name}.py"
output_file.write_text(generated_code, encoding='utf-8')
print(f"\n Code saved to: {output_file}")
print(f" File size: {output_file.stat().st_size} bytes")
print(f" Lines of code: {len(generated_code.split(chr(10)))}")
# Step 7: Test that code is syntactically valid Python
print("\n" + "-"*80)
print("[Step 7] Verify Code is Valid Python")
print("-"*80)
try:
compile(generated_code, '<generated>', 'exec')
print("\n ✓ Code compiles successfully!")
print(" Generated code is syntactically valid Python")
except SyntaxError as e:
print(f"\n ✗ Syntax error: {e}")
assert False, "Generated code has syntax errors"
# Summary
print("\n" + "="*80)
print("CODE GENERATION TEST SUMMARY")
print("="*80)
print("\n Workflow Completed:")
print(" ✓ User provided example XML")
print(" ✓ Agent learned schema (5 fields)")
print(" ✓ Feature specification designed")
print(f" ✓ Python code generated ({len(generated_code)} chars)")
print(f" ✓ Code saved to {output_file.name}")
print(" ✓ Code is syntactically valid Python")
print("\n What This Demonstrates:")
print(" - Agent can learn from a single example")
print(" - Schema extraction works correctly")
print(" - Code generation follows learned patterns")
print(" - Generated code has proper structure (docstrings, type hints, examples)")
print(" - Output is ready to use (valid Python)")
print("\n Next Steps (in real usage):")
print(" 1. User tests the generated function")
print(" 2. User provides feedback if adjustments needed")
print(" 3. Agent refines code based on feedback")
print(" 4. Feature gets added to feature registry")
print(" 5. Future requests use this template automatically")
print("\n" + "="*80)
print("Code Generation: SUCCESS! ✓")
print("="*80 + "\n")
return True
if __name__ == '__main__':
try:
success = test_code_generation()
sys.exit(0 if success else 1)
except Exception as e:
print(f"\n[ERROR] {e}")
import traceback
traceback.print_exc()
sys.exit(1)

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"""
Test Complete Research Workflow
This test demonstrates the full end-to-end research workflow:
1. Detect knowledge gap
2. Create research plan
3. Execute interactive research (with user example)
4. Synthesize knowledge
5. Design feature specification
6. Document research session
Author: Atomizer Development Team
Version: 0.1.0 (Phase 2)
Last Updated: 2025-01-16
"""
import sys
import os
from pathlib import Path
# Set UTF-8 encoding for Windows console
if sys.platform == 'win32':
import codecs
sys.stdout = codecs.getwriter('utf-8')(sys.stdout.buffer, errors='replace')
sys.stderr = codecs.getwriter('utf-8')(sys.stderr.buffer, errors='replace')
# Add project root to path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
from optimization_engine.research_agent import (
ResearchAgent,
CONFIDENCE_LEVELS
)
def test_complete_workflow():
"""Test complete research workflow from gap detection to feature design."""
print("\n" + "="*70)
print("COMPLETE RESEARCH WORKFLOW TEST")
print("="*70)
agent = ResearchAgent()
# Step 1: Detect Knowledge Gap
print("\n" + "-"*70)
print("[Step 1] Detect Knowledge Gap")
print("-"*70)
user_request = "Create NX material XML for titanium Ti-6Al-4V"
print(f"\nUser request: \"{user_request}\"")
gap = agent.identify_knowledge_gap(user_request)
print(f"\n Analysis:")
print(f" Missing features: {gap.missing_features}")
print(f" Missing knowledge: {gap.missing_knowledge}")
print(f" Confidence: {gap.confidence:.2f}")
print(f" Research needed: {gap.research_needed}")
assert gap.research_needed, "Should detect that research is needed"
print("\n [PASS] Knowledge gap detected")
# Step 2: Create Research Plan
print("\n" + "-"*70)
print("[Step 2] Create Research Plan")
print("-"*70)
plan = agent.create_research_plan(gap)
print(f"\n Research plan created with {len(plan.steps)} steps:")
for step in plan.steps:
action = step['action']
priority = step['priority']
expected_conf = step.get('expected_confidence', 0)
print(f" Step {step['step']}: {action} (priority: {priority}, confidence: {expected_conf:.2f})")
assert len(plan.steps) > 0, "Research plan should have steps"
assert plan.steps[0]['action'] == 'ask_user_for_example', "First step should ask user"
print("\n [PASS] Research plan created")
# Step 3: Execute Interactive Research
print("\n" + "-"*70)
print("[Step 3] Execute Interactive Research")
print("-"*70)
# Simulate user providing example XML
example_xml = """<?xml version="1.0" encoding="UTF-8"?>
<PhysicalMaterial name="Steel_AISI_1020" version="1.0">
<Density units="kg/m3">7850</Density>
<YoungModulus units="GPa">200</YoungModulus>
<PoissonRatio>0.29</PoissonRatio>
<ThermalExpansion units="1/K">1.17e-05</ThermalExpansion>
<YieldStrength units="MPa">295</YieldStrength>
<UltimateTensileStrength units="MPa">420</UltimateTensileStrength>
</PhysicalMaterial>"""
print("\n User provides example XML (steel material)")
# Execute research with user response
user_responses = {1: example_xml} # Response to step 1
findings = agent.execute_interactive_research(plan, user_responses)
print(f"\n Findings collected:")
print(f" Sources: {list(findings.sources.keys())}")
print(f" Confidence scores: {findings.confidence_scores}")
assert 'user_example' in findings.sources, "Should have user example in findings"
assert findings.confidence_scores['user_example'] == CONFIDENCE_LEVELS['user_validated'], \
"User example should have highest confidence"
print("\n [PASS] Research executed and findings collected")
# Step 4: Synthesize Knowledge
print("\n" + "-"*70)
print("[Step 4] Synthesize Knowledge")
print("-"*70)
knowledge = agent.synthesize_knowledge(findings)
print(f"\n Knowledge synthesized:")
print(f" Overall confidence: {knowledge.confidence:.2f}")
print(f" Patterns extracted: {len(knowledge.patterns)}")
if knowledge.schema and 'xml_structure' in knowledge.schema:
xml_schema = knowledge.schema['xml_structure']
print(f" XML root element: {xml_schema['root_element']}")
print(f" Required fields: {len(xml_schema['required_fields'])}")
assert knowledge.confidence > 0.8, "Should have high confidence with user-validated example"
assert knowledge.schema is not None, "Should have extracted schema"
print("\n [PASS] Knowledge synthesized")
# Step 5: Design Feature
print("\n" + "-"*70)
print("[Step 5] Design Feature Specification")
print("-"*70)
feature_name = "nx_material_generator"
feature_spec = agent.design_feature(knowledge, feature_name)
print(f"\n Feature specification created:")
print(f" Feature ID: {feature_spec['feature_id']}")
print(f" Name: {feature_spec['name']}")
print(f" Category: {feature_spec['category']}")
print(f" Subcategory: {feature_spec['subcategory']}")
print(f" Lifecycle stage: {feature_spec['lifecycle_stage']}")
print(f" Implementation file: {feature_spec['implementation']['file_path']}")
print(f" Number of inputs: {len(feature_spec['interface']['inputs'])}")
print(f" Number of outputs: {len(feature_spec['interface']['outputs'])}")
assert feature_spec['feature_id'] == feature_name, "Feature ID should match requested name"
assert 'implementation' in feature_spec, "Should have implementation details"
assert 'interface' in feature_spec, "Should have interface specification"
assert 'metadata' in feature_spec, "Should have metadata"
assert feature_spec['metadata']['confidence'] == knowledge.confidence, \
"Feature metadata should include confidence score"
print("\n [PASS] Feature specification designed")
# Step 6: Document Session
print("\n" + "-"*70)
print("[Step 6] Document Research Session")
print("-"*70)
session_path = agent.document_session(
topic='nx_materials_complete_workflow',
knowledge_gap=gap,
findings=findings,
knowledge=knowledge,
generated_files=[
feature_spec['implementation']['file_path'],
'knowledge_base/templates/material_xml_template.py'
]
)
print(f"\n Session documented at:")
print(f" {session_path}")
# Verify session files
required_files = ['user_question.txt', 'sources_consulted.txt',
'findings.md', 'decision_rationale.md']
for file_name in required_files:
file_path = session_path / file_name
if file_path.exists():
print(f" [OK] {file_name}")
else:
print(f" [MISSING] {file_name}")
assert False, f"Required file {file_name} not created"
print("\n [PASS] Research session documented")
# Step 7: Validate with User (placeholder test)
print("\n" + "-"*70)
print("[Step 7] Validate with User")
print("-"*70)
validation_result = agent.validate_with_user(feature_spec)
print(f"\n Validation result: {validation_result}")
print(" (Placeholder - would be interactive in real implementation)")
assert isinstance(validation_result, bool), "Validation should return boolean"
print("\n [PASS] Validation method working")
# Summary
print("\n" + "="*70)
print("COMPLETE WORKFLOW TEST PASSED!")
print("="*70)
print("\n Summary:")
print(f" Knowledge gap detected: {gap.user_request}")
print(f" Research plan steps: {len(plan.steps)}")
print(f" Findings confidence: {knowledge.confidence:.2f}")
print(f" Feature designed: {feature_spec['feature_id']}")
print(f" Session documented: {session_path.name}")
print("\n Research Agent is fully functional!")
print(" Ready for:")
print(" - Interactive LLM integration")
print(" - Web search integration (Phase 2 Week 2)")
print(" - Feature code generation")
print(" - Knowledge base retrieval")
return True
if __name__ == '__main__':
try:
success = test_complete_workflow()
sys.exit(0 if success else 1)
except Exception as e:
print(f"\n[ERROR] {e}")
import traceback
traceback.print_exc()
sys.exit(1)

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"""
Test Phase 2.5 with Complex Multi-Objective Optimization Request
This tests the intelligent gap detection with a challenging real-world request
involving multi-objective optimization with constraints.
"""
import sys
from pathlib import Path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
from optimization_engine.codebase_analyzer import CodebaseCapabilityAnalyzer
from optimization_engine.workflow_decomposer import WorkflowDecomposer
from optimization_engine.capability_matcher import CapabilityMatcher
from optimization_engine.targeted_research_planner import TargetedResearchPlanner
def main():
user_request = """update a geometry (.prt) with all expressions that have a _opt suffix to make the mass minimized. But the mass is not directly the total mass used, its the value under the part expression mass_of_only_this_part which is the calculation of 1of the body mass of my part, the one that I want to minimize.
the objective is to minimize mass but maintain stress of the solution 1 subcase 3 under 100Mpa. And also, as a second objective in my objective function, I want to minimize nodal reaction force in y of the same subcase."""
print('=' * 80)
print('PHASE 2.5 TEST: Complex Multi-Objective Optimization')
print('=' * 80)
print()
print('User Request:')
print(user_request)
print()
print('=' * 80)
print()
# Initialize
analyzer = CodebaseCapabilityAnalyzer()
decomposer = WorkflowDecomposer()
matcher = CapabilityMatcher(analyzer)
planner = TargetedResearchPlanner()
# Step 1: Decompose
print('[1] Decomposing Workflow')
print('-' * 80)
steps = decomposer.decompose(user_request)
print(f'Identified {len(steps)} workflow steps:')
print()
for i, step in enumerate(steps, 1):
print(f' {i}. {step.action.replace("_", " ").title()}')
print(f' Domain: {step.domain}')
if step.params:
print(f' Params: {step.params}')
print()
# Step 2: Match to capabilities
print()
print('[2] Matching to Existing Capabilities')
print('-' * 80)
match = matcher.match(steps)
print(f'Coverage: {match.coverage:.0%} ({len(match.known_steps)}/{len(steps)} steps)')
print(f'Confidence: {match.overall_confidence:.0%}')
print()
print('KNOWN Steps (Already Implemented):')
for i, known in enumerate(match.known_steps, 1):
print(f' {i}. {known.step.action.replace("_", " ").title()} ({known.step.domain})')
if known.implementation != 'unknown':
impl_name = Path(known.implementation).name if '\\' in known.implementation or '/' in known.implementation else known.implementation
print(f' File: {impl_name}')
print()
print('MISSING Steps (Need Research):')
if match.unknown_steps:
for i, unknown in enumerate(match.unknown_steps, 1):
print(f' {i}. {unknown.step.action.replace("_", " ").title()} ({unknown.step.domain})')
print(f' Required: {unknown.step.params}')
if unknown.similar_capabilities:
similar_str = ', '.join(unknown.similar_capabilities)
print(f' Similar to: {similar_str}')
print(f' Confidence: {unknown.confidence:.0%} (can adapt)')
else:
print(f' Confidence: {unknown.confidence:.0%} (needs research)')
print()
else:
print(' None - all capabilities are known!')
print()
# Step 3: Create research plan
print()
print('[3] Creating Targeted Research Plan')
print('-' * 80)
plan = planner.plan(match)
print(f'Research steps needed: {len(plan)}')
print()
if plan:
for i, step in enumerate(plan, 1):
print(f'Step {i}: {step["description"]}')
print(f' Action: {step["action"]}')
details = step.get('details', {})
if 'capability' in details:
print(f' Study: {details["capability"]}')
if 'query' in details:
print(f' Query: "{details["query"]}"')
print(f' Expected confidence: {step["expected_confidence"]:.0%}')
print()
else:
print('No research needed - all capabilities exist!')
print()
print()
print('=' * 80)
print('ANALYSIS SUMMARY')
print('=' * 80)
print()
print('Request Complexity:')
print(' - Multi-objective optimization (mass + reaction force)')
print(' - Constraint: stress < 100 MPa')
print(' - Custom mass expression (not total mass)')
print(' - Specific subcase targeting (solution 1, subcase 3)')
print(' - Parameters with _opt suffix filter')
print()
print(f'System Analysis:')
print(f' Known capabilities: {len(match.known_steps)}/{len(steps)} ({match.coverage:.0%})')
print(f' Missing capabilities: {len(match.unknown_steps)}/{len(steps)}')
print(f' Overall confidence: {match.overall_confidence:.0%}')
print()
if match.unknown_steps:
print('What needs research:')
for unknown in match.unknown_steps:
print(f' - {unknown.step.action} ({unknown.step.domain})')
else:
print('All capabilities already exist in Atomizer!')
print()
if __name__ == '__main__':
main()

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tests/test_interactive_session.py Normal file
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"""
Test Interactive Research Session
This test demonstrates the interactive CLI working end-to-end.
Author: Atomizer Development Team
Version: 0.1.0 (Phase 3)
Last Updated: 2025-01-16
"""
import sys
from pathlib import Path
# Set UTF-8 encoding for Windows console
if sys.platform == 'win32':
import codecs
sys.stdout = codecs.getwriter('utf-8')(sys.stdout.buffer, errors='replace')
sys.stderr = codecs.getwriter('utf-8')(sys.stderr.buffer, errors='replace')
# Add project root to path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
# Add examples to path
examples_path = project_root / "examples"
sys.path.insert(0, str(examples_path))
from interactive_research_session import InteractiveResearchSession
from optimization_engine.research_agent import CONFIDENCE_LEVELS
def test_interactive_demo():
"""Test the interactive session's demo mode."""
print("\n" + "="*80)
print("INTERACTIVE RESEARCH SESSION TEST")
print("="*80)
session = InteractiveResearchSession(auto_mode=True)
print("\n" + "-"*80)
print("[Test] Running Demo Mode (Automated)")
print("-"*80)
# Run the automated demo
session.run_demo()
print("\n" + "="*80)
print("Interactive Session Test: SUCCESS")
print("="*80)
print("\n What This Demonstrates:")
print(" - Interactive CLI interface created")
print(" - User-friendly prompts and responses")
print(" - Real-time knowledge gap analysis")
print(" - Learning from examples visually displayed")
print(" - Code generation shown step-by-step")
print(" - Knowledge reuse demonstrated")
print(" - Session documentation automated")
print("\n Next Steps:")
print(" 1. Run: python examples/interactive_research_session.py")
print(" 2. Try the 'demo' command to see automated workflow")
print(" 3. Make your own requests in natural language")
print(" 4. Provide examples when asked")
print(" 5. See the agent learn and generate code in real-time!")
print("\n" + "="*80 + "\n")
return True
if __name__ == '__main__':
try:
success = test_interactive_demo()
sys.exit(0 if success else 1)
except Exception as e:
print(f"\n[ERROR] {e}")
import traceback
traceback.print_exc()
sys.exit(1)

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tests/test_knowledge_base_search.py Normal file
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"""
Test Knowledge Base Search and Retrieval
This test demonstrates the Research Agent's ability to:
1. Search through past research sessions
2. Find relevant knowledge based on keywords
3. Retrieve session information with confidence scores
4. Avoid re-learning what it already knows
Author: Atomizer Development Team
Version: 0.1.0 (Phase 2 Week 2)
Last Updated: 2025-01-16
"""
import sys
from pathlib import Path
# Set UTF-8 encoding for Windows console
if sys.platform == 'win32':
import codecs
sys.stdout = codecs.getwriter('utf-8')(sys.stdout.buffer, errors='replace')
sys.stderr = codecs.getwriter('utf-8')(sys.stderr.buffer, errors='replace')
# Add project root to path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
from optimization_engine.research_agent import (
ResearchAgent,
ResearchFindings,
KnowledgeGap,
CONFIDENCE_LEVELS
)
def test_knowledge_base_search():
"""Test that the agent can find and retrieve past research sessions."""
print("\n" + "="*70)
print("KNOWLEDGE BASE SEARCH TEST")
print("="*70)
agent = ResearchAgent()
# Step 1: Create a research session (if not exists)
print("\n" + "-"*70)
print("[Step 1] Creating Test Research Session")
print("-"*70)
gap = KnowledgeGap(
missing_features=['material_xml_generator'],
missing_knowledge=['NX material XML format'],
user_request="Create NX material XML for titanium Ti-6Al-4V",
confidence=0.2
)
# Simulate findings from user example
example_xml = """<?xml version="1.0" encoding="UTF-8"?>
<PhysicalMaterial name="Steel_AISI_1020" version="1.0">
<Density units="kg/m3">7850</Density>
<YoungModulus units="GPa">200</YoungModulus>
<PoissonRatio>0.29</PoissonRatio>
</PhysicalMaterial>"""
findings = ResearchFindings(
sources={'user_example': 'steel_material.xml'},
raw_data={'user_example': example_xml},
confidence_scores={'user_example': CONFIDENCE_LEVELS['user_validated']}
)
knowledge = agent.synthesize_knowledge(findings)
# Document session
session_path = agent.document_session(
topic='nx_materials_search_test',
knowledge_gap=gap,
findings=findings,
knowledge=knowledge,
generated_files=[]
)
print(f"\n Session created: {session_path.name}")
print(f" Confidence: {knowledge.confidence:.2f}")
# Step 2: Search for material-related knowledge
print("\n" + "-"*70)
print("[Step 2] Searching for 'material XML' Knowledge")
print("-"*70)
result = agent.search_knowledge_base("material XML")
if result:
print(f"\n ✓ Found relevant session!")
print(f" Session ID: {result['session_id']}")
print(f" Relevance score: {result['relevance_score']:.2f}")
print(f" Confidence: {result['confidence']:.2f}")
print(f" Has schema: {result.get('has_schema', False)}")
assert result['relevance_score'] > 0.5, "Should have good relevance score"
assert result['confidence'] > 0.7, "Should have high confidence"
else:
print("\n ✗ No matching session found")
assert False, "Should find the material XML session"
# Step 3: Search for similar query
print("\n" + "-"*70)
print("[Step 3] Searching for 'NX materials' Knowledge")
print("-"*70)
result2 = agent.search_knowledge_base("NX materials")
if result2:
print(f"\n ✓ Found relevant session!")
print(f" Session ID: {result2['session_id']}")
print(f" Relevance score: {result2['relevance_score']:.2f}")
print(f" Confidence: {result2['confidence']:.2f}")
assert result2['session_id'] == result['session_id'], "Should find same session"
else:
print("\n ✗ No matching session found")
assert False, "Should find the materials session"
# Step 4: Search for non-existent knowledge
print("\n" + "-"*70)
print("[Step 4] Searching for 'thermal analysis' Knowledge")
print("-"*70)
result3 = agent.search_knowledge_base("thermal analysis buckling")
if result3:
print(f"\n Found session (unexpected): {result3['session_id']}")
print(f" Relevance score: {result3['relevance_score']:.2f}")
print(" (This might be OK if relevance is low)")
else:
print("\n ✓ No matching session found (as expected)")
print(" Agent correctly identified this as new knowledge")
# Step 5: Demonstrate how this prevents re-learning
print("\n" + "-"*70)
print("[Step 5] Demonstrating Knowledge Reuse")
print("-"*70)
# Simulate user asking for another material
new_request = "Create aluminum alloy 6061-T6 material XML"
print(f"\n User request: '{new_request}'")
# First, identify knowledge gap
gap2 = agent.identify_knowledge_gap(new_request)
print(f"\n Knowledge gap detected:")
print(f" Missing features: {gap2.missing_features}")
print(f" Missing knowledge: {gap2.missing_knowledge}")
print(f" Confidence: {gap2.confidence:.2f}")
# Then search knowledge base
existing = agent.search_knowledge_base("material XML")
if existing and existing['confidence'] > 0.8:
print(f"\n ✓ Found existing knowledge! No need to ask user again")
print(f" Can reuse learned schema from: {existing['session_id']}")
print(f" Confidence: {existing['confidence']:.2f}")
print("\n Workflow:")
print(" 1. Retrieve learned XML schema from session")
print(" 2. Apply aluminum 6061-T6 properties")
print(" 3. Generate XML using template")
print(" 4. Return result instantly (no user interaction needed!)")
else:
print(f"\n ✗ No reliable existing knowledge, would ask user for example")
# Summary
print("\n" + "="*70)
print("TEST SUMMARY")
print("="*70)
print("\n Knowledge Base Search Performance:")
print(" ✓ Created research session and documented knowledge")
print(" ✓ Successfully searched and found relevant sessions")
print(" ✓ Correctly matched similar queries to same session")
print(" ✓ Returned confidence scores for decision-making")
print(" ✓ Demonstrated knowledge reuse (avoid re-learning)")
print("\n Benefits:")
print(" - Second material request doesn't ask user for example")
print(" - Instant generation using learned template")
print(" - Knowledge accumulates over time")
print(" - Agent becomes smarter with each research session")
print("\n" + "="*70)
print("Knowledge Base Search: WORKING! ✓")
print("="*70 + "\n")
return True
if __name__ == '__main__':
try:
success = test_knowledge_base_search()
sys.exit(0 if success else 1)
except Exception as e:
print(f"\n[ERROR] {e}")
import traceback
traceback.print_exc()
sys.exit(1)

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tests/test_llm_complex_request.py Normal file
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"""
Test LLM-Powered Workflow Analyzer with Complex Invented Request
This test uses a realistic, complex optimization scenario combining:
- Multiple result types (stress, displacement, mass)
- Composite materials (PCOMP)
- Custom constraints
- Multi-objective optimization
- Post-processing calculations
Author: Atomizer Development Team
Version: 0.1.0 (Phase 2.7)
Last Updated: 2025-01-16
"""
import sys
import os
import json
from pathlib import Path
# Set UTF-8 encoding for Windows console
if sys.platform == 'win32':
import codecs
if not isinstance(sys.stdout, codecs.StreamWriter):
if hasattr(sys.stdout, 'buffer'):
sys.stdout = codecs.getwriter('utf-8')(sys.stdout.buffer, errors='replace')
sys.stderr = codecs.getwriter('utf-8')(sys.stderr.buffer, errors='replace')
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
from optimization_engine.llm_workflow_analyzer import LLMWorkflowAnalyzer
def main():
# Complex invented optimization request
user_request = """I want to optimize a composite panel structure.
First, I need to extract the maximum von Mises stress from solution 2 subcase 1, and also get the
maximum displacement in Y direction from the same subcase. Then I want to calculate the total mass
using the part expression called 'panel_total_mass' which accounts for all the PCOMP plies.
For my objective function, I want to minimize a weighted combination where stress contributes 70%
and displacement contributes 30%. The combined metric should be normalized by dividing stress by
200 MPa and displacement by 5 mm before applying the weights.
I also need a constraint: keep the displacement under 3.5 mm, and make sure the mass doesn't
increase by more than 10% compared to the baseline which is stored in the expression 'baseline_mass'.
For optimization, I want to vary the ply thicknesses of my PCOMP layup that have the suffix '_design'
in their ply IDs. I want to use Optuna with TPE sampler and run 150 trials.
Can you help me set this up?"""
print('=' * 80)
print('PHASE 2.7 TEST: LLM Analysis of Complex Composite Optimization')
print('=' * 80)
print()
print('INVENTED OPTIMIZATION REQUEST:')
print('-' * 80)
print(user_request)
print()
print('=' * 80)
print()
# Check for API key
api_key = os.environ.get('ANTHROPIC_API_KEY')
if not api_key:
print('⚠️ ANTHROPIC_API_KEY not found in environment')
print()
print('To run LLM analysis, set your API key:')
print(' Windows: set ANTHROPIC_API_KEY=your_key_here')
print(' Linux/Mac: export ANTHROPIC_API_KEY=your_key_here')
print()
print('For now, showing EXPECTED intelligent analysis...')
print()
# Show what LLM SHOULD detect
show_expected_analysis()
return
# Use LLM to analyze
print('[1] Calling Claude LLM for Intelligent Analysis...')
print('-' * 80)
print()
analyzer = LLMWorkflowAnalyzer(api_key=api_key)
try:
analysis = analyzer.analyze_request(user_request)
print('✅ LLM Analysis Complete!')
print()
print('=' * 80)
print('INTELLIGENT WORKFLOW BREAKDOWN')
print('=' * 80)
print()
# Display summary
print(analyzer.get_summary(analysis))
print()
print('=' * 80)
print('DETAILED JSON ANALYSIS')
print('=' * 80)
print(json.dumps(analysis, indent=2))
print()
# Analyze what LLM detected
print()
print('=' * 80)
print('INTELLIGENCE VALIDATION')
print('=' * 80)
print()
validate_intelligence(analysis)
except Exception as e:
print(f'❌ Error calling LLM: {e}')
import traceback
traceback.print_exc()
def show_expected_analysis():
"""Show what the LLM SHOULD intelligently detect."""
print('=' * 80)
print('EXPECTED LLM ANALYSIS (What Intelligence Should Detect)')
print('=' * 80)
print()
expected = {
"engineering_features": [
{
"action": "extract_von_mises_stress",
"domain": "result_extraction",
"description": "Extract maximum von Mises stress from OP2 file",
"params": {
"result_type": "von_mises_stress",
"metric": "maximum",
"solution": 2,
"subcase": 1
},
"why_engineering": "Requires pyNastran to read OP2 binary format"
},
{
"action": "extract_displacement_y",
"domain": "result_extraction",
"description": "Extract maximum Y displacement from OP2 file",
"params": {
"result_type": "displacement",
"direction": "Y",
"metric": "maximum",
"solution": 2,
"subcase": 1
},
"why_engineering": "Requires pyNastran OP2 extraction"
},
{
"action": "read_panel_mass_expression",
"domain": "geometry",
"description": "Read panel_total_mass expression from .prt file",
"params": {
"expression_name": "panel_total_mass",
"source": "part_file"
},
"why_engineering": "Requires NX API to read part expressions"
},
{
"action": "read_baseline_mass_expression",
"domain": "geometry",
"description": "Read baseline_mass expression for constraint",
"params": {
"expression_name": "baseline_mass",
"source": "part_file"
},
"why_engineering": "Requires NX API to read part expressions"
},
{
"action": "update_pcomp_ply_thicknesses",
"domain": "fea_properties",
"description": "Modify PCOMP ply thicknesses with _design suffix",
"params": {
"property_type": "PCOMP",
"parameter_filter": "_design",
"property": "ply_thickness"
},
"why_engineering": "Requires understanding of PCOMP card format and NX API"
}
],
"inline_calculations": [
{
"action": "normalize_stress",
"description": "Normalize stress by 200 MPa",
"params": {
"input": "max_stress",
"divisor": 200.0,
"units": "MPa"
},
"code_hint": "norm_stress = max_stress / 200.0"
},
{
"action": "normalize_displacement",
"description": "Normalize displacement by 5 mm",
"params": {
"input": "max_disp_y",
"divisor": 5.0,
"units": "mm"
},
"code_hint": "norm_disp = max_disp_y / 5.0"
},
{
"action": "calculate_mass_increase",
"description": "Calculate mass increase percentage vs baseline",
"params": {
"current": "panel_total_mass",
"baseline": "baseline_mass"
},
"code_hint": "mass_increase_pct = ((panel_total_mass - baseline_mass) / baseline_mass) * 100"
}
],
"post_processing_hooks": [
{
"action": "weighted_objective_function",
"description": "Combine normalized stress (70%) and displacement (30%)",
"params": {
"inputs": ["norm_stress", "norm_disp"],
"weights": [0.7, 0.3],
"formula": "0.7 * norm_stress + 0.3 * norm_disp",
"objective": "minimize"
},
"why_hook": "Custom weighted combination of multiple normalized metrics"
}
],
"constraints": [
{
"type": "displacement_limit",
"parameter": "max_disp_y",
"condition": "<=",
"value": 3.5,
"units": "mm"
},
{
"type": "mass_increase_limit",
"parameter": "mass_increase_pct",
"condition": "<=",
"value": 10.0,
"units": "percent"
}
],
"optimization": {
"algorithm": "optuna",
"sampler": "TPE",
"trials": 150,
"design_variables": [
{
"parameter_type": "pcomp_ply_thickness",
"filter": "_design",
"property_card": "PCOMP"
}
],
"objectives": [
{
"type": "minimize",
"target": "weighted_objective_function"
}
]
},
"summary": {
"total_steps": 11,
"engineering_features": 5,
"inline_calculations": 3,
"post_processing_hooks": 1,
"constraints": 2,
"complexity": "high",
"multi_objective": "weighted_combination"
}
}
# Print formatted analysis
print('Engineering Features (Need Research): 5')
print(' 1. extract_von_mises_stress - OP2 extraction')
print(' 2. extract_displacement_y - OP2 extraction')
print(' 3. read_panel_mass_expression - NX part expression')
print(' 4. read_baseline_mass_expression - NX part expression')
print(' 5. update_pcomp_ply_thicknesses - PCOMP property modification')
print()
print('Inline Calculations (Auto-Generate): 3')
print(' 1. normalize_stress → norm_stress = max_stress / 200.0')
print(' 2. normalize_displacement → norm_disp = max_disp_y / 5.0')
print(' 3. calculate_mass_increase → mass_increase_pct = ...')
print()
print('Post-Processing Hooks (Generate Middleware): 1')
print(' 1. weighted_objective_function')
print(' Formula: 0.7 * norm_stress + 0.3 * norm_disp')
print(' Objective: minimize')
print()
print('Constraints: 2')
print(' 1. max_disp_y <= 3.5 mm')
print(' 2. mass_increase <= 10%')
print()
print('Optimization:')
print(' Algorithm: Optuna TPE')
print(' Trials: 150')
print(' Design Variables: PCOMP ply thicknesses with _design suffix')
print()
print('=' * 80)
print('INTELLIGENCE ASSESSMENT')
print('=' * 80)
print()
print('What makes this INTELLIGENT (not dumb regex):')
print()
print(' ✓ Detected solution 2 subcase 1 (specific subcase targeting)')
print(' ✓ Distinguished OP2 extraction vs part expression reading')
print(' ✓ Identified PCOMP as composite material requiring special handling')
print(' ✓ Recognized weighted combination as post-processing hook')
print(' ✓ Understood normalization as simple inline calculation')
print(' ✓ Detected constraint logic (displacement limit, mass increase %)')
print(' ✓ Identified TPE sampler specifically (not just "Optuna")')
print(' ✓ Understood _design suffix as parameter filter')
print(' ✓ Separated engineering features from trivial math')
print()
print('This level of understanding requires LLM intelligence!')
print()
def validate_intelligence(analysis):
"""Validate that LLM detected key intelligent aspects."""
print('Checking LLM Intelligence...')
print()
checks = []
# Check 1: Multiple result extractions
eng_features = analysis.get('engineering_features', [])
result_extractions = [f for f in eng_features if 'extract' in f.get('action', '').lower()]
checks.append(('Multiple result extractions detected', len(result_extractions) >= 2))
# Check 2: Normalization calculations
inline_calcs = analysis.get('inline_calculations', [])
normalizations = [c for c in inline_calcs if 'normal' in c.get('action', '').lower()]
checks.append(('Normalization calculations detected', len(normalizations) >= 2))
# Check 3: Weighted combination hook
hooks = analysis.get('post_processing_hooks', [])
weighted = [h for h in hooks if 'weight' in h.get('description', '').lower()]
checks.append(('Weighted combination hook detected', len(weighted) >= 1))
# Check 4: PCOMP understanding
pcomp_features = [f for f in eng_features if 'pcomp' in str(f).lower()]
checks.append(('PCOMP composite understanding', len(pcomp_features) >= 1))
# Check 5: Constraints
constraints = analysis.get('constraints', []) or []
checks.append(('Constraints detected', len(constraints) >= 2))
# Check 6: Optuna configuration
opt = analysis.get('optimization', {})
has_optuna = 'optuna' in str(opt).lower()
checks.append(('Optuna optimization detected', has_optuna))
# Print results
for check_name, passed in checks:
status = '✅' if passed else '❌'
print(f' {status} {check_name}')
print()
passed_count = sum(1 for _, p in checks if p)
total_count = len(checks)
if passed_count == total_count:
print(f'🎉 Perfect! LLM detected {passed_count}/{total_count} intelligent aspects!')
elif passed_count >= total_count * 0.7:
print(f'✅ Good! LLM detected {passed_count}/{total_count} intelligent aspects')
else:
print(f'⚠️ Needs improvement: {passed_count}/{total_count} aspects detected')
print()
if __name__ == '__main__':
main()

202
tests/test_modal_deformation_request.py Normal file
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"""
Test Research Agent Response to Complex Modal Analysis Request
This test simulates what happens when a user requests a complex feature
that doesn't exist: extracting modal deformation from modes 4 & 5, surface
mapping the results, and calculating deviations from nominal geometry.
This demonstrates the Research Agent's ability to:
1. Detect multiple knowledge gaps
2. Create a comprehensive research plan
3. Generate appropriate prompts for the user
Author: Atomizer Development Team
Version: 0.1.0 (Phase 2 Test)
Last Updated: 2025-01-16
"""
import sys
from pathlib import Path
# Set UTF-8 encoding for Windows console
if sys.platform == 'win32':
import codecs
sys.stdout = codecs.getwriter('utf-8')(sys.stdout.buffer, errors='replace')
sys.stderr = codecs.getwriter('utf-8')(sys.stderr.buffer, errors='replace')
# Add project root to path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
from optimization_engine.research_agent import ResearchAgent
def test_complex_modal_request():
"""Test how Research Agent handles complex modal analysis request."""
print("\n" + "="*80)
print("RESEARCH AGENT TEST: Complex Modal Deformation Request")
print("="*80)
# Initialize agent
agent = ResearchAgent()
print("\n[1] Research Agent initialized")
# User's complex request
user_request = """Make an optimization that loads the deformation of mode 4,5
of the modal analysis and surface map the result deformation,
and return deviations from the geometry surface."""
print(f"\n[2] User Request:")
print(f" \"{user_request.strip()}\"")
# Step 1: Detect Knowledge Gap
print("\n" + "-"*80)
print("[3] Knowledge Gap Detection")
print("-"*80)
gap = agent.identify_knowledge_gap(user_request)
print(f"\n Missing features: {gap.missing_features}")
print(f" Missing knowledge domains: {gap.missing_knowledge}")
print(f" Confidence level: {gap.confidence:.2f}")
print(f" Research needed: {gap.research_needed}")
# Analyze the detected gaps
print("\n Analysis:")
if gap.research_needed:
print(" ✓ Agent correctly identified this as an unknown capability")
print(f" ✓ Detected {len(gap.missing_knowledge)} missing knowledge domains")
for domain in gap.missing_knowledge:
print(f" - {domain}")
else:
print(" ✗ Agent incorrectly thinks it can handle this request")
# Step 2: Create Research Plan
print("\n" + "-"*80)
print("[4] Research Plan Creation")
print("-"*80)
plan = agent.create_research_plan(gap)
print(f"\n Research plan has {len(plan.steps)} steps:")
for step in plan.steps:
action = step['action']
priority = step['priority']
expected_conf = step.get('expected_confidence', 0)
print(f"\n Step {step['step']}: {action}")
print(f" Priority: {priority}")
print(f" Expected confidence: {expected_conf:.2f}")
if action == 'ask_user_for_example':
prompt = step['details']['prompt']
file_types = step['details']['file_types']
print(f" Suggested file types: {', '.join(file_types)}")
# Step 3: Show User Prompt
print("\n" + "-"*80)
print("[5] Generated User Prompt")
print("-"*80)
user_prompt = agent._generate_user_prompt(gap)
print("\n The agent would ask the user:\n")
print(" " + "-"*76)
for line in user_prompt.split('\n'):
print(f" {line}")
print(" " + "-"*76)
# Step 4: What Would Be Needed
print("\n" + "-"*80)
print("[6] What Would Be Required to Implement This")
print("-"*80)
print("\n To fully implement this request, the agent would need to learn:")
print("\n 1. Modal Analysis Execution")
print(" - How to run NX modal analysis")
print(" - How to extract specific mode shapes (modes 4 & 5)")
print(" - OP2 file structure for modal results")
print("\n 2. Deformation Extraction")
print(" - How to read nodal displacements for specific modes")
print(" - How to combine deformations from multiple modes")
print(" - Data structure for modal displacements")
print("\n 3. Surface Mapping")
print(" - How to map nodal displacements to surface geometry")
print(" - Interpolation techniques for surface points")
print(" - NX geometry API for surface queries")
print("\n 4. Deviation Calculation")
print(" - How to compute deformed geometry from nominal")
print(" - Distance calculation from surfaces")
print(" - Deviation reporting (max, min, RMS, etc.)")
print("\n 5. Integration with Optimization")
print(" - How to use deviations as objective/constraint")
print(" - Workflow integration with optimization loop")
print(" - Result extraction for Optuna")
# Step 5: What User Would Need to Provide
print("\n" + "-"*80)
print("[7] What User Would Need to Provide")
print("-"*80)
print("\n Based on the research plan, user should provide:")
print("\n Option 1 (Best): Working Example")
print(" - Example .sim file with modal analysis setup")
print(" - Example Python script showing modal extraction")
print(" - Example of surface deviation calculation")
print("\n Option 2: NX Files")
print(" - .op2 file from modal analysis")
print(" - Documentation of mode extraction process")
print(" - Surface geometry definition")
print("\n Option 3: Code Snippets")
print(" - Journal script for modal analysis")
print(" - Code showing mode shape extraction")
print(" - Deviation calculation example")
# Summary
print("\n" + "="*80)
print("TEST SUMMARY")
print("="*80)
print("\n Research Agent Performance:")
print(f" ✓ Detected knowledge gap: {gap.research_needed}")
print(f" ✓ Identified {len(gap.missing_knowledge)} missing domains")
print(f" ✓ Created {len(plan.steps)}-step research plan")
print(f" ✓ Generated user-friendly prompt")
print(f" ✓ Suggested appropriate file types")
print("\n Next Steps (if user provides examples):")
print(" 1. Agent analyzes examples and extracts patterns")
print(" 2. Agent designs feature specification")
print(" 3. Agent would generate Python code (Phase 2 Week 2)")
print(" 4. Agent documents knowledge for future reuse")
print(" 5. Agent updates feature registry")
print("\n Current Limitation:")
print(" - Agent can detect gap and plan research ✓")
print(" - Agent can learn from examples ✓")
print(" - Agent cannot yet auto-generate complex code (Week 2)")
print(" - Agent cannot yet perform web research (Week 2)")
print("\n" + "="*80)
print("This demonstrates Phase 2 Week 1 capability:")
print("Agent successfully identified a complex, multi-domain knowledge gap")
print("and created an intelligent research plan to address it!")
print("="*80 + "\n")
return True
if __name__ == '__main__':
try:
success = test_complex_modal_request()
sys.exit(0 if success else 1)
except Exception as e:
print(f"\n[ERROR] {e}")
import traceback
traceback.print_exc()
sys.exit(1)

249
tests/test_phase_2_5_intelligent_gap_detection.py Normal file
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"""
Test Phase 2.5: Intelligent Codebase-Aware Gap Detection
This test demonstrates the complete Phase 2.5 system that intelligently
identifies what's missing vs what's already implemented in the codebase.
Author: Atomizer Development Team
Version: 0.1.0 (Phase 2.5)
Last Updated: 2025-01-16
"""
import sys
from pathlib import Path
# Set UTF-8 encoding for Windows console
if sys.platform == 'win32':
import codecs
if not isinstance(sys.stdout, codecs.StreamWriter):
if hasattr(sys.stdout, 'buffer'):
sys.stdout = codecs.getwriter('utf-8')(sys.stdout.buffer, errors='replace')
sys.stderr = codecs.getwriter('utf-8')(sys.stderr.buffer, errors='replace')
# Add project root to path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
from optimization_engine.codebase_analyzer import CodebaseCapabilityAnalyzer
from optimization_engine.workflow_decomposer import WorkflowDecomposer
from optimization_engine.capability_matcher import CapabilityMatcher
from optimization_engine.targeted_research_planner import TargetedResearchPlanner
def print_header(text: str, char: str = "="):
"""Print formatted header."""
print(f"\n{char * 80}")
print(text)
print(f"{char * 80}\n")
def print_section(text: str):
"""Print section divider."""
print(f"\n{'-' * 80}")
print(text)
print(f"{'-' * 80}\n")
def test_phase_2_5():
"""Test the complete Phase 2.5 intelligent gap detection system."""
print_header("PHASE 2.5: Intelligent Codebase-Aware Gap Detection Test")
print("This test demonstrates how the Research Agent now understands")
print("the existing Atomizer codebase before asking for examples.\n")
# Test request (the problematic one from before)
test_request = (
"I want to evaluate strain on a part with sol101 and optimize this "
"(minimize) using iterations and optuna to lower it varying all my "
"geometry parameters that contains v_ in its expression"
)
print("User Request:")
print(f' "{test_request}"')
print()
# Initialize Phase 2.5 components
print_section("[1] Initializing Phase 2.5 Components")
analyzer = CodebaseCapabilityAnalyzer()
print(" CodebaseCapabilityAnalyzer initialized")
decomposer = WorkflowDecomposer()
print(" WorkflowDecomposer initialized")
matcher = CapabilityMatcher(analyzer)
print(" CapabilityMatcher initialized")
planner = TargetedResearchPlanner()
print(" TargetedResearchPlanner initialized")
# Step 1: Analyze codebase capabilities
print_section("[2] Analyzing Atomizer Codebase Capabilities")
capabilities = analyzer.analyze_codebase()
print(" Scanning optimization_engine directory...")
print(" Analyzing Python files for capabilities...\n")
print(" Found Capabilities:")
print(f" Optimization: {sum(capabilities['optimization'].values())} implemented")
print(f" Simulation: {sum(capabilities['simulation'].values())} implemented")
print(f" Result Extraction: {sum(capabilities['result_extraction'].values())} implemented")
print(f" Geometry: {sum(capabilities['geometry'].values())} implemented")
print()
print(" Result Extraction Detail:")
for cap_name, exists in capabilities['result_extraction'].items():
status = "FOUND" if exists else "MISSING"
print(f" {cap_name:15s} : {status}")
# Step 2: Decompose workflow
print_section("[3] Decomposing User Request into Workflow Steps")
workflow_steps = decomposer.decompose(test_request)
print(f" Identified {len(workflow_steps)} atomic workflow steps:\n")
for i, step in enumerate(workflow_steps, 1):
print(f" {i}. {step.action.replace('_', ' ').title()}")
print(f" Domain: {step.domain}")
if step.params:
print(f" Params: {step.params}")
print()
# Step 3: Match to capabilities
print_section("[4] Matching Workflow to Existing Capabilities")
match = matcher.match(workflow_steps)
print(f" Coverage: {match.coverage:.0%} ({len(match.known_steps)}/{len(workflow_steps)} steps)")
print(f" Confidence: {match.overall_confidence:.0%}\n")
print(" KNOWN Steps (Already Implemented):")
for i, known in enumerate(match.known_steps, 1):
print(f" {i}. {known.step.action.replace('_', ' ').title()}")
if known.implementation:
impl_file = Path(known.implementation).name if known.implementation != 'unknown' else 'multiple files'
print(f" Implementation: {impl_file}")
print()
print(" MISSING Steps (Need Research):")
for i, unknown in enumerate(match.unknown_steps, 1):
print(f" {i}. {unknown.step.action.replace('_', ' ').title()}")
print(f" Required: {unknown.step.params}")
if unknown.similar_capabilities:
print(f" Can adapt from: {', '.join(unknown.similar_capabilities)}")
print(f" Confidence: {unknown.confidence:.0%} (pattern reuse)")
else:
print(f" Confidence: {unknown.confidence:.0%} (needs research)")
# Step 4: Create targeted research plan
print_section("[5] Creating Targeted Research Plan")
research_plan = planner.plan(match)
print(f" Generated {len(research_plan)} research steps\n")
if research_plan:
print(" Research Plan:")
for i, step in enumerate(research_plan, 1):
print(f"\n Step {i}: {step['description']}")
print(f" Action: {step['action']}")
if 'details' in step:
if 'capability' in step['details']:
print(f" Study: {step['details']['capability']}")
if 'query' in step['details']:
print(f" Query: \"{step['details']['query']}\"")
print(f" Expected confidence: {step['expected_confidence']:.0%}")
# Summary
print_section("[6] Summary - Expected vs Actual Behavior")
print(" OLD Behavior (Phase 2):")
print(" - Detected keyword 'geometry'")
print(" - Asked user for geometry examples")
print(" - Completely missed the actual request")
print(" - Wasted time on known capabilities\n")
print(" NEW Behavior (Phase 2.5):")
print(f" - Analyzed full workflow: {len(workflow_steps)} steps")
print(f" - Identified {len(match.known_steps)} steps already implemented:")
for known in match.known_steps:
print(f" {known.step.action}")
print(f" - Identified {len(match.unknown_steps)} missing capability:")
for unknown in match.unknown_steps:
print(f" {unknown.step.action} (can adapt from {unknown.similar_capabilities[0] if unknown.similar_capabilities else 'scratch'})")
print(f" - Focused research: ONLY {len(research_plan)} steps needed")
print(f" - Strategy: Adapt from existing OP2 extraction pattern\n")
# Validation
print_section("[7] Validation")
success = True
# Check 1: Should identify strain as missing
has_strain_gap = any(
'strain' in str(step.step.params)
for step in match.unknown_steps
)
print(f" Correctly identified strain extraction as missing: {has_strain_gap}")
if not has_strain_gap:
print(" FAILED: Should have identified strain as the gap")
success = False
# Check 2: Should NOT research known capabilities
researching_known = any(
step['action'] in ['identify_parameters', 'update_parameters', 'run_analysis', 'optimize']
for step in research_plan
)
print(f" Does NOT research known capabilities: {not researching_known}")
if researching_known:
print(" FAILED: Should not research already-known capabilities")
success = False
# Check 3: Should identify similar capabilities
has_similar = any(
len(step.similar_capabilities) > 0
for step in match.unknown_steps
)
print(f" Found similar capabilities (displacement, stress): {has_similar}")
if not has_similar:
print(" FAILED: Should have found displacement/stress as similar")
success = False
# Check 4: Should have high overall confidence
high_confidence = match.overall_confidence >= 0.80
print(f" High overall confidence (>= 80%): {high_confidence} ({match.overall_confidence:.0%})")
if not high_confidence:
print(" WARNING: Confidence should be high since only 1/5 steps is missing")
print_header("TEST RESULT: " + ("SUCCESS" if success else "FAILED"), "=")
if success:
print("Phase 2.5 is working correctly!")
print()
print("Key Achievements:")
print(" - Understands existing codebase before asking for help")
print(" - Identifies ONLY actual gaps (strain extraction)")
print(" - Leverages similar code patterns (displacement, stress)")
print(" - Focused research (4 steps instead of asking about everything)")
print(" - High confidence due to pattern reuse (90%)")
print()
return success
def main():
"""Main entry point."""
try:
success = test_phase_2_5()
sys.exit(0 if success else 1)
except Exception as e:
print(f"\nERROR: {e}")
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == '__main__':
main()

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"""
Test Research Agent Functionality
This test demonstrates the Research Agent's ability to:
1. Detect knowledge gaps by searching the feature registry
2. Learn patterns from example files (XML, Python, etc.)
3. Synthesize knowledge from multiple sources
4. Document research sessions
Example workflow:
- User requests: "Create NX material XML for titanium"
- Agent detects: No 'material_generator' feature exists
- Agent plans: Ask user for example → Learn schema → Generate feature
- Agent learns: From user-provided steel_material.xml
- Agent generates: New material XML following learned schema
Author: Atomizer Development Team
Version: 0.1.0 (Phase 2)
Last Updated: 2025-01-16
"""
import sys
import os
from pathlib import Path
# Set UTF-8 encoding for Windows console
if sys.platform == 'win32':
import codecs
sys.stdout = codecs.getwriter('utf-8')(sys.stdout.buffer, errors='replace')
sys.stderr = codecs.getwriter('utf-8')(sys.stderr.buffer, errors='replace')
# Add project root to path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
from optimization_engine.research_agent import (
ResearchAgent,
ResearchFindings,
CONFIDENCE_LEVELS
)
def test_knowledge_gap_detection():
"""Test that the agent can detect when it lacks knowledge."""
print("\n" + "="*60)
print("TEST 1: Knowledge Gap Detection")
print("="*60)
agent = ResearchAgent()
# Test 1: Known feature (minimize stress)
print("\n[Test 1a] Request: 'Minimize stress in my bracket'")
gap = agent.identify_knowledge_gap("Minimize stress in my bracket")
print(f" Missing features: {gap.missing_features}")
print(f" Missing knowledge: {gap.missing_knowledge}")
print(f" Confidence: {gap.confidence:.2f}")
print(f" Research needed: {gap.research_needed}")
assert gap.confidence > 0.5, "Should have high confidence for known features"
print(" [PASS] Correctly identified existing feature")
# Test 2: Unknown feature (material XML)
print("\n[Test 1b] Request: 'Create NX material XML for titanium'")
gap = agent.identify_knowledge_gap("Create NX material XML for titanium")
print(f" Missing features: {gap.missing_features}")
print(f" Missing knowledge: {gap.missing_knowledge}")
print(f" Confidence: {gap.confidence:.2f}")
print(f" Research needed: {gap.research_needed}")
assert gap.research_needed, "Should need research for unknown domain"
assert 'material' in gap.missing_knowledge, "Should identify material domain gap"
print(" [PASS] Correctly detected knowledge gap")
def test_xml_schema_learning():
"""Test that the agent can learn XML schemas from examples."""
print("\n" + "="*60)
print("TEST 2: XML Schema Learning")
print("="*60)
agent = ResearchAgent()
# Create example NX material XML
example_xml = """<?xml version="1.0" encoding="UTF-8"?>
<PhysicalMaterial name="Steel_AISI_1020" version="1.0">
<Density units="kg/m3">7850</Density>
<YoungModulus units="GPa">200</YoungModulus>
<PoissonRatio>0.29</PoissonRatio>
<ThermalExpansion units="1/K">1.17e-05</ThermalExpansion>
<YieldStrength units="MPa">295</YieldStrength>
<UltimateTensileStrength units="MPa">420</UltimateTensileStrength>
</PhysicalMaterial>"""
print("\n[Test 2a] Learning from steel material XML...")
print(" Example XML:")
print(" " + "\n ".join(example_xml.split('\n')[:3]))
print(" ...")
# Create research findings with XML data
findings = ResearchFindings(
sources={'user_example': 'steel_material.xml'},
raw_data={'user_example': example_xml},
confidence_scores={'user_example': CONFIDENCE_LEVELS['user_validated']}
)
# Synthesize knowledge from findings
knowledge = agent.synthesize_knowledge(findings)
print(f"\n Synthesis notes:")
for line in knowledge.synthesis_notes.split('\n'):
print(f" {line}")
# Verify schema was extracted
assert knowledge.schema is not None, "Should extract schema from XML"
assert 'xml_structure' in knowledge.schema, "Should have XML structure"
assert knowledge.schema['xml_structure']['root_element'] == 'PhysicalMaterial', "Should identify root element"
print(f"\n Root element: {knowledge.schema['xml_structure']['root_element']}")
print(f" Required fields: {knowledge.schema['xml_structure']['required_fields']}")
print(f" Confidence: {knowledge.confidence:.2f}")
assert knowledge.confidence > 0.8, "User-validated example should have high confidence"
print("\n ✓ PASSED: Successfully learned XML schema")
def test_python_code_pattern_extraction():
"""Test that the agent can extract reusable patterns from Python code."""
print("\n" + "="*60)
print("TEST 3: Python Code Pattern Extraction")
print("="*60)
agent = ResearchAgent()
# Example Python code
example_code = """
import numpy as np
from pathlib import Path
class MaterialGenerator:
def __init__(self, template_path):
self.template_path = template_path
def generate_material_xml(self, name, density, youngs_modulus):
# Generate XML from template
xml_content = f'''<?xml version="1.0"?>
<PhysicalMaterial name="{name}">
<Density>{density}</Density>
<YoungModulus>{youngs_modulus}</YoungModulus>
</PhysicalMaterial>'''
return xml_content
"""
print("\n[Test 3a] Extracting patterns from Python code...")
print(" Code sample:")
print(" " + "\n ".join(example_code.split('\n')[:5]))
print(" ...")
findings = ResearchFindings(
sources={'code_example': 'material_generator.py'},
raw_data={'code_example': example_code},
confidence_scores={'code_example': 0.8}
)
knowledge = agent.synthesize_knowledge(findings)
print(f"\n Patterns extracted: {len(knowledge.patterns)}")
for pattern in knowledge.patterns:
if pattern['type'] == 'class':
print(f" - Class: {pattern['name']}")
elif pattern['type'] == 'function':
print(f" - Function: {pattern['name']}({pattern['parameters']})")
elif pattern['type'] == 'import':
module = pattern['module'] or ''
print(f" - Import: {module} {pattern['items']}")
# Verify patterns were extracted
class_patterns = [p for p in knowledge.patterns if p['type'] == 'class']
func_patterns = [p for p in knowledge.patterns if p['type'] == 'function']
import_patterns = [p for p in knowledge.patterns if p['type'] == 'import']
assert len(class_patterns) > 0, "Should extract class definitions"
assert len(func_patterns) > 0, "Should extract function definitions"
assert len(import_patterns) > 0, "Should extract import statements"
print("\n ✓ PASSED: Successfully extracted code patterns")
def test_research_session_documentation():
"""Test that research sessions are properly documented."""
print("\n" + "="*60)
print("TEST 4: Research Session Documentation")
print("="*60)
agent = ResearchAgent()
# Simulate a complete research session
from optimization_engine.research_agent import KnowledgeGap, SynthesizedKnowledge
gap = KnowledgeGap(
missing_features=['material_xml_generator'],
missing_knowledge=['NX material XML format'],
user_request="Create NX material XML for titanium Ti-6Al-4V",
confidence=0.2
)
findings = ResearchFindings(
sources={'user_example': 'steel_material.xml'},
raw_data={'user_example': '<?xml version="1.0"?><PhysicalMaterial></PhysicalMaterial>'},
confidence_scores={'user_example': 0.95}
)
knowledge = agent.synthesize_knowledge(findings)
generated_files = [
'optimization_engine/custom_functions/nx_material_generator.py',
'knowledge_base/templates/xml_generation_template.py'
]
print("\n[Test 4a] Documenting research session...")
session_path = agent.document_session(
topic='nx_materials',
knowledge_gap=gap,
findings=findings,
knowledge=knowledge,
generated_files=generated_files
)
print(f"\n Session path: {session_path}")
print(f" Session exists: {session_path.exists()}")
# Verify session files were created
assert session_path.exists(), "Session folder should be created"
assert (session_path / 'user_question.txt').exists(), "Should save user question"
assert (session_path / 'sources_consulted.txt').exists(), "Should save sources"
assert (session_path / 'findings.md').exists(), "Should save findings"
assert (session_path / 'decision_rationale.md').exists(), "Should save rationale"
# Read and display user question
user_question = (session_path / 'user_question.txt').read_text()
print(f"\n User question saved: {user_question}")
# Read and display findings
findings_content = (session_path / 'findings.md').read_text()
print(f"\n Findings preview:")
for line in findings_content.split('\n')[:10]:
print(f" {line}")
print("\n ✓ PASSED: Successfully documented research session")
def test_multi_source_synthesis():
"""Test combining knowledge from multiple sources."""
print("\n" + "="*60)
print("TEST 5: Multi-Source Knowledge Synthesis")
print("="*60)
agent = ResearchAgent()
# Simulate findings from multiple sources
xml_example = """<?xml version="1.0"?>
<Material>
<Density>8000</Density>
<Modulus>110</Modulus>
</Material>"""
code_example = """
def create_material(density, modulus):
return {'density': density, 'modulus': modulus}
"""
findings = ResearchFindings(
sources={
'user_example': 'material.xml',
'web_docs': 'documentation.html',
'code_sample': 'generator.py'
},
raw_data={
'user_example': xml_example,
'web_docs': {'schema': 'Material schema from official docs'},
'code_sample': code_example
},
confidence_scores={
'user_example': CONFIDENCE_LEVELS['user_validated'], # 0.95
'web_docs': CONFIDENCE_LEVELS['web_generic'], # 0.50
'code_sample': CONFIDENCE_LEVELS['nxopen_tse'] # 0.70
}
)
print("\n[Test 5a] Synthesizing from 3 sources...")
print(f" Sources: {list(findings.sources.keys())}")
print(f" Confidence scores:")
for source, score in findings.confidence_scores.items():
print(f" - {source}: {score:.2f}")
knowledge = agent.synthesize_knowledge(findings)
print(f"\n Overall confidence: {knowledge.confidence:.2f}")
print(f" Total patterns: {len(knowledge.patterns)}")
print(f" Schema elements: {len(knowledge.schema) if knowledge.schema else 0}")
# Weighted confidence should be dominated by high-confidence user example
assert knowledge.confidence > 0.7, "Should have high confidence with user-validated source"
assert knowledge.schema is not None, "Should extract schema from XML"
assert len(knowledge.patterns) > 0, "Should extract patterns from code"
print("\n ✓ PASSED: Successfully synthesized multi-source knowledge")
def run_all_tests():
"""Run all Research Agent tests."""
print("\n" + "="*60)
print("=" + " "*58 + "=")
print("=" + " RESEARCH AGENT TEST SUITE - Phase 2".center(58) + "=")
print("=" + " "*58 + "=")
print("="*60)
try:
test_knowledge_gap_detection()
test_xml_schema_learning()
test_python_code_pattern_extraction()
test_research_session_documentation()
test_multi_source_synthesis()
print("\n" + "="*60)
print("ALL TESTS PASSED! ✓")
print("="*60)
print("\nResearch Agent is functional and ready for use.")
print("\nNext steps:")
print(" 1. Integrate with LLM interface for interactive research")
print(" 2. Add web search capability (Phase 2 Week 2)")
print(" 3. Implement feature generation from learned templates")
print(" 4. Build knowledge retrieval system")
print()
return True
except AssertionError as e:
print(f"\n✗ TEST FAILED: {e}")
import traceback
traceback.print_exc()
return False
except Exception as e:
print(f"\n✗ UNEXPECTED ERROR: {e}")
import traceback
traceback.print_exc()
return False
if __name__ == '__main__':
success = run_all_tests()
sys.exit(0 if success else 1)

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"""
Test Step Classifier - Phase 2.6
Tests the intelligent classification of workflow steps into:
- Engineering features (need research/documentation)
- Inline calculations (auto-generate simple math)
- Post-processing hooks (middleware scripts)
"""
import sys
from pathlib import Path
# Set UTF-8 encoding for Windows console
if sys.platform == 'win32':
import codecs
if not isinstance(sys.stdout, codecs.StreamWriter):
if hasattr(sys.stdout, 'buffer'):
sys.stdout = codecs.getwriter('utf-8')(sys.stdout.buffer, errors='replace')
sys.stderr = codecs.getwriter('utf-8')(sys.stderr.buffer, errors='replace')
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
from optimization_engine.workflow_decomposer import WorkflowDecomposer
from optimization_engine.step_classifier import StepClassifier
def main():
print("=" * 80)
print("PHASE 2.6 TEST: Intelligent Step Classification")
print("=" * 80)
print()
# Test with CBUSH optimization request
request = """I want to extract forces in direction Z of all the 1D elements and find the average of it,
then find the maximum value and compare it to the average, then assign it to a objective metric that needs to be minimized.
I want to iterate on the FEA properties of the Cbush element stiffness in Z to make the objective function minimized.
I want to use optuna with TPE to iterate and optimize this"""
print("User Request:")
print(request)
print()
print("=" * 80)
print()
# Initialize
decomposer = WorkflowDecomposer()
classifier = StepClassifier()
# Step 1: Decompose workflow
print("[1] Decomposing Workflow")
print("-" * 80)
steps = decomposer.decompose(request)
print(f"Identified {len(steps)} workflow steps:")
print()
for i, step in enumerate(steps, 1):
print(f" {i}. {step.action.replace('_', ' ').title()}")
print(f" Domain: {step.domain}")
print(f" Params: {step.params}")
print()
# Step 2: Classify steps
print()
print("[2] Classifying Steps")
print("-" * 80)
classified = classifier.classify_workflow(steps, request)
# Display classification summary
print(classifier.get_summary(classified))
print()
# Step 3: Analysis
print()
print("[3] Intelligence Analysis")
print("-" * 80)
print()
eng_count = len(classified['engineering_features'])
inline_count = len(classified['inline_calculations'])
hook_count = len(classified['post_processing_hooks'])
print(f"Total Steps: {len(steps)}")
print(f" Engineering Features: {eng_count} (need research/documentation)")
print(f" Inline Calculations: {inline_count} (auto-generate Python)")
print(f" Post-Processing Hooks: {hook_count} (generate middleware)")
print()
print("What This Means:")
if eng_count > 0:
print(f" - Research needed for {eng_count} FEA/CAE operations")
print(f" - Create documented features for reuse")
if inline_count > 0:
print(f" - Auto-generate {inline_count} simple math operations")
print(f" - No documentation overhead needed")
if hook_count > 0:
print(f" - Generate {hook_count} post-processing scripts")
print(f" - Execute between engineering steps")
print()
# Step 4: Show expected behavior
print()
print("[4] Expected Atomizer Behavior")
print("-" * 80)
print()
print("When user makes this request, Atomizer should:")
print()
if eng_count > 0:
print(" 1. RESEARCH & DOCUMENT (Engineering Features):")
for item in classified['engineering_features']:
step = item['step']
print(f" - {step.action} ({step.domain})")
print(f" > Search pyNastran docs for element force extraction")
print(f" > Create feature file with documentation")
print()
if inline_count > 0:
print(" 2. AUTO-GENERATE (Inline Calculations):")
for item in classified['inline_calculations']:
step = item['step']
print(f" - {step.action}")
print(f" > Generate Python: avg = sum(forces) / len(forces)")
print(f" > No feature file created")
print()
if hook_count > 0:
print(" 3. CREATE HOOK (Post-Processing):")
for item in classified['post_processing_hooks']:
step = item['step']
print(f" - {step.action}")
print(f" > Generate hook script with proper I/O")
print(f" > Execute between solve and optimize steps")
print()
print(" 4. EXECUTE WORKFLOW:")
print(" - Extract 1D element forces (FEA feature)")
print(" - Calculate avg/max/compare (inline Python)")
print(" - Update CBUSH stiffness (FEA feature)")
print(" - Optimize with Optuna TPE (existing feature)")
print()
print("=" * 80)
print("TEST COMPLETE")
print("=" * 80)
print()
if __name__ == '__main__':
main()