BREAKING CHANGE: Module paths have been reorganized for better maintainability. Backwards compatibility aliases with deprecation warnings are provided. New Structure: - core/ - Optimization runners (runner, intelligent_optimizer, etc.) - processors/ - Data processing - surrogates/ - Neural network surrogates - nx/ - NX/Nastran integration (solver, updater, session_manager) - study/ - Study management (creator, wizard, state, reset) - reporting/ - Reports and analysis (visualizer, report_generator) - config/ - Configuration management (manager, builder) - utils/ - Utilities (logger, auto_doc, etc.) - future/ - Research/experimental code Migration: - ~200 import changes across 125 files - All __init__.py files use lazy loading to avoid circular imports - Backwards compatibility layer supports old import paths with warnings - All existing functionality preserved To migrate existing code: OLD: from optimization_engine.nx_solver import NXSolver NEW: from optimization_engine.nx.solver import NXSolver OLD: from optimization_engine.runner import OptimizationRunner NEW: from optimization_engine.core.runner import OptimizationRunner 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
184 lines
6.1 KiB
Python
184 lines
6.1 KiB
Python
"""
|
|
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.future.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)
|