refactor: Major reorganization of optimization_engine module structure
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>
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optimization_engine/future/targeted_research_planner.py
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optimization_engine/future/targeted_research_planner.py
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"""
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Targeted Research Planner
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Creates focused research plans that target ONLY the actual knowledge gaps,
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leveraging similar existing capabilities when available.
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Author: Atomizer Development Team
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Version: 0.1.0 (Phase 2.5)
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Last Updated: 2025-01-16
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"""
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from typing import List, Dict, Any
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from pathlib import Path
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from optimization_engine.config.capability_matcher import CapabilityMatch, StepMatch
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class TargetedResearchPlanner:
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"""Creates research plan focused on actual gaps."""
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def __init__(self):
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pass
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def plan(self, capability_match: CapabilityMatch) -> List[Dict[str, Any]]:
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"""
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Create targeted research plan for missing capabilities.
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For gap='strain_from_op2', similar_to='stress_from_op2':
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Research Plan:
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1. Read existing op2_extractor_example.py to understand pattern
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2. Search pyNastran docs for strain extraction API
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3. If not found, ask user for strain extraction example
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4. Generate extract_strain() function following same pattern as extract_stress()
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"""
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if not capability_match.unknown_steps:
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return []
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research_steps = []
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for unknown_step in capability_match.unknown_steps:
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steps_for_this_gap = self._plan_for_gap(unknown_step)
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research_steps.extend(steps_for_this_gap)
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return research_steps
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def _plan_for_gap(self, step_match: StepMatch) -> List[Dict[str, Any]]:
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"""Create research plan for a single gap."""
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step = step_match.step
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similar = step_match.similar_capabilities
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plan_steps = []
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# If we have similar capabilities, start by studying them
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if similar:
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plan_steps.append({
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'action': 'read_existing_code',
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'description': f'Study existing {similar[0]} implementation to understand pattern',
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'details': {
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'capability': similar[0],
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'category': step.domain,
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'purpose': f'Learn pattern for {step.action}'
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},
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'expected_confidence': 0.7,
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'priority': 1
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})
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# Search knowledge base for previous similar work
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plan_steps.append({
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'action': 'search_knowledge_base',
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'description': f'Search for previous {step.domain} work',
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'details': {
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'query': f"{step.domain} {step.action}",
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'required_params': step.params
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},
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'expected_confidence': 0.8 if similar else 0.5,
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'priority': 2
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})
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# For result extraction, search pyNastran docs
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if step.domain == 'result_extraction':
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result_type = step.params.get('result_type', '')
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plan_steps.append({
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'action': 'search_pynastran_docs',
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'description': f'Search pyNastran documentation for {result_type} extraction',
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'details': {
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'query': f'pyNastran OP2 {result_type} extraction',
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'library': 'pyNastran',
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'expected_api': f'op2.{result_type}s or similar'
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},
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'expected_confidence': 0.85,
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'priority': 3
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})
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# For simulation, search NX docs
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elif step.domain == 'simulation':
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solver = step.params.get('solver', '')
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plan_steps.append({
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'action': 'query_nx_docs',
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'description': f'Search NX documentation for {solver}',
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'details': {
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'query': f'NX Nastran {solver} solver',
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'solver_type': solver
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},
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'expected_confidence': 0.85,
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'priority': 3
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})
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# As fallback, ask user for example
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plan_steps.append({
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'action': 'ask_user_for_example',
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'description': f'Request example from user for {step.action}',
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'details': {
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'prompt': f"Could you provide an example of {step.action.replace('_', ' ')}?",
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'suggested_file_types': self._get_suggested_file_types(step.domain),
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'params_needed': step.params
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},
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'expected_confidence': 0.95, # User examples have high confidence
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'priority': 4
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})
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return plan_steps
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def _get_suggested_file_types(self, domain: str) -> List[str]:
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"""Get suggested file types for user examples based on domain."""
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suggestions = {
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'materials': ['.xml', '.mtl'],
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'geometry': ['.py', '.prt'],
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'loads_bc': ['.py', '.xml'],
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'mesh': ['.py', '.dat'],
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'result_extraction': ['.py', '.txt'],
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'optimization': ['.py', '.json']
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}
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return suggestions.get(domain, ['.py', '.txt'])
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def get_plan_summary(self, plan: List[Dict[str, Any]]) -> str:
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"""Get human-readable summary of research plan."""
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if not plan:
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return "No research needed - all capabilities are known!"
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lines = [
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"Targeted Research Plan",
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"=" * 80,
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"",
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f"Research steps needed: {len(plan)}",
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""
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]
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current_gap = None
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for i, step in enumerate(plan, 1):
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# Group by action for clarity
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if step['action'] != current_gap:
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current_gap = step['action']
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lines.append(f"\nStep {i}: {step['description']}")
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lines.append("-" * 80)
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else:
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lines.append(f"\nStep {i}: {step['description']}")
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lines.append(f" Action: {step['action']}")
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if 'details' in step:
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if 'capability' in step['details']:
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lines.append(f" Study: {step['details']['capability']}")
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if 'query' in step['details']:
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lines.append(f" Query: \"{step['details']['query']}\"")
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if 'prompt' in step['details']:
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lines.append(f" Prompt: \"{step['details']['prompt']}\"")
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lines.append(f" Expected confidence: {step['expected_confidence']:.0%}")
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lines.append("")
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lines.append("=" * 80)
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# Add strategic summary
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lines.append("\nResearch Strategy:")
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lines.append("-" * 80)
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has_existing_code = any(s['action'] == 'read_existing_code' for s in plan)
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if has_existing_code:
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lines.append(" - Will adapt from existing similar code patterns")
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lines.append(" - Lower risk: Can follow proven implementation")
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else:
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lines.append(" - New domain: Will need to research from scratch")
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lines.append(" - Higher risk: No existing patterns to follow")
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return "\n".join(lines)
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def main():
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"""Test the targeted research planner."""
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from optimization_engine.utils.codebase_analyzer import CodebaseCapabilityAnalyzer
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from optimization_engine.future.workflow_decomposer import WorkflowDecomposer
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from optimization_engine.config.capability_matcher import CapabilityMatcher
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print("Targeted Research Planner Test")
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print("=" * 80)
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print()
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# Initialize components
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analyzer = CodebaseCapabilityAnalyzer()
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decomposer = WorkflowDecomposer()
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matcher = CapabilityMatcher(analyzer)
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planner = TargetedResearchPlanner()
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# Test with strain optimization request
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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"
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print("Request:")
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print(test_request)
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print()
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# Full pipeline
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print("Phase 2.5 Pipeline:")
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print("-" * 80)
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print("1. Decompose workflow...")
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steps = decomposer.decompose(test_request)
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print(f" Found {len(steps)} workflow steps")
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print("\n2. Match to codebase capabilities...")
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match = matcher.match(steps)
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print(f" Known: {len(match.known_steps)}/{len(steps)}")
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print(f" Unknown: {len(match.unknown_steps)}/{len(steps)}")
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print(f" Overall confidence: {match.overall_confidence:.0%}")
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print("\n3. Create targeted research plan...")
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plan = planner.plan(match)
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print(f" Generated {len(plan)} research steps")
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print("\n" + "=" * 80)
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print()
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# Display the plan
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print(planner.get_plan_summary(plan))
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# Show what's being researched
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print("\n\nWhat will be researched:")
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print("-" * 80)
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for unknown_step in match.unknown_steps:
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step = unknown_step.step
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print(f" Missing: {step.action} ({step.domain})")
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print(f" Required params: {step.params}")
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if unknown_step.similar_capabilities:
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print(f" Can adapt from: {', '.join(unknown_step.similar_capabilities)}")
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print()
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print("\nWhat will NOT be researched (already known):")
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print("-" * 80)
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for known_step in match.known_steps:
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step = known_step.step
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print(f" - {step.action} ({step.domain})")
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print()
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if __name__ == '__main__':
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main()
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