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Atomizer/optimization_engine/plugins/post_extraction/optimization_logger_results.py

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feat: Complete Phase 2.5-2.7 - Intelligent LLM-Powered Workflow Analysis 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>
2025-11-16 13:35:41 -05:00
"""
Optimization-Level Logger Hook - Results
Appends trial results to the high-level optimization.log file.
Hook Point: post_extraction
"""
from pathlib import Path
from datetime import datetime
from typing import Dict, Any, Optional
import logging
logger = logging.getLogger(__name__)
def log_optimization_results(context: Dict[str, Any]) -> Optional[Dict[str, Any]]:
"""
Append trial results to the main optimization.log file.
This hook completes the trial entry in the high-level log with:
- Objective values
- Constraint evaluations
- Trial outcome (feasible/infeasible)
Args:
context: Hook context containing:
- trial_number: Current trial number
- extracted_results: Dict of all extracted objectives and constraints
- result_path: Path to result file
Returns:
None (logging only)
"""
trial_num = context.get('trial_number', '?')
extracted_results = context.get('extracted_results', {})
result_path = context.get('result_path', '')
# Get the output directory from context (passed by runner)
output_dir = Path(context.get('output_dir', 'optimization_results'))
log_file = output_dir / 'optimization.log'
if not log_file.exists():
logger.warning(f"Optimization log file not found: {log_file}")
return None
# Find the last line for this trial and append results
with open(log_file, 'a') as f:
timestamp = datetime.now().strftime('%H:%M:%S')
# Extract objective and constraint values
results_str = " | ".join([f"{name}={value:.3f}" for name, value in extracted_results.items()])
f.write(f"[{timestamp}] Trial {trial_num:3d} COMPLETE | {results_str}\n")
return None
def register_hooks(hook_manager):
"""
Register this plugin's hooks with the manager.
This function is called automatically when the plugin is loaded.
"""
hook_manager.register_hook(
hook_point='post_extraction',
function=log_optimization_results,
description='Append trial results to optimization.log',
name='optimization_logger_results',
priority=100
)
# Hook metadata
HOOK_NAME = "optimization_logger_results"
HOOK_POINT = "post_extraction"
ENABLED = True
PRIORITY = 100