Antoine/Atomizer
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

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

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

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

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

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

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

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

- Update .gitignore to exclude docs/generated/

- Version bump: ATOMIZER_CONTEXT v1.8 -> v2.0
This commit is contained in:
Anto01
2026-01-20 10:03:45 -05:00
parent 37f73cc2be
commit ea437d360e
103 changed files with 8980 additions and 327 deletions
Download Patch File Download Diff File Expand all files Collapse all files

346
docs/archive/phase_documents/PHASE_3_2_INTEGRATION_STATUS.md Normal file
View File

@@ -0,0 +1,346 @@
# Phase 3.2 Integration Status
> **Date**: 2025-11-17
> **Status**: Partially Complete - Framework Ready, API Integration Pending
---
## Overview
Phase 3.2 aims to integrate the LLM components (Phases 2.5-3.1) into the production optimization workflow, enabling users to run optimizations using natural language requests.
**Goal**: Enable users to run:
```bash
python run_optimization.py --llm "maximize displacement, ensure safety factor > 4"
```
---
## What's Been Completed ✅
### 1. Generic Optimization Runner (`optimization_engine/run_optimization.py`)
**Created**: 2025-11-17
A flexible, command-line driven optimization runner supporting both LLM and manual modes:
```bash
# LLM Mode (Natural Language)
python optimization_engine/run_optimization.py \
--llm "maximize displacement, ensure safety factor > 4" \
--prt model/Bracket.prt \
--sim model/Bracket_sim1.sim \
--trials 20
# Manual Mode (JSON Config)
python optimization_engine/run_optimization.py \
--config config.json \
--prt model/Bracket.prt \
--sim model/Bracket_sim1.sim \
--trials 50
```
**Features**:
- ✅ Command-line argument parsing (`--llm`, `--config`, `--prt`, `--sim`, etc.)
- ✅ Integration with `LLMWorkflowAnalyzer` for natural language parsing
- ✅ Integration with `LLMOptimizationRunner` for automated extractor/hook generation
- ✅ Proper error handling and user feedback
- ✅ Comprehensive help message with examples
- ✅ Flexible output directory and study naming
**Files**:
- [optimization_engine/run_optimization.py](../optimization_engine/run_optimization.py) - Generic runner
- [tests/test_phase_3_2_llm_mode.py](../tests/test_phase_3_2_llm_mode.py) - Integration tests
### 2. Test Suite
**Test Results**: ✅ All tests passing
Tests verify:
- Argument parsing works correctly
- Help message displays `--llm` flag
- Framework is ready for LLM integration
---
## Current Limitation ⚠️
### LLM Workflow Analysis Requires API Key
The `LLMWorkflowAnalyzer` currently requires an Anthropic API key to actually parse natural language requests. The `use_claude_code` flag exists but **doesn't implement actual integration** with Claude Code's AI capabilities.
**Current Behavior**:
- `--llm` mode is implemented in the CLI
- But `LLMWorkflowAnalyzer.analyze_request()` returns empty workflow when `use_claude_code=True` and no API key provided
- Actual LLM analysis requires `--api-key` argument
**Workaround Options**:
#### Option 1: Use Anthropic API Key
```bash
python run_optimization.py \
--llm "maximize displacement" \
--prt model/part.prt \
--sim model/sim.sim \
--api-key "sk-ant-..."
```
#### Option 2: Pre-Generate Workflow JSON (Hybrid Approach)
1. Use Claude Code to help create workflow JSON manually
2. Save as `llm_workflow.json`
3. Load and use with `LLMOptimizationRunner`
Example:
```python
# In your study's run_optimization.py
from optimization_engine.llm_optimization_runner import LLMOptimizationRunner
import json
# Load pre-generated workflow (created with Claude Code assistance)
with open('llm_workflow.json', 'r') as f:
llm_workflow = json.load(f)
# Run optimization with LLM runner
runner = LLMOptimizationRunner(
llm_workflow=llm_workflow,
model_updater=model_updater,
simulation_runner=simulation_runner,
study_name='my_study'
)
results = runner.run_optimization(n_trials=20)
```
#### Option 3: Use Existing Study Scripts
The bracket study's `run_optimization.py` already demonstrates the complete workflow with hardcoded configuration - this works perfectly!
---
## Architecture
### LLM Mode Flow (When API Key Provided)
```
User Natural Language Request
↓
LLMWorkflowAnalyzer (Phase 2.7)
├─> Claude API call
└─> Parse to structured workflow JSON
↓
LLMOptimizationRunner (Phase 3.2)
├─> ExtractorOrchestrator (Phase 3.1) → Auto-generate extractors
├─> InlineCodeGenerator (Phase 2.8) → Auto-generate calculations
├─> HookGenerator (Phase 2.9) → Auto-generate hooks
└─> Run Optuna optimization with generated code
↓
Results
```
### Manual Mode Flow (Current Working Approach)
```
Hardcoded Workflow JSON (or manually created)
↓
LLMOptimizationRunner (Phase 3.2)
├─> ExtractorOrchestrator → Auto-generate extractors
├─> InlineCodeGenerator → Auto-generate calculations
├─> HookGenerator → Auto-generate hooks
└─> Run Optuna optimization
↓
Results
```
---
## What Works Right Now
### ✅ **LLM Components are Functional**
All individual components work and are tested:
1. **Phase 2.5**: Intelligent Gap Detection ✅
2. **Phase 2.7**: LLM Workflow Analysis (requires API key) ✅
3. **Phase 2.8**: Inline Code Generator ✅
4. **Phase 2.9**: Hook Generator ✅
5. **Phase 3.0**: pyNastran Research Agent ✅
6. **Phase 3.1**: Extractor Orchestrator ✅
7. **Phase 3.2**: LLM Optimization Runner ✅
### ✅ **Generic CLI Runner**
The new `run_optimization.py` provides:
- Clean command-line interface
- Argument validation
- Error handling
- Comprehensive help
### ✅ **Bracket Study Demonstrates End-to-End Workflow**
[studies/bracket_displacement_maximizing/run_optimization.py](../studies/bracket_displacement_maximizing/run_optimization.py) shows the complete integration:
- Wizard-based setup (Phase 3.3)
- LLMOptimizationRunner with hardcoded workflow
- Auto-generated extractors and hooks
- Real NX simulations
- Complete results with reports
---
## Next Steps to Complete Phase 3.2
### Short Term (Can Do Now)
1. **Document Hybrid Approach** ✅ (This document!)
- Show how to use Claude Code to create workflow JSON
- Example workflow JSON templates for common use cases
2. **Create Example Workflow JSONs**
- `examples/llm_workflows/maximize_displacement.json`
- `examples/llm_workflows/minimize_stress.json`
- `examples/llm_workflows/multi_objective.json`
3. **Update DEVELOPMENT_GUIDANCE.md**
- Mark Phase 3.2 as "Partially Complete"
- Document the API key requirement
- Provide hybrid approach guidance
### Medium Term (Requires Decision)
**Option A: Implement True Claude Code Integration**
- Modify `LLMWorkflowAnalyzer` to actually interface with Claude Code
- Would require understanding Claude Code's internal API/skill system
- Most aligned with "Development Strategy" (use Claude Code, defer API integration)
**Option B: Defer Until API Integration is Priority**
- Document current state as "Framework Ready"
- Focus on other high-priority items (NXOpen docs, Engineering pipeline)
- Return to full LLM integration when ready to integrate Anthropic API
**Option C: Hybrid Approach (Recommended for Now)**
- Keep generic CLI runner as-is
- Document how to use Claude Code to manually create workflow JSONs
- Use `LLMOptimizationRunner` with pre-generated workflows
- Provides 90% of the value with 10% of the complexity
---
## Recommendation
**For now, adopt Option C (Hybrid Approach)**:
### Why:
1. **Development Strategy Alignment**: We're using Claude Code for development, not integrating API yet
2. **Provides Value**: All automation components (extractors, hooks, calculations) work perfectly
3. **No Blocker**: Users can still leverage LLM components via pre-generated workflows
4. **Flexible**: Can add full API integration later without changing architecture
5. **Focus**: Allows us to prioritize Phase 3.3+ items (NXOpen docs, Engineering pipeline)
### What This Means:
- ✅ Phase 3.2 is "Framework Complete"
- ⚠️ Full natural language CLI requires API key (documented limitation)
- ✅ Hybrid approach (Claude Code → JSON → LLMOptimizationRunner) works today
- 🎯 Can return to full integration when API integration becomes priority
---
## Example: Using Hybrid Approach
### Step 1: Create Workflow JSON (with Claude Code assistance)
```json
{
"engineering_features": [
{
"action": "extract_displacement",
"domain": "result_extraction",
"description": "Extract displacement results from OP2 file",
"params": {"result_type": "displacement"}
},
{
"action": "extract_solid_stress",
"domain": "result_extraction",
"description": "Extract von Mises stress from CTETRA elements",
"params": {
"result_type": "stress",
"element_type": "ctetra"
}
}
],
"inline_calculations": [
{
"action": "calculate_safety_factor",
"params": {
"input": "max_von_mises",
"yield_strength": 276.0,
"operation": "divide"
},
"code_hint": "safety_factor = 276.0 / max_von_mises"
}
],
"post_processing_hooks": [],
"optimization": {
"algorithm": "TPE",
"direction": "minimize",
"design_variables": [
{
"parameter": "thickness",
"min": 3.0,
"max": 10.0,
"units": "mm"
}
]
}
}
```
### Step 2: Use in Python Script
```python
import json
from pathlib import Path
from optimization_engine.llm_optimization_runner import LLMOptimizationRunner
from optimization_engine.nx_updater import NXParameterUpdater
from optimization_engine.nx_solver import NXSolver
# Load pre-generated workflow
with open('llm_workflow.json', 'r') as f:
workflow = json.load(f)
# Setup model updater
updater = NXParameterUpdater(prt_file_path=Path("model/part.prt"))
def model_updater(design_vars):
updater.update_expressions(design_vars)
updater.save()
# Setup simulation runner
solver = NXSolver(nastran_version='2412', use_journal=True)
def simulation_runner(design_vars) -> Path:
result = solver.run_simulation(Path("model/sim.sim"), expression_updates=design_vars)
return result['op2_file']
# Run optimization
runner = LLMOptimizationRunner(
llm_workflow=workflow,
model_updater=model_updater,
simulation_runner=simulation_runner,
study_name='my_optimization'
)
results = runner.run_optimization(n_trials=20)
print(f"Best design: {results['best_params']}")
```
---
## References
- [DEVELOPMENT_GUIDANCE.md](../DEVELOPMENT_GUIDANCE.md) - Strategic direction
- [optimization_engine/run_optimization.py](../optimization_engine/run_optimization.py) - Generic CLI runner
- [optimization_engine/llm_optimization_runner.py](../optimization_engine/llm_optimization_runner.py) - LLM runner
- [optimization_engine/llm_workflow_analyzer.py](../optimization_engine/llm_workflow_analyzer.py) - Workflow analyzer
- [studies/bracket_displacement_maximizing/run_optimization.py](../studies/bracket_displacement_maximizing/run_optimization.py) - Complete example
---
**Document Maintained By**: Antoine Letarte
**Last Updated**: 2025-11-17
**Status**: Framework Complete, API Integration Pending