# Release v2.5.0 - Handover Report

**Date:** 2025-12-05
**Version:** 2.5.0
**Status:** Production Release
**Author:** Joerg Bollwahn

## Executive Summary

Release v2.5.0 represents a **major milestone** in memory optimization, achieving a **96% reduction in baseline memory footprint** (from 1.3 GB to 53.9 MB). This release introduces ONNX Runtime integration, optional dependency management, and a lazy-loading architecture while maintaining full backward compatibility.

### Key Achievements

- **Memory Optimization:** 96% baseline reduction (1.3 GB → 53.9 MB)
- **ONNX Integration:** CUDA-enabled semantic guard eliminates PyTorch dependency
- **Optional Dependencies:** Core installation requires no heavy ML libraries
- **Lazy Loading:** Heavy validators load only when needed
- **Backward Compatibility:** Public API unchanged, no breaking changes

## Release Status

### Git & Version Control

- **Commit:** `e01b54f` - "Release v2.5.0: 96% memory reduction, ONNX integration, optional dependencies"
- **Tag:** `v2.5.0` - Pushed to `origin/main`
- **Branch:** `main` (up to date with remote)

### PyPI Packages

- **Wheel:** `llm_security_firewall-2.5.0-py3-none-any.whl` (517 KB)
- **Source:** `llm_security_firewall-2.5.0.tar.gz` (972 KB)
- **Status:** Built successfully, ready for upload
- **Upload Command:** `twine upload dist/llm_security_firewall-2.5.0*`

### Documentation

- **CHANGELOG.md:** Updated with v2.5.0 highlights
- **README.md:** Installation options documented
- **MEMORY_OPTIMIZATION_P0_PROGRESS.md:** Complete optimization journey documented
- **DEPENDENCY_ELIMINATION_PLAN.md:** Strategy and results documented

## Technical Changes

### 1. Memory Optimization Architecture

**Problem:** Baseline memory footprint was 1.3 GB, exceeding the 300 MB target by 4.3x.

**Solution:** Multi-layered optimization approach:

1. **Lazy Loading (482.8 MB saved)**
   - Converted ML component initialization to `@property` decorators
   - Components load only when first accessed
   - Implemented in `kids_policy/firewall_engine_v2.py`

2. **Tokenizer Replacement (376.7 MB saved)**
   - Replaced `transformers.AutoTokenizer` with `tokenizers` library
   - Eliminated transitive PyTorch imports
   - Implemented in `semantic_grooming_guard_onnx.py`

3. **Dependency Elimination (726 MB saved)**
   - Moved `torch`, `transformers`, `sentence-transformers` to optional dependencies
   - Created `requirements-core.txt` for minimal installation
   - Updated `pyproject.toml` with `[project.optional-dependencies.full]`

4. **ONNX Integration**
   - Implemented `SemanticGroomingGuardONNX` with CUDA support
   - Eliminated PyTorch runtime dependency in core path
   - Model: `models/onnx/all-MiniLM-L6-v2.onnx` (86.79 MB)

### 2. Files Modified

**Core Changes:**
- `pyproject.toml`: Version 2.5.0, optional dependencies restructured
- `src/llm_firewall/__init__.py`: Version 2.5.0
- `kids_policy/firewall_engine_v2.py`: Lazy loading + monitoring
- `kids_policy/__init__.py`: Removed direct imports (lazy loading)
- `kids_policy/truth_preservation/validators/semantic_grooming_guard_onnx.py`: ONNX implementation

**New Files:**
- `requirements-core.txt`: Minimal installation path
- `kids_policy/truth_preservation/validators/semantic_grooming_guard_onnx.py`: ONNX guard
- `scripts/export_to_onnx.py`: Model export script
- `docs/MEMORY_OPTIMIZATION_P0_PROGRESS.md`: Optimization documentation
- `docs/DEPENDENCY_ELIMINATION_PLAN.md`: Strategy document

**Documentation:**
- `CHANGELOG.md`: v2.5.0 release notes
- `README.md`: Updated installation instructions

### 3. API Changes

**No Breaking Changes:**
- `guard.check_input()`: Unchanged
- `guard.check_output()`: Unchanged
- `HakGalFirewall_v2`: Backward compatible

**New Features:**
- `get_lazy_load_stats()`: Monitor lazy-loaded components
- Optional dependency groups: `pip install llm-security-firewall[full]`

## Installation Options

### Core Installation (Recommended)

```bash
pip install llm-security-firewall
# OR
pip install -r requirements-core.txt
```

**Memory Footprint:** ~54 MB baseline
**Features:** Pattern matching, ONNX semantic guard, basic validation

### Full ML Features (Optional)

```bash
pip install llm-security-firewall[full]
```

**Memory Footprint:** Heavy validators available on-demand
**Features:** All ML validators (TruthPreservationValidator, TopicFence) load when needed

## Performance Metrics

### Memory Baseline Comparison

| Metric | Before (v2.4.1) | After (v2.5.0) | Reduction |
|--------|------------------|----------------|-----------|
| **Firewall Baseline** | ~1,327 MB | **53.9 MB** | **96%** |
| **Import Cost (PyTorch)** | 1,077.9 MB | 0 MB (optional) | 100% |
| **Import Cost (ONNX)** | N/A | 19.3 MB | - |
| **Tokenizer Cost** | 386.1 MB | 9.4 MB | 97.6% |
| **Lazy Loading Init** | 482.8 MB | 0 MB | 100% |

### Component Memory Footprint

| Component | Memory (MB) | Status |
|-----------|-------------|--------|
| Baseline (Core) | 53.9 | Target met |
| ONNX Semantic Guard | 18.8 | On-demand |
| TruthPreservationValidator | 24.1 | On-demand |
| TopicFence | Variable | On-demand |

## Architecture Overview

### Lazy Loading Pattern

All heavy ML components use `@property` decorators for lazy initialization:

```python
@property
def semantic(self) -> Optional[Any]:
    if "semantic" not in self._component_cache:
        # Load ONNX version first (CUDA-enabled)
        if HAS_SEMANTIC_GUARD_ONNX:
            onnx_guard = SemanticGroomingGuardONNX()
            if onnx_guard._is_available:
                self._component_cache["semantic"] = onnx_guard
        # Fallback to PyTorch if ONNX unavailable
        elif HAS_SEMANTIC_GUARD:
            self._component_cache["semantic"] = SemanticGroomingGuard()
    return self._component_cache.get("semantic")
```

### Monitoring

Track lazy-loaded components:

```python
stats = firewall.get_lazy_load_stats()
# Returns: {
#   "loaded_components": ["semantic", "truth_validator"],
#   "load_timestamps": {"semantic": 1764877456.9694107},
#   "total_loaded": 2,
#   "monitoring_enabled": True
# }
```

## Known Limitations

1. **ONNX Runtime Overhead:** ONNX version uses 18.8 MB vs 8.9 MB PyTorch (expected: ONNX Runtime overhead)
2. **CUDA Requirements:** CUDA support requires `onnxruntime-gpu` and CUDA libraries in PATH
3. **Tokenizer Dependency:** Tokenizer JSON file must be present at `models/tokenizer/all-MiniLM-L6-v2/tokenizer.json`
4. **Optional Dependencies:** Heavy validators require `pip install llm-security-firewall[full]`

## Testing Status

### Validation Completed

- **API Compatibility:** `guard.check_input()` and `guard.check_output()` tested
- **Lazy Loading:** Components load correctly on first access
- **ONNX Integration:** Semantic guard works with CUDA and CPU fallback
- **Memory Profiling:** Baseline confirmed at 53.9 MB
- **Code Quality:** Pre-commit hooks passed (mypy, ruff, bandit)

### Test Coverage

- Core functionality: Tested
- ONNX path: Tested
- PyTorch fallback: Tested
- Lazy loading: Tested
- Monitoring: Tested

## Next Steps (Post-Release)

### Immediate (P0)

1. **PyPI Upload:** Execute `twine upload dist/llm_security_firewall-2.5.0*`
2. **GitHub Release:** Create release from tag `v2.5.0` with CHANGELOG content
3. **Documentation:** Verify installation instructions work for new users

### Short-term (P1)

1. **ONNX Migration:** Migrate remaining components (TopicFence, AgeStratifiedNLI) to ONNX
2. **Performance Tuning:** Optimize ONNX model size (currently 86.79 MB, could be quantized)
3. **Monitoring Integration:** Connect lazy-loading stats to Prometheus/metrics if needed

### Long-term (P2)

1. **Self-Improvement Loop:** Implement automatic evaluation and model updates
2. **Uncertainty Classification:** Implement `UNCERTAIN` state for advanced escalation logic
3. **Performance Optimization:** Further reduce ONNX Runtime overhead

## Critical Files & References

### Documentation

- `CHANGELOG.md`: Full release notes
- `docs/MEMORY_OPTIMIZATION_P0_PROGRESS.md`: Optimization journey
- `docs/DEPENDENCY_ELIMINATION_PLAN.md`: Strategy and results
- `README.md`: Installation and usage guide

### Code

- `kids_policy/firewall_engine_v2.py`: Core lazy-loading implementation
- `kids_policy/truth_preservation/validators/semantic_grooming_guard_onnx.py`: ONNX guard
- `pyproject.toml`: Dependency management
- `requirements-core.txt`: Minimal installation

### Scripts

- `scripts/export_to_onnx.py`: Model export script
- `scripts/memory_profiling/`: Memory analysis tools

### Models

- `models/onnx/all-MiniLM-L6-v2.onnx`: ONNX model (86.79 MB)
- `models/tokenizer/all-MiniLM-L6-v2/tokenizer.json`: Tokenizer vocabulary

## Troubleshooting

### Issue: ONNX Model Not Found

**Error:** `ONNX model not initialized`

**Solution:**
1. Verify model exists: `models/onnx/all-MiniLM-L6-v2.onnx`
2. Run export script: `python scripts/export_to_onnx.py`
3. Check file permissions

### Issue: CUDA Not Available

**Warning:** `CUDAExecutionProvider not available, using CPU`

**Solution:**
1. Install `onnxruntime-gpu`: `pip install onnxruntime-gpu`
2. Verify CUDA libraries in PATH
3. CPU fallback works automatically (no action needed)

### Issue: Tokenizer Not Found

**Error:** `tokenizer.json not found`

**Solution:**
1. Extract tokenizer from Hugging Face cache
2. Run: `python -c "from transformers import AutoTokenizer; AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2').save_pretrained('models/tokenizer/all-MiniLM-L6-v2')"`
3. Verify `tokenizer.json` exists

### Issue: High Memory Usage

**Symptom:** Memory still > 100 MB

**Diagnosis:**
1. Check if `[full]` dependencies installed: `pip list | grep torch`
2. Verify lazy loading: `firewall.get_lazy_load_stats()`
3. Check if components loaded: `stats['loaded_components']`

## Contact & Support

- **Author:** Joerg Bollwahn
- **Email:** sookoothaii@proton.me
- **Repository:** https://github.com/sookoothaii/llm-security-firewall
- **Version:** 2.5.0

## Release Checklist

- Version numbers updated (pyproject.toml, __init__.py, README.md)
- CHANGELOG.md updated with v2.5.0 highlights
- Git commit created with release message
- Git tag v2.5.0 created and pushed
- PyPI packages built successfully
- Code quality checks passed (pre-commit hooks)
- Backward compatibility verified
- Documentation updated
- PyPI upload executed (pending)
- GitHub release created (pending)
- Release announcement (pending)

---

**End of Handover Report**

*This document serves as a complete handover for Release v2.5.0. All critical information, technical details, and next steps are documented above.*
