# Clean Codette Repository - GitHub Setup ## Summary This is a fresh, clean Codette repository containing: - **Core Reasoning Engine** (reasoning_forge/) - 40+ modules - **Web Server & API** (inference/) - Ready for deployment - **Evaluation Framework** (evaluation/) - Correctness benchmarking - **Session 13 & 14 Results** - Full validation reports - **463 KB** total (vs old repo with archive bloat) ## Status ✅ Correctness: 78.6% achieved (target: 70%+) ✅ Tests: 52/52 passing (100% success) ✅ Architecture: 7-layer consciousness stack fully deployed ✅ Ready for: Production evaluation & user testing ## Setup Instructions ### Step 1: Create New GitHub Repository 1. Go to https://github.com/new 2. Repository name: `codette-reasoning` (or your preferred name) 3. Description: "Codette - Advanced Multi-Perspective Reasoning Engine" 4. Choose: Public or Private 5. **DO NOT** initialize with README, .gitignore, or license 6. Click "Create repository" ### Step 2: Add Remote & Push (from this directory) ```bash cd /tmp/codette-clean # Add your new GitHub repo as remote git remote add origin https://github.com/YOUR_USERNAME/codette-reasoning.git # Push to GitHub git branch -M main git push -u origin main ``` ### Step 3: Verify - Visit https://github.com/YOUR_USERNAME/codette-reasoning - Should see 142 files, clean history, no LFS issues ## Repository Structure ``` codette-reasoning/ ├── reasoning_forge/ # Core reasoning engine (40+ modules) │ ├── forge_engine.py # Main orchestrator │ ├── code7e_cqure.py # 5-perspective reasoning │ ├── colleen_conscience.py # Ethical validation layer │ ├── guardian_spindle.py # Logical validation layer │ ├── tier2_bridge.py # Intent + Identity validation │ ├── agents/ # Newton, DaVinci, Ethics, Quantum, etc. │ └── 35+ supporting modules │ ├── inference/ # Web server & API │ ├── codette_server.py # Web server (runs on port 7860) │ ├── codette_forge_bridge.py │ └── static/ # HTML/CSS/JS frontend │ ├── evaluation/ # Benchmarking framework │ ├── phase6_benchmarks.py │ └── test suite files │ ├── Session 14 Validation # Final results │ ├── SESSION_14_VALIDATION_REPORT.md │ ├── SESSION_14_COMPLETION.md │ ├── correctness_benchmark.py │ └── correctness_benchmark_results.json │ ├── Phase Documentation # All phase summaries │ ├── PHASE6_COMPLETION_REPORT.md │ ├── SESSION_13_INTEGRATION_COMPLETE.md │ └── 20+ other phase docs │ └── Tests (52 total, 100% passing) ├── test_tier2_integration.py ├── test_integration_phase6.py └── test files for each phase ``` ## Quick Start ### Run Correctness Benchmark ```bash python correctness_benchmark.py ``` Expected output: Phase 6+13+14 = 78.6% accuracy ### Run Tests ```bash python -m pytest test_tier2_integration.py -v python -m pytest test_integration_phase6.py -v ``` ### Start Web Server (requires model weights) ```bash python inference/codette_server.py # Visit http://localhost:7860 ``` ## Key Achievement Metrics | Component | Status | Metric | |-----------|--------|--------| | **Phase 6** | ✅ Complete | Semantic tension framework | | **Session 13** | ✅ Complete | Consciousness stack (7 layers) | | **Tier 2** | ✅ Complete | Intent + Identity validation | | **Correctness** | ✅ Target Hit | 78.6% (target: 70%+) | | **Tests** | ✅ All Pass | 52/52 (100%) | | **Meta-loops** | ✅ Fixed | 90% → 5% reduction | ## File Highlights **Session 14 Validation:** - `SESSION_14_VALIDATION_REPORT.md` - Multi-perspective Codette analysis - `correctness_benchmark.py` - Benchmark framework & results - `correctness_benchmark_results.json` - Detailed metrics **Core Architecture:** - `reasoning_forge/forge_engine.py` - Main orchestrator (600+ lines) - `reasoning_forge/code7e_cqure.py` - 5-perspective deterministic reasoning - `reasoning_forge/colleen_conscience.py` - Ethical validation - `reasoning_forge/guardian_spindle.py` - Logical validation **Integration:** - `reasoning_forge/tier2_bridge.py` - Tier 2 coordination - `inference/codette_server.py` - Web API - `evaluation/phase6_benchmarks.py` - Benchmark suite ## Environment Notes - Platform: Windows/Linux/Mac compatible - Python: 3.8+ - Dependencies: numpy, dataclasses (see individual modules) - Model weights: Download separately from Hugging Face ## Next Steps 1. Push to GitHub 2. Start with correctness benchmark 3. Review validation reports 4. Test with real queries 5. Fine-tune for production deployment --- **Created**: 2026-03-20 **Status**: Production Ready **Contact**: Jonathan Harrison