GITHUB_SETUP.md

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)

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

python correctness_benchmark.py

Expected output: Phase 6+13+14 = 78.6% accuracy

Run Tests

python -m pytest test_tier2_integration.py -v
python -m pytest test_integration_phase6.py -v

Start Web Server (requires model weights)

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

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Created: 2026-03-20 Status: Production Ready Contact: Jonathan Harrison