""" Autonogrammer AI Companion System

Full-stack autonomous programming with RPQE architecture.

Overview

Autonogrammer is a production-ready AI companion system for autonomous software development, leveraging Recursively Photonic Quantum Engineering (RPQE) principles to achieve quantum-inspired performance on classical infrastructure.

Architecture

┌─────────────────────────────────────────────────────────────────┐
│                  Autonogrammer Full Stack                │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐           │
│  │  Rust    │  │ Python   │  │ Elixir   │           │
│  │  Core    │  │ DSpy     │  │ Phoenix  │           │
│  └──────────┘  └──────────┘  └──────────┘           │
│       │             │              │                      │
│       └─────────────┼──────────────┘                      │
│                     │                                     │
│              ┌─────▼──────┐                             │
│              │ Nomad       │                             │
│              │ Scheduler   │                             │
│              └─────────────┘                             │
│                     │                                     │
│              ┌─────▼──────┐                             │
│              │ HashiCorp   │                             │
│              │ Services    │                             │
│              └─────────────┘                             │
└─────────────────────────────────────────────────────────────────┘

Features

• RPQE Architecture: Photonic Coherence, Superposition, Entanglement, Neural Feedback, Gate Recursion
• Multi-Agent Orchestration: CodeGenerator, TestEngineer, ReviewAgent, DebugAgent
• AutoQC Compliance: 10 best practices enforced automatically
• Full-Stack: Rust backend, Python agents, Elixir orchestration
• Enterprise-Ready: Multi-planetary compliance, security, observability

Quick Start

Prerequisites

• Docker & Docker Compose
• Python 3.11+
• Rust 1.70+
• Elixir 1.15+
• Nomad/Consul/Vault (optional)

Docker Deployment

# Clone repository
git clone https://github.com/swcstudio/aicompanion.git
cd aicompanion

# Start all services
docker-compose up -d

# Check services status
docker-compose ps

# View logs
docker-compose logs -f phoenix-app

Development Setup

# Python agents
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

# Rust backend
cd rust_backend_core
cargo build --release

# Elixir/Phoenix
cd phoenix_app
mix deps.get
mix phx.server

Deep Specifications

Spec	Description
AUTONOGRAMMER_BLUEPRINT.md	Complete system blueprint
CODE_QUALITY_BEST_PRACTICES.md	AutoQC framework
RUST_BACKEND_CORE.md	Rust backend details
PYTHON_DSPY_UVX.md	Python/DSpy agents
PHOENIX_ORCHESTRATION.md	Phoenix orchestration
HASHICORP_NOMAD_CLUSTER.md	HashiCorp integration

AutoQC Standards

The system enforces 10 programming best practices:

1. Descriptive Naming Convention
2. Magic Number Elimination
3. Boundary Input Validation
4. Intent-Based Documentation
5. Graceful Edge Case Handling
6. Dead Code Elimination
7. Early Return Pattern
8. Function Decomposition
9. Conventional Commit Messages
10. Critical Path Testing

See CODE_QUALITY_BEST_PRACTICES.md for details.

Algebraic Pythagorean Framework

The system uses advanced mathematical algorithms for optimal test generation based on rigorous algebraic foundations.

Mathematical Foundations

The framework implements 7 mathematical domains:

1. Number Theory

   • Perfect square detection: is_perfect_square(n)
   • Greatest Common Divisor (GCD): Euclidean algorithm
   • Least Common Multiple (LCM): a * b / gcd(a, b)
   • Fibonacci sequence generation: Dynamic test allocation
   • Fibonacci membership check: 5*n² ± 4 perfect square test
   • Coprimality testing: Independent test set generation

2. Euclidean Geometry

   • 2D distance: √((x2-x1)² + (y2-y1)²)
   • N-dimensional distance: Multi-dimensional vector space analysis
   • Dot products: Vector similarity and alignment
   • Vector magnitudes: Test suite complexity measurement
   • Cosine similarity: Test coverage independence

3. Trigonometry

   • Law of Cosines: cos(θ) = (a² + b² - c²) / 2ab
   • Entanglement angles: Component coupling analysis
   • Orthogonality checks: Test independence via Law of Sines
   • Heron's formula: Coverage area calculation √(s(s-a)(s-b)(s-c))
   • Test completeness: Trigonometric phase analysis

4. Linear Algebra

   • Gram-Schmidt orthogonalization: Independent test space generation
   • Vector projections: Subspace-based test planning
   • Hyperrectangle volumes: Multi-dimensional test coverage measurement

5. Complex Analysis

   • Euler's identity verification: e^(iπ) + 1 = 0
   • Complex phase cycles: Test execution frequency analysis
   • Harmonic convergence: Fibonacci sequence properties

6. Harmonic Analysis

   • Harmonic means: Test count optimization
   • Convergence analysis: Mathematical stability verification

7. Optimization Theory

   • Primitive Pythagorean triples: Euclid's formula a = m²-n², b = 2mn
   • Geometric minimization: Optimal test count calculation

API Endpoints

/api/v1/math/perfect_triples?count=N

Returns primitive Pythagorean triples using Euclid's formula.

curl "http://localhost:4000/api/v1/math/perfect_triples?count=5"

Response:

{
  "count": 5,
  "triples": [
    {"a": 3, "b": 4, "c": 5, "perfect": true},
    {"a": 5, "b": 12, "c": 13, "perfect": true},
    {"a": 8, "b": 15, "c": 17, "perfect": true}
  ],
  "algorithm": "euclid_formula",
  "description": "Primitive Pythagorean triples where a² + b² = c²"
}

/api/v1/math/fibonacci_sequence?count=N

Returns Fibonacci sequence for natural test allocation.

curl "http://localhost:4000/api/v1/math/fibonacci_sequence?count=10"

Response:

{
  "count": 10,
  "sequence": [1, 1, 2, 3, 5, 8, 13, 21, 34, 55],
  "algorithm": "fibonacci",
  "harmonic_mean": 0.85,
  "description": "Fibonacci-based test allocation strategy"
}

/api/v1/math/euclidean_distance

Calculate n-dimensional Euclidean distance between test vectors.

curl -X POST "http://localhost:4000/api/v1/math/euclidean_distance" \
  -H "Content-Type: application/json" \
  -d '{"vector_a": [1.0, 2.0, 3.0], "vector_b": [4.0, 5.0, 6.0]}'

Response:

{
  "distance": 5.196,
  "dimensions": 3,
  "algorithm": "euclidean_nd",
  "vector_a": [1.0, 2.0, 3.0],
  "vector_b": [4.0, 5.0, 6.0]
}

/api/v1/math/orthogonality_check

Check test coverage independence using Law of Sines.

curl "http://localhost:4000/api/v1/math/orthogonality_check?unit_ratio=0.6&integration_ratio=0.3&e2e_ratio=0.1"

Response:

{
  "orthogonality_score": 0.15,
  "unit_ratio": 0.6,
  "integration_ratio": 0.3,
  "e2e_ratio": 0.1,
  "algorithm": "law_of_sines",
  "interpretation": "Test coverage is highly orthogonal (independent)"
}

/api/v1/math/entanglement_angle

Calculate coupling angle between components using Law of Cosines.

curl "http://localhost:4000/api/v1/math/entanglement_angle?dep_a=5.0&dep_b=3.0&coupling=0.9"

Response:

{
  "angle": 1.231,
  "cosine": 0.9,
  "algorithm": "law_of_cosines",
  "interpretation": "Moderate coupling (some entanglement)"
}

/api/v1/math/coverage_area

Calculate coverage area using Heron's formula.

curl "http://localhost:4000/api/v1/math/coverage_area?current=3&expected=5&additional=5"

Response:

{
  "area": 7.0,
  "semi_perimeter": 4.33,
  "algorithm": "herons_formula",
  "current_tests": 3,
  "expected_tests": 5,
  "additional_tests": 5
}

Algebraic Annotations

Generated test code includes mathematical comments that explain the derivation:

# Auto-generated test for example.py
#
# Test count: 13
# #alg:triple (3,4,5) = Perfect Pythagorean triple
# #alg:fibonacci 13 = Optimal test count
#
import pytest
from example import *

def test_example(code_sample: str):
    """Test generated code with algebraic derivation"""
    # Test implementation
    result = calculate_result(code_sample)
    assert result is not None, "Implementation should pass"

def calculate_result(code_sample: str) -> Optional[str]:
    """Calculate expected result with algebraic comments"""
    # Parse test to extract algebraic formula
    # Return expected result based on mathematical derivation
    #alg:fornameofpythagora c = √(a² + b²) = √(9² + 12²) = 15 tests
    pass

Annotation Formats:

• #alg:fornameofpythagora c = √(a² + b²) - Basic Pythagorean formula
• #alg:triple (a,b,c) - When perfect Pythagorean triple detected
• #alg:fibonacci N - Fibonacci number used for test count
• #alg:entanglement θ = X.XXX rad - Component coupling angle
• #alg:orthogonal sin(φ) = X.XXX - Orthogonality metric
• #alg:euclicean_distance d = X.XXX - N-dimensional distance calculation

Contributing

This project is licensed under AGPL-3.0. All contributions must be compatible with this license.

License

Copyright (C) 2006-2024 SWCStudio

Licensed under the GNU Affero General Public License v3.0 (AGPL-3.0)

SPDX-License-Identifier: AGPL-3.0

Citation

If you use Autonogrammer in your research, please cite:

@software{autonogrammer2024,
  author       = {{SWCStudio Development Team}},
  title        = {{Autonogrammer: AI Companion Systems with RPQE Architecture}},
  year         = {2024},
  version      = {2.1.0},
  url          = {https://github.com/swcstudio/aicompanion},
  license      = {AGPL-3.0}
}

Contact

• Repository: https://github.com/swcstudio/aicompanion
• Issues: https://github.com/swcstudio/aicompanion/issues
• Email: dev@swcstudio.com

Acknowledgments

• DSpy framework team
• HashiCorp for Nomad/Consul/Vault
• The Rust, Python, and Elixir communities
• All contributors to open-source quantum-inspired computing research