Metadata-Version: 2.4
Name: cueai-architect
Version: 0.1.3
Summary: Modular AI Architecture Components based on the Collective Unified Equation (CUE) Framework
Author-email: CUE Framework Research Team <cue-research@example.com>
Maintainer-email: Karl Farah Ambrosius <karlambrosius@outlook.com.au>
License: MIT
Project-URL: Homepage, https://github.com/cue-ai-architect/cueai-architect
Project-URL: Documentation, https://cue-ai-architect.readthedocs.io
Project-URL: Repository, https://github.com/cue-ai-architect/cueai-architect.git
Project-URL: Issues, https://github.com/cue-ai-architect/cueai-architect/issues
Keywords: consciousness,quantum,neural-networks,geometric-deep-learning,renormalization-group,fiber-bundles,artificial-intelligence,cue-framework
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.9
Description-Content-Type: text/markdown
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Requires-Dist: pydantic>=1.8.0
Requires-Dist: typing-extensions>=4.0.0
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# CUE-AI Architect

**Modular AI Architecture Components based on the Collective Unified Equation (CUE) Framework**

[![PyPI version](https://badge.fury.io/py/cueai-architect.svg)](https://badge.fury.io/py/cueai-architect)
[![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Documentation Status](https://readthedocs.org/projects/cueai-architect/badge/?version=latest)](https://cue-ai-architect.readthedocs.io/en/latest/?badge=latest)

## Overview

The CUE-AI Architect is a comprehensive Python package that implements 100 modular AI architecture components based on the revolutionary Collective Unified Equation (CUE) Framework by Karl Farah Ambrosius. This framework provides a geometric approach to consciousness-matter unification, offering novel solutions to quantum measurement problems and geometric interpretations of consciousness compatible with known physics.

**Note**: This is a research framework package. Repository and documentation links will be updated upon official release.

## Key Features

🧠 **Consciousness Integration**: Native support for consciousness as a geometric structure (DΨ fiber bundle)
⚛️ **Quantum-Consciousness Interface**: Advanced quantum measurement coupling and decoherence modeling
🌊 **RG Flow Dynamics**: Complete renormalization group flow implementation with fixed point analysis
📐 **Geometric Deep Learning**: Fiber bundle neural networks and curvature-aware architectures
🔬 **Experimental Interfaces**: Direct connections to quantum optics and gravitational wave experiments
🏗️ **Modular Design**: 100 independent, composable modules across 14 specialized categories, now with a fully resolved and robust internal package structure.

## Installation

\`\`\`bash
# Install from PyPI
pip install cueai-architect

# Install with experimental dependencies
pip install cueai-architect[experimental]

# Install for development
pip install cueai-architect[dev]
\`\`\`

## Quick Start

\`\`\`python
import cueai_architect as cue

# Initialize the CUE framework
framework = cue.CUEApplicationManager()

# Create consciousness-coupled neural architecture
model = cue.ConsciousnessTransformer(
    consciousness_dim=128,
    fiber_bundle_layers=6,
    rg_flow_enabled=True
)

# Simulate consciousness-matter coupling
simulator = cue.ConsciousnessFieldSimulator()
results = simulator.run_coherence_simulation(
    duration=1000,
    consciousness_coupling=0.1
)

# Analyze RG flow dynamics
rg_analyzer = cue.RGFlowIntegrator()
fixed_points = rg_analyzer.find_critical_points()
