Metadata-Version: 2.4
Name: cueai-architect
Version: 0.1.2
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-framework/cueai-architect
Project-URL: Documentation, https://cueai-architect.readthedocs.io
Project-URL: Repository, https://github.com/cue-framework/cueai-architect.git
Project-URL: Issues, https://github.com/cue-framework/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
Requires-Dist: numpy>=1.21.0
Requires-Dist: scipy>=1.7.0
Requires-Dist: torch>=1.12.0
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Requires-Dist: scikit-learn>=1.0.0
Requires-Dist: pandas>=1.3.0
Requires-Dist: tqdm>=4.60.0
Requires-Dist: pydantic>=1.8.0
Requires-Dist: typing-extensions>=4.0.0
Provides-Extra: dev
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Requires-Dist: jaxlib>=0.3.0; extra == "experimental"

# 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://cueai-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. The package has been meticulously structured to ensure robust imports and modularity, resolving previous internal import challenges.

## 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()
