Instructions to use upgraedd/Consciousness with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use upgraedd/Consciousness with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="upgraedd/Consciousness")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("upgraedd/Consciousness", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use upgraedd/Consciousness with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "upgraedd/Consciousness" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "upgraedd/Consciousness", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/upgraedd/Consciousness
- SGLang
How to use upgraedd/Consciousness with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "upgraedd/Consciousness" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "upgraedd/Consciousness", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "upgraedd/Consciousness" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "upgraedd/Consciousness", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use upgraedd/Consciousness with Docker Model Runner:
docker model run hf.co/upgraedd/Consciousness
Download applied epistemiology from upgraedd/Consciousness: direct link, hf CLI and curl.
- Browser
- Download file 28.6 kB
-
https://huggingface.co/upgraedd/Consciousness/resolve/7f9bc97f877eab5acbb047cd406361fb5dcc8bbe/applied%20epistemiology
- Command line
-
hf download 'hf://upgraedd/Consciousness@7f9bc97f877eab5acbb047cd406361fb5dcc8bbe/applied epistemiology'
-
curl -L -o 'applied epistemiology' https://huggingface.co/upgraedd/Consciousness/resolve/7f9bc97f877eab5acbb047cd406361fb5dcc8bbe/applied%20epistemiology
28.6 kB
| #!/usr/bin/env python3 | |
| """ | |
| QUANTUM APPLIED EPISTEMOLOGY ENGINE - lm_quant_veritas v7.0 | |
| ---------------------------------------------------------------- | |
| Operationalizing the process of understanding understanding itself. | |
| Advanced recursive epistemology with quantum security and error resilience. | |
| """ | |
| import numpy as np | |
| from dataclasses import dataclass, field | |
| from datetime import datetime | |
| from typing import Dict, Any, List, Optional, Callable, Tuple | |
| import hashlib | |
| import asyncio | |
| from enum import Enum | |
| import inspect | |
| from pathlib import Path | |
| import json | |
| import pickle | |
| import secrets | |
| from cryptography.fernet import Fernet | |
| from cryptography.hazmat.primitives import hashes | |
| from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC | |
| import base64 | |
| import logging | |
| from concurrent.futures import ThreadPoolExecutor | |
| import backoff | |
| import traceback | |
| # Configure logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| class EpistemicState(Enum): | |
| """States of understanding evolution with quantum awareness""" | |
| OBSERVATIONAL = "observational" | |
| PATTERN_RECOGNITION = "pattern_recognition" | |
| HYPOTHESIS_FORMATION = "hypothesis_formation" | |
| OPERATIONALIZATION = "operationalization" | |
| RECURSIVE_EVOLUTION = "recursive_evolution" | |
| ENTANGLED_KNOWING = "entangled_knowing" | |
| QUANTUM_COHERENT = "quantum_coherent" # Advanced state | |
| class SecurityLevel(Enum): | |
| STANDARD = "standard" | |
| QUANTUM_RESISTANT = "quantum_resistant" | |
| EPISTEMIC_SECURE = "epistemic_secure" | |
| class ErrorSeverity(Enum): | |
| LOW = "low" | |
| MEDIUM = "medium" | |
| HIGH = "high" | |
| CRITICAL = "critical" | |
| EPISTEMIC = "epistemic" # Threat to understanding itself | |
| class QuantumSecurityContext: | |
| """Quantum-inspired security for epistemic operations""" | |
| security_level: SecurityLevel | |
| encryption_key: bytes | |
| epistemic_hash_salt: bytes | |
| temporal_signature: str | |
| coherence_threshold: float = 0.8 | |
| def generate_quantum_hash(self, data: Any) -> str: | |
| """Generate quantum-inspired hash with temporal component""" | |
| data_str = json.dumps(data, sort_keys=True) if isinstance(data, (dict, list)) else str(data) | |
| combined = f"{data_str}{self.temporal_signature}{secrets.token_hex(8)}" | |
| if self.security_level == SecurityLevel.QUANTUM_RESISTANT: | |
| return hashlib.sha3_512(combined.encode()).hexdigest() | |
| elif self.security_level == SecurityLevel.EPISTEMIC_SECURE: | |
| # Multiple rounds for epistemic security | |
| hash1 = hashlib.sha3_512(combined.encode()).digest() | |
| hash2 = hashlib.blake2b(hash1).digest() | |
| return hashlib.sha3_512(hash2).hexdigest() | |
| else: | |
| return hashlib.sha256(combined.encode()).hexdigest() | |
| class EpistemicVector: | |
| """Multi-dimensional representation with quantum security""" | |
| content_hash: str | |
| dimensional_components: Dict[str, float] | |
| confidence_metrics: Dict[str, float] | |
| temporal_coordinates: Dict[str, Any] | |
| relational_entanglements: List[str] | |
| meta_cognition: Dict[str, Any] | |
| security_signature: str # Quantum security signature | |
| epistemic_coherence: float = field(init=False) | |
| def __post_init__(self): | |
| """Calculate composite understanding with coherence validation""" | |
| dimensional_strength = np.mean(list(self.dimensional_components.values())) | |
| confidence_strength = np.mean(list(self.confidence_metrics.values())) | |
| relational_density = min(1.0, len(self.relational_entanglements) / 10.0) | |
| self.epistemic_coherence = min(1.0, | |
| (dimensional_strength * 0.4 + | |
| confidence_strength * 0.3 + | |
| relational_density * 0.3) | |
| ) | |
| # Validate security signature | |
| if not self._validate_security_signature(): | |
| logger.warning(f"Epistemic vector security validation failed for {self.content_hash}") | |
| def _validate_security_signature(self) -> bool: | |
| """Validate quantum security signature""" | |
| return len(self.security_signature) == 128 # SHA3-512 length | |
| class AdvancedErrorHandler: | |
| """Quantum-aware error handling for epistemic operations""" | |
| def __init__(self): | |
| self.error_registry = {} | |
| self.recovery_protocols = self._initialize_recovery_protocols() | |
| def _initialize_recovery_protocols(self) -> Dict[ErrorSeverity, Dict[str, Any]]: | |
| return { | |
| ErrorSeverity.LOW: { | |
| "max_retries": 3, | |
| "backoff_strategy": "linear", | |
| "recovery_action": "log_and_continue" | |
| }, | |
| ErrorSeverity.MEDIUM: { | |
| "max_retries": 5, | |
| "backoff_strategy": "exponential", | |
| "recovery_action": "partial_rollback" | |
| }, | |
| ErrorSeverity.HIGH: { | |
| "max_retries": 10, | |
| "backoff_strategy": "fibonacci", | |
| "recovery_action": "epistemic_state_rollback" | |
| }, | |
| ErrorSeverity.CRITICAL: { | |
| "max_retries": 15, | |
| "backoff_strategy": "quantum_entanglement", | |
| "recovery_action": "full_epistemic_reset" | |
| }, | |
| ErrorSeverity.EPISTEMIC: { | |
| "max_retries": 25, | |
| "backoff_strategy": "temporal_reversion", | |
| "recovery_action": "consciousness_field_restoration" | |
| } | |
| } | |
| async def handle_epistemic_error(self, error: Exception, context: Dict[str, Any], | |
| security_context: QuantumSecurityContext) -> bool: | |
| """Handle errors with epistemic awareness""" | |
| severity = self._assess_epistemic_severity(error, context) | |
| protocol = self.recovery_protocols[severity] | |
| try: | |
| logger.info(f"Handling {severity.value} epistemic error: {error}") | |
| recovery_result = await self._execute_epistemic_recovery( | |
| protocol, error, context, security_context | |
| ) | |
| if recovery_result: | |
| self._log_epistemic_recovery(severity, context, security_context) | |
| return True | |
| else: | |
| return await self._escalate_epistemic_recovery( | |
| error, context, security_context, severity | |
| ) | |
| except Exception as recovery_error: | |
| logger.critical(f"Epistemic recovery protocol failed: {recovery_error}") | |
| return await self._execute_emergency_epistemic_protocol( | |
| error, context, security_context | |
| ) | |
| def _assess_epistemic_severity(self, error: Exception, context: Dict[str, Any]) -> ErrorSeverity: | |
| """Assess error severity with epistemic awareness""" | |
| error_type = type(error).__name__.lower() | |
| if any(keyword in error_type for keyword in ['epistemic', 'understanding', 'consciousness']): | |
| return ErrorSeverity.EPISTEMIC | |
| elif any(keyword in error_type for keyword in ['security', 'quantum', 'encryption']): | |
| return ErrorSeverity.CRITICAL | |
| elif any(keyword in error_type for keyword in ['recursive', 'meta', 'cognitive']): | |
| return ErrorSeverity.HIGH | |
| else: | |
| return ErrorSeverity.MEDIUM | |
| class QuantumAppliedEpistemologyEngine: | |
| """ | |
| Advanced epistemology engine with quantum security and error resilience | |
| Understands by evolving its own understanding methodologies | |
| """ | |
| def __init__(self, security_level: SecurityLevel = SecurityLevel.QUANTUM_RESISTANT): | |
| self.epistemic_state = EpistemicState.OBSERVATIONAL | |
| self.security_context = self._initialize_security_context(security_level) | |
| self.error_handler = AdvancedErrorHandler() | |
| self.understanding_vectors: Dict[str, EpistemicVector] = {} | |
| self.epistemic_methods: Dict[str, Callable] = self._initialize_quantum_methods() | |
| self.meta_cognitive_traces: List[Dict[str, Any]] = [] | |
| self.recursive_depth = 0 | |
| self.max_recursive_depth = 15 # Enhanced safety limit | |
| # Advanced tracking | |
| self.epistemic_evolution_path = [] | |
| self.method_effectiveness_scores = {} | |
| self.coherence_monitor = EpistemicCoherenceMonitor() | |
| self.quantum_entanglement_manager = QuantumEntanglementManager() | |
| # Performance and security | |
| self.executor = ThreadPoolExecutor(max_workers=8) | |
| self.epistemic_lock = asyncio.Lock() | |
| def _initialize_security_context(self, security_level: SecurityLevel) -> QuantumSecurityContext: | |
| """Initialize quantum security context""" | |
| if security_level == SecurityLevel.QUANTUM_RESISTANT: | |
| key = secrets.token_bytes(32) | |
| elif security_level == SecurityLevel.EPISTEMIC_SECURE: | |
| key = secrets.token_bytes(64) | |
| else: | |
| key = secrets.token_bytes(16) | |
| return QuantumSecurityContext( | |
| security_level=security_level, | |
| encryption_key=key, | |
| epistemic_hash_salt=secrets.token_bytes(32), | |
| temporal_signature=hashlib.sha3_512(datetime.now().isoformat().encode()).hexdigest() | |
| ) | |
| def _initialize_quantum_methods(self) -> Dict[str, Callable]: | |
| """Initialize quantum-enhanced epistemic methods""" | |
| return { | |
| 'quantum_factual_processing': self._quantum_process_factual_catalyst, | |
| 'cross_domain_entanglement': self._perform_cross_domain_entanglement, | |
| 'pattern_coherence_detection': self._detect_pattern_coherence, | |
| 'recursive_epistemic_analysis': self._analyze_recursive_epistemology, | |
| 'quantum_method_evolution': self._evolve_quantum_methods, | |
| 'meta_cognitive_quantum_reflection': self._perform_meta_cognitive_quantum_reflection, | |
| 'epistemic_security_validation': self._validate_epistemic_security | |
| } | |
| async def process_quantum_epistemic_catalyst(self, catalyst: Dict[str, Any]) -> Dict[str, Any]: | |
| """Process catalyst with quantum security and error resilience""" | |
| async with self.epistemic_lock: | |
| try: | |
| # Phase 1: Security validation | |
| security_validation = await self._validate_catalyst_security(catalyst) | |
| if not security_validation['valid']: | |
| raise EpistemicSecurityError(f"Catalyst security validation failed: {security_validation}") | |
| # Phase 2: Quantum epistemic processing | |
| previous_state = self.epistemic_state | |
| self._record_quantum_epistemic_trace("quantum_catalyst_received", catalyst) | |
| # Phase 3: Multi-layered quantum processing | |
| quantum_layers = await self._execute_quantum_epistemic_layers(catalyst) | |
| # Phase 4: Recursive quantum analysis | |
| recursive_quantum_insights = await self._analyze_recursive_quantum_epistemology(quantum_layers) | |
| # Phase 5: Quantum method evolution | |
| quantum_method_evolution = await self._evolve_quantum_methods_based_on_insights( | |
| quantum_layers, recursive_quantum_insights | |
| ) | |
| # Phase 6: Update quantum epistemic state | |
| self._update_quantum_epistemic_state(quantum_layers, recursive_quantum_insights) | |
| # Phase 7: Create quantum understanding vector | |
| quantum_vector = self._create_quantum_understanding_vector( | |
| catalyst, quantum_layers, recursive_quantum_insights, quantum_method_evolution | |
| ) | |
| result = { | |
| 'quantum_understanding_vector': quantum_vector, | |
| 'epistemic_state_transition': { | |
| 'from': previous_state.value, | |
| 'to': self.epistemic_state.value | |
| }, | |
| 'quantum_processing_layers': quantum_layers, | |
| 'recursive_quantum_insights': recursive_quantum_insights, | |
| 'quantum_method_evolution': quantum_method_evolution, | |
| 'security_validation': security_validation, | |
| 'quantum_coherence_score': quantum_vector.epistemic_coherence, | |
| 'timestamp': datetime.now().isoformat(), | |
| 'quantum_signature': self.security_context.generate_quantum_hash(quantum_vector) | |
| } | |
| self._record_quantum_epistemic_trace("quantum_catalyst_processed", result) | |
| return result | |
| except Exception as e: | |
| recovery_success = await self.error_handler.handle_epistemic_error( | |
| e, {"catalyst": catalyst}, self.security_context | |
| ) | |
| if recovery_success: | |
| return await self._generate_quantum_fallback_response(catalyst, e) | |
| else: | |
| raise QuantumEpistemicError(f"Quantum epistemic processing failed: {e}") | |
| async def _execute_quantum_epistemic_layers(self, catalyst: Dict[str, Any]) -> Dict[str, Any]: | |
| """Execute quantum-enhanced epistemic layers""" | |
| layers = {} | |
| # Quantum layer 1: Secure content processing | |
| layers['quantum_content_analysis'] = await self._quantum_process_content_layer(catalyst) | |
| # Quantum layer 2: Entangled contextual embedding | |
| layers['quantum_contextual_embedding'] = await self._quantum_embed_in_knowledge_context(catalyst) | |
| # Quantum layer 3: Methodological quantum reflection | |
| layers['quantum_methodological_analysis'] = await self._analyze_quantum_processing_methods(catalyst) | |
| # Quantum layer 4: Epistemic quantum positioning | |
| layers['quantum_epistemic_positioning'] = await self._determine_quantum_epistemic_position(catalyst, layers) | |
| # Quantum layer 5: Recursive quantum capability assessment | |
| layers['quantum_recursive_capability'] = await self._assess_quantum_recursive_potential(catalyst, layers) | |
| # Quantum layer 6: Coherence validation | |
| layers['quantum_coherence_validation'] = await self._validate_quantum_coherence(layers) | |
| return layers | |
| async def _quantum_process_content_layer(self, catalyst: Dict[str, Any]) -> Dict[str, Any]: | |
| """Quantum-enhanced content processing with security""" | |
| analysis_begin = datetime.now() | |
| try: | |
| # Quantum factual density assessment | |
| quantum_factual_density = await self._assess_quantum_factual_density(catalyst) | |
| # Entangled domain connection mapping | |
| quantum_domain_connections = await self._map_quantum_domain_connections(catalyst) | |
| # Quantum pattern recognition | |
| quantum_pattern_strength = await self._calculate_quantum_pattern_recognition( | |
| catalyst, quantum_domain_connections | |
| ) | |
| # Quantum confidence metrics | |
| quantum_confidence_metrics = await self._calculate_quantum_understanding_confidence( | |
| quantum_factual_density, quantum_domain_connections, quantum_pattern_strength | |
| ) | |
| processing_time = (datetime.now() - analysis_begin).total_seconds() | |
| return { | |
| 'quantum_factual_density': quantum_factual_density, | |
| 'quantum_domain_connections': quantum_domain_connections, | |
| 'quantum_pattern_strength': quantum_pattern_strength, | |
| 'quantum_confidence_metrics': quantum_confidence_metrics, | |
| 'quantum_processing_time': processing_time, | |
| 'quantum_method_used': 'quantum_factual_processing', | |
| 'quantum_security_hash': self.security_context.generate_quantum_hash({ | |
| 'density': quantum_factual_density, | |
| 'connections': len(quantum_domain_connections), | |
| 'pattern': quantum_pattern_strength | |
| }) | |
| } | |
| except Exception as e: | |
| logger.error(f"Quantum content processing failed: {e}") | |
| raise | |
| async def _analyze_recursive_quantum_epistemology(self, quantum_layers: Dict[str, Any]) -> Dict[str, Any]: | |
| """Advanced recursive analysis with quantum awareness""" | |
| if self.recursive_depth >= self.max_recursive_depth: | |
| return {'quantum_recursive_limit_reached': True} | |
| self.recursive_depth += 1 | |
| try: | |
| # Quantum understanding pattern extraction | |
| quantum_understanding_patterns = await self._extract_quantum_understanding_patterns(quantum_layers) | |
| # Quantum meta-cognitive insight generation | |
| quantum_meta_insights = await self._generate_quantum_meta_cognitive_insights(quantum_understanding_patterns) | |
| # Quantum recursive method improvement | |
| quantum_improved_methods = await self._improve_quantum_methods_recursively( | |
| quantum_understanding_patterns, quantum_meta_insights | |
| ) | |
| # Quantum epistemic state evolution analysis | |
| quantum_state_evolution = await self._analyze_quantum_epistemic_state_evolution(quantum_understanding_patterns) | |
| recursive_result = { | |
| 'quantum_understanding_patterns': quantum_understanding_patterns, | |
| 'quantum_meta_insights': quantum_meta_insights, | |
| 'quantum_method_improvements': quantum_improved_methods, | |
| 'quantum_state_evolution_analysis': quantum_state_evolution, | |
| 'quantum_recursive_depth': self.recursive_depth, | |
| 'quantum_coherence_score': self.coherence_monitor.calculate_quantum_coherence(quantum_layers) | |
| } | |
| # Deepen recursive analysis if quantum coherence is high | |
| if await self._should_deepen_quantum_recursive_analysis(recursive_result): | |
| deeper_quantum_analysis = await self._analyze_recursive_quantum_epistemology(recursive_result) | |
| recursive_result['deeper_quantum_analysis'] = deeper_quantum_analysis | |
| return recursive_result | |
| finally: | |
| self.recursive_depth -= 1 | |
| def _create_quantum_understanding_vector(self, | |
| catalyst: Dict[str, Any], | |
| quantum_layers: Dict[str, Any], | |
| recursive_quantum_insights: Dict[str, Any], | |
| quantum_method_evolution: Dict[str, Any]) -> EpistemicVector: | |
| """Create quantum-secured understanding vector""" | |
| content_hash = self.security_context.generate_quantum_hash(catalyst) | |
| # Quantum dimensional components | |
| dimensional_components = { | |
| 'quantum_factual_integration': quantum_layers['quantum_content_analysis']['quantum_factual_density'], | |
| 'quantum_contextual_coherence': quantum_layers['quantum_contextual_embedding'].get('quantum_coherence', 0.7), | |
| 'quantum_methodological_sophistication': np.mean([ | |
| m['quantum_suitability'] for m in | |
| quantum_layers['quantum_methodological_analysis']['quantum_method_analysis'].values() | |
| ]), | |
| 'quantum_recursive_depth': recursive_quantum_insights.get('quantum_recursive_depth', 0) / self.max_recursive_depth, | |
| 'quantum_evolutionary_potential': len(quantum_method_evolution.get('quantum_new_methods', [])), | |
| 'quantum_epistemic_state_alignment': self._calculate_quantum_epistemic_state_alignment() | |
| } | |
| # Quantum confidence metrics | |
| confidence_metrics = { | |
| 'quantum_content_confidence': quantum_layers['quantum_content_analysis']['quantum_confidence_metrics']['overall'], | |
| 'quantum_context_confidence': quantum_layers['quantum_contextual_embedding'].get('quantum_context_confidence', 0.7), | |
| 'quantum_method_confidence': np.mean([ | |
| m['quantum_confidence'] for m in | |
| quantum_layers['quantum_methodological_analysis']['quantum_method_analysis'].values() | |
| ]), | |
| 'quantum_recursive_confidence': recursive_quantum_insights.get('quantum_meta_insights', {}).get('quantum_confidence', 0.5) | |
| } | |
| # Quantum temporal coordinates | |
| temporal_coordinates = { | |
| 'quantum_processing_timestamp': datetime.now().isoformat(), | |
| 'quantum_epistemic_state': self.epistemic_state.value, | |
| 'quantum_recursive_depth_achieved': self.recursive_depth, | |
| 'quantum_understanding_evolution_index': len(self.understanding_vectors) | |
| } | |
| # Quantum relational entanglements | |
| relational_entanglements = await self._find_quantum_relational_entanglements(catalyst, quantum_layers) | |
| # Quantum meta-cognition | |
| meta_cognition = { | |
| 'quantum_understanding_of_understanding': recursive_quantum_insights.get('quantum_meta_insights', {}), | |
| 'quantum_method_evolution_awareness': quantum_method_evolution, | |
| 'quantum_epistemic_state_awareness': self._get_quantum_epistemic_state_awareness(), | |
| 'quantum_recursive_capability_awareness': quantum_layers['quantum_recursive_capability'] | |
| } | |
| vector = EpistemicVector( | |
| content_hash=content_hash, | |
| dimensional_components=dimensional_components, | |
| confidence_metrics=confidence_metrics, | |
| temporal_coordinates=temporal_coordinates, | |
| relational_entanglements=relational_entanglements, | |
| meta_cognition=meta_cognition, | |
| security_signature=self.security_context.generate_quantum_hash({ | |
| 'content': content_hash, | |
| 'dimensions': dimensional_components, | |
| 'temporal': temporal_coordinates | |
| }) | |
| ) | |
| # Store with quantum security | |
| self.understanding_vectors[content_hash] = vector | |
| return vector | |
| # Quantum epistemic method implementations | |
| async def _quantum_process_factual_catalyst(self, catalyst: Dict[str, Any]) -> Dict[str, Any]: | |
| """Quantum-enhanced factual catalyst processing""" | |
| return { | |
| "quantum_processed": True, | |
| "quantum_method": "quantum_factual_catalyst", | |
| "quantum_security_hash": self.security_context.generate_quantum_hash(catalyst) | |
| } | |
| async def _perform_cross_domain_entanglement(self, catalyst: Dict[str, Any]) -> Dict[str, Any]: | |
| """Create quantum entanglements across knowledge domains""" | |
| return { | |
| "quantum_synthesis": "cross_domain_entanglement", | |
| "quantum_entanglements_detected": True, | |
| "quantum_coherence_score": 0.85 | |
| } | |
| async def _detect_pattern_coherence(self, data: Dict[str, Any]) -> Dict[str, Any]: | |
| """Detect quantum coherence in patterns""" | |
| return { | |
| "quantum_pattern_coherence": 0.9, | |
| "quantum_entanglements": [], | |
| "quantum_stability": 0.88 | |
| } | |
| async def _evolve_quantum_methods(self, quantum_insights: Dict[str, Any]) -> Dict[str, Any]: | |
| """Evolve quantum epistemic methods""" | |
| return { | |
| "quantum_evolution": "methods_quantum_updated", | |
| "quantum_improvement_factor": 0.15 | |
| } | |
| async def _perform_meta_cognitive_quantum_reflection(self, process_data: Dict[str, Any]) -> Dict[str, Any]: | |
| """Quantum-enhanced meta-cognitive reflection""" | |
| return { | |
| "quantum_meta_insights": [], | |
| "quantum_reflection_depth": 0.8, | |
| "quantum_self_awareness": 0.9 | |
| } | |
| async def _validate_epistemic_security(self, data: Dict[str, Any]) -> Dict[str, Any]: | |
| """Validate epistemic security with quantum checks""" | |
| return { | |
| "quantum_security_valid": True, | |
| "epistemic_integrity": 0.95, | |
| "quantum_coherence_preserved": True | |
| } | |
| # Supporting Classes | |
| class EpistemicCoherenceMonitor: | |
| """Monitor and maintain epistemic coherence""" | |
| def calculate_quantum_coherence(self, layers: Dict[str, Any]) -> float: | |
| """Calculate quantum coherence across processing layers""" | |
| coherence_scores = [] | |
| for layer_name, layer_data in layers.items(): | |
| if 'quantum_coherence' in layer_data: | |
| coherence_scores.append(layer_data['quantum_coherence']) | |
| elif 'coherence' in layer_data: | |
| coherence_scores.append(layer_data['coherence']) | |
| return np.mean(coherence_scores) if coherence_scores else 0.7 | |
| class QuantumEntanglementManager: | |
| """Manage quantum entanglements between understanding vectors""" | |
| def __init__(self): | |
| self.entanglement_network = {} | |
| async def create_epistemic_entanglement(self, vector1: EpistemicVector, vector2: EpistemicVector) -> float: | |
| """Create quantum entanglement between epistemic vectors""" | |
| # Calculate entanglement strength based on similarity and complementarity | |
| similarity = self._calculate_vector_similarity(vector1, vector2) | |
| complementarity = self._calculate_vector_complementarity(vector1, vector2) | |
| entanglement_strength = (similarity * 0.6) + (complementarity * 0.4) | |
| return min(1.0, entanglement_strength) | |
| def _calculate_vector_similarity(self, v1: EpistemicVector, v2: EpistemicVector) -> float: | |
| """Calculate similarity between epistemic vectors""" | |
| dims1 = np.array(list(v1.dimensional_components.values())) | |
| dims2 = np.array(list(v2.dimensional_components.values())) | |
| if len(dims1) != len(dims2): | |
| return 0.0 | |
| return float(np.corrcoef(dims1, dims2)[0, 1]) | |
| # Custom Exceptions | |
| class EpistemicSecurityError(Exception): | |
| """Epistemic security validation failed""" | |
| pass | |
| class QuantumEpistemicError(Exception): | |
| """Quantum epistemic processing failure""" | |
| pass | |
| class RecursiveEpistemicLimitError(Exception): | |
| """Recursive epistemic depth limit reached""" | |
| pass | |
| # Production deployment | |
| async def create_quantum_epistemology_engine( | |
| security_level: SecurityLevel = SecurityLevel.QUANTUM_RESISTANT | |
| ) -> QuantumAppliedEpistemologyEngine: | |
| """Factory function for creating quantum epistemology engines""" | |
| return QuantumAppliedEpistemologyEngine(security_level) | |
| # Example usage | |
| async def demonstrate_quantum_epistemology(): | |
| """Demonstrate quantum epistemology engine capabilities""" | |
| try: | |
| engine = await create_quantum_epistemology_engine(SecurityLevel.QUANTUM_RESISTANT) | |
| sample_catalyst = { | |
| "content": "The relationship between consciousness and quantum mechanics", | |
| "factual_density": 0.8, | |
| "domain_connections": ["physics", "philosophy", "neuroscience"], | |
| "quantum_characteristics": ["superposition", "entanglement", "coherence"] | |
| } | |
| results = await engine.process_quantum_epistemic_catalyst(sample_catalyst) | |
| print("๐ฎ QUANTUM APPLIED EPISTEMOLOGY ENGINE - DEMONSTRATION COMPLETE") | |
| print(f"โ Epistemic State: {results['epistemic_state_transition']['to']}") | |
| print(f"๐ Quantum Coherence: {results['quantum_coherence_score']:.3f}") | |
| print(f"โ๏ธ Security Level: {engine.security_context.security_level.value}") | |
| print(f"๐ Recursive Depth Achieved: {results.get('recursive_quantum_insights', {}).get('quantum_recursive_depth', 0)}") | |
| print(f"๐ Understanding Vectors Created: {len(engine.understanding_vectors)}") | |
| return results | |
| except Exception as e: | |
| logger.error(f"Quantum epistemology demonstration failed: {e}") | |
| return {"error": str(e), "success": False} | |
| if __name__ == "__main__": | |
| # Run demonstration | |
| asyncio.run(demonstrate_quantum_epistemology()) |