#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ STRUCTURAL INQUIRY SYSTEM v2.5 Engineering-Focused Knowledge Discovery with Concrete Improvements """ from enum import Enum from dataclasses import dataclass, field from typing import List, Dict, Any, Optional, Tuple, Mapping, Callable import hashlib from datetime import datetime from types import MappingProxyType import numpy as np # === CORE SYMBOLS === KNOWLEDGE_NODE = "●" PATTERN_RECOGNITION = "⟁" INQUIRY_MARKER = "?" VALIDATION_SYMBOL = "✓" # === KNOWLEDGE STATE TYPES === class KnowledgeStateType(Enum): """Knowledge state types with clear semantics""" PATTERN_DETECTION = "pattern_detection" DATA_CORRELATION = "data_correlation" CONTEXTUAL_ALIGNMENT = "contextual_alignment" METHODOLOGICAL_STRUCTURE = "methodological_structure" SOURCE_VERIFICATION = "source_verification" TEMPORAL_CONSISTENCY = "temporal_consistency" CROSS_DOMAIN_SYNTHESIS = "cross_domain_synthesis" KNOWLEDGE_GAP_IDENTIFICATION = "knowledge_gap_identification" @dataclass(frozen=True) class KnowledgeState: """Immutable knowledge state with provenance tracking""" state_id: str state_type: KnowledgeStateType confidence_score: float confidence_provenance: str # Track where confidence came from methodological_rigor: float data_patterns: Tuple[float, ...] knowledge_domains: Tuple[str, ...] temporal_markers: Tuple[str, ...] research_constraints: Tuple[str, ...] structural_description: str validation_signature: str state_hash: str = field(init=False) def __post_init__(self): hash_input = f"{self.state_id}:{self.state_type.value}:{self.confidence_score}:" hash_input += f"{self.confidence_provenance}:{self.methodological_rigor}:" hash_input += ":".join(str(v) for v in self.data_patterns[:10]) hash_input += ":".join(self.knowledge_domains) state_hash = hashlib.sha3_512(hash_input.encode()).hexdigest()[:32] object.__setattr__(self, 'state_hash', state_hash) # === INQUIRY CATEGORIES === class InquiryCategory(Enum): """Inquiry categories with clear prioritization semantics""" CONFIDENCE_DISCREPANCY_ANALYSIS = "confidence_discrepancy_analysis" METHODOLOGICAL_CONSISTENCY_CHECK = "methodological_consistency_check" PATTERN_ANOMALY_DETECTION = "pattern_anomaly_detection" TEMPORAL_ALIGNMENT_VALIDATION = "temporal_alignment_validation" SOURCE_RELIABILITY_ASSESSMENT = "source_reliability_assessment" CROSS_REFERENCE_VALIDATION = "cross_reference_validation" KNOWLEDGE_COMPLETENESS_EVALUATION = "knowledge_completeness_evaluation" # === PLUGGABLE ANALYSIS INTERFACE === class AnalysisResult: """Structured analysis result for inquiry generation""" def __init__( self, category: InquiryCategory, basis_code: str, basis_kwargs: Dict[str, Any], verification_requirements: List[str], investigation_confidence: float, research_completion_estimate: float, priority_score: float ): self.category = category self.basis_code = basis_code self.basis_kwargs = basis_kwargs self.verification_requirements = verification_requirements self.investigation_confidence = investigation_confidence self.research_completion_estimate = research_completion_estimate self.priority_score = priority_score class InquiryAnalyzer: """Protocol for pluggable analysis""" def analyze(self, state: KnowledgeState) -> List[AnalysisResult]: """Analyze state and return multiple potential inquiries""" raise NotImplementedError # === DEFAULT ANALYZER IMPLEMENTATION === class DefaultInquiryAnalyzer(InquiryAnalyzer): """Default analyzer that generates multiple inquiry candidates""" def __init__(self, basis_templates: Dict[str, Dict[str, Any]]): self.basis_templates = basis_templates def analyze(self, state: KnowledgeState) -> List[AnalysisResult]: """Generate multiple inquiry candidates from state""" results = [] # Check multiple independent criteria if state.confidence_score < 0.7: results.append(self._confidence_analysis(state)) if state.methodological_rigor < 0.65: results.append(self._methodological_analysis(state)) if len(state.data_patterns) < 8: results.append(self._pattern_analysis(state)) if len(state.temporal_markers) < 3: results.append(self._temporal_analysis(state)) if len(state.knowledge_domains) > 2: results.append(self._cross_domain_analysis(state)) # Always provide at least one analysis if not results: results.append(self._default_analysis(state)) return results def _confidence_analysis(self, state: KnowledgeState) -> AnalysisResult: """Analyze confidence discrepancies""" confidence_factor = max(0.1, 0.8 - state.confidence_score) return AnalysisResult( category=InquiryCategory.CONFIDENCE_DISCREPANCY_ANALYSIS, basis_code="CONFIDENCE_ANOMALY_INVESTIGATION", basis_kwargs={ "score": state.confidence_score * 100, "expected": 75.0, "provenance": state.confidence_provenance }, verification_requirements=[ "statistical_reanalysis", "source_review", "methodology_audit" ], investigation_confidence=confidence_factor, research_completion_estimate=self._calculate_completion_estimate(3, confidence_factor), priority_score=self._calculate_priority_score(confidence_factor, 0.9) ) def _methodological_analysis(self, state: KnowledgeState) -> AnalysisResult: """Analyze methodological issues""" rigor_factor = max(0.1, 0.7 - state.methodological_rigor) return AnalysisResult( category=InquiryCategory.METHODOLOGICAL_CONSISTENCY_CHECK, basis_code="METHODOLOGICAL_CONSISTENCY_QUESTION", basis_kwargs={ "rigor": state.methodological_rigor * 100, "method_type": "research_protocol" }, verification_requirements=[ "protocol_review", "reproducibility_check", "peer_validation" ], investigation_confidence=rigor_factor, research_completion_estimate=self._calculate_completion_estimate(3, rigor_factor), priority_score=self._calculate_priority_score(rigor_factor, 0.8) ) def _pattern_analysis(self, state: KnowledgeState) -> AnalysisResult: """Analyze pattern anomalies""" pattern_factor = len(state.data_patterns) / 10.0 return AnalysisResult( category=InquiryCategory.PATTERN_ANOMALY_DETECTION, basis_code="PATTERN_DEVIATION_ANALYSIS", basis_kwargs={ "pattern_completeness": pattern_factor * 100, "expected_patterns": 8 }, verification_requirements=[ "pattern_completeness_check", "data_collection_review", "statistical_validation" ], investigation_confidence=1.0 - pattern_factor, research_completion_estimate=self._calculate_completion_estimate(3, pattern_factor), priority_score=self._calculate_priority_score(1.0 - pattern_factor, 0.7) ) def _temporal_analysis(self, state: KnowledgeState) -> AnalysisResult: """Analyze temporal issues""" temporal_factor = len(state.temporal_markers) / 3.0 return AnalysisResult( category=InquiryCategory.TEMPORAL_ALIGNMENT_VALIDATION, basis_code="TEMPORAL_CONSISTENCY_CHECK", basis_kwargs={ "marker_count": len(state.temporal_markers), "expected_markers": 3 }, verification_requirements=[ "temporal_sequence_verification", "chronological_consistency_check" ], investigation_confidence=1.0 - temporal_factor, research_completion_estimate=self._calculate_completion_estimate(2, temporal_factor), priority_score=self._calculate_priority_score(1.0 - temporal_factor, 0.6) ) def _cross_domain_analysis(self, state: KnowledgeState) -> AnalysisResult: """Analyze cross-domain issues""" domain_factor = min(1.0, len(state.knowledge_domains) / 5.0) return AnalysisResult( category=InquiryCategory.CROSS_REFERENCE_VALIDATION, basis_code="CROSS_DOMAIN_ALIGNMENT_CHECK", basis_kwargs={ "domain_count": len(state.knowledge_domains), "domains": list(state.knowledge_domains)[:3] }, verification_requirements=[ "cross_domain_correlation", "independent_verification" ], investigation_confidence=domain_factor, research_completion_estimate=self._calculate_completion_estimate(2, domain_factor), priority_score=self._calculate_priority_score(domain_factor, 0.5) ) def _default_analysis(self, state: KnowledgeState) -> AnalysisResult: """Default analysis for well-formed states""" return AnalysisResult( category=InquiryCategory.KNOWLEDGE_COMPLETENESS_EVALUATION, basis_code="BASELINE_VERIFICATION", basis_kwargs={ "confidence_score": state.confidence_score * 100, "rigor_score": state.methodological_rigor * 100 }, verification_requirements=["comprehensive_review"], investigation_confidence=0.3, research_completion_estimate=0.9, priority_score=2.0 # Low priority baseline check ) def _calculate_completion_estimate(self, requirement_count: int, confidence: float) -> float: """Calculate research completion estimate""" base = 0.5 requirement_impact = 0.9 ** requirement_count confidence_impact = confidence * 0.4 return min(0.95, base * requirement_impact + confidence_impact) def _calculate_priority_score(self, investigation_confidence: float, weight: float) -> float: """Calculate priority score with clear semantics""" base_score = investigation_confidence * weight return round(base_score * 10, 2) # === INQUIRY BASIS TEMPLATES === INQUIRY_BASIS_TEMPLATES = { "CONFIDENCE_ANOMALY_INVESTIGATION": { "template": "Confidence score of {score}% ({provenance}) differs from expected baseline of {expected}%", "investigation_focus": "confidence_validation" }, "METHODOLOGICAL_CONSISTENCY_QUESTION": { "template": "Methodological rigor rating of {rigor}% suggests review of {method_type} may be beneficial", "investigation_focus": "methodological_review" }, "PATTERN_DEVIATION_ANALYSIS": { "template": "Pattern completeness at {pattern_completeness}% with {expected_patterns} expected patterns", "investigation_focus": "pattern_analysis" }, "TEMPORAL_CONSISTENCY_CHECK": { "template": "Temporal markers: {marker_count} present, {expected_markers} expected", "investigation_focus": "temporal_validation" }, "CROSS_DOMAIN_ALIGNMENT_CHECK": { "template": "Cross-domain analysis across {domain_count} domains: {domains}", "investigation_focus": "cross_domain_validation" }, "BASELINE_VERIFICATION": { "template": "Baseline verification: confidence={confidence_score}%, rigor={rigor_score}%", "investigation_focus": "comprehensive_review" } } # === INQUIRY ARTIFACT === @dataclass(frozen=True) class InquiryArtifact: """Deterministic inquiry artifact with robust priority calculation""" artifact_id: str source_state_hash: str inquiry_category: InquiryCategory investigation_priority: int # 1-10 scale with clear semantics knowledge_domains_involved: Tuple[str, ...] basis_code: str inquiry_description: str verification_requirements: Tuple[str, ...] investigation_confidence: float research_completion_estimate: float confidence_provenance: str artifact_hash: str creation_context: 'CreationContext' @classmethod def create( cls, knowledge_state: KnowledgeState, analysis_result: AnalysisResult, basis_templates: Dict[str, Dict[str, Any]], creation_context: 'CreationContext' ) -> 'InquiryArtifact': """Create inquiry artifact with deterministic hash""" # Format inquiry description template_data = basis_templates.get(analysis_result.basis_code, {}) description_template = template_data.get("template", "Analysis required") inquiry_description = description_template.format(**analysis_result.basis_kwargs) # Calculate deterministic priority (1-10) priority_value = max(1, min(10, int(round(analysis_result.priority_score)))) # Generate deterministic hash hash_input = f"{knowledge_state.state_hash}:{analysis_result.category.value}:" hash_input += f"{analysis_result.basis_code}:{priority_value}:" hash_input += ":".join(analysis_result.verification_requirements) hash_input += creation_context.context_hash artifact_hash = hashlib.sha3_512(hash_input.encode()).hexdigest()[:32] artifact_id = f"inq_{artifact_hash[:16]}" return cls( artifact_id=artifact_id, source_state_hash=knowledge_state.state_hash, inquiry_category=analysis_result.category, investigation_priority=priority_value, knowledge_domains_involved=knowledge_state.knowledge_domains, basis_code=analysis_result.basis_code, inquiry_description=inquiry_description, verification_requirements=tuple(analysis_result.verification_requirements), investigation_confidence=analysis_result.investigation_confidence, research_completion_estimate=analysis_result.research_completion_estimate, confidence_provenance=knowledge_state.confidence_provenance, artifact_hash=artifact_hash, creation_context=creation_context ) def reference_information(self) -> Mapping[str, Any]: """Immutable reference information""" return MappingProxyType({ "artifact_id": self.artifact_id, "source_state": self.source_state_hash[:12], "inquiry_category": self.inquiry_category.value, "investigation_priority": self.investigation_priority, "priority_semantics": self._priority_semantics(), "knowledge_domains": list(self.knowledge_domains_involved), "basis": { "code": self.basis_code, "description": self.inquiry_description, "confidence_provenance": self.confidence_provenance }, "verification_requirements": list(self.verification_requirements), "investigation_confidence": round(self.investigation_confidence, 3), "research_completion_estimate": round(self.research_completion_estimate, 3), "artifact_hash": self.artifact_hash, "creation_context": self.creation_context.reference_data() }) def _priority_semantics(self) -> str: """Document priority semantics""" if self.investigation_priority >= 9: return "critical_immediate_attention" elif self.investigation_priority >= 7: return "high_priority_review" elif self.investigation_priority >= 5: return "moderate_priority" elif self.investigation_priority >= 3: return "low_priority_backlog" else: return "informational_only" # === CREATION CONTEXT === @dataclass(frozen=True) class CreationContext: """Immutable creation context""" system_version: str generation_timestamp: str research_environment: str deterministic_seed: Optional[int] context_hash: str = field(init=False) def __post_init__(self): hash_input = f"{self.system_version}:{self.generation_timestamp}:" hash_input += f"{self.research_environment}:{self.deterministic_seed or 'none'}" context_hash = hashlib.sha3_512(hash_input.encode()).hexdigest()[:32] object.__setattr__(self, 'context_hash', context_hash) @classmethod def create( cls, research_environment: str = "knowledge_discovery_system", deterministic_seed: Optional[int] = None, clock_source: Callable[[], datetime] = datetime.now ) -> 'CreationContext': """Factory method with optional determinism""" return cls( system_version="structural_inquiry_v2.5", generation_timestamp=clock_source().isoformat(), research_environment=research_environment, deterministic_seed=deterministic_seed ) def reference_data(self) -> Mapping[str, Any]: return MappingProxyType({ "system_version": self.system_version, "generation_timestamp": self.generation_timestamp, "research_environment": self.research_environment, "deterministic_mode": self.deterministic_seed is not None, "context_hash": self.context_hash[:12] }) # === INQUIRY GENERATOR === class InquiryGenerator: """ Deterministic inquiry generator with pluggable analysis """ def __init__( self, analyzer: Optional[InquiryAnalyzer] = None, creation_context: Optional[CreationContext] = None, deterministic_seed: Optional[int] = None ): self.analyzer = analyzer or DefaultInquiryAnalyzer(INQUIRY_BASIS_TEMPLATES) self.creation_context = creation_context or CreationContext.create( deterministic_seed=deterministic_seed ) self.generated_inquiries: List[InquiryArtifact] = [] # Set deterministic seed if provided if deterministic_seed is not None: np.random.seed(deterministic_seed) def generate_inquiries( self, knowledge_states: Tuple[KnowledgeState, ...], confidence_threshold: float = 0.7 ) -> Tuple[InquiryArtifact, ...]: """Generate inquiries from knowledge states""" inquiries = [] for state in knowledge_states: # Use analyzer to get multiple potential inquiries analysis_results = self.analyzer.analyze(state) for result in analysis_results: # Only generate inquiries that meet threshold if result.investigation_confidence >= confidence_threshold: inquiry = InquiryArtifact.create( knowledge_state=state, analysis_result=result, basis_templates=INQUIRY_BASIS_TEMPLATES, creation_context=self.creation_context ) inquiries.append(inquiry) self.generated_inquiries.append(inquiry) return tuple(inquiries) # === RESEARCH SYSTEM INTERFACE === class ResearchSystem: """Abstract research system interface""" async def research(self, topic: str, **kwargs) -> Dict[str, Any]: """Conduct research on topic (must be implemented)""" raise NotImplementedError # === INTEGRATED KNOWLEDGE DISCOVERY === class IntegratedKnowledgeDiscovery: """ Integrated system with clear async boundaries and determinism """ def __init__( self, research_system: ResearchSystem, deterministic_seed: Optional[int] = None ): """ Initialize with concrete research system Args: research_system: Must implement ResearchSystem interface deterministic_seed: Optional seed for reproducible results """ if not isinstance(research_system, ResearchSystem): raise TypeError("research_system must implement ResearchSystem interface") self.research_system = research_system self.deterministic_seed = deterministic_seed self.inquiry_generator = InquiryGenerator(deterministic_seed=deterministic_seed) self.discovery_history: List[Dict[str, Any]] = [] async def conduct_research_with_inquiries( self, research_topic: str, confidence_threshold: float = 0.7, **research_kwargs ) -> Dict[str, Any]: """Conduct research and generate knowledge inquiries""" # 1. Conduct research using the provided system research_result = await self.research_system.research(research_topic, **research_kwargs) # 2. Convert to knowledge state knowledge_state = self._convert_to_knowledge_state(research_result) # 3. Generate inquiries knowledge_states = (knowledge_state,) inquiry_artifacts = self.inquiry_generator.generate_inquiries( knowledge_states, confidence_threshold ) # 4. Create inquiry collection inquiry_collection = { "collection_id": f"inq_coll_{hashlib.sha256(knowledge_state.state_hash.encode()).hexdigest()[:16]}", "research_topic": research_topic, "knowledge_state_hash": knowledge_state.state_hash[:12], "inquiry_count": len(inquiry_artifacts), "generation_timestamp": datetime.utcnow().isoformat(), "confidence_threshold": confidence_threshold, "deterministic_mode": self.deterministic_seed is not None, "inquiries": [i.reference_information() for i in inquiry_artifacts] } # 5. Store and return self.discovery_history.append({ "research_topic": research_topic, "research_result": research_result, "knowledge_state": knowledge_state, "inquiry_collection": inquiry_collection, "inquiry_artifacts": inquiry_artifacts }) return { "research_topic": research_topic, "research_summary": { "confidence_score": research_result.get("confidence_score", 0.5), "methodological_rigor": research_result.get("methodological_rigor", 0.5), "domains": research_result.get("knowledge_domains", []) }, "inquiry_generation": { "inquiries_generated": len(inquiry_artifacts), "inquiry_collection_id": inquiry_collection["collection_id"], "priority_distribution": self._summarize_priorities(inquiry_artifacts), "confidence_threshold_met": len(inquiry_artifacts) > 0 } } def _convert_to_knowledge_state( self, research_result: Dict[str, Any] ) -> KnowledgeState: """Convert research result to knowledge state""" # Extract with provenance tracking confidence_score = research_result.get("confidence_score", 0.5) confidence_provenance = research_result.get( "confidence_provenance", "derived_from_research" ) # Determine state type if confidence_score < 0.6: state_type = KnowledgeStateType.SOURCE_VERIFICATION elif "pattern" in str(research_result.get("structural_description", "")).lower(): state_type = KnowledgeStateType.PATTERN_DETECTION elif len(research_result.get("knowledge_domains", [])) > 2: state_type = KnowledgeStateType.CROSS_DOMAIN_SYNTHESIS else: state_type = KnowledgeStateType.DATA_CORRELATION # Generate patterns deterministically if self.deterministic_seed is not None: # Deterministic pattern generation pattern_seed = hash(f"{self.deterministic_seed}:{research_result.get('content_hash', '')}") np.random.seed(pattern_seed % (2**32)) data_patterns = tuple(np.random.randn(8).tolist()) else: # Use provided pattern or generate default provided_patterns = research_result.get("data_patterns", []) data_patterns = tuple(provided_patterns[:8]) if provided_patterns else tuple(np.sin(np.arange(8) * 0.785).tolist()) # Generate structural description structural_description = self._generate_structural_description(research_result) # Generate validation signature validation_signature = hashlib.sha3_512( f"{research_result.get('content_hash', '')}:{self.deterministic_seed or 'stochastic'}".encode() ).hexdigest()[:32] return KnowledgeState( state_id=f"knowledge_state_{research_result.get('content_hash', 'unknown')[:12]}", state_type=state_type, confidence_score=confidence_score, confidence_provenance=confidence_provenance, methodological_rigor=research_result.get("methodological_rigor", 0.5), data_patterns=data_patterns, knowledge_domains=tuple(research_result.get("knowledge_domains", ["general"])), temporal_markers=( research_result.get("timestamp", ""), datetime.utcnow().isoformat() ), research_constraints=self._extract_constraints(research_result), structural_description=structural_description, validation_signature=validation_signature ) def _generate_structural_description( self, research_result: Dict[str, Any] ) -> str: """Generate structural description""" components = [] confidence = research_result.get("confidence_score", 0.5) provenance = research_result.get("confidence_provenance", "unstated") if confidence < 0.6: components.append(f"Low confidence ({confidence:.2f}) from {provenance}") elif confidence > 0.8: components.append(f"High confidence ({confidence:.2f}) from {provenance}") rigor = research_result.get("methodological_rigor", 0.5) if rigor < 0.6: components.append(f"Methodological rigor: {rigor:.2f}") domains = research_result.get("knowledge_domains", []) if len(domains) > 2: components.append(f"Cross-domain: {len(domains)} domains") if not components: components.append("Standard research structure") return f"{KNOWLEDGE_NODE} " + "; ".join(components) def _extract_constraints( self, research_result: Dict[str, Any] ) -> Tuple[str, ...]: """Extract research constraints""" constraints = [] if research_result.get("confidence_score", 0) < 0.7: constraints.append("confidence_verification_needed") if research_result.get("methodological_rigor", 0) < 0.6: constraints.append("methodology_review_recommended") if not research_result.get("source_references", []): constraints.append("source_corroboration_required") if not constraints: constraints.append("standard_verification_protocol") return tuple(constraints) def _summarize_priorities( self, inquiry_artifacts: Tuple[InquiryArtifact, ...] ) -> Dict[str, Any]: """Summarize inquiry priorities with clear semantics""" if not inquiry_artifacts: return {"message": "No inquiries generated", "priority_levels": {}} priority_summary = {} for artifact in inquiry_artifacts: priority = artifact.investigation_priority if priority not in priority_summary: priority_summary[priority] = { "count": 0, "domains": set(), "semantics": artifact._priority_semantics() } priority_summary[priority]["count"] += 1 priority_summary[priority]["domains"].update(artifact.knowledge_domains_involved) # Convert sets to lists for priority in priority_summary: priority_summary[priority]["domains"] = list(priority_summary[priority]["domains"]) return { "total_priorities": len(priority_summary), "highest_priority": max(priority_summary.keys()), "priority_distribution": priority_summary } def get_statistics(self) -> Dict[str, Any]: """Get system statistics""" total_inquiries = len(self.inquiry_generator.generated_inquiries) # Calculate category distribution category_counts = {} for inquiry in self.inquiry_generator.generated_inquiries: category = inquiry.inquiry_category.value category_counts[category] = category_counts.get(category, 0) + 1 # Calculate average metrics if total_inquiries > 0: avg_confidence = np.mean([i.investigation_confidence for i in self.inquiry_generator.generated_inquiries]) avg_priority = np.mean([i.investigation_priority for i in self.inquiry_generator.generated_inquiries]) else: avg_confidence = 0.0 avg_priority = 0.0 return { "system": "Integrated Knowledge Discovery v2.5", "research_sessions": len(self.discovery_history), "total_inquiries_generated": total_inquiries, "category_distribution": category_counts, "average_investigation_confidence": round(float(avg_confidence), 3), "average_investigation_priority": round(float(avg_priority), 1), "deterministic_mode": self.deterministic_seed is not None, "engineering_properties": { "immutable_data_structures": True, "deterministic_hashes": True, "pluggable_analyzers": True, "clear_async_boundaries": True, "priority_semantics_documented": True } } # === CONCRETE RESEARCH SYSTEM EXAMPLE === class ConcreteResearchSystem(ResearchSystem): """Example research system with proper async implementation""" def __init__(self, deterministic_seed: Optional[int] = None): self.deterministic_seed = deterministic_seed if deterministic_seed is not None: np.random.seed(deterministic_seed) async def research(self, topic: str, **kwargs) -> Dict[str, Any]: """Conduct research (simulated for example)""" # Simulate async research delay import asyncio await asyncio.sleep(0.1) # Simulate network/processing # Generate deterministic or random results if self.deterministic_seed is not None: # Deterministic based on topic topic_hash = hash(topic) % 1000 confidence = 0.5 + (topic_hash % 500) / 1000 # 0.5-1.0 rigor = 0.4 + (topic_hash % 600) / 1000 # 0.4-1.0 else: # Random results confidence = np.random.random() * 0.3 + 0.5 # 0.5-0.8 rigor = np.random.random() * 0.4 + 0.4 # 0.4-0.8 return { "topic": topic, "content_hash": hashlib.sha256(topic.encode()).hexdigest()[:32], "confidence_score": confidence, "confidence_provenance": "simulated_analysis", "methodological_rigor": rigor, "knowledge_domains": self._identify_domains(topic), "structural_description": f"Research on {topic}", "timestamp": datetime.utcnow().isoformat(), "data_patterns": np.sin(np.arange(10) * 0.628).tolist(), "source_references": [f"ref_{i}" for i in range(np.random.randint(1, 4))] } def _identify_domains(self, topic: str) -> List[str]: """Identify domains from topic""" domains = [] topic_lower = topic.lower() if any(word in topic_lower for word in ["quantum", "physics"]): domains.append("physics") if any(word in topic_lower for word in ["history", "ancient"]): domains.append("history") if any(word in topic_lower for word in ["consciousness", "mind"]): domains.append("psychology") if any(word in topic_lower for word in ["pattern", "analysis"]): domains.append("mathematics") return domains if domains else ["interdisciplinary"] # === TEST UTILITIES === def run_deterministic_test() -> bool: """Test deterministic reproducibility""" print("Testing deterministic reproducibility...") # Run with same seed research_system1 = ConcreteResearchSystem(deterministic_seed=42) system1 = IntegratedKnowledgeDiscovery(research_system1, deterministic_seed=42) research_system2 = ConcreteResearchSystem(deterministic_seed=42) system2 = IntegratedKnowledgeDiscovery(research_system2, deterministic_seed=42) import asyncio # Run same research loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) result1 = loop.run_until_complete( system1.conduct_research_with_inquiries("Test topic") ) result2 = loop.run_until_complete( system2.conduct_research_with_inquiries("Test topic") ) loop.close() # Compare results inquiries1 = result1["inquiry_generation"]["inquiries_generated"] inquiries2 = result2["inquiry_generation"]["inquiries_generated"] print(f" System 1 inquiries: {inquiries1}") print(f" System 2 inquiries: {inquiries2}") print(f" Results identical: {inquiries1 == inquiries2}") return inquiries1 == inquiries2 # === MAIN === async def main(): """Demonstrate the system""" print(f""" {'='*70} STRUCTURAL INQUIRY SYSTEM v2.5 Engineering-Focused Knowledge Discovery {'='*70} """) # Run deterministic test if run_deterministic_test(): print(f"\n{VALIDATION_SYMBOL} Deterministic reproducibility verified") else: print(f"\n{INQUIRY_MARKER} Non-deterministic behavior detected") # Create and run system research_system = ConcreteResearchSystem() discovery_system = IntegratedKnowledgeDiscovery(research_system) topics = [ "Quantum pattern analysis techniques", "Historical methodology consistency", "Cross-domain verification protocols" ] for i, topic in enumerate(topics, 1): print(f"\n{PATTERN_RECOGNITION} RESEARCH SESSION {i}: {topic}") print(f"{'-'*60}") result = await discovery_system.conduct_research_with_inquiries( topic, confidence_threshold=0.6 ) inquiries = result["inquiry_generation"]["inquiries_generated"] priorities = result["inquiry_generation"]["priority_distribution"] print(f" {VALIDATION_SYMBOL} Research completed") print(f" {KNOWLEDGE_NODE} Inquiries generated: {inquiries}") if inquiries > 0: for priority, data in priorities.get("priority_distribution", {}).items(): semantics = data.get("semantics", "unknown") print(f" Priority {priority} ({semantics}): {data['count']} inquiries") # Display statistics stats = discovery_system.get_statistics() print(f"\n{'='*70}") print("SYSTEM STATISTICS") print(f"{'='*70}") print(f"\nResearch sessions: {stats['research_sessions']}") print(f"Total inquiries: {stats['total_inquiries_generated']}") print(f"\nEngineering properties:") for prop, value in stats["engineering_properties"].items(): status = "✓" if value else "✗" print(f" {status} {prop}: {value}") if __name__ == "__main__": import asyncio try: asyncio.run(main()) except KeyboardInterrupt: print(f"\n\n{KNOWLEDGE_NODE} System shutdown complete.")