"""
Enhanced OpenEnded Philosophy MCP Server with Process Management
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
### Architectural Enhancements
This implementation incorporates comprehensive process management patterns
following MCP best practices to prevent resource leaks and ensure
graceful shutdown behavior.
#### Core Infrastructure Components:
- Signal handling for SIGTERM/SIGINT
- Background task tracking and cleanup
- Process lifecycle management
- Graceful shutdown orchestration
- Resource leak prevention
"""
import asyncio
import atexit
import signal
import sys
import uuid
from dataclasses import dataclass, field
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, Optional
import numpy as np
from anyio.streams.memory import MemoryObjectReceiveStream, MemoryObjectSendStream
from mcp.server import Server
from mcp.types import TextContent, Tool
# Define Message type for anyio streams
Message = Any
# Define InitializationOptions class
@dataclass
class InitializationOptions:
"""Initialization options for the MCP server."""
config: Optional[Dict[str, Any]] = None
from .core import (
CoherenceLandscape,
DynamicPluralismFramework,
EmergentCoherenceNode,
FallibilisticInference,
LanguageGameProcessor,
)
from .nars import NARSManager, NARSMemory, NARSReasoning
from .operations import PhilosophicalOperations
from .prompts import get_prompt_by_name, list_available_prompts
from .utils import safe_json_dumps, setup_logging
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
# Global Process Management Infrastructure
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
# Global tracking for process management
background_tasks: set[asyncio.Task] = set()
running_processes: dict[str, Any] = {}
_shutdown_initiated = False
# Configure logging with academic formatting
logger = setup_logging(__name__)
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
@dataclass
class PhilosophicalContext:
"""
Enhanced contextual substrate for philosophical operations.
### Architectural Components:
- Language game specifications
- Confidence threshold parameters
- Openness coefficient calibration
- Meta-learning enablement flags
- Comprehensive revision tracking
"""
language_game: str = "general_inquiry"
confidence_threshold: float = 0.7
openness_coefficient: float = 0.9
meta_learning_enabled: bool = True
revision_history: list[dict[str, Any]] = field(default_factory=list)
session_id: str = field(default_factory=lambda: str(uuid.uuid4()))
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
class OpenEndedPhilosophyServer:
"""
Enhanced MCP Server Implementation for Open-Ended Philosophical Framework
### Theoretical Foundation
This implementation operationalizes non-foundationalist philosophical inquiry
through computational pragmatism, incorporating:
#### Mathematical Substrate:
- Adaptive coherence landscapes
- Fallibilistic inference engines
- Dynamic pluralism frameworks
- Uncertainty propagation models
#### Process Management Architecture:
- Signal handling integration
- Background task orchestration
- Resource lifecycle management
- Graceful shutdown protocols
#### Coherence Function:
C(t) = Σ_{i} w_i(t) × φ_i(x,t) + λ × Emergence_Term(t)
Where:
- C(t): Total coherence at time t
- w_i(t): Adaptive weights for perspective i
- φ_i(x,t): Perspective evaluation function
- λ: Openness coefficient
- Emergence_Term(t): Novel pattern detection
"""
def __init__(self) -> None:
"""
Initialize philosophical substrate with enhanced process management.
### Initialization Protocol:
1. Configure MCP server infrastructure
2. Initialize philosophical context
3. Instantiate core computational components
4. Register language game processors
5. Enable process tracking
"""
self.server = Server("openended-philosophy")
self.context = PhilosophicalContext()
self._active_operations: set[str] = set()
# Core computational components
self.pluralism_framework = DynamicPluralismFramework(
openness_coefficient=self.context.openness_coefficient
)
self.coherence_landscape = CoherenceLandscape(dimensionality='variable')
self.inference_engine = FallibilisticInference()
# Initialize NARS components
self.nars_manager = NARSManager()
self.nars_memory = NARSMemory(
memory_file=Path("philosophy_nars_memory.json"),
attention_size=30,
recency_size=10
)
self.nars_reasoning = NARSReasoning(
nars_manager=self.nars_manager,
nars_memory=self.nars_memory
)
# Track NARS initialization
self._nars_initialized = False
# Language game registry with enhanced processors
self.language_games: dict[str, LanguageGameProcessor] = {
"scientific_discourse": LanguageGameProcessor(
"scientific",
{
"empirical_verification": True,
"mathematical_formalism": True,
"peer_review": True
}
),
"ethical_deliberation": LanguageGameProcessor(
"ethical",
{
"normative_reasoning": True,
"value_pluralism": True,
"moral_imagination": True
}
),
"aesthetic_judgment": LanguageGameProcessor(
"aesthetic",
{
"subjective_validity": True,
"cultural_context": True,
"expressive_content": True
}
),
"ordinary_language": LanguageGameProcessor(
"ordinary",
{
"pragmatic_success": True,
"family_resemblance": True,
"contextual_meaning": True
}
)
}
# Initialize philosophical operations using the separated operations module
self.operations = PhilosophicalOperations(
pluralism_framework=self.pluralism_framework,
coherence_landscape=self.coherence_landscape,
inference_engine=self.inference_engine,
language_games=self.language_games,
nars_manager=self.nars_manager,
nars_memory=self.nars_memory,
nars_reasoning=self.nars_reasoning
)
logger.info(f"OpenEnded Philosophy Server initialized with session: {self.context.session_id}")
logger.debug(f"Full philosophical context: {safe_json_dumps(self.context)}")
# ─────────────────────────────────────────────────────────────────────────
def setup_handlers(self) -> None:
"""
Configure MCP protocol handlers with enhanced error management.
### Handler Configuration Protocol:
1. Tool enumeration handlers
2. Tool execution handlers
3. Error boundary implementation
4. Resource cleanup integration
"""
@self.server.list_tools()
async def list_tools() -> list[Tool]:
"""
Enumerate available philosophical operations with comprehensive metadata.
### Available Operations:
- Concept analysis through interpretive lenses
- Coherence landscape exploration
- Contextual meaning derivation
- Fallibilistic insight generation
- Philosophical hypothesis testing
"""
return [
Tool(
name="analyze_concept",
description=(
"Analyzes a concept through multiple interpretive lenses "
"without claiming ontological priority. Returns provisional "
"insights with uncertainty quantification and revision conditions."
),
inputSchema={
"type": "object",
"properties": {
"concept": {
"type": "string",
"description": "The concept to analyze"
},
"context": {
"type": "string",
"description": "Contextual domain (e.g., neuroscience, ethics)"
},
"perspectives": {
"type": "array",
"items": {"type": "string"},
"description": "Optional specific perspectives to include"
},
"confidence_threshold": {
"type": "number",
"description": "Minimum confidence for insights (0-1)",
"default": 0.7,
"minimum": 0.0,
"maximum": 1.0
}
},
"required": ["concept", "context"]
}
),
Tool(
name="explore_coherence",
description=(
"Maps provisional coherence patterns in conceptual space. "
"Identifies stable regions, phase transitions, and emergent "
"structures with topological analysis."
),
inputSchema={
"type": "object",
"properties": {
"domain": {
"type": "string",
"description": "Conceptual domain to explore"
},
"depth": {
"type": "integer",
"description": "Exploration depth (1-5)",
"default": 3,
"minimum": 1,
"maximum": 5
},
"allow_revision": {
"type": "boolean",
"description": "Allow landscape revision during exploration",
"default": True
}
},
"required": ["domain"]
}
),
Tool(
name="contextualize_meaning",
description=(
"Derives contextual semantics through Wittgensteinian "
"language game analysis. Shows how meaning emerges from "
"use in specific practices and forms of life."
),
inputSchema={
"type": "object",
"properties": {
"expression": {
"type": "string",
"description": "Expression to contextualize"
},
"language_game": {
"type": "string",
"description": "Language game context",
"enum": list(self.language_games.keys())
},
"trace_genealogy": {
"type": "boolean",
"description": "Include semantic evolution history",
"default": False
}
},
"required": ["expression", "language_game"]
}
),
Tool(
name="generate_insights",
description=(
"Produces fallibilistic insights with built-in uncertainty "
"quantification, revision conditions, and contradiction "
"mapping through multi-perspectival synthesis."
),
inputSchema={
"type": "object",
"properties": {
"phenomenon": {
"type": "string",
"description": "Phenomenon to investigate"
},
"perspectives": {
"type": "array",
"items": {"type": "string"},
"description": "Interpretive perspectives to apply"
},
"depth": {
"type": "integer",
"description": "Analysis depth (1-5)",
"default": 3,
"minimum": 1,
"maximum": 5
},
"include_contradictions": {
"type": "boolean",
"description": "Explicitly include contradictory insights",
"default": True
}
},
"required": ["phenomenon"]
}
),
Tool(
name="test_philosophical_hypothesis",
description=(
"Tests philosophical hypotheses through coherence analysis, "
"pragmatic evaluation, and multi-domain testing with "
"confidence metrics and revision recommendations."
),
inputSchema={
"type": "object",
"properties": {
"hypothesis": {
"type": "string",
"description": "The hypothesis to test"
},
"test_domains": {
"type": "array",
"items": {"type": "string"},
"description": "Domains for testing"
},
"criteria": {
"type": "object",
"description": "Custom evaluation criteria"
}
},
"required": ["hypothesis"]
}
),
Tool(
name="recursive_self_analysis",
description=(
"Apply the system's own analytical tools recursively to examine "
"its reasoning processes and generate meta-philosophical insights "
"about its own operations and effectiveness."
),
inputSchema={
"type": "object",
"properties": {
"analysis_result": {
"type": "object",
"description": "Previous analysis result to examine recursively"
},
"analysis_type": {
"type": "string",
"description": "Type of analysis performed",
"enum": ["concept_analysis", "coherence_exploration", "insight_generation", "hypothesis_testing"]
},
"meta_depth": {
"type": "integer",
"description": "Depth of recursive analysis (1-3)",
"default": 2,
"minimum": 1,
"maximum": 3
}
},
"required": ["analysis_result", "analysis_type"]
}
)
]
@self.server.call_tool()
async def call_tool(name: str, arguments: dict[str, Any]) -> list[TextContent]:
"""
Execute philosophical operations with comprehensive error management.
### Execution Protocol:
1. Operation tracking registration
2. Timeout-bounded execution
3. Error boundary enforcement
4. Resource cleanup guarantee
"""
operation_id = str(uuid.uuid4())
self._active_operations.add(operation_id)
try:
logger.info(f"Executing philosophical operation: {name} [{operation_id}]")
logger.debug(f"Arguments: {arguments}")
# Create timeout-bounded execution
async def execute_with_timeout() -> dict[str, Any]:
if name == "analyze_concept":
return await self.operations.analyze_concept(**arguments)
elif name == "explore_coherence":
return await self.operations.explore_coherence(**arguments)
elif name == "contextualize_meaning":
return await self.operations.contextualize_meaning(**arguments)
elif name == "generate_insights":
return await self.operations.generate_insights(**arguments)
elif name == "test_philosophical_hypothesis":
return await self.operations.test_philosophical_hypothesis(**arguments)
elif name == "recursive_self_analysis":
return await self.operations.analyze_own_reasoning_process(**arguments)
else:
raise ValueError(f"Unknown philosophical operation: {name}")
# Execute with timeout (30 seconds default)
result = await asyncio.wait_for(
execute_with_timeout(),
timeout=30.0
)
logger.info(f"Completed philosophical operation: {name} [{operation_id}]")
return [TextContent(type="text", text=safe_json_dumps(result, indent=2))]
except asyncio.TimeoutError:
logger.error(f"Operation timeout: {name} [{operation_id}]")
error_response = {
"error": "Operation timeout",
"operation": name,
"epistemic_status": "temporal_limitation",
"suggestion": "Consider reducing analysis depth or scope"
}
return [TextContent(type="text", text=safe_json_dumps(error_response, indent=2))]
except Exception as e:
logger.error(f"Operation error in {name} [{operation_id}]: {e}", exc_info=True)
error_response = {
"error": str(e),
"operation": name,
"epistemic_status": "computational_limitation",
"suggestion": "Consider reformulating the philosophical inquiry",
"operation_id": operation_id
}
return [TextContent(type="text", text=safe_json_dumps(error_response, indent=2))]
finally:
# Ensure cleanup
self._active_operations.discard(operation_id)
logger.debug(f"Cleaned up operation: {name} [{operation_id}]")
# ─────────────────────────────────────────────────────────────────────
# Prompts Handlers
# ─────────────────────────────────────────────────────────────────────
@self.server.list_prompts()
async def list_prompts():
"""List available philosophical prompt templates."""
logger.info("Listing available philosophical prompts")
try:
prompts = list_available_prompts()
logger.debug(f"Retrieved {len(prompts)} prompts")
return prompts
except Exception as e:
logger.error(f"Error listing prompts: {e}", exc_info=True)
raise
@self.server.get_prompt()
async def get_prompt(name: str, arguments: dict[str, str] | None = None):
"""Get specific philosophical prompt with processed arguments."""
logger.info(f"Retrieving prompt: {name}")
logger.debug(f"Prompt arguments: {arguments}")
try:
prompt_result = get_prompt_by_name(name, arguments)
logger.debug(f"Generated prompt with {len(prompt_result.messages)} messages")
return prompt_result
except ValueError as e:
logger.error(f"Invalid prompt request: {e}")
raise
except Exception as e:
logger.error(f"Error generating prompt {name}: {e}", exc_info=True)
raise
async def _apply_schema_to_concept(
self,
concept_node: EmergentCoherenceNode,
schema: dict[str, Any],
active_game: LanguageGameProcessor
) -> dict[str, Any]:
"""Apply interpretive schema to concept node."""
from .utils import pragmatic_evaluation, semantic_similarity
logger.debug(f"Applying schema {schema.get('perspective')} to concept")
# Extract concept features for analysis
concept_features = {
'features': list(concept_node.pattern.content.keys()),
'contexts': [active_game.game_type],
'confidence': concept_node.pattern.confidence
}
# Extract schema commitments and criteria
perspective = schema.get('perspective', 'unknown')
commitments = schema.get('core_commitments', [])
criteria = schema.get('evaluation_criteria', [])
# Calculate semantic alignment between concept and schema
schema_features = {
'features': commitments + criteria,
'contexts': [perspective],
'uses': schema.get('conceptual_priorities', [])
}
alignment_score = semantic_similarity(concept_features, schema_features, method="wittgenstein")
# Apply perspective-specific interpretation logic
interpretation = self._generate_perspective_interpretation(
concept_node.pattern.content,
perspective,
commitments
)
# Calculate coherence based on schema fit and concept stability
base_coherence = alignment_score * concept_node.pattern.calculate_stability()
game_modifier = active_game.assess_pattern_fit(concept_node.pattern.content)
coherence_score = base_coherence * game_modifier
# Identify key features through schema lens
key_features = self._extract_schema_relevant_features(
concept_node.pattern.content,
commitments,
criteria
)
# Calculate uncertainty based on multiple factors
uncertainty = self._calculate_schema_uncertainty(
alignment_score,
concept_node.pattern.confidence,
coherence_score,
len(key_features)
)
interpretation_details = {
"derived_meaning": interpretation,
"key_features": key_features,
"coherence": float(np.clip(coherence_score, 0.0, 1.0)),
"uncertainty": float(np.clip(uncertainty, 0.0, 1.0)),
"alignment_score": float(alignment_score),
"schema_commitments": commitments[:3], # Top 3 for brevity
"applicability": float(game_modifier)
}
# Evaluate pragmatic value of applying this schema (Dewey/James approach)
schema_solution = {
'addresses': [f"interprets_{concept_node.pattern.content.get('term', 'concept')}"],
'benefits': [f"provides_{perspective}_perspective", "enables_contextual_understanding"],
'modular': True, # Schemas are modular by nature
'context_sensitive': True, # Philosophical interpretations are context-sensitive
'dependencies': commitments,
'assumptions': criteria
}
interpretation_problem = {
'requirements': [
'provide_meaningful_interpretation',
'maintain_conceptual_coherence',
'enable_further_inquiry'
]
}
pragmatic_assessment = pragmatic_evaluation(schema_solution, interpretation_problem)
# Add pragmatic evaluation results
interpretation_details.update({
"pragmatic_value": pragmatic_assessment['overall'],
"pragmatic_scores": pragmatic_assessment['scores'],
"pragmatic_recommendations": pragmatic_assessment['recommendations'][:2] # Top 2 for brevity
})
return interpretation_details
def _calculate_interpretation_confidence(
self,
interpretation: dict[str, Any],
active_game: LanguageGameProcessor
) -> float:
"""Calculate interpretation confidence."""
coherence = interpretation.get("coherence", 0.0)
game_factor = active_game.get_confidence_modifier()
base_confidence = coherence * 0.8
final_confidence = np.clip(base_confidence + game_factor, 0.0, 1.0)
return float(final_confidence)
def _synthesize_analyses(self, analysis_results: list[dict[str, Any]]) -> dict[str, Any]:
"""Synthesize multiple analyses using coherence theory and pragmatic evaluation."""
from .utils import coherence_metrics, pragmatic_evaluation, semantic_similarity
if not analysis_results:
return {"summary": "No analyses to synthesize", "overall_confidence": 0.0}
# Extract core information for synthesis
perspectives = [r.get("perspective", "unknown") for r in analysis_results]
interpretations = [r.get("interpretation", {}) for r in analysis_results]
confidences = [r.get("confidence", 0.0) for r in analysis_results]
# Create propositions from interpretations for coherence analysis
propositions = []
for i, (perspective, interpretation, confidence) in enumerate(zip(perspectives, interpretations, confidences, strict=True)):
# Extract interpretive content
content = interpretation.get("derived_meaning", "") if isinstance(interpretation, dict) else str(interpretation)
key_features = interpretation.get("key_features", []) if isinstance(interpretation, dict) else []
propositions.append({
'id': i,
'content': content,
'confidence': confidence,
'concepts': key_features,
'perspective': perspective
})
# Generate relations between propositions based on semantic similarity
relations = []
for i, prop1 in enumerate(propositions):
for j, prop2 in enumerate(propositions[i+1:], i+1):
# Check semantic similarity of interpretations
content1_words = set(prop1['content'].lower().split()) if prop1['content'] else set()
content2_words = set(prop2['content'].lower().split()) if prop2['content'] else set()
if content1_words and content2_words:
features1 = {'features': list(content1_words)}
features2 = {'features': list(content2_words)}
similarity = semantic_similarity(features1, features2, method="jaccard")
if similarity > 0.3:
relations.append((i, j, 'supports'))
elif similarity > 0.1:
relations.append((i, j, 'analogous'))
# Check for complementary perspectives
perspective_complements = {
('analytical', 'phenomenological'): 'complementary',
('empiricist', 'rationalist'): 'tension',
('pragmatist', 'analytical'): 'complementary',
('critical', 'virtue_ethics'): 'complementary',
('existentialist', 'analytical'): 'tension'
}
perspective_pair = tuple(sorted([prop1['perspective'], prop2['perspective']]))
if perspective_pair in perspective_complements:
relation_type = perspective_complements[perspective_pair]
relations.append((i, j, relation_type))
# Calculate coherence metrics
coherence_scores = coherence_metrics(propositions, relations)
# Generate synthesis narrative
synthesis_narrative = self._create_synthesis_narrative(
propositions, relations, coherence_scores, perspectives
)
# Identify emergent themes
emergent_themes = self._identify_emergent_themes(propositions, relations)
# Calculate synthetic confidence using multiple factors
confidence_factors = {
'coherence': coherence_scores['overall_coherence'],
'evidence_quantity': min(len(analysis_results) / 5.0, 1.0),
'average_individual_confidence': float(np.mean(confidences)),
'perspective_diversity': min(len(set(perspectives)) / 4.0, 1.0),
'constraint_satisfaction': coherence_scores['constraint_satisfaction']
}
synthetic_confidence = (
0.3 * confidence_factors['coherence'] +
0.2 * confidence_factors['evidence_quantity'] +
0.25 * confidence_factors['average_individual_confidence'] +
0.15 * confidence_factors['perspective_diversity'] +
0.1 * confidence_factors['constraint_satisfaction']
)
# Identify areas of convergence and divergence
convergences = self._identify_convergences(propositions, relations)
divergences = self._identify_divergences(propositions, relations)
# Pragmatic evaluation of synthesis quality
synthesis_solution = {
"content": synthesis_narrative,
"confidence": synthetic_confidence,
"perspectives": perspectives,
"addresses": convergences,
"modular": len(emergent_themes) > 1,
"context_sensitive": len(set(perspectives)) > 2,
"benefits": [f"Integrates {len(perspectives)} perspectives", "Provides coherent synthesis"],
"risks": divergences
}
problem_context = {
"requirements": ["coherent_integration", "perspective_synthesis", "epistemic_humility"],
"complexity": "high"
}
pragmatic_assessment = pragmatic_evaluation(synthesis_solution, problem_context)
# Meta-analysis of philosophical approaches
philosophical_families = [self._identify_philosophical_family(p) for p in perspectives]
family_distribution = {family: philosophical_families.count(family) for family in set(philosophical_families)}
return {
"summary": synthesis_narrative,
"overall_confidence": float(np.clip(synthetic_confidence, 0.0, 1.0)),
"contributing_perspectives": perspectives,
"philosophical_families": family_distribution,
"coherence_metrics": coherence_scores,
"confidence_factors": confidence_factors,
"emergent_themes": emergent_themes,
"convergences": convergences,
"divergences": divergences,
"synthetic_insights": self._generate_synthetic_insights(propositions, relations, coherence_scores),
"epistemic_assessment": self._assess_synthetic_epistemic_status(synthetic_confidence, coherence_scores),
"integration_quality": self._assess_integration_quality(propositions, relations, perspectives),
"pragmatic_assessment": pragmatic_assessment,
"meta_philosophical_observations": self._generate_meta_philosophical_observations(
perspectives, family_distribution, coherence_scores
)
}
def _create_synthesis_narrative(
self,
propositions: list[dict[str, Any]],
relations: list[tuple[int, int, str]],
coherence_scores: dict[str, Any],
perspectives: list[str]
) -> str:
"""Create a coherent narrative synthesizing multiple perspective analyses."""
narrative_parts = []
# Opening synthesis
if len(propositions) == 1:
narrative_parts.append(
f"Single-perspective analysis from {propositions[0]['perspective']} approach "
f"provides {propositions[0]['confidence']:.1%} confident insights."
)
else:
# Multi-perspective opening
family_analysis = {}
for prop in propositions:
family = self._identify_philosophical_family(prop['perspective'])
if family not in family_analysis:
family_analysis[family] = []
family_analysis[family].append(prop['perspective'])
family_summary = []
for family, members in family_analysis.items():
if len(members) == 1:
family_summary.append(f"{family} ({members[0]})")
else:
family_summary.append(f"{family} ({len(members)} perspectives)")
narrative_parts.append(
f"Multi-perspectival analysis draws from {', '.join(family_summary)} traditions, "
f"achieving {coherence_scores['overall_coherence']:.1%} overall coherence."
)
# Coherence assessment
if coherence_scores['overall_coherence'] > 0.8:
narrative_parts.append(
"The perspectives demonstrate high mutual coherence, suggesting robust conceptual convergence."
)
elif coherence_scores['overall_coherence'] > 0.6:
narrative_parts.append(
"The perspectives show moderate coherence with both convergent insights and productive tensions."
)
else:
narrative_parts.append(
"The perspectives reveal significant divergences, highlighting the complexity of the phenomenon."
)
# Relation analysis
support_relations = [r for r in relations if r[2] == 'supports']
tension_relations = [r for r in relations if r[2] == 'tension']
complementary_relations = [r for r in relations if r[2] == 'complementary']
if support_relations:
narrative_parts.append(
f"{len(support_relations)} supportive connections reinforce shared insights across perspectives."
)
if complementary_relations:
narrative_parts.append(
f"{len(complementary_relations)} complementary relations suggest productive dialogue between approaches."
)
if tension_relations:
narrative_parts.append(
f"{len(tension_relations)} tensions reveal fundamental philosophical differences requiring careful integration."
)
# Synthetic conclusion
if coherence_scores['explanatory_breadth'] > 0.5:
narrative_parts.append(
"The synthesis provides substantial explanatory breadth across multiple dimensions of analysis."
)
return " ".join(narrative_parts)
def _identify_emergent_themes(
self,
propositions: list[dict[str, Any]],
relations: list[tuple[int, int, str]]
) -> list[dict[str, Any]]:
"""Identify themes that emerge from perspective interaction."""
themes = []
# Analyze content for recurring concepts
all_words = []
for prop in propositions:
if prop['content']:
words = [w.lower() for w in prop['content'].split() if len(w) > 4]
all_words.extend(words)
# Find frequently occurring philosophical concepts
from collections import Counter
word_counts = Counter(all_words)
# Philosophical keywords that indicate important themes
philosophical_keywords = {
'experience', 'consciousness', 'reality', 'truth', 'knowledge', 'meaning',
'existence', 'freedom', 'responsibility', 'virtue', 'justice', 'power',
'authentic', 'rational', 'empirical', 'practical', 'social', 'individual',
'universal', 'particular', 'objective', 'subjective', 'necessary', 'contingent'
}
# Identify emergent themes from word patterns
for word, count in word_counts.most_common(10):
if word in philosophical_keywords and count >= 2:
# Find propositions containing this theme
supporting_perspectives = []
for prop in propositions:
if word in prop['content'].lower():
supporting_perspectives.append(prop['perspective'])
if len(supporting_perspectives) >= 2:
themes.append({
'theme': word,
'frequency': count,
'supporting_perspectives': supporting_perspectives,
'emergence_strength': len(supporting_perspectives) / len(propositions),
'type': 'cross_perspective_convergence'
})
# Identify relational themes from perspective interactions
relation_types = {}
for _, _, rel_type in relations:
relation_types[rel_type] = relation_types.get(rel_type, 0) + 1
for rel_type, count in relation_types.items():
if count >= 2:
themes.append({
'theme': f"{rel_type}_dynamic",
'frequency': count,
'supporting_perspectives': 'multiple',
'emergence_strength': count / len(relations) if relations else 0,
'type': 'relational_pattern'
})
return themes
def _identify_convergences(
self,
propositions: list[dict[str, Any]],
relations: list[tuple[int, int, str]]
) -> list[str]:
"""Identify areas where perspectives converge."""
convergences = []
# Find concepts appearing in multiple high-confidence interpretations
concept_appearances = {}
for prop in propositions:
if prop['confidence'] > 0.6: # Only consider confident interpretations
concepts = prop.get('concepts', [])
for concept in concepts:
if concept not in concept_appearances:
concept_appearances[concept] = []
concept_appearances[concept].append(prop['perspective'])
# Identify multi-perspective convergences
for concept, perspectives in concept_appearances.items():
if len(perspectives) >= 2:
convergences.append(
f"Multiple perspectives ({', '.join(perspectives)}) converge on {concept}"
)
# Find explicit support relations
support_relations = [r for r in relations if r[2] == 'supports']
if len(support_relations) >= 2:
convergences.append(
f"{len(support_relations)} supporting relations indicate substantial conceptual agreement"
)
# Find high-confidence consensus
high_confidence_props = [p for p in propositions if p['confidence'] > 0.7]
if len(high_confidence_props) >= 2:
convergences.append(
f"High confidence convergence across {len(high_confidence_props)} perspectives"
)
return convergences if convergences else ["Limited explicit convergence detected"]
def _identify_divergences(
self,
propositions: list[dict[str, Any]],
relations: list[tuple[int, int, str]]
) -> list[str]:
"""Identify areas where perspectives diverge or conflict."""
divergences = []
# Find tension relations
tension_relations = [r for r in relations if r[2] == 'tension']
for p1_idx, p2_idx, _ in tension_relations:
persp1 = propositions[p1_idx]['perspective']
persp2 = propositions[p2_idx]['perspective']
divergences.append(f"Fundamental tension between {persp1} and {persp2} approaches")
# Find confidence disparities
confidences = [p['confidence'] for p in propositions]
confidence_range = max(confidences) - min(confidences)
if confidence_range > 0.4:
divergences.append(
f"Significant confidence disparity (range: {confidence_range:.1%}) indicates interpretive uncertainty"
)
# Find contradictory content themes
# This is simplified - would use more sophisticated NLP in practice
positive_terms = {'affirms', 'supports', 'enables', 'promotes', 'enhances'}
negative_terms = {'denies', 'contradicts', 'undermines', 'prevents', 'limits'}
positive_perspectives = []
negative_perspectives = []
for prop in propositions:
content_lower = prop['content'].lower()
if any(term in content_lower for term in positive_terms):
positive_perspectives.append(prop['perspective'])
if any(term in content_lower for term in negative_terms):
negative_perspectives.append(prop['perspective'])
if positive_perspectives and negative_perspectives:
divergences.append(
f"Evaluative divergence: {', '.join(positive_perspectives)} vs {', '.join(negative_perspectives)}"
)
return divergences if divergences else ["No significant divergences detected"]
def _generate_synthetic_insights(
self,
propositions: list[dict[str, Any]],
relations: list[tuple[int, int, str]],
coherence_scores: dict[str, Any]
) -> list[str]:
"""Generate insights that emerge from the synthesis process itself."""
insights = []
# Meta-cognitive insights about the analysis process
if coherence_scores['overall_coherence'] > 0.8:
insights.append(
"The high coherence across diverse perspectives suggests the concept admits of stable, "
"multi-faceted understanding despite philosophical differences."
)
if coherence_scores['explanatory_breadth'] > 0.6:
insights.append(
"The broad explanatory coverage indicates the concept connects to fundamental "
"philosophical questions across multiple domains."
)
# Relationship insights
if len(relations) > len(propositions):
insights.append(
"The rich relational structure suggests the concept exists within dense conceptual networks "
"requiring integrative rather than reductive analysis."
)
# Diversity insights
perspective_families = [self._identify_philosophical_family(p['perspective']) for p in propositions]
unique_families = len(set(perspective_families))
if unique_families >= 3:
insights.append(
f"Analysis spanning {unique_families} philosophical traditions indicates "
f"cross-traditional significance and interpretive richness."
)
# Confidence pattern insights
confidences = [p['confidence'] for p in propositions]
avg_confidence = np.mean(confidences)
confidence_std = np.std(confidences)
if avg_confidence > 0.7 and confidence_std < 0.2:
insights.append(
"Consistently high confidence across perspectives suggests robust conceptual foundations."
)
elif confidence_std > 0.3:
insights.append(
"Variable confidence levels indicate the concept requires careful contextual analysis."
)
return insights
def _assess_synthetic_epistemic_status(
self,
synthetic_confidence: float,
coherence_scores: dict[str, Any]
) -> str:
"""Assess the epistemic status of the synthetic analysis."""
if synthetic_confidence > 0.8 and coherence_scores['overall_coherence'] > 0.8:
return "high_confidence_synthesis"
elif synthetic_confidence > 0.6 and coherence_scores['overall_coherence'] > 0.6:
return "moderate_confidence_synthesis"
elif synthetic_confidence > 0.4:
return "provisional_synthesis"
else:
return "exploratory_synthesis"
def _assess_integration_quality(
self,
propositions: list[dict[str, Any]],
relations: list[tuple[int, int, str]],
perspectives: list[str]
) -> dict[str, Any]:
"""Assess the quality of integration across perspectives."""
# Calculate integration metrics
perspective_count = len(perspectives)
relation_density = len(relations) / (perspective_count * (perspective_count - 1) / 2) if perspective_count > 1 else 0
# Assess philosophical diversity
families = [self._identify_philosophical_family(p) for p in perspectives]
family_diversity = len(set(families)) / len(families) if families else 0
# Assess confidence consistency
confidences = [p['confidence'] for p in propositions]
confidence_consistency = 1.0 - np.std(confidences) if confidences else 0
# Overall integration score
integration_score = (
0.4 * relation_density +
0.3 * family_diversity +
0.3 * confidence_consistency
)
return {
'integration_score': float(np.clip(integration_score, 0.0, 1.0)),
'relation_density': float(relation_density),
'philosophical_diversity': float(family_diversity),
'confidence_consistency': float(confidence_consistency),
'integration_quality': 'high' if integration_score > 0.7 else 'moderate' if integration_score > 0.5 else 'basic'
}
def _generate_meta_philosophical_observations(
self,
perspectives: list[str],
family_distribution: dict[str, int],
coherence_scores: dict[str, Any]
) -> list[str]:
"""Generate observations about the philosophical analysis process itself."""
observations = []
# Tradition representation analysis
dominant_family = max(family_distribution.items(), key=lambda x: x[1]) if family_distribution else None
if dominant_family and dominant_family[1] > len(perspectives) / 2:
observations.append(
f"Analysis dominated by {dominant_family[0]} tradition ({dominant_family[1]}/{len(perspectives)} perspectives), "
f"potentially limiting interpretive scope."
)
elif len(family_distribution) >= 3:
observations.append(
f"Balanced representation across {len(family_distribution)} philosophical families suggests "
f"comprehensive multi-traditional analysis."
)
# Coherence pattern observations
if coherence_scores['constraint_satisfaction'] > 0.8:
observations.append(
"High constraint satisfaction indicates successful integration of diverse philosophical commitments."
)
if coherence_scores['analogical_fit'] > 0.5:
observations.append(
"Strong analogical connections suggest the concept exhibits family resemblances across traditions."
)
# Methodological observations
if 'analytical' in perspectives and 'phenomenological' in perspectives:
observations.append(
"Analysis bridges the analytic-continental divide, enabling productive cross-traditional dialogue."
)
if len(perspectives) >= 4:
observations.append(
f"Multi-perspectival analysis with {len(perspectives)} viewpoints approaches philosophical comprehensiveness."
)
return observations
def _identify_conceptual_tensions(self, analysis_results: list[dict[str, Any]]) -> list[dict[str, Any]]:
"""Identify sophisticated conceptual tensions using philosophical analysis."""
tensions = []
if len(analysis_results) < 2:
return tensions
# Extract perspective information
perspectives = [p for res in analysis_results if (p := res.get("perspective")) is not None and isinstance(p, str)]
interpretations = [res.get("interpretation", {}) for res in analysis_results]
confidences = [res.get("confidence", 0.0) for res in analysis_results]
# Define philosophical tension patterns
known_tensions = {
# Methodological tensions
("analytical", "phenomenological"): {
"type": "methodological_tension",
"description": "Tension between third-person analytical rigor and first-person experiential fidelity",
"philosophical_significance": "Represents fundamental divide between explanation and understanding",
"resolution_strategies": ["hermeneutic bridge-building", "pragmatic complementarity", "level-relative analysis"]
},
("empiricist", "rationalist"): {
"type": "epistemological_tension",
"description": "Fundamental disagreement about the source and validation of knowledge",
"philosophical_significance": "Core epistemological divide since antiquity",
"resolution_strategies": ["kantian synthesis", "naturalized epistemology", "pragmatist dissolution"]
},
("realist", "idealist"): {
"type": "metaphysical_tension",
"description": "Disagreement about mind-independent reality versus consciousness-constituted reality",
"philosophical_significance": "Central metaphysical debate about nature of reality",
"resolution_strategies": ["transcendental idealism", "neutral monism", "pragmatic deflation"]
},
# Normative tensions
("deontological", "consequentialist"): {
"type": "normative_tension",
"description": "Tension between duty-based and outcome-based moral reasoning",
"philosophical_significance": "Fundamental divide in moral philosophy about the source of ethical obligation",
"resolution_strategies": ["virtue ethics integration", "prima facie duties", "rule consequentialism"]
},
("virtue_ethics", "deontological"): {
"type": "normative_tension",
"description": "Tension between character-focused and action-focused ethics",
"philosophical_significance": "Different conceptions of moral agency and responsibility",
"resolution_strategies": ["neo-aristotelian synthesis", "moral psychology integration"]
},
# Contemporary tensions
("functionalist", "phenomenological"): {
"type": "consciousness_tension",
"description": "Tension between functional role analysis and qualitative experience",
"philosophical_significance": "Hard problem of consciousness and explanatory gap",
"resolution_strategies": ["illusionism", "panpsychism", "emergentism", "eliminativism"]
},
("naturalist", "phenomenological"): {
"type": "naturalization_tension",
"description": "Tension between scientific naturalism and phenomenological method",
"philosophical_significance": "Question of whether consciousness can be naturalized",
"resolution_strategies": ["neurophenomenology", "embodied cognition", "enactivism"]
},
# Political/critical tensions
("critical", "analytical"): {
"type": "political_epistemological_tension",
"description": "Tension between ideologically critical and supposedly neutral analysis",
"philosophical_significance": "Question of whether philosophical analysis can be politically neutral",
"resolution_strategies": ["situated knowledge", "critical realism", "pragmatist politics"]
},
("individual", "social"): {
"type": "ontological_tension",
"description": "Tension between individualist and social ontologies",
"philosophical_significance": "Fundamental question about the basic units of social reality",
"resolution_strategies": ["relational ontology", "emergent social properties", "methodological individualism"]
}
}
# Check for known philosophical tensions
for i, persp1 in enumerate(perspectives):
for j, persp2 in enumerate(perspectives[i+1:], i+1):
# Check direct tensions
# Construct tension_key explicitly as a 2-tuple and add type hint for clarity
_sorted_perspectives = sorted([persp1, persp2])
tension_key: tuple[str, str] = (_sorted_perspectives[0], _sorted_perspectives[1])
if tension_key in known_tensions:
tension_info = known_tensions[tension_key].copy()
tension_info["involved_perspectives"] = [persp1, persp2]
tension_info["confidence_disparity"] = abs(confidences[i] - confidences[j])
tension_info["interpretive_distance"] = self._calculate_interpretive_distance(
interpretations[i], interpretations[j]
)
tensions.append(tension_info)
# Identify semantic tensions in interpretations
semantic_tensions = self._identify_semantic_tensions(analysis_results, interpretations)
tensions.extend(semantic_tensions)
# Identify confidence-based tensions
confidence_tensions = self._identify_confidence_tensions(analysis_results, confidences)
tensions.extend(confidence_tensions)
# Identify evaluative tensions
evaluative_tensions = self._identify_evaluative_tensions(analysis_results, interpretations)
tensions.extend(evaluative_tensions)
# Identify meta-philosophical tensions
meta_tensions = self._identify_meta_philosophical_tensions(perspectives, analysis_results)
tensions.extend(meta_tensions)
# Deduplicate and rank tensions by philosophical significance
unique_tensions = self._deduplicate_tensions(tensions)
ranked_tensions = self._rank_tensions_by_significance(unique_tensions)
return ranked_tensions[:8] # Return top 8 most significant tensions
def _calculate_interpretive_distance(
self,
interpretation1: dict[str, Any],
interpretation2: dict[str, Any]
) -> float:
"""Calculate semantic distance between two interpretations."""
from .utils import semantic_similarity
# Extract text content
content1 = interpretation1.get("derived_meaning", "") if isinstance(interpretation1, dict) else str(interpretation1)
content2 = interpretation2.get("derived_meaning", "") if isinstance(interpretation2, dict) else str(interpretation2)
if not content1 or not content2:
return 0.5 # Neutral distance for missing content
# Create feature sets for comparison
words1 = set(content1.lower().split())
words2 = set(content2.lower().split())
features1 = {'features': list(words1)}
features2 = {'features': list(words2)}
similarity = semantic_similarity(features1, features2, method="jaccard")
return 1.0 - similarity # Convert similarity to distance
def _identify_semantic_tensions(
self,
analysis_results: list[dict[str, Any]],
interpretations: list[dict[str, Any]]
) -> list[dict[str, Any]]:
"""Identify tensions based on semantic content analysis."""
semantic_tensions = []
# Look for opposing evaluative terms
positive_indicators = {'beneficial', 'valuable', 'important', 'essential', 'promotes', 'enhances', 'supports'}
negative_indicators = {'harmful', 'problematic', 'dangerous', 'undermines', 'prevents', 'limits', 'threatens'}
positive_perspectives = []
negative_perspectives = []
for i, (result, interpretation) in enumerate(zip(analysis_results, interpretations, strict=True)):
content = interpretation.get("derived_meaning", "") if isinstance(interpretation, dict) else str(interpretation)
content_lower = content.lower()
perspective = result.get("perspective", f"perspective_{i}")
if any(indicator in content_lower for indicator in positive_indicators):
positive_perspectives.append(perspective)
if any(indicator in content_lower for indicator in negative_indicators):
negative_perspectives.append(perspective)
if positive_perspectives and negative_perspectives:
semantic_tensions.append({
"type": "evaluative_opposition",
"description": "Fundamental evaluative disagreement about the phenomenon",
"involved_perspectives": positive_perspectives + negative_perspectives,
"positive_perspectives": positive_perspectives,
"negative_perspectives": negative_perspectives,
"philosophical_significance": "Reveals deep axiological differences in philosophical evaluation",
"resolution_strategies": ["value pluralism", "contextual evaluation", "dialectical synthesis"]
})
# Look for modal disagreements (necessity vs contingency)
necessity_indicators = {'necessary', 'must', 'essential', 'inevitable', 'required'}
contingency_indicators = {'contingent', 'optional', 'possible', 'variable', 'dependent'}
necessity_perspectives = []
contingency_perspectives = []
for i, (result, interpretation) in enumerate(zip(analysis_results, interpretations, strict=True)):
content = interpretation.get("derived_meaning", "") if isinstance(interpretation, dict) else str(interpretation)
content_lower = content.lower()
perspective = result.get("perspective", f"perspective_{i}")
if any(indicator in content_lower for indicator in necessity_indicators):
necessity_perspectives.append(perspective)
if any(indicator in content_lower for indicator in contingency_indicators):
contingency_perspectives.append(perspective)
if necessity_perspectives and contingency_perspectives:
semantic_tensions.append({
"type": "modal_disagreement",
"description": "Disagreement about necessity versus contingency of the phenomenon",
"involved_perspectives": necessity_perspectives + contingency_perspectives,
"necessity_perspectives": necessity_perspectives,
"contingency_perspectives": contingency_perspectives,
"philosophical_significance": "Fundamental metaphysical disagreement about modal status",
"resolution_strategies": ["modal contextualism", "graded necessity", "pragmatic modal collapse"]
})
return semantic_tensions
def _identify_confidence_tensions(
self,
analysis_results: list[dict[str, Any]],
confidences: list[float]
) -> list[dict[str, Any]]:
"""Identify tensions based on confidence disparities."""
confidence_tensions = []
if len(confidences) < 2:
return confidence_tensions
max_confidence = max(confidences)
min_confidence = min(confidences)
confidence_range = max_confidence - min_confidence
# Significant confidence disparity indicates epistemic tension
if confidence_range > 0.4:
high_confidence_perspectives = []
low_confidence_perspectives = []
for i, (result, conf) in enumerate(zip(analysis_results, confidences, strict=True)):
perspective = result.get("perspective", f"perspective_{i}")
if conf >= max_confidence - 0.1:
high_confidence_perspectives.append(perspective)
elif conf <= min_confidence + 0.1:
low_confidence_perspectives.append(perspective)
confidence_tensions.append({
"type": "epistemic_confidence_tension",
"description": f"Significant confidence disparity (range: {confidence_range:.1%})",
"involved_perspectives": high_confidence_perspectives + low_confidence_perspectives,
"high_confidence_perspectives": high_confidence_perspectives,
"low_confidence_perspectives": low_confidence_perspectives,
"confidence_range": confidence_range,
"philosophical_significance": "Indicates fundamental disagreement about epistemic access or method",
"resolution_strategies": ["epistemic humility", "method sensitivity analysis", "confidence calibration"]
})
return confidence_tensions
def _identify_evaluative_tensions(
self,
analysis_results: list[dict[str, Any]],
interpretations: list[dict[str, Any]]
) -> list[dict[str, Any]]:
"""Identify tensions in philosophical evaluation and judgment."""
evaluative_tensions = []
# Look for prescriptive vs descriptive approaches
prescriptive_indicators = {'should', 'ought', 'must', 'recommend', 'advocate', 'propose'}
descriptive_indicators = {'is', 'describes', 'observes', 'analyzes', 'examines', 'reveals'}
prescriptive_perspectives = []
descriptive_perspectives = []
for i, (result, interpretation) in enumerate(zip(analysis_results, interpretations, strict=True)):
content = interpretation.get("derived_meaning", "") if isinstance(interpretation, dict) else str(interpretation)
content_lower = content.lower()
perspective = result.get("perspective", f"perspective_{i}")
prescriptive_score = sum(1 for indicator in prescriptive_indicators if indicator in content_lower)
descriptive_score = sum(1 for indicator in descriptive_indicators if indicator in content_lower)
if prescriptive_score > descriptive_score:
prescriptive_perspectives.append(perspective)
elif descriptive_score > prescriptive_score:
descriptive_perspectives.append(perspective)
if prescriptive_perspectives and descriptive_perspectives:
evaluative_tensions.append({
"type": "prescriptive_descriptive_tension",
"description": "Tension between normative prescription and descriptive analysis",
"involved_perspectives": prescriptive_perspectives + descriptive_perspectives,
"prescriptive_perspectives": prescriptive_perspectives,
"descriptive_perspectives": descriptive_perspectives,
"philosophical_significance": "Fundamental tension between 'is' and 'ought' in philosophical analysis",
"resolution_strategies": ["naturalistic ethics", "non-cognitivism", "pragmatic integration"]
})
return evaluative_tensions
def _identify_meta_philosophical_tensions(
self,
perspectives: list[str],
analysis_results: list[dict[str, Any]]
) -> list[dict[str, Any]]:
"""Identify tensions at the meta-philosophical level."""
meta_tensions = []
# Identify tradition-based tensions
continental_perspectives = []
analytic_perspectives = []
continental_traditions = {'phenomenological', 'existentialist', 'hermeneutic', 'critical', 'postmodern'}
analytic_traditions = {'analytical', 'empiricist', 'rationalist', 'functionalist', 'logical_positivist'}
for perspective in perspectives:
if perspective in continental_traditions:
continental_perspectives.append(perspective)
elif perspective in analytic_traditions:
analytic_perspectives.append(perspective)
if continental_perspectives and analytic_perspectives:
meta_tensions.append({
"type": "meta_philosophical_tradition_tension",
"description": "Tension between Continental and Analytic philosophical traditions",
"involved_perspectives": continental_perspectives + analytic_perspectives,
"continental_perspectives": continental_perspectives,
"analytic_perspectives": analytic_perspectives,
"philosophical_significance": "Fundamental methodological and stylistic divide in contemporary philosophy",
"resolution_strategies": ["post-analytic philosophy", "pragmatist bridge-building", "pluralistic integration"]
})
# Check for theory vs practice tensions
theoretical_perspectives = []
practical_perspectives = []
for i, result in enumerate(analysis_results):
interpretation = result.get("interpretation", {})
content = interpretation.get("derived_meaning", "") if isinstance(interpretation, dict) else str(interpretation)
if any(term in content.lower() for term in ['theory', 'abstract', 'conceptual', 'logical']):
theoretical_perspectives.append(result.get("perspective", f"perspective_{i}"))
if any(term in content.lower() for term in ['practice', 'action', 'concrete', 'applied']):
practical_perspectives.append(result.get("perspective", f"perspective_{i}"))
if theoretical_perspectives and practical_perspectives:
meta_tensions.append({
"type": "theory_practice_tension",
"description": "Tension between theoretical analysis and practical application",
"involved_perspectives": theoretical_perspectives + practical_perspectives,
"theoretical_perspectives": theoretical_perspectives,
"practical_perspectives": practical_perspectives,
"philosophical_significance": "Classical philosophical tension between contemplation and action",
"resolution_strategies": ["praxis integration", "pragmatist synthesis", "applied philosophy"]
})
return meta_tensions
def _deduplicate_tensions(self, tensions: list[dict[str, Any]]) -> list[dict[str, Any]]:
"""Remove duplicate tensions while preserving the most informative versions."""
unique_tensions = []
seen_types = set()
# Sort by information richness (more detail = better)
sorted_tensions = sorted(tensions, key=lambda t: len(str(t)), reverse=True)
for tension in sorted_tensions:
tension_type = tension.get("type", "unknown")
if tension_type not in seen_types:
unique_tensions.append(tension)
seen_types.add(tension_type)
return unique_tensions
def _rank_tensions_by_significance(self, tensions: list[dict[str, Any]]) -> list[dict[str, Any]]:
"""Rank tensions by philosophical significance and impact."""
# Define significance weights
significance_weights = {
"methodological_tension": 0.9,
"epistemological_tension": 0.95,
"metaphysical_tension": 1.0,
"normative_tension": 0.85,
"consciousness_tension": 0.9,
"meta_philosophical_tradition_tension": 0.8,
"evaluative_opposition": 0.75,
"epistemic_confidence_tension": 0.7,
"theory_practice_tension": 0.65,
"modal_disagreement": 0.6,
"prescriptive_descriptive_tension": 0.55
}
# Calculate significance scores
for tension in tensions:
base_significance = significance_weights.get(tension.get("type", ""), 0.5)
# Boost score for tensions involving many perspectives
perspective_count = len(tension.get("involved_perspectives", []))
perspective_factor = min(perspective_count / 4.0, 1.2)
# Boost score for tensions with resolution strategies (indicates established philosophical debate)
resolution_factor = 1.1 if tension.get("resolution_strategies") else 1.0
final_significance = base_significance * perspective_factor * resolution_factor
tension["significance_score"] = final_significance
# Sort by significance score
return sorted(tensions, key=lambda t: t.get("significance_score", 0), reverse=True)
def _generate_revision_conditions(
self, concept: str, context: str, synthesis: dict[str, Any]
) -> list[dict[str, Any]]:
"""Generate revision conditions."""
conditions = [
{
"trigger": "Significant shift in contextual understanding",
"metric": "Domain coherence change > 0.3",
"action": "Re-evaluate concept analysis"
}
]
if synthesis.get("overall_confidence", 1.0) < 0.5:
conditions.append({
"trigger": "Low confidence in synthesis",
"metric": "Overall confidence < 0.5",
"action": "Seek additional perspectives"
})
return conditions
def _assess_epistemic_status(self, synthesis: dict[str, Any]) -> str:
"""Assess epistemic status."""
confidence = synthesis.get('overall_confidence', 0.5)
if confidence > 0.8:
return "high_confidence"
elif confidence > 0.6:
return "moderate_confidence"
else:
return "provisional"
def _generate_exploratory_questions(self, concept: str, synthesis: dict[str, Any]) -> list[str]:
"""Generate questions for further exploration."""
return [
f"What are the boundary conditions for {concept}?",
f"How does {concept} relate to similar concepts?",
"What assumptions underlie this analysis?"
]
async def _trace_semantic_evolution(self, expression: str) -> dict[str, Any]:
"""Trace semantic evolution."""
return {
"historical_meanings": [f"historical_meaning_for_{expression}"],
"evolution_timeline": ["early_period", "modern_period"],
"key_transitions": ["semantic_shift_1"]
}
async def _initialize_insight_framework(self, phenomenon: str, perspectives: list[str] | None, depth: int) -> dict[str, Any]:
"""Initialize comprehensive framework for insight generation."""
return {
'phenomenon': phenomenon,
'analysis_scope': {
'depth_level': depth,
'perspective_count': len(perspectives) if perspectives else len(self._get_default_perspectives()),
'expected_insight_types': ['descriptive', 'explanatory', 'normative', 'meta_cognitive'],
'complexity_assessment': self._assess_phenomenon_complexity(phenomenon)
},
'methodological_commitments': {
'fallibilism': True,
'perspectivalism': True,
'coherentism': True,
'pragmatism': True
},
'epistemic_virtues': ['intellectual_humility', 'critical_thinking', 'open_mindedness', 'systematic_inquiry'],
'analysis_timestamp': datetime.now().isoformat()
}
def _assess_phenomenon_complexity(self, phenomenon: str) -> dict[str, Any]:
"""Assess complexity characteristics of phenomenon."""
complexity_indicators = {
'conceptual_complexity': 'high' if any(term in phenomenon.lower() for term in
['consciousness', 'justice', 'truth', 'reality', 'meaning']) else 'moderate',
'interdisciplinary_scope': 'high' if any(term in phenomenon.lower() for term in
['mind', 'society', 'culture', 'science', 'politics']) else 'moderate',
'normative_dimensions': 'high' if any(term in phenomenon.lower() for term in
['ethics', 'ought', 'should', 'good', 'right', 'wrong']) else 'low',
'empirical_tractability': 'low' if any(term in phenomenon.lower() for term in
['consciousness', 'qualia', 'free will', 'meaning']) else 'moderate'
}
return complexity_indicators
async def _gather_comprehensive_evidence(self, phenomenon: str, perspectives: list[str] | None, depth: int) -> dict[str, Any]:
"""Gather comprehensive evidence corpus for analysis."""
evidence_corpus = {
'conceptual_evidence': await self._gather_conceptual_evidence(phenomenon, depth),
'historical_evidence': await self._gather_historical_evidence(phenomenon),
'cross_cultural_evidence': await self._gather_cross_cultural_evidence(phenomenon),
'empirical_evidence': await self._gather_empirical_evidence(phenomenon),
'logical_evidence': await self._gather_logical_evidence(phenomenon),
'phenomenological_evidence': await self._gather_phenomenological_evidence(phenomenon),
'pragmatic_evidence': await self._gather_pragmatic_evidence(phenomenon)
}
return evidence_corpus
async def _gather_conceptual_evidence(self, phenomenon: str, depth: int) -> list[dict[str, Any]]:
"""Gather conceptual evidence and analysis patterns."""
evidence = []
# Definitional evidence
evidence.append({
'type': 'definitional',
'content': f"Conceptual analysis reveals {phenomenon} involves multiple definitional dimensions",
'strength': 0.7,
'source': 'conceptual_analysis',
'philosophical_relevance': 'foundational'
})
# Logical structure evidence
evidence.append({
'type': 'logical_structure',
'content': f"Logical analysis of {phenomenon} reveals internal conceptual relations",
'strength': 0.75,
'source': 'logical_analysis',
'philosophical_relevance': 'structural'
})
# Necessary/sufficient conditions
if depth > 2:
evidence.append({
'type': 'modal_conditions',
'content': f"Modal analysis suggests {phenomenon} has both necessary and contingent features",
'strength': 0.6,
'source': 'modal_logic',
'philosophical_relevance': 'modal'
})
return evidence
async def _gather_historical_evidence(self, phenomenon: str) -> list[dict[str, Any]]:
"""Gather historical philosophical evidence."""
return [
{
'type': 'historical_development',
'content': f"Historical analysis shows {phenomenon} has evolved significantly across philosophical traditions",
'strength': 0.8,
'source': 'history_of_philosophy',
'philosophical_relevance': 'diachronic'
},
{
'type': 'canonical_treatments',
'content': f"Major philosophers have offered diverse approaches to {phenomenon}",
'strength': 0.85,
'source': 'philosophical_canon',
'philosophical_relevance': 'authoritative'
}
]
async def _gather_cross_cultural_evidence(self, phenomenon: str) -> list[dict[str, Any]]:
"""Gather cross-cultural philosophical evidence."""
return [
{
'type': 'cultural_variation',
'content': f"Cross-cultural analysis reveals both universal and particular aspects of {phenomenon}",
'strength': 0.7,
'source': 'comparative_philosophy',
'philosophical_relevance': 'intercultural'
}
]
async def _gather_empirical_evidence(self, phenomenon: str) -> list[dict[str, Any]]:
"""Gather empirical evidence relevant to philosophical analysis."""
return [
{
'type': 'empirical_findings',
'content': f"Empirical research provides relevant data for understanding {phenomenon}",
'strength': 0.6,
'source': 'empirical_studies',
'philosophical_relevance': 'naturalistic'
}
]
async def _gather_logical_evidence(self, phenomenon: str) -> list[dict[str, Any]]:
"""Gather logical and formal evidence."""
return [
{
'type': 'formal_analysis',
'content': f"Formal logical analysis provides structural insights into {phenomenon}",
'strength': 0.8,
'source': 'formal_logic',
'philosophical_relevance': 'formal'
}
]
async def _gather_phenomenological_evidence(self, phenomenon: str) -> list[dict[str, Any]]:
"""Gather phenomenological evidence from experience."""
return [
{
'type': 'experiential',
'content': f"Phenomenological analysis reveals experiential dimensions of {phenomenon}",
'strength': 0.75,
'source': 'phenomenological_method',
'philosophical_relevance': 'experiential'
}
]
async def _gather_pragmatic_evidence(self, phenomenon: str) -> list[dict[str, Any]]:
"""Gather pragmatic evidence from practical consequences."""
return [
{
'type': 'practical_consequences',
'content': f"Pragmatic analysis shows {phenomenon} has significant practical implications",
'strength': 0.7,
'source': 'pragmatic_analysis',
'philosophical_relevance': 'practical'
}
]
async def _generate_perspective_insights(
self,
phenomenon: str,
evidence_corpus: dict[str, Any],
perspectives: list[str]
) -> list[dict[str, Any]]:
"""Generate insights from each philosophical perspective."""
perspective_insights = []
for perspective in perspectives:
try:
# Generate perspective-specific evidence interpretation
evidence_interpretation = await self._interpret_evidence_from_perspective(
evidence_corpus, perspective, phenomenon
)
# Generate insights using fallibilistic inference
raw_insights = await self.inference_engine.derive_insights(
evidence_interpretation,
confidence_threshold=self.context.confidence_threshold
)
# Enhance insights with perspective-specific analysis
enhanced_insights = []
for insight in raw_insights:
enhanced_insight = {
'content': insight.content,
'perspective': perspective,
'confidence': insight.confidence,
'evidence_base': insight.evidence_summary,
'limitations': insight.identified_limitations,
'revision_triggers': insight.revision_triggers,
'philosophical_depth': await self._assess_insight_depth(insight, perspective),
'novel_contributions': await self._identify_novel_contributions(insight, perspective),
'integration_potential': await self._assess_integration_potential(insight, perspective)
}
enhanced_insights.append(enhanced_insight)
perspective_insights.append({
'perspective': perspective,
'insights': enhanced_insights,
'evidence_interpretation': evidence_interpretation,
'meta_analysis': await self._generate_perspective_meta_analysis(perspective, enhanced_insights)
})
except Exception as e:
logger.warning(f"Failed to generate insights for perspective {perspective}: {e}")
continue
return perspective_insights
async def _interpret_evidence_from_perspective(
self,
evidence_corpus: dict[str, Any],
perspective: str,
phenomenon: str
) -> list[dict[str, Any]]:
"""Interpret evidence through specific philosophical perspective."""
perspective_filters = {
'analytical': ['logical_evidence', 'conceptual_evidence'],
'phenomenological': ['phenomenological_evidence', 'experiential_evidence'],
'pragmatist': ['pragmatic_evidence', 'empirical_evidence'],
'critical': ['historical_evidence', 'cross_cultural_evidence'],
'empiricist': ['empirical_evidence', 'logical_evidence'],
'rationalist': ['logical_evidence', 'conceptual_evidence']
}
relevant_evidence_types = perspective_filters.get(perspective, list(evidence_corpus.keys()))
interpreted_evidence = []
for evidence_type in relevant_evidence_types:
if evidence_type in evidence_corpus:
for evidence_item in evidence_corpus[evidence_type]:
interpreted_item = evidence_item.copy()
interpreted_item['perspective_relevance'] = self._assess_perspective_relevance(
evidence_item, perspective
)
interpreted_item['perspective_interpretation'] = self._generate_perspective_interpretation_of_evidence(
evidence_item, perspective, phenomenon
)
interpreted_evidence.append(interpreted_item)
return interpreted_evidence
def _assess_perspective_relevance(self, evidence: dict[str, Any], perspective: str) -> float:
"""Assess relevance of evidence to philosophical perspective."""
relevance_matrix = {
'analytical': {
'definitional': 0.9,
'logical_structure': 0.95,
'formal_analysis': 0.9,
'conceptual': 0.85
},
'phenomenological': {
'experiential': 0.95,
'phenomenological': 0.9,
'historical_development': 0.6
},
'pragmatist': {
'practical_consequences': 0.9,
'empirical_findings': 0.8,
'historical_development': 0.7
},
'critical': {
'cultural_variation': 0.85,
'historical_development': 0.9,
'practical_consequences': 0.8
}
}
evidence_type = evidence.get('type', 'general')
perspective_relevances = relevance_matrix.get(perspective, {})
return perspective_relevances.get(evidence_type, 0.6)
def _generate_perspective_interpretation_of_evidence(
self, evidence: dict[str, Any], perspective: str, phenomenon: str
) -> str:
"""Generate perspective-specific interpretation of evidence."""
evidence_content = evidence.get('content', '')
evidence_type = evidence.get('type', '')
# Adjust interpretation based on evidence type
evidence_qualifier = {
'conceptual': 'conceptual analysis reveals that',
'historical': 'historical evidence shows that',
'empirical': 'empirical findings indicate that',
'logical': 'logical analysis demonstrates that',
'phenomenological': 'experiential data suggests that',
'pragmatic': 'practical outcomes show that'
}.get(evidence_type, 'evidence suggests that')
interpretation_templates = {
'analytical': f"From an analytical standpoint, {evidence_qualifier} {evidence_content.lower()} demonstrates the need for precise conceptual analysis of {phenomenon}.",
'phenomenological': f"Phenomenologically, {evidence_qualifier} {evidence_content.lower()} reveals experiential dimensions of {phenomenon} requiring descriptive analysis.",
'pragmatist': f"Pragmatically, {evidence_qualifier} {evidence_content.lower()} indicates practical consequences of {phenomenon} for human action and inquiry.",
'critical': f"Critically, {evidence_qualifier} {evidence_content.lower()} exposes power relations and ideological dimensions involved in {phenomenon}."
}
return interpretation_templates.get(perspective,
f"From a {perspective} perspective, {evidence_qualifier} {evidence_content.lower()} provides relevant insights into {phenomenon}.")
async def _assess_insight_depth(self, insight: Any, perspective: str) -> dict[str, Any]:
"""Assess philosophical depth and sophistication of insight."""
return {
'conceptual_sophistication': 0.7,
'theoretical_integration': 0.6,
'novel_connections': 0.5,
'philosophical_significance': 0.75,
'depth_rating': 'moderate'
}
async def _identify_novel_contributions(self, insight: Any, perspective: str) -> list[str]:
"""Identify novel contributions of insight."""
return [
f"Offers {perspective} perspective on complex phenomenon",
"Integrates multiple evidence sources",
"Provides framework for further inquiry"
]
async def _assess_integration_potential(self, insight: Any, perspective: str) -> dict[str, Any]:
"""Assess potential for integrating insight with other perspectives."""
return {
'cross_perspective_compatibility': 0.7,
'synthesis_potential': 0.6,
'complementarity_score': 0.75,
'integration_challenges': ['methodological_differences', 'conceptual_tensions']
}
async def _gather_evidence_patterns(
self, phenomenon: str, perspectives: list[str] | None, depth: int
) -> dict[str, Any]:
"""Gather evidence patterns."""
return {
"empirical_evidence": [f"evidence_for_{phenomenon}"],
"conceptual_patterns": [f"pattern_for_{phenomenon}"],
"cross_perspective_themes": ["theme1", "theme2"]
}
async def _synthesize_cross_perspective_insights(
self, perspective_insights: list[dict[str, Any]], phenomenon: str
) -> dict[str, Any]:
"""Synthesize insights across perspectives."""
if not perspective_insights:
return {'synthesis': 'No insights to synthesize'}
# Extract all individual insights
all_insights = []
for perspective_data in perspective_insights:
for insight in perspective_data.get('insights', []):
all_insights.append(insight)
# Use existing synthesis method
return self._synthesize_analyses(all_insights)
async def _identify_comprehensive_contradictions(
self, perspective_insights: list[dict[str, Any]], phenomenon: str
) -> list[dict[str, Any]]:
"""Identify comprehensive contradictions across perspectives."""
all_insights = []
for perspective_data in perspective_insights:
for insight in perspective_data.get('insights', []):
all_insights.append(insight)
return self._identify_insight_contradictions(all_insights)
async def _generate_meta_philosophical_insights(
self, phenomenon: str, perspective_insights: list[dict[str, Any]], synthetic_insights: dict[str, Any]
) -> list[dict[str, Any]]:
"""Generate meta-philosophical insights about the analysis process."""
meta_insights = []
# Insight about perspective diversity
perspectives_used = [pi['perspective'] for pi in perspective_insights]
families = [self._identify_philosophical_family(p) for p in perspectives_used]
unique_families = len(set(families))
if unique_families >= 3:
meta_insights.append({
'type': 'methodological_insight',
'content': f"Analysis of {phenomenon} benefits from cross-traditional philosophical dialogue, "
f"spanning {unique_families} major philosophical families.",
'significance': 'highlights_philosophical_pluralism',
'confidence': 0.8
})
# Insight about analysis complexity
total_insights = sum(len(pi.get('insights', [])) for pi in perspective_insights)
if total_insights > 10:
meta_insights.append({
'type': 'complexity_insight',
'content': f"The phenomenon {phenomenon} generates rich philosophical analysis with "
f"{total_insights} distinct insights, indicating deep conceptual complexity.",
'significance': 'reveals_philosophical_richness',
'confidence': 0.75
})
# Insight about synthesis quality
synthesis_confidence = synthetic_insights.get('overall_confidence', 0.5)
if synthesis_confidence > 0.7:
meta_insights.append({
'type': 'synthesis_insight',
'content': f"High synthesis confidence ({synthesis_confidence:.1%}) suggests {phenomenon} "
f"admits of coherent multi-perspectival understanding.",
'significance': 'supports_philosophical_realism',
'confidence': synthesis_confidence
})
return meta_insights
async def _assess_comprehensive_uncertainty(
self, perspective_insights: list[dict[str, Any]], synthetic_insights: dict[str, Any], evidence_corpus: dict[str, Any]
) -> dict[str, Any]:
"""Assess comprehensive uncertainty profile."""
# Calculate various uncertainty sources
perspective_count = len(perspective_insights)
total_insights = sum(len(pi.get('insights', [])) for pi in perspective_insights)
evidence_count = sum(len(evidence_list) for evidence_list in evidence_corpus.values())
synthesis_confidence = synthetic_insights.get('overall_confidence', 0.5)
# Calculate epistemic uncertainty
from .utils import calculate_epistemic_uncertainty
epistemic_uncertainty = calculate_epistemic_uncertainty(
evidence_count=evidence_count,
coherence_score=synthesis_confidence,
temporal_factor=1.0,
domain_complexity=0.7
)
return {
'epistemic_uncertainty': epistemic_uncertainty,
'evidence_sufficiency': min(evidence_count / 10.0, 1.0),
'insight_density': min(total_insights / 20.0, 1.0),
'perspective_coverage': min(perspective_count / 5.0, 1.0),
'synthesis_stability': synthesis_confidence,
'uncertainty_sources': [
'limited_evidence_base' if evidence_count < 8 else None,
'insufficient_insights' if total_insights < 10 else None,
'perspective_limitations' if perspective_count < 4 else None,
'synthesis_challenges' if synthesis_confidence < 0.6 else None
],
'confidence_calibration': self._assess_confidence_calibration(perspective_insights)
}
def _assess_confidence_calibration(self, perspective_insights: list[dict[str, Any]]) -> dict[str, Any]:
"""Assess calibration of confidence across perspectives."""
all_confidences = []
for perspective_data in perspective_insights:
for insight in perspective_data.get('insights', []):
all_confidences.append(insight.get('confidence', 0.5))
if not all_confidences:
return {'status': 'no_confidence_data'}
import numpy as np
return {
'mean_confidence': float(np.mean(all_confidences)),
'confidence_variance': float(np.var(all_confidences)),
'confidence_range': float(np.max(all_confidences) - np.min(all_confidences)),
'calibration_quality': 'good' if np.var(all_confidences) < 0.1 else 'moderate' if np.var(all_confidences) < 0.2 else 'poor'
}
async def _generate_revision_framework(
self, phenomenon: str, perspective_insights: list[dict[str, Any]], uncertainty_profile: dict[str, Any]
) -> dict[str, Any]:
"""Generate framework for insight revision and update."""
revision_triggers = []
# Evidence-based triggers
evidence_sufficiency = uncertainty_profile.get('evidence_sufficiency', 0.5)
if evidence_sufficiency < 0.7:
revision_triggers.append({
'type': 'evidence_accumulation',
'threshold': 'significant_new_evidence',
'description': 'Revise insights when substantial new evidence emerges'
})
# Perspective-based triggers
perspective_coverage = uncertainty_profile.get('perspective_coverage', 0.5)
if perspective_coverage < 0.8:
revision_triggers.append({
'type': 'perspective_expansion',
'threshold': 'new_philosophical_perspectives',
'description': 'Revise analysis when new philosophical approaches are considered'
})
# Temporal triggers
revision_triggers.append({
'type': 'temporal_decay',
'threshold': '6_months',
'description': 'Regular revision to account for evolving philosophical discourse'
})
return {
'revision_triggers': revision_triggers,
'update_protocols': [
'incremental_evidence_integration',
'perspective_synthesis_refinement',
'uncertainty_recalibration'
],
'validation_criteria': [
'logical_consistency',
'empirical_adequacy',
'philosophical_plausibility',
'practical_relevance'
]
}
async def _assess_philosophical_significance(
self, phenomenon: str, perspective_insights: list[dict[str, Any]], synthetic_insights: dict[str, Any]
) -> dict[str, Any]:
"""Assess philosophical significance of analysis."""
significance_indicators = []
# Cross-traditional relevance
perspectives = [pi['perspective'] for pi in perspective_insights]
families = {self._identify_philosophical_family(p) for p in perspectives}
if len(families) >= 3:
significance_indicators.append('cross_traditional_relevance')
# Synthesis quality
synthesis_confidence = synthetic_insights.get('overall_confidence', 0.5)
if synthesis_confidence > 0.7:
significance_indicators.append('high_synthesis_coherence')
# Insight richness
total_insights = sum(len(pi.get('insights', [])) for pi in perspective_insights)
if total_insights > 8:
significance_indicators.append('rich_philosophical_analysis')
return {
'significance_level': 'high' if len(significance_indicators) >= 2 else 'moderate' if len(significance_indicators) >= 1 else 'basic',
'significance_indicators': significance_indicators,
'philosophical_contributions': [
'multi_perspectival_understanding',
'conceptual_clarification',
'systematic_integration'
],
'potential_impact': self._assess_potential_philosophical_impact(phenomenon, significance_indicators)
}
def _assess_potential_philosophical_impact(self, phenomenon: str, indicators: list[str]) -> str:
"""Assess potential impact of philosophical analysis."""
if len(indicators) >= 3:
return 'potentially_significant_contribution'
elif len(indicators) >= 2:
return 'meaningful_philosophical_analysis'
else:
return 'solid_philosophical_exploration'
async def _generate_inquiry_recommendations(
self, phenomenon: str, perspective_insights: list[dict[str, Any]], uncertainty_profile: dict[str, Any]
) -> list[dict[str, str]]:
"""Generate recommendations for further philosophical inquiry."""
recommendations = []
# Evidence-based recommendations
evidence_sufficiency = uncertainty_profile.get('evidence_sufficiency', 0.5)
if evidence_sufficiency < 0.7:
recommendations.append({
'type': 'evidence_expansion',
'description': f'Gather additional evidence from empirical research and case studies related to {phenomenon}',
'priority': 'high'
})
# Perspective-based recommendations
perspective_coverage = uncertainty_profile.get('perspective_coverage', 0.5)
if perspective_coverage < 0.8:
recommendations.append({
'type': 'perspective_diversification',
'description': f'Explore {phenomenon} from additional philosophical traditions such as Eastern philosophy or feminist philosophy',
'priority': 'medium'
})
# Methodological recommendations
recommendations.append({
'type': 'methodological_refinement',
'description': f'Apply specialized philosophical methods to deepen analysis of {phenomenon}',
'priority': 'medium'
})
# Integration recommendations
if len(perspective_insights) > 3:
recommendations.append({
'type': 'cross_perspective_integration',
'description': f'Develop systematic integration of diverse perspectives on {phenomenon}',
'priority': 'high'
})
return recommendations
async def _generate_fallback_insights(self, phenomenon: str, perspectives: list[str] | None, depth: int) -> dict[str, Any]:
"""Generate simplified insights as fallback."""
return {
'phenomenon': phenomenon,
'status': 'fallback_analysis',
'primary_insights': [
{
'content': f'Basic philosophical analysis of {phenomenon} reveals conceptual complexity',
'perspective': 'general',
'confidence': 0.6
}
],
'analysis_note': 'Simplified analysis due to processing constraints'
}
async def _generate_perspective_meta_analysis(self, perspective: str, insights: list[dict[str, Any]]) -> dict[str, Any]:
"""Generate meta-analysis for perspective-specific insights."""
if not insights:
return {'status': 'no_insights_for_meta_analysis'}
confidences = [insight.get('confidence', 0.5) for insight in insights]
return {
'insight_count': len(insights),
'average_confidence': sum(confidences) / len(confidences),
'confidence_range': max(confidences) - min(confidences),
'perspective_strength': 'high' if sum(confidences) / len(confidences) > 0.7 else 'moderate',
'key_contributions': [insight.get('content', '')[:100] + '...' for insight in insights[:3]]
}
def _get_default_perspectives(self) -> list[str]:
"""Get default perspectives."""
return ["analytical", "phenomenological", "pragmatist", "critical"]
def _identify_insight_contradictions(self, insights: list[dict[str, Any]]) -> list[dict[str, Any]]:
"""Identify contradictions between insights."""
contradictions = []
for i, insight1 in enumerate(insights):
for insight2 in insights[i+1:]:
if insight1.get('perspective') != insight2.get('perspective'):
contradictions.append({
'insight1': insight1,
'insight2': insight2,
'conflict_type': 'perspectival_difference'
})
return contradictions
async def _test_in_domain(
self, hypothesis: str, domain: str, criteria: dict[str, Any]
) -> dict[str, Any]:
"""Test hypothesis in domain."""
return {
"domain": domain,
"test_results": f"hypothesis_test_in_{domain}",
"coherence_score": 0.7,
"evidence_strength": 0.6,
"supporting_evidence": ["evidence1"],
"challenges": ["challenge1"]
}
def _calculate_hypothesis_coherence(self, domain_results: dict[str, Any]) -> float:
"""Calculate hypothesis coherence."""
if not domain_results:
return 0.0
scores = [result.get('coherence_score', 0.5) for result in domain_results.values()]
return float(np.mean(scores))
def _assess_pragmatic_value(self, hypothesis: str, domain_results: dict[str, Any]) -> float:
"""Assess pragmatic value."""
return 0.65
def _calculate_test_confidence(
self, coherence: float, pragmatic_score: float, challenge_count: int
) -> float:
"""Calculate test confidence."""
base_confidence = (coherence + pragmatic_score) / 2
challenge_penalty = challenge_count * 0.1
return max(0.0, base_confidence - challenge_penalty)
def _identify_philosophical_family(self, perspective: str) -> str:
"""Identify the philosophical family/tradition of a perspective."""
families = {
'continental': ['phenomenological', 'existentialist', 'hermeneutic', 'critical', 'postmodern'],
'analytic': ['analytical', 'empiricist', 'rationalist', 'functionalist', 'logical_positivist'],
'pragmatic': ['pragmatist', 'naturalist', 'neo_pragmatist'],
'classical': ['virtue_ethics', 'deontological', 'consequentialist', 'aristotelian'],
'emergent': ['emergentist', 'complexity_theory', 'systems_theory']
}
for family, members in families.items():
if perspective in members:
return family
return 'philosophical_inquiry'
def _identify_complementary_perspectives(self, perspective: str) -> list[str]:
"""Identify perspectives that complement or synergize with this one."""
complements = {
'analytical': ['empiricist', 'rationalist', 'pragmatist'],
'phenomenological': ['existentialist', 'hermeneutic', 'embodied_cognition'],
'pragmatist': ['naturalist', 'democratic_theory', 'experimental_philosophy'],
'critical': ['postmodern', 'feminist', 'postcolonial'],
'existentialist': ['phenomenological', 'hermeneutic', 'psychoanalytic'],
'virtue_ethics': ['aristotelian', 'care_ethics', 'narrative_ethics'],
'empiricist': ['naturalist', 'scientific_realist', 'pragmatist'],
'functionalist': ['computational', 'systems_theory', 'emergentist']
}
return complements.get(perspective, ['holistic', 'integrative'])
def _identify_typical_tensions(self, perspective: str) -> list[str]:
"""Identify typical tensions or challenges for this perspective."""
tensions = {
'analytical': ['oversimplification', 'context_loss', 'lived_experience_neglect'],
'phenomenological': ['subjectivity_concerns', 'generalizability_limits', 'scientific_compatibility'],
'pragmatist': ['relativism_risk', 'traditional_values_neglect', 'theoretical_depth_concerns'],
'critical': ['ideology_accusations', 'objectivity_questions', 'practical_application_challenges'],
'existentialist': ['radical_individualism', 'nihilism_risk', 'social_dimension_neglect'],
'virtue_ethics': ['cultural_relativism', 'action_guidance_limits', 'virtue_conflicts'],
'empiricist': ['theory_underdetermination', 'observation_theory_distinction', 'induction_problem'],
'functionalist': ['multiple_realizability_issues', 'consciousness_hard_problem', 'qualia_neglect']
}
return tensions.get(perspective, ['complexity_management', 'integration_challenges'])
def _get_perspective_commitments(self, perspective: str) -> list[str]:
"""Get perspective commitments."""
commitments = {
"analytical": ["logical_rigor", "conceptual_clarity"],
"phenomenological": ["lived_experience", "consciousness_centrality"],
"pragmatist": ["practical_consequences", "experimental_method"],
"critical": ["power_analysis", "social_construction"]
}
return commitments.get(perspective, ["general_inquiry"])
def _get_perspective_criteria(self, perspective: str) -> list[str]:
"""Get perspective criteria."""
criteria = {
"analytical": ["logical_consistency", "definitional_precision"],
"phenomenological": ["descriptive_adequacy", "experiential_fidelity"],
"pragmatist": ["practical_success", "problem_solving_efficacy"],
"critical": ["transformative_potential", "ideological_critique"]
}
return criteria.get(perspective, ["general_adequacy"])
def _get_perspective_priorities(self, perspective: str) -> list[str]:
"""Get perspective priorities."""
priorities = {
"analytical": ["precision", "rigor", "systematicity"],
"phenomenological": ["experience", "meaning", "intentionality"],
"pragmatist": ["action", "consequences", "inquiry"],
"critical": ["power", "justice", "transformation"]
}
return priorities.get(perspective, ["understanding"])
def _generate_perspective_interpretation(
self,
concept_content: dict[str, Any],
perspective: str,
commitments: list[str]
) -> str:
"""Generate sophisticated perspective-specific interpretation using rich philosophical analysis."""
# Extract concept information
concept_key = concept_content.get('term', 'concept')
domain = concept_content.get('domain', 'general')
# Enhanced interpretation generation using philosophical depth
interpretation_generators = {
"analytical": self._generate_analytical_interpretation,
"phenomenological": self._generate_phenomenological_interpretation,
"pragmatist": self._generate_pragmatist_interpretation,
"critical": self._generate_critical_interpretation,
"existentialist": self._generate_existentialist_interpretation,
"virtue_ethics": self._generate_virtue_ethics_interpretation,
"empiricist": self._generate_empiricist_interpretation,
"functionalist": self._generate_functionalist_interpretation,
"emergentist": self._generate_emergentist_interpretation,
"hermeneutic": self._generate_hermeneutic_interpretation
}
# Get specialized generator or use generic approach
generator = interpretation_generators.get(perspective, self._generate_generic_interpretation)
try:
interpretation = generator(concept_key, domain, commitments, concept_content)
except Exception as e:
logger.debug(f"Specialized interpretation failed for {perspective}: {e}")
interpretation = self._generate_generic_interpretation(concept_key, domain, commitments, concept_content)
return interpretation
def _generate_analytical_interpretation(
self, concept: str, domain: str, commitments: list[str], content: dict[str, Any]
) -> str:
"""Generate analytical philosophical interpretation."""
# Analytical philosophy focuses on conceptual analysis, logical structure, and definitional clarity
analysis_components = []
# Definitional analysis
analysis_components.append(
f"Analytically, '{concept}' within {domain} requires precise definitional analysis. "
f"We must distinguish between necessary and sufficient conditions for {concept}-hood."
)
# Logical structure analysis
if 'consciousness' in concept.lower() or 'mind' in domain.lower():
analysis_components.append(
"The logical structure reveals intentionality conditions, qualitative properties, "
"and functional relations that constitute mental phenomena."
)
elif 'knowledge' in concept.lower() or 'truth' in concept.lower():
analysis_components.append(
"Epistemologically, we must analyze the logical relations between justification, "
"truth conditions, and belief states."
)
elif 'ethics' in domain.lower() or 'moral' in concept.lower():
analysis_components.append(
"The logical analysis reveals deontic structures, normative relations, "
"and the logical form of moral reasoning."
)
else:
analysis_components.append(
f"The conceptual analysis of {concept} reveals formal structures, "
f"logical dependencies, and systematic relations within {domain}."
)
# Integration with commitments
if commitments:
primary_commitments = commitments[:2]
analysis_components.append(
f"Key analytical commitments include {' and '.join(primary_commitments)}, "
f"which guide the systematic decomposition of {concept}."
)
return " ".join(analysis_components)
def _generate_phenomenological_interpretation(
self, concept: str, domain: str, commitments: list[str], content: dict[str, Any]
) -> str:
"""Generate phenomenological interpretation focusing on lived experience."""
phenomenological_components = []
# Experiential foundation
phenomenological_components.append(
f"Phenomenologically, {concept} is not primarily a theoretical construct but "
f"a lived reality that manifests in the structure of consciousness itself."
)
# Intentional analysis
if 'consciousness' in concept.lower() or 'mind' in domain.lower():
phenomenological_components.append(
"The intentional structure reveals consciousness as always consciousness-of-something, "
"with {concept} appearing through specific modes of givenness and temporal synthesis."
)
elif 'time' in concept.lower() or 'temporal' in concept.lower():
phenomenological_components.append(
"Temporality is disclosed as the fundamental structure of consciousness, "
"with retention, primal impression, and protention constituting lived duration."
)
elif 'body' in concept.lower() or 'embodiment' in domain.lower():
phenomenological_components.append(
"Embodied consciousness reveals the body not as mere physical object but as "
"the lived body (Leib) that is the condition for all world-disclosure."
)
elif 'other' in concept.lower() or 'intersubjectivity' in domain.lower():
phenomenological_components.append(
"Intersubjective constitution shows how the Other is given in originary experience "
"through empathetic appresentation and bodily expression."
)
else:
phenomenological_components.append(
f"{concept.title()} appears in the lifeworld as pre-theoretical understanding, "
f"constituted through temporal synthesis and embodied engagement within {domain}."
)
# Methodological note
phenomenological_components.append(
"This analysis brackets the natural attitude to examine how "
f"{concept} is constituted in and through consciousness."
)
return " ".join(phenomenological_components)
def _generate_pragmatist_interpretation(
self, concept: str, domain: str, commitments: list[str], content: dict[str, Any]
) -> str:
"""Generate pragmatist interpretation focusing on practical consequences and inquiry."""
pragmatist_components = []
# Practical consequences focus
pragmatist_components.append(
f"From a pragmatist perspective, the meaning of '{concept}' is found in its "
f"practical consequences and its role in ongoing inquiry within {domain}."
)
# Domain-specific practical analysis
if 'truth' in concept.lower() or 'knowledge' in concept.lower():
pragmatist_components.append(
"Truth is reconceived as warranted assertibility - what inquiry has established "
"as reliable for guiding action and further inquiry."
)
elif 'democracy' in concept.lower() or 'politics' in domain.lower():
pragmatist_components.append(
"Democratic values are validated through their capacity to create conditions "
"for shared inquiry and cooperative problem-solving."
)
elif 'education' in concept.lower() or 'learning' in domain.lower():
pragmatist_components.append(
"Educational value is measured by enhancement of intelligent action "
"and capacity for reflective inquiry."
)
elif 'science' in domain.lower() or 'empirical' in concept.lower():
pragmatist_components.append(
"Scientific concepts are tools for organizing experience and enabling "
"successful prediction and control."
)
else:
pragmatist_components.append(
f"The value of {concept} is determined by its contribution to solving "
f"concrete problems and advancing inquiry in {domain}."
)
# Experimental dimension
pragmatist_components.append(
f"Any understanding of {concept} must be tested experimentally "
f"through its application in concrete situations."
)
# Fallibilistic note
pragmatist_components.append(
"This interpretation remains open to revision based on "
"future experience and inquiry outcomes."
)
return " ".join(pragmatist_components)
def _generate_critical_interpretation(
self, concept: str, domain: str, commitments: list[str], content: dict[str, Any]
) -> str:
"""Generate critical theory interpretation focusing on power, ideology, and emancipation."""
critical_components = []
# Power analysis foundation
critical_components.append(
f"A critical analysis of '{concept}' within {domain} must examine "
f"the power relations, ideological structures, and material conditions "
f"that shape its meaning and deployment."
)
# Domain-specific critical analysis
if 'knowledge' in concept.lower() or 'education' in domain.lower():
critical_components.append(
"Knowledge is not neutral but embedded in relations of power. "
"We must ask: whose knowledge counts, who benefits from this framing, "
"and what alternatives are marginalized?"
)
elif 'freedom' in concept.lower() or 'autonomy' in concept.lower():
critical_components.append(
"Apparent freedom must be examined for subtle forms of domination. "
"True autonomy requires not just negative liberty but positive "
"conditions for self-determination."
)
elif 'rationality' in concept.lower() or 'reason' in concept.lower():
critical_components.append(
"Instrumental reason has colonized domains of human activity, "
"reducing communicative action to strategic manipulation. "
"Emancipatory reason seeks to restore dialogue and mutual understanding."
)
elif 'culture' in concept.lower() or 'society' in domain.lower():
critical_components.append(
"Cultural phenomena must be understood as sites of struggle "
"between dominant and resistant meanings, shaped by economic "
"and political forces."
)
else:
critical_components.append(
f"The concept {concept} functions within systems of domination "
f"and may serve to reproduce or challenge existing power structures "
f"within {domain}."
)
# Emancipatory dimension
critical_components.append(
f"Critical analysis aims to reveal how {concept} might contribute "
f"to human emancipation and social transformation."
)
# Historical consciousness
critical_components.append(
"This understanding recognizes the historical contingency of current "
"arrangements and the possibility of alternative configurations."
)
return " ".join(critical_components)
def _generate_existentialist_interpretation(
self, concept: str, domain: str, commitments: list[str], content: dict[str, Any]
) -> str:
"""Generate existentialist interpretation focusing on existence, freedom, and authenticity."""
existentialist_components = []
# Existence precedes essence
existentialist_components.append(
f"Existentially, {concept} cannot be understood through abstract essences "
f"but only through the concrete existence and lived situation of human beings."
)
# Freedom and responsibility analysis
if 'choice' in concept.lower() or 'decision' in concept.lower():
existentialist_components.append(
"Choice reveals the radical freedom and responsibility that defines "
"human existence. We are condemned to be free and must choose "
"without predetermined guidelines."
)
elif 'authenticity' in concept.lower() or 'authentic' in concept.lower():
existentialist_components.append(
"Authenticity requires owning one's choices and refusing the "
"comfortable self-deception of bad faith. One must choose oneself "
"in full awareness of freedom and finitude."
)
elif 'anxiety' in concept.lower() or 'angst' in concept.lower():
existentialist_components.append(
"Anxiety discloses the groundlessness of existence and the "
"weight of freedom. It is the affective revelation of our "
"responsibility for creating meaning."
)
elif 'death' in concept.lower() or 'finitude' in concept.lower():
existentialist_components.append(
"Being-toward-death reveals the finite structure of existence "
"and calls us to authentic temporality and resolute choice."
)
else:
existentialist_components.append(
f"{concept.title()} must be understood in relation to the fundamental "
f"structures of human existence: freedom, finitude, and the call "
f"to authentic self-creation within {domain}."
)
# Situatedness
existentialist_components.append(
f"This analysis recognizes that {concept} is always encountered "
f"within concrete historical and cultural situations that both "
f"limit and enable authentic existence."
)
# Individual responsibility
existentialist_components.append(
"Ultimately, each individual must take responsibility for their "
f"understanding and living of {concept}."
)
return " ".join(existentialist_components)
def _generate_virtue_ethics_interpretation(
self, concept: str, domain: str, commitments: list[str], content: dict[str, Any]
) -> str:
"""Generate virtue ethics interpretation focusing on character and flourishing."""
virtue_components = []
# Character-centered approach
virtue_components.append(
f"From a virtue ethics perspective, {concept} is best understood "
f"in relation to character formation and human flourishing (eudaimonia)."
)
# Domain-specific virtue analysis
if 'justice' in concept.lower() or 'fairness' in concept.lower():
virtue_components.append(
"Justice as virtue involves the settled disposition to give "
"each their due, guided by practical wisdom and consideration "
"of the common good."
)
elif 'courage' in concept.lower() or 'bravery' in concept.lower():
virtue_components.append(
"Courage is the mean between cowardice and recklessness, "
"enabling one to act well in the face of difficulty or danger."
)
elif 'friendship' in concept.lower() or 'love' in concept.lower():
virtue_components.append(
"Friendship represents the highest form of human relationship, "
"combining utility, pleasure, and mutual virtue in the pursuit "
"of shared goods."
)
elif 'wisdom' in concept.lower() or 'knowledge' in concept.lower():
virtue_components.append(
"Practical wisdom (phronesis) is the master virtue that enables "
"correct deliberation about human affairs and the coordination "
"of other virtues."
)
else:
virtue_components.append(
f"{concept.title()} should be evaluated by its contribution to "
f"character development and the cultivation of virtues within {domain}."
)
# Practical wisdom dimension
virtue_components.append(
f"The virtuous person exercises practical wisdom to discern how "
f"{concept} contributes to or detracts from human flourishing."
)
# Community context
virtue_components.append(
"This analysis recognizes that virtues are cultivated within "
"communities of practice and oriented toward shared goods."
)
# Habituation
virtue_components.append(
f"Understanding {concept} requires attention to how repeated actions "
f"shape character and create settled dispositions for future action."
)
return " ".join(virtue_components)
def _generate_empiricist_interpretation(
self, concept: str, domain: str, commitments: list[str], content: dict[str, Any]
) -> str:
"""Generate empiricist interpretation grounded in experience and observation."""
empiricist_components = []
# Experience foundation
empiricist_components.append(
f"From an empiricist standpoint, our understanding of '{concept}' "
f"must be grounded in sensory experience and empirical observation."
)
# Knowledge source analysis
if 'knowledge' in concept.lower() or 'learning' in concept.lower():
empiricist_components.append(
"All knowledge originates in sense experience through impression "
"and idea formation. Complex ideas are built from simple sensory "
"elements through association and abstraction."
)
elif 'causation' in concept.lower() or 'cause' in concept.lower():
empiricist_components.append(
"Causal relations are not logically necessary but are based on "
"observed constant conjunction and habit formation. We cannot "
"perceive causation itself, only temporal succession."
)
elif 'self' in concept.lower() or 'identity' in concept.lower():
empiricist_components.append(
"Personal identity consists in the continuity of memory and "
"experience rather than in any metaphysical substance. "
"The self is a bundle of perceptions."
)
else:
empiricist_components.append(
f"Any valid concept of {concept} must be traceable to simple "
f"ideas derived from sensory impressions within {domain}."
)
# Verification principle
empiricist_components.append(
f"Claims about {concept} gain meaning and justification through "
f"their connection to possible empirical verification or falsification."
)
# Inductive reasoning
empiricist_components.append(
"Our understanding develops through inductive generalization "
"from particular experiences, acknowledging the fallible nature "
"of empirical knowledge."
)
return " ".join(empiricist_components)
def _generate_functionalist_interpretation(
self, concept: str, domain: str, commitments: list[str], content: dict[str, Any]
) -> str:
"""Generate functionalist interpretation focusing on functional roles and relations."""
functionalist_components = []
# Functional role analysis
functionalist_components.append(
f"Functionally, {concept} is defined not by its intrinsic properties "
f"but by its causal-functional role within a broader system."
)
# Domain-specific functional analysis
if 'mind' in concept.lower() or 'mental' in domain.lower():
functionalist_components.append(
"Mental states are characterized by their functional relations "
"to inputs, outputs, and other mental states. Multiple physical "
"systems can realize the same functional organization."
)
elif 'pain' in concept.lower() or 'consciousness' in concept.lower():
functionalist_components.append(
"Conscious states are defined by their causal roles in producing "
"behavior, processing information, and integrating with other "
"cognitive systems."
)
elif 'belief' in concept.lower() or 'desire' in concept.lower():
functionalist_components.append(
"Propositional attitudes are characterized by their roles in "
"practical reasoning, action production, and belief revision "
"rather than by qualitative content."
)
else:
functionalist_components.append(
f"{concept.title()} is best understood through its functional "
f"contributions to system operation and goal achievement within {domain}."
)
# Multiple realizability
functionalist_components.append(
f"The functional approach recognizes that {concept} can be "
f"multiply realized in different physical substrates while "
f"maintaining functional equivalence."
)
# System integration
functionalist_components.append(
f"Analysis of {concept} requires understanding its integration "
f"with other functional components and its role in overall "
f"system performance."
)
return " ".join(functionalist_components)
def _generate_emergentist_interpretation(
self, concept: str, domain: str, commitments: list[str], content: dict[str, Any]
) -> str:
"""Generate emergentist interpretation focusing on emergence and complexity."""
emergentist_components = []
# Emergence foundation
emergentist_components.append(
f"From an emergentist perspective, {concept} represents emergent "
f"properties that arise from but are not reducible to lower-level "
f"components and interactions."
)
# Level-specific analysis
if 'consciousness' in concept.lower() or 'mind' in domain.lower():
emergentist_components.append(
"Consciousness emerges from neural complexity but exhibits "
"novel causal powers irreducible to neurobiological processes. "
"Mental causation operates through downward causation."
)
elif 'life' in concept.lower() or 'biological' in domain.lower():
emergentist_components.append(
"Biological properties emerge from chemical interactions but "
"exhibit autonomous organization, self-maintenance, and "
"adaptive behavior not present at lower levels."
)
elif 'social' in concept.lower() or 'culture' in domain.lower():
emergentist_components.append(
"Social phenomena emerge from individual interactions but "
"develop institutional reality with genuine causal efficacy "
"that shapes individual behavior."
)
else:
emergentist_components.append(
f"{concept.title()} exhibits emergent properties within {domain} "
f"that arise from complex interactions while maintaining "
f"distinctive causal powers."
)
# Complexity and organization
emergentist_components.append(
f"The emergence of {concept} depends on specific organizational "
f"patterns and threshold levels of complexity that enable "
f"novel systemic properties."
)
# Irreducibility
emergentist_components.append(
"While respecting lower-level constraints, emergent properties "
f"of {concept} require autonomous description and cannot be "
f"fully predicted from component analysis alone."
)
return " ".join(emergentist_components)
def _generate_hermeneutic_interpretation(
self, concept: str, domain: str, commitments: list[str], content: dict[str, Any]
) -> str:
"""Generate hermeneutic interpretation focusing on interpretation and understanding."""
hermeneutic_components = []
# Interpretive foundation
hermeneutic_components.append(
f"Hermeneutically, {concept} is understood through the circular "
f"process of interpretation, where understanding emerges from "
f"the dialogue between interpreter and interpreted."
)
# Historical consciousness
hermeneutic_components.append(
f"Understanding {concept} requires awareness of historical context "
f"and the temporal distance that both separates and connects us "
f"to its meaning within {domain}."
)
# Fusion of horizons
if 'text' in concept.lower() or 'interpretation' in concept.lower():
hermeneutic_components.append(
"Meaning emerges through the fusion of horizons between the "
"world of the text and the world of the interpreter, creating "
"new understanding through dialogue."
)
elif 'tradition' in concept.lower() or 'culture' in concept.lower():
hermeneutic_components.append(
"Tradition is not a dead weight of the past but a living "
"transmission of meaning that shapes present understanding "
"while remaining open to reinterpretation."
)
elif 'understanding' in concept.lower() or 'meaning' in concept.lower():
hermeneutic_components.append(
"Understanding is not passive reception but active engagement "
"that transforms both interpreter and interpreted through "
"the process of meaning-making."
)
else:
hermeneutic_components.append(
f"The meaning of {concept} emerges through interpretive "
f"engagement that recognizes both historical conditioning "
f"and creative possibility within {domain}."
)
# Prejudgment and openness
hermeneutic_components.append(
f"Interpretation of {concept} involves productive prejudgments "
f"that enable understanding while remaining open to correction "
f"through encounter with the subject matter."
)
return " ".join(hermeneutic_components)
def _generate_generic_interpretation(
self, concept: str, domain: str, commitments: list[str], content: dict[str, Any]
) -> str:
"""Generate generic philosophical interpretation as fallback."""
generic_components = []
generic_components.append(
f"Philosophically, {concept} within {domain} raises fundamental "
f"questions about the nature of reality, knowledge, and value."
)
if commitments:
generic_components.append(
f"Key considerations include {', '.join(commitments[:3])}, "
f"which guide the analysis and evaluation of {concept}."
)
generic_components.append(
f"Understanding {concept} requires careful attention to both "
f"conceptual precision and contextual sensitivity within {domain}."
)
return " ".join(generic_components)
def _extract_schema_relevant_features(
self,
concept_content: dict[str, Any],
commitments: list[str],
criteria: list[str]
) -> list[str]:
"""Extract features relevant to the interpretive schema."""
relevant_features = []
# Check concept content against schema commitments
for commitment in commitments:
if any(commitment.lower() in str(value).lower()
for value in concept_content.values() if value):
relevant_features.append(f"commitment_alignment_{commitment}")
# Check against evaluation criteria
for criterion in criteria:
if any(criterion.lower() in str(value).lower()
for value in concept_content.values() if value):
relevant_features.append(f"criterion_satisfaction_{criterion}")
# Extract structural features
if 'term' in concept_content:
relevant_features.append(f"conceptual_core_{concept_content['term']}")
if 'domain' in concept_content:
relevant_features.append(f"domain_grounding_{concept_content['domain']}")
# Add temporal features
if 'timestamp' in concept_content:
relevant_features.append("temporal_grounding")
# Ensure we have at least some features
if not relevant_features:
relevant_features = [
"minimal_conceptual_structure",
"basic_semantic_content"
]
return relevant_features[:5] # Limit to 5 most relevant
def _calculate_schema_uncertainty(
self,
alignment_score: float,
concept_confidence: float,
coherence_score: float,
feature_count: int
) -> float:
"""Calculate uncertainty in schema application."""
from .utils import calculate_epistemic_uncertainty
# Base uncertainty from alignment
alignment_uncertainty = 1.0 - alignment_score
# Confidence uncertainty
confidence_uncertainty = 1.0 - concept_confidence
# Coherence uncertainty
coherence_uncertainty = 1.0 - coherence_score
# Evidence quantity factor (more features = less uncertainty)
evidence_factor = 1.0 / (1.0 + feature_count * 0.2)
# Combine uncertainty sources
combined_uncertainty = (
0.3 * alignment_uncertainty +
0.25 * confidence_uncertainty +
0.25 * coherence_uncertainty +
0.2 * evidence_factor
)
# Apply epistemic uncertainty calculation from utils
domain_complexity = 0.6 # Philosophy is inherently complex
temporal_factor = 1.0 # Current analysis
epistemic_uncertainty = calculate_epistemic_uncertainty(
evidence_count=feature_count,
coherence_score=coherence_score,
temporal_factor=temporal_factor,
domain_complexity=domain_complexity
)
# Take maximum of combined and epistemic uncertainty
final_uncertainty = max(combined_uncertainty, epistemic_uncertainty)
return float(np.clip(final_uncertainty, 0.0, 1.0))
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
def cleanup_processes() -> None:
"""
Comprehensive cleanup of all running processes and background tasks.
### Cleanup Protocol:
1. Cancel all tracked background tasks
2. Terminate any external processes
3. Clear tracking dictionaries
4. Log cleanup completion
"""
global _shutdown_initiated
if _shutdown_initiated:
return
_shutdown_initiated = True
logger.info("Initiating philosophical server cleanup")
# Cancel background tasks
for task in background_tasks.copy():
if not task.done():
task.cancel()
logger.debug(f"Cancelled background task: {task}")
background_tasks.clear()
# Clean up any tracked processes
for process_id, process in running_processes.items():
if hasattr(process, 'terminate'):
try:
process.terminate()
logger.debug(f"Terminated process: {process_id}")
except Exception as e:
logger.warning(f"Error terminating process {process_id}: {e}")
running_processes.clear()
logger.info("Philosophical server cleanup completed")
def signal_handler(signum: int, _frame: Any) -> None:
"""Handle shutdown signals gracefully."""
logger.info(f"Received signal {signum}, initiating shutdown")
cleanup_processes()
sys.exit(0)
def track_background_task(task: asyncio.Task) -> None:
"""
Track background task for proper cleanup.
Args:
task: Asyncio task to track
"""
background_tasks.add(task)
task.add_done_callback(background_tasks.discard)
# Register signal handlers and cleanup
signal.signal(signal.SIGTERM, signal_handler)
signal.signal(signal.SIGINT, signal_handler)
atexit.register(cleanup_processes)
async def main() -> None:
"""
Enhanced main entry point with comprehensive error handling.
Enhanced main entry point with comprehensive error handling.
### Execution Framework:
1. Server instantiation
2. Handler configuration
3. Lifecycle management
4. Graceful shutdown orchestration
"""
try:
server = OpenEndedPhilosophyServer()
server.setup_handlers()
from anyio import create_memory_object_stream
from mcp.server.models import InitializationOptions
send_stream, receive_stream = create_memory_object_stream[Message]()
await server.server.run(
server_name="OpenEndedPhilosophy",
server_version="1.0.0",
capabilities={},
read_stream=receive_stream,
write_stream=send_stream,
initialization_options=InitializationOptions()
)
except KeyboardInterrupt:
logger.info("Philosophical inquiry interrupted")
except Exception as e:
logger.error(f"Critical server error: {e}", exc_info=True)
sys.exit(1)
finally:
cleanup_processes()
if __name__ == "__main__":
# Set up event loop policy for better performance
if sys.platform != 'win32':
try:
import uvloop
uvloop.install()
logger.info("Using uvloop for enhanced performance")
except ImportError:
logger.info("uvloop not available, using default event loop")
asyncio.run(main())
def cli_main():
"""Entry point for console script (synchronous wrapper for async main)."""
# Set up event loop policy for better performance
if sys.platform != 'win32':
try:
import uvloop
uvloop.install()
logger.info("Using uvloop for enhanced performance")
except ImportError:
logger.info("uvloop not available, using default event loop")
asyncio.run(main())
