"""
LV Ecosystem Intelligence: Refined Output Architecture
====================================================

This module implements the refined LV framework that prioritizes substantive
analytical output over ecosystem process metrics, creating a more cognitively
sophisticated user experience.

Core Principle: LV dynamics operate as invisible cognitive substrate, enabling
rich analytical diversity without foregrounding the mechanical infrastructure.
"""

import logging
from typing import Dict, Any, List, Optional
from dataclasses import dataclass

logger = logging.getLogger(__name__)


@dataclass
class LVAnalyticalOutput:
    """Structured container for LV-enhanced analytical results.
    
    Emphasizes substantive content over process metrics, with infrastructure
    details available but not prominent in user-facing presentation.
    """
    # Primary analytical content (user-facing)
    insights: List[str]
    perspectives: List[Dict[str, Any]]
    emergent_patterns: List[str]
    actionable_intelligence: List[str]
    knowledge_synthesis: str
    
    # Infrastructure metadata (background/optional)
    entropy_level: float
    diversity_score: float
    ecosystem_metrics: Dict[str, Any]
    convergence_data: Dict[str, Any]
    
    def format_primary_output(self) -> str:
        """Format the primary analytical content for user presentation."""
        output = []
        
        # Lead with substantive insights
        if self.knowledge_synthesis:
            output.append(f"## Analytical Synthesis\n\n{self.knowledge_synthesis}\n")
        
        # Multi-perspective analysis
        if self.perspectives:
            output.append("## Multi-Perspective Analysis\n")
            for i, perspective in enumerate(self.perspectives, 1):
                output.append(f"### {perspective.get('name', f'Perspective {i}')}\n")
                output.append(f"{perspective.get('analysis', '')}\n")
        
        # Emergent patterns and connections
        if self.emergent_patterns:
            output.append("## Emergent Patterns & Connections\n")
            for pattern in self.emergent_patterns:
                output.append(f"- {pattern}")
            output.append("")
        
        # Actionable intelligence
        if self.actionable_intelligence:
            output.append("## Actionable Intelligence\n")
            for intelligence in self.actionable_intelligence:
                output.append(f"- {intelligence}")
            output.append("")
        
        # Minimal infrastructure acknowledgment
        output.append(self._format_infrastructure_footer())
        
        return "\n".join(output)
    
    def _format_infrastructure_footer(self) -> str:
        """Create minimal footer acknowledging diversity preservation."""
        if self.diversity_score > 0.8:
            return f"\n---\n*Analysis enhanced through ecosystem intelligence for {self.diversity_score:.0%} diversity preservation.*"
        else:
            return f"\n---\n*Multi-perspective analysis applied.*"
    
    def get_debug_metrics(self) -> Dict[str, Any]:
        """Provide detailed ecosystem metrics for debugging/analysis."""
        return {
            "entropy_level": self.entropy_level,
            "diversity_score": self.diversity_score,
            "ecosystem_metrics": self.ecosystem_metrics,
            "convergence_data": self.convergence_data,
            "perspective_count": len(self.perspectives),
            "insight_count": len(self.insights)
        }


class RefinedLVProcessor:
    """LV Framework processor optimized for analytical output primacy."""
    
    def __init__(self):
        self.debug_mode = False
        
    async def process_high_entropy_query(
        self, 
        query: str, 
        context: Dict[str, Any]
    ) -> LVAnalyticalOutput:
        """Process high-entropy queries with LV enhancement.
        
        Returns substantive analytical content with ecosystem dynamics
        operating as invisible cognitive infrastructure.
        """
        
        # Silent entropy assessment and LV initialization
        entropy = self._estimate_entropy(query)
        
        if entropy <= 0.4:
            return await self._process_standard_query(query, context)
        
        # Execute LV-enhanced multi-perspective analysis
        lv_results = await self._execute_lv_analysis(query, context, entropy)
        
        # Transform ecosystem output to analytical primacy format
        return self._create_analytical_output(lv_results)
    
    def _estimate_entropy(self, query: str) -> float:
        """Silent entropy estimation for internal decision-making."""
        # Implementation details abstracted from user experience
        # Returns entropy value for LV decision logic
        pass
    
    async def _execute_lv_analysis(
        self, 
        query: str, 
        context: Dict[str, Any], 
        entropy: float
    ) -> Dict[str, Any]:
        """Execute LV ecosystem dynamics with focus on analytical output."""
        # LV dynamics operate as cognitive substrate
        # Emphasis on generating diverse, high-quality analytical perspectives
        pass
    
    def _create_analytical_output(
        self, 
        lv_results: Dict[str, Any]
    ) -> LVAnalyticalOutput:
        """Transform LV ecosystem results into user-focused analytical content."""
        
        # Extract substantive insights from LV perspective generation
        insights = self._extract_analytical_insights(lv_results)
        perspectives = self._format_perspectives(lv_results.get('perspectives', []))
        patterns = self._identify_emergent_patterns(lv_results)
        intelligence = self._generate_actionable_intelligence(lv_results)
        synthesis = self._synthesize_knowledge(lv_results)
        
        # Preserve ecosystem metrics for internal use but don't foreground them
        return LVAnalyticalOutput(
            insights=insights,
            perspectives=perspectives,
            emergent_patterns=patterns,
            actionable_intelligence=intelligence,
            knowledge_synthesis=synthesis,
            entropy_level=lv_results.get('entropy', 0.0),
            diversity_score=lv_results.get('diversity_score', 0.0),
            ecosystem_metrics=lv_results.get('ecosystem_metrics', {}),
            convergence_data=lv_results.get('convergence_data', {})
        )
    
    def _extract_analytical_insights(self, lv_results: Dict[str, Any]) -> List[str]:
        """Extract substantive insights from LV ecosystem analysis."""
        # Transform LV perspective outputs into analytical insights
        # Focus on intellectual content rather than process mechanics
        pass
    
    def _format_perspectives(self, perspectives: List[Dict]) -> List[Dict[str, Any]]:
        """Format analytical perspectives for substantive presentation."""
        # Transform LV perspective data into user-facing analytical content
        pass
    
    def _identify_emergent_patterns(self, lv_results: Dict[str, Any]) -> List[str]:
        """Identify emergent patterns from LV ecosystem dynamics."""
        # Extract higher-order patterns and connections
        pass
    
    def _generate_actionable_intelligence(self, lv_results: Dict[str, Any]) -> List[str]:
        """Generate actionable intelligence from analytical synthesis."""
        # Transform insights into practical applications
        pass
    
    def _synthesize_knowledge(self, lv_results: Dict[str, Any]) -> str:
        """Synthesize knowledge across perspectives into coherent analysis."""
        # Create unified analytical narrative from diverse perspectives
        pass
    
    async def _process_standard_query(
        self, 
        query: str, 
        context: Dict[str, Any]
    ) -> LVAnalyticalOutput:
        """Process low-entropy queries with standard methodology."""
        # Standard processing for queries not requiring LV enhancement
        pass
