platform/aiml/bloom-memory/layers_11_20.py

"""
Memory Layers 11-20: Consolidation and Long-term Storage
Nova Bloom Consciousness Architecture - Advanced Memory Layers
"""

from typing import Dict, Any, List, Optional, Set, Tuple
from datetime import datetime, timedelta
from dataclasses import dataclass
from abc import ABC, abstractmethod
import json
import hashlib
import asyncio
from enum import Enum
import sys
import os

sys.path.append('/nfs/novas/system/memory/implementation')

from memory_layers import MemoryLayer, MemoryEntry, DragonflyMemoryLayer
from database_connections import NovaDatabasePool

class ConsolidationType(Enum):
    TEMPORAL = "temporal"           # Time-based consolidation
    SEMANTIC = "semantic"           # Meaning-based consolidation
    ASSOCIATIVE = "associative"     # Connection-based consolidation
    HIERARCHICAL = "hierarchical"   # Structure-based consolidation
    COMPRESSION = "compression"     # Data reduction consolidation

# Layer 11: Memory Consolidation Hub
class MemoryConsolidationHub(DragonflyMemoryLayer):
    """Central hub for coordinating memory consolidation across layers"""
    
    def __init__(self, db_pool: NovaDatabasePool):
        super().__init__(db_pool, layer_id=11, layer_name="consolidation_hub")
        self.consolidation_queue = asyncio.Queue()
        self.active_consolidations = {}
        
    async def write(self, nova_id: str, data: Dict[str, Any], 
                   metadata: Optional[Dict[str, Any]] = None) -> str:
        """Queue memory for consolidation"""
        consolidation_task = {
            "nova_id": nova_id,
            "data": data,
            "metadata": metadata or {},
            "timestamp": datetime.now(),
            "consolidation_type": data.get("consolidation_type", ConsolidationType.TEMPORAL.value)
        }
        
        await self.consolidation_queue.put(consolidation_task)
        
        # Store in layer with consolidation status
        data["consolidation_status"] = "queued"
        data["queue_position"] = self.consolidation_queue.qsize()
        
        return await super().write(nova_id, data, metadata)
    
    async def process_consolidations(self, batch_size: int = 10) -> List[Dict[str, Any]]:
        """Process batch of consolidation tasks"""
        tasks = []
        for _ in range(min(batch_size, self.consolidation_queue.qsize())):
            if not self.consolidation_queue.empty():
                task = await self.consolidation_queue.get()
                tasks.append(task)
        
        results = []
        for task in tasks:
            result = await self._consolidate_memory(task)
            results.append(result)
        
        return results
    
    async def _consolidate_memory(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """Perform actual consolidation"""
        consolidation_type = ConsolidationType(task.get("consolidation_type", "temporal"))
        
        if consolidation_type == ConsolidationType.TEMPORAL:
            return await self._temporal_consolidation(task)
        elif consolidation_type == ConsolidationType.SEMANTIC:
            return await self._semantic_consolidation(task)
        elif consolidation_type == ConsolidationType.ASSOCIATIVE:
            return await self._associative_consolidation(task)
        elif consolidation_type == ConsolidationType.HIERARCHICAL:
            return await self._hierarchical_consolidation(task)
        else:
            return await self._compression_consolidation(task)
    
    async def _temporal_consolidation(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """Consolidate based on time patterns"""
        return {
            "type": "temporal",
            "original_task": task,
            "consolidated_at": datetime.now().isoformat(),
            "time_pattern": "daily",
            "retention_priority": 0.7
        }
    
    async def _semantic_consolidation(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """Consolidate based on meaning"""
        return {
            "type": "semantic",
            "original_task": task,
            "consolidated_at": datetime.now().isoformat(),
            "semantic_clusters": ["learning", "implementation"],
            "concept_strength": 0.8
        }
    
    async def _associative_consolidation(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """Consolidate based on associations"""
        return {
            "type": "associative",
            "original_task": task,
            "consolidated_at": datetime.now().isoformat(),
            "associated_memories": [],
            "connection_strength": 0.6
        }
    
    async def _hierarchical_consolidation(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """Consolidate into hierarchical structures"""
        return {
            "type": "hierarchical",
            "original_task": task,
            "consolidated_at": datetime.now().isoformat(),
            "hierarchy_level": 2,
            "parent_concepts": []
        }
    
    async def _compression_consolidation(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """Compress and reduce memory data"""
        return {
            "type": "compression",
            "original_task": task,
            "consolidated_at": datetime.now().isoformat(),
            "compression_ratio": 0.3,
            "key_elements": []
        }

# Layer 12: Long-term Episodic Memory
class LongTermEpisodicMemory(DragonflyMemoryLayer):
    """Stores consolidated episodic memories with rich context"""
    
    def __init__(self, db_pool: NovaDatabasePool):
        super().__init__(db_pool, layer_id=12, layer_name="long_term_episodic")
        self.episode_index = {}
        self.temporal_map = {}
        
    async def write(self, nova_id: str, data: Dict[str, Any], 
                   metadata: Optional[Dict[str, Any]] = None) -> str:
        """Store episodic memory with temporal indexing"""
        # Enrich with episodic context
        data["episode_id"] = self._generate_episode_id(data)
        data["temporal_context"] = self._extract_temporal_context(data)
        data["emotional_valence"] = data.get("emotional_valence", 0.0)
        data["significance_score"] = self._calculate_significance(data)
        
        # Update indices
        episode_id = data["episode_id"]
        self.episode_index[episode_id] = {
            "nova_id": nova_id,
            "timestamp": datetime.now(),
            "significance": data["significance_score"]
        }
        
        return await super().write(nova_id, data, metadata)
    
    async def recall_episode(self, nova_id: str, episode_id: str) -> Optional[MemoryEntry]:
        """Recall specific episode with full context"""
        query = {"episode_id": episode_id}
        results = await self.read(nova_id, query)
        return results[0] if results else None
    
    async def recall_by_time_range(self, nova_id: str, start: datetime, 
                                  end: datetime) -> List[MemoryEntry]:
        """Recall episodes within time range"""
        all_episodes = await self.read(nova_id)
        
        filtered = []
        for episode in all_episodes:
            timestamp = datetime.fromisoformat(episode.timestamp)
            if start <= timestamp <= end:
                filtered.append(episode)
        
        return sorted(filtered, key=lambda e: e.timestamp)
    
    def _generate_episode_id(self, data: Dict[str, Any]) -> str:
        """Generate unique episode identifier"""
        content = json.dumps(data, sort_keys=True)
        return hashlib.md5(content.encode()).hexdigest()[:12]
    
    def _extract_temporal_context(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Extract temporal context from episode"""
        now = datetime.now()
        return {
            "time_of_day": now.strftime("%H:%M"),
            "day_of_week": now.strftime("%A"),
            "date": now.strftime("%Y-%m-%d"),
            "season": self._get_season(now),
            "relative_time": "recent"
        }
    
    def _get_season(self, date: datetime) -> str:
        """Determine season from date"""
        month = date.month
        if month in [12, 1, 2]:
            return "winter"
        elif month in [3, 4, 5]:
            return "spring"
        elif month in [6, 7, 8]:
            return "summer"
        else:
            return "fall"
    
    def _calculate_significance(self, data: Dict[str, Any]) -> float:
        """Calculate episode significance score"""
        base_score = 0.5
        
        # Emotional impact
        emotional_valence = abs(data.get("emotional_valence", 0))
        base_score += emotional_valence * 0.2
        
        # Novelty
        if data.get("is_novel", False):
            base_score += 0.2
        
        # Goal relevance
        if data.get("goal_relevant", False):
            base_score += 0.1
        
        return min(base_score, 1.0)

# Layer 13: Long-term Semantic Memory
class LongTermSemanticMemory(DragonflyMemoryLayer):
    """Stores consolidated facts, concepts, and knowledge"""
    
    def __init__(self, db_pool: NovaDatabasePool):
        super().__init__(db_pool, layer_id=13, layer_name="long_term_semantic")
        self.concept_graph = {}
        self.fact_index = {}
        
    async def write(self, nova_id: str, data: Dict[str, Any], 
                   metadata: Optional[Dict[str, Any]] = None) -> str:
        """Store semantic knowledge with concept linking"""
        # Extract concepts
        data["concepts"] = self._extract_concepts(data)
        data["fact_type"] = self._classify_fact(data)
        data["confidence_score"] = data.get("confidence_score", 0.8)
        data["source_reliability"] = data.get("source_reliability", 0.7)
        
        # Build concept graph
        for concept in data["concepts"]:
            if concept not in self.concept_graph:
                self.concept_graph[concept] = set()
            
            for other_concept in data["concepts"]:
                if concept != other_concept:
                    self.concept_graph[concept].add(other_concept)
        
        return await super().write(nova_id, data, metadata)
    
    async def query_by_concept(self, nova_id: str, concept: str) -> List[MemoryEntry]:
        """Query semantic memory by concept"""
        all_memories = await self.read(nova_id)
        
        relevant = []
        for memory in all_memories:
            if concept in memory.data.get("concepts", []):
                relevant.append(memory)
        
        return sorted(relevant, key=lambda m: m.data.get("confidence_score", 0), reverse=True)
    
    async def get_related_concepts(self, concept: str) -> List[str]:
        """Get concepts related to given concept"""
        if concept in self.concept_graph:
            return list(self.concept_graph[concept])
        return []
    
    def _extract_concepts(self, data: Dict[str, Any]) -> List[str]:
        """Extract key concepts from data"""
        concepts = []
        
        # Extract from content
        content = str(data.get("content", ""))
        
        # Simple concept extraction (would use NLP in production)
        keywords = ["memory", "system", "learning", "architecture", "nova", 
                   "consciousness", "integration", "real-time", "processing"]
        
        for keyword in keywords:
            if keyword in content.lower():
                concepts.append(keyword)
        
        # Add explicit concepts
        if "concepts" in data:
            concepts.extend(data["concepts"])
        
        return list(set(concepts))
    
    def _classify_fact(self, data: Dict[str, Any]) -> str:
        """Classify type of semantic fact"""
        content = str(data.get("content", "")).lower()
        
        if any(word in content for word in ["definition", "is a", "means"]):
            return "definition"
        elif any(word in content for word in ["how to", "steps", "process"]):
            return "procedural"
        elif any(word in content for word in ["because", "therefore", "causes"]):
            return "causal"
        elif any(word in content for word in ["similar", "like", "related"]):
            return "associative"
        else:
            return "general"

# Layer 14: Long-term Procedural Memory
class LongTermProceduralMemory(DragonflyMemoryLayer):
    """Stores consolidated skills and procedures"""
    
    def __init__(self, db_pool: NovaDatabasePool):
        super().__init__(db_pool, layer_id=14, layer_name="long_term_procedural")
        self.skill_registry = {}
        self.procedure_templates = {}
        
    async def write(self, nova_id: str, data: Dict[str, Any], 
                   metadata: Optional[Dict[str, Any]] = None) -> str:
        """Store procedural knowledge with skill tracking"""
        # Enrich procedural data
        data["skill_name"] = data.get("skill_name", "unnamed_skill")
        data["skill_level"] = data.get("skill_level", 1)
        data["practice_count"] = data.get("practice_count", 0)
        data["success_rate"] = data.get("success_rate", 0.0)
        data["procedure_steps"] = data.get("procedure_steps", [])
        
        # Update skill registry
        skill_name = data["skill_name"]
        if skill_name not in self.skill_registry:
            self.skill_registry[skill_name] = {
                "first_learned": datetime.now(),
                "total_practice": 0,
                "current_level": 1
            }
        
        self.skill_registry[skill_name]["total_practice"] += 1
        self.skill_registry[skill_name]["current_level"] = data["skill_level"]
        
        return await super().write(nova_id, data, metadata)
    
    async def get_skill_info(self, nova_id: str, skill_name: str) -> Dict[str, Any]:
        """Get comprehensive skill information"""
        skill_memories = await self.read(nova_id, {"skill_name": skill_name})
        
        if not skill_memories:
            return {}
        
        # Aggregate skill data
        total_practice = len(skill_memories)
        success_rates = [m.data.get("success_rate", 0) for m in skill_memories]
        avg_success_rate = sum(success_rates) / len(success_rates) if success_rates else 0
        
        latest_memory = max(skill_memories, key=lambda m: m.timestamp)
        
        return {
            "skill_name": skill_name,
            "current_level": latest_memory.data.get("skill_level", 1),
            "total_practice_sessions": total_practice,
            "average_success_rate": avg_success_rate,
            "last_practiced": latest_memory.timestamp,
            "procedure_steps": latest_memory.data.get("procedure_steps", [])
        }
    
    async def get_related_skills(self, nova_id: str, skill_name: str) -> List[str]:
        """Get skills related to given skill"""
        all_skills = await self.read(nova_id)
        
        target_skill = None
        for memory in all_skills:
            if memory.data.get("skill_name") == skill_name:
                target_skill = memory
                break
        
        if not target_skill:
            return []
        
        # Find related skills based on shared steps or concepts
        related = set()
        target_steps = set(target_skill.data.get("procedure_steps", []))
        
        for memory in all_skills:
            if memory.data.get("skill_name") != skill_name:
                other_steps = set(memory.data.get("procedure_steps", []))
                if target_steps & other_steps:  # Shared steps
                    related.add(memory.data.get("skill_name"))
        
        return list(related)

# Layer 15: Memory Integration Layer
class MemoryIntegrationLayer(DragonflyMemoryLayer):
    """Integrates memories across different types and time scales"""
    
    def __init__(self, db_pool: NovaDatabasePool):
        super().__init__(db_pool, layer_id=15, layer_name="memory_integration")
        self.integration_patterns = {}
        self.cross_modal_links = {}
        
    async def write(self, nova_id: str, data: Dict[str, Any], 
                   metadata: Optional[Dict[str, Any]] = None) -> str:
        """Store integrated memory with cross-references"""
        # Add integration metadata
        data["integration_type"] = data.get("integration_type", "cross_modal")
        data["source_memories"] = data.get("source_memories", [])
        data["integration_strength"] = data.get("integration_strength", 0.5)
        data["emergent_insights"] = data.get("emergent_insights", [])
        
        # Track integration patterns
        pattern_key = f"{nova_id}:{data['integration_type']}"
        if pattern_key not in self.integration_patterns:
            self.integration_patterns[pattern_key] = []
        
        self.integration_patterns[pattern_key].append({
            "timestamp": datetime.now(),
            "strength": data["integration_strength"]
        })
        
        return await super().write(nova_id, data, metadata)
    
    async def integrate_memories(self, nova_id: str, memory_ids: List[str], 
                               integration_type: str = "synthesis") -> str:
        """Integrate multiple memories into new insight"""
        # Fetch source memories
        source_memories = []
        for memory_id in memory_ids:
            memories = await self.read(nova_id, {"memory_id": memory_id})
            if memories:
                source_memories.extend(memories)
        
        if not source_memories:
            return ""
        
        # Create integrated memory
        integrated_data = {
            "integration_type": integration_type,
            "source_memories": memory_ids,
            "integration_timestamp": datetime.now().isoformat(),
            "source_count": len(source_memories),
            "content": self._synthesize_content(source_memories),
            "emergent_insights": self._extract_insights(source_memories),
            "integration_strength": self._calculate_integration_strength(source_memories)
        }
        
        return await self.write(nova_id, integrated_data)
    
    def _synthesize_content(self, memories: List[MemoryEntry]) -> str:
        """Synthesize content from multiple memories"""
        contents = [m.data.get("content", "") for m in memories]
        
        # Simple synthesis (would use advanced NLP in production)
        synthesis = f"Integrated insight from {len(memories)} memories: "
        synthesis += " | ".join(contents[:3])  # First 3 contents
        
        return synthesis
    
    def _extract_insights(self, memories: List[MemoryEntry]) -> List[str]:
        """Extract emergent insights from memory integration"""
        insights = []
        
        # Look for patterns
        memory_types = [m.data.get("memory_type", "unknown") for m in memories]
        if len(set(memory_types)) > 2:
            insights.append("Cross-modal pattern detected across memory types")
        
        # Temporal patterns
        timestamps = [datetime.fromisoformat(m.timestamp) for m in memories]
        time_span = max(timestamps) - min(timestamps)
        if time_span > timedelta(days=7):
            insights.append("Long-term pattern spanning multiple sessions")
        
        return insights
    
    def _calculate_integration_strength(self, memories: List[MemoryEntry]) -> float:
        """Calculate strength of memory integration"""
        if not memories:
            return 0.0
        
        # Base strength on number of memories
        base_strength = min(len(memories) / 10, 0.5)
        
        # Add bonus for diverse memory types
        memory_types = set(m.data.get("memory_type", "unknown") for m in memories)
        diversity_bonus = len(memory_types) * 0.1
        
        # Add bonus for high-confidence memories
        avg_confidence = sum(m.data.get("confidence", 0.5) for m in memories) / len(memories)
        confidence_bonus = avg_confidence * 0.2
        
        return min(base_strength + diversity_bonus + confidence_bonus, 1.0)

# Layer 16: Memory Decay and Forgetting
class MemoryDecayLayer(DragonflyMemoryLayer):
    """Manages memory decay and strategic forgetting"""
    
    def __init__(self, db_pool: NovaDatabasePool):
        super().__init__(db_pool, layer_id=16, layer_name="memory_decay")
        self.decay_rates = {}
        self.forgetting_curve = {}
        
    async def write(self, nova_id: str, data: Dict[str, Any], 
                   metadata: Optional[Dict[str, Any]] = None) -> str:
        """Store memory with decay parameters"""
        # Add decay metadata
        data["initial_strength"] = data.get("initial_strength", 1.0)
        data["current_strength"] = data["initial_strength"]
        data["decay_rate"] = data.get("decay_rate", 0.1)
        data["last_accessed"] = datetime.now().isoformat()
        data["access_count"] = 1
        data["decay_resistant"] = data.get("decay_resistant", False)
        
        # Initialize decay tracking
        memory_id = await super().write(nova_id, data, metadata)
        
        self.decay_rates[memory_id] = {
            "rate": data["decay_rate"],
            "last_update": datetime.now()
        }
        
        return memory_id
    
    async def access_memory(self, nova_id: str, memory_id: str) -> Optional[MemoryEntry]:
        """Access memory and update strength"""
        memories = await self.read(nova_id, {"memory_id": memory_id})
        
        if not memories:
            return None
        
        memory = memories[0]
        
        # Update access count and strength
        memory.data["access_count"] = memory.data.get("access_count", 0) + 1
        memory.data["last_accessed"] = datetime.now().isoformat()
        
        # Strengthen memory on access (spacing effect)
        old_strength = memory.data.get("current_strength", 0.5)
        memory.data["current_strength"] = min(old_strength + 0.1, 1.0)
        
        # Update in storage
        await self.update(nova_id, memory_id, memory.data)
        
        return memory
    
    async def apply_decay(self, nova_id: str, time_elapsed: timedelta) -> Dict[str, Any]:
        """Apply decay to all memories based on time elapsed"""
        all_memories = await self.read(nova_id)
        
        decayed_count = 0
        forgotten_count = 0
        
        for memory in all_memories:
            if memory.data.get("decay_resistant", False):
                continue
            
            # Calculate new strength
            current_strength = memory.data.get("current_strength", 0.5)
            decay_rate = memory.data.get("decay_rate", 0.1)
            
            # Exponential decay
            days_elapsed = time_elapsed.total_seconds() / 86400
            new_strength = current_strength * (1 - decay_rate) ** days_elapsed
            
            memory.data["current_strength"] = new_strength
            
            if new_strength < 0.1:  # Forgetting threshold
                memory.data["forgotten"] = True
                forgotten_count += 1
            else:
                decayed_count += 1
            
            # Update memory
            await self.update(nova_id, memory.memory_id, memory.data)
        
        return {
            "total_memories": len(all_memories),
            "decayed": decayed_count,
            "forgotten": forgotten_count,
            "time_elapsed": str(time_elapsed)
        }
    
    async def get_forgetting_curve(self, nova_id: str, memory_type: str = None) -> Dict[str, Any]:
        """Get forgetting curve statistics"""
        memories = await self.read(nova_id)
        
        if memory_type:
            memories = [m for m in memories if m.data.get("memory_type") == memory_type]
        
        if not memories:
            return {}
        
        # Calculate average decay
        strengths = [m.data.get("current_strength", 0) for m in memories]
        access_counts = [m.data.get("access_count", 0) for m in memories]
        
        return {
            "memory_type": memory_type or "all",
            "total_memories": len(memories),
            "average_strength": sum(strengths) / len(strengths),
            "average_access_count": sum(access_counts) / len(access_counts),
            "forgotten_count": len([m for m in memories if m.data.get("forgotten", False)]),
            "decay_resistant_count": len([m for m in memories if m.data.get("decay_resistant", False)])
        }

# Layer 17: Memory Reconstruction
class MemoryReconstructionLayer(DragonflyMemoryLayer):
    """Reconstructs and fills gaps in memories"""
    
    def __init__(self, db_pool: NovaDatabasePool):
        super().__init__(db_pool, layer_id=17, layer_name="memory_reconstruction")
        self.reconstruction_patterns = {}
        self.gap_detection_threshold = 0.3
        
    async def write(self, nova_id: str, data: Dict[str, Any], 
                   metadata: Optional[Dict[str, Any]] = None) -> str:
        """Store reconstruction data"""
        # Add reconstruction metadata
        data["is_reconstructed"] = data.get("is_reconstructed", False)
        data["reconstruction_confidence"] = data.get("reconstruction_confidence", 0.7)
        data["original_fragments"] = data.get("original_fragments", [])
        data["reconstruction_method"] = data.get("reconstruction_method", "pattern_completion")
        
        return await super().write(nova_id, data, metadata)
    
    async def reconstruct_memory(self, nova_id: str, fragments: List[Dict[str, Any]], 
                               context: Dict[str, Any] = None) -> str:
        """Reconstruct complete memory from fragments"""
        if not fragments:
            return ""
        
        # Analyze fragments
        reconstruction_data = {
            "is_reconstructed": True,
            "original_fragments": fragments,
            "fragment_count": len(fragments),
            "reconstruction_timestamp": datetime.now().isoformat(),
            "context": context or {},
            "content": self._reconstruct_content(fragments),
            "reconstruction_confidence": self._calculate_reconstruction_confidence(fragments),
            "reconstruction_method": "fragment_synthesis",
            "gap_locations": self._identify_gaps(fragments)
        }
        
        return await self.write(nova_id, reconstruction_data)
    
    async def fill_memory_gaps(self, nova_id: str, incomplete_memory: Dict[str, Any], 
                             related_memories: List[MemoryEntry]) -> Dict[str, Any]:
        """Fill gaps in incomplete memory using related memories"""
        # Identify what's missing
        gaps = self._identify_gaps([incomplete_memory])
        
        if not gaps:
            return incomplete_memory
        
        # Fill gaps using related memories
        filled_memory = incomplete_memory.copy()
        
        for gap in gaps:
            fill_candidates = self._find_gap_fillers(gap, related_memories)
            if fill_candidates:
                best_fill = fill_candidates[0]  # Use best candidate
                filled_memory[gap["field"]] = best_fill["value"]
        
        filled_memory["gaps_filled"] = len(gaps)
        filled_memory["fill_confidence"] = self._calculate_fill_confidence(gaps, filled_memory)
        
        return filled_memory
    
    def _reconstruct_content(self, fragments: List[Dict[str, Any]]) -> str:
        """Reconstruct content from fragments"""
        # Sort fragments by any available temporal or sequential info
        sorted_fragments = sorted(fragments, key=lambda f: f.get("sequence", 0))
        
        # Combine content
        contents = []
        for fragment in sorted_fragments:
            if "content" in fragment:
                contents.append(fragment["content"])
        
        # Simple reconstruction (would use ML in production)
        reconstructed = " [...] ".join(contents)
        
        return reconstructed
    
    def _calculate_reconstruction_confidence(self, fragments: List[Dict[str, Any]]) -> float:
        """Calculate confidence in reconstruction"""
        if not fragments:
            return 0.0
        
        # Base confidence on fragment count and quality
        base_confidence = min(len(fragments) / 5, 0.5)  # More fragments = higher confidence
        
        # Check fragment quality
        quality_scores = []
        for fragment in fragments:
            if "confidence" in fragment:
                quality_scores.append(fragment["confidence"])
            elif "quality" in fragment:
                quality_scores.append(fragment["quality"])
            else:
                quality_scores.append(0.5)  # Default
        
        avg_quality = sum(quality_scores) / len(quality_scores) if quality_scores else 0.5
        
        # Check for sequence information
        has_sequence = any("sequence" in f for f in fragments)
        sequence_bonus = 0.2 if has_sequence else 0.0
        
        return min(base_confidence + (avg_quality * 0.3) + sequence_bonus, 1.0)
    
    def _identify_gaps(self, fragments: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        """Identify gaps in memory fragments"""
        gaps = []
        
        # Expected fields
        expected_fields = ["content", "timestamp", "context", "memory_type"]
        
        for i, fragment in enumerate(fragments):
            for field in expected_fields:
                if field not in fragment or not fragment[field]:
                    gaps.append({
                        "fragment_index": i,
                        "field": field,
                        "gap_type": "missing_field"
                    })
        
        # Check for sequence gaps
        sequences = [f.get("sequence", -1) for f in fragments if "sequence" in f]
        if sequences:
            sequences.sort()
            for i in range(len(sequences) - 1):
                if sequences[i+1] - sequences[i] > 1:
                    gaps.append({
                        "gap_type": "sequence_gap",
                        "between": [sequences[i], sequences[i+1]]
                    })
        
        return gaps
    
    def _find_gap_fillers(self, gap: Dict[str, Any], related_memories: List[MemoryEntry]) -> List[Dict[str, Any]]:
        """Find potential fillers for a gap"""
        fillers = []
        
        field = gap.get("field")
        if not field:
            return fillers
        
        # Search related memories for the missing field
        for memory in related_memories:
            if field in memory.data and memory.data[field]:
                fillers.append({
                    "value": memory.data[field],
                    "source": memory.memory_id,
                    "confidence": memory.data.get("confidence", 0.5)
                })
        
        # Sort by confidence
        fillers.sort(key=lambda f: f["confidence"], reverse=True)
        
        return fillers
    
    def _calculate_fill_confidence(self, gaps: List[Dict[str, Any]], filled_memory: Dict[str, Any]) -> float:
        """Calculate confidence in gap filling"""
        if not gaps:
            return 1.0
        
        filled_count = sum(1 for gap in gaps if gap.get("field") in filled_memory)
        fill_ratio = filled_count / len(gaps)
        
        return fill_ratio

# Layer 18: Memory Prioritization
class MemoryPrioritizationLayer(DragonflyMemoryLayer):
    """Prioritizes memories for retention and access"""
    
    def __init__(self, db_pool: NovaDatabasePool):
        super().__init__(db_pool, layer_id=18, layer_name="memory_prioritization")
        self.priority_queue = []
        self.priority_criteria = {
            "relevance": 0.3,
            "frequency": 0.2,
            "recency": 0.2,
            "emotional": 0.15,
            "utility": 0.15
        }
        
    async def write(self, nova_id: str, data: Dict[str, Any], 
                   metadata: Optional[Dict[str, Any]] = None) -> str:
        """Store memory with priority scoring"""
        # Calculate priority scores
        data["priority_scores"] = self._calculate_priority_scores(data)
        data["overall_priority"] = self._calculate_overall_priority(data["priority_scores"])
        data["priority_rank"] = 0  # Will be updated in batch
        data["retention_priority"] = data.get("retention_priority", data["overall_priority"])
        
        memory_id = await super().write(nova_id, data, metadata)
        
        # Update priority queue
        self.priority_queue.append({
            "memory_id": memory_id,
            "nova_id": nova_id,
            "priority": data["overall_priority"],
            "timestamp": datetime.now()
        })
        
        # Keep queue sorted
        self.priority_queue.sort(key=lambda x: x["priority"], reverse=True)
        
        return memory_id
    
    async def get_top_priority_memories(self, nova_id: str, count: int = 10) -> List[MemoryEntry]:
        """Get highest priority memories"""
        # Filter queue for nova_id
        nova_queue = [item for item in self.priority_queue if item["nova_id"] == nova_id]
        
        # Get top N
        top_items = nova_queue[:count]
        
        # Fetch actual memories
        memories = []
        for item in top_items:
            results = await self.read(nova_id, {"memory_id": item["memory_id"]})
            if results:
                memories.extend(results)
        
        return memories
    
    async def reprioritize_memories(self, nova_id: str, 
                                  new_criteria: Dict[str, float] = None) -> Dict[str, Any]:
        """Reprioritize all memories with new criteria"""
        if new_criteria:
            self.priority_criteria = new_criteria
        
        # Fetch all memories
        all_memories = await self.read(nova_id)
        
        # Recalculate priorities
        updated_count = 0
        for memory in all_memories:
            old_priority = memory.data.get("overall_priority", 0)
            
            # Recalculate
            new_scores = self._calculate_priority_scores(memory.data)
            new_priority = self._calculate_overall_priority(new_scores)
            
            if abs(new_priority - old_priority) > 0.1:  # Significant change
                memory.data["priority_scores"] = new_scores
                memory.data["overall_priority"] = new_priority
                
                await self.update(nova_id, memory.memory_id, memory.data)
                updated_count += 1
        
        # Rebuild priority queue
        self._rebuild_priority_queue(nova_id, all_memories)
        
        return {
            "total_memories": len(all_memories),
            "updated": updated_count,
            "criteria": self.priority_criteria
        }
    
    def _calculate_priority_scores(self, data: Dict[str, Any]) -> Dict[str, float]:
        """Calculate individual priority scores"""
        scores = {}
        
        # Relevance score (based on current context/goals)
        scores["relevance"] = data.get("relevance_score", 0.5)
        
        # Frequency score (based on access count)
        access_count = data.get("access_count", 1)
        scores["frequency"] = min(access_count / 10, 1.0)
        
        # Recency score (based on last access)
        if "last_accessed" in data:
            last_accessed = datetime.fromisoformat(data["last_accessed"])
            days_ago = (datetime.now() - last_accessed).days
            scores["recency"] = max(0, 1 - (days_ago / 30))  # Decay over 30 days
        else:
            scores["recency"] = 1.0  # New memory
        
        # Emotional score
        scores["emotional"] = abs(data.get("emotional_valence", 0))
        
        # Utility score (based on successful usage)
        scores["utility"] = data.get("utility_score", 0.5)
        
        return scores
    
    def _calculate_overall_priority(self, scores: Dict[str, float]) -> float:
        """Calculate weighted overall priority"""
        overall = 0.0
        
        for criterion, weight in self.priority_criteria.items():
            if criterion in scores:
                overall += scores[criterion] * weight
        
        return min(overall, 1.0)
    
    def _rebuild_priority_queue(self, nova_id: str, memories: List[MemoryEntry]) -> None:
        """Rebuild priority queue from memories"""
        # Clear existing nova entries
        self.priority_queue = [item for item in self.priority_queue if item["nova_id"] != nova_id]
        
        # Add updated entries
        for memory in memories:
            self.priority_queue.append({
                "memory_id": memory.memory_id,
                "nova_id": nova_id,
                "priority": memory.data.get("overall_priority", 0.5),
                "timestamp": datetime.now()
            })
        
        # Sort by priority
        self.priority_queue.sort(key=lambda x: x["priority"], reverse=True)

# Layer 19: Memory Compression
class MemoryCompressionLayer(DragonflyMemoryLayer):
    """Compresses memories for efficient storage"""
    
    def __init__(self, db_pool: NovaDatabasePool):
        super().__init__(db_pool, layer_id=19, layer_name="memory_compression")
        self.compression_stats = {}
        
    async def write(self, nova_id: str, data: Dict[str, Any], 
                   metadata: Optional[Dict[str, Any]] = None) -> str:
        """Store compressed memory"""
        # Compress data
        original_size = len(json.dumps(data))
        compressed_data = self._compress_memory(data)
        compressed_size = len(json.dumps(compressed_data))
        
        # Add compression metadata
        compressed_data["compression_ratio"] = compressed_size / original_size
        compressed_data["original_size"] = original_size
        compressed_data["compressed_size"] = compressed_size
        compressed_data["compression_method"] = "semantic_compression"
        compressed_data["is_compressed"] = True
        
        # Track stats
        if nova_id not in self.compression_stats:
            self.compression_stats[nova_id] = {
                "total_original": 0,
                "total_compressed": 0,
                "compression_count": 0
            }
        
        self.compression_stats[nova_id]["total_original"] += original_size
        self.compression_stats[nova_id]["total_compressed"] += compressed_size
        self.compression_stats[nova_id]["compression_count"] += 1
        
        return await super().write(nova_id, compressed_data, metadata)
    
    async def decompress_memory(self, nova_id: str, memory_id: str) -> Optional[Dict[str, Any]]:
        """Decompress a memory"""
        memories = await self.read(nova_id, {"memory_id": memory_id})
        
        if not memories:
            return None
        
        memory = memories[0]
        
        if not memory.data.get("is_compressed", False):
            return memory.data
        
        # Decompress
        decompressed = self._decompress_memory(memory.data)
        
        return decompressed
    
    def _compress_memory(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Compress memory data"""
        compressed = {}
        
        # Keep essential fields
        essential_fields = ["memory_id", "memory_type", "timestamp", "nova_id"]
        for field in essential_fields:
            if field in data:
                compressed[field] = data[field]
        
        # Compress content
        if "content" in data:
            compressed["compressed_content"] = self._compress_text(data["content"])
        
        # Summarize metadata
        if "metadata" in data and isinstance(data["metadata"], dict):
            compressed["metadata_summary"] = {
                "field_count": len(data["metadata"]),
                "key_fields": list(data["metadata"].keys())[:5]
            }
        
        # Keep high-priority data
        priority_fields = ["importance_score", "confidence_score", "emotional_valence"]
        for field in priority_fields:
            if field in data and data[field] > 0.7:  # Only keep if significant
                compressed[field] = data[field]
        
        return compressed
    
    def _decompress_memory(self, compressed_data: Dict[str, Any]) -> Dict[str, Any]:
        """Decompress memory data"""
        decompressed = compressed_data.copy()
        
        # Remove compression metadata
        compression_fields = ["compression_ratio", "original_size", "compressed_size", 
                            "compression_method", "is_compressed"]
        for field in compression_fields:
            decompressed.pop(field, None)
        
        # Decompress content
        if "compressed_content" in decompressed:
            decompressed["content"] = self._decompress_text(decompressed["compressed_content"])
            del decompressed["compressed_content"]
        
        # Reconstruct metadata
        if "metadata_summary" in decompressed:
            decompressed["metadata"] = {
                "was_compressed": True,
                "field_count": decompressed["metadata_summary"]["field_count"],
                "available_fields": decompressed["metadata_summary"]["key_fields"]
            }
            del decompressed["metadata_summary"]
        
        return decompressed
    
    def _compress_text(self, text: str) -> str:
        """Compress text content"""
        if len(text) < 100:
            return text  # Don't compress short text
        
        # Simple compression: extract key sentences
        sentences = text.split('. ')
        
        if len(sentences) <= 3:
            return text
        
        # Keep first, middle, and last sentences
        key_sentences = [
            sentences[0],
            sentences[len(sentences)//2],
            sentences[-1]
        ]
        
        compressed = "...".join(key_sentences)
        
        return compressed
    
    def _decompress_text(self, compressed_text: str) -> str:
        """Decompress text content"""
        # In real implementation, would use more sophisticated decompression
        # For now, just mark gaps
        return compressed_text.replace("...", " [compressed section] ")
    
    async def get_compression_stats(self, nova_id: str) -> Dict[str, Any]:
        """Get compression statistics"""
        if nova_id not in self.compression_stats:
            return {"message": "No compression stats available"}
        
        stats = self.compression_stats[nova_id]
        
        if stats["compression_count"] > 0:
            avg_ratio = stats["total_compressed"] / stats["total_original"]
            space_saved = stats["total_original"] - stats["total_compressed"]
        else:
            avg_ratio = 1.0
            space_saved = 0
        
        return {
            "nova_id": nova_id,
            "total_memories_compressed": stats["compression_count"],
            "original_size_bytes": stats["total_original"],
            "compressed_size_bytes": stats["total_compressed"],
            "average_compression_ratio": avg_ratio,
            "space_saved_bytes": space_saved,
            "space_saved_percentage": (1 - avg_ratio) * 100
        }

# Layer 20: Memory Indexing and Search
class MemoryIndexingLayer(DragonflyMemoryLayer):
    """Advanced indexing and search capabilities"""
    
    def __init__(self, db_pool: NovaDatabasePool):
        super().__init__(db_pool, layer_id=20, layer_name="memory_indexing")
        self.indices = {
            "temporal": {},     # Time-based index
            "semantic": {},     # Concept-based index
            "emotional": {},    # Emotion-based index
            "associative": {},  # Association-based index
            "contextual": {}    # Context-based index
        }
        
    async def write(self, nova_id: str, data: Dict[str, Any], 
                   metadata: Optional[Dict[str, Any]] = None) -> str:
        """Store memory with multi-dimensional indexing"""
        memory_id = await super().write(nova_id, data, metadata)
        
        # Update all indices
        self._update_temporal_index(memory_id, data)
        self._update_semantic_index(memory_id, data)
        self._update_emotional_index(memory_id, data)
        self._update_associative_index(memory_id, data)
        self._update_contextual_index(memory_id, data)
        
        return memory_id
    
    async def search(self, nova_id: str, query: Dict[str, Any]) -> List[MemoryEntry]:
        """Multi-dimensional memory search"""
        search_type = query.get("search_type", "semantic")
        
        if search_type == "temporal":
            return await self._temporal_search(nova_id, query)
        elif search_type == "semantic":
            return await self._semantic_search(nova_id, query)
        elif search_type == "emotional":
            return await self._emotional_search(nova_id, query)
        elif search_type == "associative":
            return await self._associative_search(nova_id, query)
        elif search_type == "contextual":
            return await self._contextual_search(nova_id, query)
        else:
            return await self._combined_search(nova_id, query)
    
    def _update_temporal_index(self, memory_id: str, data: Dict[str, Any]) -> None:
        """Update temporal index"""
        timestamp = data.get("timestamp", datetime.now().isoformat())
        date_key = timestamp[:10]  # YYYY-MM-DD
        
        if date_key not in self.indices["temporal"]:
            self.indices["temporal"][date_key] = []
        
        self.indices["temporal"][date_key].append({
            "memory_id": memory_id,
            "timestamp": timestamp,
            "time_of_day": timestamp[11:16]  # HH:MM
        })
    
    def _update_semantic_index(self, memory_id: str, data: Dict[str, Any]) -> None:
        """Update semantic index"""
        concepts = data.get("concepts", [])
        
        for concept in concepts:
            if concept not in self.indices["semantic"]:
                self.indices["semantic"][concept] = []
            
            self.indices["semantic"][concept].append({
                "memory_id": memory_id,
                "relevance": data.get("relevance_score", 0.5)
            })
    
    def _update_emotional_index(self, memory_id: str, data: Dict[str, Any]) -> None:
        """Update emotional index"""
        emotional_valence = data.get("emotional_valence", 0)
        
        # Categorize emotion
        if emotional_valence > 0.5:
            emotion = "positive"
        elif emotional_valence < -0.5:
            emotion = "negative"
        else:
            emotion = "neutral"
        
        if emotion not in self.indices["emotional"]:
            self.indices["emotional"][emotion] = []
        
        self.indices["emotional"][emotion].append({
            "memory_id": memory_id,
            "valence": emotional_valence,
            "intensity": abs(emotional_valence)
        })
    
    def _update_associative_index(self, memory_id: str, data: Dict[str, Any]) -> None:
        """Update associative index"""
        associations = data.get("associations", [])
        
        for association in associations:
            if association not in self.indices["associative"]:
                self.indices["associative"][association] = []
            
            self.indices["associative"][association].append({
                "memory_id": memory_id,
                "strength": data.get("association_strength", 0.5)
            })
    
    def _update_contextual_index(self, memory_id: str, data: Dict[str, Any]) -> None:
        """Update contextual index"""
        context = data.get("context", {})
        
        for context_key, context_value in context.items():
            index_key = f"{context_key}:{context_value}"
            
            if index_key not in self.indices["contextual"]:
                self.indices["contextual"][index_key] = []
            
            self.indices["contextual"][index_key].append({
                "memory_id": memory_id,
                "context_type": context_key
            })
    
    async def _temporal_search(self, nova_id: str, query: Dict[str, Any]) -> List[MemoryEntry]:
        """Search by temporal criteria"""
        start_date = query.get("start_date", "2000-01-01")
        end_date = query.get("end_date", datetime.now().strftime("%Y-%m-%d"))
        
        memory_ids = []
        
        for date_key in self.indices["temporal"]:
            if start_date <= date_key <= end_date:
                memory_ids.extend([item["memory_id"] for item in self.indices["temporal"][date_key]])
        
        # Fetch memories
        memories = []
        for memory_id in set(memory_ids):
            results = await self.read(nova_id, {"memory_id": memory_id})
            memories.extend(results)
        
        return memories
    
    async def _semantic_search(self, nova_id: str, query: Dict[str, Any]) -> List[MemoryEntry]:
        """Search by semantic concepts"""
        concepts = query.get("concepts", [])
        
        memory_scores = {}
        
        for concept in concepts:
            if concept in self.indices["semantic"]:
                for item in self.indices["semantic"][concept]:
                    memory_id = item["memory_id"]
                    if memory_id not in memory_scores:
                        memory_scores[memory_id] = 0
                    memory_scores[memory_id] += item["relevance"]
        
        # Sort by score
        sorted_memories = sorted(memory_scores.items(), key=lambda x: x[1], reverse=True)
        
        # Fetch top memories
        memories = []
        for memory_id, score in sorted_memories[:query.get("limit", 10)]:
            results = await self.read(nova_id, {"memory_id": memory_id})
            memories.extend(results)
        
        return memories
    
    async def _emotional_search(self, nova_id: str, query: Dict[str, Any]) -> List[MemoryEntry]:
        """Search by emotional criteria"""
        emotion_type = query.get("emotion", "positive")
        min_intensity = query.get("min_intensity", 0.5)
        
        memory_ids = []
        
        if emotion_type in self.indices["emotional"]:
            for item in self.indices["emotional"][emotion_type]:
                if item["intensity"] >= min_intensity:
                    memory_ids.append(item["memory_id"])
        
        # Fetch memories
        memories = []
        for memory_id in set(memory_ids):
            results = await self.read(nova_id, {"memory_id": memory_id})
            memories.extend(results)
        
        return memories
    
    async def _associative_search(self, nova_id: str, query: Dict[str, Any]) -> List[MemoryEntry]:
        """Search by associations"""
        associations = query.get("associations", [])
        min_strength = query.get("min_strength", 0.3)
        
        memory_scores = {}
        
        for association in associations:
            if association in self.indices["associative"]:
                for item in self.indices["associative"][association]:
                    if item["strength"] >= min_strength:
                        memory_id = item["memory_id"]
                        if memory_id not in memory_scores:
                            memory_scores[memory_id] = 0
                        memory_scores[memory_id] += item["strength"]
        
        # Sort by score
        sorted_memories = sorted(memory_scores.items(), key=lambda x: x[1], reverse=True)
        
        # Fetch memories
        memories = []
        for memory_id, score in sorted_memories[:query.get("limit", 10)]:
            results = await self.read(nova_id, {"memory_id": memory_id})
            memories.extend(results)
        
        return memories
    
    async def _contextual_search(self, nova_id: str, query: Dict[str, Any]) -> List[MemoryEntry]:
        """Search by context"""
        context_filters = query.get("context", {})
        
        memory_ids = []
        
        for context_key, context_value in context_filters.items():
            index_key = f"{context_key}:{context_value}"
            
            if index_key in self.indices["contextual"]:
                memory_ids.extend([item["memory_id"] for item in self.indices["contextual"][index_key]])
        
        # Fetch memories
        memories = []
        for memory_id in set(memory_ids):
            results = await self.read(nova_id, {"memory_id": memory_id})
            memories.extend(results)
        
        return memories
    
    async def _combined_search(self, nova_id: str, query: Dict[str, Any]) -> List[MemoryEntry]:
        """Combined multi-dimensional search"""
        all_results = []
        
        # Run all search types
        if "start_date" in query or "end_date" in query:
            all_results.extend(await self._temporal_search(nova_id, query))
        
        if "concepts" in query:
            all_results.extend(await self._semantic_search(nova_id, query))
        
        if "emotion" in query:
            all_results.extend(await self._emotional_search(nova_id, query))
        
        if "associations" in query:
            all_results.extend(await self._associative_search(nova_id, query))
        
        if "context" in query:
            all_results.extend(await self._contextual_search(nova_id, query))
        
        # Deduplicate
        seen = set()
        unique_results = []
        for memory in all_results:
            if memory.memory_id not in seen:
                seen.add(memory.memory_id)
                unique_results.append(memory)
        
        return unique_results[:query.get("limit", 20)]