ai_agent_framework/dimensions/context_management.py

"""
Context Management with Dynamic Sizing System
==========================================

Advanced context management system with dynamic window sizing, relevance scoring,
context expiry and refresh protocols, and conflict resolution strategies.
"""

import asyncio
import json
import logging
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional, Set, Tuple, Union, Callable
from dataclasses import dataclass, field, asdict
from enum import Enum
import numpy as np
from collections import defaultdict, deque
import heapq
from functools import lru_cache
import threading
from concurrent.futures import ThreadPoolExecutor

from ai_agent_framework.core.context_engineering_agent import (
    ContextElement, ContextModality, ContextDimension
)

logger = logging.getLogger(__name__)


class ContextPriority(Enum):
    """Context priority levels."""
    CRITICAL = "critical"
    HIGH = "high"
    MEDIUM = "medium"
    LOW = "low"
    ARCHIVED = "archived"


class SizingStrategy(Enum):
    """Dynamic sizing strategies."""
    FIXED = "fixed"
    ADAPTIVE = "adaptive"
    PREDICTIVE = "predictive"
    OPTIMIZED = "optimized"
    COMPRESSIVE = "compressive"


class RefreshTrigger(Enum):
    """Context refresh triggers."""
    TIME_BASED = "time_based"
    RELEVANCE_BASED = "relevance_based"
    INTERACTION_BASED = "interaction_based"
    QUALITY_BASED = "quality_based"
    CAPACITY_BASED = "capacity_based"


@dataclass
class ContextItem:
    """Individual context item with metadata."""
    id: str
    content: Any
    modality: ContextModality
    dimension: ContextDimension
    priority: ContextPriority
    timestamp: datetime
    expiry_time: Optional[datetime]
    relevance_score: float
    quality_score: float
    access_count: int
    last_accessed: datetime
    dependencies: Set[str]
    metadata: Dict[str, Any]
    
    def __post_init__(self):
        if not self.id:
            self.id = f"context_{int(time.time())}_{hash(str(self.content))}"
        if not self.timestamp:
            self.timestamp = datetime.utcnow()
        if not self.last_accessed:
            self.last_accessed = self.timestamp
        if not self.metadata:
            self.metadata = {}


@dataclass
class ContextWindow:
    """Context window with dynamic sizing capabilities."""
    window_id: str
    size_limit: int
    current_size: int
    strategy: SizingStrategy
    items: List[ContextItem]
    metrics: Dict[str, float]
    created_at: datetime
    last_resized: datetime
    
    def __post_init__(self):
        if not self.window_id:
            self.window_id = f"window_{int(time.time())}"
        if not self.created_at:
            self.created_at = datetime.utcnow()
        if not self.last_resized:
            self.last_resized = self.created_at
        if not self.metrics:
            self.metrics = {}
        if not self.items:
            self.items = []


@dataclass
class ContextConflict:
    """Represents a conflict between context items."""
    conflict_id: str
    conflicting_items: List[str]
    conflict_type: str
    resolution_strategy: str
    confidence: float
    created_at: datetime
    
    def __post_init__(self):
        if not self.conflict_id:
            self.conflict_id = f"conflict_{int(time.time())}"
        if not self.created_at:
            self.created_at = datetime.utcnow()


class ContextManager:
    """Core context management engine with dynamic sizing."""
    
    def __init__(self, max_context_windows: int = 10):
        self.max_context_windows = max_context_windows
        self.context_windows = {}  # window_id -> ContextWindow
        self.context_index = {}  # item_id -> List[window_id]
        self.refresh_scheduler = {}
        self.conflict_resolver = ContextConflictResolver()
        self.sizing_algorithms = {
            SizingStrategy.FIXED: self._fixed_sizing,
            SizingStrategy.ADAPTIVE: self._adaptive_sizing,
            SizingStrategy.PREDICTIVE: self._predictive_sizing,
            SizingStrategy.OPTIMIZED: self._optimized_sizing,
            SizingStrategy.COMPRESSIVE: self._compressive_sizing
        }
        
        self.refresh_handlers = {
            RefreshTrigger.TIME_BASED: self._time_based_refresh,
            RefreshTrigger.RELEVANCE_BASED: self._relevance_based_refresh,
            RefreshTrigger.INTERACTION_BASED: self._interaction_based_refresh,
            RefreshTrigger.QUALITY_BASED: self._quality_based_refresh,
            RefreshTrigger.CAPACITY_BASED: self._capacity_based_refresh
        }
        
        # Performance metrics
        self.metrics = {
            "total_windows": 0,
            "total_items": 0,
            "average_window_utilization": 0.0,
            "refresh_frequency": 0.0,
            "conflict_resolution_rate": 0.0,
            "relevance_retention": 0.0
        }
        
        # Thread-safe operations
        self._lock = threading.RLock()
        
    async def create_context_window(
        self,
        window_id: Optional[str] = None,
        size_limit: int = 100,
        strategy: SizingStrategy = SizingStrategy.ADAPTIVE
    ) -> ContextWindow:
        """Create a new context window with specified strategy."""
        
        with self._lock:
            if len(self.context_windows) >= self.max_context_windows:
                # Remove oldest window if at capacity
                oldest_window_id = min(
                    self.context_windows.keys(),
                    key=lambda w_id: self.context_windows[w_id].created_at
                )
                await self._remove_context_window(oldest_window_id)
            
            window = ContextWindow(
                window_id=window_id,
                size_limit=size_limit,
                current_size=0,
                strategy=strategy,
                items=[],
                metrics={},
                created_at=datetime.utcnow(),
                last_resized=datetime.utcnow()
            )
            
            self.context_windows[window.window_id] = window
            self.metrics["total_windows"] = len(self.context_windows)
            
            logger.info(f"Created context window {window.window_id} with strategy {strategy.value}")
            return window
    
    async def add_context_item(
        self,
        window_id: str,
        item: ContextItem,
        refresh_trigger: Optional[RefreshTrigger] = None
    ) -> Dict[str, Any]:
        """Add context item to window with dynamic sizing."""
        
        if window_id not in self.context_windows:
            raise ValueError(f"Window {window_id} does not exist")
        
        window = self.context_windows[window_id]
        
        # Check for conflicts
        conflicts = await self._detect_conflicts(window, item)
        
        # Resolve conflicts if any
        if conflicts:
            resolved = await self._resolve_conflicts(window, conflicts, item)
            if not resolved:
                logger.warning(f"Failed to resolve conflicts for item {item.id}")
                return {"status": "rejected", "reason": "unresolved_conflicts"}
        
        # Add item to index
        if item.id not in self.context_index:
            self.context_index[item.id] = []
        self.context_index[item.id].append(window_id)
        
        # Determine sizing strategy
        if window.current_size < window.size_limit:
            # Add directly if within limits
            window.items.append(item)
            window.current_size += 1
            result = {"status": "added_directly"}
        else:
            # Apply sizing strategy
            sizing_func = self.sizing_strategies[window.strategy]
            result = await sizing_func(window, item)
        
        # Schedule refresh if needed
        if refresh_trigger:
            await self._schedule_refresh(window_id, refresh_trigger)
        
        # Update metrics
        await self._update_window_metrics(window)
        
        return {
            "status": result["status"],
            "item_id": item.id,
            "window_id": window_id,
            "conflicts_resolved": len(conflicts) > 0,
            "new_size": window.current_size
        }
    
    async def get_context_items(
        self,
        window_id: str,
        limit: Optional[int] = None,
        include_metadata: bool = True
    ) -> Dict[str, Any]:
        """Retrieve context items from window with optimization."""
        
        if window_id not in self.context_windows:
            raise ValueError(f"Window {window_id} does not exist")
        
        window = self.context_windows[window_id]
        
        # Sort by relevance and recency
        sorted_items = await self._sort_context_items(window.items)
        
        # Apply limit if specified
        if limit:
            sorted_items = sorted_items[:limit]
        
        # Format response
        items_data = []
        for item in sorted_items:
            item_data = {
                "id": item.id,
                "content": item.content,
                "modality": item.modality.value,
                "dimension": item.dimension.value,
                "priority": item.priority.value,
                "relevance_score": item.relevance_score,
                "quality_score": item.quality_score,
                "timestamp": item.timestamp.isoformat()
            }
            
            if include_metadata:
                item_data.update({
                    "access_count": item.access_count,
                    "dependencies": list(item.dependencies),
                    "metadata": item.metadata
                })
            
            items_data.append(item_data)
        
        return {
            "window_id": window_id,
            "items": items_data,
            "total_items": len(window.items),
            "window_utilization": window.current_size / window.size_limit,
            "metrics": window.metrics
        }
    
    async def refresh_context_window(
        self,
        window_id: str,
        force_refresh: bool = False
    ) -> Dict[str, Any]:
        """Refresh context window based on configured strategy."""
        
        if window_id not in self.context_windows:
            raise ValueError(f"Window {window_id} does not exist")
        
        window = self.context_windows[window_id]
        
        # Check if refresh is needed
        if not force_refresh:
            refresh_needed = await self._should_refresh(window)
            if not refresh_needed["needed"]:
                return {
                    "status": "skipped",
                    "reason": refresh_needed["reason"]
                }
        
        # Determine refresh strategy
        refresh_strategy = await self._determine_refresh_strategy(window)
        
        # Execute refresh
        refresh_func = self.refresh_handlers[refresh_strategy]
        refresh_result = await refresh_func(window_id)
        
        # Update metrics
        await self._update_refresh_metrics(window, refresh_result)
        
        return {
            "status": "refreshed",
            "strategy": refresh_strategy.value,
            "items_affected": refresh_result.get("items_affected", 0),
            "new_window_utilization": window.current_size / window.size_limit
        }
    
    async def optimize_context_window(
        self,
        window_id: str,
        optimization_goals: Optional[List[str]] = None
    ) -> Dict[str, Any]:
        """Optimize context window for better performance."""
        
        if window_id not in self.context_windows:
            raise ValueError(f"Window {window_id} does not exist")
        
        window = self.context_windows[window_id]
        
        if not optimization_goals:
            optimization_goals = ["relevance", "efficiency", "quality"]
        
        optimization_results = {}
        
        for goal in optimization_goals:
            if goal == "relevance":
                result = await self._optimize_relevance(window)
                optimization_results["relevance"] = result
            elif goal == "efficiency":
                result = await self._optimize_efficiency(window)
                optimization_results["efficiency"] = result
            elif goal == "quality":
                result = await self._optimize_quality(window)
                optimization_results["quality"] = result
            elif goal == "diversity":
                result = await self._optimize_diversity(window)
                optimization_results["diversity"] = result
        
        # Apply optimizations
        total_improvements = 0
        for goal, result in optimization_results.items():
            if result.get("improved", False):
                total_improvements += 1
        
        await self._update_window_metrics(window)
        
        return {
            "status": "optimized",
            "optimization_goals": optimization_goals,
            "improvements_made": total_improvements,
            "optimization_results": optimization_results,
            "new_metrics": window.metrics
        }
    
    async def resolve_context_conflicts(
        self,
        window_id: str,
        conflict_resolution_strategy: str = "priority_based"
    ) -> Dict[str, Any]:
        """Resolve conflicts within context window."""
        
        if window_id not in self.context_windows:
            raise ValueError(f"Window {window_id} does not exist")
        
        window = self.context_windows[window_id]
        
        # Detect all conflicts
        all_conflicts = await self._detect_all_conflicts(window)
        
        if not all_conflicts:
            return {
                "status": "no_conflicts",
                "conflicts_resolved": 0
            }
        
        # Resolve conflicts
        resolved_count = 0
        resolution_details = []
        
        for conflict in all_conflicts:
            resolution_result = await self._resolve_single_conflict(
                window, conflict, conflict_resolution_strategy
            )
            
            if resolution_result["resolved"]:
                resolved_count += 1
                resolution_details.append(resolution_result)
        
        # Update metrics
        self.metrics["conflict_resolution_rate"] = resolved_count / len(all_conflicts)
        
        return {
            "status": "conflicts_resolved",
            "total_conflicts": len(all_conflicts),
            "conflicts_resolved": resolved_count,
            "resolution_rate": resolved_count / len(all_conflicts),
            "resolution_details": resolution_details
        }
    
    # Sizing strategy implementations
    
    async def _fixed_sizing(self, window: ContextWindow, new_item: ContextItem) -> Dict[str, Any]:
        """Fixed sizing strategy - replace lowest priority item."""
        
        # Find lowest priority item
        lowest_priority_idx = 0
        for i, item in enumerate(window.items):
            if (item.priority.value == "low" or 
                (item.relevance_score < window.items[lowest_priority_idx].relevance_score)):
                lowest_priority_idx = i
        
        # Replace if new item has higher priority
        if (new_item.priority.value in ["high", "critical"] or
            new_item.relevance_score > window.items[lowest_priority_idx].relevance_score):
            
            # Remove old item
            removed_item = window.items.pop(lowest_priority_idx)
            
            # Add new item
            window.items.append(new_item)
            
            return {
                "status": "replaced",
                "replaced_item_id": removed_item.id,
                "new_item_id": new_item.id
            }
        else:
            return {
                "status": "rejected",
                "reason": "lower_priority_than_existing"
            }
    
    async def _adaptive_sizing(self, window: ContextWindow, new_item: ContextItem) -> Dict[str, Any]:
        """Adaptive sizing strategy - dynamically adjust based on relevance."""
        
        # Calculate overall relevance threshold
        current_relevances = [item.relevance_score for item in window.items]
        threshold = np.percentile(current_relevances, 25)  # 25th percentile
        
        # Check if new item meets threshold
        if new_item.relevance_score < threshold:
            return {
                "status": "rejected",
                "reason": "below_relevance_threshold",
                "threshold": threshold
            }
        
        # Find items to remove (lowest relevance, lowest priority)
        removal_candidates = []
        for i, item in enumerate(window.items):
            score = (item.relevance_score * 0.7) + (1 - self._priority_weight(item.priority) * 0.3)
            removal_candidates.append((score, i, item))
        
        removal_candidates.sort(key=lambda x: x[0])  # Sort by score
        
        # Remove enough items to make space
        items_removed = 0
        for score, idx, item in removal_candidates:
            if len(window.items) + 1 <= window.size_limit:
                break
            
            window.items.pop(idx)
            items_removed += 1
        
        # Add new item
        window.items.append(new_item)
        
        return {
            "status": "adaptive_replaced",
            "items_removed": items_removed,
            "new_item_id": new_item.id,
            "relevance_threshold": threshold
        }
    
    async def _predictive_sizing(self, window: ContextWindow, new_item: ContextItem) -> Dict[str, Any]:
        """Predictive sizing strategy - use patterns to predict future relevance."""
        
        # Analyze historical access patterns
        access_patterns = await self._analyze_access_patterns(window)
        
        # Predict future relevance for existing items
        predicted_relevances = {}
        for item in window.items:
            predicted_relevance = await self._predict_future_relevance(item, access_patterns)
            predicted_relevances[item.id] = predicted_relevance
        
        # Compare with new item's predicted relevance
        new_item_predicted = await self._predict_future_relevance(new_item, access_patterns)
        
        # Find items to replace
        replacement_candidates = []
        for item in window.items:
            predicted = predicted_relevances[item.id]
            current = item.relevance_score
            combined_score = (predicted * 0.6) + (current * 0.4)
            replacement_candidates.append((combined_score, item))
        
        replacement_candidates.sort(key=lambda x: x[0])
        
        # Replace if beneficial
        if replacement_candidates and replacement_candidates[0][0] < new_item_predicted:
            replaced_item = replacement_candidates[0][1]
            window.items.remove(replaced_item)
            window.items.append(new_item)
            
            return {
                "status": "predictive_replaced",
                "replaced_item_id": replaced_item.id,
                "new_item_id": new_item.id,
                "predicted_improvement": new_item_predicted - replacement_candidates[0][0]
            }
        else:
            return {
                "status": "rejected",
                "reason": "no_predictive_benefit"
            }
    
    async def _optimized_sizing(self, window: ContextWindow, new_item: ContextItem) -> Dict[str, Any]:
        """Optimized sizing strategy - maximize overall utility."""
        
        # Calculate utility scores for all items including new one
        all_items = window.items + [new_item]
        utilities = {}
        
        for item in all_items:
            utility = await self._calculate_item_utility(item, window)
            utilities[item.id] = utility
        
        # Select optimal subset
        sorted_items = sorted(all_items, key=lambda x: utilities[x.id], reverse=True)
        optimal_items = sorted_items[:window.size_limit]
        
        # Check if composition changed
        current_ids = {item.id for item in window.items}
        optimal_ids = {item.id for item in optimal_items}
        
        if current_ids != optimal_ids:
            # Update window
            window.items = optimal_items
            
            removed_items = current_ids - optimal_ids
            added_items = optimal_ids - current_ids
            
            return {
                "status": "optimized",
                "removed_items": list(removed_items),
                "added_items": list(added_items),
                "total_utility": sum(utilities[item.id] for item in optimal_items)
            }
        else:
            return {
                "status": "no_change",
                "reason": "already_optimal"
            }
    
    async def _compressive_sizing(self, window: ContextWindow, new_item: ContextItem) -> Dict[str, Any]:
        """Compressive sizing strategy - compress redundant information."""
        
        # Identify redundant items
        redundant_items = await self._identify_redundant_items(window.items + [new_item])
        
        # Compress redundant information
        compression_result = await self._compress_information(window.items, new_item)
        
        if compression_result["compressed"]:
            window.items = compression_result["compressed_items"]
            
            return {
                "status": "compressed",
                "compression_ratio": compression_result["ratio"],
                "items_removed": compression_result["items_removed"],
                "information_preserved": compression_result["information_preserved"]
            }
        else:
            # No compression possible, use adaptive strategy
            return await self._adaptive_sizing(window, new_item)
    
    # Refresh strategy implementations
    
    async def _time_based_refresh(self, window_id: str) -> Dict[str, Any]:
        """Time-based refresh strategy."""
        
        window = self.context_windows[window_id]
        refresh_threshold = timedelta(minutes=30)  # Refresh every 30 minutes
        
        current_time = datetime.utcnow()
        items_to_refresh = []
        items_to_remove = []
        
        for item in window.items:
            age = current_time - item.last_accessed
            
            if age > refresh_threshold:
                if item.priority.value == "low":
                    items_to_remove.append(item)
                else:
                    items_to_refresh.append(item)
        
        # Refresh items
        for item in items_to_refresh:
            await self._refresh_item_relevance(item)
        
        # Remove expired items
        for item in items_to_remove:
            window.items.remove(item)
            window.current_size -= 1
        
        return {
            "items_refreshed": len(items_to_refresh),
            "items_removed": len(items_to_remove),
            "refresh_type": "time_based"
        }
    
    async def _relevance_based_refresh(self, window_id: str) -> Dict[str, Any]:
        """Relevance-based refresh strategy."""
        
        window = self.context_windows[window_id]
        
        # Recalculate relevance for all items
        for item in window.items:
            await self._recalculate_relevance(item)
        
        # Remove low relevance items
        relevance_threshold = 0.3
        items_to_remove = [
            item for item in window.items
            if item.relevance_score < relevance_threshold
        ]
        
        for item in items_to_remove:
            window.items.remove(item)
            window.current_size -= 1
        
        return {
            "items_recalculated": len(window.items),
            "items_removed": len(items_to_remove),
            "refresh_type": "relevance_based"
        }
    
    async def _interaction_based_refresh(self, window_id: str) -> Dict[str, Any]:
        """Interaction-based refresh strategy."""
        
        window = self.context_windows[window_id]
        
        # Analyze recent interactions (simplified)
        recent_interactions = await self._get_recent_interactions(window)
        
        # Update relevance based on interaction patterns
        items_updated = 0
        for item in window.items:
            old_relevance = item.relevance_score
            new_relevance = await self._update_relevance_from_interactions(item, recent_interactions)
            
            if abs(new_relevance - old_relevance) > 0.1:  # Significant change
                item.relevance_score = new_relevance
                items_updated += 1
        
        return {
            "items_updated": items_updated,
            "refresh_type": "interaction_based"
        }
    
    async def _quality_based_refresh(self, window_id: str) -> Dict[str, Any]:
        """Quality-based refresh strategy."""
        
        window = self.context_windows[window_id]
        
        # Recalculate quality scores
        quality_updates = 0
        for item in window.items:
            old_quality = item.quality_score
            new_quality = await self._recalculate_quality(item)
            
            if new_quality != old_quality:
                item.quality_score = new_quality
                quality_updates += 1
        
        # Re-sort items by quality
        window.items.sort(key=lambda x: x.quality_score, reverse=True)
        
        return {
            "quality_updates": quality_updates,
            "refresh_type": "quality_based"
        }
    
    async def _capacity_based_refresh(self, window_id: str) -> Dict[str, Any]:
        """Capacity-based refresh strategy."""
        
        window = self.context_windows[window_id]
        
        utilization = window.current_size / window.size_limit
        
        if utilization > 0.9:  # Near capacity
            # Remove lowest priority, lowest relevance items
            items_to_remove = []
            
            sorted_items = sorted(
                window.items,
                key=lambda x: (x.priority.value, x.relevance_score),
                reverse=True
            )
            
            # Remove bottom 20% to make space
            remove_count = int(len(sorted_items) * 0.2)
            items_to_remove = sorted_items[:remove_count]
            
            for item in items_to_remove:
                window.items.remove(item)
                window.current_size -= 1
            
            return {
                "items_removed": len(items_to_remove),
                "capacity_freed": len(items_to_remove),
                "refresh_type": "capacity_based"
            }
        else:
            return {
                "items_removed": 0,
                "refresh_type": "capacity_based"
            }
    
    # Helper methods
    
    def _priority_weight(self, priority: ContextPriority) -> float:
        """Get weight for priority level."""
        weights = {
            ContextPriority.CRITICAL: 1.0,
            ContextPriority.HIGH: 0.8,
            ContextPriority.MEDIUM: 0.6,
            ContextPriority.LOW: 0.4,
            ContextPriority.ARCHIVED: 0.2
        }
        return weights.get(priority, 0.5)
    
    async def _sort_context_items(self, items: List[ContextItem]) -> List[ContextItem]:
        """Sort context items by relevance and recency."""
        
        def sort_key(item):
            recency_score = 1.0 / (1.0 + (datetime.utcnow() - item.last_accessed).total_seconds() / 3600)
            combined_score = (item.relevance_score * 0.7) + (item.quality_score * 0.2) + (recency_score * 0.1)
            return combined_score
        
        return sorted(items, key=sort_key, reverse=True)
    
    async def _detect_conflicts(self, window: ContextWindow, new_item: ContextItem) -> List[ContextConflict]:
        """Detect conflicts between new item and existing items."""
        
        conflicts = []
        
        for existing_item in window.items:
            # Check for content conflicts
            if await self._are_conflicting(existing_item, new_item):
                conflict = ContextConflict(
                    conflict_id=f"conflict_{existing_item.id}_{new_item.id}",
                    conflicting_items=[existing_item.id, new_item.id],
                    conflict_type="content_conflict",
                    resolution_strategy="priority_based",
                    confidence=0.8
                )
                conflicts.append(conflict)
        
        return conflicts
    
    async def _are_conflicting(self, item1: ContextItem, item2: ContextItem) -> bool:
        """Check if two items conflict."""
        
        # Check for dependency conflicts
        if item1.dependencies & {item2.id} or item2.dependencies & {item1.id}:
            return True
        
        # Check for contradictory information (simplified)
        if (item1.modality == item2.modality and 
            item1.dimension == item2.dimension and
            item1.priority == item2.priority):
            # Additional logic would check for contradictory content
            return False  # Simplified for this example
        
        return False
    
    async def _resolve_conflicts(
        self,
        window: ContextWindow,
        conflicts: List[ContextConflict],
        new_item: ContextItem
    ) -> bool:
        """Resolve conflicts and update window."""
        
        resolver = self.conflict_resolver
        
        for conflict in conflicts:
            resolution_result = await resolver.resolve_conflict(window, conflict)
            
            if not resolution_result["success"]:
                return False
        
        return True
    
    async def _detect_all_conflicts(self, window: ContextWindow) -> List[ContextConflict]:
        """Detect all conflicts in window."""
        
        conflicts = []
        items = window.items
        
        for i in range(len(items)):
            for j in range(i + 1, len(items)):
                if await self._are_conflicting(items[i], items[j]):
                    conflict = ContextConflict(
                        conflict_id=f"conflict_{items[i].id}_{items[j].id}",
                        conflicting_items=[items[i].id, items[j].id],
                        conflict_type="pairwise_conflict",
                        resolution_strategy="priority_based",
                        confidence=0.8
                    )
                    conflicts.append(conflict)
        
        return conflicts
    
    async def _resolve_single_conflict(
        self,
        window: ContextWindow,
        conflict: ContextConflict,
        strategy: str
    ) -> Dict[str, Any]:
        """Resolve a single conflict."""
        
        items = {item.id: item for item in window.items}
        conflicting_ids = conflict.conflicting_items
        
        if not all(item_id in items for item_id in conflicting_ids):
            return {"resolved": False, "reason": "missing_items"}
        
        if strategy == "priority_based":
            # Keep highest priority item
            items_list = [items[item_id] for item_id in conflicting_ids]
            items_list.sort(key=lambda x: x.priority.value, reverse=True)
            
            winner = items_list[0]
            losers = items_list[1:]
            
            for loser in losers:
                window.items.remove(loser)
                window.current_size -= 1
            
            return {
                "resolved": True,
                "winner": winner.id,
                "losers": [l.id for l in losers],
                "strategy": "priority_based"
            }
        
        elif strategy == "relevance_based":
            # Keep highest relevance item
            items_list = [items[item_id] for item_id in conflicting_ids]
            items_list.sort(key=lambda x: x.relevance_score, reverse=True)
            
            winner = items_list[0]
            losers = items_list[1:]
            
            for loser in losers:
                window.items.remove(loser)
                window.current_size -= 1
            
            return {
                "resolved": True,
                "winner": winner.id,
                "losers": [l.id for l in losers],
                "strategy": "relevance_based"
            }
        
        return {"resolved": False, "reason": "unknown_strategy"}
    
    # Utility methods for sizing and optimization
    
    async def _analyze_access_patterns(self, window: ContextWindow) -> Dict[str, Any]:
        """Analyze historical access patterns."""
        
        # Simplified access pattern analysis
        access_patterns = {
            "frequent_items": [],
            "recent_activity": {},
            "access_distribution": {}
        }
        
        for item in window.items:
            if item.access_count > 5:  # Frequently accessed
                access_patterns["frequent_items"].append(item.id)
            
            access_patterns["recent_activity"][item.id] = item.access_count
            access_patterns["access_distribution"][item.priority.value] = \
                access_patterns["access_distribution"].get(item.priority.value, 0) + 1
        
        return access_patterns
    
    async def _predict_future_relevance(self, item: ContextItem, patterns: Dict[str, Any]) -> float:
        """Predict future relevance of an item."""
        
        # Simple prediction based on access patterns
        base_relevance = item.relevance_score
        
        # Adjust based on access frequency
        access_factor = min(1.5, 1.0 + (item.access_count * 0.1))
        
        # Adjust based on recency
        recency_hours = (datetime.utcnow() - item.last_accessed).total_seconds() / 3600
        recency_factor = max(0.5, 1.0 - (recency_hours / 168))  # Decay over a week
        
        predicted_relevance = base_relevance * access_factor * recency_factor
        return min(1.0, predicted_relevance)
    
    async def _calculate_item_utility(self, item: ContextWindow, window: ContextWindow) -> float:
        """Calculate utility score for an item."""
        
        # Multi-factor utility calculation
        relevance_utility = item.relevance_score * 0.4
        quality_utility = item.quality_score * 0.3
        priority_utility = self._priority_weight(item.priority) * 0.2
        recency_utility = 1.0 / (1.0 + (datetime.utcnow() - item.last_accessed).total_seconds() / 3600) * 0.1
        
        total_utility = relevance_utility + quality_utility + priority_utility + recency_utility
        return total_utility
    
    async def _identify_redundant_items(self, items: List[ContextItem]) -> List[str]:
        """Identify redundant items in the list."""
        
        redundant_ids = []
        
        # Group by modality and dimension
        groups = defaultdict(list)
        for item in items:
            key = f"{item.modality.value}_{item.dimension.value}"
            groups[key].append(item)
        
        # Find redundant items within groups
        for group_items in groups.values():
            if len(group_items) > 1:
                # Sort by relevance and quality
                group_items.sort(key=lambda x: (x.relevance_score, x.quality_score), reverse=True)
                
                # Mark items below threshold as redundant
                threshold = group_items[0].relevance_score * 0.8
                for item in group_items[1:]:
                    if item.relevance_score < threshold:
                        redundant_ids.append(item.id)
        
        return redundant_ids
    
    async def _compress_information(self, items: List[ContextItem], new_item: ContextItem) -> Dict[str, Any]:
        """Compress redundant information."""
        
        # Simplified compression
        all_items = items + [new_item]
        redundant_ids = await self._identify_redundant_items(all_items)
        
        if not redundant_ids:
            return {"compressed": False}
        
        # Remove redundant items
        compressed_items = [item for item in all_items if item.id not in redundant_ids]
        
        return {
            "compressed": True,
            "compressed_items": compressed_items,
            "items_removed": len(redundant_ids),
            "ratio": len(compressed_items) / len(all_items),
            "information_preserved": sum(item.quality_score for item in compressed_items) / max(1, sum(item.quality_score for item in all_items))
        }
    
    async def _should_refresh(self, window: ContextWindow) -> Dict[str, Any]:
        """Determine if window should be refreshed."""
        
        time_since_refresh = datetime.utcnow() - window.last_resized
        
        # Time-based threshold
        if time_since_refresh > timedelta(minutes=60):
            return {"needed": True, "reason": "time_threshold"}
        
        # Capacity-based threshold
        utilization = window.current_size / window.size_limit
        if utilization > 0.95:
            return {"needed": True, "reason": "high_utilization"}
        
        # Quality-based threshold
        low_quality_items = sum(1 for item in window.items if item.quality_score < 0.5)
        if low_quality_items > len(window.items) * 0.3:
            return {"needed": True, "reason": "low_quality"}
        
        return {"needed": False, "reason": "no_refresh_needed"}
    
    async def _determine_refresh_strategy(self, window: ContextWindow) -> RefreshTrigger:
        """Determine best refresh strategy for window."""
        
        # Analyze window characteristics
        utilization = window.current_size / window.size_limit
        age = (datetime.utcnow() - window.last_resized).total_seconds() / 60  # minutes
        
        # Choose strategy based on conditions
        if utilization > 0.9:
            return RefreshTrigger.CAPACITY_BASED
        elif age > 45:
            return RefreshTrigger.TIME_BASED
        elif window.strategy == SizingStrategy.PREDICTIVE:
            return RefreshTrigger.INTERACTION_BASED
        else:
            return RefreshTrigger.RELEVANCE_BASED
    
    async def _update_window_metrics(self, window: ContextWindow) -> None:
        """Update window metrics."""
        
        window.metrics.update({
            "utilization": window.current_size / window.size_limit,
            "avg_relevance": np.mean([item.relevance_score for item in window.items]) if window.items else 0,
            "avg_quality": np.mean([item.quality_score for item in window.items]) if window.items else 0,
            "diversity_score": len(set(item.modality for item in window.items)) / len(window.items) if window.items else 0,
            "last_updated": datetime.utcnow().isoformat()
        })
        
        # Update global metrics
        self.metrics["total_items"] = sum(w.current_size for w in self.context_windows.values())
        self.metrics["average_window_utilization"] = np.mean([w.current_size / w.size_limit for w in self.context_windows.values()])
    
    # Placeholder methods for item operations
    
    async def _remove_context_window(self, window_id: str) -> None:
        """Remove a context window."""
        if window_id in self.context_windows:
            del self.context_windows[window_id]
            self.metrics["total_windows"] = len(self.context_windows)
    
    async def _refresh_item_relevance(self, item: ContextItem) -> None:
        """Refresh item relevance score."""
        # Simplified refresh
        item.relevance_score *= 0.95  # Gradual decay
    
    async def _recalculate_relevance(self, item: ContextItem) -> None:
        """Recalculate item relevance score."""
        # Simplified recalculation
        pass
    
    async def _get_recent_interactions(self, window: ContextWindow) -> List[Dict[str, Any]]:
        """Get recent interactions affecting the window."""
        return []  # Simplified
    
    async def _update_relevance_from_interactions(self, item: ContextItem, interactions: List[Dict[str, Any]]) -> float:
        """Update relevance based on recent interactions."""
        return item.relevance_score  # Simplified
    
    async def _recalculate_quality(self, item: ContextItem) -> float:
        """Recalculate item quality score."""
        return item.quality_score  # Simplified
    
    async def _update_refresh_metrics(self, window: ContextWindow, refresh_result: Dict[str, Any]) -> None:
        """Update refresh-related metrics."""
        window.last_resized = datetime.utcnow()
    
    # Optimization methods
    
    async def _optimize_relevance(self, window: ContextWindow) -> Dict[str, Any]:
        """Optimize for relevance."""
        
        initial_avg_relevance = np.mean([item.relevance_score for item in window.items]) if window.items else 0
        
        # Remove low-relevance items
        threshold = initial_avg_relevance * 0.7
        items_to_remove = [item for item in window.items if item.relevance_score < threshold]
        
        for item in items_to_remove:
            window.items.remove(item)
            window.current_size -= 1
        
        final_avg_relevance = np.mean([item.relevance_score for item in window.items]) if window.items else 0
        
        return {
            "improved": final_avg_relevance > initial_avg_relevance,
            "initial_avg": initial_avg_relevance,
            "final_avg": final_avg_relevance,
            "items_removed": len(items_to_remove)
        }
    
    async def _optimize_efficiency(self, window: ContextWindow) -> Dict[str, Any]:
        """Optimize for efficiency."""
        
        # Improve processing efficiency
        initial_diversity = len(set(item.modality for item in window.items)) / len(window.items) if window.items else 0
        
        # Ensure good diversity while maintaining focus
        target_diversity = 0.6
        if initial_diversity < target_diversity:
            # Items are too similar, encourage diversity
            pass  # Simplified
        
        return {
            "improved": True,
            "initial_diversity": initial_diversity,
            "target_diversity": target_diversity
        }
    
    async def _optimize_quality(self, window: ContextWindow) -> Dict[str, Any]:
        """Optimize for quality."""
        
        initial_avg_quality = np.mean([item.quality_score for item in window.items]) if window.items else 0
        
        # Promote high-quality items
        window.items.sort(key=lambda x: x.quality_score, reverse=True)
        
        final_avg_quality = np.mean([item.quality_score for item in window.items]) if window.items else 0
        
        return {
            "improved": final_avg_quality >= initial_avg_quality,
            "initial_avg": initial_avg_quality,
            "final_avg": final_avg_quality
        }
    
    async def _optimize_diversity(self, window: ContextWindow) -> Dict[str, Any]:
        """Optimize for diversity."""
        
        modality_counts = defaultdict(int)
        for item in window.items:
            modality_counts[item.modality] += 1
        
        # Ensure balanced representation
        max_modality_count = max(modality_counts.values()) if modality_counts else 0
        target_per_modality = len(window.items) // len(modality_counts) if modality_counts else 0
        
        # Simplified diversity optimization
        return {
            "improved": True,
            "modality_distribution": dict(modality_counts),
            "max_modality_count": max_modality_count
        }
    
    async def _schedule_refresh(self, window_id: str, trigger: RefreshTrigger) -> None:
        """Schedule a refresh for the window."""
        # Simplified scheduling
        pass
    
    # Properties
    
    @property
    def sizing_strategies(self) -> Dict[SizingStrategy, Callable]:
        """Get available sizing strategies."""
        return self._sizing_strategies if hasattr(self, '_sizing_strategies') else self.sizing_algorithms


class ContextConflictResolver:
    """Specialized conflict resolution for context items."""
    
    def __init__(self):
        self.resolution_strategies = {
            "priority_based": self._priority_resolution,
            "relevance_based": self._relevance_resolution,
            "quality_based": self._quality_resolution,
            "temporal_based": self._temporal_resolution
        }
    
    async def resolve_conflict(self, window: ContextWindow, conflict: ContextConflict) -> Dict[str, Any]:
        """Resolve a context conflict."""
        
        strategy_func = self.resolution_strategies.get(conflict.resolution_strategy)
        if not strategy_func:
            return {"success": False, "reason": "unknown_strategy"}
        
        return await strategy_func(window, conflict)
    
    async def _priority_resolution(self, window: ContextWindow, conflict: ContextConflict) -> Dict[str, Any]:
        """Resolve conflict based on priority."""
        # Implementation would resolve based on priority
        return {"success": True, "strategy": "priority_based"}
    
    async def _relevance_resolution(self, window: ContextWindow, conflict: ContextConflict) -> Dict[str, Any]:
        """Resolve conflict based on relevance."""
        # Implementation would resolve based on relevance
        return {"success": True, "strategy": "relevance_based"}
    
    async def _quality_resolution(self, window: ContextWindow, conflict: ContextConflict) -> Dict[str, Any]:
        """Resolve conflict based on quality."""
        # Implementation would resolve based on quality
        return {"success": True, "strategy": "quality_based"}
    
    async def _temporal_resolution(self, window: ContextWindow, conflict: ContextConflict) -> Dict[str, Any]:
        """Resolve conflict based on temporal information."""
        # Implementation would resolve based on timing
        return {"success": True, "strategy": "temporal_based"}


if __name__ == "__main__":
    print("Context Management with Dynamic Sizing System Initialized")
    print("=" * 60)
    manager = ContextManager()
    print("Ready for advanced context management and dynamic sizing!")