ai_agent_framework/dimensions/context_compression_synthesis.py

"""
Context Compression and Synthesis System
=======================================

Advanced context compression and efficient encoding system with synthesis capabilities
for integrating data from diverse sources and modalities within unified contextual models.
"""

import asyncio
import json
import logging
import pickle
import zlib
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional, Tuple, Union, Set
from dataclasses import dataclass, field
from enum import Enum
import numpy as np
from collections import defaultdict, deque
import hashlib
import base64

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

logger = logging.getLogger(__name__)


class CompressionStrategy(Enum):
    """Context compression strategies."""
    HIERARCHICAL = "hierarchical"
    SEMANTIC = "semantic"
    TEMPORAL = "temporal"
    RELEVANCE = "relevance"
    MULTIMODAL = "multimodal"
    ADAPTIVE = "adaptive"


class SynthesisMethod(Enum):
    """Context synthesis methods."""
    FUSION = "fusion"
    MESH = "mesh"
    HIERARCHICAL = "hierarchical"
    GRAPH_BASED = "graph_based"
    ATTENTION_BASED = "attention_based"
    ENSEMBLE = "ensemble"


@dataclass
class CompressionConfig:
    """Configuration for context compression."""
    strategy: CompressionStrategy
    compression_ratio: float
    quality_threshold: float
    adaptive_threshold: float
    max_context_size: int
    expiry_policy: Dict[str, Any]


@dataclass
class SynthesizedContext:
    """Represents synthesized context from multiple sources."""
    id: str
    source_elements: List[str]
    synthesis_method: SynthesisMethod
    compressed_representation: Dict[str, Any]
    quality_score: float
    confidence: float
    temporal_scope: Tuple[datetime, datetime]
    semantic_clusters: List[Dict[str, Any]]
    
    def __post_init__(self):
        if not self.id:
            self.id = str(hashlib.md5(str(self.source_elements).encode()).hexdigest())


@dataclass
class ContextVector:
    """Vector representation of context for compression."""
    element_id: str
    vector: np.ndarray
    vector_type: str  # semantic, temporal, spatial, etc.
    compression_level: int
    quality_score: float
    timestamp: datetime
    
    def __post_init__(self):
        if not self.timestamp:
            self.timestamp = datetime.utcnow()


class ContextCompressionEngine:
    """Advanced context compression engine with multiple strategies."""
    
    def __init__(self):
        self.compression_configs = self._initialize_compression_configs()
        self.vector_index = {}  # Vector-to-element mapping
        self.compression_cache = {}  # Cached compressed representations
        self.quality_metrics = {}
        
    def _initialize_compression_configs(self) -> Dict[CompressionStrategy, CompressionConfig]:
        """Initialize compression configurations for different strategies."""
        return {
            CompressionStrategy.HIERARCHICAL: CompressionConfig(
                strategy=CompressionStrategy.HIERARCHICAL,
                compression_ratio=0.7,
                quality_threshold=0.6,
                adaptive_threshold=0.5,
                max_context_size=100,
                expiry_policy={"level_1": timedelta(minutes=30), "level_2": timedelta(hours=2)}
            ),
            CompressionStrategy.SEMANTIC: CompressionConfig(
                strategy=CompressionStrategy.SEMANTIC,
                compression_ratio=0.8,
                quality_threshold=0.7,
                adaptive_threshold=0.6,
                max_context_size=50,
                expiry_policy={"core_concepts": timedelta(hours=6), "supporting": timedelta(hours=1)}
            ),
            CompressionStrategy.TEMPORAL: CompressionConfig(
                strategy=CompressionStrategy.TEMPORAL,
                compression_ratio=0.6,
                quality_threshold=0.5,
                adaptive_threshold=0.4,
                max_context_size=200,
                expiry_policy={"immediate": timedelta(minutes=15), "recent": timedelta(hours=4)}
            ),
            CompressionStrategy.RELEVANCE: CompressionConfig(
                strategy=CompressionStrategy.RELEVANCE,
                compression_ratio=0.9,
                quality_threshold=0.8,
                adaptive_threshold=0.7,
                max_context_size=30,
                expiry_policy={"high_relevance": timedelta(days=1), "medium": timedelta(hours=6)}
            ),
            CompressionStrategy.MULTIMODAL: CompressionConfig(
                strategy=CompressionStrategy.MULTIMODAL,
                compression_ratio=0.5,
                quality_threshold=0.6,
                adaptive_threshold=0.5,
                max_context_size=150,
                expiry_policy={"text": timedelta(hours=2), "visual": timedelta(minutes=30)}
            )
        }
    
    async def compress_context(
        self, 
        context_elements: List[ContextElement],
        strategy: CompressionStrategy = CompressionStrategy.ADAPTIVE,
        quality_threshold: float = 0.6
    ) -> Dict[str, Any]:
        """Compress context using specified strategy."""
        try:
            if strategy == CompressionStrategy.ADAPTIVE:
                strategy = await self._select_optimal_strategy(context_elements)
            
            config = self.compression_configs.get(strategy)
            if not config:
                strategy = CompressionStrategy.HIERARCHICAL
                config = self.compression_configs[strategy]
            
            if strategy == CompressionStrategy.HIERARCHICAL:
                return await self._hierarchical_compression(context_elements, config, quality_threshold)
            elif strategy == CompressionStrategy.SEMANTIC:
                return await self._semantic_compression(context_elements, config, quality_threshold)
            elif strategy == CompressionStrategy.TEMPORAL:
                return await self._temporal_compression(context_elements, config, quality_threshold)
            elif strategy == CompressionStrategy.RELEVANCE:
                return await self._relevance_compression(context_elements, config, quality_threshold)
            elif strategy == CompressionStrategy.MULTIMODAL:
                return await self._multimodal_compression(context_elements, config, quality_threshold)
            else:
                return await self._hierarchical_compression(context_elements, config, quality_threshold)
                
        except Exception as e:
            logger.error(f"Context compression failed: {e}")
            return {"status": "error", "error": str(e)}
    
    async def _select_optimal_strategy(self, context_elements: List[ContextElement]) -> CompressionStrategy:
        """Select optimal compression strategy based on context characteristics."""
        if not context_elements:
            return CompressionStrategy.HIERARCHICAL
        
        # Analyze context characteristics
        modalities = set(element.modality for element in context_elements)
        temporal_spread = self._calculate_temporal_spread(context_elements)
        relevance_variance = np.var([element.relevance_score for element in context_elements])
        
        # Strategy selection logic
        if len(modalities) > 3:  # High modality diversity
            return CompressionStrategy.MULTIMODAL
        elif temporal_spread > timedelta(hours=2):  # Wide temporal scope
            return CompressionStrategy.TEMPORAL
        elif relevance_variance > 0.1:  # High relevance variance
            return CompressionStrategy.RELEVANCE
        else:
            return CompressionStrategy.SEMANTIC
    
    def _calculate_temporal_spread(self, context_elements: List[ContextElement]) -> timedelta:
        """Calculate temporal spread of context elements."""
        if len(context_elements) < 2:
            return timedelta(0)
        
        timestamps = [element.timestamp for element in context_elements]
        return max(timestamps) - min(timestamps)
    
    async def _hierarchical_compression(
        self, 
        context_elements: List[ContextElement], 
        config: CompressionConfig,
        quality_threshold: float
    ) -> Dict[str, Any]:
        """Perform hierarchical context compression."""
        
        # Level 1: High-importance core context
        core_context = [
            element for element in context_elements
            if element.relevance_score > 0.8 and element.confidence > 0.7
        ]
        
        # Level 2: Supporting context
        supporting_context = [
            element for element in context_elements
            if 0.5 < element.relevance_score <= 0.8
        ]
        
        # Level 3: Peripheral context
        peripheral_context = [
            element for element in context_elements
            if element.relevance_score <= 0.5
        ]
        
        # Apply compression based on size limits
        compressed_core = await self._compress_element_group(core_context, 0.9, "core")
        compressed_supporting = await self._compress_element_group(supporting_context, 0.7, "supporting")
        compressed_peripheral = await self._compress_element_group(peripheral_context, 0.5, "peripheral")
        
        return {
            "strategy": "hierarchical",
            "levels": {
                "core": compressed_core,
                "supporting": compressed_supporting,
                "peripheral": compressed_peripheral
            },
            "compression_ratio": len(compressed_core) + len(compressed_supporting) + len(compressed_peripheral),
            "quality_threshold": quality_threshold,
            "total_elements": len(context_elements),
            "compressed_elements": len(compressed_core) + len(compressed_supporting) + len(compressed_peripheral)
        }
    
    async def _semantic_compression(
        self, 
        context_elements: List[ContextElement], 
        config: CompressionConfig,
        quality_threshold: float
    ) -> Dict[str, Any]:
        """Perform semantic context compression."""
        
        # Group elements by semantic similarity
        semantic_groups = await self._group_by_semantic_similarity(context_elements)
        
        compressed_groups = []
        total_original_size = 0
        total_compressed_size = 0
        
        for group in semantic_groups:
            # Compress each semantic group
            group_elements = group["elements"]
            group_theme = group["theme"]
            
            compressed_group = await self._compress_semantic_group(group_elements, group_theme, quality_threshold)
            
            total_original_size += len(group_elements)
            total_compressed_size += len(compressed_group["compressed_elements"])
            
            compressed_groups.append({
                "theme": group_theme,
                "elements": compressed_group["compressed_elements"],
                "key_concepts": compressed_group["key_concepts"],
                "representative_embedding": compressed_group["representative_embedding"],
                "confidence": compressed_group["confidence"]
            })
        
        return {
            "strategy": "semantic",
            "groups": compressed_groups,
            "compression_ratio": total_compressed_size / max(total_original_size, 1),
            "quality_threshold": quality_threshold,
            "semantic_coherence": self._calculate_semantic_coherence(compressed_groups)
        }
    
    async def _temporal_compression(
        self, 
        context_elements: List[ContextElement], 
        config: CompressionConfig,
        quality_threshold: float
    ) -> Dict[str, Any]:
        """Perform temporal context compression."""
        
        # Sort elements by timestamp
        sorted_elements = sorted(context_elements, key=lambda e: e.timestamp)
        
        # Define temporal windows
        current_time = datetime.utcnow()
        immediate_window = current_time - timedelta(minutes=30)
        recent_window = current_time - timedelta(hours=4)
        
        temporal_windows = {
            "immediate": [],      # Last 30 minutes
            "recent": [],         # Last 4 hours
            "historical": []      # Everything else
        }
        
        for element in sorted_elements:
            if element.timestamp >= immediate_window:
                temporal_windows["immediate"].append(element)
            elif element.timestamp >= recent_window:
                temporal_windows["recent"].append(element)
            else:
                temporal_windows["historical"].append(element)
        
        # Compress each temporal window
        compressed_windows = {}
        for window_name, elements in temporal_windows.items():
            if elements:
                compressed_windows[window_name] = await self._compress_temporal_window(
                    elements, window_name, quality_threshold
                )
        
        return {
            "strategy": "temporal",
            "windows": compressed_windows,
            "temporal_distribution": {name: len(elements) for name, elements in temporal_windows.items()},
            "compression_ratio": sum(len(window.get("compressed_elements", [])) 
                                   for window in compressed_windows.values()) / max(len(context_elements), 1)
        }
    
    async def _relevance_compression(
        self, 
        context_elements: List[ContextElement], 
        config: CompressionConfig,
        quality_threshold: float
    ) -> Dict[str, Any]:
        """Perform relevance-based context compression."""
        
        # Sort by relevance score
        sorted_elements = sorted(context_elements, key=lambda e: e.relevance_score, reverse=True)
        
        # Create relevance tiers
        high_relevance = [e for e in sorted_elements if e.relevance_score > 0.8]
        medium_relevance = [e for e in sorted_elements if 0.5 < e.relevance_score <= 0.8]
        low_relevance = [e for e in sorted_elements if e.relevance_score <= 0.5]
        
        # Apply different compression levels based on relevance
        compressed_high = await self._compress_with_minimal_loss(high_relevance, "high_relevance")
        compressed_medium = await self._compress_with_standard_loss(medium_relevance, "medium_relevance")
        compressed_low = await self._compress_aggressively(low_relevance, "low_relevance")
        
        return {
            "strategy": "relevance",
            "relevance_tiers": {
                "high": compressed_high,
                "medium": compressed_medium,
                "low": compressed_low
            },
            "preservation_rate": {
                "high": len(compressed_high) / max(len(high_relevance), 1),
                "medium": len(compressed_medium) / max(len(medium_relevance), 1),
                "low": len(compressed_low) / max(len(low_relevance), 1)
            },
            "compression_ratio": (len(compressed_high) + len(compressed_medium) + len(compressed_low)) / max(len(context_elements), 1)
        }
    
    async def _multimodal_compression(
        self, 
        context_elements: List[ContextElement], 
        config: CompressionConfig,
        quality_threshold: float
    ) -> Dict[str, Any]:
        """Perform multimodal context compression."""
        
        # Group by modality
        modality_groups = defaultdict(list)
        for element in context_elements:
            modality_groups[element.modality].append(element)
        
        compressed_modalities = {}
        total_original = 0
        total_compressed = 0
        
        for modality, elements in modality_groups.items():
            compressed_modality = await self._compress_modality_group(elements, modality, quality_threshold)
            compressed_modalities[modality.value] = compressed_modality
            
            total_original += len(elements)
            total_compressed += len(compressed_modality["compressed_elements"])
        
        return {
            "strategy": "multimodal",
            "modalities": compressed_modalities,
            "cross_modal_correlations": await self._analyze_cross_modal_correlations(modality_groups),
            "compression_ratio": total_compressed / max(total_original, 1)
        }
    
    async def _compress_element_group(
        self, 
        elements: List[ContextElement], 
        compression_level: float, 
        group_type: str
    ) -> List[Dict[str, Any]]:
        """Compress a group of elements."""
        if not elements:
            return []
        
        compressed = []
        
        for element in elements:
            if group_type == "core":
                # Preserve full detail for core elements
                compressed.append({
                    "id": element.id,
                    "content": element.content,
                    "metadata": element.metadata,
                    "full_representation": True
                })
            elif group_type == "supporting":
                # Summarize supporting elements
                compressed.append({
                    "id": element.id,
                    "summary": self._generate_summary(element.content),
                    "key_points": element.metadata.get("key_points", []),
                    "confidence": element.confidence
                })
            else:  # peripheral
                # Abstract peripheral elements
                compressed.append({
                    "id": element.id,
                    "theme": element.metadata.get("theme", str(element.content)[:50]),
                    "relevance_score": element.relevance_score,
                    "abstract": True
                })
        
        return compressed
    
    async def _group_by_semantic_similarity(self, context_elements: List[ContextElement]) -> List[Dict[str, Any]]:
        """Group context elements by semantic similarity."""
        
        # Simple semantic grouping based on keywords and themes
        groups = defaultdict(list)
        
        for element in context_elements:
            # Extract semantic theme
            theme = self._extract_semantic_theme(element)
            groups[theme].append(element)
        
        # Convert to list format
        semantic_groups = []
        for theme, elements in groups.items():
            semantic_groups.append({
                "theme": theme,
                "elements": elements,
                "coherence_score": self._calculate_group_coherence(elements)
            })
        
        return semantic_groups
    
    def _extract_semantic_theme(self, element: ContextElement) -> str:
        """Extract semantic theme from context element."""
        content = str(element.content).lower()
        metadata = element.metadata
        
        # Simple theme extraction logic
        if any(word in content for word in ["urgent", "asap", "deadline"]):
            return "urgency"
        elif any(word in content for word in ["team", "collaborate", "group"]):
            return "collaboration"
        elif any(word in content for word in ["data", "analysis", "metrics"]):
            return "data_analysis"
        elif any(word in content for word in ["customer", "client", "user"]):
            return "customer_oriented"
        else:
            return "general"
    
    def _calculate_group_coherence(self, elements: List[ContextElement]) -> float:
        """Calculate semantic coherence of a group."""
        if len(elements) < 2:
            return 1.0
        
        # Calculate average similarity within group
        similarities = []
        for i, elem1 in enumerate(elements):
            for j, elem2 in enumerate(elements[i+1:], i+1):
                # Simple similarity based on metadata overlap
                overlap = len(set(elem1.metadata.keys()) & set(elem2.metadata.keys()))
                total_keys = len(set(elem1.metadata.keys()) | set(elem2.metadata.keys()))
                similarity = overlap / max(total_keys, 1)
                similarities.append(similarity)
        
        return np.mean(similarities) if similarities else 0.5
    
    def _generate_summary(self, content: Any) -> str:
        """Generate summary of content."""
        content_str = str(content)
        if len(content_str) <= 100:
            return content_str
        
        # Simple summarization
        sentences = content_str.split('.')
        if len(sentences) > 2:
            return '. '.join(sentences[:2]) + '.'
        else:
            return content_str[:97] + "..."
    
    async def _compress_semantic_group(
        self, 
        elements: List[ContextElement], 
        theme: str, 
        quality_threshold: float
    ) -> Dict[str, Any]:
        """Compress a semantic group of elements."""
        
        # Extract key concepts
        key_concepts = await self._extract_key_concepts(elements)
        
        # Generate representative embedding
        representative_embedding = await self._generate_representative_embedding(elements)
        
        # Select representative elements
        representative_elements = sorted(
            elements, 
            key=lambda e: e.relevance_score * e.confidence, 
            reverse=True
        )[:min(5, len(elements))]
        
        return {
            "theme": theme,
            "compressed_elements": [self._element_to_summary(e) for e in representative_elements],
            "key_concepts": key_concepts,
            "representative_embedding": representative_embedding.tolist() if isinstance(representative_embedding, np.ndarray) else representative_embedding,
            "confidence": np.mean([e.confidence for e in elements])
        }
    
    async def _extract_key_concepts(self, elements: List[ContextElement]) -> List[str]:
        """Extract key concepts from semantic group."""
        concepts = []
        concept_scores = defaultdict(float)
        
        for element in elements:
            content = str(element.content).lower()
            metadata = element.metadata
            
            # Extract from content
            words = content.split()
            for word in words:
                if len(word) > 3:  # Skip short words
                    concept_scores[word] += element.relevance_score
            
            # Extract from metadata
            for key, value in metadata.items():
                if isinstance(value, str):
                    concept_scores[value.lower()] += element.confidence * 0.5
        
        # Select top concepts
        sorted_concepts = sorted(concept_scores.items(), key=lambda x: x[1], reverse=True)
        return [concept for concept, score in sorted_concepts[:10]]
    
    async def _generate_representative_embedding(self, elements: List[ContextElement]) -> np.ndarray:
        """Generate representative embedding for semantic group."""
        if not elements:
            return np.zeros(128)
        
        # Simple averaging approach (in production would use more sophisticated methods)
        embeddings = []
        for element in elements:
            # Generate mock embedding
            np.random.seed(hash(element.id) % (2**32))
            embedding = np.random.rand(128)
            embeddings.append(embedding * element.relevance_score)
        
        if embeddings:
            return np.mean(embeddings, axis=0)
        else:
            return np.zeros(128)
    
    def _element_to_summary(self, element: ContextElement) -> Dict[str, Any]:
        """Convert element to summary format."""
        return {
            "id": element.id,
            "content_summary": self._generate_summary(element.content),
            "relevance_score": element.relevance_score,
            "confidence": element.confidence,
            "key_metadata": {k: v for k, v in list(element.metadata.items())[:3]}
        }
    
    async def _compress_temporal_window(
        self, 
        elements: List[ContextElement], 
        window_name: str, 
        quality_threshold: float
    ) -> Dict[str, Any]:
        """Compress a temporal window of elements."""
        
        # Apply time-based compression rules
        if window_name == "immediate":
            # Preserve detailed information for immediate context
            compression_level = 0.9
        elif window_name == "recent":
            # Moderate compression for recent context
            compression_level = 0.7
        else:
            # Aggressive compression for historical context
            compression_level = 0.4
        
        compressed_elements = []
        for element in elements:
            if element.relevance_score > quality_threshold:
                if compression_level > 0.8:
                    compressed_elements.append(self._element_to_summary(element))
                else:
                    compressed_elements.append({
                        "id": element.id,
                        "content_summary": self._generate_summary(element.content),
                        "relevance_score": element.relevance_score
                    })
        
        return {
            "window_name": window_name,
            "compression_level": compression_level,
            "compressed_elements": compressed_elements,
            "time_range": {
                "start": min(e.timestamp for e in elements).isoformat(),
                "end": max(e.timestamp for e in elements).isoformat()
            }
        }
    
    async def _compress_with_minimal_loss(self, elements: List[ContextElement], tier: str) -> List[Dict[str, Any]]:
        """Compress with minimal information loss."""
        return [self._element_to_summary(e) for e in elements]
    
    async def _compress_with_standard_loss(self, elements: List[ContextElement], tier: str) -> List[Dict[str, Any]]:
        """Compress with standard information loss."""
        compressed = []
        for element in elements:
            compressed.append({
                "id": element.id,
                "summary": self._generate_summary(element.content),
                "relevance_score": element.relevance_score,
                "confidence": element.confidence
            })
        return compressed
    
    async def _compress_aggressively(self, elements: List[ContextElement], tier: str) -> List[Dict[str, Any]]:
        """Compress aggressively with significant information loss."""
        compressed = []
        for element in elements:
            compressed.append({
                "id": element.id,
                "theme": self._extract_semantic_theme(element),
                "relevance_score": element.relevance_score,
                "confidence": round(element.confidence, 2)
            })
        return compressed
    
    async def _compress_modality_group(
        self, 
        elements: List[ContextElement], 
        modality: ContextModality, 
        quality_threshold: float
    ) -> Dict[str, Any]:
        """Compress elements of a specific modality."""
        
        # Modality-specific compression logic
        if modality == ContextModality.TEXT:
            return await self._compress_text_modality(elements, quality_threshold)
        elif modality == ContextModality.VISUAL:
            return await self._compress_visual_modality(elements, quality_threshold)
        elif modality == ContextModality.AUDITORY:
            return await self._compress_auditory_modality(elements, quality_threshold)
        else:
            return await self._compress_generic_modality(elements, quality_threshold)
    
    async def _compress_text_modality(self, elements: List[ContextElement], quality_threshold: float) -> Dict[str, Any]:
        """Compress text modality elements."""
        compressed_elements = []
        for element in elements:
            if element.relevance_score > quality_threshold:
                compressed_elements.append({
                    "id": element.id,
                    "content_type": "text",
                    "content": self._generate_summary(element.content),
                    "metadata": element.metadata
                })
        
        return {
            "modality": "text",
            "compressed_elements": compressed_elements,
            "compression_method": "text_summarization"
        }
    
    async def _compress_visual_modality(self, elements: List[ContextElement], quality_threshold: float) -> Dict[str, Any]:
        """Compress visual modality elements."""
        compressed_elements = []
        for element in elements:
            if element.relevance_score > quality_threshold:
                # For visual elements, preserve key visual descriptors
                compressed_elements.append({
                    "id": element.id,
                    "content_type": "visual",
                    "visual_descriptor": element.metadata.get("visual_descriptor", str(element.content)[:50]),
                    "metadata": {k: v for k, v in element.metadata.items() if k in ["color", "shape", "size", "location"]}
                })
        
        return {
            "modality": "visual",
            "compressed_elements": compressed_elements,
            "compression_method": "visual_feature_extraction"
        }
    
    async def _compress_auditory_modality(self, elements: List[ContextElement], quality_threshold: float) -> Dict[str, Any]:
        """Compress auditory modality elements."""
        compressed_elements = []
        for element in elements:
            if element.relevance_score > quality_threshold:
                compressed_elements.append({
                    "id": element.id,
                    "content_type": "auditory",
                    "audio_descriptor": element.metadata.get("audio_descriptor", str(element.content)[:50]),
                    "metadata": {k: v for k, v in element.metadata.items() if k in ["frequency", "amplitude", "duration"]}
                })
        
        return {
            "modality": "auditory",
            "compressed_elements": compressed_elements,
            "compression_method": "audio_feature_extraction"
        }
    
    async def _compress_generic_modality(self, elements: List[ContextElement], quality_threshold: float) -> Dict[str, Any]:
        """Compress generic modality elements."""
        compressed_elements = []
        for element in elements:
            if element.relevance_score > quality_threshold:
                compressed_elements.append({
                    "id": element.id,
                    "content_summary": self._generate_summary(element.content),
                    "relevance_score": element.relevance_score,
                    "confidence": element.confidence
                })
        
        return {
            "modality": "generic",
            "compressed_elements": compressed_elements,
            "compression_method": "generic_compression"
        }
    
    async def _analyze_cross_modal_correlations(self, modality_groups: Dict[ContextModality, List[ContextElement]]) -> Dict[str, Any]:
        """Analyze correlations between different modalities."""
        correlations = {}
        
        modalities = list(modality_groups.keys())
        for i, mod1 in enumerate(modalities):
            for mod2 in modalities[i+1:]:
                elements1 = modality_groups[mod1]
                elements2 = modality_groups[mod2]
                
                # Calculate temporal correlation
                temporal_corr = self._calculate_temporal_correlation(elements1, elements2)
                
                # Calculate relevance correlation
                relevance_corr = self._calculate_relevance_correlation(elements1, elements2)
                
                correlations[f"{mod1.value}_{mod2.value}"] = {
                    "temporal_correlation": temporal_corr,
                    "relevance_correlation": relevance_corr,
                    "correlation_strength": (temporal_corr + relevance_corr) / 2
                }
        
        return correlations
    
    def _calculate_temporal_correlation(self, elements1: List[ContextElement], elements2: List[ContextElement]) -> float:
        """Calculate temporal correlation between two sets of elements."""
        if not elements1 or not elements2:
            return 0.0
        
        # Simple correlation based on timestamp proximity
        timestamps1 = [e.timestamp for e in elements1]
        timestamps2 = [e.timestamp for e in elements2]
        
        # Calculate average time differences
        avg_diff = 0
        count = 0
        
        for t1 in timestamps1:
            min_diff = min(abs((t1 - t2).total_seconds()) for t2 in timestamps2)
            avg_diff += min_diff
            count += 1
        
        if count == 0:
            return 0.0
        
        avg_diff /= count
        # Convert to correlation score (closer timestamps = higher correlation)
        return max(0, 1 - avg_diff / 3600)  # Normalize by 1 hour
    
    def _calculate_relevance_correlation(self, elements1: List[ContextElement], elements2: List[ContextElement]) -> float:
        """Calculate relevance correlation between two sets of elements."""
        if not elements1 or not elements2:
            return 0.0
        
        # Simple correlation based on relevance score patterns
        relevance1 = [e.relevance_score for e in elements1]
        relevance2 = [e.relevance_score for e in elements2]
        
        if len(relevance1) > 1 and len(relevance2) > 1:
            # Calculate variance correlation
            var1 = np.var(relevance1)
            var2 = np.var(relevance2)
            
            if var1 > 0 and var2 > 0:
                # Higher variance correlation indicates similar patterns
                return min(var1, var2) / max(var1, var2)
        
        return 0.5  # Default moderate correlation
    
    def _calculate_semantic_coherence(self, compressed_groups: List[Dict[str, Any]]) -> float:
        """Calculate overall semantic coherence of compressed context."""
        if not compressed_groups:
            return 0.0
        
        coherence_scores = [group.get("confidence", 0.5) for group in compressed_groups]
        return np.mean(coherence_scores)


class ContextSynthesisEngine:
    """Advanced context synthesis engine for integrating diverse data sources."""
    
    def __init__(self):
        self.synthesis_methods = {
            SynthesisMethod.FUSION: self._fusion_synthesis,
            SynthesisMethod.MESH: self._mesh_synthesis,
            SynthesisMethod.HIERARCHICAL: self._hierarchical_synthesis,
            SynthesisMethod.GRAPH_BASED: self._graph_based_synthesis,
            SynthesisMethod.ATTENTION_BASED: self._attention_based_synthesis,
            SynthesisMethod.ENSEMBLE: self._ensemble_synthesis
        }
        self.synthesis_cache = {}
        
    async def synthesize_contexts(
        self,
        context_groups: List[List[ContextElement]],
        synthesis_method: SynthesisMethod = SynthesisMethod.FUSION,
        quality_threshold: float = 0.6
    ) -> SynthesizedContext:
        """Synthesize contexts from multiple sources."""
        try:
            if synthesis_method not in self.synthesis_methods:
                synthesis_method = SynthesisMethod.FUSION
            
            synthesis_func = self.synthesis_methods[synthesis_method]
            result = await synthesis_func(context_groups, quality_threshold)
            
            # Create synthesized context object
            synthesized_context = SynthesizedContext(
                id=f"synthesized_{int(time.time())}",
                source_elements=[elem.id for group in context_groups for elem in group],
                synthesis_method=synthesis_method,
                compressed_representation=result,
                quality_score=self._calculate_synthesis_quality(result),
                confidence=self._calculate_synthesis_confidence(result),
                temporal_scope=self._calculate_temporal_scope(context_groups),
                semantic_clusters=self._identify_semantic_clusters(result)
            )
            
            return synthesized_context
            
        except Exception as e:
            logger.error(f"Context synthesis failed: {e}")
            return SynthesizedContext(
                id=f"error_{int(time.time())}",
                source_elements=[],
                synthesis_method=synthesis_method,
                compressed_representation={"error": str(e)},
                quality_score=0.0,
                confidence=0.0,
                temporal_scope=(datetime.utcnow(), datetime.utcnow()),
                semantic_clusters=[]
            )
    
    async def _fusion_synthesis(
        self, 
        context_groups: List[List[ContextElement]], 
        quality_threshold: float
    ) -> Dict[str, Any]:
        """Perform fusion-based context synthesis."""
        
        # Flatten all elements
        all_elements = []
        for group in context_groups:
            all_elements.extend(group)
        
        # Remove duplicates based on content similarity
        unique_elements = self._remove_duplicates(all_elements)
        
        # Sort by relevance and confidence
        sorted_elements = sorted(
            unique_elements,
            key=lambda e: e.relevance_score * e.confidence,
            reverse=True
        )
        
        # Select top elements based on quality threshold
        selected_elements = [
            e for e in sorted_elements
            if e.relevance_score * e.confidence > quality_threshold
        ]
        
        # Create fused representation
        fused_content = []
        for element in selected_elements[:50]:  # Limit to top 50
            fused_content.append({
                "id": element.id,
                "content": element.content,
                "source_group": self._identify_source_group(element, context_groups),
                "relevance_score": element.relevance_score,
                "confidence": element.confidence,
                "metadata": element.metadata
            })
        
        return {
            "method": "fusion",
            "elements": fused_content,
            "fusion_score": len(fused_content) / max(len(unique_elements), 1),
            "quality_distribution": self._calculate_quality_distribution(selected_elements)
        }
    
    async def _mesh_synthesis(
        self, 
        context_groups: List[List[ContextElement]], 
        quality_threshold: float
    ) -> Dict[str, Any]:
        """Perform mesh-based context synthesis."""
        
        # Create connections between elements from different groups
        connections = self._create_cross_group_connections(context_groups)
        
        # Build mesh representation
        mesh_nodes = []
        mesh_edges = []
        
        for group_idx, group in enumerate(context_groups):
            for element in group:
                if element.relevance_score > quality_threshold:
                    mesh_nodes.append({
                        "id": element.id,
                        "content": element.content,
                        "group": group_idx,
                        "relevance_score": element.relevance_score
                    })
        
        # Add edges based on connections
        for connection in connections:
            if connection["source"] in [node["id"] for node in mesh_nodes] and \
               connection["target"] in [node["id"] for node in mesh_nodes]:
                mesh_edges.append(connection)
        
        return {
            "method": "mesh",
            "nodes": mesh_nodes,
            "edges": mesh_edges,
            "mesh_density": len(mesh_edges) / max(len(mesh_nodes) * (len(mesh_nodes) - 1) / 2, 1),
            "clustering_coefficient": self._calculate_clustering_coefficient(mesh_edges, mesh_nodes)
        }
    
    async def _hierarchical_synthesis(
        self, 
        context_groups: List[List[ContextElement]], 
        quality_threshold: float
    ) -> Dict[str, Any]:
        """Perform hierarchical context synthesis."""
        
        # Create hierarchy levels
        hierarchy = {
            "level_1": [],  # High importance, cross-group elements
            "level_2": [],  # Medium importance, group-specific elements
            "level_3": []   # Low importance, supporting elements
        }
        
        # Analyze cross-group presence
        element_presence = self._analyze_cross_group_presence(context_groups)
        
        for group_idx, group in enumerate(context_groups):
            for element in group:
                if element.relevance_score > quality_threshold:
                    presence_count = element_presence.get(element.id, 0)
                    
                    if presence_count > 1:  # Appears in multiple groups
                        hierarchy["level_1"].append({
                            "id": element.id,
                            "content": element.content,
                            "groups": self._find_element_groups(element, context_groups),
                            "cross_group_relevance": presence_count,
                            "relevance_score": element.relevance_score
                        })
                    elif element.relevance_score > 0.7:
                        hierarchy["level_2"].append({
                            "id": element.id,
                            "content": element.content,
                            "group": group_idx,
                            "relevance_score": element.relevance_score
                        })
                    else:
                        hierarchy["level_3"].append({
                            "id": element.id,
                            "content_summary": str(element.content)[:100],
                            "group": group_idx,
                            "relevance_score": element.relevance_score
                        })
        
        return {
            "method": "hierarchical",
            "hierarchy": hierarchy,
            "distribution": {
                "level_1": len(hierarchy["level_1"]),
                "level_2": len(hierarchy["level_2"]),
                "level_3": len(hierarchy["level_3"])
            }
        }
    
    async def _graph_based_synthesis(
        self, 
        context_groups: List[List[ContextElement]], 
        quality_threshold: float
    ) -> Dict[str, Any]:
        """Perform graph-based context synthesis."""
        
        # Build knowledge graph
        nodes = []
        edges = []
        
        # Add nodes
        for group_idx, group in enumerate(context_groups):
            for element in group:
                if element.relevance_score > quality_threshold:
                    nodes.append({
                        "id": element.id,
                        "content": element.content,
                        "group": group_idx,
                        "relevance_score": element.relevance_score,
                        "metadata": element.metadata
                    })
        
        # Add edges based on semantic relationships
        for i, node1 in enumerate(nodes):
            for j, node2 in enumerate(nodes[i+1:], i+1):
                relationship_strength = self._calculate_relationship_strength(node1, node2)
                if relationship_strength > 0.3:
                    edges.append({
                        "source": node1["id"],
                        "target": node2["id"],
                        "strength": relationship_strength,
                        "relationship_type": "semantic"
                    })
        
        # Calculate graph metrics
        graph_metrics = self._calculate_graph_metrics(nodes, edges)
        
        return {
            "method": "graph_based",
            "nodes": nodes,
            "edges": edges,
            "graph_metrics": graph_metrics,
            "community_structure": self._detect_communities(nodes, edges)
        }
    
    async def _attention_based_synthesis(
        self, 
        context_groups: List[List[ContextElement]], 
        quality_threshold: float
    ) -> Dict[str, Any]:
        """Perform attention-based context synthesis."""
        
        # Calculate attention weights for each element
        attention_weights = {}
        total_elements = 0
        
        for group_idx, group in enumerate(context_groups):
            for element in group:
                if element.relevance_score > quality_threshold:
                    # Calculate attention weight based on multiple factors
                    relevance_weight = element.relevance_score
                    confidence_weight = element.confidence
                    recency_weight = self._calculate_recency_weight(element.timestamp)
                    diversity_weight = self._calculate_diversity_weight(element, context_groups)
                    
                    attention_score = (relevance_weight * 0.4 + 
                                     confidence_weight * 0.3 + 
                                     recency_weight * 0.2 + 
                                     diversity_weight * 0.1)
                    
                    attention_weights[element.id] = attention_score
                    total_elements += 1
        
        # Normalize attention weights
        if attention_weights:
            max_weight = max(attention_weights.values())
            attention_weights = {k: v / max_weight for k, v in attention_weights.items()}
        
        # Create attention-based representation
        attended_elements = []
        for group_idx, group in enumerate(context_groups):
            for element in group:
                if element.id in attention_weights and attention_weights[element.id] > 0.1:
                    attended_elements.append({
                        "id": element.id,
                        "content": element.content,
                        "group": group_idx,
                        "attention_weight": attention_weights[element.id],
                        "relevance_score": element.relevance_score
                    })
        
        return {
            "method": "attention_based",
            "attended_elements": attended_elements,
            "attention_distribution": self._calculate_attention_distribution(attention_weights),
            "attention_entropy": self._calculate_attention_entropy(attention_weights)
        }
    
    async def _ensemble_synthesis(
        self, 
        context_groups: List[List[ContextElement]], 
        quality_threshold: float
    ) -> Dict[str, Any]:
        """Perform ensemble-based context synthesis combining multiple methods."""
        
        # Apply multiple synthesis methods
        synthesis_results = {}
        
        for method in [SynthesisMethod.FUSION, SynthesisMethod.HIERARCHICAL, SynthesisMethod.ATTENTION_BASED]:
            try:
                result = await self.synthesis_methods[method](context_groups, quality_threshold)
                synthesis_results[method.value] = result
            except Exception as e:
                logger.warning(f"Ensemble synthesis method {method.value} failed: {e}")
        
        # Combine results using voting/weighted approach
        ensemble_result = self._combine_synthesis_results(synthesis_results)
        
        return {
            "method": "ensemble",
            "individual_results": synthesis_results,
            "ensemble_result": ensemble_result,
            "method_agreement": self._calculate_method_agreement(synthesis_results)
        }
    
    def _remove_duplicates(self, elements: List[ContextElement]) -> List[ContextElement]:
        """Remove duplicate elements based on content similarity."""
        unique_elements = []
        seen_content_hashes = set()
        
        for element in elements:
            content_hash = hashlib.md5(str(element.content).encode()).hexdigest()
            if content_hash not in seen_content_hashes:
                seen_content_hashes.add(content_hash)
                unique_elements.append(element)
        
        return unique_elements
    
    def _identify_source_group(self, element: ContextElement, context_groups: List[List[ContextElement]]) -> int:
        """Identify which group an element belongs to."""
        for group_idx, group in enumerate(context_groups):
            if element in group:
                return group_idx
        return -1
    
    def _find_element_groups(self, element: ContextElement, context_groups: List[List[ContextElement]]) -> List[int]:
        """Find all groups an element appears in."""
        groups = []
        for group_idx, group in enumerate(context_groups):
            if element in group:
                groups.append(group_idx)
        return groups
    
    def _calculate_quality_distribution(self, elements: List[ContextElement]) -> Dict[str, float]:
        """Calculate quality score distribution."""
        if not elements:
            return {}
        
        relevance_scores = [e.relevance_score for e in elements]
        confidence_scores = [e.confidence for e in elements]
        
        return {
            "mean_relevance": np.mean(relevance_scores),
            "std_relevance": np.std(relevance_scores),
            "mean_confidence": np.mean(confidence_scores),
            "std_confidence": np.std(confidence_scores)
        }
    
    def _create_cross_group_connections(self, context_groups: List[List[ContextElement]]) -> List[Dict[str, Any]]:
        """Create connections between elements from different groups."""
        connections = []
        
        for i, group1 in enumerate(context_groups):
            for j, group2 in enumerate(context_groups[i+1:], i+1):
                for elem1 in group1:
                    for elem2 in group2:
                        similarity = self._calculate_element_similarity(elem1, elem2)
                        if similarity > 0.5:
                            connections.append({
                                "source": elem1.id,
                                "target": elem2.id,
                                "strength": similarity,
                                "groups": (i, j)
                            })
        
        return connections
    
    def _calculate_element_similarity(self, elem1: ContextElement, elem2: ContextElement) -> float:
        """Calculate similarity between two elements."""
        # Simple similarity based on metadata overlap
        common_metadata = set(elem1.metadata.keys()) & set(elem2.metadata.keys())
        total_metadata = set(elem1.metadata.keys()) | set(elem2.metadata.keys())
        
        if not total_metadata:
            return 0.0
        
        metadata_similarity = len(common_metadata) / len(total_metadata)
        
        # Content similarity (simplified)
        content1 = str(elem1.content).lower()
        content2 = str(elem2.content).lower()
        
        content_words1 = set(content1.split())
        content_words2 = set(content2.split())
        
        if not content_words1 and not content_words2:
            content_similarity = 1.0
        elif not content_words1 or not content_words2:
            content_similarity = 0.0
        else:
            content_similarity = len(content_words1 & content_words2) / len(content_words1 | content_words2)
        
        return (metadata_similarity + content_similarity) / 2
    
    def _calculate_clustering_coefficient(self, edges: List[Dict[str, Any]], nodes: List[Dict[str, Any]]) -> float:
        """Calculate clustering coefficient of the mesh."""
        if len(nodes) < 3:
            return 0.0
        
        # Create adjacency list
        adjacency = defaultdict(set)
        for edge in edges:
            adjacency[edge["source"]].add(edge["target"])
            adjacency[edge["target"]].add(edge["source"])
        
        # Calculate local clustering coefficients
        clustering_coeffs = []
        for node in nodes:
            neighbors = adjacency[node["id"]]
            if len(neighbors) < 2:
                continue
            
            possible_edges = len(neighbors) * (len(neighbors) - 1) / 2
            actual_edges = 0
            
            for neighbor1 in neighbors:
                for neighbor2 in neighbors:
                    if neighbor1 != neighbor2 and neighbor2 in adjacency[neighbor1]:
                        actual_edges += 1
            
            if possible_edges > 0:
                clustering_coeffs.append(actual_edges / possible_edges)
        
        return np.mean(clustering_coeffs) if clustering_coeffs else 0.0
    
    def _analyze_cross_group_presence(self, context_groups: List[List[ContextElement]]) -> Dict[str, int]:
        """Analyze which elements appear in multiple groups."""
        element_presence = defaultdict(int)
        
        for group in context_groups:
            group_elements = set(elem.id for elem in group)
            for elem_id in group_elements:
                element_presence[elem_id] += 1
        
        return dict(element_presence)
    
    def _calculate_relationship_strength(self, node1: Dict[str, Any], node2: Dict[str, Any]) -> float:
        """Calculate relationship strength between two nodes."""
        # Simple relationship strength based on relevance scores and metadata overlap
        relevance_factor = min(node1["relevance_score"], node2["relevance_score"])
        
        # Metadata similarity
        metadata1 = node1.get("metadata", {})
        metadata2 = node2.get("metadata", {})
        
        if not metadata1 and not metadata2:
            metadata_similarity = 1.0
        elif not metadata1 or not metadata2:
            metadata_similarity = 0.0
        else:
            common_keys = set(metadata1.keys()) & set(metadata2.keys())
            total_keys = set(metadata1.keys()) | set(metadata2.keys())
            metadata_similarity = len(common_keys) / max(len(total_keys), 1)
        
        return (relevance_factor + metadata_similarity) / 2
    
    def _calculate_graph_metrics(self, nodes: List[Dict[str, Any]], edges: List[Dict[str, Any]]) -> Dict[str, float]:
        """Calculate metrics of the knowledge graph."""
        if not nodes:
            return {}
        
        num_nodes = len(nodes)
        num_edges = len(edges)
        
        # Basic metrics
        density = num_edges / max(num_nodes * (num_nodes - 1) / 2, 1)
        
        # Average degree
        degrees = defaultdict(int)
        for edge in edges:
            degrees[edge["source"]] += 1
            degrees[edge["target"]] += 1
        
        avg_degree = np.mean(list(degrees.values())) if degrees else 0
        
        return {
            "nodes": num_nodes,
            "edges": num_edges,
            "density": density,
            "average_degree": avg_degree,
            "connected_components": self._count_connected_components(nodes, edges)
        }
    
    def _count_connected_components(self, nodes: List[Dict[str, Any]], edges: List[Dict[str, Any]]) -> int:
        """Count connected components in the graph."""
        if not nodes:
            return 0
        
        # Build adjacency list
        adjacency = defaultdict(set)
        for edge in edges:
            adjacency[edge["source"]].add(edge["target"])
            adjacency[edge["target"]].add(edge["source"])
        
        # Find connected components using DFS
        visited = set()
        components = 0
        
        for node in nodes:
            if node["id"] not in visited:
                self._dfs(node["id"], adjacency, visited)
                components += 1
        
        return components
    
    def _dfs(self, node: str, adjacency: Dict[str, set], visited: set):
        """Depth-first search for connected components."""
        visited.add(node)
        for neighbor in adjacency[node]:
            if neighbor not in visited:
                self._dfs(neighbor, adjacency, visited)
    
    def _detect_communities(self, nodes: List[Dict[str, Any]], edges: List[Dict[str, Any]]) -> List[List[str]]:
        """Detect communities in the graph (simplified algorithm)."""
        # Simple community detection based on edge density
        if not nodes or not edges:
            return [[node["id"]] for node in nodes]
        
        # Group nodes by their connectivity patterns
        communities = []
        remaining_nodes = [node["id"] for node in nodes]
        
        while remaining_nodes:
            # Start a new community
            community = [remaining_nodes.pop(0)]
            
            # Add connected nodes
            to_add = True
            while to_add:
                to_add = False
                for node_id in remaining_nodes[:]:
                    # Check if node is well-connected to community
                    connections = sum(1 for edge in edges 
                                    if (edge["source"] == node_id and edge["target"] in community) or
                                       (edge["target"] == node_id and edge["source"] in community))
                    
                    if connections >= len(community) * 0.3:  # At least 30% connection rate
                        community.append(node_id)
                        remaining_nodes.remove(node_id)
                        to_add = True
                        break
            
            communities.append(community)
        
        return communities
    
    def _calculate_recency_weight(self, timestamp: datetime) -> float:
        """Calculate recency weight based on timestamp."""
        age_seconds = (datetime.utcnow() - timestamp).total_seconds()
        return max(0, 1 - age_seconds / 86400)  # Decay over 24 hours
    
    def _calculate_diversity_weight(self, element: ContextElement, context_groups: List[List[ContextElement]]) -> float:
        """Calculate diversity weight based on element uniqueness."""
        # Simple diversity calculation based on content uniqueness
        content = str(element.content).lower()
        word_count = len(content.split())
        
        # Normalize by typical word count (assuming 20 words is average)
        diversity_score = min(1.0, word_count / 20.0)
        
        return diversity_score
    
    def _calculate_attention_distribution(self, attention_weights: Dict[str, float]) -> Dict[str, float]:
        """Calculate attention weight distribution."""
        if not attention_weights:
            return {}
        
        weights = list(attention_weights.values())
        
        return {
            "mean": np.mean(weights),
            "std": np.std(weights),
            "min": np.min(weights),
            "max": np.max(weights),
            "top_10_percent": np.percentile(weights, 90)
        }
    
    def _calculate_attention_entropy(self, attention_weights: Dict[str, float]) -> float:
        """Calculate entropy of attention distribution."""
        if not attention_weights:
            return 0.0
        
        weights = list(attention_weights.values())
        total = sum(weights)
        
        if total == 0:
            return 0.0
        
        # Normalize to probabilities
        probs = [w / total for w in weights]
        
        # Calculate entropy
        entropy = -sum(p * np.log2(p) for p in probs if p > 0)
        
        return entropy
    
    def _combine_synthesis_results(self, synthesis_results: Dict[str, Any]) -> Dict[str, Any]:
        """Combine results from multiple synthesis methods."""
        combined_elements = {}
        
        # Combine elements from all methods
        for method_name, result in synthesis_results.items():
            if "elements" in result:
                for element in result["elements"]:
                    element_id = element["id"]
                    if element_id not in combined_elements:
                        combined_elements[element_id] = {
                            "id": element_id,
                            "content": element["content"],
                            "methods": [method_name],
                            "confidence_scores": [element.get("confidence", element.get("relevance_score", 0.5))]
                        }
                    else:
                        combined_elements[element_id]["methods"].append(method_name)
                        if "confidence" in element:
                            combined_elements[element_id]["confidence_scores"].append(element["confidence"])
        
        # Calculate combined confidence
        for element_id, element_data in combined_elements.items():
            scores = element_data["confidence_scores"]
            element_data["combined_confidence"] = np.mean(scores) if scores else 0.5
            element_data["method_agreement"] = len(element_data["methods"])
        
        return {
            "combined_elements": list(combined_elements.values()),
            "total_unique_elements": len(combined_elements),
            "average_method_agreement": np.mean([elem["method_agreement"] for elem in combined_elements.values()])
        }
    
    def _calculate_method_agreement(self, synthesis_results: Dict[str, Any]) -> float:
        """Calculate agreement between synthesis methods."""
        if len(synthesis_results) < 2:
            return 1.0
        
        element_sets = []
        for method_name, result in synthesis_results.items():
            if "elements" in result:
                element_ids = set(elem["id"] for elem in result["elements"])
                element_sets.append(element_ids)
        
        if not element_sets:
            return 0.0
        
        # Calculate Jaccard similarity between all pairs
        similarities = []
        for i in range(len(element_sets)):
            for j in range(i+1, len(element_sets)):
                intersection = len(element_sets[i] & element_sets[j])
                union = len(element_sets[i] | element_sets[j])
                similarity = intersection / max(union, 1)
                similarities.append(similarity)
        
        return np.mean(similarities) if similarities else 0.0
    
    def _calculate_synthesis_quality(self, result: Dict[str, Any]) -> float:
        """Calculate overall quality of synthesis result."""
        if "elements" in result:
            elements = result["elements"]
            if not elements:
                return 0.0
            
            avg_relevance = np.mean([elem.get("relevance_score", 0.5) for elem in elements])
            avg_confidence = np.mean([elem.get("confidence", 0.5) for elem in elements])
            
            return (avg_relevance + avg_confidence) / 2
        
        return 0.5  # Default quality score
    
    def _calculate_synthesis_confidence(self, result: Dict[str, Any]) -> float:
        """Calculate confidence in synthesis result."""
        # Base confidence on method and coverage
        method_confidence = {
            "fusion": 0.8,
            "mesh": 0.7,
            "hierarchical": 0.9,
            "graph_based": 0.6,
            "attention_based": 0.8,
            "ensemble": 0.95
        }
        
        base_confidence = method_confidence.get(result.get("method", ""), 0.7)
        
        # Adjust based on result coverage
        if "elements" in result:
            coverage_bonus = min(0.2, len(result["elements"]) / 100)
            return min(1.0, base_confidence + coverage_bonus)
        
        return base_confidence
    
    def _calculate_temporal_scope(self, context_groups: List[List[ContextElement]]) -> Tuple[datetime, datetime]:
        """Calculate temporal scope of synthesized context."""
        all_timestamps = []
        for group in context_groups:
            for element in group:
                all_timestamps.append(element.timestamp)
        
        if all_timestamps:
            return (min(all_timestamps), max(all_timestamps))
        else:
            current_time = datetime.utcnow()
            return (current_time, current_time)
    
    def _identify_semantic_clusters(self, result: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Identify semantic clusters in the synthesis result."""
        clusters = []
        
        if "elements" in result:
            elements = result["elements"]
            
            # Simple clustering based on content similarity
            if len(elements) > 1:
                # Group elements with similar themes
                themes = defaultdict(list)
                for element in elements:
                    # Extract theme from content
                    content = str(element.get("content", "")).lower()
                    if "urgent" in content or "asap" in content:
                        themes["urgency"].append(element)
                    elif "team" in content or "collaborate" in content:
                        themes["collaboration"].append(element)
                    elif "data" in content or "analysis" in content:
                        themes["data_analysis"].append(element)
                    else:
                        themes["general"].append(element)
                
                for theme, theme_elements in themes.items():
                    if len(theme_elements) > 1:
                        clusters.append({
                            "theme": theme,
                            "elements": [elem["id"] for elem in theme_elements],
                            "size": len(theme_elements),
                            "coherence": len(theme_elements) / len(elements)
                        })
        
        return clusters


# Integration with main context engineering system
class ContextCompressionAndSynthesis:
    """Integrated context compression and synthesis system."""
    
    def __init__(self):
        self.compression_engine = ContextCompressionEngine()
        self.synthesis_engine = ContextSynthesisEngine()
        
    async def process_context_pipeline(
        self,
        input_context: List[ContextElement],
        compression_strategy: CompressionStrategy = CompressionStrategy.ADAPTIVE,
        synthesis_method: SynthesisMethod = SynthesisMethod.FUSION,
        quality_threshold: float = 0.6
    ) -> Dict[str, Any]:
        """Process context through compression and synthesis pipeline."""
        
        # Step 1: Compress context
        compression_result = await self.compression_engine.compress_context(
            input_context, compression_strategy, quality_threshold
        )
        
        # Step 2: Prepare compressed elements for synthesis
        compressed_elements = self._extract_compressed_elements(compression_result)
        
        # Step 3: Synthesize contexts if multiple groups
        # For demo, treat all compressed elements as one group
        synthesis_result = await self.synthesis_engine.synthesize_contexts(
            [compressed_elements], synthesis_method, quality_threshold
        )
        
        return {
            "compression": compression_result,
            "synthesis": {
                "id": synthesis_result.id,
                "method": synthesis_result.synthesis_method.value,
                "quality_score": synthesis_result.quality_score,
                "confidence": synthesis_result.confidence,
                "temporal_scope": {
                    "start": synthesis_result.temporal_scope[0].isoformat(),
                    "end": synthesis_result.temporal_scope[1].isoformat()
                },
                "semantic_clusters": synthesis_result.semantic_clusters
            },
            "pipeline_metrics": self._calculate_pipeline_metrics(compression_result, synthesis_result)
        }
    
    def _extract_compressed_elements(self, compression_result: Dict[str, Any]) -> List[ContextElement]:
        """Extract context elements from compression result."""
        elements = []
        
        if compression_result.get("strategy") == "hierarchical":
            for level_name, level_data in compression_result.get("levels", {}).items():
                for compressed_elem in level_data:
                    if isinstance(compressed_elem, dict):
                        # Convert back to ContextElement-like object
                        elements.append(type('ContextElement', (), {
                            'id': compressed_elem.get("id", f"compressed_{level_name}_{len(elements)}"),
                            'content': compressed_elem.get("content", compressed_elem.get("summary", "")),
                            'relevance_score': compressed_elem.get("relevance_score", 0.5),
                            'confidence': compressed_elem.get("confidence", 0.5),
                            'timestamp': datetime.utcnow(),
                            'metadata': compressed_elem.get("metadata", {})
                        })())
        
        return elements
    
    def _calculate_pipeline_metrics(
        self, 
        compression_result: Dict[str, Any], 
        synthesis_result: SynthesizedContext
    ) -> Dict[str, Any]:
        """Calculate metrics for the compression-synthesis pipeline."""
        
        return {
            "compression_ratio": compression_result.get("compression_ratio", 0),
            "synthesis_quality": synthesis_result.quality_score,
            "synthesis_confidence": synthesis_result.confidence,
            "semantic_clusters": len(synthesis_result.semantic_clusters),
            "temporal_span_hours": (synthesis_result.temporal_scope[1] - synthesis_result.temporal_scope[0]).total_seconds() / 3600,
            "processing_efficiency": synthesis_result.quality_score / max(compression_result.get("compression_ratio", 1), 0.1)
        }


if __name__ == "__main__":
    print("Context Compression and Synthesis System Initialized")
    print("=" * 60)
    system = ContextCompressionAndSynthesis()
    print("Ready for advanced context compression and multi-source synthesis!")