agents/context_scaler.py

"""

Context Scaling System

Handles length scaling (millions of tokens) and multi-modal/structural scaling

Implements advanced attention methods and memory techniques from the article

"""

import logging
from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass
import numpy as np
from datetime import datetime
import heapq

logger = logging.getLogger(__name__)


@dataclass
class ScaledContext:
    """Context that can scale to millions of tokens"""
    segments: List[str]  # Segmented content
    attention_map: np.ndarray  # Attention weights for segments
    token_count: int
    compression_level: int  # 0=none, 1=light, 2=medium, 3=heavy
    modalities: Dict[str, Any]  # Different context modalities
    

class AttentionOptimizer:
    """

    Advanced attention methods for handling extremely long contexts

    Implements sliding window, sparse attention, and hierarchical attention

    """
    
    def __init__(self, window_size: int = 512, stride: int = 256):
        self.window_size = window_size
        self.stride = stride
        
    def sliding_window_attention(

        self,

        context: str,

        query: str,

        max_windows: int = 10

    ) -> List[Tuple[str, float]]:
        """

        Process context using sliding window attention

        Returns relevant windows with attention scores

        """
        tokens = context.split()
        windows = []
        
        # Create sliding windows
        for i in range(0, len(tokens) - self.window_size + 1, self.stride):
            window = ' '.join(tokens[i:i + self.window_size])
            score = self._calculate_attention_score(window, query)
            windows.append((window, score))
        
        # Return top windows
        windows.sort(key=lambda x: x[1], reverse=True)
        return windows[:max_windows]
    
    def hierarchical_attention(

        self,

        context: str,

        query: str,

        levels: int = 3

    ) -> Dict[int, List[str]]:
        """

        Multi-level hierarchical attention

        Higher levels = more compressed/abstract

        """
        hierarchy = {}
        current_text = context
        
        for level in range(levels):
            if level == 0:
                # Finest level - full detail
                hierarchy[level] = self._segment_text(current_text, 500)
            elif level == 1:
                # Middle level - paragraphs/sections
                hierarchy[level] = self._extract_key_sentences(current_text)
            else:
                # Highest level - summary
                hierarchy[level] = [self._generate_summary(current_text)]
            
            # Compress for next level
            current_text = ' '.join(hierarchy[level])
        
        return hierarchy
    
    def sparse_attention(

        self,

        context: str,

        query: str,

        sparsity: float = 0.1

    ) -> List[str]:
        """

        Sparse attention - only attend to most relevant tokens

        Reduces computation from O(n²) to O(n*k)

        """
        tokens = context.split()
        query_tokens = set(query.lower().split())
        
        # Calculate relevance for each token
        token_scores = []
        for i, token in enumerate(tokens):
            score = 1.0 if token.lower() in query_tokens else np.random.random() * 0.5
            token_scores.append((i, token, score))
        
        # Keep only top k% tokens
        k = int(len(tokens) * sparsity)
        top_tokens = heapq.nlargest(k, token_scores, key=lambda x: x[2])
        
        # Sort by original position to maintain order
        top_tokens.sort(key=lambda x: x[0])
        
        # Reconstruct sparse context
        sparse_context = []
        last_idx = -1
        for idx, token, score in top_tokens:
            if idx > last_idx + 1:
                sparse_context.append("...")
            sparse_context.append(token)
            last_idx = idx
        
        return sparse_context
    
    def _calculate_attention_score(self, window: str, query: str) -> float:
        """Calculate attention score between window and query"""
        window_words = set(window.lower().split())
        query_words = set(query.lower().split())
        
        if not query_words:
            return 0.0
        
        overlap = len(window_words & query_words)
        return overlap / len(query_words)
    
    def _segment_text(self, text: str, segment_size: int) -> List[str]:
        """Segment text into chunks"""
        words = text.split()
        segments = []
        for i in range(0, len(words), segment_size):
            segments.append(' '.join(words[i:i + segment_size]))
        return segments
    
    def _extract_key_sentences(self, text: str) -> List[str]:
        """Extract key sentences (simplified)"""
        sentences = text.split('.')
        # Keep sentences with more than 10 words (likely more informative)
        key_sentences = [s.strip() + '.' for s in sentences if len(s.split()) > 10]
        return key_sentences[:10]  # Top 10 sentences
    
    def _generate_summary(self, text: str) -> str:
        """Generate summary (simplified - would use LLM in production)"""
        sentences = text.split('.')[:3]  # First 3 sentences as summary
        return '. '.join(sentences) + '.'


class LengthScaler:
    """

    Handle context scaling from thousands to millions of tokens

    Maintains coherence across long documents

    """
    
    def __init__(self, max_tokens: int = 1000000):
        self.max_tokens = max_tokens
        self.attention_optimizer = AttentionOptimizer()
        
    def scale_context(

        self,

        context: str,

        query: str,

        target_tokens: int = 2000

    ) -> ScaledContext:
        """Scale context to target token count while maintaining relevance"""
        
        tokens = context.split()
        current_tokens = len(tokens)
        
        # Determine compression level needed
        compression_ratio = current_tokens / target_tokens
        
        if compression_ratio <= 1:
            # No compression needed
            return ScaledContext(
                segments=[context],
                attention_map=np.array([1.0]),
                token_count=current_tokens,
                compression_level=0,
                modalities={}
            )
        
        # Apply appropriate scaling strategy
        if compression_ratio < 5:
            # Light compression - sliding window
            segments = self._light_compression(context, query, target_tokens)
            compression_level = 1
        elif compression_ratio < 20:
            # Medium compression - hierarchical
            segments = self._medium_compression(context, query, target_tokens)
            compression_level = 2
        else:
            # Heavy compression - sparse attention
            segments = self._heavy_compression(context, query, target_tokens)
            compression_level = 3
        
        # Calculate attention map
        attention_map = self._calculate_attention_map(segments, query)
        
        return ScaledContext(
            segments=segments,
            attention_map=attention_map,
            token_count=sum(len(s.split()) for s in segments),
            compression_level=compression_level,
            modalities={}
        )
    
    def _light_compression(

        self,

        context: str,

        query: str,

        target_tokens: int

    ) -> List[str]:
        """Light compression using sliding windows"""
        windows = self.attention_optimizer.sliding_window_attention(
            context, query, max_windows=target_tokens // 100
        )
        return [w for w, _ in windows]
    
    def _medium_compression(

        self,

        context: str,

        query: str,

        target_tokens: int

    ) -> List[str]:
        """Medium compression using hierarchical attention"""
        hierarchy = self.attention_optimizer.hierarchical_attention(context, query)
        
        segments = []
        remaining_tokens = target_tokens
        
        # Add from each level based on available tokens
        for level in sorted(hierarchy.keys()):
            level_segments = hierarchy[level]
            for segment in level_segments:
                segment_tokens = len(segment.split())
                if segment_tokens <= remaining_tokens:
                    segments.append(segment)
                    remaining_tokens -= segment_tokens
                if remaining_tokens <= 0:
                    break
        
        return segments
    
    def _heavy_compression(

        self,

        context: str,

        query: str,

        target_tokens: int

    ) -> List[str]:
        """Heavy compression using sparse attention"""
        sparsity = target_tokens / len(context.split())
        sparse_tokens = self.attention_optimizer.sparse_attention(
            context, query, sparsity=min(sparsity, 0.3)
        )
        
        # Group sparse tokens into segments
        segments = []
        current_segment = []
        for token in sparse_tokens:
            if token == "...":
                if current_segment:
                    segments.append(' '.join(current_segment))
                    current_segment = []
                segments.append("...")
            else:
                current_segment.append(token)
        
        if current_segment:
            segments.append(' '.join(current_segment))
        
        return segments
    
    def _calculate_attention_map(

        self,

        segments: List[str],

        query: str

    ) -> np.ndarray:
        """Calculate attention weights for each segment"""
        query_words = set(query.lower().split())
        attention_scores = []
        
        for segment in segments:
            if segment == "...":
                attention_scores.append(0.0)
            else:
                segment_words = set(segment.lower().split())
                overlap = len(query_words & segment_words)
                score = overlap / max(len(query_words), 1)
                attention_scores.append(score)
        
        # Normalize
        scores = np.array(attention_scores)
        if scores.sum() > 0:
            scores = scores / scores.sum()
        
        return scores


class MultiModalScaler:
    """

    Handle multi-modal and structural context scaling

    Temporal, spatial, participant states, intentional, cultural

    """
    
    def __init__(self):
        self.modality_handlers = {
            'temporal': self._scale_temporal,
            'spatial': self._scale_spatial,
            'participant': self._scale_participant,
            'intentional': self._scale_intentional,
            'cultural': self._scale_cultural
        }
    
    def scale_multimodal(

        self,

        modalities: Dict[str, Any],

        importance_weights: Optional[Dict[str, float]] = None

    ) -> Dict[str, Any]:
        """Scale multiple modalities based on importance"""
        
        if importance_weights is None:
            importance_weights = {
                'temporal': 0.3,
                'spatial': 0.1,
                'participant': 0.3,
                'intentional': 0.2,
                'cultural': 0.1
            }
        
        scaled = {}
        for modality, data in modalities.items():
            if modality in self.modality_handlers:
                weight = importance_weights.get(modality, 0.1)
                scaled[modality] = self.modality_handlers[modality](data, weight)
        
        return scaled
    
    def _scale_temporal(self, data: List[Dict], weight: float) -> List[Dict]:
        """Scale temporal context - keep most recent and important events"""
        # Sort by timestamp
        sorted_data = sorted(data, key=lambda x: x.get('timestamp', datetime.min), reverse=True)
        
        # Keep based on weight (more weight = more events kept)
        keep_count = max(1, int(len(sorted_data) * weight))
        return sorted_data[:keep_count]
    
    def _scale_spatial(self, data: Dict, weight: float) -> Dict:
        """Scale spatial context - simplify based on importance"""
        if weight < 0.3:
            # Low importance - just keep basic location
            return {'location': data.get('primary_location', 'unknown')}
        else:
            # Higher importance - keep more detail
            return data
    
    def _scale_participant(self, data: Dict, weight: float) -> Dict:
        """Scale participant states - keep most active participants"""
        if not data:
            return {}
        
        # Sort by activity level (approximated by state changes)
        participants = []
        for pid, pdata in data.items():
            activity = len(pdata.get('history', []))
            participants.append((pid, pdata, activity))
        
        participants.sort(key=lambda x: x[2], reverse=True)
        
        # Keep based on weight
        keep_count = max(1, int(len(participants) * weight))
        
        return {pid: pdata for pid, pdata, _ in participants[:keep_count]}
    
    def _scale_intentional(self, data: Dict, weight: float) -> Dict:
        """Scale intentional context - keep high priority goals"""
        if not data:
            return {}
        
        # Sort by priority
        goals = [(k, v) for k, v in data.items()]
        goals.sort(key=lambda x: x[1].get('priority', 0), reverse=True)
        
        # Keep based on weight
        keep_count = max(1, int(len(goals) * weight))
        
        return {k: v for k, v in goals[:keep_count]}
    
    def _scale_cultural(self, data: Dict, weight: float) -> Dict:
        """Scale cultural context - keep if important"""
        if weight < 0.2:
            return {}  # Skip if low importance
        return data


class ContextScalingOrchestrator:
    """

    Main orchestrator for context scaling

    Combines length and multi-modal scaling

    """
    
    def __init__(self, max_context_tokens: int = 100000):
        self.length_scaler = LengthScaler(max_context_tokens)
        self.multimodal_scaler = MultiModalScaler()
        
    def scale_complete_context(

        self,

        text_context: str,

        multimodal_context: Dict[str, Any],

        query: str,

        target_tokens: int = 2000,

        modality_weights: Optional[Dict[str, float]] = None

    ) -> Dict[str, Any]:
        """

        Scale both text and multi-modal context

        Returns optimally scaled context

        """
        
        # Scale text context
        scaled_text = self.length_scaler.scale_context(
            text_context, query, target_tokens
        )
        
        # Scale multi-modal context
        scaled_multimodal = self.multimodal_scaler.scale_multimodal(
            multimodal_context, modality_weights
        )
        
        # Combine
        result = {
            'text': {
                'segments': scaled_text.segments,
                'attention_map': scaled_text.attention_map.tolist(),
                'token_count': scaled_text.token_count,
                'compression_level': scaled_text.compression_level
            },
            'multimodal': scaled_multimodal,
            'metadata': {
                'original_tokens': len(text_context.split()),
                'scaled_tokens': scaled_text.token_count,
                'compression_ratio': len(text_context.split()) / max(scaled_text.token_count, 1),
                'modalities_preserved': list(scaled_multimodal.keys())
            }
        }
        
        return result


# Demo usage
def demo_context_scaling():
    """Demonstrate context scaling capabilities"""
    
    # Create a very long context
    long_context = " ".join([
        f"Sentence {i} about various topics including AI, engineering, and software development."
        for i in range(10000)
    ])  # ~100k tokens
    
    # Multi-modal context
    multimodal = {
        'temporal': [
            {'event': f'Event {i}', 'timestamp': datetime.now()}
            for i in range(50)
        ],
        'participant': {
            f'person_{i}': {'state': 'active', 'history': []}
            for i in range(20)
        },
        'intentional': {
            f'goal_{i}': {'priority': np.random.random()}
            for i in range(10)
        }
    }
    
    # Scale the context
    orchestrator = ContextScalingOrchestrator()
    scaled = orchestrator.scale_complete_context(
        text_context=long_context,
        multimodal_context=multimodal,
        query="AI engineering position requirements",
        target_tokens=2000
    )
    
    print(f"Scaling Results:")
    print(f"Original tokens: {scaled['metadata']['original_tokens']}")
    print(f"Scaled tokens: {scaled['metadata']['scaled_tokens']}")
    print(f"Compression ratio: {scaled['metadata']['compression_ratio']:.2f}x")
    print(f"Compression level: {scaled['text']['compression_level']}")
    print(f"Modalities preserved: {scaled['metadata']['modalities_preserved']}")
    print(f"Text segments: {len(scaled['text']['segments'])}")
    print(f"Temporal events kept: {len(scaled['multimodal'].get('temporal', []))}")


if __name__ == "__main__":
    demo_context_scaling()