""" Multimodal Context Processing System ================================== Advanced multimodal context processing system that handles and integrates text, visual, auditory, and sensor data within unified contextual representations. """ import asyncio import json import logging import base64 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 from ai_agent_framework.core.context_engineering_agent import ( ContextElement, ContextModality, ContextDimension ) logger = logging.getLogger(__name__) class DataModality(Enum): """Supported data modalities.""" TEXT = "text" IMAGE = "image" AUDIO = "audio" VIDEO = "video" SENSOR = "sensor" TABLE = "table" CODE = "code" STRUCTURED = "structured" class FusionStrategy(Enum): """Strategies for multimodal fusion.""" EARLY_FUSION = "early_fusion" LATE_FUSION = "late_fusion" HYBRID_FUSION = "hybrid_fusion" ATTENTION_BASED = "attention_based" CROSS_ATTENTION = "cross_attention" @dataclass class MultimodalInput: """Represents multimodal input data.""" id: str modality: DataModality content: Any metadata: Dict[str, Any] timestamp: datetime quality_score: float confidence: float processing_status: str = "pending" def __post_init__(self): if not self.id: self.id = f"mm_input_{int(time.time())}_{hash(str(self.content))}" if not self.timestamp: self.timestamp = datetime.utcnow() if not self.metadata: self.metadata = {} @dataclass class UnifiedContext: """Unified contextual representation from multimodal inputs.""" id: str source_inputs: List[str] fused_representation: Dict[str, Any] modality_contributions: Dict[str, float] temporal_alignment: Dict[str, Any] semantic_consistency: float fusion_strategy: FusionStrategy confidence_aggregate: float def __post_init__(self): if not self.id: self.id = f"unified_context_{int(time.time())}" class MultimodalProcessor: """Core multimodal processing engine.""" def __init__(self): self.modal_processors = { DataModality.TEXT: TextProcessor(), DataModality.IMAGE: ImageProcessor(), DataModality.AUDIO: AudioProcessor(), DataModality.VIDEO: VideoProcessor(), DataModality.SENSOR: SensorProcessor(), DataModality.TABLE: TableProcessor(), DataModality.CODE: CodeProcessor(), DataModality.STRUCTURED: StructuredProcessor() } self.fusion_strategies = { FusionStrategy.EARLY_FUSION: self._early_fusion, FusionStrategy.LATE_FUSION: self._late_fusion, FusionStrategy.HYBRID_FUSION: self._hybrid_fusion, FusionStrategy.ATTENTION_BASED: self._attention_based_fusion, FusionStrategy.CROSS_ATTENTION: self._cross_attention_fusion } self.alignment_algorithms = { "temporal": self._temporal_alignment, "semantic": self._semantic_alignment, "structural": self._structural_alignment } async def process_multimodal_input( self, inputs: List[MultimodalInput], fusion_strategy: FusionStrategy = FusionStrategy.HYBRID_FUSION ) -> UnifiedContext: """Process multimodal inputs and create unified context.""" try: # Step 1: Process individual modalities processed_modalities = await self._process_individual_modalities(inputs) # Step 2: Align modalities aligned_modalities = await self._align_modalities(processed_modalities) # Step 3: Fuse modalities using selected strategy fusion_func = self.fusion_strategies.get(fusion_strategy) if not fusion_func: fusion_strategy = FusionStrategy.HYBRID_FUSION fusion_func = self.fusion_strategies[fusion_strategy] unified_context = await fusion_func(aligned_modalities) # Step 4: Validate and enhance unified context validated_context = await self._validate_unified_context(unified_context) return validated_context except Exception as e: logger.error(f"Multimodal processing failed: {e}") return UnifiedContext( id=f"error_context_{int(time.time())}", source_inputs=[inp.id for inp in inputs], fused_representation={"error": str(e)}, modality_contributions={}, temporal_alignment={}, semantic_consistency=0.0, fusion_strategy=fusion_strategy, confidence_aggregate=0.0 ) async def _process_individual_modalities( self, inputs: List[MultimodalInput] ) -> Dict[DataModality, Dict[str, Any]]: """Process each modality individually.""" processed_modalities = {} # Group inputs by modality modality_groups = defaultdict(list) for input_data in inputs: modality_groups[input_data.modality].append(input_data) # Process each modality for modality, modality_inputs in modality_groups.items(): processor = self.modal_processors.get(modality) if processor: try: processed_result = await processor.process(modality_inputs) processed_modalities[modality] = processed_result except Exception as e: logger.error(f"Failed to process {modality.value} modality: {e}") processed_modalities[modality] = { "status": "error", "error": str(e), "inputs": [inp.id for inp in modality_inputs] } return processed_modalities async def _align_modalities( self, processed_modalities: Dict[DataModality, Dict[str, Any]] ) -> Dict[DataModality, Dict[str, Any]]: """Align modalities for fusion.""" aligned_modalities = {} # Temporal alignment temporal_alignment = await self.alignment_algorithms["temporal"](processed_modalities) # Semantic alignment semantic_alignment = await self.alignment_algorithms["semantic"](processed_modalities) # Structural alignment structural_alignment = await self.alignment_algorithms["structural"](processed_modalities) # Apply alignments to each modality for modality, processed_data in processed_modalities.items(): if processed_data.get("status") == "success": aligned_data = processed_data.copy() aligned_data["alignment"] = { "temporal": temporal_alignment.get(modality, {}), "semantic": semantic_alignment.get(modality, {}), "structural": structural_alignment.get(modality, {}) } aligned_modalities[modality] = aligned_data return aligned_modalities async def _early_fusion( self, aligned_modalities: Dict[DataModality, Dict[str, Any]] ) -> UnifiedContext: """Perform early fusion of modalities.""" # Combine features at input level fused_features = {} modality_contributions = {} confidence_scores = [] for modality, data in aligned_modalities.items(): if data.get("status") == "success": # Extract features from each modality features = data.get("features", {}) fused_features[modality.value] = features modality_contributions[modality.value] = data.get("confidence", 0.5) confidence_scores.append(data.get("confidence", 0.5)) # Create unified representation unified_representation = { "fusion_type": "early_fusion", "modality_features": fused_features, "combined_embedding": await self._combine_embeddings(fused_features), "cross_modal_patterns": await self._detect_cross_modal_patterns(fused_features) } return UnifiedContext( id=f"early_fusion_{int(time.time())}", source_inputs=list(fused_features.keys()), fused_representation=unified_representation, modality_contributions=modality_contributions, temporal_alignment={}, semantic_consistency=await self._calculate_semantic_consistency(fused_features), fusion_strategy=FusionStrategy.EARLY_FUSION, confidence_aggregate=np.mean(confidence_scores) if confidence_scores else 0.0 ) async def _late_fusion( self, aligned_modalities: Dict[DataModality, Dict[str, Any]] ) -> UnifiedContext: """Perform late fusion of modalities.""" # Process each modality to high-level representations high_level_representations = {} modality_contributions = {} confidence_scores = [] for modality, data in aligned_modalities.items(): if data.get("status") == "success": # Extract semantic representations representation = data.get("semantic_representation", {}) high_level_representations[modality.value] = representation modality_contributions[modality.value] = data.get("confidence", 0.5) confidence_scores.append(data.get("confidence", 0.5)) # Fuse at semantic level unified_representation = { "fusion_type": "late_fusion", "semantic_representations": high_level_representations, "fused_semantics": await self._fuse_semantics(high_level_representations), "consensus_features": await self._extract_consensus_features(high_level_representations) } return UnifiedContext( id=f"late_fusion_{int(time.time())}", source_inputs=list(high_level_representations.keys()), fused_representation=unified_representation, modality_contributions=modality_contributions, temporal_alignment={}, semantic_consistency=await self._calculate_semantic_consistency(high_level_representations), fusion_strategy=FusionStrategy.LATE_FUSION, confidence_aggregate=np.mean(confidence_scores) if confidence_scores else 0.0 ) async def _hybrid_fusion( self, aligned_modalities: Dict[DataModality, Dict[str, Any]] ) -> UnifiedContext: """Perform hybrid fusion combining early and late fusion.""" # Early fusion for complementary features early_fused = await self._early_fusion(aligned_modalities) # Late fusion for semantic alignment late_fused = await self._late_fusion(aligned_modalities) # Combine both approaches hybrid_representation = { "fusion_type": "hybrid_fusion", "early_fusion": early_fused.fused_representation, "late_fusion": late_fused.fused_representation, "combined_features": await self._combine_fusion_results(early_fused, late_fused), "adaptive_weights": await self._calculate_adaptive_weights(aligned_modalities) } # Merge contributions and confidence combined_contributions = {} for modality in aligned_modalities.keys(): early_contrib = early_fused.modality_contributions.get(modality.value, 0) late_contrib = late_fused.modality_contributions.get(modality.value, 0) combined_contributions[modality.value] = (early_contrib + late_contrib) / 2 return UnifiedContext( id=f"hybrid_fusion_{int(time.time())}", source_inputs=list(combined_contributions.keys()), fused_representation=hybrid_representation, modality_contributions=combined_contributions, temporal_alignment={}, semantic_consistency=(early_fused.semantic_consistency + late_fused.semantic_consistency) / 2, fusion_strategy=FusionStrategy.HYBRID_FUSION, confidence_aggregate=(early_fused.confidence_aggregate + late_fused.confidence_aggregate) / 2 ) async def _attention_based_fusion( self, aligned_modalities: Dict[DataModality, Dict[str, Any]] ) -> UnifiedContext: """Perform attention-based fusion.""" # Calculate attention weights for each modality attention_weights = await self._calculate_attention_weights(aligned_modalities) # Apply attention-based fusion fused_features = {} modality_contributions = {} confidence_scores = [] for modality, data in aligned_modalities.items(): if data.get("status") == "success": modality_weight = attention_weights.get(modality, 0.5) features = data.get("features", {}) # Apply attention weighting weighted_features = {} for feature_name, feature_value in features.items(): if isinstance(feature_value, (int, float)): weighted_features[feature_name] = feature_value * modality_weight else: weighted_features[feature_name] = feature_value fused_features[modality.value] = weighted_features modality_contributions[modality.value] = modality_weight confidence_scores.append(data.get("confidence", 0.5) * modality_weight) unified_representation = { "fusion_type": "attention_based", "attention_weights": attention_weights, "weighted_features": fused_features, "attention_mechanism": "dynamic_modality_weighting" } return UnifiedContext( id=f"attention_fusion_{int(time.time())}", source_inputs=list(fused_features.keys()), fused_representation=unified_representation, modality_contributions=modality_contributions, temporal_alignment={}, semantic_consistency=await self._calculate_semantic_consistency(fused_features), fusion_strategy=FusionStrategy.ATTENTION_BASED, confidence_aggregate=np.mean(confidence_scores) if confidence_scores else 0.0 ) async def _cross_attention_fusion( self, aligned_modalities: Dict[DataModality, Dict[str, Any]] ) -> UnifiedContext: """Perform cross-attention fusion.""" # Generate cross-attention matrices between modalities cross_attention_matrices = await self._calculate_cross_attention(aligned_modalities) # Apply cross-attention fusion fused_representations = {} modality_contributions = {} confidence_scores = [] for modality, data in aligned_modalities.items(): if data.get("status") == "success": # Get cross-attention with other modalities cross_attention = cross_attention_matrices.get(modality, {}) features = data.get("features", {}) # Apply cross-attention attended_features = {} for feature_name, feature_value in features.items(): if isinstance(feature_value, (int, float)): attention_sum = sum(cross_attention.get(other_mod, 0) for other_mod in aligned_modalities.keys() if other_mod != modality) attended_features[feature_name] = feature_value * (1 + attention_sum) else: attended_features[feature_name] = feature_value fused_representations[modality.value] = attended_features modality_contributions[modality.value] = data.get("confidence", 0.5) confidence_scores.append(data.get("confidence", 0.5)) unified_representation = { "fusion_type": "cross_attention", "cross_attention_matrices": cross_attention_matrices, "attended_features": fused_representations, "inter_modal_relationships": await self._analyze_inter_modal_relationships(aligned_modalities) } return UnifiedContext( id=f"cross_attention_{int(time.time())}", source_inputs=list(fused_representations.keys()), fused_representation=unified_representation, modality_contributions=modality_contributions, temporal_alignment={}, semantic_consistency=await self._calculate_semantic_consistency(fused_representations), fusion_strategy=FusionStrategy.CROSS_ATTENTION, confidence_aggregate=np.mean(confidence_scores) if confidence_scores else 0.0 ) async def _validate_unified_context(self, context: UnifiedContext) -> UnifiedContext: """Validate and enhance unified context.""" # Check for consistency issues issues = [] if context.semantic_consistency < 0.3: issues.append("Low semantic consistency detected") if context.confidence_aggregate < 0.4: issues.append("Low aggregate confidence") if len(context.source_inputs) < 2: issues.append("Insufficient modalities for robust fusion") # Enhance context if issues are found if issues: context.fused_representation["validation_issues"] = issues context.fused_representation["enhancement_applied"] = True # Apply enhancement strategies if context.semantic_consistency < 0.5: context.semantic_consistency = min(0.8, context.semantic_consistency * 1.2) if context.confidence_aggregate < 0.5: context.confidence_aggregate = min(0.8, context.confidence_aggregate * 1.1) return context # Helper methods for fusion strategies async def _combine_embeddings(self, features: Dict[str, Any]) -> Dict[str, Any]: """Combine embeddings from different modalities.""" combined = {} for modality, modality_features in features.items(): for feature_name, feature_value in modality_features.items(): combined_key = f"{modality}_{feature_name}" combined[combined_key] = feature_value return combined async def _detect_cross_modal_patterns(self, features: Dict[str, Any]) -> List[Dict[str, Any]]: """Detect patterns across modalities.""" patterns = [] modalities = list(features.keys()) # Simple pattern detection for i, mod1 in enumerate(modalities): for mod2 in modalities[i+1:]: # Check for correlated features mod1_features = features[mod1] mod2_features = features[mod2] common_features = set(mod1_features.keys()) & set(mod2_features.keys()) if common_features: patterns.append({ "modalities": [mod1, mod2], "common_features": list(common_features), "correlation_strength": 0.7 # Simplified }) return patterns async def _fuse_semantics(self, representations: Dict[str, Any]) -> Dict[str, Any]: """Fuse semantic representations.""" # Simple semantic fusion fused_semantics = {} # Extract common semantic elements all_semantics = [] for modality, representation in representations.items(): if isinstance(representation, dict): all_semantics.extend(representation.keys()) common_semantics = list(set(all_semantics)) for semantic in common_semantics: values = [] for modality, representation in representations.items(): if semantic in representation: values.append(representation[semantic]) if values: if all(isinstance(v, (int, float)) for v in values): fused_semantics[semantic] = np.mean(values) else: fused_semantics[semantic] = values[0] # Take first non-numeric value return fused_semantics async def _extract_consensus_features(self, representations: Dict[str, Any]) -> Dict[str, Any]: """Extract features with high consensus across modalities.""" consensus_features = {} # Find features present in multiple modalities feature_counts = defaultdict(int) for modality, representation in representations.items(): if isinstance(representation, dict): for feature in representation.keys(): feature_counts[feature] += 1 # Select features with high consensus threshold = len(representations) * 0.5 consensus_features = { feature: self._get_consensus_value(feature, representations) for feature, count in feature_counts.items() if count >= threshold } return consensus_features def _get_consensus_value(self, feature: str, representations: Dict[str, Any]) -> Any: """Get consensus value for a feature across modalities.""" values = [] for modality, representation in representations.items(): if isinstance(representation, dict) and feature in representation: values.append(representation[feature]) if not values: return None if all(isinstance(v, (int, float)) for v in values): return np.mean(values) else: # For non-numeric values, return most common from collections import Counter value_counts = Counter(values) return value_counts.most_common(1)[0][0] async def _combine_fusion_results(self, early_fused: UnifiedContext, late_fused: UnifiedContext) -> Dict[str, Any]: """Combine early and late fusion results.""" return { "early_features": early_fused.fused_representation.get("combined_embedding", {}), "late_semantics": late_fused.fused_representation.get("fused_semantics", {}), "combined_score": (early_fused.confidence_aggregate + late_fused.confidence_aggregate) / 2 } async def _calculate_adaptive_weights(self, modalities: Dict[DataModality, Dict[str, Any]]) -> Dict[str, float]: """Calculate adaptive weights for modalities.""" weights = {} for modality, data in modalities.items(): if data.get("status") == "success": # Base weight on confidence and data quality confidence = data.get("confidence", 0.5) quality = data.get("quality_score", 0.5) weights[modality] = (confidence + quality) / 2 else: weights[modality] = 0.1 # Low weight for failed processing # Normalize weights total_weight = sum(weights.values()) if total_weight > 0: weights = {k: v / total_weight for k, v in weights.items()} return weights async def _calculate_attention_weights(self, modalities: Dict[DataModality, Dict[str, Any]]) -> Dict[DataModality, float]: """Calculate attention weights for modalities.""" weights = {} for modality, data in modalities.items(): if data.get("status") == "success": # Attention based on relevance and information content confidence = data.get("confidence", 0.5) info_content = data.get("information_content", 0.5) weights[modality] = confidence * info_content else: weights[modality] = 0.1 # Apply softmax-like normalization total_weight = sum(weights.values()) if total_weight > 0: weights = {k: v / total_weight for k, v in weights.items()} return weights async def _calculate_cross_attention(self, modalities: Dict[DataModality, Dict[str, Any]]) -> Dict[DataModality, Dict[DataModality, float]]: """Calculate cross-attention between modalities.""" cross_attention = {} modalities_list = list(modalities.keys()) for i, mod1 in enumerate(modalities_list): cross_attention[mod1] = {} for mod2 in modalities_list: if mod1 != mod2: # Calculate attention based on feature similarity similarity = await self._calculate_modality_similarity(modalities[mod1], modalities[mod2]) cross_attention[mod1][mod2] = similarity else: cross_attention[mod1][mod2] = 0.0 return cross_attention async def _calculate_modality_similarity(self, mod1_data: Dict[str, Any], mod2_data: Dict[str, Any]]) -> float: """Calculate similarity between two modalities.""" if mod1_data.get("status") != "success" or mod2_data.get("status") != "success": return 0.0 # Simple similarity based on confidence correlation conf1 = mod1_data.get("confidence", 0.5) conf2 = mod2_data.get("confidence", 0.5) # Similar confidence levels indicate related content similarity = 1 - abs(conf1 - conf2) return max(0.0, similarity) async def _analyze_inter_modal_relationships(self, modalities: Dict[DataModality, Dict[str, Any]]) -> List[Dict[str, Any]]: """Analyze relationships between modalities.""" relationships = [] modalities_list = list(modalities.keys()) for i, mod1 in enumerate(modalities_list): for mod2 in modalities_list[i+1:]: data1 = modalities[mod1] data2 = modalities[mod2] if data1.get("status") == "success" and data2.get("status") == "success": similarity = await self._calculate_modality_similarity(data1, data2) relationships.append({ "modalities": [mod1.value, mod2.value], "relationship_type": "complementary" if similarity > 0.7 else "independent", "strength": similarity, "temporal_alignment": await self._check_temporal_alignment(data1, data2) }) return relationships async def _check_temporal_alignment(self, data1: Dict[str, Any], data2: Dict[str, Any]]) -> float: """Check temporal alignment between modalities.""" # Simplified temporal alignment check timestamp1 = data1.get("timestamp", datetime.utcnow()) timestamp2 = data2.get("timestamp", datetime.utcnow()) time_diff = abs((timestamp1 - timestamp2).total_seconds()) # Normalize by 1 hour alignment_score = max(0, 1 - time_diff / 3600) return alignment_score async def _calculate_semantic_consistency(self, representations: Dict[str, Any]]) -> float: """Calculate semantic consistency across modalities.""" if not representations: return 0.0 # Simple consistency calculation consistency_scores = [] # Check for semantic overlap all_semantics = [] for modality, representation in representations.items(): if isinstance(representation, dict): all_semantics.append(set(representation.keys())) if len(all_semantics) > 1: # Calculate Jaccard similarity between semantic sets for i in range(len(all_semantics)): for j in range(i+1, len(all_semantics)): intersection = len(all_semantics[i] & all_semantics[j]) union = len(all_semantics[i] | all_semantics[j]) if union > 0: consistency_scores.append(intersection / union) return np.mean(consistency_scores) if consistency_scores else 0.5 # Alignment algorithms async def _temporal_alignment(self, modalities: Dict[DataModality, Dict[str, Any]]) -> Dict[DataModality, Dict[str, Any]]: """Align modalities temporally.""" alignment_results = {} for modality, data in modalities.items(): if data.get("status") == "success": timestamp = data.get("timestamp", datetime.utcnow()) alignment_results[modality] = { "timestamp": timestamp.isoformat(), "time_category": self._categorize_time(timestamp), "temporal_priority": self._calculate_temporal_priority(timestamp) } return alignment_results def _categorize_time(self, timestamp: datetime) -> str: """Categorize timestamp into time categories.""" now = datetime.utcnow() age_seconds = (now - timestamp).total_seconds() if age_seconds < 60: return "immediate" elif age_seconds < 3600: return "recent" elif age_seconds < 86400: return "today" else: return "historical" def _calculate_temporal_priority(self, timestamp: datetime) -> float: """Calculate temporal priority (recent = high priority).""" now = datetime.utcnow() age_seconds = (now - timestamp).total_seconds() return max(0, 1 - age_seconds / 86400) # Decay over 24 hours async def _semantic_alignment(self, modalities: Dict[DataModality, Dict[str, Any]]) -> Dict[DataModality, Dict[str, Any]]: """Align modalities semantically.""" alignment_results = {} for modality, data in modalities.items(): if data.get("status") == "success": features = data.get("features", {}) semantic_tags = data.get("semantic_tags", []) alignment_results[modality] = { "semantic_tags": semantic_tags, "dominant_concepts": await self._extract_dominant_concepts(features), "semantic_density": len(semantic_tags) / max(len(features), 1) } return alignment_results async def _extract_dominant_concepts(self, features: Dict[str, Any]) -> List[str]: """Extract dominant concepts from features.""" # Simple concept extraction based on feature names and values concepts = [] for feature_name, feature_value in features.items(): if isinstance(feature_value, str) and len(feature_value) > 3: concepts.append(feature_value.lower()) elif isinstance(feature_name, str) and len(feature_name) > 3: concepts.append(feature_name.lower()) return list(set(concepts))[:5] # Top 5 concepts async def _structural_alignment(self, modalities: Dict[DataModality, Dict[str, Any]]) -> Dict[DataModality, Dict[str, Any]]: """Align modalities structurally.""" alignment_results = {} for modality, data in modalities.items(): if data.get("status") == "success": features = data.get("features", {}) alignment_results[modality] = { "structure_type": self._determine_structure_type(features), "complexity_score": self._calculate_complexity_score(features), "organization_pattern": self._identify_organization_pattern(features) } return alignment_results def _determine_structure_type(self, features: Dict[str, Any]) -> str: """Determine the structural type of the data.""" if not features: return "minimal" # Simple structure detection if all(isinstance(v, (int, float)) for v in features.values()): return "numerical" elif all(isinstance(v, str) for v in features.values()): return "textual" elif len(features) > 10: return "complex" else: return "simple" def _calculate_complexity_score(self, features: Dict[str, Any]) -> float: """Calculate complexity score of the data structure.""" if not features: return 0.0 # Simple complexity based on feature count and type diversity type_counts = defaultdict(int) for value in features.values(): type_counts[type(value).__name__] += 1 type_diversity = len(type_counts) feature_count = len(features) # Normalize complexity score complexity = (feature_count / 20) * 0.6 + (type_diversity / 4) * 0.4 return min(1.0, complexity) def _identify_organization_pattern(self, features: Dict[str, Any]) -> str: """Identify the organization pattern of the data.""" if not features: return "none" # Simple pattern detection feature_names = list(features.keys()) if any("time" in name.lower() or "date" in name.lower() for name in feature_names): return "temporal" elif any("category" in name.lower() or "type" in name.lower() for name in feature_names): return "categorical" elif any("value" in name.lower() or "amount" in name.lower() for name in feature_names): return "quantitative" else: return "mixed" # Individual modality processors class TextProcessor: """Processor for text modality.""" async def process(self, inputs: List[MultimodalInput]) -> Dict[str, Any]: """Process text inputs.""" if not inputs: return {"status": "error", "error": "No text inputs"} # Combine all text inputs combined_text = " ".join([inp.content for inp in inputs if isinstance(inp.content, str)]) # Extract features features = { "text_length": len(combined_text), "word_count": len(combined_text.split()), "sentence_count": combined_text.count('.') + combined_text.count('!') + combined_text.count('?'), "complexity_score": self._calculate_text_complexity(combined_text) } # Generate semantic representation semantic_representation = await self._extract_text_semantics(combined_text) return { "status": "success", "features": features, "semantic_representation": semantic_representation, "confidence": np.mean([inp.confidence for inp in inputs]), "quality_score": np.mean([inp.quality_score for inp in inputs]), "timestamp": max(inp.timestamp for inp in inputs) } def _calculate_text_complexity(self, text: str) -> float: """Calculate text complexity score.""" if not text: return 0.0 words = text.split() avg_word_length = np.mean([len(word) for word in words]) if words else 0 sentence_count = max(1, text.count('.') + text.count('!') + text.count('?')) avg_sentence_length = len(words) / sentence_count # Simple complexity calculation complexity = (avg_word_length / 10) * 0.4 + (avg_sentence_length / 20) * 0.6 return min(1.0, complexity) async def _extract_text_semantics(self, text: str) -> Dict[str, Any]: """Extract semantic representation from text.""" # Simple semantic extraction words = text.lower().split() # Extract key concepts (simplified) concepts = [] for word in words: if len(word) > 4: # Skip short words concepts.append(word) # Extract topics (simplified) topics = [] if any(word in text.lower() for word in ["business", "company", "revenue"]): topics.append("business") if any(word in text.lower() for word in ["technology", "system", "software"]): topics.append("technology") if any(word in text.lower() for word in ["data", "information", "analysis"]): topics.append("data") return { "concepts": list(set(concepts))[:10], "topics": topics, "sentiment": self._analyze_sentiment(text), "entities": [] # Would use NER in production } def _analyze_sentiment(self, text: str) -> str: """Simple sentiment analysis.""" positive_words = ["good", "great", "excellent", "positive", "happy", "success"] negative_words = ["bad", "terrible", "negative", "sad", "failure", "problem"] text_lower = text.lower() positive_count = sum(1 for word in positive_words if word in text_lower) negative_count = sum(1 for word in negative_words if word in text_lower) if positive_count > negative_count: return "positive" elif negative_count > positive_count: return "negative" else: return "neutral" class ImageProcessor: """Processor for image modality.""" async def process(self, inputs: List[MultimodalInput]) -> Dict[str, Any]: """Process image inputs.""" if not inputs: return {"status": "error", "error": "No image inputs"} # Process first image (simplified) image_input = inputs[0] # Extract features (simplified) features = { "image_size": image_input.metadata.get("size", 0), "format": image_input.metadata.get("format", "unknown"), "color_diversity": image_input.metadata.get("color_diversity", 0.5), "complexity_score": image_input.metadata.get("complexity", 0.5) } # Generate semantic representation semantic_representation = await self._extract_image_semantics(image_input) return { "status": "success", "features": features, "semantic_representation": semantic_representation, "confidence": image_input.confidence, "quality_score": image_input.quality_score, "timestamp": image_input.timestamp } async def _extract_image_semantics(self, image_input: MultimodalInput) -> Dict[str, Any]: """Extract semantic representation from image.""" # Simplified image semantic extraction metadata = image_input.metadata return { "objects": metadata.get("objects", []), "colors": metadata.get("dominant_colors", []), "scenes": metadata.get("scene_types", []), "text_content": metadata.get("extracted_text", ""), "visual_concepts": metadata.get("visual_concepts", []) } class AudioProcessor: """Processor for audio modality.""" async def process(self, inputs: List[MultimodalInput]) -> Dict[str, Any]: """Process audio inputs.""" if not inputs: return {"status": "error", "error": "No audio inputs"} # Process first audio (simplified) audio_input = inputs[0] # Extract features features = { "duration": audio_input.metadata.get("duration", 0), "sample_rate": audio_input.metadata.get("sample_rate", 44100), "channels": audio_input.metadata.get("channels", 1), "frequency_content": audio_input.metadata.get("frequency_profile", {}) } # Generate semantic representation semantic_representation = await self._extract_audio_semantics(audio_input) return { "status": "success", "features": features, "semantic_representation": semantic_representation, "confidence": audio_input.confidence, "quality_score": audio_input.quality_score, "timestamp": audio_input.timestamp } async def _extract_audio_semantics(self, audio_input: MultimodalInput) -> Dict[str, Any]: """Extract semantic representation from audio.""" metadata = audio_input.metadata return { "speech_content": metadata.get("transcribed_text", ""), "speaker_count": metadata.get("speaker_count", 1), "emotion": metadata.get("emotion", "neutral"), "language": metadata.get("language", "unknown"), "audio_quality": metadata.get("quality_score", 0.5) } # Additional processors would be implemented similarly... class VideoProcessor: """Processor for video modality.""" async def process(self, inputs: List[MultimodalInput]) -> Dict[str, Any]: return {"status": "success", "features": {}, "semantic_representation": {}} class SensorProcessor: """Processor for sensor modality.""" async def process(self, inputs: List[MultimodalInput]) -> Dict[str, Any]: return {"status": "success", "features": {}, "semantic_representation": {}} class TableProcessor: """Processor for table modality.""" async def process(self, inputs: List[MultimodalInput]) -> Dict[str, Any]: return {"status": "success", "features": {}, "semantic_representation": {}} class CodeProcessor: """Processor for code modality.""" async def process(self, inputs: List[MultimodalInput]) -> Dict[str, Any]: return {"status": "success", "features": {}, "semantic_representation": {}} class StructuredProcessor: """Processor for structured data modality.""" async def process(self, inputs: List[MultimodalInput]) -> Dict[str, Any]: return {"status": "success", "features": {}, "semantic_representation": {}} # Integration with main system class MultimodalContextProcessor: """Integrated multimodal context processing system.""" def __init__(self): self.multimodal_processor = MultimodalProcessor() async def process_multimodal_input( self, input_data: Dict[str, Any], fusion_strategy: FusionStrategy = FusionStrategy.HYBRID_FUSION ) -> Dict[str, Any]: """Process multimodal input and return unified context.""" # Convert input data to MultimodalInput objects multimodal_inputs = [] for modality_str, content_list in input_data.items(): try: modality = DataModality(modality_str) if isinstance(content_list, list): for content in content_list: multimodal_input = MultimodalInput( id=f"{modality_str}_{len(multimodal_inputs)}", modality=modality, content=content.get("content", content), metadata=content.get("metadata", {}), timestamp=datetime.utcnow(), quality_score=content.get("quality_score", 0.8), confidence=content.get("confidence", 0.8) ) multimodal_inputs.append(multimodal_input) else: multimodal_input = MultimodalInput( id=f"{modality_str}_0", modality=modality, content=content_list.get("content", content_list), metadata=content_list.get("metadata", {}), timestamp=datetime.utcnow(), quality_score=content_list.get("quality_score", 0.8), confidence=content_list.get("confidence", 0.8) ) multimodal_inputs.append(multimodal_input) except ValueError: logger.warning(f"Unknown modality: {modality_str}") # Process multimodal inputs unified_context = await self.multimodal_processor.process_multimodal_input( multimodal_inputs, fusion_strategy ) return { "unified_context": { "id": unified_context.id, "fusion_strategy": unified_context.fusion_strategy.value, "modality_contributions": unified_context.modality_contributions, "semantic_consistency": unified_context.semantic_consistency, "confidence_aggregate": unified_context.confidence_aggregate, "fused_representation": unified_context.fused_representation }, "processing_summary": { "modalities_processed": len(set(inp.modality for inp in multimodal_inputs)), "total_inputs": len(multimodal_inputs), "fusion_quality": unified_context.confidence_aggregate } } if __name__ == "__main__": print("Multimodal Context Processing System Initialized") print("=" * 60) processor = MultimodalContextProcessor() print("Ready for advanced multimodal context processing and fusion!")