"""Advanced multimodal reasoning combining different types of information.""" import logging from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple import json from dataclasses import dataclass, field from enum import Enum from datetime import datetime import numpy as np from collections import defaultdict from .base import ReasoningStrategy @dataclass class ModalityFeatures: """Features extracted from different modalities.""" text: List[Dict[str, Any]] image: Optional[List[Dict[str, Any]]] = None audio: Optional[List[Dict[str, Any]]] = None video: Optional[List[Dict[str, Any]]] = None structured: Optional[List[Dict[str, Any]]] = None class MultiModalReasoning(ReasoningStrategy): """ Advanced multimodal reasoning that: 1. Processes different types of information 2. Aligns cross-modal features 3. Integrates multimodal context 4. Generates coherent responses 5. Handles uncertainty """ def __init__(self, config: Optional[Dict[str, Any]] = None): """Initialize multimodal reasoning.""" super().__init__() self.config = config or {} # Standard reasoning parameters self.min_confidence = self.config.get('min_confidence', 0.7) self.parallel_threshold = self.config.get('parallel_threshold', 3) self.learning_rate = self.config.get('learning_rate', 0.1) self.strategy_weights = self.config.get('strategy_weights', { "LOCAL_LLM": 0.8, "CHAIN_OF_THOUGHT": 0.6, "TREE_OF_THOUGHTS": 0.5, "META_LEARNING": 0.4 }) # Configure model repositories self.models = self.config.get('models', { 'img2img': { 'repo_id': 'enhanceaiteam/Flux-Uncensored-V2', 'filename': 'Flux-Uncensored-V2.safetensors' }, 'img2vid': { 'repo_id': 'stabilityai/stable-video-diffusion-img2vid-xt', 'filename': 'svd_xt.safetensors' }, 'any2any': { 'repo_id': 'deepseek-ai/JanusFlow-1.3B', 'filename': 'janusflow-1.3b.safetensors' } }) # Configure modality weights self.weights = self.config.get('modality_weights', { 'text': 0.4, 'image': 0.3, 'audio': 0.1, 'video': 0.1, 'structured': 0.1 }) async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: """ Apply multimodal reasoning to process and integrate different types of information. Args: query: The input query to reason about context: Additional context and parameters Returns: Dict containing reasoning results and confidence scores """ try: # Process across modalities modalities = await self._process_modalities(query, context) # Align cross-modal information alignment = await self._cross_modal_alignment(modalities, context) # Integrate aligned information integration = await self._integrated_analysis(alignment, context) # Generate final response response = await self._generate_response(integration, context) return { 'answer': response.get('text', ''), 'confidence': self._calculate_confidence(integration), 'modalities': modalities, 'alignment': alignment, 'integration': integration } except Exception as e: logging.error(f"Multimodal reasoning failed: {str(e)}") return { 'error': f"Multimodal reasoning failed: {str(e)}", 'confidence': 0.0 } async def _process_modalities( self, query: str, context: Dict[str, Any] ) -> Dict[str, List[Dict[str, Any]]]: """Process query across different modalities.""" modalities = {} # Process text if 'text' in context: modalities['text'] = self._process_text(context['text']) # Process images if 'images' in context: modalities['image'] = self._process_images(context['images']) # Process audio if 'audio' in context: modalities['audio'] = self._process_audio(context['audio']) # Process video if 'video' in context: modalities['video'] = self._process_video(context['video']) # Process structured data if 'structured' in context: modalities['structured'] = self._process_structured(context['structured']) return modalities async def _cross_modal_alignment( self, modalities: Dict[str, List[Dict[str, Any]]], context: Dict[str, Any] ) -> List[Dict[str, Any]]: """Align information across different modalities.""" alignments = [] # Get all modality pairs modality_pairs = [ (m1, m2) for i, m1 in enumerate(modalities.keys()) for m2 in list(modalities.keys())[i+1:] ] # Align each pair for mod1, mod2 in modality_pairs: items1 = modalities[mod1] items2 = modalities[mod2] # Calculate cross-modal similarities for item1 in items1: for item2 in items2: similarity = self._calculate_similarity(item1, item2) if similarity > 0.7: # Alignment threshold alignments.append({ 'modality1': mod1, 'modality2': mod2, 'item1': item1, 'item2': item2, 'similarity': similarity }) return alignments def _calculate_similarity( self, item1: Dict[str, Any], item2: Dict[str, Any] ) -> float: """Calculate similarity between two items from different modalities.""" # Simple feature overlap for now features1 = set(str(v) for v in item1.values()) features2 = set(str(v) for v in item2.values()) if not features1 or not features2: return 0.0 overlap = len(features1.intersection(features2)) total = len(features1.union(features2)) return overlap / total if total > 0 else 0.0 async def _integrated_analysis( self, alignment: List[Dict[str, Any]], context: Dict[str, Any] ) -> List[Dict[str, Any]]: """Perform integrated analysis of aligned information.""" integrated = [] # Group alignments by similarity similarity_groups = defaultdict(list) for align in alignment: similarity_groups[align['similarity']].append(align) # Process groups in order of similarity for similarity, group in sorted( similarity_groups.items(), key=lambda x: x[0], reverse=True ): # Combine aligned features for align in group: integrated.append({ 'features': { **align['item1'], **align['item2'] }, 'modalities': [align['modality1'], align['modality2']], 'confidence': align['similarity'] }) return integrated async def _generate_response( self, integration: List[Dict[str, Any]], context: Dict[str, Any] ) -> Dict[str, Any]: """Generate coherent response from integrated analysis.""" if not integration: return {'text': '', 'confidence': 0.0} # Combine all integrated features all_features = {} for item in integration: all_features.update(item['features']) # Generate response text response_text = [] # Add main findings response_text.append("Main findings across modalities:") for feature, value in all_features.items(): response_text.append(f"- {feature}: {value}") # Add confidence confidence = sum(item['confidence'] for item in integration) / len(integration) response_text.append(f"\nOverall confidence: {confidence:.2f}") return { 'text': "\n".join(response_text), 'confidence': confidence } def _calculate_confidence(self, integration: List[Dict[str, Any]]) -> float: """Calculate overall confidence score.""" if not integration: return 0.0 # Base confidence confidence = 0.5 # Adjust based on number of modalities unique_modalities = set() for item in integration: unique_modalities.update(item['modalities']) modality_bonus = len(unique_modalities) * 0.1 confidence += min(modality_bonus, 0.3) # Adjust based on integration quality avg_similarity = sum( item['confidence'] for item in integration ) / len(integration) confidence += avg_similarity * 0.2 return min(confidence, 1.0) def _process_text(self, text: str) -> List[Dict[str, Any]]: """Process text modality.""" # Simple text processing for now return [{'text': text}] def _process_images(self, images: List[str]) -> List[Dict[str, Any]]: """Process image modality.""" # Simple image processing for now return [{'image': image} for image in images] def _process_audio(self, audio: List[str]) -> List[Dict[str, Any]]: """Process audio modality.""" # Simple audio processing for now return [{'audio': audio_file} for audio_file in audio] def _process_video(self, video: List[str]) -> List[Dict[str, Any]]: """Process video modality.""" # Simple video processing for now return [{'video': video_file} for video_file in video] def _process_structured(self, structured: Dict[str, Any]) -> List[Dict[str, Any]]: """Process structured data modality.""" # Simple structured data processing for now return [{'structured': structured}]