""" Multi-Modal Reasoning Implementation ---------------------------------- Implements reasoning across different types of information. """ import logging from typing import Dict, Any, List, Optional from datetime import datetime import json import numpy as np from .reasoning import ReasoningStrategy class MultiModalReasoning(ReasoningStrategy): """Implements multi-modal reasoning across different types of information.""" def __init__(self, config: Optional[Dict[str, Any]] = None): """Initialize multi-modal 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 }) # Multi-modal specific parameters self.modality_weights = self.config.get('modality_weights', { 'text': 0.8, 'image': 0.7, 'audio': 0.6, 'video': 0.5, 'structured': 0.7 }) self.cross_modal_threshold = self.config.get('cross_modal_threshold', 0.6) self.integration_steps = self.config.get('integration_steps', 3) self.alignment_method = self.config.get('alignment_method', 'attention') async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: try: # Process different modalities modalities = await self._process_modalities(query, context) # Align across modalities alignment = await self._cross_modal_alignment(modalities, context) # Integrated analysis integration = await self._integrated_analysis(alignment, context) # Generate final response response = await self._generate_response(integration, context) return { "success": True, "answer": response["conclusion"], "modalities": modalities, "alignment": alignment, "integration": integration, "confidence": response["confidence"] } except Exception as e: logging.error(f"Error in multi-modal reasoning: {str(e)}") return {"success": False, "error": str(e)} async def _process_modalities(self, query: str, context: Dict[str, Any]) -> Dict[str, List[Dict[str, Any]]]: """Process query across different modalities.""" prompt = f""" Process query across modalities: Query: {query} Context: {json.dumps(context)} For each modality extract: 1. [Type]: Modality type 2. [Content]: Relevant content 3. [Features]: Key features 4. [Quality]: Content quality Format as: [M1] Type: ... Content: ... Features: ... Quality: ... """ response = await context["groq_api"].predict(prompt) return self._parse_modalities(response["answer"]) 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.""" try: # Extract modality types modal_types = list(modalities.keys()) # Initialize alignment results alignments = [] # Process each modality pair for i in range(len(modal_types)): for j in range(i + 1, len(modal_types)): type1, type2 = modal_types[i], modal_types[j] # Get items from each modality items1 = modalities[type1] items2 = modalities[type2] # Find alignments between items for item1 in items1: for item2 in items2: similarity = self._calculate_similarity(item1, item2) if similarity > self.cross_modal_threshold: # Threshold for alignment alignments.append({ "type1": type1, "type2": type2, "item1": item1, "item2": item2, "similarity": similarity }) # Sort alignments by similarity alignments.sort(key=lambda x: x["similarity"], reverse=True) return alignments except Exception as e: logging.error(f"Error in cross-modal alignment: {str(e)}") return [] def _calculate_similarity(self, item1: Dict[str, Any], item2: Dict[str, Any]) -> float: """Calculate similarity between two items from different modalities.""" try: # Extract content from items content1 = str(item1.get("content", "")) content2 = str(item2.get("content", "")) # Calculate basic similarity (can be enhanced with more sophisticated methods) common_words = set(content1.lower().split()) & set(content2.lower().split()) total_words = set(content1.lower().split()) | set(content2.lower().split()) if not total_words: return 0.0 return len(common_words) / len(total_words) except Exception as e: logging.error(f"Error calculating similarity: {str(e)}") return 0.0 async def _integrated_analysis(self, alignment: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: prompt = f""" Perform integrated multi-modal analysis: Alignment: {json.dumps(alignment)} Context: {json.dumps(context)} For each insight: 1. [Insight]: Key finding 2. [Sources]: Contributing modalities 3. [Support]: Supporting evidence 4. [Confidence]: Confidence level Format as: [I1] Insight: ... Sources: ... Support: ... Confidence: ... """ response = await context["groq_api"].predict(prompt) return self._parse_integration(response["answer"]) async def _generate_response(self, integration: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]: prompt = f""" Generate unified multi-modal response: Integration: {json.dumps(integration)} Context: {json.dumps(context)} Provide: 1. Main conclusion 2. Modal contributions 3. Integration benefits 4. Confidence level (0-1) """ response = await context["groq_api"].predict(prompt) return self._parse_response(response["answer"]) def _parse_modalities(self, response: str) -> Dict[str, List[Dict[str, Any]]]: """Parse modalities from response.""" modalities = {} current_modality = None for line in response.split('\n'): line = line.strip() if not line: continue if line.startswith('[M'): if current_modality: if current_modality["type"] not in modalities: modalities[current_modality["type"]] = [] modalities[current_modality["type"]].append(current_modality) current_modality = { "type": "", "content": "", "features": "", "quality": "" } elif current_modality: if line.startswith('Type:'): current_modality["type"] = line[5:].strip() elif line.startswith('Content:'): current_modality["content"] = line[8:].strip() elif line.startswith('Features:'): current_modality["features"] = line[9:].strip() elif line.startswith('Quality:'): current_modality["quality"] = line[8:].strip() if current_modality: if current_modality["type"] not in modalities: modalities[current_modality["type"]] = [] modalities[current_modality["type"]].append(current_modality) return modalities def _parse_integration(self, response: str) -> List[Dict[str, Any]]: """Parse integration from response.""" integration = [] current_insight = None for line in response.split('\n'): line = line.strip() if not line: continue if line.startswith('[I'): if current_insight: integration.append(current_insight) current_insight = { "insight": "", "sources": "", "support": "", "confidence": 0.0 } elif current_insight: if line.startswith('Insight:'): current_insight["insight"] = line[8:].strip() elif line.startswith('Sources:'): current_insight["sources"] = line[8:].strip() elif line.startswith('Support:'): current_insight["support"] = line[8:].strip() elif line.startswith('Confidence:'): try: current_insight["confidence"] = float(line[11:].strip()) except: pass if current_insight: integration.append(current_insight) return integration def _parse_response(self, response: str) -> Dict[str, Any]: """Parse response from response.""" response_dict = { "conclusion": "", "modal_contributions": [], "integration_benefits": [], "confidence": 0.0 } mode = None for line in response.split('\n'): line = line.strip() if not line: continue if line.startswith('Conclusion:'): response_dict["conclusion"] = line[11:].strip() elif line.startswith('Modal Contributions:'): mode = "modal" elif line.startswith('Integration Benefits:'): mode = "integration" elif line.startswith('Confidence:'): try: response_dict["confidence"] = float(line[11:].strip()) except: response_dict["confidence"] = 0.5 mode = None elif mode == "modal" and line.startswith('- '): response_dict["modal_contributions"].append(line[2:].strip()) elif mode == "integration" and line.startswith('- '): response_dict["integration_benefits"].append(line[2:].strip()) return response_dict