""" Enhanced Reasoning Engine for Supernova AI Provides sophisticated problem-solving capabilities through structured reasoning, multi-tool coordination, and knowledge synthesis. """ import torch import numpy as np try: import sympy as sp except ImportError: sp = None import re import json from typing import List, Dict, Any, Optional, Tuple from dataclasses import dataclass from enum import Enum from .tools import ToolOrchestrator, ToolCall class ReasoningType(Enum): ANALYTICAL = "analytical" CREATIVE = "creative" COMPARATIVE = "comparative" CAUSAL = "causal" SEQUENTIAL = "sequential" EVALUATIVE = "evaluative" @dataclass class ReasoningStep: step_number: int description: str reasoning_type: ReasoningType tool_needed: Optional[str] = None query: Optional[str] = None result: Optional[str] = None confidence: float = 0.8 @dataclass class KnowledgeDomain: domain: str confidence: float sources: List[str] key_facts: List[str] class EnhancedReasoningEngine: """Advanced reasoning engine that mimics sophisticated AI reasoning patterns.""" def __init__(self, tool_orchestrator: ToolOrchestrator): self.tools = tool_orchestrator self.conversation_context = [] self.domain_expertise = { 'science': ['physics', 'chemistry', 'biology', 'mathematics', 'astronomy'], 'technology': ['programming', 'ai', 'computing', 'engineering', 'electronics'], 'humanities': ['history', 'literature', 'philosophy', 'psychology', 'sociology'], 'medicine': ['anatomy', 'pharmacology', 'diagnosis', 'treatment', 'research'], 'business': ['finance', 'management', 'economics', 'marketing', 'strategy'], 'arts': ['music', 'visual arts', 'design', 'architecture', 'performance'] } def analyze_query_complexity(self, query: str) -> Dict[str, Any]: """Analyze the complexity and requirements of a user query.""" complexity_indicators = { 'simple': ['what is', 'define', 'who is', 'when did'], 'moderate': ['how does', 'why does', 'explain', 'compare', 'analyze'], 'complex': ['evaluate', 'synthesize', 'create', 'design', 'solve for multiple', 'consider all factors'] } domains_detected = [] for domain, keywords in self.domain_expertise.items(): if any(keyword in query.lower() for keyword in keywords): domains_detected.append(domain) complexity_level = 'simple' for level, indicators in complexity_indicators.items(): if any(indicator in query.lower() for indicator in indicators): complexity_level = level requires_multi_step = any(phrase in query.lower() for phrase in [ 'step by step', 'first...then', 'multiple', 'several', 'both', 'compare and contrast' ]) return { 'complexity': complexity_level, 'domains': domains_detected, 'multi_step_needed': requires_multi_step, 'estimated_steps': min(5, len(domains_detected) + (2 if requires_multi_step else 1)) } def decompose_complex_query(self, query: str, analysis: Dict[str, Any]) -> List[ReasoningStep]: """Break down complex queries into manageable reasoning steps.""" steps = [] step_num = 1 # Step 1: Information Gathering if analysis['complexity'] in ['moderate', 'complex']: # Determine if we need current information if any(term in query.lower() for term in ['current', 'latest', 'recent', 'today', '2024', '2025']): steps.append(ReasoningStep( step_number=step_num, description="Gather current information from web sources", reasoning_type=ReasoningType.ANALYTICAL, tool_needed="serper", query=query )) step_num += 1 # Check if mathematical computation is needed if any(term in query.lower() for term in ['calculate', 'compute', 'solve', 'derivative', 'integral']): steps.append(ReasoningStep( step_number=step_num, description="Perform mathematical computation", reasoning_type=ReasoningType.ANALYTICAL, tool_needed="math_engine", query=query )) step_num += 1 # Step 2: Domain-specific analysis for domain in analysis['domains']: steps.append(ReasoningStep( step_number=step_num, description=f"Analyze from {domain} perspective", reasoning_type=ReasoningType.ANALYTICAL, tool_needed=None, # Will use model generation with domain context query=f"From a {domain} perspective: {query}" )) step_num += 1 # Step 3: Synthesis and evaluation if analysis['complexity'] == 'complex': steps.append(ReasoningStep( step_number=step_num, description="Synthesize information and provide comprehensive analysis", reasoning_type=ReasoningType.EVALUATIVE, tool_needed=None, query=query )) return steps if steps else [ReasoningStep(1, "Direct response", ReasoningType.ANALYTICAL, query=query)] def execute_reasoning_chain(self, steps: List[ReasoningStep], model, tokenizer) -> List[ReasoningStep]: """Execute a chain of reasoning steps, using tools and model generation as needed.""" results = [] context_info = [] for step in steps: if step.tool_needed: # Use appropriate tool tool_call = ToolCall(tool=step.tool_needed, query=step.query) executed_call = self.tools.execute_tool_call(tool_call) if executed_call.result: step.result = executed_call.result step.confidence = 0.9 context_info.append(f"{step.description}: {executed_call.result}") else: step.result = f"Tool execution failed: {executed_call.error}" step.confidence = 0.3 else: # Use model generation with enhanced context enhanced_context = self._build_enhanced_context(step, context_info) try: response = self._generate_with_context(model, tokenizer, enhanced_context, step.query) step.result = response step.confidence = 0.7 context_info.append(f"{step.description}: {response}") except Exception as e: step.result = f"Generation failed: {str(e)}" step.confidence = 0.2 results.append(step) return results def _build_enhanced_context(self, step: ReasoningStep, context_info: List[str]) -> str: """Build enhanced context for model generation.""" context_parts = [ "You are Supernova, an advanced AI assistant with deep expertise across multiple domains.", "Apply sophisticated reasoning and provide comprehensive, nuanced responses.", "" ] if context_info: context_parts.extend([ "Previous analysis steps:", *[f"- {info}" for info in context_info], "" ]) reasoning_guidance = { ReasoningType.ANALYTICAL: "Analyze systematically, consider multiple factors, and provide evidence-based insights.", ReasoningType.CREATIVE: "Think creatively, explore innovative solutions, and consider unconventional approaches.", ReasoningType.COMPARATIVE: "Compare different perspectives, weigh pros and cons, and identify key differences.", ReasoningType.CAUSAL: "Identify cause-and-effect relationships, trace underlying mechanisms, and explain why things happen.", ReasoningType.SEQUENTIAL: "Break down into logical steps, show progression, and maintain clear sequencing.", ReasoningType.EVALUATIVE: "Make judgments based on criteria, assess quality and effectiveness, and provide recommendations." } context_parts.extend([ f"Reasoning approach: {reasoning_guidance.get(step.reasoning_type, 'Provide thorough analysis.')}", f"Focus area: {step.description}", "" ]) return "\n".join(context_parts) def _generate_with_context(self, model, tokenizer, context: str, query: str, max_tokens: int = 400) -> str: """Generate response using the model with enhanced context.""" full_prompt = f"{context}\nUser Query: {query}\n\nDetailed Response:" # Use the existing generate function (simplified version) model.eval() device = next(model.parameters()).device input_ids = tokenizer.encode(full_prompt, return_tensors="pt").to(device) with torch.no_grad(): for _ in range(max_tokens): if input_ids.size(1) >= model.cfg.n_positions: input_cond = input_ids[:, -model.cfg.n_positions:] else: input_cond = input_ids logits, _ = model(input_cond) logits = logits[:, -1, :] / 0.8 # temperature # Top-k sampling v, _ = torch.topk(logits, min(50, logits.size(-1))) logits[logits < v[:, [-1]]] = -float("Inf") probs = torch.softmax(logits, dim=-1) next_id = torch.multinomial(probs, num_samples=1) input_ids = torch.cat([input_ids, next_id], dim=1) response = tokenizer.decode(input_ids[0].tolist()) # Extract the response part if "Detailed Response:" in response: response = response.split("Detailed Response:", 1)[1].strip() return response def synthesize_final_response(self, steps: List[ReasoningStep], original_query: str) -> str: """Synthesize all reasoning steps into a comprehensive final response.""" successful_steps = [step for step in steps if step.result and step.confidence > 0.5] if not successful_steps: return "I apologize, but I encountered difficulties processing your request. Could you please rephrase or provide more specific details?" # Build comprehensive response response_parts = [] # Add executive summary for complex queries if len(successful_steps) > 2: response_parts.append("Here's my comprehensive analysis:") response_parts.append("") # Include results from each step for step in successful_steps: if step.tool_needed in ['math_engine', 'serper']: # Tool results are already well-formatted response_parts.append(step.result) else: # Model-generated responses response_parts.append(step.result) response_parts.append("") # Add synthesis for multi-step responses if len(successful_steps) > 2: confidence_score = sum(step.confidence for step in successful_steps) / len(successful_steps) synthesis_parts = [ "**Key Insights:**", "• Multiple perspectives have been considered", f"• Analysis confidence: {confidence_score:.1%}", "• Both current information and domain expertise were utilized" ] response_parts.extend(synthesis_parts) return "\n".join(response_parts).strip() def process_complex_query(self, query: str, model, tokenizer) -> str: """Main method to process complex queries with enhanced reasoning.""" # Analyze query complexity and requirements analysis = self.analyze_query_complexity(query) # For simple queries, use direct processing if analysis['complexity'] == 'simple' and not analysis['multi_step_needed']: tool_call = self.tools.route_query(query) if tool_call: executed_call = self.tools.execute_tool_call(tool_call) if executed_call.result: return executed_call.result # Fall back to enhanced model generation context = self._build_enhanced_context( ReasoningStep(1, "Direct response", ReasoningType.ANALYTICAL), [] ) return self._generate_with_context(model, tokenizer, context, query) # For complex queries, use multi-step reasoning reasoning_steps = self.decompose_complex_query(query, analysis) executed_steps = self.execute_reasoning_chain(reasoning_steps, model, tokenizer) return self.synthesize_final_response(executed_steps, query) # Import torch and other needed modules here to avoid import issues import torch try: import sympy as sp import numpy as np except ImportError: pass