src/agents/synthesizer.py

#!/usr/bin/env python3
"""
Synthesizer Agent for GAIA Agent System
Combines results from multiple agents and produces final answers
"""

import logging
from typing import Dict, List, Optional, Any
from statistics import mean

from agents.state import GAIAAgentState, AgentRole, AgentResult
from models.qwen_client import QwenClient, ModelTier

logger = logging.getLogger(__name__)

class SynthesizerAgent:
    """
    Synthesizer agent that combines multiple agent results into a final answer
    """
    
    def __init__(self, llm_client: QwenClient):
        self.llm_client = llm_client
        
    def process(self, state: GAIAAgentState) -> GAIAAgentState:
        """
        Synthesize final answer from multiple agent results
        """
        logger.info("Synthesizer: Starting result synthesis")
        state.add_processing_step("Synthesizer: Analyzing agent results")
        
        try:
            # Check if we have any agent results to synthesize
            if not state.agent_results:
                error_msg = "No agent results available for synthesis"
                state.add_error(error_msg)
                state.final_answer = "Unable to process question - no agent results available"
                state.final_confidence = 0.0
                state.final_reasoning = error_msg
                state.is_complete = True
                return state
            
            # Determine synthesis strategy based on available results
            synthesis_strategy = self._determine_synthesis_strategy(state)
            state.add_processing_step(f"Synthesizer: Using {synthesis_strategy} strategy")
            
            # Execute synthesis based on strategy
            if synthesis_strategy == "single_agent":
                final_result = self._synthesize_single_agent(state)
            elif synthesis_strategy == "multi_agent_consensus":
                final_result = self._synthesize_multi_agent_consensus(state)
            elif synthesis_strategy == "confidence_weighted":
                final_result = self._synthesize_confidence_weighted(state)
            elif synthesis_strategy == "llm_synthesis":
                final_result = self._synthesize_with_llm(state)
            elif synthesis_strategy == "failure_analysis":
                final_result = self._synthesize_failure_analysis(state)
            else:
                final_result = self._synthesize_fallback(state)
            
            # Update state with final results
            state.final_answer = final_result["answer"]
            state.final_confidence = final_result["confidence"]
            state.final_reasoning = final_result["reasoning"]
            state.answer_source = final_result["source"]
            state.is_complete = True
            
            # Check if confidence threshold is met
            state.confidence_threshold_met = state.final_confidence >= 0.7
            
            # Determine if human review is needed
            state.requires_human_review = (
                state.final_confidence < 0.5 or
                len(state.error_messages) > 0 or
                state.difficulty_level >= 3
            )
            
            logger.info(f"✅ Synthesis complete: confidence={state.final_confidence:.2f}")
            state.add_processing_step(f"Synthesizer: Final answer generated (confidence: {state.final_confidence:.2f})")
            
            return state
            
        except Exception as e:
            error_msg = f"Synthesis failed: {str(e)}"
            state.add_error(error_msg)
            logger.error(error_msg)
            
            # Provide fallback answer
            state.final_answer = "Processing failed due to synthesis error"
            state.final_confidence = 0.0
            state.final_reasoning = error_msg
            state.answer_source = "error_fallback"
            state.is_complete = True
            state.requires_human_review = True
            
            return state
    
    def _determine_synthesis_strategy(self, state: GAIAAgentState) -> str:
        """Determine the best synthesis strategy based on available results"""
        
        successful_results = [r for r in state.agent_results.values() if r.success]
        failed_results = [r for r in state.agent_results.values() if not r.success]
        
        # If we have some results but they're mostly failures, try to extract useful info
        if len(successful_results) == 0 and len(failed_results) > 0:
            return "failure_analysis"
        elif len(successful_results) == 1:
            return "single_agent"
        elif len(successful_results) == 2:
            return "confidence_weighted"
        elif all(r.confidence > 0.6 for r in successful_results):
            return "multi_agent_consensus"
        else:
            return "llm_synthesis"
    
    def _synthesize_single_agent(self, state: GAIAAgentState) -> Dict[str, Any]:
        """Synthesize result from a single agent"""
        
        successful_results = [r for r in state.agent_results.values() if r.success]
        if not successful_results:
            return self._create_fallback_result("No successful agent results")
        
        best_result = max(successful_results, key=lambda r: r.confidence)
        
        return {
            "answer": best_result.result,
            "confidence": best_result.confidence,
            "reasoning": f"Single agent result from {best_result.agent_role.value}: {best_result.reasoning}",
            "source": best_result.agent_role.value
        }
    
    def _synthesize_multi_agent_consensus(self, state: GAIAAgentState) -> Dict[str, Any]:
        """Synthesize results when multiple agents agree (high confidence)"""
        
        successful_results = [r for r in state.agent_results.values() if r.success]
        high_confidence_results = [r for r in successful_results if r.confidence > 0.6]
        
        if not high_confidence_results:
            return self._synthesize_confidence_weighted(state)
        
        # Use the highest confidence result as primary
        primary_result = max(high_confidence_results, key=lambda r: r.confidence)
        
        # Calculate consensus confidence
        avg_confidence = mean([r.confidence for r in high_confidence_results])
        consensus_confidence = min(0.95, avg_confidence * 1.1)  # Boost for consensus
        
        # Create reasoning summary
        agent_summaries = []
        for result in high_confidence_results:
            agent_summaries.append(f"{result.agent_role.value} (conf: {result.confidence:.2f})")
        
        reasoning = f"Consensus from {len(high_confidence_results)} agents: {', '.join(agent_summaries)}. Primary result: {primary_result.reasoning}"
        
        return {
            "answer": primary_result.result,
            "confidence": consensus_confidence,
            "reasoning": reasoning,
            "source": f"consensus_{len(high_confidence_results)}_agents"
        }
    
    def _synthesize_confidence_weighted(self, state: GAIAAgentState) -> Dict[str, Any]:
        """Synthesize results using confidence weighting"""
        
        successful_results = [r for r in state.agent_results.values() if r.success]
        
        if not successful_results:
            return self._create_fallback_result("No successful results for confidence weighting")
        
        # Weight by confidence
        total_weight = sum(r.confidence for r in successful_results)
        if total_weight == 0:
            return self._synthesize_single_agent(state)
        
        # Select primary result (highest confidence)
        primary_result = max(successful_results, key=lambda r: r.confidence)
        
        # Calculate weighted confidence
        weighted_confidence = sum(r.confidence ** 2 for r in successful_results) / total_weight
        
        # Create reasoning
        result_summaries = []
        for result in successful_results:
            weight = result.confidence / total_weight
            result_summaries.append(f"{result.agent_role.value} (weight: {weight:.2f})")
        
        reasoning = f"Confidence-weighted synthesis: {', '.join(result_summaries)}. Primary: {primary_result.reasoning}"
        
        return {
            "answer": primary_result.result,
            "confidence": min(0.9, weighted_confidence),
            "reasoning": reasoning,
            "source": f"weighted_{len(successful_results)}_agents"
        }
    
    def _synthesize_with_llm(self, state: GAIAAgentState) -> Dict[str, Any]:
        """Use LLM to synthesize conflicting or complex results"""
        
        successful_results = [r for r in state.agent_results.values() if r.success]
        
        # Prepare synthesis prompt
        agent_results_text = []
        for i, result in enumerate(successful_results, 1):
            agent_results_text.append(f"""
Agent {i} ({result.agent_role.value}):
- Answer: {result.result}
- Confidence: {result.confidence:.2f}
- Reasoning: {result.reasoning}
""")
        
        synthesis_prompt = f"""
Question: {state.question}

Multiple agents have provided different answers/insights. Please synthesize these into a single, coherent final answer:

{chr(10).join(agent_results_text)}

Please provide:
1. A clear, direct final answer
2. Your confidence level (0.0 to 1.0)
3. Brief reasoning explaining how you synthesized the results

Focus on accuracy and be direct in your response.
"""
        
        # Use complex model for synthesis
        model_tier = ModelTier.COMPLEX if state.should_use_complex_model() else ModelTier.MAIN
        llm_result = self.llm_client.generate(synthesis_prompt, tier=model_tier, max_tokens=400)
        
        if llm_result.success:
            # Parse LLM response for structured output
            llm_answer = llm_result.response
            
            # Extract confidence if mentioned in response
            confidence_match = re.search(r'confidence[:\s]*([0-9.]+)', llm_answer.lower())
            llm_confidence = float(confidence_match.group(1)) if confidence_match else 0.7
            
            # Adjust confidence based on input quality
            avg_input_confidence = mean([r.confidence for r in successful_results])
            final_confidence = min(0.85, (llm_confidence + avg_input_confidence) / 2)
            
            return {
                "answer": llm_answer,
                "confidence": final_confidence,
                "reasoning": f"LLM synthesis of {len(successful_results)} agent results using {llm_result.model_used}",
                "source": "llm_synthesis"
            }
        else:
            # Fallback to confidence weighted if LLM fails
            return self._synthesize_confidence_weighted(state)
    
    def _synthesize_fallback(self, state: GAIAAgentState) -> Dict[str, Any]:
        """Enhanced fallback synthesis when other strategies fail"""
        
        # Try to get any result, even if not successful
        all_results = list(state.agent_results.values())
        
        if all_results:
            # First try successful results
            successful_results = [r for r in all_results if r.success]
            if successful_results:
                best_attempt = max(successful_results, key=lambda r: r.confidence)
                return {
                    "answer": best_attempt.result,
                    "confidence": max(0.3, best_attempt.confidence * 0.8),  # Reduce confidence for fallback
                    "reasoning": f"Fallback result from {best_attempt.agent_role.value}: {best_attempt.reasoning}",
                    "source": f"fallback_{best_attempt.agent_role.value}"
                }
            
            # If no successful results, try to extract useful info from failures
            return self._synthesize_failure_analysis(state)
        else:
            return self._create_fallback_result("No agent results available")
    
    def _synthesize_failure_analysis(self, state: GAIAAgentState) -> Dict[str, Any]:
        """Analyze failed results to provide some useful response"""
        
        failed_results = [r for r in state.agent_results.values() if not r.success]
        
        if not failed_results:
            return self._create_fallback_result("No results to analyze")
        
        # Look for patterns in failures
        error_patterns = []
        attempted_agents = []
        
        for result in failed_results:
            attempted_agents.append(result.agent_role.value)
            
            # Extract meaningful error information
            result_text = result.result.lower()
            if "research sources failed" in result_text:
                error_patterns.append("external_research_unavailable")
            elif "reasoning failed" in result_text:
                error_patterns.append("complex_reasoning_required")
            elif "conversion" in result_text:
                error_patterns.append("conversion_difficulty")
            elif "mathematical" in result_text:
                error_patterns.append("mathematical_complexity")
        
        # Try to provide a helpful response based on the question type and failures
        try:
            analysis_prompt = f"""
            Question: {state.question}
            
            Multiple specialized agents attempted to answer this question but encountered difficulties:
            - Agents tried: {', '.join(attempted_agents)}
            - Common issues: {', '.join(set(error_patterns)) if error_patterns else 'processing difficulties'}
            
            Based on the question itself, please provide the best answer you can using basic reasoning and knowledge.
            Even if external resources failed, try to answer based on general knowledge.
            
            Be honest about limitations but try to be helpful.
            """
            
            # Use main model for analysis
            llm_result = self.llm_client.generate(analysis_prompt, tier=ModelTier.MAIN, max_tokens=300)
            
            if llm_result.success:
                return {
                    "answer": llm_result.response,
                    "confidence": 0.25,  # Low confidence but still attempting
                    "reasoning": f"Generated from failure analysis. Agents tried: {', '.join(attempted_agents)}",
                    "source": "failure_analysis"
                }
        
        except Exception as analysis_error:
            logger.warning(f"Failure analysis also failed: {analysis_error}")
        
        # Final fallback - provide structured error message
        return {
            "answer": f"Processing encountered difficulties: All research sources failed",
            "confidence": 0.1,
            "reasoning": f"Multiple agents failed: {', '.join(attempted_agents)}. {', '.join(set(error_patterns)) if error_patterns else 'Various processing issues encountered'}",
            "source": "structured_failure"
        }
    
    def _create_fallback_result(self, reason: str) -> Dict[str, Any]:
        """Create a fallback result when synthesis is impossible"""
        return {
            "answer": f"Unable to process question: {reason}",
            "confidence": 0.0,
            "reasoning": f"Synthesis failed: {reason}",
            "source": "synthesis_failure"
        }

# Import regex for LLM response parsing
import re