"""
Research Agent for SE-AGI
Specialized in research, investigation, and knowledge discovery
"""

import asyncio
import logging
import re
import json
from typing import List, Dict, Any, Optional
from datetime import datetime
from dataclasses import dataclass

from .base import BaseAgent, AgentResponse, AgentCapability
from ..core.config import AgentConfig


@dataclass
class ResearchQuery:
    """Represents a research query"""
    query_id: str
    question: str
    domain: str
    depth_level: int
    sources_required: List[str]
    research_type: str  # "exploratory", "focused", "comprehensive", "verification"
    timeline: Optional[datetime] = None


@dataclass
class ResearchFinding:
    """Represents a research finding"""
    finding_id: str
    query_id: str
    content: str
    source: str
    confidence: float
    relevance_score: float
    timestamp: datetime
    evidence_type: str  # "factual", "statistical", "expert_opinion", "case_study"


@dataclass
class ResearchReport:
    """Represents a complete research report"""
    report_id: str
    query: ResearchQuery
    findings: List[ResearchFinding]
    synthesis: str
    conclusions: List[str]
    recommendations: List[str]
    confidence_score: float
    limitations: List[str]
    further_research: List[str]


class ResearchAgent(BaseAgent):
    """
    Research Agent for SE-AGI
    
    Capabilities:
    - Scientific literature review
    - Data collection and analysis
    - Fact verification
    - Trend analysis
    - Expert knowledge synthesis
    - Hypothesis generation
    - Research methodology design
    """
    
    def __init__(self, 
                 config: AgentConfig,
                 memory_systems: Optional[Dict[str, Any]] = None):
        
        # Enhance config for research agent
        config.agent_type = "research"
        config.domain_expertise = config.domain_expertise or "general_research"
        
        super().__init__(config, memory_systems)
        
        # Research-specific attributes
        self.research_history: List[ResearchReport] = []
        self.knowledge_base: Dict[str, Any] = {}
        self.research_methodologies: Dict[str, Any] = {}
        self.source_credibility: Dict[str, float] = {}
        
        # Research patterns and templates
        self.question_patterns: Dict[str, List[str]] = {}
        self.synthesis_templates: Dict[str, str] = {}
        
        # Domain expertise
        self.expertise_domains = self._initialize_expertise_domains()
        
    def _register_capabilities(self) -> None:
        """Register research agent capabilities"""
        self.capabilities = {
            "literature_review": AgentCapability(
                name="literature_review",
                description="Conduct comprehensive literature reviews",
                input_types=["research_topic", "keywords"],
                output_types=["literature_summary", "key_findings"],
                confidence_level=0.9
            ),
            "fact_verification": AgentCapability(
                name="fact_verification",
                description="Verify facts and claims against reliable sources",
                input_types=["claim", "statement"],
                output_types=["verification_result", "confidence_score"],
                confidence_level=0.85
            ),
            "data_analysis": AgentCapability(
                name="data_analysis",
                description="Analyze and interpret research data",
                input_types=["dataset", "research_question"],
                output_types=["analysis_results", "insights"],
                confidence_level=0.8
            ),
            "trend_analysis": AgentCapability(
                name="trend_analysis",
                description="Identify and analyze trends in research areas",
                input_types=["time_series_data", "domain"],
                output_types=["trend_report", "predictions"],
                confidence_level=0.75
            ),
            "hypothesis_generation": AgentCapability(
                name="hypothesis_generation",
                description="Generate research hypotheses based on existing knowledge",
                input_types=["research_gap", "background_knowledge"],
                output_types=["hypotheses", "research_design"],
                confidence_level=0.7
            ),
            "expert_synthesis": AgentCapability(
                name="expert_synthesis",
                description="Synthesize expert knowledge from multiple sources",
                input_types=["expert_opinions", "domain_knowledge"],
                output_types=["consensus_view", "knowledge_synthesis"],
                confidence_level=0.8
            ),
            "research_design": AgentCapability(
                name="research_design",
                description="Design research methodologies and experiments",
                input_types=["research_question", "constraints"],
                output_types=["research_plan", "methodology"],
                confidence_level=0.75
            )
        }
    
    async def _initialize_agent_specifics(self) -> None:
        """Initialize research agent specific components"""
        self.logger.info("Initializing research agent specifics...")
        
        # Load research methodologies
        await self._load_research_methodologies()
        
        # Initialize knowledge base
        await self._initialize_knowledge_base()
        
        # Load source credibility data
        await self._load_source_credibility()
        
        # Setup research templates
        await self._setup_research_templates()
        
        self.logger.info("Research agent initialization complete")
    
    async def _process_task(self, 
                           task_description: str, 
                           context: Optional[Dict[str, Any]] = None) -> AgentResponse:
        """Process research task"""
        try:
            # Classify research type
            research_type = await self._classify_research_type(task_description)
            
            # Create research query
            query = await self._create_research_query(task_description, research_type, context)
            
            # Execute research based on type
            if research_type == "literature_review":
                report = await self._conduct_literature_review(query)
            elif research_type == "fact_verification":
                report = await self._verify_facts(query)
            elif research_type == "data_analysis":
                report = await self._analyze_data(query)
            elif research_type == "trend_analysis":
                report = await self._analyze_trends(query)
            elif research_type == "hypothesis_generation":
                report = await self._generate_hypotheses(query)
            elif research_type == "comprehensive_research":
                report = await self._conduct_comprehensive_research(query)
            else:
                report = await self._conduct_general_research(query)
            
            # Store research report
            self.research_history.append(report)
            
            # Generate response
            response_content = await self._generate_research_response(report)
            
            return AgentResponse(
                content=response_content,
                confidence=report.confidence_score,
                reasoning=f"Conducted {research_type} research with {len(report.findings)} findings",
                metadata={
                    "research_type": research_type,
                    "findings_count": len(report.findings),
                    "report_id": report.report_id,
                    "domain": query.domain
                }
            )
            
        except Exception as e:
            self.logger.error(f"Error in research task processing: {e}")
            return AgentResponse(
                content="I encountered an error while conducting research.",
                confidence=0.0,
                success=False,
                error_message=str(e)
            )
    
    async def _classify_research_type(self, task_description: str) -> str:
        """Classify the type of research needed"""
        task_lower = task_description.lower()
        
        # Pattern matching for research types
        if any(phrase in task_lower for phrase in ["literature review", "review literature", "research papers"]):
            return "literature_review"
        
        elif any(phrase in task_lower for phrase in ["verify", "fact check", "is it true", "confirm"]):
            return "fact_verification"
        
        elif any(phrase in task_lower for phrase in ["analyze data", "data analysis", "interpret data"]):
            return "data_analysis"
        
        elif any(phrase in task_lower for phrase in ["trend", "pattern", "over time", "historical"]):
            return "trend_analysis"
        
        elif any(phrase in task_lower for phrase in ["hypothesis", "theory", "propose", "suggest explanation"]):
            return "hypothesis_generation"
        
        elif any(phrase in task_lower for phrase in ["comprehensive", "thorough", "complete study"]):
            return "comprehensive_research"
        
        elif any(phrase in task_lower for phrase in ["investigate", "research", "study", "explore"]):
            return "general_research"
        
        else:
            return "general_research"
    
    async def _create_research_query(self, 
                                   task_description: str, 
                                   research_type: str,
                                   context: Optional[Dict[str, Any]]) -> ResearchQuery:
        """Create structured research query"""
        
        # Extract domain from task or context
        domain = await self._extract_domain(task_description, context)
        
        # Determine depth level
        depth_level = await self._determine_depth_level(task_description, research_type)
        
        # Identify required sources
        sources_required = await self._identify_required_sources(task_description, domain)
        
        query = ResearchQuery(
            query_id=f"query_{datetime.now().timestamp()}",
            question=task_description,
            domain=domain,
            depth_level=depth_level,
            sources_required=sources_required,
            research_type=research_type
        )
        
        return query
    
    async def _extract_domain(self, 
                            task_description: str, 
                            context: Optional[Dict[str, Any]]) -> str:
        """Extract research domain from task"""
        
        # Check context first
        if context and "domain" in context:
            return context["domain"]
        
        # Domain keywords mapping
        domain_keywords = {
            "science": ["science", "scientific", "physics", "chemistry", "biology", "research"],
            "technology": ["technology", "tech", "software", "hardware", "computing", "AI", "algorithm"],
            "medicine": ["medical", "health", "disease", "treatment", "medicine", "clinical"],
            "economics": ["economic", "finance", "market", "business", "economic", "trade"],
            "social": ["social", "society", "culture", "psychology", "sociology", "anthropology"],
            "environment": ["environment", "climate", "ecology", "sustainability", "green"],
            "education": ["education", "learning", "teaching", "academic", "school", "university"],
            "history": ["history", "historical", "past", "ancient", "timeline", "era"],
            "politics": ["political", "government", "policy", "law", "legal", "legislation"],
            "arts": ["art", "artistic", "creative", "design", "music", "literature", "culture"]
        }
        
        task_lower = task_description.lower()
        
        for domain, keywords in domain_keywords.items():
            if any(keyword in task_lower for keyword in keywords):
                return domain
        
        return "general"
    
    async def _determine_depth_level(self, task_description: str, research_type: str) -> int:
        """Determine research depth level (1-5)"""
        task_lower = task_description.lower()
        
        # Depth indicators
        if any(word in task_lower for word in ["comprehensive", "thorough", "detailed", "in-depth"]):
            return 5
        elif any(word in task_lower for word in ["analyze", "examine", "investigate"]):
            return 4
        elif any(word in task_lower for word in ["explore", "study", "research"]):
            return 3
        elif any(word in task_lower for word in ["overview", "summary", "brief"]):
            return 2
        else:
            return 3  # Default medium depth
    
    async def _identify_required_sources(self, task_description: str, domain: str) -> List[str]:
        """Identify required source types for research"""
        task_lower = task_description.lower()
        sources = []
        
        # Academic sources
        if any(word in task_lower for word in ["academic", "peer-reviewed", "journal", "study"]):
            sources.append("academic_papers")
        
        # News sources
        if any(word in task_lower for word in ["news", "current", "recent", "latest"]):
            sources.append("news_articles")
        
        # Expert opinions
        if any(word in task_lower for word in ["expert", "specialist", "professional"]):
            sources.append("expert_opinions")
        
        # Government data
        if any(word in task_lower for word in ["government", "official", "policy", "regulation"]):
            sources.append("government_sources")
        
        # Industry reports
        if any(word in task_lower for word in ["industry", "market", "business", "commercial"]):
            sources.append("industry_reports")
        
        # If no specific sources identified, use domain defaults
        if not sources:
            domain_defaults = {
                "science": ["academic_papers", "research_databases"],
                "technology": ["industry_reports", "technical_documentation"],
                "medicine": ["medical_journals", "clinical_studies"],
                "economics": ["economic_reports", "financial_data"],
                "social": ["academic_papers", "survey_data"],
                "general": ["multiple_sources", "credible_websites"]
            }
            sources = domain_defaults.get(domain, ["multiple_sources"])
        
        return sources
    
    async def _conduct_literature_review(self, query: ResearchQuery) -> ResearchReport:
        """Conduct literature review"""
        findings = []
        
        # Simulate literature search and analysis
        # In practice, this would integrate with academic databases
        
        # Generate simulated findings based on query
        base_findings = await self._simulate_literature_findings(query)
        findings.extend(base_findings)
        
        # Synthesize findings
        synthesis = await self._synthesize_literature_findings(findings, query)
        
        # Generate conclusions
        conclusions = await self._generate_literature_conclusions(findings, query)
        
        # Calculate confidence
        confidence = await self._calculate_research_confidence(findings, "literature_review")
        
        report = ResearchReport(
            report_id=f"lit_review_{datetime.now().timestamp()}",
            query=query,
            findings=findings,
            synthesis=synthesis,
            conclusions=conclusions,
            recommendations=await self._generate_recommendations(findings, query),
            confidence_score=confidence,
            limitations=["Limited to available literature", "May have publication bias"],
            further_research=["Conduct empirical studies", "Update review periodically"]
        )
        
        return report
    
    async def _verify_facts(self, query: ResearchQuery) -> ResearchReport:
        """Verify facts and claims"""
        findings = []
        
        # Extract claims from query
        claims = await self._extract_claims(query.question)
        
        # Verify each claim
        for claim in claims:
            verification_result = await self._verify_single_claim(claim, query.domain)
            findings.append(verification_result)
        
        # Synthesize verification results
        synthesis = await self._synthesize_verification_results(findings, query)
        
        # Generate conclusions
        conclusions = await self._generate_verification_conclusions(findings)
        
        confidence = await self._calculate_research_confidence(findings, "fact_verification")
        
        report = ResearchReport(
            report_id=f"fact_check_{datetime.now().timestamp()}",
            query=query,
            findings=findings,
            synthesis=synthesis,
            conclusions=conclusions,
            recommendations=["Cross-reference with additional sources", "Monitor for updates"],
            confidence_score=confidence,
            limitations=["Based on available sources at time of check"],
            further_research=["Continuous monitoring for updates"]
        )
        
        return report
    
    async def _analyze_data(self, query: ResearchQuery) -> ResearchReport:
        """Analyze research data"""
        findings = []
        
        # Simulate data analysis process
        analysis_results = await self._simulate_data_analysis(query)
        findings.extend(analysis_results)
        
        # Generate insights
        synthesis = await self._synthesize_data_analysis(findings, query)
        
        # Statistical conclusions
        conclusions = await self._generate_data_conclusions(findings, query)
        
        confidence = await self._calculate_research_confidence(findings, "data_analysis")
        
        report = ResearchReport(
            report_id=f"data_analysis_{datetime.now().timestamp()}",
            query=query,
            findings=findings,
            synthesis=synthesis,
            conclusions=conclusions,
            recommendations=["Validate with additional data", "Consider confounding variables"],
            confidence_score=confidence,
            limitations=["Data quality dependent", "Statistical assumptions"],
            further_research=["Collect additional data", "Longitudinal study"]
        )
        
        return report
    
    async def _analyze_trends(self, query: ResearchQuery) -> ResearchReport:
        """Analyze trends and patterns"""
        findings = []
        
        # Simulate trend analysis
        trend_data = await self._simulate_trend_analysis(query)
        findings.extend(trend_data)
        
        # Trend synthesis
        synthesis = await self._synthesize_trend_analysis(findings, query)
        
        # Trend conclusions and predictions
        conclusions = await self._generate_trend_conclusions(findings, query)
        
        confidence = await self._calculate_research_confidence(findings, "trend_analysis")
        
        report = ResearchReport(
            report_id=f"trend_analysis_{datetime.now().timestamp()}",
            query=query,
            findings=findings,
            synthesis=synthesis,
            conclusions=conclusions,
            recommendations=["Monitor for trend changes", "Consider external factors"],
            confidence_score=confidence,
            limitations=["Past performance doesn't guarantee future results", "External factors"],
            further_research=["Continuous monitoring", "Causal analysis"]
        )
        
        return report
    
    async def _generate_hypotheses(self, query: ResearchQuery) -> ResearchReport:
        """Generate research hypotheses"""
        findings = []
        
        # Generate hypotheses based on existing knowledge
        hypotheses = await self._generate_research_hypotheses(query)
        
        for hypothesis in hypotheses:
            finding = ResearchFinding(
                finding_id=f"hypothesis_{datetime.now().timestamp()}",
                query_id=query.query_id,
                content=hypothesis["statement"],
                source="hypothesis_generation",
                confidence=hypothesis["confidence"],
                relevance_score=hypothesis["relevance"],
                timestamp=datetime.now(),
                evidence_type="theoretical"
            )
            findings.append(finding)
        
        # Synthesize hypotheses
        synthesis = await self._synthesize_hypotheses(findings, query)
        
        # Generate research design recommendations
        conclusions = await self._generate_hypothesis_conclusions(findings, query)
        
        confidence = await self._calculate_research_confidence(findings, "hypothesis_generation")
        
        report = ResearchReport(
            report_id=f"hypothesis_gen_{datetime.now().timestamp()}",
            query=query,
            findings=findings,
            synthesis=synthesis,
            conclusions=conclusions,
            recommendations=["Test hypotheses empirically", "Design controlled experiments"],
            confidence_score=confidence,
            limitations=["Theoretical basis only", "Requires empirical validation"],
            further_research=["Experimental testing", "Pilot studies"]
        )
        
        return report
    
    async def _conduct_comprehensive_research(self, query: ResearchQuery) -> ResearchReport:
        """Conduct comprehensive research combining multiple approaches"""
        all_findings = []
        
        # Conduct literature review
        lit_review = await self._conduct_literature_review(query)
        all_findings.extend(lit_review.findings)
        
        # Add data analysis if applicable
        if "data" in query.question.lower():
            data_analysis = await self._analyze_data(query)
            all_findings.extend(data_analysis.findings)
        
        # Add trend analysis if temporal aspects
        if any(word in query.question.lower() for word in ["trend", "over time", "change"]):
            trend_analysis = await self._analyze_trends(query)
            all_findings.extend(trend_analysis.findings)
        
        # Comprehensive synthesis
        synthesis = await self._synthesize_comprehensive_research(all_findings, query)
        
        # Comprehensive conclusions
        conclusions = await self._generate_comprehensive_conclusions(all_findings, query)
        
        confidence = await self._calculate_research_confidence(all_findings, "comprehensive")
        
        report = ResearchReport(
            report_id=f"comprehensive_{datetime.now().timestamp()}",
            query=query,
            findings=all_findings,
            synthesis=synthesis,
            conclusions=conclusions,
            recommendations=["Implement findings", "Monitor developments", "Update periodically"],
            confidence_score=confidence,
            limitations=["Scope limitations", "Time constraints", "Source availability"],
            further_research=["Specialized studies", "Longitudinal tracking", "Validation studies"]
        )
        
        return report
    
    async def _conduct_general_research(self, query: ResearchQuery) -> ResearchReport:
        """Conduct general research"""
        findings = []
        
        # Simulate general research process
        general_findings = await self._simulate_general_research(query)
        findings.extend(general_findings)
        
        # General synthesis
        synthesis = await self._synthesize_general_research(findings, query)
        
        # General conclusions
        conclusions = await self._generate_general_conclusions(findings, query)
        
        confidence = await self._calculate_research_confidence(findings, "general")
        
        report = ResearchReport(
            report_id=f"general_{datetime.now().timestamp()}",
            query=query,
            findings=findings,
            synthesis=synthesis,
            conclusions=conclusions,
            recommendations=["Further specialized research", "Verify with experts"],
            confidence_score=confidence,
            limitations=["General overview only", "May lack depth in specific areas"],
            further_research=["Specialized investigation", "Expert consultation"]
        )
        
        return report
    
    # Simulation methods (would be replaced with actual research capabilities)
    
    async def _simulate_literature_findings(self, query: ResearchQuery) -> List[ResearchFinding]:
        """Simulate literature review findings"""
        findings = []
        
        # Generate representative findings based on query
        finding_templates = [
            f"Recent studies in {query.domain} suggest that {query.question.lower()} is an active area of research.",
            f"Literature review reveals multiple perspectives on {query.question.lower()}.",
            f"Systematic analysis of {query.domain} literature shows consistent patterns.",
            f"Meta-analysis indicates significant findings related to {query.question.lower()}."
        ]
        
        for i, template in enumerate(finding_templates):
            finding = ResearchFinding(
                finding_id=f"lit_finding_{i}_{datetime.now().timestamp()}",
                query_id=query.query_id,
                content=template,
                source=f"academic_database_{i+1}",
                confidence=0.7 + (i * 0.05),
                relevance_score=0.8 + (i * 0.02),
                timestamp=datetime.now(),
                evidence_type="literature_review"
            )
            findings.append(finding)
        
        return findings
    
    async def _simulate_data_analysis(self, query: ResearchQuery) -> List[ResearchFinding]:
        """Simulate data analysis results"""
        findings = []
        
        analysis_types = ["descriptive", "correlation", "regression", "trend"]
        
        for analysis_type in analysis_types:
            finding = ResearchFinding(
                finding_id=f"data_{analysis_type}_{datetime.now().timestamp()}",
                query_id=query.query_id,
                content=f"{analysis_type.title()} analysis reveals significant patterns in the data related to {query.question}.",
                source="data_analysis_engine",
                confidence=0.75,
                relevance_score=0.8,
                timestamp=datetime.now(),
                evidence_type="statistical"
            )
            findings.append(finding)
        
        return findings
    
    async def _simulate_trend_analysis(self, query: ResearchQuery) -> List[ResearchFinding]:
        """Simulate trend analysis results"""
        findings = []
        
        trend_patterns = ["increasing", "decreasing", "cyclical", "stable"]
        
        for pattern in trend_patterns:
            finding = ResearchFinding(
                finding_id=f"trend_{pattern}_{datetime.now().timestamp()}",
                query_id=query.query_id,
                content=f"Trend analysis shows {pattern} pattern in {query.question} over the analyzed period.",
                source="trend_analysis_system",
                confidence=0.7,
                relevance_score=0.75,
                timestamp=datetime.now(),
                evidence_type="statistical"
            )
            findings.append(finding)
        
        return findings
    
    async def _simulate_general_research(self, query: ResearchQuery) -> List[ResearchFinding]:
        """Simulate general research findings"""
        findings = []
        
        general_aspects = ["background", "current_state", "challenges", "opportunities"]
        
        for aspect in general_aspects:
            finding = ResearchFinding(
                finding_id=f"general_{aspect}_{datetime.now().timestamp()}",
                query_id=query.query_id,
                content=f"Research into {aspect} of {query.question} reveals important insights.",
                source="general_research",
                confidence=0.6,
                relevance_score=0.7,
                timestamp=datetime.now(),
                evidence_type="general"
            )
            findings.append(finding)
        
        return findings
    
    # Synthesis methods
    
    async def _synthesize_literature_findings(self, 
                                            findings: List[ResearchFinding], 
                                            query: ResearchQuery) -> str:
        """Synthesize literature review findings"""
        synthesis = f"# Literature Review: {query.question}\n\n"
        synthesis += f"## Domain: {query.domain.title()}\n\n"
        synthesis += "## Key Findings\n\n"
        
        for i, finding in enumerate(findings, 1):
            synthesis += f"{i}. {finding.content} (Confidence: {finding.confidence:.2f})\n"
        
        synthesis += "\n## Synthesis\n"
        synthesis += f"The literature review on {query.question} reveals a comprehensive body of research "
        synthesis += f"in the {query.domain} domain. The findings suggest multiple approaches and perspectives "
        synthesis += "that contribute to our understanding of this topic."
        
        return synthesis
    
    async def _synthesize_verification_results(self, 
                                             findings: List[ResearchFinding], 
                                             query: ResearchQuery) -> str:
        """Synthesize fact verification results"""
        verified_count = sum(1 for f in findings if f.confidence > 0.7)
        total_count = len(findings)
        
        synthesis = f"# Fact Verification Report: {query.question}\n\n"
        synthesis += f"## Verification Summary\n"
        synthesis += f"- Total claims verified: {total_count}\n"
        synthesis += f"- High confidence verifications: {verified_count}\n"
        synthesis += f"- Verification rate: {(verified_count/total_count)*100:.1f}%\n\n"
        
        synthesis += "## Detailed Results\n\n"
        for finding in findings:
            status = "VERIFIED" if finding.confidence > 0.7 else "INCONCLUSIVE"
            synthesis += f"- **{status}**: {finding.content} (Confidence: {finding.confidence:.2f})\n"
        
        return synthesis
    
    async def _generate_research_response(self, report: ResearchReport) -> str:
        """Generate final research response"""
        response = f"# Research Report: {report.query.question}\n\n"
        
        response += f"**Research Type**: {report.query.research_type.title()}\n"
        response += f"**Domain**: {report.query.domain.title()}\n"
        response += f"**Confidence Score**: {report.confidence_score:.2f}\n\n"
        
        response += "## Summary\n"
        response += report.synthesis + "\n\n"
        
        if report.conclusions:
            response += "## Key Conclusions\n"
            for i, conclusion in enumerate(report.conclusions, 1):
                response += f"{i}. {conclusion}\n"
            response += "\n"
        
        if report.recommendations:
            response += "## Recommendations\n"
            for i, rec in enumerate(report.recommendations, 1):
                response += f"{i}. {rec}\n"
            response += "\n"
        
        if report.limitations:
            response += "## Limitations\n"
            for limitation in report.limitations:
                response += f"- {limitation}\n"
            response += "\n"
        
        if report.further_research:
            response += "## Further Research\n"
            for research in report.further_research:
                response += f"- {research}\n"
        
        return response
    
    # Helper methods
    
    def _initialize_expertise_domains(self) -> Dict[str, float]:
        """Initialize domain expertise levels"""
        base_expertise = {
            "general": 0.8,
            "science": 0.7,
            "technology": 0.75,
            "medicine": 0.6,
            "economics": 0.65,
            "social": 0.7,
            "environment": 0.65,
            "education": 0.7,
            "history": 0.6,
            "politics": 0.6,
            "arts": 0.5
        }
        
        # Adjust based on agent configuration
        if self.config.domain_expertise != "general_research":
            if self.config.domain_expertise in base_expertise:
                base_expertise[self.config.domain_expertise] = 0.9
        
        return base_expertise
    
    async def _load_research_methodologies(self) -> None:
        """Load research methodologies"""
        self.research_methodologies = {
            "systematic_review": {
                "steps": ["define_criteria", "search_databases", "screen_studies", "extract_data", "synthesize"],
                "confidence_adjustment": 0.1
            },
            "meta_analysis": {
                "steps": ["literature_search", "quality_assessment", "data_extraction", "statistical_analysis"],
                "confidence_adjustment": 0.15
            },
            "case_study": {
                "steps": ["case_selection", "data_collection", "analysis", "interpretation"],
                "confidence_adjustment": -0.1
            },
            "survey_research": {
                "steps": ["design_survey", "collect_responses", "analyze_data", "interpret_results"],
                "confidence_adjustment": 0.05
            }
        }
    
    async def _initialize_knowledge_base(self) -> None:
        """Initialize domain knowledge base"""
        # Load from memory if available
        knowledge_data = await self._retrieve_from_memory("semantic", {
            "type": "research_knowledge_base"
        })
        
        if knowledge_data:
            self.knowledge_base = knowledge_data[0].get("knowledge", {})
        else:
            # Initialize with basic structure
            self.knowledge_base = {
                "concepts": {},
                "relationships": {},
                "methodologies": {},
                "sources": {}
            }
    
    async def _load_source_credibility(self) -> None:
        """Load source credibility ratings"""
        # Default credibility ratings
        self.source_credibility = {
            "peer_reviewed_journals": 0.9,
            "government_sources": 0.85,
            "academic_institutions": 0.8,
            "expert_opinions": 0.75,
            "industry_reports": 0.7,
            "news_articles": 0.6,
            "general_websites": 0.4,
            "social_media": 0.2
        }
    
    async def _setup_research_templates(self) -> None:
        """Setup research response templates"""
        self.synthesis_templates = {
            "literature_review": "Based on the comprehensive literature review of {topic}, the research reveals {summary}.",
            "fact_verification": "Verification of {claim} shows {result} with confidence level {confidence}.",
            "data_analysis": "Statistical analysis of {dataset} indicates {findings} with significance {significance}.",
            "trend_analysis": "Trend analysis of {topic} over {timeframe} shows {pattern} with {confidence} confidence."
        }
    
    # Additional helper methods for specific research tasks
    
    async def _extract_claims(self, question: str) -> List[str]:
        """Extract verifiable claims from question"""
        # Simple claim extraction - would be more sophisticated in practice
        claims = []
        
        # Look for factual statements
        if "is" in question.lower() or "are" in question.lower():
            claims.append(question)
        
        # Split compound questions
        if " and " in question:
            claims.extend(question.split(" and "))
        
        if not claims:
            claims.append(question)
        
        return claims
    
    async def _verify_single_claim(self, claim: str, domain: str) -> ResearchFinding:
        """Verify a single claim"""
        # Simulate verification process
        confidence = 0.7  # Default confidence
        
        # Adjust confidence based on domain expertise
        if domain in self.expertise_domains:
            confidence *= self.expertise_domains[domain]
        
        return ResearchFinding(
            finding_id=f"verification_{datetime.now().timestamp()}",
            query_id="",
            content=f"Verification of '{claim}' completed",
            source="verification_engine",
            confidence=confidence,
            relevance_score=0.9,
            timestamp=datetime.now(),
            evidence_type="verification"
        )
    
    async def _generate_research_hypotheses(self, query: ResearchQuery) -> List[Dict[str, Any]]:
        """Generate research hypotheses"""
        hypotheses = []
        
        # Generate hypotheses based on query and domain
        base_hypotheses = [
            f"There is a significant relationship between variables in {query.question}",
            f"The phenomenon described in {query.question} follows predictable patterns",
            f"External factors influence the outcomes related to {query.question}",
            f"The current understanding of {query.question} may need revision"
        ]
        
        for i, hypothesis in enumerate(base_hypotheses):
            hypotheses.append({
                "statement": hypothesis,
                "confidence": 0.6 + (i * 0.05),
                "relevance": 0.7 + (i * 0.03),
                "testability": 0.8
            })
        
        return hypotheses
    
    async def _calculate_research_confidence(self, 
                                           findings: List[ResearchFinding], 
                                           research_type: str) -> float:
        """Calculate overall research confidence"""
        if not findings:
            return 0.0
        
        # Base confidence from findings
        avg_confidence = sum(f.confidence for f in findings) / len(findings)
        
        # Adjust based on research type
        type_adjustments = {
            "literature_review": 0.1,
            "fact_verification": 0.05,
            "data_analysis": 0.15,
            "trend_analysis": -0.05,
            "hypothesis_generation": -0.1,
            "comprehensive": 0.2,
            "general": 0.0
        }
        
        adjustment = type_adjustments.get(research_type, 0.0)
        
        # Adjust based on number of findings
        if len(findings) > 5:
            adjustment += 0.05
        elif len(findings) < 3:
            adjustment -= 0.05
        
        final_confidence = min(max(avg_confidence + adjustment, 0.0), 1.0)
        return final_confidence
    
    # Conclusion generation methods
    
    async def _generate_literature_conclusions(self, 
                                             findings: List[ResearchFinding], 
                                             query: ResearchQuery) -> List[str]:
        """Generate conclusions from literature review"""
        conclusions = [
            f"Literature review of {query.question} reveals {len(findings)} significant findings",
            f"Research in {query.domain} shows consistent patterns regarding {query.question}",
            "Multiple methodological approaches support the main findings",
            "Further research would benefit from longitudinal studies"
        ]
        return conclusions
    
    async def _generate_verification_conclusions(self, findings: List[ResearchFinding]) -> List[str]:
        """Generate conclusions from fact verification"""
        high_conf_count = sum(1 for f in findings if f.confidence > 0.7)
        conclusions = [
            f"Verification process completed for {len(findings)} claims",
            f"{high_conf_count} claims verified with high confidence",
            "Verification based on available credible sources",
            "Continuous monitoring recommended for claim updates"
        ]
        return conclusions
    
    async def _generate_data_conclusions(self, 
                                       findings: List[ResearchFinding], 
                                       query: ResearchQuery) -> List[str]:
        """Generate conclusions from data analysis"""
        conclusions = [
            f"Data analysis supports {len(findings)} key findings",
            "Statistical significance observed in primary measures",
            "Results consistent with theoretical expectations",
            "Additional data collection recommended for validation"
        ]
        return conclusions
    
    async def _generate_recommendations(self, 
                                      findings: List[ResearchFinding], 
                                      query: ResearchQuery) -> List[str]:
        """Generate research recommendations"""
        recommendations = [
            "Continue monitoring developments in this research area",
            "Consider practical applications of research findings",
            "Validate findings through independent replication",
            "Integrate findings with broader theoretical framework"
        ]
        
        # Customize based on research type and domain
        if query.research_type == "hypothesis_generation":
            recommendations.extend([
                "Design empirical studies to test generated hypotheses",
                "Conduct pilot studies before full-scale research"
            ])
        
        return recommendations
    
    # Additional synthesis methods for different research types
    
    async def _synthesize_data_analysis(self, 
                                      findings: List[ResearchFinding], 
                                      query: ResearchQuery) -> str:
        """Synthesize data analysis findings"""
        synthesis = f"# Data Analysis Report: {query.question}\n\n"
        synthesis += f"## Analysis Domain: {query.domain.title()}\n\n"
        
        # Categorize findings by analysis type
        analysis_types = {}
        for finding in findings:
            if "descriptive" in finding.content.lower():
                analysis_types.setdefault("descriptive", []).append(finding)
            elif "correlation" in finding.content.lower():
                analysis_types.setdefault("correlation", []).append(finding)
            elif "regression" in finding.content.lower():
                analysis_types.setdefault("regression", []).append(finding)
            elif "trend" in finding.content.lower():
                analysis_types.setdefault("trend", []).append(finding)
            else:
                analysis_types.setdefault("general", []).append(finding)
        
        # Present findings by analysis type
        for analysis_type, type_findings in analysis_types.items():
            if type_findings:
                synthesis += f"## {analysis_type.title()} Analysis\n"
                for finding in type_findings:
                    synthesis += f"- {finding.content} (Confidence: {finding.confidence:.2f})\n"
                synthesis += "\n"
        
        synthesis += "## Statistical Summary\n"
        avg_confidence = sum(f.confidence for f in findings) / len(findings) if findings else 0
        synthesis += f"- Average confidence across analyses: {avg_confidence:.2f}\n"
        synthesis += f"- Number of analytical approaches: {len(analysis_types)}\n"
        synthesis += f"- Total findings: {len(findings)}\n\n"
        
        synthesis += "## Interpretation\n"
        synthesis += "The data analysis reveals consistent patterns across multiple analytical approaches. "
        synthesis += "The findings provide quantitative insights that support evidence-based conclusions."
        
        return synthesis
    
    async def _synthesize_trend_analysis(self, 
                                       findings: List[ResearchFinding], 
                                       query: ResearchQuery) -> str:
        """Synthesize trend analysis findings"""
        synthesis = f"# Trend Analysis Report: {query.question}\n\n"
        synthesis += f"## Analysis Period: Historical data for {query.domain}\n\n"
        
        # Extract trend patterns
        trend_patterns = []
        for finding in findings:
            content_lower = finding.content.lower()
            if "increasing" in content_lower:
                trend_patterns.append("upward")
            elif "decreasing" in content_lower:
                trend_patterns.append("downward")
            elif "cyclical" in content_lower:
                trend_patterns.append("cyclical")
            elif "stable" in content_lower:
                trend_patterns.append("stable")
        
        # Summary of trend patterns
        if trend_patterns:
            pattern_counts = {pattern: trend_patterns.count(pattern) for pattern in set(trend_patterns)}
            synthesis += "## Observed Trend Patterns\n"
            for pattern, count in pattern_counts.items():
                synthesis += f"- {pattern.title()} trends: {count} occurrences\n"
            synthesis += "\n"
        
        # Detailed findings
        synthesis += "## Detailed Trend Analysis\n"
        for i, finding in enumerate(findings, 1):
            synthesis += f"{i}. {finding.content}\n"
            synthesis += f"   - Confidence: {finding.confidence:.2f}\n"
            synthesis += f"   - Relevance: {finding.relevance_score:.2f}\n\n"
        
        synthesis += "## Trend Implications\n"
        synthesis += "The trend analysis provides insights into temporal patterns and potential future directions. "
        synthesis += "These patterns can inform predictive modeling and strategic planning efforts."
        
        return synthesis
    
    async def _synthesize_general_research(self, 
                                         findings: List[ResearchFinding], 
                                         query: ResearchQuery) -> str:
        """Synthesize general research findings"""
        synthesis = f"# General Research Report: {query.question}\n\n"
        synthesis += f"## Research Scope: {query.domain.title()} Domain\n\n"
        
        # Organize findings by aspect
        aspects = {}
        for finding in findings:
            content_lower = finding.content.lower()
            if "background" in content_lower:
                aspects.setdefault("background", []).append(finding)
            elif "current" in content_lower:
                aspects.setdefault("current_state", []).append(finding)
            elif "challenge" in content_lower:
                aspects.setdefault("challenges", []).append(finding)
            elif "opportunit" in content_lower:
                aspects.setdefault("opportunities", []).append(finding)
            else:
                aspects.setdefault("general_insights", []).append(finding)
        
        # Present organized findings
        aspect_titles = {
            "background": "Background Information",
            "current_state": "Current State",
            "challenges": "Identified Challenges",
            "opportunities": "Opportunities",
            "general_insights": "General Insights"
        }
        
        for aspect_key, aspect_findings in aspects.items():
            if aspect_findings:
                synthesis += f"## {aspect_titles.get(aspect_key, aspect_key.title())}\n"
                for finding in aspect_findings:
                    synthesis += f"- {finding.content}\n"
                synthesis += "\n"
        
        synthesis += "## Research Summary\n"
        synthesis += f"This general research investigation into {query.question} has identified "
        synthesis += f"{len(findings)} key findings across {len(aspects)} different aspects. "
        synthesis += "The research provides a broad foundation for understanding the topic and "
        synthesis += "identifies areas for more focused investigation."
        
        return synthesis
    
    async def _synthesize_hypotheses(self, 
                                   findings: List[ResearchFinding], 
                                   query: ResearchQuery) -> str:
        """Synthesize generated hypotheses"""
        synthesis = f"# Hypothesis Generation Report: {query.question}\n\n"
        synthesis += f"## Research Domain: {query.domain.title()}\n\n"
        
        # Categorize hypotheses by confidence level
        high_conf = [f for f in findings if f.confidence > 0.7]
        medium_conf = [f for f in findings if 0.5 <= f.confidence <= 0.7]
        exploratory = [f for f in findings if f.confidence < 0.5]
        
        if high_conf:
            synthesis += "## High-Confidence Hypotheses\n"
            for finding in high_conf:
                synthesis += f"- **H{findings.index(finding)+1}**: {finding.content}\n"
                synthesis += f"  - Confidence: {finding.confidence:.2f}\n"
                synthesis += f"  - Relevance: {finding.relevance_score:.2f}\n\n"
        
        if medium_conf:
            synthesis += "## Medium-Confidence Hypotheses\n"
            for finding in medium_conf:
                synthesis += f"- **H{findings.index(finding)+1}**: {finding.content}\n"
                synthesis += f"  - Confidence: {finding.confidence:.2f}\n"
                synthesis += f"  - Relevance: {finding.relevance_score:.2f}\n\n"
        
        if exploratory:
            synthesis += "## Exploratory Hypotheses\n"
            for finding in exploratory:
                synthesis += f"- **H{findings.index(finding)+1}**: {finding.content}\n"
                synthesis += f"  - Confidence: {finding.confidence:.2f}\n"
                synthesis += f"  - Relevance: {finding.relevance_score:.2f}\n\n"
        
        synthesis += "## Hypothesis Testing Recommendations\n"
        synthesis += "The generated hypotheses provide a foundation for empirical research. "
        synthesis += "Priority should be given to high-confidence hypotheses, while exploratory "
        synthesis += "hypotheses may warrant preliminary investigation or pilot studies."
        
        return synthesis
    
    # Public API methods
    
    def get_research_history(self) -> List[Dict[str, Any]]:
        """Get research history summary"""
        return [
            {
                "report_id": report.report_id,
                "query": report.query.question,
                "research_type": report.query.research_type,
                "domain": report.query.domain,
                "confidence": report.confidence_score,
                "findings_count": len(report.findings),
                "timestamp": report.findings[0].timestamp if report.findings else None
            }
            for report in self.research_history
        ]
    
    def get_domain_expertise(self) -> Dict[str, float]:
        """Get domain expertise levels"""
        return self.expertise_domains.copy()
    
    async def update_domain_expertise(self, domain: str, expertise_level: float) -> None:
        """Update expertise level for a domain"""
        self.expertise_domains[domain] = min(max(expertise_level, 0.0), 1.0)
        self.logger.info(f"Updated {domain} expertise to {expertise_level}")
    
    def get_research_statistics(self) -> Dict[str, Any]:
        """Get research performance statistics"""
        if not self.research_history:
            return {"status": "no_research_conducted"}
        
        total_reports = len(self.research_history)
        avg_confidence = sum(r.confidence_score for r in self.research_history) / total_reports
        
        # Research type distribution
        type_counts = {}
        for report in self.research_history:
            rtype = report.query.research_type
            type_counts[rtype] = type_counts.get(rtype, 0) + 1
        
        # Domain distribution
        domain_counts = {}
        for report in self.research_history:
            domain = report.query.domain
            domain_counts[domain] = domain_counts.get(domain, 0) + 1
        
        return {
            "total_reports": total_reports,
            "average_confidence": avg_confidence,
            "research_types": type_counts,
            "domains_researched": domain_counts,
            "expertise_domains": len(self.expertise_domains)
        }
    
    async def _synthesize_comprehensive_research(self, 
                                               findings: List[ResearchFinding], 
                                               query: ResearchQuery) -> str:
        """Synthesize comprehensive research findings"""
        synthesis = f"# Comprehensive Research Report: {query.question}\n\n"
        synthesis += f"## Multi-Method Research Analysis in {query.domain.title()}\n\n"
        
        # Group findings by evidence type
        evidence_groups = {}
        for finding in findings:
            evidence_groups.setdefault(finding.evidence_type, []).append(finding)
        
        # Present findings by evidence type
        for evidence_type, type_findings in evidence_groups.items():
            synthesis += f"## {evidence_type.title()} Evidence\n"
            for finding in type_findings:
                synthesis += f"- {finding.content}\n"
                synthesis += f"  - Confidence: {finding.confidence:.2f}, Relevance: {finding.relevance_score:.2f}\n"
            synthesis += "\n"
        
        # Cross-validation section
        synthesis += "## Cross-Method Validation\n"
        synthesis += f"Evidence collected from {len(evidence_groups)} different methodological approaches:\n"
        for evidence_type in evidence_groups.keys():
            synthesis += f"- {evidence_type.title()}: {len(evidence_groups[evidence_type])} findings\n"
        synthesis += "\n"
        
        # Overall assessment
        avg_confidence = sum(f.confidence for f in findings) / len(findings) if findings else 0
        avg_relevance = sum(f.relevance_score for f in findings) / len(findings) if findings else 0
        
        synthesis += "## Overall Assessment\n"
        synthesis += f"- Average confidence across all methods: {avg_confidence:.2f}\n"
        synthesis += f"- Average relevance across all findings: {avg_relevance:.2f}\n"
        synthesis += f"- Methodological diversity index: {len(evidence_groups)}\n\n"
        
        synthesis += "## Integrated Conclusions\n"
        synthesis += "The comprehensive research approach provides convergent evidence across multiple "
        synthesis += "methodological frameworks. The diversity of evidence types strengthens the "
        synthesis += "overall validity of the findings and provides a robust foundation for "
        synthesis += "evidence-based decision making."
        
        return synthesis
