"""
Advanced Research Engine - Gather research data and generate topic-specific content
Fixes: Generic content issue by researching the actual topic and creating relevant content
"""

import logging
from typing import Dict, List, Optional, Tuple, Any
from textwrap import dedent
import json

logger = logging.getLogger(__name__)


class ResearchDataAggregator:
    """
    Aggregate research data from papers, journals, and online sources.
    Simulates research gathering (in production, would integrate with academic databases).
    """

    def __init__(self):
        """Initialize research aggregator."""
        self.research_sources = {
            "machine_learning": {
                "key_concepts": ["neural networks", "deep learning", "supervised learning", "unsupervised learning", "reinforcement learning"],
                "research_areas": ["computer vision", "natural language processing", "speech recognition", "recommendation systems"],
                "challenges": ["data quality", "model interpretability", "computational cost", "bias in AI", "generalization"],
                "applications": ["healthcare", "finance", "robotics", "autonomous vehicles", "content recommendation"],
                "recent_trends": ["transformer models", "few-shot learning", "federated learning", "quantum machine learning"],
                "key_researchers": ["Yann LeCun", "Geoffrey Hinton", "Yoshua Bengio", "Andrew Ng", "Fei-Fei Li"],
                "key_papers": [
                    "Attention is All You Need (Vaswani et al., 2017)",
                    "Deep Residual Learning for Image Recognition (He et al., 2015)",
                    "A Theoretically Grounded Application of Dropout in RNNs (Gal & Ghahramani, 2016)",
                    "BERT: Pre-training of Deep Bidirectional Transformers (Devlin et al., 2018)",
                ]
            },
            "natural_language_processing": {
                "key_concepts": ["tokenization", "sentiment analysis", "named entity recognition", "machine translation", "question answering"],
                "research_areas": ["language models", "text summarization", "dialogue systems", "semantic understanding"],
                "challenges": ["context understanding", "multilingual processing", "low-resource languages", "domain adaptation"],
                "applications": ["chatbots", "machine translation", "information extraction", "document classification"],
                "recent_trends": ["large language models", "prompt engineering", "in-context learning", "multimodal models"],
                "key_researchers": ["Christopher Manning", "Hinrich Schütze", "Preslav Nakov", "Graham Neubig"],
                "key_papers": [
                    "Language Models are Unsupervised Multitask Learners (Radford et al., 2019)",
                    "ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators (Clark et al., 2020)",
                    "XLNet: Generalized Autoregressive Pretraining for Language Understanding (Yang et al., 2019)",
                ]
            },
            "computer_vision": {
                "key_concepts": ["image classification", "object detection", "semantic segmentation", "pose estimation", "image generation"],
                "research_areas": ["3D vision", "video understanding", "scene understanding", "visual reasoning"],
                "challenges": ["scale variation", "occlusion", "real-time processing", "domain shift"],
                "applications": ["autonomous driving", "medical imaging", "facial recognition", "augmented reality"],
                "recent_trends": ["vision transformers", "self-supervised learning", "multi-modal learning", "efficient architectures"],
                "key_researchers": ["Fei-Fei Li", "Silvio Savarese", "Justin Johnson", "Alexei Efros"],
                "key_papers": [
                    "An Image is Worth 16x16 Words: Transformers for Image Recognition (Dosovitskiy et al., 2020)",
                    "Mask R-CNN (He et al., 2017)",
                    "YOLO: You Only Look Once (Redmon et al., 2016)",
                ]
            },
            "deep_learning": {
                "key_concepts": ["backpropagation", "gradient descent", "convolutional networks", "recurrent networks", "attention mechanisms"],
                "research_areas": ["architecture design", "training optimization", "regularization", "initialization"],
                "challenges": ["vanishing gradients", "overfitting", "computational efficiency", "hyperparameter tuning"],
                "applications": ["all AI/ML tasks", "speech processing", "game playing"],
                "recent_trends": ["neural architecture search", "knowledge distillation", "pruning", "quantization"],
                "key_researchers": ["Yann LeCun", "Geoffrey Hinton", "Yoshua Bengio"],
                "key_papers": [
                    "ImageNet Classification with Deep Convolutional Neural Networks (Krizhevsky et al., 2012)",
                    "Going Deeper with Convolutions (Szegedy et al., 2015)",
                    "Batch Normalization: Accelerating Deep Network Training (Ioffe & Szegedy, 2015)",
                ]
            },
            "data_science": {
                "key_concepts": ["data collection", "data cleaning", "exploratory analysis", "statistical modeling", "predictive analytics"],
                "research_areas": ["data mining", "big data", "data visualization", "statistical inference"],
                "challenges": ["missing data", "class imbalance", "feature engineering", "interpretability"],
                "applications": ["business analytics", "market analysis", "risk assessment"],
                "recent_trends": ["automated machine learning", "explainable AI", "causal inference", "privacy-preserving analytics"],
                "key_researchers": ["Jeff Leek", "Hadley Wickham", "Claudia Perlich"],
                "key_papers": [
                    "Big Data: The New Data for Development (Letouze et al., 2014)",
                    "A Few Useful Things to Know about Machine Learning (Domingos, 2012)",
                ]
            },
        }

    def search_topic_research(self, topic: str) -> Dict[str, Any]:
        """
        Search for research data related to a specific topic.
        
        Args:
            topic: Research topic
            
        Returns:
            Dictionary of research data (concepts, papers, trends, etc.)
        """
        topic_lower = topic.lower()
        
        # Try to match topic to research areas
        for research_area, data in self.research_sources.items():
            if any(keyword in topic_lower for keyword in research_area.split("_")):
                return self._enhance_topic_data(topic, data)
        
        # Fallback: generic research data
        return self._generate_generic_research(topic)

    def _enhance_topic_data(self, topic: str, base_data: Dict) -> Dict:
        """Enhance base research data with topic-specific information."""
        enhanced = base_data.copy()
        enhanced["topic"] = topic
        enhanced["search_context"] = f"Research on {topic}"
        enhanced["research_impact"] = "High - Actively researched area with significant industry application"
        
        return enhanced

    def _generate_generic_research(self, topic: str) -> Dict:
        """Generate research data for unknown topics."""
        return {
            "topic": topic,
            "key_concepts": [
                f"Fundamentals of {topic}",
                f"Current state of {topic} research",
                f"Methodologies in {topic}",
                f"Applications of {topic}",
                f"Challenges in {topic}",
            ],
            "research_areas": [
                f"Theoretical aspects of {topic}",
                f"Practical implementation of {topic}",
                f"Future directions in {topic}",
                f"Interdisciplinary applications",
            ],
            "challenges": [
                f"Current limitations in {topic}",
                f"Scalability issues",
                f"Integration challenges",
                f"Resource constraints",
            ],
            "applications": [
                f"Industry applications",
                f"Healthcare applications",
                f"Business applications",
                f"Research applications",
            ],
            "recent_trends": [
                f"Automation in {topic}",
                f"AI integration in {topic}",
                f"Cloud-based solutions",
                f"Real-time processing",
            ],
        }

    def extract_key_insights(self, research_data: Dict, section: str) -> List[str]:
        """Extract key insights for specific document section."""
        insights = []
        
        section_lower = section.lower()
        
        if "introduction" in section_lower:
            insights = self._get_introduction_insights(research_data)
        elif "literature" in section_lower or "background" in section_lower:
            insights = self._get_literature_insights(research_data)
        elif "method" in section_lower:
            insights = self._get_methodology_insights(research_data)
        elif "result" in section_lower or "finding" in section_lower:
            insights = self._get_results_insights(research_data)
        elif "discussion" in section_lower:
            insights = self._get_discussion_insights(research_data)
        elif "conclusion" in section_lower:
            insights = self._get_conclusion_insights(research_data)
        
        return insights if insights else self._get_generic_insights(research_data)

    def _get_introduction_insights(self, data: Dict) -> List[str]:
        """Get introduction section insights."""
        return [
            f"Overview of {data.get('topic', 'the topic')} and its importance",
            f"Key concepts: {', '.join(data.get('key_concepts', [])[:3])}",
            f"Current research landscape and gaps",
            f"Objectives and scope of investigation",
        ]

    def _get_literature_insights(self, data: Dict) -> List[str]:
        """Get literature review insights."""
        return [
            f"Historical development of {data.get('topic', 'the field')}",
            f"Major research contributions: {', '.join(data.get('key_papers', [])[:2])}",
            f"Research areas: {', '.join(data.get('research_areas', [])[:3])}",
            f"Emerging trends: {', '.join(data.get('recent_trends', [])[:3])}",
            "Consensus and controversies in research",
        ]

    def _get_methodology_insights(self, data: Dict) -> List[str]:
        """Get methodology insights."""
        return [
            f"Research approaches in {data.get('topic', 'the field')}",
            "Experimental design and validation methods",
            "Data collection and analysis techniques",
            "Comparative evaluation frameworks",
            "Quality assurance and reproducibility",
        ]

    def _get_results_insights(self, data: Dict) -> List[str]:
        """Get results insights."""
        challenges = data.get('challenges', [])
        applications = data.get('applications', [])
        return [
            f"Performance benchmarks in {data.get('topic', 'the field')}",
            f"Comparative analysis of approaches",
            f"Practical applications: {', '.join(applications[:2]) if applications else 'Multiple domains'}",
            f"Identified limitations: {', '.join(challenges[:2]) if challenges else 'Various technical challenges'}",
        ]

    def _get_discussion_insights(self, data: Dict) -> List[str]:
        """Get discussion insights."""
        return [
            f"Implications of findings for {data.get('topic', 'the field')}",
            f"Relationship to existing research",
            f"Theoretical contributions",
            f"Practical significance and applications",
            f"Future research directions",
        ]

    def _get_conclusion_insights(self, data: Dict) -> List[str]:
        """Get conclusion insights."""
        return [
            f"Summary of key findings in {data.get('topic', 'the field')}",
            "Contributions to advancing knowledge",
            "Practical implications",
            "Unresolved questions and limitations",
            "Recommendations for future work",
        ]

    def _get_generic_insights(self, data: Dict) -> List[str]:
        """Get generic insights."""
        return [
            f"Overview of {data.get('topic', 'the topic')}",
            "Key research findings",
            "Practical applications",
            "Current challenges",
            "Future opportunities",
        ]


class TopicAnalyzer:
    """
    Analyze topics to extract key concepts, terminology, and context.
    """

    def __init__(self):
        """Initialize topic analyzer."""
        self.analyzer = ResearchDataAggregator()

    def analyze_topic(self, topic: str, context: str = "") -> Dict:
        """
        Comprehensive topic analysis.
        
        Args:
            topic: Main topic
            context: Additional context (requirements, notes)
            
        Returns:
            Analyzed topic information
        """
        research_data = self.analyzer.search_topic_research(topic)
        
        return {
            "topic": topic,
            "context": context,
            "research_data": research_data,
            "key_concepts": research_data.get("key_concepts", []),
            "research_areas": research_data.get("research_areas", []),
            "key_applications": research_data.get("applications", []),
            "challenges": research_data.get("challenges", []),
            "trends": research_data.get("recent_trends", []),
            "papers": research_data.get("key_papers", []),
        }

    def extract_key_concepts(self, topic: str) -> List[str]:
        """Extract key concepts from topic."""
        analysis = self.analyze_topic(topic)
        return analysis["key_concepts"]

    def get_topic_context(self, topic: str) -> str:
        """Get context description for topic."""
        analysis = self.analyze_topic(topic)
        concepts = ", ".join(analysis["key_concepts"][:3])
        applications = ", ".join(analysis["key_applications"][:2])
        return f"{topic} encompasses concepts like {concepts} with applications in {applications}."


class ContentSynthesizer:
    """
    Synthesize research data into coherent, topic-specific content.
    """

    def __init__(self):
        """Initialize content synthesizer."""
        self.research_engine = ResearchDataAggregator()
        self.topic_analyzer = TopicAnalyzer()

    def synthesize_section(
        self,
        section_title: str,
        topic: str,
        context: str = "",
        word_count: int = 500,
    ) -> str:
        """
        Synthesize topic-specific content for a section.
        
        Args:
            section_title: Section title
            topic: Research topic
            context: Additional context
            word_count: Target word count
            
        Returns:
            Synthesized section content
        """
        # Analyze topic
        topic_analysis = self.topic_analyzer.analyze_topic(topic, context)
        
        # Get research insights for this section
        insights = self.research_engine.extract_key_insights(
            topic_analysis["research_data"], section_title
        )
        
        # Generate section content based on type
        section_lower = section_title.lower()
        
        if "introduction" in section_lower:
            return self._synthesize_introduction(topic, topic_analysis, insights)
        elif "literature" in section_lower or "background" in section_lower:
            return self._synthesize_literature(topic, topic_analysis, insights)
        elif "method" in section_lower:
            return self._synthesize_methodology(topic, topic_analysis, insights)
        elif "result" in section_lower or "finding" in section_lower:
            return self._synthesize_results(topic, topic_analysis, insights)
        elif "discussion" in section_lower:
            return self._synthesize_discussion(topic, topic_analysis, insights)
        elif "conclusion" in section_lower:
            return self._synthesize_conclusion(topic, topic_analysis, insights)
        else:
            return self._synthesize_generic(topic, topic_analysis, insights, section_title)

    def _synthesize_introduction(self, topic: str, analysis: Dict, insights: List[str]) -> str:
        """Synthesize introduction section with topic-specific content."""
        concepts = ", ".join(analysis["key_concepts"][:3])
        areas = ", ".join(analysis["research_areas"][:2])
        
        content = dedent(f"""
        ## Introduction
        
        The study of {topic} represents a critical and rapidly evolving area in contemporary research. 
        {topic} encompasses fundamental concepts including {concepts}, which have become increasingly 
        important due to their applications across multiple domains.
        
        ### Significance of {topic}
        
        The importance of {topic} cannot be overstated in today's technological landscape. 
        Key research areas include {areas}, each contributing to our understanding of different 
        aspects of the field. Recent advances have opened new possibilities for both theoretical 
        understanding and practical application.
        
        ### Current Research Landscape
        
        The research community has made substantial progress in understanding {topic}. 
        Major contributions include investigations into {analysis['key_concepts'][0]} and 
        {analysis['key_concepts'][1]}, which form the foundation for modern approaches. 
        However, several challenges remain, including {analysis['challenges'][0]} and 
        {analysis['challenges'][1]}, which continue to drive research efforts.
        
        ### Research Objectives
        
        This investigation aims to:
        - Provide comprehensive understanding of {topic} and its applications
        - Analyze current methodologies and their effectiveness
        - Identify emerging trends and future research directions
        - Contribute to advancing knowledge in {topic}
        
        ### Document Structure
        
        Following this introduction, we examine the existing literature on {topic}, 
        explore relevant methodologies, analyze findings and implications, and conclude 
        with recommendations for future research. Throughout this document, we emphasize 
        the practical significance of {topic} in real-world applications.
        """).strip()
        
        return content

    def _synthesize_literature(self, topic: str, analysis: Dict, insights: List[str]) -> str:
        """Synthesize literature review with research data."""
        concepts = analysis["key_concepts"]
        papers = analysis.get("papers", [])
        trends = analysis.get("trends", [])
        
        content = dedent(f"""
        ## Literature Review
        
        The field of {topic} has developed substantially over the past decades, with extensive 
        research documenting key principles and methodologies. This review synthesizes major 
        findings and identifies patterns in the literature.
        
        ### Foundational Concepts and Historical Development
        
        Research in {topic} is built upon several foundational concepts: {', '.join(concepts[:3])}. 
        These concepts have evolved through iterative research and theoretical refinement. Early work 
        established principles that continue to guide contemporary investigation.
        
        ### Major Research Contributions
        
        Significant contributions to {topic} include:
        
        - {papers[0] if papers else 'Foundational theoretical work establishing core principles'}
        - {papers[1] if len(papers) > 1 else 'Methodological innovations expanding research approaches'}
        - {papers[2] if len(papers) > 2 else 'Empirical studies validating theoretical predictions'}
        
        ### Current Research Trends
        
        Recent developments in {topic} show particular focus on:
        
        - {trends[0] if trends else 'Novel applications of existing methodologies'}
        - {trends[1] if len(trends) > 1 else 'Integration across disciplinary boundaries'}
        - {trends[2] if len(trends) > 2 else 'Computational efficiency improvements'}
        
        ### Identified Gaps and Research Questions
        
        Despite significant progress, several important gaps remain in our understanding of {topic}:
        
        - Incomplete understanding of {concepts[0]} in novel contexts
        - Limited research on {analysis['challenges'][0]}
        - Insufficient investigation of {concepts[1]} interactions
        - Need for large-scale empirical validation of recent theoretical developments
        
        ### Synthesis and Implications
        
        Current literature on {topic} reveals both substantial consensus on core principles 
        and ongoing debate regarding optimal approaches. The evidence base supports the 
        importance of {topic} while highlighting the need for continued research addressing 
        identified gaps.
        """).strip()
        
        return content

    def _synthesize_methodology(self, topic: str, analysis: Dict, insights: List[str]) -> str:
        """Synthesize methodology section."""
        content = dedent(f"""
        ## Methodology
        
        This investigation employs comprehensive methodological approaches to advance understanding of {topic}. 
        The methodology integrates multiple research techniques and validation approaches.
        
        ### Research Design
        
        The investigation of {topic} utilizes a multi-method approach that combines:
        - Systematic literature review and analysis
        - Empirical investigation of key concepts: {', '.join(analysis['key_concepts'][:2])}
        - Comparative analysis across different approaches and contexts
        - Evaluation of practical applications and implications
        
        ### Data Collection
        
        Data collection focuses on gathering information relevant to {topic}:
        - Research publications and academic sources
        - Industry case studies and applications
        - Empirical evidence from previous studies
        - Expert assessments and insights
        
        ### Analysis Approaches
        
        Multiple analytical methods ensure comprehensive understanding:
        - Thematic analysis of research findings
        - Comparative evaluation of methodologies
        - Synthesis across research areas: {', '.join(analysis['research_areas'][:2])}
        - Evaluation of practical implications and applications
        
        ### Research Areas Investigated
        
        Investigation encompasses the following research areas:
        {self._format_list(analysis['research_areas'][:4])}
        
        ### Validation and Quality Assurance
        
        Multiple measures ensure validity and reliability:
        - Cross-referencing with multiple sources
        - Evaluation against established frameworks
        - Assessment of methodological rigor
        - Consideration of alternative interpretations
        
        ### Limitations and Scope
        
        This investigation acknowledges the following limitations:
        - Focus on documented research and literature
        - Constraints in empirical data collection
        - Domain-specific nature of {topic}
        - Evolving nature of the field requiring ongoing updates
        """).strip()
        
        return content

    def _synthesize_results(self, topic: str, analysis: Dict, insights: List[str]) -> str:
        """Synthesize results section with topic findings."""
        applications = analysis.get("applications", [])
        challenges = analysis.get("challenges", [])
        
        content = dedent(f"""
        ## Results and Findings
        
        Investigation of {topic} reveals important findings regarding current state of research, 
        practical applications, and emerging opportunities.
        
        ### Current State of {topic}
        
        Analysis reveals that {topic} has advanced significantly with:
        - Established core concepts and methodologies
        - Growing industry adoption and applications
        - Emerging research directions and innovations
        - Increasing interdisciplinary collaboration
        
        ### Key Findings
        
        Research on {topic} demonstrates:
        
        **Finding 1: Practical Applications**
        {topic} finds widespread application in: {', '.join(applications[:3])}. 
        Each application domain benefits from specific aspects of {topic} research.
        
        **Finding 2: Methodological Consensus**
        While some variation exists, research shows consensus regarding effective approaches to {topic}. 
        Established methodologies demonstrate consistent effectiveness across contexts.
        
        **Finding 3: Persistent Challenges**
        Despite advances, several challenges continue to challenge researchers:
        - {challenges[0] if challenges else 'Technical limitations'}
        - {challenges[1] if len(challenges) > 1 else 'Integration complexity'}
        - {challenges[2] if len(challenges) > 2 else 'Resource constraints'}
        
        ### Comparative Analysis
        
        Comparison of different approaches to {topic} reveals:
        - Varying strengths and limitations of methodologies
        - Context-dependent effectiveness of approaches
        - Trade-offs between different technical solutions
        - Opportunities for methodological innovation
        
        ### Application Areas
        
        {topic} demonstrates practical significance across multiple domains:
        {self._format_list(applications[:4])}
        
        ### Identified Trends
        
        Recent developments show particular attention to:
        {self._format_list(analysis.get('trends', [])[:3])}
        """).strip()
        
        return content

    def _synthesize_discussion(self, topic: str, analysis: Dict, insights: List[str]) -> str:
        """Synthesize discussion section."""
        content = dedent(f"""
        ## Discussion and Analysis
        
        The findings regarding {topic} carry important implications for both theory and practice. 
        This discussion integrates results with existing knowledge and explores their significance.
        
        ### Interpretation of Key Findings
        
        **Theoretical Implications**
        
        The research reveals important insights into {topic} with implications for theoretical understanding:
        - Validation of existing theoretical frameworks
        - Identification of previously underexplored aspects
        - Support for emerging theoretical perspectives
        - Clarification of relationships between key concepts: {', '.join(analysis['key_concepts'][:2])}
        
        **Practical Implications**
        
        Findings have direct practical significance for {analysis['key_applications'][0]} and 
        {analysis['key_applications'][1]} applications:
        - Improved understanding of implementation approaches
        - Guidance for practical decision-making
        - Identification of promising new applications
        - Validation of existing practices
        
        ### Relationship to Existing Literature
        
        The findings advance existing knowledge of {topic} by:
        - Confirming prior theoretical predictions
        - Extending findings to new contexts
        - Resolving previously contested questions
        - Opening new research directions
        
        ### Methodological Contributions
        
        This investigation demonstrates the effectiveness of integrated approaches to {topic}:
        - Combining multiple research methods provides comprehensive understanding
        - Literature review reveals consensus and controversies
        - Systematic analysis enables identification of patterns
        - Integration across domains enriches insights
        
        ### Challenges and Limitations
        
        Important qualifications to interpretations include:
        - {analysis['challenges'][0] if analysis['challenges'] else 'Technical limitations'}
        - Contextual factors affecting generalizability
        - Evolving nature of the field
        - Need for continued research
        
        ### Future Research Directions
        
        The investigation identifies several productive directions for future research:
        - Deeper investigation of identified gaps
        - Application of findings to new domains
        - Integration with related research areas
        - Development of novel methodologies
        """).strip()
        
        return content

    def _synthesize_conclusion(self, topic: str, analysis: Dict, insights: List[str]) -> str:
        """Synthesize conclusion section."""
        content = dedent(f"""
        ## Conclusion
        
        This comprehensive investigation of {topic} contributes significantly to our understanding 
        of this important field. Key findings, implications, and directions for future work are summarized.
        
        ### Summary of Key Findings
        
        Investigation of {topic} establishes:
        - Current state of research and knowledge
        - Practical applications across multiple domains: {', '.join(analysis['key_applications'][:2])}
        - Both achievements and remaining challenges in the field
        - Promising directions for future investigation
        
        ### Contributions to the Field
        
        This work contributes to {topic} through:
        - Comprehensive synthesis of existing research
        - Systematic analysis of current methodologies
        - Identification of research gaps and opportunities
        - Integration of findings with practical applications
        
        ### Implications for Practice
        
        The findings have direct implications for practitioners in {analysis['key_applications'][0]}:
        - Evidence-based guidance for implementation
        - Understanding of best practices and approaches
        - Awareness of current limitations and challenges
        - Foundation for decision-making
        
        ### Unresolved Questions
        
        Important questions for future research include:
        - Advanced understanding of {analysis['key_concepts'][0]}
        - Solutions to identified challenges: {', '.join(analysis['challenges'][:2])}
        - Novel applications of {topic}
        - Integration with emerging technologies and approaches
        
        ### Final Perspectives
        
        {topic} remains a vital and evolving field with significant research and practical importance. 
        The comprehensive analysis provided here demonstrates both the maturity of current knowledge and 
        the exciting opportunities for future advancement. Continued research and practical application 
        of {topic} promises to address current challenges while opening new possibilities for innovation 
        and impact.
        
        As the field continues to evolve, the foundation provided by current research will prove essential 
        for advancing toward deeper understanding and more effective practical solutions in {topic}.
        """).strip()
        
        return content

    def _synthesize_generic(self, topic: str, analysis: Dict, insights: List[str], section_title: str) -> str:
        """Synthesize generic section content."""
        content = dedent(f"""
        ## {section_title}
        
        This section explores {topic} in the context of {section_title.lower()}. 
        The analysis synthesizes relevant research and practical insights.
        
        ### Overview
        
        {section_title} in the context of {topic} addresses several important dimensions:
        {self._format_list(insights[:3])}
        
        ### Key Concepts
        
        Central to understanding this aspect of {topic} are:
        {self._format_list(analysis['key_concepts'][:3])}
        
        ### Research and Evidence
        
        The literature on this aspect of {topic} demonstrates:
        - Established methodologies and approaches
        - Validated findings across multiple contexts
        - Both theoretical and practical significance
        - Ongoing research addressing remaining questions
        
        ### Current Understanding
        
        Present knowledge regarding this aspect of {topic} includes:
        - Core principles and foundational concepts
        - Effective approaches and best practices
        - Known limitations and challenges
        - Emerging opportunities and innovations
        
        ### Implications and Significance
        
        This aspect of {topic} carries importance for:
        {self._format_list(analysis['key_applications'][:2])}
        
        ### Conclusion
        
        Understanding {section_title.lower()} in {topic} provides essential foundation 
        for advancing knowledge and practice in this important field.
        """).strip()
        
        return content

    def _format_list(self, items: List[str], bullet: str = "- ") -> str:
        """Format list of items as bullet points."""
        return "\n".join(f"{bullet}{item}" for item in items if item)