Deep Research Agent

"""
Research Planning Module

Handles research planning and methodology design.
"""

from typing import Dict, List


class ResearchPlanner:
    """Plans and structures research initiatives."""

    def create_research_plan(self, topic: str, scope: str) -> Dict:
        """
        Create a structured research plan.

        Args:
            topic: Research topic
            scope: Research scope (quick, comprehensive, etc.)

        Returns:
            Dictionary with research plan
        """
        plan = {
            "research_question": self._generate_research_question(topic),
            "key_areas": self._identify_key_areas(topic),
            "information_sources": [
                "academic databases",
                "news sources",
                "industry reports",
                "expert interviews"
            ],
            "evaluation_criteria": {
                "relevance": "Does it answer the research question?",
                "credibility": "Is the source reliable?",
                "recency": "Is the information current?",
                "bias": "Are there apparent biases?"
            },
            "timeline": self._create_timeline(scope)
        }
        return plan

    def _generate_research_question(self, topic: str) -> str:
        """Generate focused research question."""
        return f"What are the key aspects and implications of {topic}?"

    def _identify_key_areas(self, topic: str) -> List[str]:
        """Identify key research areas."""
        return [f"Introduction to {topic}", "Key factors", "Industry trends", "Future outlook"]

    def _create_timeline(self, scope: str) -> Dict:
        """Create research timeline."""
        return {"scope": scope, "estimated_duration": "2-4 weeks"}

"""
Research Report Generation Module

Generates comprehensive research reports.
"""

from typing import Dict


class ResearchReportGenerator:
    """Generates research reports from synthesis."""

    def generate_comprehensive_report(self, synthesis: Dict, topic: str) -> str:
        """
        Generate comprehensive research report.

        Args:
            synthesis: Synthesized research findings
            topic: Research topic

        Returns:
            Formatted report as string
        """
        report = f"""
# Research Report: {topic}

## Executive Summary
{self.create_executive_summary(synthesis)}

## Main Findings
{self.format_findings(synthesis["main_conclusions"])}

## Supporting Evidence
{self.format_evidence(synthesis["supporting_evidence"])}

## Conflicting Views & Debates
{self.format_conflicts(synthesis["conflicting_views"])}

## Research Gaps
{self.format_gaps(synthesis["research_gaps"])}

## Recommendations
{self.generate_recommendations(synthesis)}

## References
{self.generate_references(synthesis)}
        """
        return report

    def create_executive_summary(self, synthesis: Dict) -> str:
        """Create executive summary."""
        return "Summary of key research findings and conclusions."

    def format_findings(self, findings: list) -> str:
        """Format findings for report."""
        if not findings:
            return "No key findings identified."
        return "\n".join([f"- {finding}" for finding, count in findings])

    def format_evidence(self, evidence: Dict) -> str:
        """Format supporting evidence."""
        result = ""
        for category, items in evidence.items():
            result += f"\n### {category.replace('_', ' ').title()}\n"
            result += "\n".join([f"- {item}" for item in items[:5]])
        return result

    def format_conflicts(self, conflicts: list) -> str:
        """Format conflicting views."""
        if not conflicts:
            return "No major conflicting views identified."
        return "\n".join([f"- {conflict}" for conflict in conflicts])

    def format_gaps(self, gaps: list) -> str:
        """Format research gaps."""
        if not gaps:
            return "Research appears comprehensive."
        return "\n".join([f"- {gap}" for gap in gaps])

    def generate_recommendations(self, synthesis: Dict) -> str:
        """Generate recommendations."""
        return "Based on findings, recommended actions and next steps."

    def generate_references(self, synthesis: Dict) -> str:
        """Generate references section."""
        return "List of sources cited in report."

"""
Research Synthesis Module

Synthesizes and analyzes research findings.
"""

from typing import Dict, List
from collections import Counter


class ResearchSynthesizer:
    """Synthesizes research findings from multiple sources."""

    def synthesize_findings(self, information: Dict) -> Dict:
        """
        Synthesize research findings.

        Args:
            information: Extracted information from sources

        Returns:
            Dictionary with synthesized findings
        """
        synthesis = {
            "main_conclusions": self.identify_main_conclusions(information),
            "supporting_evidence": self.organize_evidence(information),
            "conflicting_views": self.identify_conflicts(information),
            "research_gaps": self.identify_gaps(information),
            "future_directions": self.suggest_future_research(information)
        }
        return synthesis

    def identify_main_conclusions(self, information: Dict) -> List:
        """Find most consistent findings across sources."""
        findings_frequency = Counter(information.get("key_findings", []))
        main_conclusions = findings_frequency.most_common(5)
        return main_conclusions

    def organize_evidence(self, information: Dict) -> Dict:
        """Organize supporting evidence."""
        return {
            "statistics": information.get("statistics", []),
            "expert_opinions": information.get("expert_opinions", []),
            "trends": information.get("trends", [])
        }

    def identify_conflicts(self, information: Dict) -> List:
        """Identify conflicting viewpoints."""
        # Placeholder for conflict identification
        return []

    def identify_gaps(self, information: Dict) -> List:
        """Identify research gaps."""
        return information.get("gaps", [])

    def suggest_future_research(self, information: Dict) -> List:
        """Suggest future research directions."""
        return ["Address identified gaps", "Explore emerging trends"]

"""
Source Evaluation Module

Evaluates and scores information sources for credibility.
"""

from datetime import datetime
from typing import Dict


class SourceEvaluator:
    """Evaluates credibility of information sources."""

    def evaluate_source(self, source: Dict) -> int:
        """
        Evaluate source credibility.

        Args:
            source: Source object with attributes

        Returns:
            Credibility score (0-100)
        """
        score = 0

        # Authority: Is author qualified?
        if hasattr(source, 'author_expertise_level') and source.author_expertise_level > 0.7:
            score += 25

        # Credibility: Is publisher trusted?
        if hasattr(source, 'publisher_reputation') and source.publisher_reputation > 0.8:
            score += 25

        # Recency: Is information current?
        if hasattr(source, 'publication_date'):
            days_old = (datetime.now() - source.publication_date).days
            if days_old < 365:
                score += 25

        # Bias: Are there clear biases?
        if hasattr(source, 'bias_score') and source.bias_score < 0.3:
            score += 25

        return score