#!/usr/bin/env python3
"""
Luzia Research Agent - Smart Task Routing and Analysis

Routes research tasks to appropriate Zen tools based on:
- Security implications
- Speed requirements
- Complexity/depth needed

Stores findings in research KG.
"""

import json
import sqlite3
import uuid
import time
from pathlib import Path
from datetime import datetime
from typing import Optional, Dict, List, Tuple
from enum import Enum


class SecurityLevel(Enum):
    """Security classification for tasks"""
    PUBLIC = "public"           # No sensitive data
    INTERNAL = "internal"       # Internal infrastructure
    SENSITIVE = "sensitive"     # Auth, credentials, compliance
    CRITICAL = "critical"       # Full infrastructure control


class SpeedRequirement(Enum):
    """Speed classification for tasks"""
    INTERACTIVE = "interactive"   # <2 seconds (chat)
    RESPONSIVE = "responsive"     # <10 seconds (debug, chat)
    THOROUGH = "thorough"        # <60 seconds (thinkdeep, consensus)
    RESEARCH = "research"        # No time limit (deep investigation)


class ComplexityLevel(Enum):
    """Complexity classification"""
    TRIVIAL = "trivial"          # Simple answer
    STRAIGHTFORWARD = "straightforward"  # Clear problem, known solution
    COMPLEX = "complex"           # Multiple considerations
    EXPLORATORY = "exploratory"   # Unknown territory


class TaskFilter:
    """Smart filter evaluating security, speed, complexity"""

    SECURITY_KEYWORDS = {
        'critical': SecurityLevel.CRITICAL,
        'infrastructure': SecurityLevel.SENSITIVE,
        'credentials': SecurityLevel.CRITICAL,
        'auth': SecurityLevel.SENSITIVE,
        'permission': SecurityLevel.SENSITIVE,
        'rbac': SecurityLevel.SENSITIVE,
        'vulnerability': SecurityLevel.CRITICAL,
        'exploit': SecurityLevel.CRITICAL,
        'secret': SecurityLevel.CRITICAL,
        'token': SecurityLevel.SENSITIVE,
        'api key': SecurityLevel.CRITICAL,
        'password': SecurityLevel.CRITICAL,
        'deploy': SecurityLevel.SENSITIVE,
        'production': SecurityLevel.SENSITIVE,
    }

    SPEED_KEYWORDS = {
        'urgent': SpeedRequirement.INTERACTIVE,
        'asap': SpeedRequirement.INTERACTIVE,
        'now': SpeedRequirement.INTERACTIVE,
        'blocking': SpeedRequirement.RESPONSIVE,
        'quick': SpeedRequirement.INTERACTIVE,
        'quick answer': SpeedRequirement.INTERACTIVE,
        'fast': SpeedRequirement.RESPONSIVE,
        'slow': SpeedRequirement.THOROUGH,
        'analyze': SpeedRequirement.THOROUGH,
        'research': SpeedRequirement.RESEARCH,
        'explore': SpeedRequirement.RESEARCH,
        'investigate': SpeedRequirement.RESEARCH,
        'comprehensive': SpeedRequirement.THOROUGH,
    }

    COMPLEXITY_KEYWORDS = {
        'simple': ComplexityLevel.TRIVIAL,
        'quick answer': ComplexityLevel.TRIVIAL,
        'obvious': ComplexityLevel.TRIVIAL,
        'tradeoff': ComplexityLevel.COMPLEX,
        'decision': ComplexityLevel.COMPLEX,
        'architecture': ComplexityLevel.COMPLEX,
        'design': ComplexityLevel.COMPLEX,
        'bug': ComplexityLevel.STRAIGHTFORWARD,
        'error': ComplexityLevel.STRAIGHTFORWARD,
        'fix': ComplexityLevel.STRAIGHTFORWARD,
        'explore': ComplexityLevel.EXPLORATORY,
        'research': ComplexityLevel.EXPLORATORY,
        'unknown': ComplexityLevel.EXPLORATORY,
        'investigation': ComplexityLevel.EXPLORATORY,
    }

    @staticmethod
    def evaluate_security(task: str) -> SecurityLevel:
        """Determine security level from task description"""
        task_lower = task.lower()

        # Check for critical keywords first
        for keyword, level in TaskFilter.SECURITY_KEYWORDS.items():
            if keyword in task_lower:
                if level == SecurityLevel.CRITICAL:
                    return SecurityLevel.CRITICAL
                elif level == SecurityLevel.SENSITIVE:
                    return SecurityLevel.SENSITIVE

        # Check for infrastructure-related tasks
        if any(word in task_lower for word in ['deploy', 'systemd', 'service', 'nginx', 'database', 'firewall']):
            return SecurityLevel.SENSITIVE

        return SecurityLevel.INTERNAL

    @staticmethod
    def evaluate_speed(task: str) -> SpeedRequirement:
        """Determine speed requirement from task description"""
        task_lower = task.lower()

        for keyword, level in TaskFilter.SPEED_KEYWORDS.items():
            if keyword in task_lower:
                return level

        # Default to thorough for unknown tasks
        return SpeedRequirement.THOROUGH

    @staticmethod
    def evaluate_complexity(task: str) -> ComplexityLevel:
        """Determine complexity level from task description"""
        task_lower = task.lower()

        for keyword, level in TaskFilter.COMPLEXITY_KEYWORDS.items():
            if keyword in task_lower:
                return level

        # Check task length as proxy for complexity
        word_count = len(task.split())
        if word_count > 100:
            return ComplexityLevel.COMPLEX
        elif word_count > 50:
            return ComplexityLevel.STRAIGHTFORWARD

        return ComplexityLevel.STRAIGHTFORWARD


class ToolRouter:
    """Routes tasks to appropriate Zen tools based on filters"""

    @staticmethod
    def recommend_tools(
        security: SecurityLevel,
        speed: SpeedRequirement,
        complexity: ComplexityLevel,
        task_text: str = ""
    ) -> Tuple[str, str]:
        """
        Recommend best Zen tool(s) for the task.

        Returns: (primary_tool, reason)
        """

        # Critical security + complex → codereview
        if security == SecurityLevel.CRITICAL and complexity == ComplexityLevel.COMPLEX:
            return "codereview", "Critical security + complex design requires security review and deep thinking"

        # Critical security → codereview
        if security == SecurityLevel.CRITICAL:
            return "codereview", "Critical security implications require thorough code/design review"

        # Time critical + trivial → chat (fastest)
        if speed == SpeedRequirement.INTERACTIVE and complexity == ComplexityLevel.TRIVIAL:
            return "chat", "Simple answer needed immediately"

        # Time critical → chat
        if speed == SpeedRequirement.INTERACTIVE:
            return "chat", "Time critical - using fastest response tool"

        # Architecture/design decisions → consensus (multi-perspective)
        if complexity == ComplexityLevel.COMPLEX and ("design" in task_text.lower() or "decision" in task_text.lower()):
            return "consensus", "Complex architectural decision needs multi-perspective analysis"

        # Exploratory research → thinkdeep
        if complexity == ComplexityLevel.EXPLORATORY or speed == SpeedRequirement.RESEARCH:
            return "thinkdeep", "Exploratory research needs deep investigation and analysis"

        # Bug/error diagnosis → debug
        if complexity == ComplexityLevel.STRAIGHTFORWARD and ("bug" in task_text.lower() or "error" in task_text.lower() or "fix" in task_text.lower()):
            return "debug", "Systematic debugging and error diagnosis"

        # Complex + sensitive infrastructure → thinkdeep
        if complexity == ComplexityLevel.COMPLEX and security == SecurityLevel.SENSITIVE:
            return "thinkdeep", "Complex infrastructure task needs thorough analysis"

        # General thorough analysis → thinkdeep
        if speed == SpeedRequirement.THOROUGH:
            return "thinkdeep", "Thorough analysis and deep reasoning needed"

        # Default: balanced approach
        return "thinkdeep", "Comprehensive analysis and reasoning"

    @staticmethod
    def get_routing_summary(
        security: SecurityLevel,
        speed: SpeedRequirement,
        complexity: ComplexityLevel,
        tool: str,
        reason: str
    ) -> str:
        """Generate human-readable routing summary"""
        return f"""
📊 Task Analysis:
  🔒 Security:    {security.value}
  ⚡ Speed:       {speed.value}
  🧠 Complexity:  {complexity.value}

🎯 Routing Decision:
  Tool:    {tool}
  Reason:  {reason}
"""


class LuziaResearchAgent:
    """Luzia research agent with smart filtering and routing"""

    def __init__(self):
        self.research_kg = Path("/etc/luz-knowledge/research.db")
        self.log_file = Path("/opt/server-agents/logs/research-agent.log")
        self.log_file.parent.mkdir(parents=True, exist_ok=True)

    def log(self, message):
        """Log research action"""
        timestamp = datetime.now().isoformat()
        log_entry = f"[{timestamp}] {message}\n"
        with open(self.log_file, 'a') as f:
            f.write(log_entry)
        print(message)

    def analyze_task(self, task: str) -> Dict:
        """
        Analyze incoming research task.

        Returns evaluation with security, speed, complexity, and tool recommendation.
        """
        security = TaskFilter.evaluate_security(task)
        speed = TaskFilter.evaluate_speed(task)
        complexity = TaskFilter.evaluate_complexity(task)

        tool, reason = ToolRouter.recommend_tools(security, speed, complexity, task)

        return {
            'security': security.value,
            'speed': speed.value,
            'complexity': complexity.value,
            'recommended_tool': tool,
            'reasoning': reason,
            'routing_summary': ToolRouter.get_routing_summary(security, speed, complexity, tool, reason),
        }

    def clarify_task(self, task: str, analysis: Dict) -> Optional[Dict]:
        """
        Determine if clarification is needed based on analysis.

        Returns clarification questions or None if ready to proceed.
        """
        questions = []

        # Clarify sensitive tasks
        if analysis['security'] in ['sensitive', 'critical']:
            questions.append("🔒 Is this for production infrastructure? (yes/no)")

        # Clarify timing for quick tasks
        if analysis['speed'] == 'interactive':
            questions.append("⚡ Is this blocking other work? (yes/no)")

        # Clarify scope for exploratory work
        if analysis['complexity'] == 'exploratory':
            questions.append("🧭 What's the scope of research? (e.g., feasibility study, comparison, deep investigation)")

        if questions:
            return {
                'needs_clarification': True,
                'questions': questions,
            }

        return None

    def store_research_finding(
        self,
        task: str,
        tool_used: str,
        finding: str,
        tags: Optional[List[str]] = None,
    ) -> bool:
        """Store research finding in research KG"""
        try:
            conn = sqlite3.connect(self.research_kg)
            cursor = conn.cursor()

            entity_id = str(uuid.uuid4())
            now = time.time()

            # Create finding entity
            cursor.execute("""
                INSERT INTO entities
                (id, name, type, domain, content, metadata, created_at, updated_at, source)
                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
            """, (
                entity_id,
                f"Research: {task[:50]}",  # name
                f"research_{tool_used}",   # type
                'research',                # domain
                finding,                   # content
                json.dumps({
                    'task': task,
                    'tool_used': tool_used,
                    'tags': tags or [],
                }),
                now,
                now,
                'research_agent'
            ))

            conn.commit()
            conn.close()

            self.log(f"✅ Stored finding: {task[:40]}...")
            return True

        except Exception as e:
            self.log(f"❌ Error storing finding: {e}")
            return False

    def process_research_task(self, task: str) -> Dict:
        """
        Main entry point: analyze task and provide routing recommendation.
        """
        self.log(f"🔍 Processing research task: {task[:60]}...")

        # Analyze the task
        analysis = self.analyze_task(task)
        self.log(analysis['routing_summary'])

        # Check if clarification needed
        clarification = self.clarify_task(task, analysis)

        return {
            'task': task,
            'analysis': analysis,
            'clarification': clarification,
            'status': 'ready' if not clarification else 'needs_clarification',
        }

    def get_summary(self) -> Dict:
        """Get summary of research findings stored"""
        try:
            conn = sqlite3.connect(self.research_kg)
            cursor = conn.cursor()

            # Count research findings by tool
            cursor.execute("""
                SELECT type, COUNT(*) as count
                FROM entities
                WHERE type LIKE 'research_%'
                GROUP BY type
            """)

            findings_by_tool = {row[0].replace('research_', ''): row[1] for row in cursor.fetchall()}

            # Count total research entities
            cursor.execute("SELECT COUNT(*) FROM entities WHERE type LIKE 'research_%'")
            total_research = cursor.fetchone()[0]

            conn.close()

            return {
                'total_research_findings': total_research,
                'findings_by_tool': findings_by_tool,
                'tools_used': list(findings_by_tool.keys()),
            }

        except Exception as e:
            self.log(f"❌ Error getting summary: {e}")
            return {}


if __name__ == '__main__':
    agent = LuziaResearchAgent()

    # Example tasks
    test_tasks = [
        "quick answer: what's the difference between async and await?",
        "urgent critical security review needed for authentication implementation",
        "research and explore different approaches to distributed caching",
        "fix the bug in the zen-proxy max_tokens handling",
        "design decision: should we use REST or GraphQL API?",
    ]

    print("=" * 70)
    print("LUZIA RESEARCH AGENT - SMART FILTER DEMONSTRATION")
    print("=" * 70)

    for task in test_tasks:
        result = agent.process_research_task(task)
        print()