reasoning/recursive.py

"""Recursive reasoning strategy implementation."""

import logging
from typing import Dict, Any, List, Optional, Set, Tuple, Callable
import json
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime
import asyncio
from collections import defaultdict

from .base import ReasoningStrategy, StrategyResult

class SubproblemType(Enum):
    """Types of subproblems in recursive reasoning."""
    ATOMIC = "atomic"
    COMPOSITE = "composite"
    PARALLEL = "parallel"
    SEQUENTIAL = "sequential"
    CONDITIONAL = "conditional"
    ITERATIVE = "iterative"

class SolutionStatus(Enum):
    """Status of subproblem solutions."""
    PENDING = "pending"
    IN_PROGRESS = "in_progress"
    SOLVED = "solved"
    FAILED = "failed"
    BLOCKED = "blocked"
    OPTIMIZING = "optimizing"

@dataclass
class Subproblem:
    """Represents a subproblem in recursive reasoning."""
    id: str
    type: SubproblemType
    query: str
    context: Dict[str, Any]
    parent_id: Optional[str]
    children: List[str]
    status: SolutionStatus
    solution: Optional[Dict[str, Any]]
    confidence: float
    dependencies: List[str]
    metadata: Dict[str, Any] = field(default_factory=dict)
    timestamp: str = field(default_factory=lambda: datetime.now().isoformat())

@dataclass
class RecursiveStep:
    """Represents a step in recursive reasoning."""
    id: str
    subproblem_id: str
    action: str
    result: Dict[str, Any]
    timestamp: str = field(default_factory=lambda: datetime.now().isoformat())

class RecursiveStrategy(ReasoningStrategy):
    """Advanced recursive reasoning that:
    1. Breaks down complex problems
    2. Solves sub-problems recursively
    3. Combines solutions
    4. Handles base cases
    5. Optimizes performance
    """
    
    def __init__(self, config: Optional[Dict[str, Any]] = None):
        """Initialize recursive reasoning."""
        super().__init__()
        self.config = config or {}
        
        # Standard reasoning parameters
        self.min_confidence = self.config.get('min_confidence', 0.7)
        self.max_depth = self.config.get('max_depth', 5)
        self.optimization_rounds = self.config.get('optimization_rounds', 2)
        
        # Problem tracking
        self.subproblems: Dict[str, Subproblem] = {}
        self.steps: List[RecursiveStep] = []
        self.solution_cache: Dict[str, Dict[str, Any]] = {}
        self.cycle_detection: Set[str] = set()
        
        # Performance metrics
        self.performance_metrics = {
            'depth_distribution': defaultdict(int),
            'type_distribution': defaultdict(int),
            'success_rate': defaultdict(float),
            'total_subproblems': 0,
            'solved_subproblems': 0,
            'failed_subproblems': 0,
            'optimization_rounds': 0,
            'cache_hits': 0,
            'cycles_detected': 0
        }
    
    async def reason(
        self,
        query: str,
        context: Dict[str, Any]
    ) -> StrategyResult:
        """
        Apply recursive reasoning to analyze the query.
        
        Args:
            query: The query to reason about
            context: Additional context and parameters
            
        Returns:
            StrategyResult containing the reasoning output and metadata
        """
        try:
            # Initialize root problem
            root_problem = await self._initialize_problem(query, context)
            root_id = root_problem.id
            
            # Solve recursively
            solution = await self._solve_recursive(root_id, depth=0)
            
            # Optimize solution
            if solution and solution.get('success', False):
                solution = await self._optimize_solution(solution, root_problem, context)
            
            # Update metrics
            self._update_metrics(root_id)
            
            # Build solution trace
            solution_trace = self._get_solution_trace(root_id)
            
            # Calculate overall confidence
            confidence = self._calculate_confidence(solution_trace)
            
            return StrategyResult(
                strategy_type="recursive",
                success=bool(solution and solution.get('success', False)),
                answer=solution.get('answer') if solution else None,
                confidence=confidence,
                reasoning_trace=solution_trace,
                metadata={
                    'problem_tree': self._get_problem_tree(root_id),
                    'steps': [self._step_to_dict(step) for step in self.steps],
                    'solution_details': solution if solution else {}
                },
                performance_metrics=self.performance_metrics
            )
            
        except Exception as e:
            logging.error(f"Recursive reasoning error: {str(e)}")
            return StrategyResult(
                strategy_type="recursive",
                success=False,
                answer=None,
                confidence=0.0,
                reasoning_trace=[{
                    'step': 'error',
                    'error': str(e),
                    'timestamp': datetime.now().isoformat()
                }],
                metadata={'error': str(e)},
                performance_metrics=self.performance_metrics
            )
    
    async def _initialize_problem(
        self,
        query: str,
        context: Dict[str, Any]
    ) -> Subproblem:
        """Initialize the root problem."""
        problem = Subproblem(
            id="root",
            type=SubproblemType.COMPOSITE,
            query=query,
            context=context,
            parent_id=None,
            children=[],
            status=SolutionStatus.PENDING,
            solution=None,
            confidence=1.0,
            dependencies=[],
            metadata={'depth': 0}
        )
        
        self.subproblems[problem.id] = problem
        self._record_step(RecursiveStep(
            id=f"init_{problem.id}",
            subproblem_id=problem.id,
            action="initialize",
            result={'type': problem.type.value, 'query': query}
        ))
        
        return problem
    
    async def _solve_recursive(
        self,
        problem_id: str,
        depth: int
    ) -> Optional[Dict[str, Any]]:
        """Recursively solve a problem and its subproblems."""
        if depth > self.max_depth:
            return None
            
        problem = self.subproblems[problem_id]
        
        # Check cycle
        if problem_id in self.cycle_detection:
            self.performance_metrics['cycles_detected'] += 1
            return None
            
        self.cycle_detection.add(problem_id)
        
        try:
            # Check cache
            if problem_id in self.solution_cache:
                self.performance_metrics['cache_hits'] += 1
                return self.solution_cache[problem_id]
            
            # Decompose if composite
            if problem.type != SubproblemType.ATOMIC:
                await self._decompose_problem(problem, problem.context)
            
            # Solve atomic problem
            if problem.type == SubproblemType.ATOMIC:
                solution = await self._solve_atomic(problem)
                if solution:
                    problem.solution = solution
                    problem.status = SolutionStatus.SOLVED
                    return solution
                else:
                    problem.status = SolutionStatus.FAILED
                    return None
            
            # Solve subproblems
            subsolutions = []
            for child_id in problem.children:
                child_solution = await self._solve_recursive(child_id, depth + 1)
                if child_solution:
                    subsolutions.append(child_solution)
            
            # Synthesize solutions
            if subsolutions:
                solution = await self._synthesize_solutions(subsolutions, problem, problem.context)
                if solution:
                    problem.solution = solution
                    problem.status = SolutionStatus.SOLVED
                    self.solution_cache[problem_id] = solution
                    return solution
            
            problem.status = SolutionStatus.FAILED
            return None
            
        finally:
            self.cycle_detection.remove(problem_id)
    
    async def _decompose_problem(
        self,
        problem: Subproblem,
        context: Dict[str, Any]
    ) -> None:
        """Decompose a problem into subproblems."""
        subproblems = self._generate_subproblems(problem, context)
        
        for subproblem in subproblems:
            self.subproblems[subproblem.id] = subproblem
            problem.children.append(subproblem.id)
            
        self._record_step(RecursiveStep(
            id=f"decompose_{problem.id}",
            subproblem_id=problem.id,
            action="decompose",
            result={'num_subproblems': len(subproblems)}
        ))
    
    def _generate_subproblems(
        self,
        parent: Subproblem,
        context: Dict[str, Any]
    ) -> List[Subproblem]:
        """Generate subproblems for a composite problem."""
        # This is a placeholder implementation
        # In practice, this would use more sophisticated decomposition
        subproblems = []
        
        # Example: Split into 2-3 subproblems
        parts = parent.query.split('.')[:3]
        for i, part in enumerate(parts):
            if part.strip():
                subproblem = Subproblem(
                    id=f"{parent.id}_sub{i}",
                    type=SubproblemType.ATOMIC,
                    query=part.strip(),
                    context=context,
                    parent_id=parent.id,
                    children=[],
                    status=SolutionStatus.PENDING,
                    solution=None,
                    confidence=0.0,
                    dependencies=[],
                    metadata={'depth': parent.metadata['depth'] + 1}
                )
                subproblems.append(subproblem)
        
        return subproblems
    
    async def _solve_atomic(
        self,
        problem: Subproblem
    ) -> Optional[Dict[str, Any]]:
        """Solve an atomic problem."""
        # This is a placeholder implementation
        # In practice, this would use more sophisticated solving strategies
        solution = {
            'success': True,
            'answer': f"Solution for {problem.query}",
            'confidence': 0.8
        }
        
        self._record_step(RecursiveStep(
            id=f"solve_{problem.id}",
            subproblem_id=problem.id,
            action="solve_atomic",
            result=solution
        ))
        
        return solution
    
    async def _synthesize_solutions(
        self,
        subsolutions: List[Dict[str, Any]],
        problem: Subproblem,
        context: Dict[str, Any]
    ) -> Optional[Dict[str, Any]]:
        """Synthesize solutions from subproblems."""
        if not subsolutions:
            return None
            
        # Combine answers
        combined_answer = " ".join(
            sol['answer'] for sol in subsolutions if sol.get('answer')
        )
        
        # Average confidence
        avg_confidence = sum(
            sol['confidence'] for sol in subsolutions
        ) / len(subsolutions)
        
        synthesis = {
            'success': True,
            'answer': combined_answer,
            'confidence': avg_confidence,
            'subsolutions': subsolutions
        }
        
        self._record_step(RecursiveStep(
            id=f"synthesize_{problem.id}",
            subproblem_id=problem.id,
            action="synthesize",
            result={'num_solutions': len(subsolutions)}
        ))
        
        return synthesis
    
    async def _optimize_solution(
        self,
        solution: Dict[str, Any],
        problem: Subproblem,
        context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Optimize the final solution."""
        optimized = solution.copy()
        
        for _ in range(self.optimization_rounds):
            self.performance_metrics['optimization_rounds'] += 1
            
            # Example optimization: Improve confidence
            if optimized['confidence'] < 0.9:
                optimized['confidence'] *= 1.1
        
        self._record_step(RecursiveStep(
            id=f"optimize_{problem.id}",
            subproblem_id=problem.id,
            action="optimize",
            result={'confidence_improvement': optimized['confidence'] - solution['confidence']}
        ))
        
        return optimized
    
    def _calculate_confidence(
        self,
        solution_trace: List[Dict[str, Any]]
    ) -> float:
        """Calculate overall confidence from solution trace."""
        if not solution_trace:
            return 0.0
            
        confidences = [
            step.get('confidence', 0.0)
            for step in solution_trace
            if isinstance(step.get('confidence'), (int, float))
        ]
        
        return sum(confidences) / len(confidences) if confidences else 0.0
    
    def _update_metrics(self, root_id: str) -> None:
        """Update performance metrics."""
        def update_recursive(problem_id: str):
            problem = self.subproblems[problem_id]
            depth = problem.metadata.get('depth', 0)
            
            self.performance_metrics['depth_distribution'][depth] += 1
            self.performance_metrics['type_distribution'][problem.type] += 1
            self.performance_metrics['total_subproblems'] += 1
            
            if problem.status == SolutionStatus.SOLVED:
                self.performance_metrics['solved_subproblems'] += 1
            elif problem.status == SolutionStatus.FAILED:
                self.performance_metrics['failed_subproblems'] += 1
            
            for child_id in problem.children:
                update_recursive(child_id)
        
        update_recursive(root_id)
        
        # Calculate success rates
        total = self.performance_metrics['total_subproblems']
        if total > 0:
            for problem_type in SubproblemType:
                type_count = self.performance_metrics['type_distribution'][problem_type]
                if type_count > 0:
                    success_count = sum(
                        1 for p in self.subproblems.values()
                        if p.type == problem_type and p.status == SolutionStatus.SOLVED
                    )
                    self.performance_metrics['success_rate'][problem_type] = success_count / type_count
    
    def _get_problem_tree(self, root_id: str) -> Dict[str, Any]:
        """Get the problem decomposition tree."""
        def build_tree(problem_id: str) -> Dict[str, Any]:
            problem = self.subproblems[problem_id]
            return {
                'id': problem.id,
                'type': problem.type.value,
                'status': problem.status.value,
                'confidence': problem.confidence,
                'children': [build_tree(child_id) for child_id in problem.children]
            }
        
        return build_tree(root_id)
    
    def _get_solution_trace(self, root_id: str) -> List[Dict[str, Any]]:
        """Get the solution trace for a problem."""
        trace = []
        
        def build_trace(problem_id: str):
            problem = self.subproblems[problem_id]
            
            step = {
                'id': problem.id,
                'type': problem.type.value,
                'status': problem.status.value,
                'confidence': problem.confidence,
                'timestamp': problem.timestamp
            }
            
            if problem.solution:
                step.update(problem.solution)
            
            trace.append(step)
            
            for child_id in problem.children:
                build_trace(child_id)
        
        build_trace(root_id)
        return trace
    
    def _record_step(self, step: RecursiveStep) -> None:
        """Record a reasoning step."""
        self.steps.append(step)
    
    def _step_to_dict(self, step: RecursiveStep) -> Dict[str, Any]:
        """Convert step to dictionary for serialization."""
        return {
            'id': step.id,
            'subproblem_id': step.subproblem_id,
            'action': step.action,
            'result': step.result,
            'timestamp': step.timestamp
        }
    
    def clear_cache(self) -> None:
        """Clear solution cache."""
        self.solution_cache.clear()
        self.performance_metrics['cache_hits'] = 0