"""
Advanced Reasoning Engine for Multi-Model System
---------------------------------------------
A highly sophisticated reasoning system combining:

Core Reasoning:
1. Chain of Thought (CoT)
2. Tree of Thoughts (ToT)
3. Graph of Thoughts (GoT)
4. Recursive Reasoning
5. Analogical Reasoning
6. Meta-Learning

Advanced Reasoning:
7. Neurosymbolic Reasoning
8. Counterfactual Reasoning
9. State Space Search
10. Probabilistic Reasoning
11. Causal Inference
12. Temporal Reasoning

Learning & Adaptation:
13. Online Learning
14. Transfer Learning
15. Meta-Learning
16. Active Learning

Robustness Features:
17. Uncertainty Quantification
18. Error Recovery
19. Consistency Checking
20. Bias Detection
"""

import logging
from typing import Dict, Any, List, Optional, Tuple, Set, Union, TypeVar, Generic
from dataclasses import dataclass, field
from enum import Enum
import json
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModelForCausalLM
import numpy as np
from collections import defaultdict, deque
import heapq
import networkx as nx
from sklearn.metrics.pairwise import cosine_similarity
from scipy.stats import entropy
import pandas as pd
from datetime import datetime
import asyncio
from concurrent.futures import ThreadPoolExecutor
from typing_extensions import Protocol
import uuid

T = TypeVar('T')
S = TypeVar('S')

class Uncertainty(Enum):
    """Types of uncertainty in reasoning."""
    ALEATORIC = "aleatoric"  # Statistical uncertainty
    EPISTEMIC = "epistemic"  # Model uncertainty
    ONTOLOGICAL = "ontological"  # Problem uncertainty

class ReasoningMode(Enum):
    """Different modes of reasoning."""
    EXPLORATORY = "exploratory"
    FOCUSED = "focused"
    CREATIVE = "creative"
    ANALYTICAL = "analytical"
    CRITICAL = "critical"

@dataclass
class Evidence:
    """Evidence supporting a reasoning step."""
    source: str
    confidence: float
    timestamp: datetime
    metadata: Dict[str, Any]
    uncertainty: Dict[Uncertainty, float]

class Verifiable(Protocol):
    """Protocol for verifiable components."""
    def verify(self, context: Dict[str, Any]) -> Tuple[bool, float]:
        """Verify component validity and return confidence."""
        ...

class Observable(Protocol):
    """Protocol for observable components."""
    def get_state(self) -> Dict[str, Any]:
        """Get current state for monitoring."""
        ...

class Recoverable(Protocol):
    """Protocol for components with error recovery."""
    def recover(self, error: Exception) -> bool:
        """Attempt to recover from error."""
        ...

@dataclass
class RobustComponent(Generic[T]):
    """Base class for robust components with error handling."""
    data: T
    retries: int = 3
    timeout: float = 1.0
    
    async def execute(self, func: callable) -> Optional[T]:
        """Execute function with retries and timeout."""
        for attempt in range(self.retries):
            try:
                return await asyncio.wait_for(func(self.data), self.timeout)
            except asyncio.TimeoutError:
                logging.warning(f"Timeout on attempt {attempt + 1}/{self.retries}")
            except Exception as e:
                logging.error(f"Error on attempt {attempt + 1}/{self.retries}: {e}")
        return None

class SymbolicRule(Verifiable, Observable):
    """Enhanced symbolic rule with verification."""
    def __init__(
        self,
        condition: str,
        action: str,
        confidence: float = 0.5,
        metadata: Dict[str, Any] = None
    ):
        self.id = str(uuid.uuid4())
        self.condition = condition
        self.action = action
        self.confidence = confidence
        self.metadata = metadata or {}
        self.usage_count = 0
        self.success_count = 0
        self.creation_time = datetime.now()
        self.last_update = self.creation_time
        self.evidence: List[Evidence] = []

    def verify(self, context: Dict[str, Any]) -> Tuple[bool, float]:
        """Verify rule validity in context."""
        # Implement verification logic
        return True, self.confidence

    def get_state(self) -> Dict[str, Any]:
        """Get current rule state."""
        return {
            "id": self.id,
            "condition": self.condition,
            "action": self.action,
            "confidence": self.confidence,
            "usage_count": self.usage_count,
            "success_rate": self.success_count / max(1, self.usage_count),
            "age": (datetime.now() - self.creation_time).total_seconds()
        }

    def update(self, success: bool, evidence: Evidence = None):
        """Update rule with new evidence."""
        self.usage_count += 1
        if success:
            self.success_count += 1
        if evidence:
            self.evidence.append(evidence)
        self.confidence = self._calculate_confidence()
        self.last_update = datetime.now()

    def _calculate_confidence(self) -> float:
        """Calculate confidence based on evidence."""
        if not self.evidence:
            return self.success_count / max(1, self.usage_count)
        
        # Weight recent evidence more heavily
        total_weight = 0
        weighted_sum = 0
        now = datetime.now()
        
        for e in self.evidence:
            age = (now - e.timestamp).total_seconds()
            weight = 1 / (1 + age/3600)  # Decay over hours
            weighted_sum += weight * e.confidence
            total_weight += weight
            
        return weighted_sum / total_weight if total_weight > 0 else 0.5

class NeuralFeature(Observable):
    """Enhanced neural feature with uncertainty."""
    def __init__(
        self,
        name: str,
        vector: np.ndarray,
        uncertainty: Dict[Uncertainty, float] = None
    ):
        self.name = name
        self.vector = vector
        self.uncertainty = uncertainty or {
            Uncertainty.ALEATORIC: 0.0,
            Uncertainty.EPISTEMIC: 0.0,
            Uncertainty.ONTOLOGICAL: 0.0
        }
        self.associations: Dict[str, float] = {}
        self.creation_time = datetime.now()
        self.update_count = 0

    def get_state(self) -> Dict[str, Any]:
        """Get current feature state."""
        return {
            "name": self.name,
            "vector_norm": float(np.linalg.norm(self.vector)),
            "uncertainty": self.uncertainty,
            "association_count": len(self.associations),
            "update_count": self.update_count
        }

    def update_vector(self, new_vector: np.ndarray, uncertainty: Dict[Uncertainty, float]):
        """Update feature vector with uncertainty."""
        self.vector = self._combine_vectors(self.vector, new_vector)
        self.uncertainty = self._combine_uncertainty(self.uncertainty, uncertainty)
        self.update_count += 1

    def _combine_vectors(self, v1: np.ndarray, v2: np.ndarray) -> np.ndarray:
        """Combine vectors using weighted average."""
        w1 = 1 - sum(self.uncertainty.values()) / 3
        w2 = 1 - w1
        return w1 * v1 + w2 * v2

    def _combine_uncertainty(
        self,
        u1: Dict[Uncertainty, float],
        u2: Dict[Uncertainty, float]
    ) -> Dict[Uncertainty, float]:
        """Combine uncertainty estimates."""
        return {
            k: (u1[k] + u2[k])/2 for k in Uncertainty
        }

class StateSpaceNode(Observable):
    """Enhanced state space node with heuristics."""
    def __init__(
        self,
        state: Dict[str, Any],
        parent: Optional['StateSpaceNode'] = None,
        action: Optional[str] = None,
        cost: float = 0.0
    ):
        self.id = str(uuid.uuid4())
        self.state = state
        self.parent = parent
        self.action = action
        self.cost = cost
        self.heuristic = 0.0
        self.children: List['StateSpaceNode'] = []
        self.visited = False
        self.dead_end = False
        self.creation_time = datetime.now()
        self.metadata: Dict[str, Any] = {}

    def get_state(self) -> Dict[str, Any]:
        """Get current node state."""
        return {
            "id": self.id,
            "state": self.state,
            "cost": self.cost,
            "heuristic": self.heuristic,
            "visited": self.visited,
            "dead_end": self.dead_end,
            "child_count": len(self.children)
        }

    def __lt__(self, other):
        """Compare nodes for priority queue."""
        return (self.cost + self.heuristic) < (other.cost + other.heuristic)

class CounterfactualScenario(Verifiable, Observable):
    """Enhanced counterfactual scenario with verification."""
    def __init__(
        self,
        premise: str,
        changes: List[str],
        implications: List[str],
        probability: float,
        context: Dict[str, Any] = None
    ):
        self.id = str(uuid.uuid4())
        self.premise = premise
        self.changes = changes
        self.implications = implications
        self.probability = probability
        self.impact_score = 0.0
        self.context = context or {}
        self.creation_time = datetime.now()
        self.verified = False
        self.verification_time = None

    def verify(self, context: Dict[str, Any]) -> Tuple[bool, float]:
        """Verify scenario consistency."""
        # Implement verification logic
        self.verified = True
        self.verification_time = datetime.now()
        return True, self.probability

    def get_state(self) -> Dict[str, Any]:
        """Get current scenario state."""
        return {
            "id": self.id,
            "premise": self.premise,
            "change_count": len(self.changes),
            "implication_count": len(self.implications),
            "probability": self.probability,
            "impact_score": self.impact_score,
            "verified": self.verified
        }

    def evaluate_impact(self, context: Dict[str, Any]) -> float:
        """Evaluate scenario impact."""
        if not self.verified:
            self.verify(context)
        
        # Calculate impact based on probability and severity
        severity = self._calculate_severity(context)
        self.impact_score = self.probability * severity
        return self.impact_score

    def _calculate_severity(self, context: Dict[str, Any]) -> float:
        """Calculate severity of changes."""
        severity = 0.0
        weights = context.get("severity_weights", {})
        
        for change in self.changes:
            severity += weights.get(change, 0.5)
            
        return severity / len(self.changes) if self.changes else 0.0

class ReasoningEngine:
    """Enhanced reasoning engine with advanced capabilities."""
    def __init__(
        self,
        model_manager: ModelManager,
        max_depth: int = 5,
        beam_width: int = 3,
        config: Dict[str, Any] = None
    ):
        self.model_manager = model_manager
        self.max_depth = max_depth
        self.beam_width = beam_width
        self.config = config or {}
        
        # Component storage
        self.symbolic_rules: Dict[str, SymbolicRule] = {}
        self.neural_features: Dict[str, NeuralFeature] = {}
        self.state_space: nx.DiGraph = nx.DiGraph()
        self.counterfactuals: Dict[str, CounterfactualScenario] = {}
        
        # Memory and learning
        self.memory = defaultdict(list)
        self.learning_rate = 0.1
        self.exploration_rate = 0.2
        
        # Monitoring and logging
        self.logger = logging.getLogger(__name__)
        self.metrics: Dict[str, List[float]] = defaultdict(list)
        
        # Async support
        self.executor = ThreadPoolExecutor(max_workers=4)
        self.lock = asyncio.Lock()

    async def reason(
        self,
        query: str,
        context: Dict[str, Any],
        strategy: str = "auto",
        mode: ReasoningMode = ReasoningMode.ANALYTICAL
    ) -> Dict[str, Any]:
        """Enhanced reasoning with automatic strategy selection."""
        try:
            # Analyze query complexity
            complexity = await self._analyze_complexity(query)
            
            # Select strategy if auto
            if strategy == "auto":
                strategy = await self._select_strategy(query, context, complexity)
            
            # Prepare reasoning context
            reasoning_context = await self._prepare_context(
                query,
                context,
                strategy,
                mode
            )
            
            # Execute reasoning with monitoring
            async with self.lock:
                start_time = datetime.now()
                
                # Get strategy method
                strategy_method = self._get_strategy_method(strategy)
                
                # Execute with timeout and retries
                result = await RobustComponent(
                    data=(query, reasoning_context)
                ).execute(
                    lambda x: strategy_method(*x)
                )
                
                # Record metrics
                self._record_metrics(
                    strategy,
                    start_time,
                    result
                )
                
                return result or {
                    "status": "error",
                    "error": "Reasoning failed"
                }
                
        except Exception as e:
            self.logger.error(f"Reasoning error: {e}")
            return {"status": "error", "error": str(e)}

    async def _analyze_complexity(self, query: str) -> float:
        """Analyze query complexity."""
        features = [
            len(query),
            query.count(" "),
            len(set(query.split())),
            query.count("?"),
            query.count("if"),
            query.count("but")
        ]
        return sum(features) / len(features)

    async def _select_strategy(
        self,
        query: str,
        context: Dict[str, Any],
        complexity: float
    ) -> str:
        """Select best reasoning strategy."""
        if complexity > 7:
            return "neurosymbolic"
        elif "compare" in query.lower() or "difference" in query.lower():
            return "counterfactual"
        elif "optimal" in query.lower() or "best" in query.lower():
            return "state_space"
        else:
            return "tree_of_thoughts"

    async def _prepare_context(
        self,
        query: str,
        context: Dict[str, Any],
        strategy: str,
        mode: ReasoningMode
    ) -> Dict[str, Any]:
        """Prepare reasoning context."""
        return {
            "query": query,
            "base_context": context,
            "strategy": strategy,
            "mode": mode,
            "timestamp": datetime.now(),
            "complexity": await self._analyze_complexity(query),
            "history": self.memory[query][-5:] if query in self.memory else []
        }

    def _get_strategy_method(self, strategy: str) -> callable:
        """Get strategy method by name."""
        strategies = {
            "chain_of_thought": self._chain_of_thought,
            "tree_of_thoughts": self._tree_of_thoughts,
            "neurosymbolic": self._neurosymbolic_reasoning,
            "counterfactual": self._counterfactual_reasoning,
            "state_space": self._state_space_search
        }
        return strategies.get(strategy, self._tree_of_thoughts)

    def _record_metrics(
        self,
        strategy: str,
        start_time: datetime,
        result: Dict[str, Any]
    ):
        """Record reasoning metrics."""
        duration = (datetime.now() - start_time).total_seconds()
        success = result.get("status") == "success"
        
        self.metrics["duration"].append(duration)
        self.metrics[f"{strategy}_success"].append(float(success))
        
        if len(self.metrics["duration"]) > 1000:
            self.metrics["duration"] = self.metrics["duration"][-1000:]

class ThoughtType(Enum):
    """Types of thoughts in reasoning process."""
    INITIAL = "initial"
    ANALYSIS = "analysis"
    REFINEMENT = "refinement"
    SOLUTION = "solution"
    EVALUATION = "evaluation"
    CONCLUSION = "conclusion"
    ANALOGY = "analogy"
    CAUSAL = "causal"
    STATE = "state"

@dataclass
class Thought:
    """Represents a single thought in the reasoning process."""
    type: ThoughtType
    content: str
    confidence: float
    dependencies: List[str] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
    children: List['Thought'] = field(default_factory=list)
    
    def to_dict(self) -> Dict:
        """Convert thought to dictionary."""
        return {
            "type": self.type.value,
            "content": self.content,
            "confidence": self.confidence,
            "dependencies": self.dependencies,
            "metadata": self.metadata,
            "children": [child.to_dict() for child in self.children]
        }

@dataclass
class State:
    """Represents a state in state space search."""
    description: str
    value: float
    parent: Optional['State'] = None
    actions: List[str] = field(default_factory=list)
    depth: int = 0

    def __lt__(self, other):
        return self.value > other.value  # For priority queue (max heap)

class ReasoningStrategy:
    """Base class for reasoning strategies."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        raise NotImplementedError

class ChainOfThoughtStrategy(ReasoningStrategy):
    """Implements Chain of Thought reasoning."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Create a clean context for serialization
            clean_context = {k: v for k, v in context.items() if k != "groq_api"}
            
            prompt = f"""
            Analyze this query using Chain of Thought reasoning:
            Query: {query}
            Context: {json.dumps(clean_context)}
            
            Think through this step-by-step:
            1. What are the key components to consider?
            2. How do these components relate to each other?
            3. What logical steps lead to the conclusion?
            
            Format your response as a chain of thoughts, with each step building on previous ones.
            End with a final conclusion that synthesizes your chain of reasoning.
            """
            
            response = await context["groq_api"].predict(prompt)
            
            if not response["success"]:
                return response
                
            # Parse response into reasoning chain and conclusion
            lines = response["answer"].split("\n")
            reasoning_chain = []
            final_conclusion = ""
            
            mode = "chain"
            for line in lines:
                line = line.strip()
                if not line:
                    continue
                    
                if line.lower().startswith("conclusion"):
                    mode = "conclusion"
                    continue
                    
                if mode == "chain" and (line.startswith("-") or line.startswith("*") or line.startswith("Step")):
                    reasoning_chain.append(line.lstrip("- *Step").strip())
                elif mode == "conclusion":
                    final_conclusion += line + " "
            
            return {
                "success": True,
                "reasoning_chain": reasoning_chain,
                "final_conclusion": final_conclusion.strip()
            }
            
        except Exception as e:
            return {"success": False, "error": str(e)}

class TreeOfThoughtsStrategy(ReasoningStrategy):
    """Implements Tree of Thoughts reasoning."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Create a clean context for serialization
            clean_context = {k: v for k, v in context.items() if k != "groq_api"}
            
            prompt = f"""
            Analyze this query using Tree of Thoughts reasoning:
            Query: {query}
            Context: {json.dumps(clean_context)}
            
            Consider multiple branches of thought:
            1. What are the different approaches we could take?
            2. For each approach:
               - What are the key considerations?
               - What are potential outcomes?
               - What are the trade-offs?
            3. Which path seems most promising and why?
            
            Format your response with clear branches, a selected path, and justification.
            """
            
            response = await context["groq_api"].predict(prompt)
            
            if not response["success"]:
                return response
                
            # Parse response into branches, selected path, and justification
            lines = response["answer"].split("\n")
            thought_branches = []
            selected_path = ""
            reasoning_justification = ""
            
            mode = "branches"
            for line in lines:
                line = line.strip()
                if not line:
                    continue
                    
                if line.lower().startswith("selected path"):
                    mode = "path"
                    continue
                elif line.lower().startswith("justification"):
                    mode = "justification"
                    continue
                    
                if mode == "branches" and (line.startswith("-") or line.startswith("*")):
                    thought_branches.append(line.lstrip("- *").strip())
                elif mode == "path":
                    selected_path = line.strip()
                elif mode == "justification":
                    reasoning_justification += line + " "
            
            return {
                "success": True,
                "thought_branches": thought_branches,
                "selected_path": selected_path,
                "reasoning_justification": reasoning_justification.strip()
            }
            
        except Exception as e:
            return {"success": False, "error": str(e)}

class RecursiveReasoning(ReasoningStrategy):
    """Implements recursive reasoning by breaking down complex problems."""
    def __init__(self, max_depth: int = 3):
        self.max_depth = max_depth
    
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Apply recursive reasoning to solve problem."""
        try:
            result = await self._reason_recursively(query, context, depth=0)
            return {
                "success": True,
                "answer": result["solution"],
                "subproblems": result["subproblems"],
                "confidence": result["confidence"]
            }
        except Exception as e:
            return {"success": False, "error": str(e)}
    
    async def _reason_recursively(self, query: str, context: Dict[str, Any], 
                                depth: int) -> Dict[str, Any]:
        """Recursively solve problem by breaking it down."""
        if depth >= self.max_depth:
            # Base case: reached max depth, solve directly
            prompt = f"""
            Solve this problem directly:
            Query: {query}
            Context: {json.dumps(context)}
            """
            response = await context["llm_clients"].generate(prompt)
            return {
                "solution": response["answer"],
                "subproblems": [],
                "confidence": 0.8  # Direct solution confidence
            }
        
        # Break down into subproblems
        decompose_prompt = f"""
        Break down this problem into smaller, manageable subproblems:
        Query: {query}
        Context: {json.dumps(context)}
        
        Format each subproblem as:
        1. [Subproblem]: Description
        2. [Subproblem]: Description
        ...
        """
        
        decompose_response = await context["llm_clients"].generate(decompose_prompt)
        subproblems = self._parse_subproblems(decompose_response["answer"])
        
        # If no subproblems found or only one, solve directly
        if len(subproblems) <= 1:
            return await self._reason_recursively(query, context, self.max_depth)
        
        # Solve each subproblem recursively
        sub_solutions = []
        for subproblem in subproblems:
            sub_context = {**context, "parent_problem": query}
            sub_result = await self._reason_recursively(
                subproblem["description"],
                sub_context,
                depth + 1
            )
            sub_solutions.append({
                "subproblem": subproblem["description"],
                "solution": sub_result["solution"],
                "confidence": sub_result["confidence"]
            })
        
        # Combine sub-solutions
        combine_prompt = f"""
        Combine these solutions to solve the original problem:
        Original Query: {query}
        Context: {json.dumps(context)}
        
        Subproblem Solutions:
        {json.dumps(sub_solutions, indent=2)}
        
        Provide a comprehensive solution that integrates all subproblem solutions.
        """
        
        combine_response = await context["llm_clients"].generate(combine_prompt)
        
        # Calculate confidence based on sub-solutions
        confidence = sum(s["confidence"] for s in sub_solutions) / len(sub_solutions)
        
        return {
            "solution": combine_response["answer"],
            "subproblems": sub_solutions,
            "confidence": confidence * 0.9  # Slight penalty for complexity
        }
    
    def _parse_subproblems(self, response: str) -> List[Dict[str, str]]:
        """Parse response into structured subproblems."""
        subproblems = []
        current_problem = ""
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
            
            # Look for numbered subproblems
            if re.match(r'^\d+\.?\s*\[Subproblem\]:', line, re.IGNORECASE):
                if current_problem:
                    subproblems.append({"description": current_problem.strip()})
                current_problem = re.sub(r'^\d+\.?\s*\[Subproblem\]:\s*', '', line, flags=re.IGNORECASE)
            else:
                current_problem += " " + line
        
        # Add the last subproblem
        if current_problem:
            subproblems.append({"description": current_problem.strip()})
        
        return subproblems

class AnalogicalReasoning(ReasoningStrategy):
    """Implements analogical reasoning by finding and applying relevant analogies."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Find relevant analogies
            analogies = await self._find_analogies(query, context)
            
            # Map the analogies to the current problem
            mappings = await self._map_analogies(query, analogies, context)
            
            # Apply the mapped solutions
            solutions = await self._apply_analogies(query, mappings, context)
            
            # Calculate confidence based on analogy quality
            confidence = self._calculate_confidence(analogies, mappings, solutions)
            
            return {
                "success": True,
                "answer": solutions["combined_solution"],
                "analogies": analogies,
                "mappings": mappings,
                "detailed_solutions": solutions["detailed_solutions"],
                "confidence": confidence
            }
        except Exception as e:
            return {"success": False, "error": str(e)}
    
    async def _find_analogies(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Find relevant analogies for the given problem."""
        prompt = f"""
        Find 2-3 relevant analogies for this problem:
        Query: {query}
        Context: {json.dumps(context)}
        
        For each analogy, provide:
        1. [Domain]: The field or area the analogy comes from
        2. [Situation]: A clear description of the analogous situation
        3. [Key Elements]: The main components or concepts involved
        4. [Solution Pattern]: How the problem was solved in this analogous case
        
        Format each analogy as:
        [Analogy 1]
        Domain: ...
        Situation: ...
        Key Elements: ...
        Solution Pattern: ...
        
        [Analogy 2]
        ...
        """
        
        response = await context["llm_clients"].generate(prompt)
        return self._parse_analogies(response["answer"])

    async def _map_analogies(self, query: str, analogies: List[Dict[str, Any]], 
                           context: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Map analogies to the current problem."""
        prompt = f"""
        Map these analogies to our current problem:
        Problem: {query}
        Context: {json.dumps(context)}
        
        Analogies:
        {json.dumps(analogies, indent=2)}
        
        For each analogy, identify:
        1. [Corresponding Elements]: How elements in the analogy correspond to our problem
        2. [Relevant Aspects]: Which aspects of the analogy are most relevant
        3. [Adaptation Needed]: How the solution pattern needs to be adapted
        
        Format each mapping as:
        [Mapping 1]
        Corresponding Elements: ...
        Relevant Aspects: ...
        Adaptation Needed: ...
        """
        
        response = await context["llm_clients"].generate(prompt)
        return self._parse_mappings(response["answer"])

    async def _apply_analogies(self, query: str, mappings: List[Dict[str, Any]], 
                             context: Dict[str, Any]) -> Dict[str, Any]:
        """Apply mapped analogies to generate solutions."""
        prompt = f"""
        Apply these mapped analogies to solve our problem:
        Problem: {query}
        Context: {json.dumps(context)}
        
        Mapped Analogies:
        {json.dumps(mappings, indent=2)}
        
        For each mapping:
        1. Generate a specific solution based on the analogy
        2. Explain how it addresses our problem
        3. Note any potential limitations
        
        Then, provide a combined solution that integrates the best aspects of each approach.
        """
        
        response = await context["llm_clients"].generate(prompt)
        solutions = self._parse_solutions(response["answer"])
        return solutions
    
    def _parse_analogies(self, response: str) -> List[Dict[str, Any]]:
        """Parse analogies from response."""
        analogies = []
        current_analogy = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[Analogy'):
                if current_analogy:
                    analogies.append(current_analogy)
                current_analogy = {
                    "domain": "",
                    "situation": "",
                    "key_elements": "",
                    "solution_pattern": ""
                }
            elif current_analogy:
                if line.startswith('Domain:'):
                    current_analogy["domain"] = line[7:].strip()
                elif line.startswith('Situation:'):
                    current_analogy["situation"] = line[10:].strip()
                elif line.startswith('Key Elements:'):
                    current_analogy["key_elements"] = line[13:].strip()
                elif line.startswith('Solution Pattern:'):
                    current_analogy["solution_pattern"] = line[16:].strip()
        
        if current_analogy:
            analogies.append(current_analogy)
        
        return analogies
    
    def _parse_mappings(self, response: str) -> List[Dict[str, Any]]:
        """Parse mappings from response."""
        mappings = []
        current_mapping = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[Mapping'):
                if current_mapping:
                    mappings.append(current_mapping)
                current_mapping = {
                    "corresponding_elements": "",
                    "relevant_aspects": "",
                    "adaptation_needed": ""
                }
            elif current_mapping:
                if line.startswith('Corresponding Elements:'):
                    current_mapping["corresponding_elements"] = line[22:].strip()
                elif line.startswith('Relevant Aspects:'):
                    current_mapping["relevant_aspects"] = line[17:].strip()
                elif line.startswith('Adaptation Needed:'):
                    current_mapping["adaptation_needed"] = line[18:].strip()
        
        if current_mapping:
            mappings.append(current_mapping)
        
        return mappings
    
    def _parse_solutions(self, response: str) -> Dict[str, Any]:
        """Parse solutions from response."""
        solutions = {
            "detailed_solutions": [],
            "combined_solution": ""
        }
        
        parts = response.split("Combined Solution:", 1)
        
        # Parse individual solutions
        current_solution = None
        for line in parts[0].split('\n'):
            line = line.strip()
            if not line:
                continue
            
            if line.startswith('Solution'):
                if current_solution:
                    solutions["detailed_solutions"].append(current_solution)
                current_solution = {
                    "approach": "",
                    "explanation": "",
                    "limitations": ""
                }
            elif current_solution:
                if "Approach:" in line:
                    current_solution["approach"] = line.split("Approach:", 1)[1].strip()
                elif "Explanation:" in line:
                    current_solution["explanation"] = line.split("Explanation:", 1)[1].strip()
                elif "Limitations:" in line:
                    current_solution["limitations"] = line.split("Limitations:", 1)[1].strip()
        
        if current_solution:
            solutions["detailed_solutions"].append(current_solution)
        
        # Parse combined solution
        if len(parts) > 1:
            solutions["combined_solution"] = parts[1].strip()
        
        return solutions
    
    def _calculate_confidence(self, analogies: List[Dict[str, Any]], 
                            mappings: List[Dict[str, Any]], 
                            solutions: Dict[str, Any]) -> float:
        """Calculate confidence score based on analogy quality."""
        confidence = 0.0
        
        # Quality of analogies (0.4 weight)
        if analogies:
            analogy_score = sum(
                bool(a["domain"]) * 0.25 +
                bool(a["situation"]) * 0.25 +
                bool(a["key_elements"]) * 0.25 +
                bool(a["solution_pattern"]) * 0.25
                for a in analogies
            ) / len(analogies)
            confidence += analogy_score * 0.4
        
        # Quality of mappings (0.3 weight)
        if mappings:
            mapping_score = sum(
                bool(m["corresponding_elements"]) * 0.4 +
                bool(m["relevant_aspects"]) * 0.3 +
                bool(m["adaptation_needed"]) * 0.3
                for m in mappings
            ) / len(mappings)
            confidence += mapping_score * 0.3
        
        # Quality of solutions (0.3 weight)
        if solutions["detailed_solutions"]:
            solution_score = sum(
                bool(s["approach"]) * 0.4 +
                bool(s["explanation"]) * 0.4 +
                bool(s["limitations"]) * 0.2
                for s in solutions["detailed_solutions"]
            ) / len(solutions["detailed_solutions"])
            confidence += solution_score * 0.3
        
        return min(confidence, 1.0)

class CausalReasoning(ReasoningStrategy):
    """Implements causal reasoning by identifying cause-effect relationships."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        # Identify causal factors
        factors = await self._identify_causal_factors(query, context)
        
        # Build causal graph
        causal_graph = await self._build_causal_graph(factors, context)
        
        # Analyze interventions
        interventions = await self._analyze_interventions(causal_graph, context)
        
        return {
            "success": True,
            "causal_factors": factors,
            "causal_graph": causal_graph,
            "interventions": interventions
        }

    async def _identify_causal_factors(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Identify causal factors in the problem."""
        prompt = f"""
        Identify causal factors in this problem:
        Query: {query}
        Context: {json.dumps(context)}
        
        For each factor:
        1. Describe the factor
        2. Explain its causal role
        3. Identify dependencies
        4. Rate its importance (1-5)
        """
        
        response = await context["llm_clients"].generate(prompt)
        return self._parse_factors(response["answer"]) if response["success"] else []
    
    async def _build_causal_graph(self, factors: List[Dict[str, Any]], 
                                context: Dict[str, Any]) -> Dict[str, Any]:
        """Build a causal graph from identified factors."""
        prompt = f"""
        Build a causal graph from these factors:
        Factors: {json.dumps(factors, indent=2)}
        Context: {json.dumps(context)}
        
        For each relationship:
        1. Identify cause and effect
        2. Describe the relationship
        3. Rate the strength (1-5)
        4. Note any conditions
        """
        
        response = await context["llm_clients"].generate(prompt)
        return self._parse_graph(response["answer"]) if response["success"] else {}
    
    async def _analyze_interventions(self, causal_graph: Dict[str, Any], 
                                   context: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Analyze possible interventions based on causal graph."""
        prompt = f"""
        Analyze possible interventions based on this causal graph:
        Graph: {json.dumps(causal_graph, indent=2)}
        Context: {json.dumps(context)}
        
        For each intervention:
        1. Describe the intervention
        2. Identify target factors
        3. Predict effects
        4. Rate effectiveness (1-5)
        """
        
        response = await context["llm_clients"].generate(prompt)
        return self._parse_interventions(response["answer"]) if response["success"] else []
    
    def _parse_factors(self, response: str) -> List[Dict[str, Any]]:
        """Parse causal factors from response."""
        factors = []
        current_factor = None
        
        for line in response.split("\n"):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith("Factor"):
                if current_factor:
                    factors.append(current_factor)
                current_factor = {
                    "description": "",
                    "role": "",
                    "dependencies": [],
                    "importance": 0
                }
            elif current_factor:
                if line.startswith("Role:"):
                    current_factor["role"] = line[5:].strip()
                elif line.startswith("Dependencies:"):
                    mode = "dependencies"
                elif line.startswith("Importance:"):
                    try:
                        current_factor["importance"] = int(line[11:].strip())
                    except:
                        pass
                elif line.startswith("- "):
                    if mode == "dependencies":
                        current_factor["dependencies"].append(line[2:].strip())
                else:
                    current_factor["description"] += line + "\n"
        
        if current_factor:
            factors.append(current_factor)
            
        return factors
    
    def _parse_graph(self, response: str) -> Dict[str, Any]:
        """Parse causal graph from response."""
        nodes = {}
        edges = []
        current_relationship = None
        
        for line in response.split("\n"):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith("Relationship"):
                if current_relationship:
                    edges.append(current_relationship)
                current_relationship = {
                    "cause": "",
                    "effect": "",
                    "description": "",
                    "strength": 0,
                    "conditions": []
                }
            elif current_relationship:
                if line.startswith("Cause:"):
                    current_relationship["cause"] = line[6:].strip()
                elif line.startswith("Effect:"):
                    current_relationship["effect"] = line[7:].strip()
                elif line.startswith("Strength:"):
                    try:
                        current_relationship["strength"] = int(line[9:].strip())
                    except:
                        pass
                elif line.startswith("Conditions:"):
                    mode = "conditions"
                elif line.startswith("- "):
                    if mode == "conditions":
                        current_relationship["conditions"].append(line[2:].strip())
                else:
                    current_relationship["description"] += line + "\n"
        
        if current_relationship:
            edges.append(current_relationship)
            
        return {"nodes": nodes, "edges": edges}
    
    def _parse_interventions(self, response: str) -> List[Dict[str, Any]]:
        """Parse interventions from response."""
        interventions = []
        current_intervention = None
        
        for line in response.split("\n"):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith("Intervention"):
                if current_intervention:
                    interventions.append(current_intervention)
                current_intervention = {
                    "description": "",
                    "targets": [],
                    "effects": [],
                    "effectiveness": 0
                }
            elif current_intervention:
                if line.startswith("Targets:"):
                    mode = "targets"
                elif line.startswith("Effects:"):
                    mode = "effects"
                elif line.startswith("Effectiveness:"):
                    try:
                        current_intervention["effectiveness"] = int(line[14:].strip())
                    except:
                        pass
                elif line.startswith("- "):
                    if mode == "targets":
                        current_intervention["targets"].append(line[2:].strip())
                    elif mode == "effects":
                        current_intervention["effects"].append(line[2:].strip())
                else:
                    current_intervention["description"] += line + "\n"
        
        if current_intervention:
            interventions.append(current_intervention)
            
        return interventions

class StateSpaceSearch(ReasoningStrategy):
    """Implements state space search for problem solving."""
    def __init__(self, max_states: int = 100):
        self.max_states = max_states
    
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        initial_state = await self._create_initial_state(query, context)
        goal_state = await self._define_goal_state(query, context)
        
        path = await self._a_star_search(initial_state, goal_state)
        
        return {
            "success": True,
            "initial_state": initial_state.description,
            "goal_state": goal_state.description,
            "solution_path": path
        }
    
    async def _a_star_search(self, start: State, goal: State) -> List[str]:
        frontier = []
        heapq.heappush(frontier, start)
        came_from = {start: None}
        cost_so_far = {start: 0}
        
        while frontier and len(came_from) < self.max_states:
            current = heapq.heappop(frontier)
            
            if self._is_goal(current, goal):
                return self._reconstruct_path(came_from, current)
            
            for next_state in await self._get_neighbors(current):
                new_cost = cost_so_far[current] + 1
                
                if next_state not in cost_so_far or new_cost < cost_so_far[next_state]:
                    cost_so_far[next_state] = new_cost
                    priority = new_cost + self._heuristic(next_state, goal)
                    heapq.heappush(frontier, next_state)
                    came_from[next_state] = current
        
        return []  # No path found

    async def _create_initial_state(self, query: str, context: Dict[str, Any]) -> State:
        """Create initial state from query and context."""
        prompt = f"""
        Create an initial state for this problem:
        Query: {query}
        Context: {json.dumps(context)}
        
        Describe:
        1. Current system state
        2. Available actions
        3. Initial value estimate
        """
        
        response = await context["llm_clients"].generate(prompt)
        if response["success"]:
            parsed = self._parse_state(response["answer"])
            return State(
                description=parsed["description"],
                value=parsed["value"],
                actions=parsed["actions"]
            )
        return None
    
    async def _define_goal_state(self, query: str, context: Dict[str, Any]) -> State:
        """Define goal state from query and context."""
        prompt = f"""
        Define a goal state for this problem:
        Query: {query}
        Context: {json.dumps(context)}
        
        Describe:
        1. Desired system state
        2. Success criteria
        3. Value estimate
        """
        
        response = await context["llm_clients"].generate(prompt)
        if response["success"]:
            parsed = self._parse_state(response["answer"])
            return State(
                description=parsed["description"],
                value=parsed["value"],
                actions=[]
            )
        return None
    
    async def _get_neighbors(self, state: State) -> List[State]:
        """Get neighboring states by applying possible actions."""
        prompt = f"""
        Generate neighboring states by applying these actions:
        Current State: {state.description}
        Actions: {json.dumps(state.actions)}
        
        For each action:
        1. Describe resulting state
        2. Estimate new value
        3. List new available actions
        """
        
        response = await context["llm_clients"].generate(prompt)
        neighbors = []
        
        if response["success"]:
            for parsed in self._parse_neighbors(response["answer"]):
                neighbor = State(
                    description=parsed["description"],
                    value=parsed["value"],
                    parent=state,
                    actions=parsed["actions"],
                    depth=state.depth + 1
                )
                neighbors.append(neighbor)
        
        return neighbors
    
    def _is_goal(self, current: State, goal: State) -> bool:
        """Check if current state matches goal state."""
        return current.description == goal.description
    
    def _heuristic(self, state: State, goal: State) -> float:
        """Estimate distance from state to goal."""
        # Simple heuristic based on value difference
        return abs(state.value - goal.value)
    
    def _reconstruct_path(self, came_from: Dict[State, State], 
                         current: State) -> List[str]:
        """Reconstruct path from start to current state."""
        path = []
        while current:
            path.append(current.description)
            current = came_from.get(current)
        return list(reversed(path))
    
    def _parse_state(self, response: str) -> Dict[str, Any]:
        """Parse state from response."""
        state = {
            "description": "",
            "value": 0.0,
            "actions": []
        }
        
        mode = None
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('State:'):
                mode = "description"
            elif line.startswith('Value:'):
                try:
                    state["value"] = float(line[6:].strip())
                except:
                    pass
            elif line.startswith('Actions:'):
                mode = "actions"
            elif line.startswith("- "):
                if mode == "actions":
                    state["actions"].append(line[2:].strip())
                elif mode == "description":
                    state["description"] += line[2:].strip() + "\n"
            elif mode == "description":
                state["description"] += line + "\n"
        
        return state
    
    def _parse_neighbors(self, response: str) -> List[Dict[str, Any]]:
        """Parse neighboring states from response."""
        neighbors = []
        current_neighbor = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith("Neighbor"):
                if current_neighbor:
                    neighbors.append(current_neighbor)
                current_neighbor = {
                    "description": "",
                    "value": 0.0,
                    "actions": []
                }
            elif current_neighbor:
                if line.startswith("Value:"):
                    try:
                        current_neighbor["value"] = float(line[6:].strip())
                    except:
                        pass
                elif line.startswith("Actions:"):
                    mode = "actions"
                elif line.startswith("- "):
                    if mode == "actions":
                        current_neighbor["actions"].append(line[2:].strip())
                    else:
                        current_neighbor["description"] += line[2:].strip() + "\n"
                else:
                    current_neighbor["description"] += line + "\n"
        
        if current_neighbor:
            neighbors.append(current_neighbor)
            
        return neighbors

class BayesianReasoning(ReasoningStrategy):
    """Implements Bayesian reasoning for probabilistic analysis."""
    def __init__(self, prior_weight: float = 0.3):
        self.prior_weight = prior_weight
    
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Generate hypotheses
            hypotheses = await self._generate_hypotheses(query, context)
            
            # Calculate prior probabilities
            priors = await self._calculate_priors(hypotheses, context)
            
            # Update with evidence
            posteriors = await self._update_with_evidence(hypotheses, priors, context)
            
            # Generate final analysis
            analysis = await self._generate_analysis(posteriors, context)
            
            return {
                "success": True,
                "answer": analysis["conclusion"],
                "hypotheses": hypotheses,
                "priors": priors,
                "posteriors": posteriors,
                "confidence": analysis["confidence"],
                "reasoning_path": analysis["reasoning_path"]
            }
        except Exception as e:
            return {"success": False, "error": str(e)}
    
    async def _generate_hypotheses(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Generate 3-4 hypotheses for this problem:
        Query: {query}
        Context: {json.dumps(context)}
        
        For each hypothesis:
        1. [Statement]: Clear statement of the hypothesis
        2. [Assumptions]: Key assumptions made
        3. [Testability]: How it could be tested/verified
        
        Format as:
        [H1]
        Statement: ...
        Assumptions: ...
        Testability: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_hypotheses(response["answer"])

    async def _calculate_priors(self, hypotheses: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, float]:
        prompt = f"""
        Calculate prior probabilities for these hypotheses:
        Context: {json.dumps(context)}
        
        Hypotheses:
        {json.dumps(hypotheses, indent=2)}
        
        For each hypothesis, estimate its prior probability (0-1) based on:
        1. Alignment with known principles
        2. Historical precedent
        3. Domain expertise
        
        Format: [H1]: 0.XX, [H2]: 0.XX, ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_probabilities(response["answer"])

    async def _update_with_evidence(self, hypotheses: List[Dict[str, Any]], priors: Dict[str, float], 
                                  context: Dict[str, Any]) -> Dict[str, float]:
        prompt = f"""
        Update probabilities with available evidence:
        Context: {json.dumps(context)}
        
        Hypotheses and Priors:
        {json.dumps(list(zip(hypotheses, priors.values())), indent=2)}
        
        Consider:
        1. How well each hypothesis explains the evidence
        2. Any new evidence from the context
        3. Potential conflicts or support between hypotheses
        
        Format: [H1]: 0.XX, [H2]: 0.XX, ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_probabilities(response["answer"])

    async def _generate_analysis(self, posteriors: Dict[str, float], context: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"""
        Generate final Bayesian analysis:
        Context: {json.dumps(context)}
        
        Posterior Probabilities:
        {json.dumps(posteriors, indent=2)}
        
        Provide:
        1. Main conclusion based on highest probability hypotheses
        2. Confidence level (0-1)
        3. Key reasoning steps taken
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_analysis(response["answer"])

    def _parse_hypotheses(self, response: str) -> List[Dict[str, Any]]:
        """Parse hypotheses from response."""
        hypotheses = []
        current = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
            
            if line.startswith('[H'):
                if current:
                    hypotheses.append(current)
                current = {
                    "statement": "",
                    "assumptions": "",
                    "testability": ""
                }
            elif current:
                if line.startswith('Statement:'):
                    current["statement"] = line[10:].strip()
                elif line.startswith('Assumptions:'):
                    current["assumptions"] = line[12:].strip()
                elif line.startswith('Testability:'):
                    current["testability"] = line[12:].strip()
        
        if current:
            hypotheses.append(current)
        
        return hypotheses

    def _parse_probabilities(self, response: str) -> Dict[str, float]:
        """Parse probabilities from response."""
        probs = {}
        pattern = r'\[H(\d+)\]:\s*(0\.\d+)'
        
        for match in re.finditer(pattern, response):
            h_num = int(match.group(1))
            prob = float(match.group(2))
            probs[f"H{h_num}"] = prob
        
        return probs

    def _parse_analysis(self, response: str) -> Dict[str, Any]:
        """Parse analysis from response."""
        lines = response.split('\n')
        analysis = {
            "conclusion": "",
            "confidence": 0.0,
            "reasoning_path": []
        }
        
        for line in lines:
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('Conclusion:'):
                analysis["conclusion"] = line[11:].strip()
            elif line.startswith('Confidence:'):
                try:
                    analysis["confidence"] = float(line[11:].strip())
                except:
                    analysis["confidence"] = 0.5
            elif line.startswith('- '):
                analysis["reasoning_path"].append(line[2:].strip())
        
        return analysis

class CounterfactualReasoning(ReasoningStrategy):
    """Implements counterfactual reasoning to explore alternative scenarios."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Generate counterfactuals
            counterfactuals = await self._generate_counterfactuals(query, context)
            
            # Analyze implications
            implications = await self._analyze_implications(counterfactuals, context)
            
            # Synthesize insights
            synthesis = await self._synthesize_insights(counterfactuals, implications, context)
            
            return {
                "success": True,
                "answer": synthesis["conclusion"],
                "counterfactuals": counterfactuals,
                "implications": implications,
                "confidence": synthesis["confidence"],
                "key_insights": synthesis["key_insights"]
            }
        except Exception as e:
            return {"success": False, "error": str(e)}

    async def _generate_counterfactuals(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Generate 3-4 counterfactual scenarios for this problem:
        Query: {query}
        Context: {json.dumps(context)}
        
        For each counterfactual:
        1. [Scenario]: What if...? description
        2. [Changes]: Key changes from current situation
        3. [Plausibility]: How likely/realistic is this scenario
        
        Format as:
        [CF1]
        Scenario: ...
        Changes: ...
        Plausibility: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_counterfactuals(response["answer"])

    async def _analyze_implications(self, counterfactuals: List[Dict[str, Any]], 
                                  context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Analyze implications of these counterfactual scenarios:
        Context: {json.dumps(context)}
        
        Counterfactuals:
        {json.dumps(counterfactuals, indent=2)}
        
        For each scenario analyze:
        1. Direct effects
        2. Indirect consequences
        3. System-wide impacts
        
        Format as:
        [CF1 Analysis]
        Direct: ...
        Indirect: ...
        Systemic: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_implications(response["answer"])

    async def _synthesize_insights(self, counterfactuals: List[Dict[str, Any]], 
                                 implications: List[Dict[str, Any]], 
                                 context: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"""
        Synthesize insights from counterfactual analysis:
        Context: {json.dumps(context)}
        
        Counterfactuals:
        {json.dumps(counterfactuals, indent=2)}
        
        Implications:
        {json.dumps(implications, indent=2)}
        
        Provide:
        1. Key insights learned
        2. Main conclusion
        3. Confidence level (0-1)
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_synthesis(response["answer"])

    def _parse_counterfactuals(self, response: str) -> List[Dict[str, Any]]:
        """Parse counterfactuals from response."""
        counterfactuals = []
        current = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
            
            if line.startswith('[CF'):
                if current:
                    counterfactuals.append(current)
                current = {
                    "scenario": "",
                    "changes": "",
                    "plausibility": ""
                }
            elif current:
                if line.startswith('Scenario:'):
                    current["scenario"] = line[9:].strip()
                elif line.startswith('Changes:'):
                    current["changes"] = line[8:].strip()
                elif line.startswith('Plausibility:'):
                    current["plausibility"] = line[12:].strip()
        
        if current:
            counterfactuals.append(current)
        
        return counterfactuals

    def _parse_implications(self, response: str) -> List[Dict[str, Any]]:
        """Parse implications from response."""
        implications = []
        current = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
            
            if line.startswith('[CF'):
                if current:
                    implications.append(current)
                current = {
                    "direct": "",
                    "indirect": "",
                    "systemic": ""
                }
            elif current:
                if line.startswith('Direct:'):
                    current["direct"] = line[7:].strip()
                elif line.startswith('Indirect:'):
                    current["indirect"] = line[9:].strip()
                elif line.startswith('Systemic:'):
                    current["systemic"] = line[9:].strip()
        
        if current:
            implications.append(current)
        
        return implications

    def _parse_synthesis(self, response: str) -> Dict[str, Any]:
        """Parse synthesis from response."""
        synthesis = {
            "key_insights": [],
            "conclusion": "",
            "confidence": 0.0
        }
        
        mode = None
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('Key Insights:'):
                mode = "insights"
            elif line.startswith('Conclusion:'):
                synthesis["conclusion"] = line[11:].strip()
                mode = None
            elif line.startswith('Confidence:'):
                try:
                    synthesis["confidence"] = float(line[11:].strip())
                except:
                    synthesis["confidence"] = 0.5
                mode = None
            elif mode == "insights" and line.startswith('- '):
                synthesis["key_insights"].append(line[2:].strip())
        
        return synthesis

class MetaReasoning(ReasoningStrategy):
    """Implements meta-reasoning to analyze and improve the reasoning process itself."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Analyze reasoning requirements
            requirements = await self._analyze_requirements(query, context)
            
            # Generate reasoning strategies
            strategies = await self._generate_strategies(requirements, context)
            
            # Evaluate strategies
            evaluation = await self._evaluate_strategies(strategies, context)
            
            # Select and apply best strategy
            result = await self._apply_strategy(evaluation["best_strategy"], query, context)
            
            return {
                "success": True,
                "answer": result["conclusion"],
                "requirements": requirements,
                "strategies": strategies,
                "evaluation": evaluation,
                "confidence": result["confidence"],
                "meta_insights": result["meta_insights"]
            }
            
        except Exception as e:
            return {"success": False, "error": str(e)}

    async def _analyze_requirements(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"""
        Analyze reasoning requirements for this problem:
        Query: {query}
        Context: {json.dumps(context)}
        
        Consider:
        1. Complexity level
        2. Required knowledge types
        3. Constraints and limitations
        4. Success criteria
        
        Format as:
        Complexity: ...
        Knowledge: ...
        Constraints: ...
        Criteria: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_requirements(response["answer"])

    async def _generate_strategies(self, requirements: Dict[str, Any], 
                                 context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Generate potential reasoning strategies based on requirements:
        Requirements: {json.dumps(requirements)}
        Context: {json.dumps(context)}
        
        For each strategy:
        1. [Approach]: Description of reasoning approach
        2. [Strengths]: Key advantages
        3. [Weaknesses]: Potential limitations
        
        Format as:
        [S1]
        Approach: ...
        Strengths: ...
        Weaknesses: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_strategies(response["answer"])

    async def _evaluate_strategies(self, strategies: List[Dict[str, Any]], 
                                 context: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"""
        Evaluate proposed reasoning strategies:
        Context: {json.dumps(context)}
        
        Strategies:
        {json.dumps(strategies, indent=2)}
        
        Evaluate each strategy on:
        1. Effectiveness (0-1)
        2. Efficiency (0-1)
        3. Reliability (0-1)
        
        Then select the best strategy and explain why.
        
        Format as:
        [S1 Evaluation]
        Effectiveness: 0.XX
        Efficiency: 0.XX
        Reliability: 0.XX
        
        Best Strategy: [SX]
        Rationale: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_evaluation(response["answer"])

    async def _apply_strategy(self, strategy: Dict[str, Any], query: str, 
                            context: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"""
        Apply the selected reasoning strategy:
        Strategy: {json.dumps(strategy)}
        Query: {query}
        Context: {json.dumps(context)}
        
        Provide:
        1. Step-by-step application
        2. Main conclusion
        3. Confidence level (0-1)
        4. Meta-insights about the reasoning process
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_result(response["answer"])

    def _parse_requirements(self, response: str) -> Dict[str, Any]:
        """Parse requirements from response."""
        requirements = {
            "complexity": "",
            "knowledge": "",
            "constraints": "",
            "criteria": ""
        }
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('Complexity:'):
                requirements["complexity"] = line[11:].strip()
            elif line.startswith('Knowledge:'):
                requirements["knowledge"] = line[10:].strip()
            elif line.startswith('Constraints:'):
                requirements["constraints"] = line[12:].strip()
            elif line.startswith('Criteria:'):
                requirements["criteria"] = line[9:].strip()
        
        return requirements

    def _parse_strategies(self, response: str) -> List[Dict[str, Any]]:
        """Parse strategies from response."""
        strategies = []
        current = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
            
            if line.startswith('[S'):
                if current:
                    strategies.append(current)
                current = {
                    "approach": "",
                    "strengths": "",
                    "weaknesses": ""
                }
            elif current:
                if line.startswith('Approach:'):
                    current["approach"] = line[9:].strip()
                elif line.startswith('Strengths:'):
                    current["strengths"] = line[10:].strip()
                elif line.startswith('Weaknesses:'):
                    current["weaknesses"] = line[11:].strip()
        
        if current:
            strategies.append(current)
        
        return strategies

    def _parse_evaluation(self, response: str) -> Dict[str, Any]:
        """Parse evaluation from response."""
        evaluation = {
            "evaluations": [],
            "best_strategy": None,
            "rationale": ""
        }
        
        current = None
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[S'):
                if current:
                    evaluation["evaluations"].append(current)
                current = {
                    "effectiveness": 0.0,
                    "efficiency": 0.0,
                    "reliability": 0.0
                }
            elif current:
                if line.startswith('Effectiveness:'):
                    current["effectiveness"] = float(line[14:].strip())
                elif line.startswith('Efficiency:'):
                    current["efficiency"] = float(line[11:].strip())
                elif line.startswith('Reliability:'):
                    current["reliability"] = float(line[12:].strip())
            elif line.startswith('Best Strategy:'):
                strategy_num = re.search(r'\[S(\d+)\]', line)
                if strategy_num:
                    evaluation["best_strategy"] = int(strategy_num.group(1))
            elif line.startswith('Rationale:'):
                evaluation["rationale"] = line[10:].strip()
        
        if current:
            evaluation["evaluations"].append(current)
        
        return evaluation

    def _parse_result(self, response: str) -> Dict[str, Any]:
        """Parse result from response."""
        result = {
            "steps": [],
            "conclusion": "",
            "confidence": 0.0,
            "meta_insights": []
        }
        
        mode = None
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('Step '):
                result["steps"].append(line)
            elif line.startswith('Conclusion:'):
                result["conclusion"] = line[11:].strip()
            elif line.startswith('Confidence:'):
                try:
                    result["confidence"] = float(line[11:].strip())
                except:
                    result["confidence"] = 0.5
            elif line.startswith('Meta-insights:'):
                mode = "meta"
            elif mode == "meta" and line.startswith('- '):
                result["meta_insights"].append(line[2:].strip())
        
        return result

class EmergentReasoning(ReasoningStrategy):
    """Implements emergent reasoning by analyzing collective patterns and system-level behaviors."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Identify system components
            components = await self._identify_components(query, context)
            
            # Analyze interactions
            interactions = await self._analyze_interactions(components, context)
            
            # Detect emergent patterns
            patterns = await self._detect_patterns(interactions, context)
            
            # Synthesize emergent properties
            synthesis = await self._synthesize_properties(patterns, context)
            
            return {
                "success": True,
                "answer": synthesis["conclusion"],
                "components": components,
                "interactions": interactions,
                "patterns": patterns,
                "emergent_properties": synthesis["properties"],
                "confidence": synthesis["confidence"]
            }
        except Exception as e:
            return {"success": False, "error": str(e)}

    async def _identify_components(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Identify key system components for analysis:
        Query: {query}
        Context: {json.dumps(context)}
        
        For each component identify:
        1. [Name]: Component identifier
        2. [Properties]: Key characteristics
        3. [Role]: Function in the system
        4. [Dependencies]: Related components
        
        Format as:
        [C1]
        Name: ...
        Properties: ...
        Role: ...
        Dependencies: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_components(response["answer"])

    async def _analyze_interactions(self, components: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Analyze interactions between components:
        Components: {json.dumps(components)}
        Context: {json.dumps(context)}
        
        For each interaction describe:
        1. [Components]: Participating components
        2. [Type]: Nature of interaction
        3. [Effects]: Impact on system
        4. [Dynamics]: How it changes over time
        
        Format as:
        [I1]
        Components: ...
        Type: ...
        Effects: ...
        Dynamics: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_interactions(response["answer"])

    async def _detect_patterns(self, interactions: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Detect emergent patterns from interactions:
        Interactions: {json.dumps(interactions)}
        Context: {json.dumps(context)}
        
        For each pattern identify:
        1. [Pattern]: Description of the pattern
        2. [Scale]: At what level it emerges
        3. [Conditions]: Required conditions
        4. [Stability]: How stable/persistent it is
        
        Format as:
        [P1]
        Pattern: ...
        Scale: ...
        Conditions: ...
        Stability: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_patterns(response["answer"])

    async def _synthesize_properties(self, patterns: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"""
        Synthesize emergent properties from patterns:
        Patterns: {json.dumps(patterns)}
        Context: {json.dumps(context)}
        
        Provide:
        1. List of emergent properties
        2. How they arise from patterns
        3. Their significance
        4. Overall conclusion
        5. Confidence level (0-1)
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_synthesis(response["answer"])

    def _parse_components(self, response: str) -> List[Dict[str, Any]]:
        """Parse components from response."""
        components = []
        current_component = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[C'):
                if current_component:
                    components.append(current_component)
                current_component = {
                    "name": "",
                    "properties": "",
                    "role": "",
                    "dependencies": []
                }
            elif current_component:
                if line.startswith('Name:'):
                    current_component["name"] = line[5:].strip()
                elif line.startswith('Properties:'):
                    current_component["properties"] = line[11:].strip()
                elif line.startswith('Role:'):
                    current_component["role"] = line[5:].strip()
                elif line.startswith('Dependencies:'):
                    mode = "dependencies"
                elif line.startswith("- "):
                    if mode == "dependencies":
                        current_component["dependencies"].append(line[2:].strip())
        
        if current_component:
            components.append(current_component)
        
        return components

    def _parse_interactions(self, response: str) -> List[Dict[str, Any]]:
        """Parse interactions from response."""
        interactions = []
        current_interaction = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[I'):
                if current_interaction:
                    interactions.append(current_interaction)
                current_interaction = {
                    "components": "",
                    "type": "",
                    "effects": "",
                    "dynamics": ""
                }
            elif current_interaction:
                if line.startswith('Components:'):
                    current_interaction["components"] = line[11:].strip()
                elif line.startswith('Type:'):
                    current_interaction["type"] = line[5:].strip()
                elif line.startswith('Effects:'):
                    current_interaction["effects"] = line[7:].strip()
                elif line.startswith('Dynamics:'):
                    current_interaction["dynamics"] = line[9:].strip()
        
        if current_interaction:
            interactions.append(current_interaction)
        
        return interactions

    def _parse_patterns(self, response: str) -> List[Dict[str, Any]]:
        """Parse patterns from response."""
        patterns = []
        current_pattern = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[P'):
                if current_pattern:
                    patterns.append(current_pattern)
                current_pattern = {
                    "pattern": "",
                    "scale": "",
                    "conditions": "",
                    "stability": ""
                }
            elif current_pattern:
                if line.startswith('Pattern:'):
                    current_pattern["pattern"] = line[8:].strip()
                elif line.startswith('Scale:'):
                    current_pattern["scale"] = line[6:].strip()
                elif line.startswith('Conditions:'):
                    current_pattern["conditions"] = line[11:].strip()
                elif line.startswith('Stability:'):
                    current_pattern["stability"] = line[10:].strip()
        
        if current_pattern:
            patterns.append(current_pattern)
        
        return patterns

    def _parse_synthesis(self, response: str) -> Dict[str, Any]:
        """Parse synthesis from response."""
        synthesis = {
            "properties": [],
            "conclusion": "",
            "confidence": 0.0
        }
        
        mode = None
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('Properties:'):
                mode = "properties"
            elif line.startswith('Conclusion:'):
                synthesis["conclusion"] = line[11:].strip()
                mode = None
            elif line.startswith('Confidence:'):
                try:
                    synthesis["confidence"] = float(line[11:].strip())
                except:
                    synthesis["confidence"] = 0.5
                mode = None
            elif mode == "properties" and line.startswith('- '):
                synthesis["properties"].append(line[2:].strip())
        
        return synthesis

class QuantumReasoning(ReasoningStrategy):
    """Implements quantum-inspired reasoning using superposition and entanglement principles."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Create superposition of possibilities
            superposition = await self._create_superposition(query, context)
            
            # Analyze entanglements
            entanglements = await self._analyze_entanglements(superposition, context)
            
            # Perform quantum interference
            interference = await self._quantum_interference(superposition, entanglements, context)
            
            # Collapse to solution
            solution = await self._collapse_to_solution(interference, context)
            
            return {
                "success": True,
                "answer": solution["conclusion"],
                "superposition": superposition,
                "entanglements": entanglements,
                "interference_patterns": interference,
                "measurement": solution["measurement"],
                "confidence": solution["confidence"]
            }
        except Exception as e:
            return {"success": False, "error": str(e)}

    async def _create_superposition(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Create superposition of possible solutions:
        Query: {query}
        Context: {json.dumps(context)}
        
        For each possibility state:
        1. [State]: Description of possibility
        2. [Amplitude]: Relative strength (0-1)
        3. [Phase]: Relationship to other states
        4. [Basis]: Underlying assumptions
        
        Format as:
        [S1]
        State: ...
        Amplitude: ...
        Phase: ...
        Basis: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_superposition(response["answer"])

    async def _analyze_entanglements(self, superposition: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Analyze entanglements between possibilities:
        Superposition: {json.dumps(superposition)}
        Context: {json.dumps(context)}
        
        For each entanglement describe:
        1. [States]: Entangled states
        2. [Type]: Nature of entanglement
        3. [Strength]: Correlation strength
        4. [Impact]: Effect on outcomes
        
        Format as:
        [E1]
        States: ...
        Type: ...
        Strength: ...
        Impact: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_entanglements(response["answer"])

    async def _quantum_interference(self, superposition: List[Dict[str, Any]], entanglements: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Calculate quantum interference patterns:
        Superposition: {json.dumps(superposition)}
        Entanglements: {json.dumps(entanglements)}
        Context: {json.dumps(context)}
        
        For each interference pattern:
        1. [Pattern]: Description
        2. [Amplitude]: Combined strength
        3. [Phase]: Combined phase
        4. [Effect]: Impact on solution space
        
        Format as:
        [I1]
        Pattern: ...
        Amplitude: ...
        Phase: ...
        Effect: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_interference(response["answer"])

    async def _collapse_to_solution(self, interference: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"""
        Collapse quantum state to final solution:
        Interference: {json.dumps(interference)}
        Context: {json.dumps(context)}
        
        Provide:
        1. Final measured state
        2. Measurement confidence
        3. Key quantum effects utilized
        4. Overall conclusion
        5. Confidence level (0-1)
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_collapse(response["answer"])

    def _parse_superposition(self, response: str) -> List[Dict[str, Any]]:
        """Parse superposition states from response."""
        superposition = []
        current_state = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[S'):
                if current_state:
                    superposition.append(current_state)
                current_state = {
                    "state": "",
                    "amplitude": 0.0,
                    "phase": "",
                    "basis": ""
                }
            elif current_state:
                if line.startswith('State:'):
                    current_state["state"] = line[6:].strip()
                elif line.startswith('Amplitude:'):
                    try:
                        current_state["amplitude"] = float(line[10:].strip())
                    except:
                        pass
                elif line.startswith('Phase:'):
                    current_state["phase"] = line[6:].strip()
                elif line.startswith('Basis:'):
                    current_state["basis"] = line[6:].strip()
        
        if current_state:
            superposition.append(current_state)
        
        return superposition

    def _parse_entanglements(self, response: str) -> List[Dict[str, Any]]:
        """Parse entanglements from response."""
        entanglements = []
        current_entanglement = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[E'):
                if current_entanglement:
                    entanglements.append(current_entanglement)
                current_entanglement = {
                    "states": "",
                    "type": "",
                    "strength": 0.0,
                    "impact": ""
                }
            elif current_entanglement:
                if line.startswith('States:'):
                    current_entanglement["states"] = line[7:].strip()
                elif line.startswith('Type:'):
                    current_entanglement["type"] = line[5:].strip()
                elif line.startswith('Strength:'):
                    try:
                        current_entanglement["strength"] = float(line[9:].strip())
                    except:
                        pass
                elif line.startswith('Impact:'):
                    current_entanglement["impact"] = line[7:].strip()
        
        if current_entanglement:
            entanglements.append(current_entanglement)
        
        return entanglements

    def _parse_interference(self, response: str) -> List[Dict[str, Any]]:
        """Parse interference patterns from response."""
        interference = []
        current_pattern = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[I'):
                if current_pattern:
                    interference.append(current_pattern)
                current_pattern = {
                    "pattern": "",
                    "amplitude": 0.0,
                    "phase": "",
                    "effect": ""
                }
            elif current_pattern:
                if line.startswith('Pattern:'):
                    current_pattern["pattern"] = line[8:].strip()
                elif line.startswith('Amplitude:'):
                    try:
                        current_pattern["amplitude"] = float(line[10:].strip())
                    except:
                        pass
                elif line.startswith('Phase:'):
                    current_pattern["phase"] = line[6:].strip()
                elif line.startswith('Effect:'):
                    current_pattern["effect"] = line[7:].strip()
        
        if current_pattern:
            interference.append(current_pattern)
        
        return interference

    def _parse_collapse(self, response: str) -> Dict[str, Any]:
        """Parse collapse to solution from response."""
        collapse = {
            "measurement": "",
            "confidence": 0.0,
            "quantum_effects": [],
            "conclusion": ""
        }
        
        mode = None
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('Measurement:'):
                collapse["measurement"] = line[12:].strip()
            elif line.startswith('Confidence:'):
                try:
                    collapse["confidence"] = float(line[11:].strip())
                except:
                    collapse["confidence"] = 0.5
            elif line.startswith('Quantum Effects:'):
                mode = "effects"
            elif mode == "effects" and line.startswith('- '):
                collapse["quantum_effects"].append(line[2:].strip())
            elif line.startswith('Conclusion:'):
                collapse["conclusion"] = line[11:].strip()
        
        return collapse

class QuantumInspiredStrategy(ReasoningStrategy):
    """Implements Quantum-Inspired reasoning."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Create a clean context for serialization
            clean_context = {k: v for k, v in context.items() if k != "groq_api"}
            
            prompt = f"""
            You are a meta-learning reasoning system that adapts its approach based on problem characteristics.
            
            Problem Type: 
            Query: {query}
            Context: {json.dumps(clean_context)}
            
            Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows:

            PROBLEM ANALYSIS:
            - [First key aspect or complexity factor]
            - [Second key aspect or complexity factor]
            - [Third key aspect or complexity factor]

            SOLUTION PATHS:
            - Path 1: [Specific solution approach]
            - Path 2: [Alternative solution approach]
            - Path 3: [Another alternative approach]

            META INSIGHTS:
            - Learning 1: [Key insight about the problem space]
            - Learning 2: [Key insight about solution approaches]
            - Learning 3: [Key insight about trade-offs]

            CONCLUSION:
            [Final synthesized solution incorporating meta-learnings]
            """
            
            response = await context["groq_api"].predict(prompt)
            
            if not response["success"]:
                return response
                
            # Parse response into components
            lines = response["answer"].split("\n")
            problem_analysis = []
            solution_paths = []
            meta_insights = []
            conclusion = ""
            
            section = None
            for line in lines:
                line = line.strip()
                if not line:
                    continue
                    
                if "PROBLEM ANALYSIS:" in line:
                    section = "analysis"
                elif "SOLUTION PATHS:" in line:
                    section = "paths"
                elif "META INSIGHTS:" in line:
                    section = "insights"
                elif "CONCLUSION:" in line:
                    section = "conclusion"
                elif line.startswith("-"):
                    content = line.lstrip("- ").strip()
                    if section == "analysis":
                        problem_analysis.append(content)
                    elif section == "paths":
                        solution_paths.append(content)
                    elif section == "insights":
                        meta_insights.append(content)
                elif section == "conclusion":
                    conclusion += line + " "
            
            return {
                "success": True,
                "problem_analysis": problem_analysis,
                "solution_paths": solution_paths,
                "meta_insights": meta_insights,
                "conclusion": conclusion.strip(),
                # Add standard fields for compatibility
                "reasoning_path": problem_analysis + solution_paths + meta_insights,
                "conclusion": conclusion.strip()
            }
            
        except Exception as e:
            return {"success": False, "error": str(e)}

class NeurosymbolicReasoning(ReasoningStrategy):
    """Implements neurosymbolic reasoning combining neural and symbolic approaches."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Extract neural features
            neural_features = await self._extract_neural_features(query)
            
            # Generate symbolic rules
            symbolic_rules = await self._generate_symbolic_rules(
                neural_features,
                context
            )
            
            # Combine neural and symbolic reasoning
            combined_result = await self._combine_neural_symbolic(
                neural_features,
                symbolic_rules,
                context
            )
            
            # Update knowledge base
            self._update_knowledge_base(
                neural_features,
                symbolic_rules,
                combined_result
            )
            
            return {
                "success": True,
                "neural_features": [
                    {
                        "name": f.name,
                        "associations": f.associations
                    }
                    for f in neural_features
                ],
                "symbolic_rules": [
                    {
                        "condition": r.condition,
                        "action": r.action,
                        "confidence": r.confidence
                    }
                    for r in symbolic_rules
                ],
                "combined_result": combined_result
            }
            
        except Exception as e:
            return {"success": False, "error": str(e)}
    
    async def _extract_neural_features(self, query: str) -> List[NeuralFeature]:
        """Extract neural features from the query."""
        try:
            # Use text generation model to extract features
            prompt = f"""
            Extract key features from this query:
            {query}
            
            List each feature with its properties:
            """
            
            result = await self.model_manager.generate(
                "text_gen",
                prompt,
                max_length=150,
                temperature=0.7
            )
            
            features = []
            for line in result.split("\n"):
                if line.strip():
                    # Create feature vector using simple embedding
                    vector = np.random.rand(768)  # Placeholder
                    feature = NeuralFeature(
                        name=line.strip(),
                        vector=vector
                    )
                    features.append(feature)
            
            return features
            
        except Exception as e:
            return []

    async def _generate_symbolic_rules(self, features: List[NeuralFeature], context: Dict[str, Any]) -> List[SymbolicRule]:
        """Generate symbolic rules based on features."""
        try:
            # Use features to generate rules
            feature_desc = "\n".join(f.name for f in features)
            prompt = f"""
            Given these features:
            {feature_desc}
            
            Generate logical rules in if-then format:
            """
            
            result = await self.model_manager.generate(
                "text_gen",
                prompt,
                max_length=200,
                temperature=0.7
            )
            
            rules = []
            for line in result.split("\n"):
                if "if" in line.lower() and "then" in line.lower():
                    parts = line.lower().split("then")
                    condition = parts[0].replace("if", "").strip()
                    action = parts[1].strip()
                    rule = SymbolicRule(condition, action)
                    rules.append(rule)
            
            return rules
            
        except Exception as e:
            return []

    async def _combine_neural_symbolic(self, features: List[NeuralFeature], rules: List[SymbolicRule], context: Dict[str, Any]) -> Dict[str, Any]:
        """Combine neural and symbolic reasoning."""
        try:
            # Use neural features to evaluate symbolic rules
            evaluated_rules = []
            for rule in rules:
                # Calculate confidence based on feature associations
                confidence = 0.0
                for feature in features:
                    if feature.name in rule.condition:
                        confidence += feature.associations.get(rule.action, 0.0)
                rule.confidence = confidence / len(features)
                evaluated_rules.append(rule)
            
            # Generate combined result
            prompt = f"""
            Combine these evaluated rules to generate a solution:
            Rules: {json.dumps(evaluated_rules, indent=2)}
            Context: {json.dumps(context)}
            
            Provide:
            1. Main conclusion
            2. Confidence level (0-1)
            """
            
            result = await self.model_manager.generate(
                "text_gen",
                prompt,
                max_length=150,
                temperature=0.7
            )
            
            return {
                "conclusion": result["answer"],
                "confidence": 0.8  # Placeholder confidence
            }
            
        except Exception as e:
            return {}

    def _update_knowledge_base(self, features: List[NeuralFeature], rules: List[SymbolicRule], result: Dict[str, Any]) -> None:
        """Update knowledge base with new features and rules."""
        # Update feature associations
        for feature in features:
            for rule in rules:
                if feature.name in rule.condition:
                    feature.associations[rule.action] = rule.confidence
        
        # Update symbolic rules
        for rule in rules:
            rule.update_confidence(result["confidence"])

class MultiModalReasoning(ReasoningStrategy):
    """Implements multi-modal reasoning across different types of information."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Process different modalities
            modalities = await self._process_modalities(query, context)
            
            # Cross-modal alignment
            alignment = await self._cross_modal_alignment(modalities, context)
            
            # Integrated analysis
            integration = await self._integrated_analysis(alignment, context)
            
            # Generate unified response
            response = await self._generate_response(integration, context)
            
            return {
                "success": True,
                "answer": response["conclusion"],
                "modalities": modalities,
                "alignment": alignment,
                "integration": integration,
                "confidence": response["confidence"]
            }
        except Exception as e:
            return {"success": False, "error": str(e)}

    async def _process_modalities(self, query: str, context: Dict[str, Any]) -> Dict[str, List[Dict[str, Any]]]:
        prompt = f"""
        Process information across modalities:
        Query: {query}
        Context: {json.dumps(context)}
        
        For each modality analyze:
        1. [Type]: Modality type
        2. [Content]: Key information
        3. [Features]: Important features
        4. [Quality]: Information quality
        
        Format as:
        [M1]
        Type: ...
        Content: ...
        Features: ...
        Quality: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_modalities(response["answer"])

        async def _cross_modal_alignment(self, modalities: Dict[str, List[Dict[str, Any]]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
            """Align information across different modalities."""
            try:
                # Extract modality types
                modal_types = list(modalities.keys())
                
                # Initialize alignment results
                alignments = []
                
                # Process each modality pair
                for i in range(len(modal_types)):
                    for j in range(i + 1, len(modal_types)):
                        type1, type2 = modal_types[i], modal_types[j]
                        
                        # Get items from each modality
                        items1 = modalities[type1]
                        items2 = modalities[type2]
                        
                        # Find alignments between items
                        for item1 in items1:
                            for item2 in items2:
                                similarity = self._calculate_similarity(item1, item2)
                                if similarity > 0.5:  # Threshold for alignment
                                    alignments.append({
                                        "type1": type1,
                                        "type2": type2,
                                        "item1": item1,
                                        "item2": item2,
                                        "similarity": similarity
                                    })
                
                # Sort alignments by similarity
                alignments.sort(key=lambda x: x["similarity"], reverse=True)
                
                return alignments
                
            except Exception as e:
                logging.error(f"Error in cross-modal alignment: {str(e)}")
                return []
            
    def _calculate_similarity(self, item1: Dict[str, Any], item2: Dict[str, Any]) -> float:
        """Calculate similarity between two items from different modalities."""
        try:
            # Extract content from items
            content1 = str(item1.get("content", ""))
            content2 = str(item2.get("content", ""))
            
            # Calculate basic similarity (can be enhanced with more sophisticated methods)
            common_words = set(content1.lower().split()) & set(content2.lower().split())
            total_words = set(content1.lower().split()) | set(content2.lower().split())
            
            if not total_words:
                return 0.0
                
            return len(common_words) / len(total_words)
            
        except Exception as e:
            logging.error(f"Error calculating similarity: {str(e)}")
            return 0.0

    async def _integrated_analysis(self, alignment: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Perform integrated multi-modal analysis:
        Alignment: {json.dumps(alignment)}
        Context: {json.dumps(context)}
        
        For each insight:
        1. [Insight]: Key finding
        2. [Sources]: Contributing modalities
        3. [Support]: Supporting evidence
        4. [Confidence]: Confidence level
        
        Format as:
        [I1]
        Insight: ...
        Sources: ...
        Support: ...
        Confidence: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_integration(response["answer"])

    async def _generate_response(self, integration: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"""
        Generate unified multi-modal response:
        Integration: {json.dumps(integration)}
        Context: {json.dumps(context)}
        
        Provide:
        1. Main conclusion
        2. Modal contributions
        3. Integration benefits
        4. Confidence level (0-1)
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_response(response["answer"])

    def _parse_modalities(self, response: str) -> Dict[str, List[Dict[str, Any]]]:
        """Parse modalities from response."""
        modalities = {}
        current_modality = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[M'):
                if current_modality:
                    if current_modality["type"] not in modalities:
                        modalities[current_modality["type"]] = []
                    modalities[current_modality["type"]].append(current_modality)
                current_modality = {
                    "type": "",
                    "content": "",
                    "features": "",
                    "quality": ""
                }
            elif current_modality:
                if line.startswith('Type:'):
                    current_modality["type"] = line[5:].strip()
                elif line.startswith('Content:'):
                    current_modality["content"] = line[8:].strip()
                elif line.startswith('Features:'):
                    current_modality["features"] = line[9:].strip()
                elif line.startswith('Quality:'):
                    current_modality["quality"] = line[8:].strip()
        
        if current_modality:
            if current_modality["type"] not in modalities:
                modalities[current_modality["type"]] = []
            modalities[current_modality["type"]].append(current_modality)
        
        return modalities

    def _parse_alignment(self, response: str) -> List[Dict[str, Any]]:
        """Parse alignment from response."""
        alignment = []
        current_alignment = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[A'):
                if current_alignment:
                    alignment.append(current_alignment)
                current_alignment = {
                    "modalities": "",
                    "mapping": "",
                    "confidence": 0.0,
                    "conflicts": []
                }
            elif current_alignment:
                if line.startswith('Modalities:'):
                    current_alignment["modalities"] = line[11:].strip()
                elif line.startswith('Mapping:'):
                    current_alignment["mapping"] = line[7:].strip()
                elif line.startswith('Confidence:'):
                    try:
                        current_alignment["confidence"] = float(line[11:].strip())
                    except:
                        pass
                elif line.startswith('Conflicts:'):
                    mode = "conflicts"
                elif line.startswith("- "):
                    if mode == "conflicts":
                        current_alignment["conflicts"].append(line[2:].strip())
        
        if current_alignment:
            alignment.append(current_alignment)
        
        return alignment

    def _parse_integration(self, response: str) -> List[Dict[str, Any]]:
        """Parse integration from response."""
        integration = []
        current_insight = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[I'):
                if current_insight:
                    integration.append(current_insight)
                current_insight = {
                    "insight": "",
                    "sources": "",
                    "support": "",
                    "confidence": 0.0
                }
            elif current_insight:
                if line.startswith('Insight:'):
                    current_insight["insight"] = line[8:].strip()
                elif line.startswith('Sources:'):
                    current_insight["sources"] = line[8:].strip()
                elif line.startswith('Support:'):
                    current_insight["support"] = line[8:].strip()
                elif line.startswith('Confidence:'):
                    try:
                        current_insight["confidence"] = float(line[11:].strip())
                    except:
                        pass
        
        if current_insight:
            integration.append(current_insight)
        
        return integration

    def _parse_response(self, response: str) -> Dict[str, Any]:
        """Parse response from response."""
        response_dict = {
            "conclusion": "",
            "modal_contributions": [],
            "integration_benefits": [],
            "confidence": 0.0
        }
        
        mode = None
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('Conclusion:'):
                response_dict["conclusion"] = line[11:].strip()
            elif line.startswith('Modal Contributions:'):
                mode = "modal"
            elif line.startswith('Integration Benefits:'):
                mode = "integration"
            elif line.startswith('Confidence:'):
                try:
                    response_dict["confidence"] = float(line[11:].strip())
                except:
                    response_dict["confidence"] = 0.5
                mode = None
            elif mode == "modal" and line.startswith('- '):
                response_dict["modal_contributions"].append(line[2:].strip())
            elif mode == "integration" and line.startswith('- '):
                response_dict["integration_benefits"].append(line[2:].strip())
        
        return response_dict

class MetaLearningStrategy(ReasoningStrategy):
    """A meta-learning strategy that adapts its reasoning approach based on problem characteristics."""
    
    def __init__(self):
        self.strategy_patterns = {
            "analytical": ["analyze", "compare", "evaluate", "measure"],
            "creative": ["design", "create", "innovate", "imagine"],
            "systematic": ["organize", "structure", "plan", "implement"],
            "critical": ["critique", "assess", "validate", "test"]
        }
        
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Create a clean context for serialization
            clean_context = {k: v for k, v in context.items() if k != "groq_api"}
            
            # Analyze query to determine best reasoning patterns
            patterns = self._identify_patterns(query.lower())
            
            prompt = f"""
            You are a meta-learning reasoning system that adapts its approach based on problem characteristics.
            
            Problem Type: {', '.join(patterns)}
            Query: {query}
            Context: {json.dumps(clean_context)}
            
            Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows:

            PROBLEM ANALYSIS:
            - [First key aspect or complexity factor]
            - [Second key aspect or complexity factor]
            - [Third key aspect or complexity factor]

            SOLUTION PATHS:
            - Path 1: [Specific solution approach]
            - Path 2: [Alternative solution approach]
            - Path 3: [Another alternative approach]

            META INSIGHTS:
            - Learning 1: [Key insight about the problem space]
            - Learning 2: [Key insight about solution approaches]
            - Learning 3: [Key insight about trade-offs]

            CONCLUSION:
            [Final synthesized solution incorporating meta-learnings]
            """
            
            response = await context["groq_api"].predict(prompt)
            
            if not response["success"]:
                return response
                
            # Parse response into components
            lines = response["answer"].split("\n")
            problem_analysis = []
            solution_paths = []
            meta_insights = []
            conclusion = ""
            
            section = None
            for line in lines:
                line = line.strip()
                if not line:
                    continue
                    
                if "PROBLEM ANALYSIS:" in line:
                    section = "analysis"
                elif "SOLUTION PATHS:" in line:
                    section = "paths"
                elif "META INSIGHTS:" in line:
                    section = "insights"
                elif "CONCLUSION:" in line:
                    section = "conclusion"
                elif line.startswith("-"):
                    content = line.lstrip("- ").strip()
                    if section == "analysis":
                        problem_analysis.append(content)
                    elif section == "paths":
                        solution_paths.append(content)
                    elif section == "insights":
                        meta_insights.append(content)
                elif section == "conclusion":
                    conclusion += line + " "
            
            return {
                "success": True,
                "problem_analysis": problem_analysis,
                "solution_paths": solution_paths,
                "meta_insights": meta_insights,
                "conclusion": conclusion.strip(),
                # Add standard fields for compatibility
                "reasoning_path": problem_analysis + solution_paths + meta_insights,
                "conclusion": conclusion.strip()
            }
            
        except Exception as e:
            return {"success": False, "error": str(e)}
            
    def _identify_patterns(self, query: str) -> List[str]:
        """Identify which reasoning patterns are most relevant for the query."""
        patterns = []
        for pattern, keywords in self.strategy_patterns.items():
            if any(keyword in query for keyword in keywords):
                patterns.append(pattern)
        
        # Default to analytical if no patterns match
        if not patterns:
            patterns = ["analytical"]
            
        return patterns

class BavePantherReasoning:
    """Advanced reasoning engine combining multiple reasoning strategies."""
    
    def __init__(self, verbose: bool = True):
        """Initialize reasoning engine with multiple strategies."""
        self.logger = logging.getLogger(__name__)
        self.groq_api = GroqAPI()
        self.verbose = verbose
        
        # Configure verbose logging
        if verbose:
            logging.basicConfig(
                level=logging.DEBUG,
                format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
            )
        
        # Initialize core strategies
        self.strategies = {
            "cot": ChainOfThoughtStrategy(),
            "tot": TreeOfThoughtsStrategy(),
            "quantum": QuantumInspiredStrategy(),
            "meta_learning": MetaLearningStrategy()
        }
    
    def _log_verbose(self, message: str, level: str = "info"):
        """Log message if verbose mode is enabled."""
        if self.verbose:
            if level == "debug":
                self.logger.debug(message)
            elif level == "info":
                self.logger.info(message)
            elif level == "warning":
                self.logger.warning(message)
            elif level == "error":
                self.logger.error(message)
    
    async def process(self, query: str, context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
        """Process query using selected reasoning strategies."""
        try:
            if context is None:
                context = {}
            
            # Create a clean context for serialization
            clean_context = {
                k: v for k, v in context.items() 
                if k != "groq_api"
            }
                
            # Determine which strategies to use based on query
            selected_strategies = []
            
            if "chain of thought" in query.lower():
                selected_strategies.append("cot")
            elif "tree of thoughts" in query.lower():
                selected_strategies.append("tot")
            elif "quantum-inspired" in query.lower():
                selected_strategies.append("quantum")
            elif "meta-learning" in query.lower():
                selected_strategies.append("meta_learning")
            else:
                # For basic reasoning, use the base strategy
                prompt = f"""
                Analyze this query using basic reasoning:
                Query: {query}
                Context: {json.dumps(clean_context)}
                
                Please provide:
                1. A step-by-step reasoning path
                2. A clear conclusion
                """
                
                response = await self.groq_api.predict(prompt)
                
                if not response["success"]:
                    return response
                    
                # Parse response into reasoning path and conclusion
                lines = response["answer"].split("\n")
                reasoning_path = []
                conclusion = ""
                
                mode = "path"
                for line in lines:
                    line = line.strip()
                    if not line:
                        continue
                        
                    if mode == "path" and (line.startswith("-") or line.startswith("*") or line.startswith("Step")):
                        reasoning_path.append(line.lstrip("- *Step").strip())
                    elif mode == "conclusion":
                        conclusion += line + " "
            
                return {
                    "success": True,
                    "reasoning_path": reasoning_path,
                    "conclusion": conclusion.strip(),
                    "reasoning_chain": [],
                    "final_conclusion": "",
                    "thought_branches": [],
                    "selected_path": "",
                    "reasoning_justification": ""
                }
                
            # Apply selected strategies
            results = {}
            for name in selected_strategies:
                try:
                    strategy = self.strategies[name]
                    strategy_context = {**clean_context, "groq_api": self.groq_api}
                    result = await strategy.reason(query, strategy_context)
                    results[name] = result
                except Exception as e:
                    self.logger.error(f"Error in {name} strategy: {e}")
                    results[name] = {"error": str(e), "success": False}
            
            # Combine insights from different strategies
            combined = self._combine_insights(results, query, clean_context)
            
            # Add reasoning_path and conclusion for compatibility
            combined["reasoning_path"] = combined.get("reasoning_chain", [])
            combined["conclusion"] = combined.get("final_conclusion", "")
            
            return {
                "success": True,
                **combined
            }
            
        except Exception as e:
            self.logger.error(f"Error in reasoning process: {e}")
            return {
                "error": str(e),
                "success": False
            }
    
    def _combine_insights(self, results: Dict[str, Any], query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Combine insights from different reasoning strategies."""
        combined = {
            "reasoning_chain": [],
            "final_conclusion": "",
            "thought_branches": [],
            "selected_path": "",
            "reasoning_justification": ""
        }
        
        # Extract insights from each strategy
        if "cot" in results and results["cot"].get("success"):
            combined["reasoning_chain"] = results["cot"].get("reasoning_chain", [])
            combined["final_conclusion"] = results["cot"].get("final_conclusion", "")
            
        if "tot" in results and results["tot"].get("success"):
            combined["thought_branches"] = results["tot"].get("thought_branches", [])
            combined["selected_path"] = results["tot"].get("selected_path", "")
            combined["reasoning_justification"] = results["tot"].get("reasoning_justification", "")
            if not combined["final_conclusion"]:
                combined["final_conclusion"] = results["tot"].get("selected_path", "")
        
        if "quantum" in results and results["quantum"].get("success"):
            combined["reasoning_chain"] = results["quantum"].get("quantum_states", [])
            combined["final_conclusion"] = results["quantum"].get("measured_outcome", "")
        
        if "meta_learning" in results and results["meta_learning"].get("success"):
            combined["reasoning_chain"] = results["meta_learning"].get("problem_analysis", []) + results["meta_learning"].get("solution_paths", [])
            combined["final_conclusion"] = results["meta_learning"].get("conclusion", "")
        
        return combined

class SymbolicRule:
    """Represents a symbolic rule for neurosymbolic reasoning."""
    def __init__(self, condition: str, action: str, confidence: float = 0.5):
        self.id = str(uuid.uuid4())
        self.condition = condition
        self.action = action
        self.confidence = confidence
        self.usage_count = 0
        self.success_count = 0

    def update_confidence(self, success: bool):
        """Update rule confidence based on usage."""
        self.usage_count += 1
        if success:
            self.success_count += 1
        self.confidence = self.success_count / max(1, self.usage_count)

class NeuralFeature:
    """Represents a neural feature for neurosymbolic reasoning."""
    def __init__(self, name: str, vector: np.ndarray):
        self.name = name
        self.vector = vector
        self.associations: Dict[str, float] = {}

    def update_association(self, concept: str, strength: float):
        """Update association strength with a concept."""
        self.associations[concept] = strength

class StateSpaceNode:
    """Represents a node in the state space search."""
    def __init__(
        self,
        state: Dict[str, Any],
        parent: Optional['StateSpaceNode'] = None,
        action: Optional[str] = None,
        cost: float = 0.0
    ):
        self.id = str(uuid.uuid4())
        self.state = state
        self.parent = parent
        self.action = action
        self.cost = cost
        self.heuristic = 0.0
        self.children: List['StateSpaceNode'] = []

    def __lt__(self, other):
        return (self.cost + self.heuristic) < (other.cost + other.heuristic)

class CounterfactualScenario:
    """Represents a counterfactual scenario."""
    def __init__(
        self,
        premise: str,
        changes: List[str],
        implications: List[str],
        probability: float
    ):
        self.id = str(uuid.uuid4())
        self.premise = premise
        self.changes = changes
        self.implications = implications
        self.probability = probability
        self.impact_score = 0.0

    def evaluate_impact(self, context: Dict[str, Any]) -> float:
        """Evaluate the impact of this counterfactual scenario."""
        # Implementation will vary based on the specific domain
        return self.impact_score

class MetaLearningStrategy(ReasoningStrategy):
    """A meta-learning strategy that adapts its reasoning approach based on problem characteristics."""
    
    def __init__(self):
        self.strategy_patterns = {
            "analytical": ["analyze", "compare", "evaluate", "measure"],
            "creative": ["design", "create", "innovate", "imagine"],
            "systematic": ["organize", "structure", "plan", "implement"],
            "critical": ["critique", "assess", "validate", "test"]
        }
        
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Create a clean context for serialization
            clean_context = {k: v for k, v in context.items() if k != "groq_api"}
            
            # Analyze query to determine best reasoning patterns
            patterns = self._identify_patterns(query.lower())
            
            prompt = f"""
            You are a meta-learning reasoning system that adapts its approach based on problem characteristics.
            
            Problem Type: {', '.join(patterns)}
            Query: {query}
            Context: {json.dumps(clean_context)}
            
            Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows:

            PROBLEM ANALYSIS:
            - [First key aspect or complexity factor]
            - [Second key aspect or complexity factor]
            - [Third key aspect or complexity factor]

            SOLUTION PATHS:
            - Path 1: [Specific solution approach]
            - Path 2: [Alternative solution approach]
            - Path 3: [Another alternative approach]

            META INSIGHTS:
            - Learning 1: [Key insight about the problem space]
            - Learning 2: [Key insight about solution approaches]
            - Learning 3: [Key insight about trade-offs]

            CONCLUSION:
            [Final synthesized solution incorporating meta-learnings]
            """
            
            response = await context["groq_api"].predict(prompt)
            
            if not response["success"]:
                return response
                
            # Parse response into components
            lines = response["answer"].split("\n")
            problem_analysis = []
            solution_paths = []
            meta_insights = []
            conclusion = ""
            
            section = None
            for line in lines:
                line = line.strip()
                if not line:
                    continue
                    
                if "PROBLEM ANALYSIS:" in line:
                    section = "analysis"
                elif "SOLUTION PATHS:" in line:
                    section = "paths"
                elif "META INSIGHTS:" in line:
                    section = "insights"
                elif "CONCLUSION:" in line:
                    section = "conclusion"
                elif line.startswith("-"):
                    content = line.lstrip("- ").strip()
                    if section == "analysis":
                        problem_analysis.append(content)
                    elif section == "paths":
                        solution_paths.append(content)
                    elif section == "insights":
                        meta_insights.append(content)
                elif section == "conclusion":
                    conclusion += line + " "
            
            return {
                "success": True,
                "problem_analysis": problem_analysis,
                "solution_paths": solution_paths,
                "meta_insights": meta_insights,
                "conclusion": conclusion.strip(),
                # Add standard fields for compatibility
                "reasoning_path": problem_analysis + solution_paths + meta_insights,
                "conclusion": conclusion.strip()
            }
            
        except Exception as e:
            return {"success": False, "error": str(e)}
            
    def _identify_patterns(self, query: str) -> List[str]:
        """Identify which reasoning patterns are most relevant for the query."""
        patterns = []
        for pattern, keywords in self.strategy_patterns.items():
            if any(keyword in query for keyword in keywords):
                patterns.append(pattern)
        
        # Default to analytical if no patterns match
        if not patterns:
            patterns = ["analytical"]
            
        return patterns

class BayesianReasoning(ReasoningStrategy):
    """Implements Bayesian reasoning for probabilistic analysis."""
    def __init__(self, prior_weight: float = 0.3):
        self.prior_weight = prior_weight
    
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Generate hypotheses
            hypotheses = await self._generate_hypotheses(query, context)
            
            # Calculate prior probabilities
            priors = await self._calculate_priors(hypotheses, context)
            
            # Update with evidence
            posteriors = await self._update_with_evidence(hypotheses, priors, context)
            
            # Generate final analysis
            analysis = await self._generate_analysis(posteriors, context)
            
            return {
                "success": True,
                "answer": analysis["conclusion"],
                "hypotheses": hypotheses,
                "priors": priors,
                "posteriors": posteriors,
                "confidence": analysis["confidence"],
                "reasoning_path": analysis["reasoning_path"]
            }
        except Exception as e:
            return {"success": False, "error": str(e)}
    
    async def _generate_hypotheses(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Generate 3-4 hypotheses for this problem:
        Query: {query}
        Context: {json.dumps(context)}
        
        For each hypothesis:
        1. [Statement]: Clear statement of the hypothesis
        2. [Assumptions]: Key assumptions made
        3. [Testability]: How it could be tested/verified
        
        Format as:
        [H1]
        Statement: ...
        Assumptions: ...
        Testability: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_hypotheses(response["answer"])

    async def _calculate_priors(self, hypotheses: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, float]:
        prompt = f"""
        Calculate prior probabilities for these hypotheses:
        Context: {json.dumps(context)}
        
        Hypotheses:
        {json.dumps(hypotheses, indent=2)}
        
        For each hypothesis, estimate its prior probability (0-1) based on:
        1. Alignment with known principles
        2. Historical precedent
        3. Domain expertise
        
        Format: [H1]: 0.XX, [H2]: 0.XX, ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_probabilities(response["answer"])

    async def _update_with_evidence(self, hypotheses: List[Dict[str, Any]], priors: Dict[str, float], 
                                  context: Dict[str, Any]) -> Dict[str, float]:
        prompt = f"""
        Update probabilities with available evidence:
        Context: {json.dumps(context)}
        
        Hypotheses and Priors:
        {json.dumps(list(zip(hypotheses, priors.values())), indent=2)}
        
        Consider:
        1. How well each hypothesis explains the evidence
        2. Any new evidence from the context
        3. Potential conflicts or support between hypotheses
        
        Format: [H1]: 0.XX, [H2]: 0.XX, ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_probabilities(response["answer"])

    async def _generate_analysis(self, posteriors: Dict[str, float], context: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"""
        Generate final Bayesian analysis:
        Context: {json.dumps(context)}
        
        Posterior Probabilities:
        {json.dumps(posteriors, indent=2)}
        
        Provide:
        1. Main conclusion based on highest probability hypotheses
        2. Confidence level (0-1)
        3. Key reasoning steps taken
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_analysis(response["answer"])

    def _parse_hypotheses(self, response: str) -> List[Dict[str, Any]]:
        """Parse hypotheses from response."""
        hypotheses = []
        current = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
            
            if line.startswith('[H'):
                if current:
                    hypotheses.append(current)
                current = {
                    "statement": "",
                    "assumptions": "",
                    "testability": ""
                }
            elif current:
                if line.startswith('Statement:'):
                    current["statement"] = line[10:].strip()
                elif line.startswith('Assumptions:'):
                    current["assumptions"] = line[12:].strip()
                elif line.startswith('Testability:'):
                    current["testability"] = line[12:].strip()
        
        if current:
            hypotheses.append(current)
        
        return hypotheses

    def _parse_probabilities(self, response: str) -> Dict[str, float]:
        """Parse probabilities from response."""
        probs = {}
        pattern = r'\[H(\d+)\]:\s*(0\.\d+)'
        
        for match in re.finditer(pattern, response):
            h_num = int(match.group(1))
            prob = float(match.group(2))
            probs[f"H{h_num}"] = prob
        
        return probs

    def _parse_analysis(self, response: str) -> Dict[str, Any]:
        """Parse analysis from response."""
        lines = response.split('\n')
        analysis = {
            "conclusion": "",
            "confidence": 0.0,
            "reasoning_path": []
        }
        
        for line in lines:
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('Conclusion:'):
                analysis["conclusion"] = line[11:].strip()
            elif line.startswith('Confidence:'):
                try:
                    analysis["confidence"] = float(line[11:].strip())
                except:
                    analysis["confidence"] = 0.5
            elif line.startswith('- '):
                analysis["reasoning_path"].append(line[2:].strip())
        
        return analysis

class EmergentReasoning(ReasoningStrategy):
    """Implements emergent reasoning by analyzing collective patterns and system-level behaviors."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Identify system components
            components = await self._identify_components(query, context)
            
            # Analyze interactions
            interactions = await self._analyze_interactions(components, context)
            
            # Detect emergent patterns
            patterns = await self._detect_patterns(interactions, context)
            
            # Synthesize emergent properties
            synthesis = await self._synthesize_properties(patterns, context)
            
            return {
                "success": True,
                "answer": synthesis["conclusion"],
                "components": components,
                "interactions": interactions,
                "patterns": patterns,
                "emergent_properties": synthesis["properties"],
                "confidence": synthesis["confidence"]
            }
        except Exception as e:
            return {"success": False, "error": str(e)}

    async def _identify_components(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Identify key system components for analysis:
        Query: {query}
        Context: {json.dumps(context)}
        
        For each component identify:
        1. [Name]: Component identifier
        2. [Properties]: Key characteristics
        3. [Role]: Function in the system
        4. [Dependencies]: Related components
        
        Format as:
        [C1]
        Name: ...
        Properties: ...
        Role: ...
        Dependencies: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_components(response["answer"])

    async def _analyze_interactions(self, components: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Analyze interactions between components:
        Components: {json.dumps(components)}
        Context: {json.dumps(context)}
        
        For each interaction describe:
        1. [Components]: Participating components
        2. [Type]: Nature of interaction
        3. [Effects]: Impact on system
        4. [Dynamics]: How it changes over time
        
        Format as:
        [I1]
        Components: ...
        Type: ...
        Effects: ...
        Dynamics: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_interactions(response["answer"])

    async def _detect_patterns(self, interactions: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Detect emergent patterns from interactions:
        Interactions: {json.dumps(interactions)}
        Context: {json.dumps(context)}
        
        For each pattern identify:
        1. [Pattern]: Description of the pattern
        2. [Scale]: At what level it emerges
        3. [Conditions]: Required conditions
        4. [Stability]: How stable/persistent it is
        
        Format as:
        [P1]
        Pattern: ...
        Scale: ...
        Conditions: ...
        Stability: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_patterns(response["answer"])

    async def _synthesize_properties(self, patterns: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"""
        Synthesize emergent properties from patterns:
        Patterns: {json.dumps(patterns)}
        Context: {json.dumps(context)}
        
        Provide:
        1. List of emergent properties
        2. How they arise from patterns
        3. Their significance
        4. Overall conclusion
        5. Confidence level (0-1)
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_synthesis(response["answer"])

    def _parse_components(self, response: str) -> List[Dict[str, Any]]:
        """Parse components from response."""
        components = []
        current_component = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[C'):
                if current_component:
                    components.append(current_component)
                current_component = {
                    "name": "",
                    "properties": "",
                    "role": "",
                    "dependencies": []
                }
            elif current_component:
                if line.startswith('Name:'):
                    current_component["name"] = line[5:].strip()
                elif line.startswith('Properties:'):
                    current_component["properties"] = line[11:].strip()
                elif line.startswith('Role:'):
                    current_component["role"] = line[5:].strip()
                elif line.startswith('Dependencies:'):
                    mode = "dependencies"
                elif line.startswith("- "):
                    if mode == "dependencies":
                        current_component["dependencies"].append(line[2:].strip())
        
        if current_component:
            components.append(current_component)
        
        return components

    def _parse_interactions(self, response: str) -> List[Dict[str, Any]]:
        """Parse interactions from response."""
        interactions = []
        current_interaction = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[I'):
                if current_interaction:
                    interactions.append(current_interaction)
                current_interaction = {
                    "components": "",
                    "type": "",
                    "effects": "",
                    "dynamics": ""
                }
            elif current_interaction:
                if line.startswith('Components:'):
                    current_interaction["components"] = line[11:].strip()
                elif line.startswith('Type:'):
                    current_interaction["type"] = line[5:].strip()
                elif line.startswith('Effects:'):
                    current_interaction["effects"] = line[7:].strip()
                elif line.startswith('Dynamics:'):
                    current_interaction["dynamics"] = line[9:].strip()
        
        if current_interaction:
            interactions.append(current_interaction)
        
        return interactions

    def _parse_patterns(self, response: str) -> List[Dict[str, Any]]:
        """Parse patterns from response."""
        patterns = []
        current_pattern = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[P'):
                if current_pattern:
                    patterns.append(current_pattern)
                current_pattern = {
                    "pattern": "",
                    "scale": "",
                    "conditions": "",
                    "stability": ""
                }
            elif current_pattern:
                if line.startswith('Pattern:'):
                    current_pattern["pattern"] = line[8:].strip()
                elif line.startswith('Scale:'):
                    current_pattern["scale"] = line[6:].strip()
                elif line.startswith('Conditions:'):
                    current_pattern["conditions"] = line[11:].strip()
                elif line.startswith('Stability:'):
                    current_pattern["stability"] = line[10:].strip()
        
        if current_pattern:
            patterns.append(current_pattern)
        
        return patterns

    def _parse_synthesis(self, response: str) -> Dict[str, Any]:
        """Parse synthesis from response."""
        synthesis = {
            "properties": [],
            "conclusion": "",
            "confidence": 0.0
        }
        
        mode = None
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('Properties:'):
                mode = "properties"
            elif line.startswith('Conclusion:'):
                synthesis["conclusion"] = line[11:].strip()
                mode = None
            elif line.startswith('Confidence:'):
                try:
                    synthesis["confidence"] = float(line[11:].strip())
                except:
                    synthesis["confidence"] = 0.5
                mode = None
            elif mode == "properties" and line.startswith('- '):
                synthesis["properties"].append(line[2:].strip())
        
        return synthesis

class QuantumReasoning(ReasoningStrategy):
    """Implements quantum-inspired reasoning using superposition and entanglement principles."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Create superposition of possibilities
            superposition = await self._create_superposition(query, context)
            
            # Analyze entanglements
            entanglements = await self._analyze_entanglements(superposition, context)
            
            # Perform quantum interference
            interference = await self._quantum_interference(superposition, entanglements, context)
            
            # Collapse to solution
            solution = await self._collapse_to_solution(interference, context)
            
            return {
                "success": True,
                "answer": solution["conclusion"],
                "superposition": superposition,
                "entanglements": entanglements,
                "interference_patterns": interference,
                "measurement": solution["measurement"],
                "confidence": solution["confidence"]
            }
        except Exception as e:
            return {"success": False, "error": str(e)}

    async def _create_superposition(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Create superposition of possible solutions:
        Query: {query}
        Context: {json.dumps(context)}
        
        For each possibility state:
        1. [State]: Description of possibility
        2. [Amplitude]: Relative strength (0-1)
        3. [Phase]: Relationship to other states
        4. [Basis]: Underlying assumptions
        
        Format as:
        [S1]
        State: ...
        Amplitude: ...
        Phase: ...
        Basis: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_superposition(response["answer"])

    async def _analyze_entanglements(self, superposition: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Analyze entanglements between possibilities:
        Superposition: {json.dumps(superposition)}
        Context: {json.dumps(context)}
        
        For each entanglement describe:
        1. [States]: Entangled states
        2. [Type]: Nature of entanglement
        3. [Strength]: Correlation strength
        4. [Impact]: Effect on outcomes
        
        Format as:
        [E1]
        States: ...
        Type: ...
        Strength: ...
        Impact: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_entanglements(response["answer"])

    async def _quantum_interference(self, superposition: List[Dict[str, Any]], entanglements: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Calculate quantum interference patterns:
        Superposition: {json.dumps(superposition)}
        Entanglements: {json.dumps(entanglements)}
        Context: {json.dumps(context)}
        
        For each interference pattern:
        1. [Pattern]: Description
        2. [Amplitude]: Combined strength
        3. [Phase]: Combined phase
        4. [Effect]: Impact on solution space
        
        Format as:
        [I1]
        Pattern: ...
        Amplitude: ...
        Phase: ...
        Effect: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_interference(response["answer"])

    async def _collapse_to_solution(self, interference: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"""
        Collapse quantum state to final solution:
        Interference: {json.dumps(interference)}
        Context: {json.dumps(context)}
        
        Provide:
        1. Final measured state
        2. Measurement confidence
        3. Key quantum effects utilized
        4. Overall conclusion
        5. Confidence level (0-1)
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_collapse(response["answer"])

    def _parse_superposition(self, response: str) -> List[Dict[str, Any]]:
        """Parse superposition states from response."""
        superposition = []
        current_state = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[S'):
                if current_state:
                    superposition.append(current_state)
                current_state = {
                    "state": "",
                    "amplitude": 0.0,
                    "phase": "",
                    "basis": ""
                }
            elif current_state:
                if line.startswith('State:'):
                    current_state["state"] = line[6:].strip()
                elif line.startswith('Amplitude:'):
                    try:
                        current_state["amplitude"] = float(line[10:].strip())
                    except:
                        pass
                elif line.startswith('Phase:'):
                    current_state["phase"] = line[6:].strip()
                elif line.startswith('Basis:'):
                    current_state["basis"] = line[6:].strip()
        
        if current_state:
            superposition.append(current_state)
        
        return superposition

    def _parse_entanglements(self, response: str) -> List[Dict[str, Any]]:
        """Parse entanglements from response."""
        entanglements = []
        current_entanglement = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[E'):
                if current_entanglement:
                    entanglements.append(current_entanglement)
                current_entanglement = {
                    "states": "",
                    "type": "",
                    "strength": 0.0,
                    "impact": ""
                }
            elif current_entanglement:
                if line.startswith('States:'):
                    current_entanglement["states"] = line[7:].strip()
                elif line.startswith('Type:'):
                    current_entanglement["type"] = line[5:].strip()
                elif line.startswith('Strength:'):
                    try:
                        current_entanglement["strength"] = float(line[9:].strip())
                    except:
                        pass
                elif line.startswith('Impact:'):
                    current_entanglement["impact"] = line[7:].strip()
        
        if current_entanglement:
            entanglements.append(current_entanglement)
        
        return entanglements

    def _parse_interference(self, response: str) -> List[Dict[str, Any]]:
        """Parse interference patterns from response."""
        interference = []
        current_pattern = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[I'):
                if current_pattern:
                    interference.append(current_pattern)
                current_pattern = {
                    "pattern": "",
                    "amplitude": 0.0,
                    "phase": "",
                    "effect": ""
                }
            elif current_pattern:
                if line.startswith('Pattern:'):
                    current_pattern["pattern"] = line[8:].strip()
                elif line.startswith('Amplitude:'):
                    try:
                        current_pattern["amplitude"] = float(line[10:].strip())
                    except:
                        pass
                elif line.startswith('Phase:'):
                    current_pattern["phase"] = line[6:].strip()
                elif line.startswith('Effect:'):
                    current_pattern["effect"] = line[7:].strip()
        
        if current_pattern:
            interference.append(current_pattern)
        
        return interference

    def _parse_collapse(self, response: str) -> Dict[str, Any]:
        """Parse collapse to solution from response."""
        collapse = {
            "measurement": "",
            "confidence": 0.0,
            "quantum_effects": [],
            "conclusion": ""
        }
        
        mode = None
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('Measurement:'):
                collapse["measurement"] = line[12:].strip()
            elif line.startswith('Confidence:'):
                try:
                    collapse["confidence"] = float(line[11:].strip())
                except:
                    collapse["confidence"] = 0.5
            elif line.startswith('Quantum Effects:'):
                mode = "effects"
            elif mode == "effects" and line.startswith('- '):
                collapse["quantum_effects"].append(line[2:].strip())
            elif line.startswith('Conclusion:'):
                collapse["conclusion"] = line[11:].strip()
        
        return collapse

class QuantumInspiredStrategy(ReasoningStrategy):
    """Implements Quantum-Inspired reasoning."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Create a clean context for serialization
            clean_context = {k: v for k, v in context.items() if k != "groq_api"}
            
            prompt = f"""
            You are a meta-learning reasoning system that adapts its approach based on problem characteristics.
            
            Problem Type: 
            Query: {query}
            Context: {json.dumps(clean_context)}
            
            Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows:

            PROBLEM ANALYSIS:
            - [First key aspect or complexity factor]
            - [Second key aspect or complexity factor]
            - [Third key aspect or complexity factor]

            SOLUTION PATHS:
            - Path 1: [Specific solution approach]
            - Path 2: [Alternative solution approach]
            - Path 3: [Another alternative approach]

            META INSIGHTS:
            - Learning 1: [Key insight about the problem space]
            - Learning 2: [Key insight about solution approaches]
            - Learning 3: [Key insight about trade-offs]

            CONCLUSION:
            [Final synthesized solution incorporating meta-learnings]
            """
            
            response = await context["groq_api"].predict(prompt)
            
            if not response["success"]:
                return response
                
            # Parse response into components
            lines = response["answer"].split("\n")
            problem_analysis = []
            solution_paths = []
            meta_insights = []
            conclusion = ""
            
            section = None
            for line in lines:
                line = line.strip()
                if not line:
                    continue
                    
                if "PROBLEM ANALYSIS:" in line:
                    section = "analysis"
                elif "SOLUTION PATHS:" in line:
                    section = "paths"
                elif "META INSIGHTS:" in line:
                    section = "insights"
                elif "CONCLUSION:" in line:
                    section = "conclusion"
                elif line.startswith("-"):
                    content = line.lstrip("- ").strip()
                    if section == "analysis":
                        problem_analysis.append(content)
                    elif section == "paths":
                        solution_paths.append(content)
                    elif section == "insights":
                        meta_insights.append(content)
                elif section == "conclusion":
                    conclusion += line + " "
            
            return {
                "success": True,
                "problem_analysis": problem_analysis,
                "solution_paths": solution_paths,
                "meta_insights": meta_insights,
                "conclusion": conclusion.strip(),
                # Add standard fields for compatibility
                "reasoning_path": problem_analysis + solution_paths + meta_insights,
                "conclusion": conclusion.strip()
            }
            
        except Exception as e:
            return {"success": False, "error": str(e)}

class NeurosymbolicReasoning(ReasoningStrategy):
    """Implements neurosymbolic reasoning combining neural and symbolic approaches."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Extract neural features
            neural_features = await self._extract_neural_features(query)
            
            # Generate symbolic rules
            symbolic_rules = await self._generate_symbolic_rules(
                neural_features,
                context
            )
            
            # Combine neural and symbolic reasoning
            combined_result = await self._combine_neural_symbolic(
                neural_features,
                symbolic_rules,
                context
            )
            
            # Update knowledge base
            self._update_knowledge_base(
                neural_features,
                symbolic_rules,
                combined_result
            )
            
            return {
                "success": True,
                "neural_features": [
                    {
                        "name": f.name,
                        "associations": f.associations
                    }
                    for f in neural_features
                ],
                "symbolic_rules": [
                    {
                        "condition": r.condition,
                        "action": r.action,
                        "confidence": r.confidence
                    }
                    for r in symbolic_rules
                ],
                "combined_result": combined_result
            }
            
        except Exception as e:
            return {"success": False, "error": str(e)}
    
    async def _extract_neural_features(self, query: str) -> List[NeuralFeature]:
        """Extract neural features from the query."""
        try:
            # Use text generation model to extract features
            prompt = f"""
            Extract key features from this query:
            {query}
            
            List each feature with its properties:
            """
            
            result = await self.model_manager.generate(
                "text_gen",
                prompt,
                max_length=150,
                temperature=0.7
            )
            
            features = []
            for line in result.split("\n"):
                if line.strip():
                    # Create feature vector using simple embedding
                    vector = np.random.rand(768)  # Placeholder
                    feature = NeuralFeature(
                        name=line.strip(),
                        vector=vector
                    )
                    features.append(feature)
            
            return features
            
        except Exception as e:
            return []

    async def _generate_symbolic_rules(self, features: List[NeuralFeature], context: Dict[str, Any]) -> List[SymbolicRule]:
        """Generate symbolic rules based on features."""
        try:
            # Use features to generate rules
            feature_desc = "\n".join(f.name for f in features)
            prompt = f"""
            Given these features:
            {feature_desc}
            
            Generate logical rules in if-then format:
            """
            
            result = await self.model_manager.generate(
                "text_gen",
                prompt,
                max_length=200,
                temperature=0.7
            )
            
            rules = []
            for line in result.split("\n"):
                if "if" in line.lower() and "then" in line.lower():
                    parts = line.lower().split("then")
                    condition = parts[0].replace("if", "").strip()
                    action = parts[1].strip()
                    rule = SymbolicRule(condition, action)
                    rules.append(rule)
            
            return rules
            
        except Exception as e:
            return []

    async def _combine_neural_symbolic(self, features: List[NeuralFeature], rules: List[SymbolicRule], context: Dict[str, Any]) -> Dict[str, Any]:
        """Combine neural and symbolic reasoning."""
        try:
            # Use neural features to evaluate symbolic rules
            evaluated_rules = []
            for rule in rules:
                # Calculate confidence based on feature associations
                confidence = 0.0
                for feature in features:
                    if feature.name in rule.condition:
                        confidence += feature.associations.get(rule.action, 0.0)
                rule.confidence = confidence / len(features)
                evaluated_rules.append(rule)
            
            # Generate combined result
            prompt = f"""
            Combine these evaluated rules to generate a solution:
            Rules: {json.dumps(evaluated_rules, indent=2)}
            Context: {json.dumps(context)}
            
            Provide:
            1. Main conclusion
            2. Confidence level (0-1)
            """
            
            result = await self.model_manager.generate(
                "text_gen",
                prompt,
                max_length=150,
                temperature=0.7
            )
            
            return {
                "conclusion": result["answer"],
                "confidence": 0.8  # Placeholder confidence
            }
            
        except Exception as e:
            return {}

    def _update_knowledge_base(self, features: List[NeuralFeature], rules: List[SymbolicRule], result: Dict[str, Any]) -> None:
        """Update knowledge base with new features and rules."""
        # Update feature associations
        for feature in features:
            for rule in rules:
                if feature.name in rule.condition:
                    feature.associations[rule.action] = rule.confidence
        
        # Update symbolic rules
        for rule in rules:
            rule.update_confidence(result["confidence"])

class MultiModalReasoning(ReasoningStrategy):
    """Implements multi-modal reasoning across different types of information."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Process different modalities
            modalities = await self._process_modalities(query, context)
            
            # Cross-modal alignment
            alignment = await self._cross_modal_alignment(modalities, context)
            
            # Integrated analysis
            integration = await self._integrated_analysis(alignment, context)
            
            # Generate unified response
            response = await self._generate_response(integration, context)
            
            return {
                "success": True,
                "answer": response["conclusion"],
                "modalities": modalities,
                "alignment": alignment,
                "integration": integration,
                "confidence": response["confidence"]
            }
        except Exception as e:
            return {"success": False, "error": str(e)}

    async def _process_modalities(self, query: str, context: Dict[str, Any]) -> Dict[str, List[Dict[str, Any]]]:
        prompt = f"""
        Process information across modalities:
        Query: {query}
        Context: {json.dumps(context)}
        
        For each modality analyze:
        1. [Type]: Modality type
        2. [Content]: Key information
        3. [Features]: Important features
        4. [Quality]: Information quality
        
        Format as:
        [M1]
        Type: ...
        Content: ...
        Features: ...
        Quality: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_modalities(response["answer"])

        async def _cross_modal_alignment(self, modalities: Dict[str, List[Dict[str, Any]]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
            """Align information across different modalities."""
            try:
                # Extract modality types
                modal_types = list(modalities.keys())
                
                # Initialize alignment results
                alignments = []
                
                # Process each modality pair
                for i in range(len(modal_types)):
                    for j in range(i + 1, len(modal_types)):
                        type1, type2 = modal_types[i], modal_types[j]
                        
                        # Get items from each modality
                        items1 = modalities[type1]
                        items2 = modalities[type2]
                        
                        # Find alignments between items
                        for item1 in items1:
                            for item2 in items2:
                                similarity = self._calculate_similarity(item1, item2)
                                if similarity > 0.5:  # Threshold for alignment
                                    alignments.append({
                                        "type1": type1,
                                        "type2": type2,
                                        "item1": item1,
                                        "item2": item2,
                                        "similarity": similarity
                                    })
                
                # Sort alignments by similarity
                alignments.sort(key=lambda x: x["similarity"], reverse=True)
                
                return alignments
                
            except Exception as e:
                logging.error(f"Error in cross-modal alignment: {str(e)}")
                return []
            
    def _calculate_similarity(self, item1: Dict[str, Any], item2: Dict[str, Any]) -> float:
        """Calculate similarity between two items from different modalities."""
        try:
            # Extract content from items
            content1 = str(item1.get("content", ""))
            content2 = str(item2.get("content", ""))
            
            # Calculate basic similarity (can be enhanced with more sophisticated methods)
            common_words = set(content1.lower().split()) & set(content2.lower().split())
            total_words = set(content1.lower().split()) | set(content2.lower().split())
            
            if not total_words:
                return 0.0
                
            return len(common_words) / len(total_words)
            
        except Exception as e:
            logging.error(f"Error calculating similarity: {str(e)}")
            return 0.0

    async def _integrated_analysis(self, alignment: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Perform integrated multi-modal analysis:
        Alignment: {json.dumps(alignment)}
        Context: {json.dumps(context)}
        
        For each insight:
        1. [Insight]: Key finding
        2. [Sources]: Contributing modalities
        3. [Support]: Supporting evidence
        4. [Confidence]: Confidence level
        
        Format as:
        [I1]
        Insight: ...
        Sources: ...
        Support: ...
        Confidence: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_integration(response["answer"])

    async def _generate_response(self, integration: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"""
        Generate unified multi-modal response:
        Integration: {json.dumps(integration)}
        Context: {json.dumps(context)}
        
        Provide:
        1. Main conclusion
        2. Modal contributions
        3. Integration benefits
        4. Confidence level (0-1)
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_response(response["answer"])

    def _parse_modalities(self, response: str) -> Dict[str, List[Dict[str, Any]]]:
        """Parse modalities from response."""
        modalities = {}
        current_modality = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[M'):
                if current_modality:
                    if current_modality["type"] not in modalities:
                        modalities[current_modality["type"]] = []
                    modalities[current_modality["type"]].append(current_modality)
                current_modality = {
                    "type": "",
                    "content": "",
                    "features": "",
                    "quality": ""
                }
            elif current_modality:
                if line.startswith('Type:'):
                    current_modality["type"] = line[5:].strip()
                elif line.startswith('Content:'):
                    current_modality["content"] = line[8:].strip()
                elif line.startswith('Features:'):
                    current_modality["features"] = line[9:].strip()
                elif line.startswith('Quality:'):
                    current_modality["quality"] = line[8:].strip()
        
        if current_modality:
            if current_modality["type"] not in modalities:
                modalities[current_modality["type"]] = []
            modalities[current_modality["type"]].append(current_modality)
        
        return modalities

    def _parse_alignment(self, response: str) -> List[Dict[str, Any]]:
        """Parse alignment from response."""
        alignment = []
        current_alignment = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[A'):
                if current_alignment:
                    alignment.append(current_alignment)
                current_alignment = {
                    "modalities": "",
                    "mapping": "",
                    "confidence": 0.0,
                    "conflicts": []
                }
            elif current_alignment:
                if line.startswith('Modalities:'):
                    current_alignment["modalities"] = line[11:].strip()
                elif line.startswith('Mapping:'):
                    current_alignment["mapping"] = line[7:].strip()
                elif line.startswith('Confidence:'):
                    try:
                        current_alignment["confidence"] = float(line[11:].strip())
                    except:
                        pass
                elif line.startswith('Conflicts:'):
                    mode = "conflicts"
                elif line.startswith("- "):
                    if mode == "conflicts":
                        current_alignment["conflicts"].append(line[2:].strip())
        
        if current_alignment:
            alignment.append(current_alignment)
        
        return alignment

    def _parse_integration(self, response: str) -> List[Dict[str, Any]]:
        """Parse integration from response."""
        integration = []
        current_insight = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[I'):
                if current_insight:
                    integration.append(current_insight)
                current_insight = {
                    "insight": "",
                    "sources": "",
                    "support": "",
                    "confidence": 0.0
                }
            elif current_insight:
                if line.startswith('Insight:'):
                    current_insight["insight"] = line[8:].strip()
                elif line.startswith('Sources:'):
                    current_insight["sources"] = line[8:].strip()
                elif line.startswith('Support:'):
                    current_insight["support"] = line[8:].strip()
                elif line.startswith('Confidence:'):
                    try:
                        current_insight["confidence"] = float(line[11:].strip())
                    except:
                        pass
        
        if current_insight:
            integration.append(current_insight)
        
        return integration

    def _parse_response(self, response: str) -> Dict[str, Any]:
        """Parse response from response."""
        response_dict = {
            "conclusion": "",
            "modal_contributions": [],
            "integration_benefits": [],
            "confidence": 0.0
        }
        
        mode = None
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('Conclusion:'):
                response_dict["conclusion"] = line[11:].strip()
            elif line.startswith('Modal Contributions:'):
                mode = "modal"
            elif line.startswith('Integration Benefits:'):
                mode = "integration"
            elif line.startswith('Confidence:'):
                try:
                    response_dict["confidence"] = float(line[11:].strip())
                except:
                    response_dict["confidence"] = 0.5
                mode = None
            elif mode == "modal" and line.startswith('- '):
                response_dict["modal_contributions"].append(line[2:].strip())
            elif mode == "integration" and line.startswith('- '):
                response_dict["integration_benefits"].append(line[2:].strip())
        
        return response_dict

class MetaLearningStrategy(ReasoningStrategy):
    """A meta-learning strategy that adapts its reasoning approach based on problem characteristics."""
    
    def __init__(self):
        self.strategy_patterns = {
            "analytical": ["analyze", "compare", "evaluate", "measure"],
            "creative": ["design", "create", "innovate", "imagine"],
            "systematic": ["organize", "structure", "plan", "implement"],
            "critical": ["critique", "assess", "validate", "test"]
        }
        
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Create a clean context for serialization
            clean_context = {k: v for k, v in context.items() if k != "groq_api"}
            
            # Analyze query to determine best reasoning patterns
            patterns = self._identify_patterns(query.lower())
            
            prompt = f"""
            You are a meta-learning reasoning system that adapts its approach based on problem characteristics.
            
            Problem Type: {', '.join(patterns)}
            Query: {query}
            Context: {json.dumps(clean_context)}
            
            Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows:

            PROBLEM ANALYSIS:
            - [First key aspect or complexity factor]
            - [Second key aspect or complexity factor]
            - [Third key aspect or complexity factor]

            SOLUTION PATHS:
            - Path 1: [Specific solution approach]
            - Path 2: [Alternative solution approach]
            - Path 3: [Another alternative approach]

            META INSIGHTS:
            - Learning 1: [Key insight about the problem space]
            - Learning 2: [Key insight about solution approaches]
            - Learning 3: [Key insight about trade-offs]

            CONCLUSION:
            [Final synthesized solution incorporating meta-learnings]
            """
            
            response = await context["groq_api"].predict(prompt)
            
            if not response["success"]:
                return response
                
            # Parse response into components
            lines = response["answer"].split("\n")
            problem_analysis = []
            solution_paths = []
            meta_insights = []
            conclusion = ""
            
            section = None
            for line in lines:
                line = line.strip()
                if not line:
                    continue
                    
                if "PROBLEM ANALYSIS:" in line:
                    section = "analysis"
                elif "SOLUTION PATHS:" in line:
                    section = "paths"
                elif "META INSIGHTS:" in line:
                    section = "insights"
                elif "CONCLUSION:" in line:
                    section = "conclusion"
                elif line.startswith("-"):
                    content = line.lstrip("- ").strip()
                    if section == "analysis":
                        problem_analysis.append(content)
                    elif section == "paths":
                        solution_paths.append(content)
                    elif section == "insights":
                        meta_insights.append(content)
                elif section == "conclusion":
                    conclusion += line + " "
            
            return {
                "success": True,
                "problem_analysis": problem_analysis,
                "solution_paths": solution_paths,
                "meta_insights": meta_insights,
                "conclusion": conclusion.strip(),
                # Add standard fields for compatibility
                "reasoning_path": problem_analysis + solution_paths + meta_insights,
                "conclusion": conclusion.strip()
            }
            
        except Exception as e:
            return {"success": False, "error": str(e)}
            
    def _identify_patterns(self, query: str) -> List[str]:
        """Identify which reasoning patterns are most relevant for the query."""
        patterns = []
        for pattern, keywords in self.strategy_patterns.items():
            if any(keyword in query for keyword in keywords):
                patterns.append(pattern)
        
        # Default to analytical if no patterns match
        if not patterns:
            patterns = ["analytical"]
            
        return patterns

class BayesianReasoning(ReasoningStrategy):
    """Implements Bayesian reasoning for probabilistic analysis."""
    def __init__(self, prior_weight: float = 0.3):
        self.prior_weight = prior_weight
    
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Generate hypotheses
            hypotheses = await self._generate_hypotheses(query, context)
            
            # Calculate prior probabilities
            priors = await self._calculate_priors(hypotheses, context)
            
            # Update with evidence
            posteriors = await self._update_with_evidence(hypotheses, priors, context)
            
            # Generate final analysis
            analysis = await self._generate_analysis(posteriors, context)
            
            return {
                "success": True,
                "answer": analysis["conclusion"],
                "hypotheses": hypotheses,
                "priors": priors,
                "posteriors": posteriors,
                "confidence": analysis["confidence"],
                "reasoning_path": analysis["reasoning_path"]
            }
        except Exception as e:
            return {"success": False, "error": str(e)}
    
    async def _generate_hypotheses(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Generate 3-4 hypotheses for this problem:
        Query: {query}
        Context: {json.dumps(context)}
        
        For each hypothesis:
        1. [Statement]: Clear statement of the hypothesis
        2. [Assumptions]: Key assumptions made
        3. [Testability]: How it could be tested/verified
        
        Format as:
        [H1]
        Statement: ...
        Assumptions: ...
        Testability: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_hypotheses(response["answer"])

    async def _calculate_priors(self, hypotheses: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, float]:
        prompt = f"""
        Calculate prior probabilities for these hypotheses:
        Context: {json.dumps(context)}
        
        Hypotheses:
        {json.dumps(hypotheses, indent=2)}
        
        For each hypothesis, estimate its prior probability (0-1) based on:
        1. Alignment with known principles
        2. Historical precedent
        3. Domain expertise
        
        Format: [H1]: 0.XX, [H2]: 0.XX, ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_probabilities(response["answer"])

    async def _update_with_evidence(self, hypotheses: List[Dict[str, Any]], priors: Dict[str, float], 
                                  context: Dict[str, Any]) -> Dict[str, float]:
        prompt = f"""
        Update probabilities with available evidence:
        Context: {json.dumps(context)}
        
        Hypotheses and Priors:
        {json.dumps(list(zip(hypotheses, priors.values())), indent=2)}
        
        Consider:
        1. How well each hypothesis explains the evidence
        2. Any new evidence from the context
        3. Potential conflicts or support between hypotheses
        
        Format: [H1]: 0.XX, [H2]: 0.XX, ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_probabilities(response["answer"])

    async def _generate_analysis(self, posteriors: Dict[str, float], context: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"""
        Generate final Bayesian analysis:
        Context: {json.dumps(context)}
        
        Posterior Probabilities:
        {json.dumps(posteriors, indent=2)}
        
        Provide:
        1. Main conclusion based on highest probability hypotheses
        2. Confidence level (0-1)
        3. Key reasoning steps taken
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_analysis(response["answer"])

    def _parse_hypotheses(self, response: str) -> List[Dict[str, Any]]:
        """Parse hypotheses from response."""
        hypotheses = []
        current = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
            
            if line.startswith('[H'):
                if current:
                    hypotheses.append(current)
                current = {
                    "statement": "",
                    "assumptions": "",
                    "testability": ""
                }
            elif current:
                if line.startswith('Statement:'):
                    current["statement"] = line[10:].strip()
                elif line.startswith('Assumptions:'):
                    current["assumptions"] = line[12:].strip()
                elif line.startswith('Testability:'):
                    current["testability"] = line[12:].strip()
        
        if current:
            hypotheses.append(current)
        
        return hypotheses

    def _parse_probabilities(self, response: str) -> Dict[str, float]:
        """Parse probabilities from response."""
        probs = {}
        pattern = r'\[H(\d+)\]:\s*(0\.\d+)'
        
        for match in re.finditer(pattern, response):
            h_num = int(match.group(1))
            prob = float(match.group(2))
            probs[f"H{h_num}"] = prob
        
        return probs

    def _parse_analysis(self, response: str) -> Dict[str, Any]:
        """Parse analysis from response."""
        lines = response.split('\n')
        analysis = {
            "conclusion": "",
            "confidence": 0.0,
            "reasoning_path": []
        }
        
        for line in lines:
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('Conclusion:'):
                analysis["conclusion"] = line[11:].strip()
            elif line.startswith('Confidence:'):
                try:
                    analysis["confidence"] = float(line[11:].strip())
                except:
                    analysis["confidence"] = 0.5
            elif line.startswith('- '):
                analysis["reasoning_path"].append(line[2:].strip())
        
        return analysis

class EmergentReasoning(ReasoningStrategy):
    """Implements emergent reasoning by analyzing collective patterns and system-level behaviors."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Identify system components
            components = await self._identify_components(query, context)
            
            # Analyze interactions
            interactions = await self._analyze_interactions(components, context)
            
            # Detect emergent patterns
            patterns = await self._detect_patterns(interactions, context)
            
            # Synthesize emergent properties
            synthesis = await self._synthesize_properties(patterns, context)
            
            return {
                "success": True,
                "answer": synthesis["conclusion"],
                "components": components,
                "interactions": interactions,
                "patterns": patterns,
                "emergent_properties": synthesis["properties"],
                "confidence": synthesis["confidence"]
            }
        except Exception as e:
            return {"success": False, "error": str(e)}

    async def _identify_components(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Identify key system components for analysis:
        Query: {query}
        Context: {json.dumps(context)}
        
        For each component identify:
        1. [Name]: Component identifier
        2. [Properties]: Key characteristics
        3. [Role]: Function in the system
        4. [Dependencies]: Related components
        
        Format as:
        [C1]
        Name: ...
        Properties: ...
        Role: ...
        Dependencies: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_components(response["answer"])

    async def _analyze_interactions(self, components: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Analyze interactions between components:
        Components: {json.dumps(components)}
        Context: {json.dumps(context)}
        
        For each interaction describe:
        1. [Components]: Participating components
        2. [Type]: Nature of interaction
        3. [Effects]: Impact on system
        4. [Dynamics]: How it changes over time
        
        Format as:
        [I1]
        Components: ...
        Type: ...
        Effects: ...
        Dynamics: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_interactions(response["answer"])

    async def _detect_patterns(self, interactions: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Detect emergent patterns from interactions:
        Interactions: {json.dumps(interactions)}
        Context: {json.dumps(context)}
        
        For each pattern identify:
        1. [Pattern]: Description of the pattern
        2. [Scale]: At what level it emerges
        3. [Conditions]: Required conditions
        4. [Stability]: How stable/persistent it is
        
        Format as:
        [P1]
        Pattern: ...
        Scale: ...
        Conditions: ...
        Stability: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_patterns(response["answer"])

    async def _synthesize_properties(self, patterns: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"""
        Synthesize emergent properties from patterns:
        Patterns: {json.dumps(patterns)}
        Context: {json.dumps(context)}
        
        Provide:
        1. List of emergent properties
        2. How they arise from patterns
        3. Their significance
        4. Overall conclusion
        5. Confidence level (0-1)
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_synthesis(response["answer"])

    def _parse_components(self, response: str) -> List[Dict[str, Any]]:
        """Parse components from response."""
        components = []
        current_component = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[C'):
                if current_component:
                    components.append(current_component)
                current_component = {
                    "name": "",
                    "properties": "",
                    "role": "",
                    "dependencies": []
                }
            elif current_component:
                if line.startswith('Name:'):
                    current_component["name"] = line[5:].strip()
                elif line.startswith('Properties:'):
                    current_component["properties"] = line[11:].strip()
                elif line.startswith('Role:'):
                    current_component["role"] = line[5:].strip()
                elif line.startswith('Dependencies:'):
                    mode = "dependencies"
                elif line.startswith("- "):
                    if mode == "dependencies":
                        current_component["dependencies"].append(line[2:].strip())
        
        if current_component:
            components.append(current_component)
        
        return components

    def _parse_interactions(self, response: str) -> List[Dict[str, Any]]:
        """Parse interactions from response."""
        interactions = []
        current_interaction = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[I'):
                if current_interaction:
                    interactions.append(current_interaction)
                current_interaction = {
                    "components": "",
                    "type": "",
                    "effects": "",
                    "dynamics": ""
                }
            elif current_interaction:
                if line.startswith('Components:'):
                    current_interaction["components"] = line[11:].strip()
                elif line.startswith('Type:'):
                    current_interaction["type"] = line[5:].strip()
                elif line.startswith('Effects:'):
                    current_interaction["effects"] = line[7:].strip()
                elif line.startswith('Dynamics:'):
                    current_interaction["dynamics"] = line[9:].strip()
        
        if current_interaction:
            interactions.append(current_interaction)
        
        return interactions

    def _parse_patterns(self, response: str) -> List[Dict[str, Any]]:
        """Parse patterns from response."""
        patterns = []
        current_pattern = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[P'):
                if current_pattern:
                    patterns.append(current_pattern)
                current_pattern = {
                    "pattern": "",
                    "scale": "",
                    "conditions": "",
                    "stability": ""
                }
            elif current_pattern:
                if line.startswith('Pattern:'):
                    current_pattern["pattern"] = line[8:].strip()
                elif line.startswith('Scale:'):
                    current_pattern["scale"] = line[6:].strip()
                elif line.startswith('Conditions:'):
                    current_pattern["conditions"] = line[11:].strip()
                elif line.startswith('Stability:'):
                    current_pattern["stability"] = line[10:].strip()
        
        if current_pattern:
            patterns.append(current_pattern)
        
        return patterns

    def _parse_synthesis(self, response: str) -> Dict[str, Any]:
        """Parse synthesis from response."""
        synthesis = {
            "properties": [],
            "conclusion": "",
            "confidence": 0.0
        }
        
        mode = None
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('Properties:'):
                mode = "properties"
            elif line.startswith('Conclusion:'):
                synthesis["conclusion"] = line[11:].strip()
                mode = None
            elif line.startswith('Confidence:'):
                try:
                    synthesis["confidence"] = float(line[11:].strip())
                except:
                    synthesis["confidence"] = 0.5
                mode = None
            elif mode == "properties" and line.startswith('- '):
                synthesis["properties"].append(line[2:].strip())
        
        return synthesis

class QuantumReasoning(ReasoningStrategy):
    """Implements quantum-inspired reasoning using superposition and entanglement principles."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Create superposition of possibilities
            superposition = await self._create_superposition(query, context)
            
            # Analyze entanglements
            entanglements = await self._analyze_entanglements(superposition, context)
            
            # Perform quantum interference
            interference = await self._quantum_interference(superposition, entanglements, context)
            
            # Collapse to solution
            solution = await self._collapse_to_solution(interference, context)
            
            return {
                "success": True,
                "answer": solution["conclusion"],
                "superposition": superposition,
                "entanglements": entanglements,
                "interference_patterns": interference,
                "measurement": solution["measurement"],
                "confidence": solution["confidence"]
            }
        except Exception as e:
            return {"success": False, "error": str(e)}

    async def _create_superposition(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Create superposition of possible solutions:
        Query: {query}
        Context: {json.dumps(context)}
        
        For each possibility state:
        1. [State]: Description of possibility
        2. [Amplitude]: Relative strength (0-1)
        3. [Phase]: Relationship to other states
        4. [Basis]: Underlying assumptions
        
        Format as:
        [S1]
        State: ...
        Amplitude: ...
        Phase: ...
        Basis: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_superposition(response["answer"])

    async def _analyze_entanglements(self, superposition: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Analyze entanglements between possibilities:
        Superposition: {json.dumps(superposition)}
        Context: {json.dumps(context)}
        
        For each entanglement describe:
        1. [States]: Entangled states
        2. [Type]: Nature of entanglement
        3. [Strength]: Correlation strength
        4. [Impact]: Effect on outcomes
        
        Format as:
        [E1]
        States: ...
        Type: ...
        Strength: ...
        Impact: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_entanglements(response["answer"])

    async def _quantum_interference(self, superposition: List[Dict[str, Any]], entanglements: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Calculate quantum interference patterns:
        Superposition: {json.dumps(superposition)}
        Entanglements: {json.dumps(entanglements)}
        Context: {json.dumps(context)}
        
        For each interference pattern:
        1. [Pattern]: Description
        2. [Amplitude]: Combined strength
        3. [Phase]: Combined phase
        4. [Effect]: Impact on solution space
        
        Format as:
        [I1]
        Pattern: ...
        Amplitude: ...
        Phase: ...
        Effect: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_interference(response["answer"])

    async def _collapse_to_solution(self, interference: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"""
        Collapse quantum state to final solution:
        Interference: {json.dumps(interference)}
        Context: {json.dumps(context)}
        
        Provide:
        1. Final measured state
        2. Measurement confidence
        3. Key quantum effects utilized
        4. Overall conclusion
        5. Confidence level (0-1)
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_collapse(response["answer"])

    def _parse_superposition(self, response: str) -> List[Dict[str, Any]]:
        """Parse superposition states from response."""
        superposition = []
        current_state = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[S'):
                if current_state:
                    superposition.append(current_state)
                current_state = {
                    "state": "",
                    "amplitude": 0.0,
                    "phase": "",
                    "basis": ""
                }
            elif current_state:
                if line.startswith('State:'):
                    current_state["state"] = line[6:].strip()
                elif line.startswith('Amplitude:'):
                    try:
                        current_state["amplitude"] = float(line[10:].strip())
                    except:
                        pass
                elif line.startswith('Phase:'):
                    current_state["phase"] = line[6:].strip()
                elif line.startswith('Basis:'):
                    current_state["basis"] = line[6:].strip()
        
        if current_state:
            superposition.append(current_state)
        
        return superposition

    def _parse_entanglements(self, response: str) -> List[Dict[str, Any]]:
        """Parse entanglements from response."""
        entanglements = []
        current_entanglement = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[E'):
                if current_entanglement:
                    entanglements.append(current_entanglement)
                current_entanglement = {
                    "states": "",
                    "type": "",
                    "strength": 0.0,
                    "impact": ""
                }
            elif current_entanglement:
                if line.startswith('States:'):
                    current_entanglement["states"] = line[7:].strip()
                elif line.startswith('Type:'):
                    current_entanglement["type"] = line[5:].strip()
                elif line.startswith('Strength:'):
                    try:
                        current_entanglement["strength"] = float(line[9:].strip())
                    except:
                        pass
                elif line.startswith('Impact:'):
                    current_entanglement["impact"] = line[7:].strip()
        
        if current_entanglement:
            entanglements.append(current_entanglement)
        
        return entanglements

    def _parse_interference(self, response: str) -> List[Dict[str, Any]]:
        """Parse interference patterns from response."""
        interference = []
        current_pattern = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[I'):
                if current_pattern:
                    interference.append(current_pattern)
                current_pattern = {
                    "pattern": "",
                    "amplitude": 0.0,
                    "phase": "",
                    "effect": ""
                }
            elif current_pattern:
                if line.startswith('Pattern:'):
                    current_pattern["pattern"] = line[8:].strip()
                elif line.startswith('Amplitude:'):
                    try:
                        current_pattern["amplitude"] = float(line[10:].strip())
                    except:
                        pass
                elif line.startswith('Phase:'):
                    current_pattern["phase"] = line[6:].strip()
                elif line.startswith('Effect:'):
                    current_pattern["effect"] = line[7:].strip()
        
        if current_pattern:
            interference.append(current_pattern)
        
        return interference

    def _parse_collapse(self, response: str) -> Dict[str, Any]:
        """Parse collapse to solution from response."""
        collapse = {
            "measurement": "",
            "confidence": 0.0,
            "quantum_effects": [],
            "conclusion": ""
        }
        
        mode = None
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('Measurement:'):
                collapse["measurement"] = line[12:].strip()
            elif line.startswith('Confidence:'):
                try:
                    collapse["confidence"] = float(line[11:].strip())
                except:
                    collapse["confidence"] = 0.5
            elif line.startswith('Quantum Effects:'):
                mode = "effects"
            elif mode == "effects" and line.startswith('- '):
                collapse["quantum_effects"].append(line[2:].strip())
            elif line.startswith('Conclusion:'):
                collapse["conclusion"] = line[11:].strip()
        
        return collapse

class QuantumInspiredStrategy(ReasoningStrategy):
    """Implements Quantum-Inspired reasoning."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Create a clean context for serialization
            clean_context = {k: v for k, v in context.items() if k != "groq_api"}
            
            prompt = f"""
            You are a meta-learning reasoning system that adapts its approach based on problem characteristics.
            
            Problem Type: 
            Query: {query}
            Context: {json.dumps(clean_context)}
            
            Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows:

            PROBLEM ANALYSIS:
            - [First key aspect or complexity factor]
            - [Second key aspect or complexity factor]
            - [Third key aspect or complexity factor]

            SOLUTION PATHS:
            - Path 1: [Specific solution approach]
            - Path 2: [Alternative solution approach]
            - Path 3: [Another alternative approach]

            META INSIGHTS:
            - Learning 1: [Key insight about the problem space]
            - Learning 2: [Key insight about solution approaches]
            - Learning 3: [Key insight about trade-offs]

            CONCLUSION:
            [Final synthesized solution incorporating meta-learnings]
            """
            
            response = await context["groq_api"].predict(prompt)
            
            if not response["success"]:
                return response
                
            # Parse response into components
            lines = response["answer"].split("\n")
            problem_analysis = []
            solution_paths = []
            meta_insights = []
            conclusion = ""
            
            section = None
            for line in lines:
                line = line.strip()
                if not line:
                    continue
                    
                if "PROBLEM ANALYSIS:" in line:
                    section = "analysis"
                elif "SOLUTION PATHS:" in line:
                    section = "paths"
                elif "META INSIGHTS:" in line:
                    section = "insights"
                elif "CONCLUSION:" in line:
                    section = "conclusion"
                elif line.startswith("-"):
                    content = line.lstrip("- ").strip()
                    if section == "analysis":
                        problem_analysis.append(content)
                    elif section == "paths":
                        solution_paths.append(content)
                    elif section == "insights":
                        meta_insights.append(content)
                elif section == "conclusion":
                    conclusion += line + " "
            
            return {
                "success": True,
                "problem_analysis": problem_analysis,
                "solution_paths": solution_paths,
                "meta_insights": meta_insights,
                "conclusion": conclusion.strip(),
                # Add standard fields for compatibility
                "reasoning_path": problem_analysis + solution_paths + meta_insights,
                "conclusion": conclusion.strip()
            }
            
        except Exception as e:
            return {"success": False, "error": str(e)}

class NeurosymbolicReasoning(ReasoningStrategy):
    """Implements neurosymbolic reasoning combining neural and symbolic approaches."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Extract neural features
            neural_features = await self._extract_neural_features(query)
            
            # Generate symbolic rules
            symbolic_rules = await self._generate_symbolic_rules(
                neural_features,
                context
            )
            
            # Combine neural and symbolic reasoning
            combined_result = await self._combine_neural_symbolic(
                neural_features,
                symbolic_rules,
                context
            )
            
            # Update knowledge base
            self._update_knowledge_base(
                neural_features,
                symbolic_rules,
                combined_result
            )
            
            return {
                "success": True,
                "neural_features": [
                    {
                        "name": f.name,
                        "associations": f.associations
                    }
                    for f in neural_features
                ],
                "symbolic_rules": [
                    {
                        "condition": r.condition,
                        "action": r.action,
                        "confidence": r.confidence
                    }
                    for r in symbolic_rules
                ],
                "combined_result": combined_result
            }
            
        except Exception as e:
            return {"success": False, "error": str(e)}
    
    async def _extract_neural_features(self, query: str) -> List[NeuralFeature]:
        """Extract neural features from the query."""
        try:
            # Use text generation model to extract features
            prompt = f"""
            Extract key features from this query:
            {query}
            
            List each feature with its properties:
            """
            
            result = await self.model_manager.generate(
                "text_gen",
                prompt,
                max_length=150,
                temperature=0.7
            )
            
            features = []
            for line in result.split("\n"):
                if line.strip():
                    # Create feature vector using simple embedding
                    vector = np.random.rand(768)  # Placeholder
                    feature = NeuralFeature(
                        name=line.strip(),
                        vector=vector
                    )
                    features.append(feature)
            
            return features
            
        except Exception as e:
            return []

    async def _generate_symbolic_rules(self, features: List[NeuralFeature], context: Dict[str, Any]) -> List[SymbolicRule]:
        """Generate symbolic rules based on features."""
        try:
            # Use features to generate rules
            feature_desc = "\n".join(f.name for f in features)
            prompt = f"""
            Given these features:
            {feature_desc}
            
            Generate logical rules in if-then format:
            """
            
            result = await self.model_manager.generate(
                "text_gen",
                prompt,
                max_length=200,
                temperature=0.7
            )
            
            rules = []
            for line in result.split("\n"):
                if "if" in line.lower() and "then" in line.lower():
                    parts = line.lower().split("then")
                    condition = parts[0].replace("if", "").strip()
                    action = parts[1].strip()
                    rule = SymbolicRule(condition, action)
                    rules.append(rule)
            
            return rules
            
        except Exception as e:
            return []

    async def _combine_neural_symbolic(self, features: List[NeuralFeature], rules: List[SymbolicRule], context: Dict[str, Any]) -> Dict[str, Any]:
        """Combine neural and symbolic reasoning."""
        try:
            # Use neural features to evaluate symbolic rules
            evaluated_rules = []
            for rule in rules:
                # Calculate confidence based on feature associations
                confidence = 0.0
                for feature in features:
                    if feature.name in rule.condition:
                        confidence += feature.associations.get(rule.action, 0.0)
                rule.confidence = confidence / len(features)
                evaluated_rules.append(rule)
            
            # Generate combined result
            prompt = f"""
            Combine these evaluated rules to generate a solution:
            Rules: {json.dumps(evaluated_rules, indent=2)}
            Context: {json.dumps(context)}
            
            Provide:
            1. Main conclusion
            2. Confidence level (0-1)
            """
            
            result = await self.model_manager.generate(
                "text_gen",
                prompt,
                max_length=150,
                temperature=0.7
            )
            
            return {
                "conclusion": result["answer"],
                "confidence": 0.8  # Placeholder confidence
            }
            
        except Exception as e:
            return {}

    def _update_knowledge_base(self, features: List[NeuralFeature], rules: List[SymbolicRule], result: Dict[str, Any]) -> None:
        """Update knowledge base with new features and rules."""
        # Update feature associations
        for feature in features:
            for rule in rules:
                if feature.name in rule.condition:
                    feature.associations[rule.action] = rule.confidence
        
        # Update symbolic rules
        for rule in rules:
            rule.update_confidence(result["confidence"])

class MultiModalReasoning(ReasoningStrategy):
    """Implements multi-modal reasoning across different types of information."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Process different modalities
            modalities = await self._process_modalities(query, context)
            
            # Cross-modal alignment
            alignment = await self._cross_modal_alignment(modalities, context)
            
            # Integrated analysis
            integration = await self._integrated_analysis(alignment, context)
            
            # Generate unified response
            response = await self._generate_response(integration, context)
            
            return {
                "success": True,
                "answer": response["conclusion"],
                "modalities": modalities,
                "alignment": alignment,
                "integration": integration,
                "confidence": response["confidence"]
            }
        except Exception as e:
            return {"success": False, "error": str(e)}

    async def _process_modalities(self, query: str, context: Dict[str, Any]) -> Dict[str, List[Dict[str, Any]]]:
        prompt = f"""
        Process information across modalities:
        Query: {query}
        Context: {json.dumps(context)}
        
        For each modality analyze:
        1. [Type]: Modality type
        2. [Content]: Key information
        3. [Features]: Important features
        4. [Quality]: Information quality
        
        Format as:
        [M1]
        Type: ...
        Content: ...
        Features: ...
        Quality: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_modalities(response["answer"])

        async def _cross_modal_alignment(self, modalities: Dict[str, List[Dict[str, Any]]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
            """Align information across different modalities."""
            try:
                # Extract modality types
                modal_types = list(modalities.keys())
                
                # Initialize alignment results
                alignments = []
                
                # Process each modality pair
                for i in range(len(modal_types)):
                    for j in range(i + 1, len(modal_types)):
                        type1, type2 = modal_types[i], modal_types[j]
                        
                        # Get items from each modality
                        items1 = modalities[type1]
                        items2 = modalities[type2]
                        
                        # Find alignments between items
                        for item1 in items1:
                            for item2 in items2:
                                similarity = self._calculate_similarity(item1, item2)
                                if similarity > 0.5:  # Threshold for alignment
                                    alignments.append({
                                        "type1": type1,
                                        "type2": type2,
                                        "item1": item1,
                                        "item2": item2,
                                        "similarity": similarity
                                    })
                
                # Sort alignments by similarity
                alignments.sort(key=lambda x: x["similarity"], reverse=True)
                
                return alignments
                
            except Exception as e:
                logging.error(f"Error in cross-modal alignment: {str(e)}")
                return []
            
    def _calculate_similarity(self, item1: Dict[str, Any], item2: Dict[str, Any]) -> float:
        """Calculate similarity between two items from different modalities."""
        try:
            # Extract content from items
            content1 = str(item1.get("content", ""))
            content2 = str(item2.get("content", ""))
            
            # Calculate basic similarity (can be enhanced with more sophisticated methods)
            common_words = set(content1.lower().split()) & set(content2.lower().split())
            total_words = set(content1.lower().split()) | set(content2.lower().split())
            
            if not total_words:
                return 0.0
                
            return len(common_words) / len(total_words)
            
        except Exception as e:
            logging.error(f"Error calculating similarity: {str(e)}")
            return 0.0

    async def _integrated_analysis(self, alignment: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Perform integrated multi-modal analysis:
        Alignment: {json.dumps(alignment)}
        Context: {json.dumps(context)}
        
        For each insight:
        1. [Insight]: Key finding
        2. [Sources]: Contributing modalities
        3. [Support]: Supporting evidence
        4. [Confidence]: Confidence level
        
        Format as:
        [I1]
        Insight: ...
        Sources: ...
        Support: ...
        Confidence: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_integration(response["answer"])

    async def _generate_response(self, integration: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"""
        Generate unified multi-modal response:
        Integration: {json.dumps(integration)}
        Context: {json.dumps(context)}
        
        Provide:
        1. Main conclusion
        2. Modal contributions
        3. Integration benefits
        4. Confidence level (0-1)
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_response(response["answer"])

    def _parse_modalities(self, response: str) -> Dict[str, List[Dict[str, Any]]]:
        """Parse modalities from response."""
        modalities = {}
        current_modality = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[M'):
                if current_modality:
                    if current_modality["type"] not in modalities:
                        modalities[current_modality["type"]] = []
                    modalities[current_modality["type"]].append(current_modality)
                current_modality = {
                    "type": "",
                    "content": "",
                    "features": "",
                    "quality": ""
                }
            elif current_modality:
                if line.startswith('Type:'):
                    current_modality["type"] = line[5:].strip()
                elif line.startswith('Content:'):
                    current_modality["content"] = line[8:].strip()
                elif line.startswith('Features:'):
                    current_modality["features"] = line[9:].strip()
                elif line.startswith('Quality:'):
                    current_modality["quality"] = line[8:].strip()
        
        if current_modality:
            if current_modality["type"] not in modalities:
                modalities[current_modality["type"]] = []
            modalities[current_modality["type"]].append(current_modality)
        
        return modalities

    def _parse_alignment(self, response: str) -> List[Dict[str, Any]]:
        """Parse alignment from response."""
        alignment = []
        current_alignment = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[A'):
                if current_alignment:
                    alignment.append(current_alignment)
                current_alignment = {
                    "modalities": "",
                    "mapping": "",
                    "confidence": 0.0,
                    "conflicts": []
                }
            elif current_alignment:
                if line.startswith('Modalities:'):
                    current_alignment["modalities"] = line[11:].strip()
                elif line.startswith('Mapping:'):
                    current_alignment["mapping"] = line[7:].strip()
                elif line.startswith('Confidence:'):
                    try:
                        current_alignment["confidence"] = float(line[11:].strip())
                    except:
                        pass
                elif line.startswith('Conflicts:'):
                    mode = "conflicts"
                elif line.startswith("- "):
                    if mode == "conflicts":
                        current_alignment["conflicts"].append(line[2:].strip())
        
        if current_alignment:
            alignment.append(current_alignment)
        
        return alignment

    def _parse_integration(self, response: str) -> List[Dict[str, Any]]:
        """Parse integration from response."""
        integration = []
        current_insight = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[I'):
                if current_insight:
                    integration.append(current_insight)
                current_insight = {
                    "insight": "",
                    "sources": "",
                    "support": "",
                    "confidence": 0.0
                }
            elif current_insight:
                if line.startswith('Insight:'):
                    current_insight["insight"] = line[8:].strip()
                elif line.startswith('Sources:'):
                    current_insight["sources"] = line[8:].strip()
                elif line.startswith('Support:'):
                    current_insight["support"] = line[8:].strip()
                elif line.startswith('Confidence:'):
                    try:
                        current_insight["confidence"] = float(line[11:].strip())
                    except:
                        pass
        
        if current_insight:
            integration.append(current_insight)
        
        return integration

    def _parse_response(self, response: str) -> Dict[str, Any]:
        """Parse response from response."""
        response_dict = {
            "conclusion": "",
            "modal_contributions": [],
            "integration_benefits": [],
            "confidence": 0.0
        }
        
        mode = None
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('Conclusion:'):
                response_dict["conclusion"] = line[11:].strip()
            elif line.startswith('Modal Contributions:'):
                mode = "modal"
            elif line.startswith('Integration Benefits:'):
                mode = "integration"
            elif line.startswith('Confidence:'):
                try:
                    response_dict["confidence"] = float(line[11:].strip())
                except:
                    response_dict["confidence"] = 0.5
                mode = None
            elif mode == "modal" and line.startswith('- '):
                response_dict["modal_contributions"].append(line[2:].strip())
            elif mode == "integration" and line.startswith('- '):
                response_dict["integration_benefits"].append(line[2:].strip())
        
        return response_dict

class MetaLearningStrategy(ReasoningStrategy):
    """A meta-learning strategy that adapts its reasoning approach based on problem characteristics."""
    
    def __init__(self):
        self.strategy_patterns = {
            "analytical": ["analyze", "compare", "evaluate", "measure"],
            "creative": ["design", "create", "innovate", "imagine"],
            "systematic": ["organize", "structure", "plan", "implement"],
            "critical": ["critique", "assess", "validate", "test"]
        }
        
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Create a clean context for serialization
            clean_context = {k: v for k, v in context.items() if k != "groq_api"}
            
            # Analyze query to determine best reasoning patterns
            patterns = self._identify_patterns(query.lower())
            
            prompt = f"""
            You are a meta-learning reasoning system that adapts its approach based on problem characteristics.
            
            Problem Type: {', '.join(patterns)}
            Query: {query}
            Context: {json.dumps(clean_context)}
            
            Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows:

            PROBLEM ANALYSIS:
            - [First key aspect or complexity factor]
            - [Second key aspect or complexity factor]
            - [Third key aspect or complexity factor]

            SOLUTION PATHS:
            - Path 1: [Specific solution approach]
            - Path 2: [Alternative solution approach]
            - Path 3: [Another alternative approach]

            META INSIGHTS:
            - Learning 1: [Key insight about the problem space]
            - Learning 2: [Key insight about solution approaches]
            - Learning 3: [Key insight about trade-offs]

            CONCLUSION:
            [Final synthesized solution incorporating meta-learnings]
            """
            
            response = await context["groq_api"].predict(prompt)
            
            if not response["success"]:
                return response
                
            # Parse response into components
            lines = response["answer"].split("\n")
            problem_analysis = []
            solution_paths = []
            meta_insights = []
            conclusion = ""
            
            section = None
            for line in lines:
                line = line.strip()
                if not line:
                    continue
                    
                if "PROBLEM ANALYSIS:" in line:
                    section = "analysis"
                elif "SOLUTION PATHS:" in line:
                    section = "paths"
                elif "META INSIGHTS:" in line:
                    section = "insights"
                elif "CONCLUSION:" in line:
                    section = "conclusion"
                elif line.startswith("-"):
                    content = line.lstrip("- ").strip()
                    if section == "analysis":
                        problem_analysis.append(content)
                    elif section == "paths":
                        solution_paths.append(content)
                    elif section == "insights":
                        meta_insights.append(content)
                elif section == "conclusion":
                    conclusion += line + " "
            
            return {
                "success": True,
                "problem_analysis": problem_analysis,
                "solution_paths": solution_paths,
                "meta_insights": meta_insights,
                "conclusion": conclusion.strip(),
                # Add standard fields for compatibility
                "reasoning_path": problem_analysis + solution_paths + meta_insights,
                "conclusion": conclusion.strip()
            }
            
        except Exception as e:
            return {"success": False, "error": str(e)}
            
    def _identify_patterns(self, query: str) -> List[str]:
        """Identify which reasoning patterns are most relevant for the query."""
        patterns = []
        for pattern, keywords in self.strategy_patterns.items():
            if any(keyword in query for keyword in keywords):
                patterns.append(pattern)
        
        # Default to analytical if no patterns match
        if not patterns:
            patterns = ["analytical"]
            
        return patterns

class BayesianReasoning(ReasoningStrategy):
    """Implements Bayesian reasoning for probabilistic analysis."""
    def __init__(self, prior_weight: float = 0.3):
        self.prior_weight = prior_weight
    
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Generate hypotheses
            hypotheses = await self._generate_hypotheses(query, context)
            
            # Calculate prior probabilities
            priors = await self._calculate_priors(hypotheses, context)
            
            # Update with evidence
            posteriors = await self._update_with_evidence(hypotheses, priors, context)
            
            # Generate final analysis
            analysis = await self._generate_analysis(posteriors, context)
            
            return {
                "success": True,
                "answer": analysis["conclusion"],
                "hypotheses": hypotheses,
                "priors": priors,
                "posteriors": posteriors,
                "confidence": analysis["confidence"],
                "reasoning_path": analysis["reasoning_path"]
            }
        except Exception as e:
            return {"success": False, "error": str(e)}
    
    async def _generate_hypotheses(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Generate 3-4 hypotheses for this problem:
        Query: {query}
        Context: {json.dumps(context)}
        
        For each hypothesis:
        1. [Statement]: Clear statement of the hypothesis
        2. [Assumptions]: Key assumptions made
        3. [Testability]: How it could be tested/verified
        
        Format as:
        [H1]
        Statement: ...
        Assumptions: ...
        Testability: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_hypotheses(response["answer"])

    async def _calculate_priors(self, hypotheses: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, float]:
        prompt = f"""
        Calculate prior probabilities for these hypotheses:
        Context: {json.dumps(context)}
        
        Hypotheses:
        {json.dumps(hypotheses, indent=2)}
        
        For each hypothesis, estimate its prior probability (0-1) based on:
        1. Alignment with known principles
        2. Historical precedent
        3. Domain expertise
        
        Format: [H1]: 0.XX, [H2]: 0.XX, ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_probabilities(response["answer"])

    async def _update_with_evidence(self, hypotheses: List[Dict[str, Any]], priors: Dict[str, float], 
                                  context: Dict[str, Any]) -> Dict[str, float]:
        prompt = f"""
        Update probabilities with available evidence:
        Context: {json.dumps(context)}
        
        Hypotheses and Priors:
        {json.dumps(list(zip(hypotheses, priors.values())), indent=2)}
        
        Consider:
        1. How well each hypothesis explains the evidence
        2. Any new evidence from the context
        3. Potential conflicts or support between hypotheses
        
        Format: [H1]: 0.XX, [H2]: 0.XX, ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_probabilities(response["answer"])

    async def _generate_analysis(self, posteriors: Dict[str, float], context: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"""
        Generate final Bayesian analysis:
        Context: {json.dumps(context)}
        
        Posterior Probabilities:
        {json.dumps(posteriors, indent=2)}
        
        Provide:
        1. Main conclusion based on highest probability hypotheses
        2. Confidence level (0-1)
        3. Key reasoning steps taken
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_analysis(response["answer"])

    def _parse_hypotheses(self, response: str) -> List[Dict[str, Any]]:
        """Parse hypotheses from response."""
        hypotheses = []
        current = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
            
            if line.startswith('[H'):
                if current:
                    hypotheses.append(current)
                current = {
                    "statement": "",
                    "assumptions": "",
                    "testability": ""
                }
            elif current:
                if line.startswith('Statement:'):
                    current["statement"] = line[10:].strip()
                elif line.startswith('Assumptions:'):
                    current["assumptions"] = line[12:].strip()
                elif line.startswith('Testability:'):
                    current["testability"] = line[12:].strip()
        
        if current:
            hypotheses.append(current)
        
        return hypotheses

    def _parse_probabilities(self, response: str) -> Dict[str, float]:
        """Parse probabilities from response."""
        probs = {}
        pattern = r'\[H(\d+)\]:\s*(0\.\d+)'
        
        for match in re.finditer(pattern, response):
            h_num = int(match.group(1))
            prob = float(match.group(2))
            probs[f"H{h_num}"] = prob
        
        return probs

    def _parse_analysis(self, response: str) -> Dict[str, Any]:
        """Parse analysis from response."""
        lines = response.split('\n')
        analysis = {
            "conclusion": "",
            "confidence": 0.0,
            "reasoning_path": []
        }
        
        for line in lines:
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('Conclusion:'):
                analysis["conclusion"] = line[11:].strip()
            elif line.startswith('Confidence:'):
                try:
                    analysis["confidence"] = float(line[11:].strip())
                except:
                    analysis["confidence"] = 0.5
            elif line.startswith('- '):
                analysis["reasoning_path"].append(line[2:].strip())
        
        return analysis

class EmergentReasoning(ReasoningStrategy):
    """Implements emergent reasoning by analyzing collective patterns and system-level behaviors."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Identify system components
            components = await self._identify_components(query, context)
            
            # Analyze interactions
            interactions = await self._analyze_interactions(components, context)
            
            # Detect emergent patterns
            patterns = await self._detect_patterns(interactions, context)
            
            # Synthesize emergent properties
            synthesis = await self._synthesize_properties(patterns, context)
            
            return {
                "success": True,
                "answer": synthesis["conclusion"],
                "components": components,
                "interactions": interactions,
                "patterns": patterns,
                "emergent_properties": synthesis["properties"],
                "confidence": synthesis["confidence"]
            }
        except Exception as e:
            return {"success": False, "error": str(e)}

    async def _identify_components(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Identify key system components for analysis:
        Query: {query}
        Context: {json.dumps(context)}
        
        For each component identify:
        1. [Name]: Component identifier
        2. [Properties]: Key characteristics
        3. [Role]: Function in the system
        4. [Dependencies]: Related components
        
        Format as:
        [C1]
        Name: ...
        Properties: ...
        Role: ...
        Dependencies: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_components(response["answer"])

    async def _analyze_interactions(self, components: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Analyze interactions between components:
        Components: {json.dumps(components)}
        Context: {json.dumps(context)}
        
        For each interaction describe:
        1. [Components]: Participating components
        2. [Type]: Nature of interaction
        3. [Effects]: Impact on system
        4. [Dynamics]: How it changes over time
        
        Format as:
        [I1]
        Components: ...
        Type: ...
        Effects: ...
        Dynamics: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_interactions(response["answer"])

    async def _detect_patterns(self, interactions: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Detect emergent patterns from interactions:
        Interactions: {json.dumps(interactions)}
        Context: {json.dumps(context)}
        
        For each pattern identify:
        1. [Pattern]: Description of the pattern
        2. [Scale]: At what level it emerges
        3. [Conditions]: Required conditions
        4. [Stability]: How stable/persistent it is
        
        Format as:
        [P1]
        Pattern: ...
        Scale: ...
        Conditions: ...
        Stability: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_patterns(response["answer"])

    async def _synthesize_properties(self, patterns: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"""
        Synthesize emergent properties from patterns:
        Patterns: {json.dumps(patterns)}
        Context: {json.dumps(context)}
        
        Provide:
        1. List of emergent properties
        2. How they arise from patterns
        3. Their significance
        4. Overall conclusion
        5. Confidence level (0-1)
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_synthesis(response["answer"])

    def _parse_components(self, response: str) -> List[Dict[str, Any]]:
        """Parse components from response."""
        components = []
        current_component = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[C'):
                if current_component:
                    components.append(current_component)
                current_component = {
                    "name": "",
                    "properties": "",
                    "role": "",
                    "dependencies": []
                }
            elif current_component:
                if line.startswith('Name:'):
                    current_component["name"] = line[5:].strip()
                elif line.startswith('Properties:'):
                    current_component["properties"] = line[11:].strip()
                elif line.startswith('Role:'):
                    current_component["role"] = line[5:].strip()
                elif line.startswith('Dependencies:'):
                    mode = "dependencies"
                elif line.startswith("- "):
                    if mode == "dependencies":
                        current_component["dependencies"].append(line[2:].strip())
        
        if current_component:
            components.append(current_component)
        
        return components

    def _parse_interactions(self, response: str) -> List[Dict[str, Any]]:
        """Parse interactions from response."""
        interactions = []
        current_interaction = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[I'):
                if current_interaction:
                    interactions.append(current_interaction)
                current_interaction = {
                    "components": "",
                    "type": "",
                    "effects": "",
                    "dynamics": ""
                }
            elif current_interaction:
                if line.startswith('Components:'):
                    current_interaction["components"] = line[11:].strip()
                elif line.startswith('Type:'):
                    current_interaction["type"] = line[5:].strip()
                elif line.startswith('Effects:'):
                    current_interaction["effects"] = line[7:].strip()
                elif line.startswith('Dynamics:'):
                    current_interaction["dynamics"] = line[9:].strip()
        
        if current_interaction:
            interactions.append(current_interaction)
        
        return interactions

    def _parse_patterns(self, response: str) -> List[Dict[str, Any]]:
        """Parse patterns from response."""
        patterns = []
        current_pattern = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[P'):
                if current_pattern:
                    patterns.append(current_pattern)
                current_pattern = {
                    "pattern": "",
                    "scale": "",
                    "conditions": "",
                    "stability": ""
                }
            elif current_pattern:
                if line.startswith('Pattern:'):
                    current_pattern["pattern"] = line[8:].strip()
                elif line.startswith('Scale:'):
                    current_pattern["scale"] = line[6:].strip()
                elif line.startswith('Conditions:'):
                    current_pattern["conditions"] = line[11:].strip()
                elif line.startswith('Stability:'):
                    current_pattern["stability"] = line[10:].strip()
        
        if current_pattern:
            patterns.append(current_pattern)
        
        return patterns

    def _parse_synthesis(self, response: str) -> Dict[str, Any]:
        """Parse synthesis from response."""
        synthesis = {
            "properties": [],
            "conclusion": "",
            "confidence": 0.0
        }
        
        mode = None
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('Properties:'):
                mode = "properties"
            elif line.startswith('Conclusion:'):
                synthesis["conclusion"] = line[11:].strip()
                mode = None
            elif line.startswith('Confidence:'):
                try:
                    synthesis["confidence"] = float(line[11:].strip())
                except:
                    synthesis["confidence"] = 0.5
                mode = None
            elif mode == "properties" and line.startswith('- '):
                synthesis["properties"].append(line[2:].strip())
        
        return synthesis

class QuantumReasoning(ReasoningStrategy):
    """Implements quantum-inspired reasoning using superposition and entanglement principles."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Create superposition of possibilities
            superposition = await self._create_superposition(query, context)
            
            # Analyze entanglements
            entanglements = await self._analyze_entanglements(superposition, context)
            
            # Perform quantum interference
            interference = await self._quantum_interference(superposition, entanglements, context)
            
            # Collapse to solution
            solution = await self._collapse_to_solution(interference, context)
            
            return {
                "success": True,
                "answer": solution["conclusion"],
                "superposition": superposition,
                "entanglements": entanglements,
                "interference_patterns": interference,
                "measurement": solution["measurement"],
                "confidence": solution["confidence"]
            }
        except Exception as e:
            return {"success": False, "error": str(e)}

    async def _create_superposition(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Create superposition of possible solutions:
        Query: {query}
        Context: {json.dumps(context)}
        
        For each possibility state:
        1. [State]: Description of possibility
        2. [Amplitude]: Relative strength (0-1)
        3. [Phase]: Relationship to other states
        4. [Basis]: Underlying assumptions
        
        Format as:
        [S1]
        State: ...
        Amplitude: ...
        Phase: ...
        Basis: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_superposition(response["answer"])

    async def _analyze_entanglements(self, superposition: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Analyze entanglements between possibilities:
        Superposition: {json.dumps(superposition)}
        Context: {json.dumps(context)}
        
        For each entanglement describe:
        1. [States]: Entangled states
        2. [Type]: Nature of entanglement
        3. [Strength]: Correlation strength
        4. [Impact]: Effect on outcomes
        
        Format as:
        [E1]
        States: ...
        Type: ...
        Strength: ...
        Impact: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_entanglements(response["answer"])

    async def _quantum_interference(self, superposition: List[Dict[str, Any]], entanglements: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Calculate quantum interference patterns:
        Superposition: {json.dumps(superposition)}
        Entanglements: {json.dumps(entanglements)}
        Context: {json.dumps(context)}
        
        For each interference pattern:
        1. [Pattern]: Description
        2. [Amplitude]: Combined strength
        3. [Phase]: Combined phase
        4. [Effect]: Impact on solution space
        
        Format as:
        [I1]
        Pattern: ...
        Amplitude: ...
        Phase: ...
        Effect: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_interference(response["answer"])

    async def _collapse_to_solution(self, interference: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"""
        Collapse quantum state to final solution:
        Interference: {json.dumps(interference)}
        Context: {json.dumps(context)}
        
        Provide:
        1. Final measured state
        2. Measurement confidence
        3. Key quantum effects utilized
        4. Overall conclusion
        5. Confidence level (0-1)
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_collapse(response["answer"])

    def _parse_superposition(self, response: str) -> List[Dict[str, Any]]:
        """Parse superposition states from response."""
        superposition = []
        current_state = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[S'):
                if current_state:
                    superposition.append(current_state)
                current_state = {
                    "state": "",
                    "amplitude": 0.0,
                    "phase": "",
                    "basis": ""
                }
            elif current_state:
                if line.startswith('State:'):
                    current_state["state"] = line[6:].strip()
                elif line.startswith('Amplitude:'):
                    try:
                        current_state["amplitude"] = float(line[10:].strip())
                    except:
                        pass
                elif line.startswith('Phase:'):
                    current_state["phase"] = line[6:].strip()
                elif line.startswith('Basis:'):
                    current_state["basis"] = line[6:].strip()
        
        if current_state:
            superposition.append(current_state)
        
        return superposition

    def _parse_entanglements(self, response: str) -> List[Dict[str, Any]]:
        """Parse entanglements from response."""
        entanglements = []
        current_entanglement = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[E'):
                if current_entanglement:
                    entanglements.append(current_entanglement)
                current_entanglement = {
                    "states": "",
                    "type": "",
                    "strength": 0.0,
                    "impact": ""
                }
            elif current_entanglement:
                if line.startswith('States:'):
                    current_entanglement["states"] = line[7:].strip()
                elif line.startswith('Type:'):
                    current_entanglement["type"] = line[5:].strip()
                elif line.startswith('Strength:'):
                    try:
                        current_entanglement["strength"] = float(line[9:].strip())
                    except:
                        pass
                elif line.startswith('Impact:'):
                    current_entanglement["impact"] = line[7:].strip()
        
        if current_entanglement:
            entanglements.append(current_entanglement)
        
        return entanglements

    def _parse_interference(self, response: str) -> List[Dict[str, Any]]:
        """Parse interference patterns from response."""
        interference = []
        current_pattern = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[I'):
                if current_pattern:
                    interference.append(current_pattern)
                current_pattern = {
                    "pattern": "",
                    "amplitude": 0.0,
                    "phase": "",
                    "effect": ""
                }
            elif current_pattern:
                if line.startswith('Pattern:'):
                    current_pattern["pattern"] = line[8:].strip()
                elif line.startswith('Amplitude:'):
                    try:
                        current_pattern["amplitude"] = float(line[10:].strip())
                    except:
                        pass
                elif line.startswith('Phase:'):
                    current_pattern["phase"] = line[6:].strip()
                elif line.startswith('Effect:'):
                    current_pattern["effect"] = line[7:].strip()
        
        if current_pattern:
            interference.append(current_pattern)
        
        return interference

    def _parse_collapse(self, response: str) -> Dict[str, Any]:
        """Parse collapse to solution from response."""
        collapse = {
            "measurement": "",
            "confidence": 0.0,
            "quantum_effects": [],
            "conclusion": ""
        }
        
        mode = None
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('Measurement:'):
                collapse["measurement"] = line[12:].strip()
            elif line.startswith('Confidence:'):
                try:
                    collapse["confidence"] = float(line[11:].strip())
                except:
                    collapse["confidence"] = 0.5
            elif line.startswith('Quantum Effects:'):
                mode = "effects"
            elif mode == "effects" and line.startswith('- '):
                collapse["quantum_effects"].append(line[2:].strip())
            elif line.startswith('Conclusion:'):
                collapse["conclusion"] = line[11:].strip()
        
        return collapse

class QuantumInspiredStrategy(ReasoningStrategy):
    """Implements Quantum-Inspired reasoning."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Create a clean context for serialization
            clean_context = {k: v for k, v in context.items() if k != "groq_api"}
            
            prompt = f"""
            You are a meta-learning reasoning system that adapts its approach based on problem characteristics.
            
            Problem Type: 
            Query: {query}
            Context: {json.dumps(clean_context)}
            
            Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows:

            PROBLEM ANALYSIS:
            - [First key aspect or complexity factor]
            - [Second key aspect or complexity factor]
            - [Third key aspect or complexity factor]

            SOLUTION PATHS:
            - Path 1: [Specific solution approach]
            - Path 2: [Alternative solution approach]
            - Path 3: [Another alternative approach]

            META INSIGHTS:
            - Learning 1: [Key insight about the problem space]
            - Learning 2: [Key insight about solution approaches]
            - Learning 3: [Key insight about trade-offs]

            CONCLUSION:
            [Final synthesized solution incorporating meta-learnings]
            """
            
            response = await context["groq_api"].predict(prompt)
            
            if not response["success"]:
                return response
                
            # Parse response into components
            lines = response["answer"].split("\n")
            problem_analysis = []
            solution_paths = []
            meta_insights = []
            conclusion = ""
            
            section = None
            for line in lines:
                line = line.strip()
                if not line:
                    continue
                    
                if "PROBLEM ANALYSIS:" in line:
                    section = "analysis"
                elif "SOLUTION PATHS:" in line:
                    section = "paths"
                elif "META INSIGHTS:" in line:
                    section = "insights"
                elif "CONCLUSION:" in line:
                    section = "conclusion"
                elif line.startswith("-"):
                    content = line.lstrip("- ").strip()
                    if section == "analysis":
                        problem_analysis.append(content)
                    elif section == "paths":
                        solution_paths.append(content)
                    elif section == "insights":
                        meta_insights.append(content)
                elif section == "conclusion":
                    conclusion += line + " "
            
            return {
                "success": True,
                "problem_analysis": problem_analysis,
                "solution_paths": solution_paths,
                "meta_insights": meta_insights,
                "conclusion": conclusion.strip(),
                # Add standard fields for compatibility
                "reasoning_path": problem_analysis + solution_paths + meta_insights,
                "conclusion": conclusion.strip()
            }
            
        except Exception as e:
            return {"success": False, "error": str(e)}

class NeurosymbolicReasoning(ReasoningStrategy):
    """Implements neurosymbolic reasoning combining neural and symbolic approaches."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Extract neural features
            neural_features = await self._extract_neural_features(query)
            
            # Generate symbolic rules
            symbolic_rules = await self._generate_symbolic_rules(
                neural_features,
                context
            )
            
            # Combine neural and symbolic reasoning
            combined_result = await self._combine_neural_symbolic(
                neural_features,
                symbolic_rules,
                context
            )
            
            # Update knowledge base
            self._update_knowledge_base(
                neural_features,
                symbolic_rules,
                combined_result
            )
            
            return {
                "success": True,
                "neural_features": [
                    {
                        "name": f.name,
                        "associations": f.associations
                    }
                    for f in neural_features
                ],
                "symbolic_rules": [
                    {
                        "condition": r.condition,
                        "action": r.action,
                        "confidence": r.confidence
                    }
                    for r in symbolic_rules
                ],
                "combined_result": combined_result
            }
            
        except Exception as e:
            return {"success": False, "error": str(e)}
    
    async def _extract_neural_features(self, query: str) -> List[NeuralFeature]:
        """Extract neural features from the query."""
        try:
            # Use text generation model to extract features
            prompt = f"""
            Extract key features from this query:
            {query}
            
            List each feature with its properties:
            """
            
            result = await self.model_manager.generate(
                "text_gen",
                prompt,
                max_length=150,
                temperature=0.7
            )
            
            features = []
            for line in result.split("\n"):
                if line.strip():
                    # Create feature vector using simple embedding
                    vector = np.random.rand(768)  # Placeholder
                    feature = NeuralFeature(
                        name=line.strip(),
                        vector=vector
                    )
                    features.append(feature)
            
            return features
            
        except Exception as e:
            return []

    async def _generate_symbolic_rules(self, features: List[NeuralFeature], context: Dict[str, Any]) -> List[SymbolicRule]:
        """Generate symbolic rules based on features."""
        try:
            # Use features to generate rules
            feature_desc = "\n".join(f.name for f in features)
            prompt = f"""
            Given these features:
            {feature_desc}
            
            Generate logical rules in if-then format:
            """
            
            result = await self.model_manager.generate(
                "text_gen",
                prompt,
                max_length=200,
                temperature=0.7
            )
            
            rules = []
            for line in result.split("\n"):
                if "if" in line.lower() and "then" in line.lower():
                    parts = line.lower().split("then")
                    condition = parts[0].replace("if", "").strip()
                    action = parts[1].strip()
                    rule = SymbolicRule(condition, action)
                    rules.append(rule)
            
            return rules
            
        except Exception as e:
            return []

    async def _combine_neural_symbolic(self, features: List[NeuralFeature], rules: List[SymbolicRule], context: Dict[str, Any]) -> Dict[str, Any]:
        """Combine neural and symbolic reasoning."""
        try:
            # Use neural features to evaluate symbolic rules
            evaluated_rules = []
            for rule in rules:
                # Calculate confidence based on feature associations
                confidence = 0.0
                for feature in features:
                    if feature.name in rule.condition:
                        confidence += feature.associations.get(rule.action, 0.0)
                rule.confidence = confidence / len(features)
                evaluated_rules.append(rule)
            
            # Generate combined result
            prompt = f"""
            Combine these evaluated rules to generate a solution:
            Rules: {json.dumps(evaluated_rules, indent=2)}
            Context: {json.dumps(context)}
            
            Provide:
            1. Main conclusion
            2. Confidence level (0-1)
            """
            
            result = await self.model_manager.generate(
                "text_gen",
                prompt,
                max_length=150,
                temperature=0.7
            )
            
            return {
                "conclusion": result["answer"],
                "confidence": 0.8  # Placeholder confidence
            }
            
        except Exception as e:
            return {}

    def _update_knowledge_base(self, features: List[NeuralFeature], rules: List[SymbolicRule], result: Dict[str, Any]) -> None:
        """Update knowledge base with new features and rules."""
        # Update feature associations
        for feature in features:
            for rule in rules:
                if feature.name in rule.condition:
                    feature.associations[rule.action] = rule.confidence
        
        # Update symbolic rules
        for rule in rules:
            rule.update_confidence(result["confidence"])

class MultiModalReasoning(ReasoningStrategy):
    """Implements multi-modal reasoning across different types of information."""
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Process different modalities
            modalities = await self._process_modalities(query, context)
            
            # Cross-modal alignment
            alignment = await self._cross_modal_alignment(modalities, context)
            
            # Integrated analysis
            integration = await self._integrated_analysis(alignment, context)
            
            # Generate unified response
            response = await self._generate_response(integration, context)
            
            return {
                "success": True,
                "answer": response["conclusion"],
                "modalities": modalities,
                "alignment": alignment,
                "integration": integration,
                "confidence": response["confidence"]
            }
        except Exception as e:
            return {"success": False, "error": str(e)}

    async def _process_modalities(self, query: str, context: Dict[str, Any]) -> Dict[str, List[Dict[str, Any]]]:
        prompt = f"""
        Process information across modalities:
        Query: {query}
        Context: {json.dumps(context)}
        
        For each modality analyze:
        1. [Type]: Modality type
        2. [Content]: Key information
        3. [Features]: Important features
        4. [Quality]: Information quality
        
        Format as:
        [M1]
        Type: ...
        Content: ...
        Features: ...
        Quality: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_modalities(response["answer"])

        async def _cross_modal_alignment(self, modalities: Dict[str, List[Dict[str, Any]]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
            """Align information across different modalities."""
            try:
                # Extract modality types
                modal_types = list(modalities.keys())
                
                # Initialize alignment results
                alignments = []
                
                # Process each modality pair
                for i in range(len(modal_types)):
                    for j in range(i + 1, len(modal_types)):
                        type1, type2 = modal_types[i], modal_types[j]
                        
                        # Get items from each modality
                        items1 = modalities[type1]
                        items2 = modalities[type2]
                        
                        # Find alignments between items
                        for item1 in items1:
                            for item2 in items2:
                                similarity = self._calculate_similarity(item1, item2)
                                if similarity > 0.5:  # Threshold for alignment
                                    alignments.append({
                                        "type1": type1,
                                        "type2": type2,
                                        "item1": item1,
                                        "item2": item2,
                                        "similarity": similarity
                                    })
                
                # Sort alignments by similarity
                alignments.sort(key=lambda x: x["similarity"], reverse=True)
                
                return alignments
                
            except Exception as e:
                logging.error(f"Error in cross-modal alignment: {str(e)}")
                return []
            
    def _calculate_similarity(self, item1: Dict[str, Any], item2: Dict[str, Any]) -> float:
        """Calculate similarity between two items from different modalities."""
        try:
            # Extract content from items
            content1 = str(item1.get("content", ""))
            content2 = str(item2.get("content", ""))
            
            # Calculate basic similarity (can be enhanced with more sophisticated methods)
            common_words = set(content1.lower().split()) & set(content2.lower().split())
            total_words = set(content1.lower().split()) | set(content2.lower().split())
            
            if not total_words:
                return 0.0
                
            return len(common_words) / len(total_words)
            
        except Exception as e:
            logging.error(f"Error calculating similarity: {str(e)}")
            return 0.0

    async def _integrated_analysis(self, alignment: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Perform integrated multi-modal analysis:
        Alignment: {json.dumps(alignment)}
        Context: {json.dumps(context)}
        
        For each insight:
        1. [Insight]: Key finding
        2. [Sources]: Contributing modalities
        3. [Support]: Supporting evidence
        4. [Confidence]: Confidence level
        
        Format as:
        [I1]
        Insight: ...
        Sources: ...
        Support: ...
        Confidence: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_integration(response["answer"])

    async def _generate_response(self, integration: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"""
        Generate unified multi-modal response:
        Integration: {json.dumps(integration)}
        Context: {json.dumps(context)}
        
        Provide:
        1. Main conclusion
        2. Modal contributions
        3. Integration benefits
        4. Confidence level (0-1)
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_response(response["answer"])

    def _parse_modalities(self, response: str) -> Dict[str, List[Dict[str, Any]]]:
        """Parse modalities from response."""
        modalities = {}
        current_modality = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[M'):
                if current_modality:
                    if current_modality["type"] not in modalities:
                        modalities[current_modality["type"]] = []
                    modalities[current_modality["type"]].append(current_modality)
                current_modality = {
                    "type": "",
                    "content": "",
                    "features": "",
                    "quality": ""
                }
            elif current_modality:
                if line.startswith('Type:'):
                    current_modality["type"] = line[5:].strip()
                elif line.startswith('Content:'):
                    current_modality["content"] = line[8:].strip()
                elif line.startswith('Features:'):
                    current_modality["features"] = line[9:].strip()
                elif line.startswith('Quality:'):
                    current_modality["quality"] = line[8:].strip()
        
        if current_modality:
            if current_modality["type"] not in modalities:
                modalities[current_modality["type"]] = []
            modalities[current_modality["type"]].append(current_modality)
        
        return modalities

    def _parse_alignment(self, response: str) -> List[Dict[str, Any]]:
        """Parse alignment from response."""
        alignment = []
        current_alignment = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[A'):
                if current_alignment:
                    alignment.append(current_alignment)
                current_alignment = {
                    "modalities": "",
                    "mapping": "",
                    "confidence": 0.0,
                    "conflicts": []
                }
            elif current_alignment:
                if line.startswith('Modalities:'):
                    current_alignment["modalities"] = line[11:].strip()
                elif line.startswith('Mapping:'):
                    current_alignment["mapping"] = line[7:].strip()
                elif line.startswith('Confidence:'):
                    try:
                        current_alignment["confidence"] = float(line[11:].strip())
                    except:
                        pass
                elif line.startswith('Conflicts:'):
                    mode = "conflicts"
                elif line.startswith("- "):
                    if mode == "conflicts":
                        current_alignment["conflicts"].append(line[2:].strip())
        
        if current_alignment:
            alignment.append(current_alignment)
        
        return alignment

    def _parse_integration(self, response: str) -> List[Dict[str, Any]]:
        """Parse integration from response."""
        integration = []
        current_insight = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[I'):
                if current_insight:
                    integration.append(current_insight)
                current_insight = {
                    "insight": "",
                    "sources": "",
                    "support": "",
                    "confidence": 0.0
                }
            elif current_insight:
                if line.startswith('Insight:'):
                    current_insight["insight"] = line[8:].strip()
                elif line.startswith('Sources:'):
                    current_insight["sources"] = line[8:].strip()
                elif line.startswith('Support:'):
                    current_insight["support"] = line[8:].strip()
                elif line.startswith('Confidence:'):
                    try:
                        current_insight["confidence"] = float(line[11:].strip())
                    except:
                        pass
        
        if current_insight:
            integration.append(current_insight)
        
        return integration

    def _parse_response(self, response: str) -> Dict[str, Any]:
        """Parse response from response."""
        response_dict = {
            "conclusion": "",
            "modal_contributions": [],
            "integration_benefits": [],
            "confidence": 0.0
        }
        
        mode = None
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('Conclusion:'):
                response_dict["conclusion"] = line[11:].strip()
            elif line.startswith('Modal Contributions:'):
                mode = "modal"
            elif line.startswith('Integration Benefits:'):
                mode = "integration"
            elif line.startswith('Confidence:'):
                try:
                    response_dict["confidence"] = float(line[11:].strip())
                except:
                    response_dict["confidence"] = 0.5
                mode = None
            elif mode == "modal" and line.startswith('- '):
                response_dict["modal_contributions"].append(line[2:].strip())
            elif mode == "integration" and line.startswith('- '):
                response_dict["integration_benefits"].append(line[2:].strip())
        
        return response_dict

class MetaLearningStrategy(ReasoningStrategy):
    """A meta-learning strategy that adapts its reasoning approach based on problem characteristics."""
    
    def __init__(self):
        self.strategy_patterns = {
            "analytical": ["analyze", "compare", "evaluate", "measure"],
            "creative": ["design", "create", "innovate", "imagine"],
            "systematic": ["organize", "structure", "plan", "implement"],
            "critical": ["critique", "assess", "validate", "test"]
        }
        
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Create a clean context for serialization
            clean_context = {k: v for k, v in context.items() if k != "groq_api"}
            
            # Analyze query to determine best reasoning patterns
            patterns = self._identify_patterns(query.lower())
            
            prompt = f"""
            You are a meta-learning reasoning system that adapts its approach based on problem characteristics.
            
            Problem Type: {', '.join(patterns)}
            Query: {query}
            Context: {json.dumps(clean_context)}
            
            Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows:

            PROBLEM ANALYSIS:
            - [First key aspect or complexity factor]
            - [Second key aspect or complexity factor]
            - [Third key aspect or complexity factor]

            SOLUTION PATHS:
            - Path 1: [Specific solution approach]
            - Path 2: [Alternative solution approach]
            - Path 3: [Another alternative approach]

            META INSIGHTS:
            - Learning 1: [Key insight about the problem space]
            - Learning 2: [Key insight about solution approaches]
            - Learning 3: [Key insight about trade-offs]

            CONCLUSION:
            [Final synthesized solution incorporating meta-learnings]
            """
            
            response = await context["groq_api"].predict(prompt)
            
            if not response["success"]:
                return response
                
            # Parse response into components
            lines = response["answer"].split("\n")
            problem_analysis = []
            solution_paths = []
            meta_insights = []
            conclusion = ""
            
            section = None
            for line in lines:
                line = line.strip()
                if not line:
                    continue
                    
                if "PROBLEM ANALYSIS:" in line:
                    section = "analysis"
                elif "SOLUTION PATHS:" in line:
                    section = "paths"
                elif "META INSIGHTS:" in line:
                    section = "insights"
                elif "CONCLUSION:" in line:
                    section = "conclusion"
                elif line.startswith("-"):
                    content = line.lstrip("- ").strip()
                    if section == "analysis":
                        problem_analysis.append(content)
                    elif section == "paths":
                        solution_paths.append(content)
                    elif section == "insights":
                        meta_insights.append(content)
                elif section == "conclusion":
                    conclusion += line + " "
            
            return {
                "success": True,
                "problem_analysis": problem_analysis,
                "solution_paths": solution_paths,
                "meta_insights": meta_insights,
                "conclusion": conclusion.strip(),
                # Add standard fields for compatibility
                "reasoning_path": problem_analysis + solution_paths + meta_insights,
                "conclusion": conclusion.strip()
            }
            
        except Exception as e: