"""
OpenDistillery Advanced Prompting Strategies
Implements state-of-the-art prompting techniques for compound AI systems.
"""

import asyncio
import json
from typing import Dict, Any, List, Optional, Callable, Union, Tuple
from dataclasses import dataclass, field
from enum import Enum
from abc import ABC, abstractmethod
import logging
from datetime import datetime
import numpy as np
from collections import defaultdict
import random
import math
from itertools import combinations

logger = logging.getLogger(__name__)

class PromptingStrategy(Enum):
    """Available prompting strategies"""
    ZERO_SHOT = "zero_shot"
    FEW_SHOT = "few_shot"
    CHAIN_OF_THOUGHT = "chain_of_thought"
    TREE_OF_THOUGHTS = "tree_of_thoughts"
    SELF_CONSISTENCY = "self_consistency"
    STEP_BY_STEP = "step_by_step"
    POLYPROMPTING = "polyprompting"
    METAPROMPTING = "metaprompting"
    CONSTITUTIONAL = "constitutional"
    REACT = "react"
    REFLEXION = "reflexion"
    AUTO_COT = "auto_cot"
    DIFFUSION_PROMPTING = "diffusion_prompting"  # Latest 2025 technique
    RECURSIVE_REPROMPTING = "recursive_reprompting"  # 2025
    SEMANTIC_BRIDGING = "semantic_bridging"  # 2025
    COGNITIVE_SCAFFOLDING = "cognitive_scaffolding"  # 2025
    NEUROMORPHIC_PROMPTING = "neuromorphic_prompting"  # 2025
    QUANTUM_SUPERPOSITION = "quantum_superposition"  # 2025
    ADAPTIVE_TEMPERATURE = "adaptive_temperature"  # 2025
    CONTEXTUAL_PRIMING = "contextual_priming"  # 2025

@dataclass
class PromptResult:
    """Result of a prompting strategy"""
    content: str
    strategy: str
    confidence: float
    reasoning_steps: List[str] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
    execution_time: float = 0.0
    token_usage: int = 0

@dataclass
class ThoughtNode:
    """Node in the Tree of Thoughts"""
    content: str
    parent: Optional['ThoughtNode'] = None
    children: List['ThoughtNode'] = field(default_factory=list)
    score: float = 0.0
    depth: int = 0
    metadata: Dict[str, Any] = field(default_factory=dict)

class BasePromptingStrategy(ABC):
    """Base class for all prompting strategies"""
    
    def __init__(self, llm_client=None):
        self.llm_client = llm_client
        self.execution_history = []
    
    @abstractmethod
    async def execute(self, prompt: str, **kwargs) -> PromptResult:
        """Execute the prompting strategy"""
        pass
    
    def get_strategy_name(self) -> str:
        """Get the name of the strategy"""
        return self.__class__.__name__

class ZeroShotStrategy(BasePromptingStrategy):
    """Zero-shot prompting strategy"""
    
    async def execute(self, prompt: str, **kwargs) -> PromptResult:
        start_time = datetime.now()
        
        enhanced_prompt = f"""
        Task: {prompt}
        
        Instructions:
        - Provide a clear, accurate response
        - Base your answer on your training knowledge
        - Be specific and actionable where possible
        """
        
        if self.llm_client:
            response = await self.llm_client.chat_completion(
                messages=[{"role": "user", "content": enhanced_prompt}],
                model=kwargs.get("model", "o4"),
                temperature=kwargs.get("temperature", 0.1)
            )
            content = response.content
        else:
            content = f"Zero-shot response to: {prompt}"
        
        execution_time = (datetime.now() - start_time).total_seconds()
        
        return PromptResult(
            content=content,
            strategy="zero_shot",
            confidence=0.7,
            execution_time=execution_time,
            metadata={"approach": "direct"}
        )

class ChainOfThoughtStrategy(BasePromptingStrategy):
    """Chain of Thought prompting strategy"""
    
    async def execute(self, prompt: str, **kwargs) -> PromptResult:
        start_time = datetime.now()
        
        cot_prompt = f"""
        Problem: {prompt}
        
        Let me think through this step by step:
        
        Step 1: Understanding the problem
        - What is being asked?
        - What information do I have?
        - What approach should I take?
        
        Step 2: Breaking down the solution
        - What are the key components?
        - How do they relate to each other?
        - What logical sequence should I follow?
        
        Step 3: Detailed reasoning
        - Work through each component systematically
        - Consider potential challenges or edge cases
        - Validate reasoning at each step
        
        Step 4: Final answer
        - Synthesize the analysis
        - Provide clear conclusion
        - Explain the reasoning behind the answer
        
        Let me work through this:
        """
        
        if self.llm_client:
            response = await self.llm_client.chat_completion(
                messages=[{"role": "user", "content": cot_prompt}],
                model=kwargs.get("model", "o4"),
                temperature=kwargs.get("temperature", 0.1)
            )
            content = response.content
            reasoning_steps = content.split("Step ")
        else:
            content = f"Chain of thought analysis for: {prompt}"
            reasoning_steps = ["Analysis step 1", "Analysis step 2", "Conclusion"]
        
        execution_time = (datetime.now() - start_time).total_seconds()
        
        return PromptResult(
            content=content,
            strategy="chain_of_thought",
            confidence=0.85,
            reasoning_steps=reasoning_steps,
            execution_time=execution_time,
            metadata={"steps": len(reasoning_steps)}
        )

class TreeOfThoughtsStrategy(BasePromptingStrategy):
    """Advanced Tree of Thoughts prompting strategy"""
    
    def __init__(self, llm_client=None, max_depth=3, max_branches=3):
        super().__init__(llm_client)
        self.max_depth = max_depth
        self.max_branches = max_branches
    
    async def execute(self, prompt: str, **kwargs) -> PromptResult:
        start_time = datetime.now()
        
        root = ThoughtNode(content=f"Problem: {prompt}", depth=0)
        await self._expand_tree(root, kwargs)
        
        # Find the best path through the tree
        best_path = self._find_best_path(root)
        solution = self._synthesize_solution(best_path)
        
        execution_time = (datetime.now() - start_time).total_seconds()
        
        return PromptResult(
            content=solution,
            strategy="tree_of_thoughts",
            confidence=0.9,
            reasoning_steps=[node.content for node in best_path],
            execution_time=execution_time,
            metadata={
                "tree_depth": len(best_path),
                "total_nodes": self._count_nodes(root)
            }
        )
    
    async def _expand_tree(self, node: ThoughtNode, kwargs: Dict):
        """Recursively expand the thought tree"""
        if node.depth >= self.max_depth:
            return
        
        # Generate possible thoughts at this level
        expansion_prompt = f"""
        Current thought: {node.content}
        
        Generate {self.max_branches} different approaches or sub-thoughts to explore this further.
        Each should be a distinct angle or method to advance the reasoning.
        
        Format as:
        1. [First approach]
        2. [Second approach]
        3. [Third approach]
        """
        
        if self.llm_client:
            response = await self.llm_client.chat_completion(
                messages=[{"role": "user", "content": expansion_prompt}],
                model=kwargs.get("model", "o4"),
                temperature=kwargs.get("temperature", 0.3)
            )
            
            # Parse the response to extract thoughts
            thoughts = self._parse_thoughts(response.content)
        else:
            thoughts = [f"Sub-thought {i+1} for: {node.content}" for i in range(self.max_branches)]
        
        # Create child nodes and evaluate them
        for thought in thoughts:
            child = ThoughtNode(
                content=thought,
                parent=node,
                depth=node.depth + 1
            )
            child.score = await self._evaluate_thought(child, kwargs)
            node.children.append(child)
            
            # Recursively expand promising children
            if child.score > 0.6:
                await self._expand_tree(child, kwargs)
    
    def _parse_thoughts(self, response: str) -> List[str]:
        """Parse numbered thoughts from response"""
        lines = response.strip().split('\n')
        thoughts = []
        for line in lines:
            if line.strip() and any(line.strip().startswith(f"{i}.") for i in range(1, 10)):
                thought = line.split('.', 1)[1].strip()
                if thought:
                    thoughts.append(thought)
        return thoughts[:self.max_branches]
    
    async def _evaluate_thought(self, node: ThoughtNode, kwargs: Dict) -> float:
        """Evaluate the quality of a thought"""
        evaluation_prompt = f"""
        Evaluate this reasoning step on a scale of 0.0 to 1.0:
        
        Thought: {node.content}
        Context: Depth {node.depth} in reasoning tree
        
        Consider:
        - Logical soundness
        - Relevance to the problem
        - Likelihood to lead to correct solution
        - Creativity and insight
        
        Respond with just a number between 0.0 and 1.0.
        """
        
        if self.llm_client:
            response = await self.llm_client.chat_completion(
                messages=[{"role": "user", "content": evaluation_prompt}],
                model=kwargs.get("model", "o4"),
                temperature=0.0
            )
            try:
                score = float(response.content.strip())
                return max(0.0, min(1.0, score))
            except ValueError:
                return 0.5
        else:
            return random.uniform(0.4, 0.9)
    
    def _find_best_path(self, root: ThoughtNode) -> List[ThoughtNode]:
        """Find the highest-scoring path through the tree"""
        def path_score(path: List[ThoughtNode]) -> float:
            if not path:
                return 0.0
            return sum(node.score for node in path) / len(path)
        
        def get_all_paths(node: ThoughtNode) -> List[List[ThoughtNode]]:
            if not node.children:
                return [[node]]
            
            all_paths = []
            for child in node.children:
                child_paths = get_all_paths(child)
                for path in child_paths:
                    all_paths.append([node] + path)
            return all_paths
        
        all_paths = get_all_paths(root)
        if not all_paths:
            return [root]
        
        best_path = max(all_paths, key=path_score)
        return best_path
    
    def _synthesize_solution(self, path: List[ThoughtNode]) -> str:
        """Synthesize the final solution from the best path"""
        synthesis = "Tree of Thoughts Analysis:\n\n"
        for i, node in enumerate(path):
            synthesis += f"Level {i}: {node.content}\n"
        
        synthesis += f"\nFinal Solution: Based on this reasoning path, "
        synthesis += f"the most promising approach considers {path[-1].content}"
        
        return synthesis
    
    def _count_nodes(self, root: ThoughtNode) -> int:
        """Count total nodes in the tree"""
        count = 1
        for child in root.children:
            count += self._count_nodes(child)
        return count

class DiffusionPromptingStrategy(BasePromptingStrategy):
    """
    Diffusion-based prompting strategy (2025)
    Inspired by diffusion models, gradually refines prompts through noise injection and denoising
    """
    
    def __init__(self, llm_client=None, diffusion_steps=5, noise_level=0.3):
        super().__init__(llm_client)
        self.diffusion_steps = diffusion_steps
        self.noise_level = noise_level
    
    async def execute(self, prompt: str, **kwargs) -> PromptResult:
        start_time = datetime.now()
        
        # Start with a noisy version of the prompt
        current_prompt = self._add_semantic_noise(prompt)
        refinement_history = [current_prompt]
        
        # Iteratively denoise and refine
        for step in range(self.diffusion_steps):
            denoising_prompt = f"""
            I have a partially corrupted or unclear version of a task:
            "{current_prompt}"
            
            Please clean this up and make it clearer while preserving the core intent.
            Remove ambiguity, add necessary context, and improve precision.
            Step {step + 1} of {self.diffusion_steps} refinement process.
            """
            
            if self.llm_client:
                response = await self.llm_client.chat_completion(
                    messages=[{"role": "user", "content": denoising_prompt}],
                    model=kwargs.get("model", "o4"),
                    temperature=max(0.1, self.noise_level - (step * 0.05))
                )
                current_prompt = response.content
            else:
                current_prompt = f"Refined prompt step {step + 1}: {prompt}"
            
            refinement_history.append(current_prompt)
        
        # Generate final response with the refined prompt
        final_response_prompt = f"""
        Refined task through diffusion process:
        {current_prompt}
        
        Provide a comprehensive response to this refined task.
        """
        
        if self.llm_client:
            final_response = await self.llm_client.chat_completion(
                messages=[{"role": "user", "content": final_response_prompt}],
                model=kwargs.get("model", "o4"),
                temperature=kwargs.get("temperature", 0.1)
            )
            content = final_response.content
        else:
            content = f"Diffusion-refined response to: {current_prompt}"
        
        execution_time = (datetime.now() - start_time).total_seconds()
        
        return PromptResult(
            content=content,
            strategy="diffusion_prompting",
            confidence=0.95,
            reasoning_steps=refinement_history,
            execution_time=execution_time,
            metadata={
                "diffusion_steps": self.diffusion_steps,
                "refinement_iterations": len(refinement_history)
            }
        )
    
    def _add_semantic_noise(self, prompt: str) -> str:
        """Add semantic noise to the prompt"""
        noise_patterns = [
            "unclear task involving",
            "something related to",
            "need help with",
            "figure out how to",
            "complex problem about"
        ]
        
        if random.random() < self.noise_level:
            noise = random.choice(noise_patterns)
            return f"{noise} {prompt.lower()}"
        
        return prompt

class QuantumSuperpositionStrategy(BasePromptingStrategy):
    """
    Quantum Superposition prompting (2025)
    Explores multiple solution states simultaneously before collapse
    """
    
    def __init__(self, llm_client=None, superposition_states=4):
        super().__init__(llm_client)
        self.superposition_states = superposition_states
    
    async def execute(self, prompt: str, **kwargs) -> PromptResult:
        start_time = datetime.now()
        
        # Generate multiple superposition states
        states = await self._generate_superposition_states(prompt, kwargs)
        
        # Quantum entanglement - find correlations between states
        entangled_insights = self._find_quantum_entanglements(states)
        
        # Collapse to final solution
        collapsed_solution = await self._quantum_collapse(states, entangled_insights, kwargs)
        
        execution_time = (datetime.now() - start_time).total_seconds()
        
        return PromptResult(
            content=collapsed_solution,
            strategy="quantum_superposition",
            confidence=0.92,
            reasoning_steps=[f"State {i+1}: {state[:100]}..." for i, state in enumerate(states)],
            execution_time=execution_time,
            metadata={
                "superposition_states": len(states),
                "entanglements": len(entangled_insights)
            }
        )
    
    async def _generate_superposition_states(self, prompt: str, kwargs: Dict) -> List[str]:
        """Generate multiple solution states in superposition"""
        state_prompts = [
            f"Approach {prompt} from a logical/analytical perspective:",
            f"Approach {prompt} from a creative/intuitive perspective:",
            f"Approach {prompt} from a systematic/methodical perspective:",
            f"Approach {prompt} from an innovative/disruptive perspective:"
        ]
        
        states = []
        for i, state_prompt in enumerate(state_prompts[:self.superposition_states]):
            if self.llm_client:
                response = await self.llm_client.chat_completion(
                    messages=[{"role": "user", "content": state_prompt}],
                    model=kwargs.get("model", "o4"),
                    temperature=0.2 + (i * 0.2)  # Varying temperature for diversity
                )
                states.append(response.content)
            else:
                states.append(f"Quantum state {i+1} for: {prompt}")
        
        return states
    
    def _find_quantum_entanglements(self, states: List[str]) -> List[str]:
        """Find correlations and patterns across states"""
        entanglements = []
        
        # Simple keyword-based entanglement detection
        all_words = set()
        for state in states:
            words = state.lower().split()
            all_words.update(words)
        
        # Find words that appear in multiple states
        common_themes = []
        for word in all_words:
            if len(word) > 4 and sum(1 for state in states if word in state.lower()) >= 2:
                common_themes.append(word)
        
        if common_themes:
            entanglements.append(f"Common themes: {', '.join(common_themes[:5])}")
        
        return entanglements
    
    async def _quantum_collapse(self, states: List[str], entanglements: List[str], kwargs: Dict) -> str:
        """Collapse superposition to final solution"""
        collapse_prompt = f"""
        Multiple solution approaches have been explored simultaneously:
        
        {chr(10).join([f"State {i+1}: {state}" for i, state in enumerate(states)])}
        
        Quantum entanglements (common patterns): {'; '.join(entanglements)}
        
        Now collapse these superposed states into a single, optimal solution that incorporates
        the best insights from each approach while resolving any contradictions.
        """
        
        if self.llm_client:
            response = await self.llm_client.chat_completion(
                messages=[{"role": "user", "content": collapse_prompt}],
                model=kwargs.get("model", "o4"),
                temperature=0.1
            )
            return response.content
        else:
            return f"Quantum collapsed solution incorporating {len(states)} states"

class NeuromorphicPromptingStrategy(BasePromptingStrategy):
    """
    Neuromorphic prompting (2025)
    Mimics neural network structure with layers of processing
    """
    
    def __init__(self, llm_client=None, neural_layers=3, neurons_per_layer=4):
        super().__init__(llm_client)
        self.neural_layers = neural_layers
        self.neurons_per_layer = neurons_per_layer
    
    async def execute(self, prompt: str, **kwargs) -> PromptResult:
        start_time = datetime.now()
        
        # Input layer processing
        layer_outputs = [prompt]
        
        # Process through neural layers
        for layer in range(self.neural_layers):
            layer_input = layer_outputs[-1]
            layer_output = await self._process_neural_layer(layer_input, layer, kwargs)
            layer_outputs.append(layer_output)
        
        # Output layer synthesis
        final_output = await self._synthesize_neural_output(layer_outputs, kwargs)
        
        execution_time = (datetime.now() - start_time).total_seconds()
        
        return PromptResult(
            content=final_output,
            strategy="neuromorphic_prompting",
            confidence=0.88,
            reasoning_steps=[f"Layer {i}: {output[:100]}..." for i, output in enumerate(layer_outputs)],
            execution_time=execution_time,
            metadata={
                "neural_layers": self.neural_layers,
                "processing_depth": len(layer_outputs)
            }
        )
    
    async def _process_neural_layer(self, input_data: str, layer_num: int, kwargs: Dict) -> str:
        """Process information through a neural layer"""
        layer_functions = [
            "Extract key concepts and relationships",
            "Apply logical reasoning and analysis", 
            "Generate creative solutions and alternatives",
            "Validate and refine conclusions"
        ]
        
        function = layer_functions[layer_num % len(layer_functions)]
        
        layer_prompt = f"""
        Neural Layer {layer_num + 1} Processing:
        Function: {function}
        
        Input: {input_data}
        
        Process this information through the lens of {function.lower()}.
        Transform and enhance the input while preserving essential information.
        """
        
        if self.llm_client:
            response = await self.llm_client.chat_completion(
                messages=[{"role": "user", "content": layer_prompt}],
                model=kwargs.get("model", "o4"),
                temperature=0.2 + (layer_num * 0.1)
            )
            return response.content
        else:
            return f"Neural layer {layer_num + 1} output for: {input_data[:50]}..."
    
    async def _synthesize_neural_output(self, layer_outputs: List[str], kwargs: Dict) -> str:
        """Synthesize final output from all neural layers"""
        synthesis_prompt = f"""
        Neural Network Processing Complete:
        
        {chr(10).join([f"Layer {i} Output: {output}" for i, output in enumerate(layer_outputs)])}
        
        Synthesize these neural processing results into a coherent, comprehensive response.
        Integrate insights from each layer while ensuring logical consistency.
        """
        
        if self.llm_client:
            response = await self.llm_client.chat_completion(
                messages=[{"role": "user", "content": synthesis_prompt}],
                model=kwargs.get("model", "o4"),
                temperature=0.1
            )
            return response.content
        else:
            return f"Neuromorphic synthesis of {len(layer_outputs)} processing layers"

class AdaptiveTemperatureStrategy(BasePromptingStrategy):
    """
    Adaptive Temperature prompting (2025)
    Dynamically adjusts temperature based on task complexity and uncertainty
    """
    
    async def execute(self, prompt: str, **kwargs) -> PromptResult:
        start_time = datetime.now()
        
        # Analyze prompt complexity
        complexity_score = self._analyze_complexity(prompt)
        uncertainty_score = self._analyze_uncertainty(prompt)
        
        # Calculate adaptive temperature
        base_temp = kwargs.get("temperature", 0.1)
        adaptive_temp = self._calculate_adaptive_temperature(
            complexity_score, uncertainty_score, base_temp
        )
        
        # Multi-stage processing with temperature adaptation
        stages = [
            ("exploration", adaptive_temp * 1.5),
            ("refinement", adaptive_temp),
            ("finalization", adaptive_temp * 0.5)
        ]
        
        stage_outputs = []
        current_content = prompt
        
        for stage_name, temp in stages:
            stage_prompt = f"""
            Stage: {stage_name.upper()}
            Temperature: {temp:.2f}
            
            {current_content}
            
            Process this with the appropriate level of creativity/determinism for the {stage_name} stage.
            """
            
            if self.llm_client:
                response = await self.llm_client.chat_completion(
                    messages=[{"role": "user", "content": stage_prompt}],
                    model=kwargs.get("model", "o4"),
                    temperature=temp
                )
                current_content = response.content
                stage_outputs.append(f"{stage_name}: {current_content}")
            else:
                current_content = f"Adaptive temp {temp:.2f} output for: {prompt}"
                stage_outputs.append(current_content)
        
        execution_time = (datetime.now() - start_time).total_seconds()
        
        return PromptResult(
            content=current_content,
            strategy="adaptive_temperature",
            confidence=0.86,
            reasoning_steps=stage_outputs,
            execution_time=execution_time,
            metadata={
                "complexity_score": complexity_score,
                "uncertainty_score": uncertainty_score,
                "adaptive_temperature": adaptive_temp
            }
        )
    
    def _analyze_complexity(self, prompt: str) -> float:
        """Analyze prompt complexity (0.0 to 1.0)"""
        complexity_indicators = [
            len(prompt.split()) > 50,  # Length
            prompt.count('?') > 1,  # Multiple questions
            any(word in prompt.lower() for word in ['complex', 'sophisticated', 'advanced']),
            prompt.count(',') > 5,  # Multiple clauses
            any(word in prompt.lower() for word in ['analyze', 'evaluate', 'synthesize'])
        ]
        return sum(complexity_indicators) / len(complexity_indicators)
    
    def _analyze_uncertainty(self, prompt: str) -> float:
        """Analyze prompt uncertainty (0.0 to 1.0)"""
        uncertainty_indicators = [
            any(word in prompt.lower() for word in ['maybe', 'possibly', 'might', 'could']),
            prompt.count('?') > 0,
            any(word in prompt.lower() for word in ['unclear', 'ambiguous', 'uncertain']),
            'creative' in prompt.lower(),
            'innovative' in prompt.lower()
        ]
        return sum(uncertainty_indicators) / len(uncertainty_indicators)
    
    def _calculate_adaptive_temperature(self, complexity: float, uncertainty: float, base_temp: float) -> float:
        """Calculate adaptive temperature based on complexity and uncertainty"""
        # Higher complexity and uncertainty warrant higher temperature
        adaptation_factor = (complexity + uncertainty) / 2
        adaptive_temp = base_temp + (adaptation_factor * 0.5)
        return max(0.0, min(1.0, adaptive_temp))

class PromptingOrchestrator:
    """
    Advanced orchestrator for managing multiple prompting strategies
    """
    
    def __init__(self, llm_client=None):
        self.llm_client = llm_client
        self.strategies = {
            PromptingStrategy.ZERO_SHOT: ZeroShotStrategy(llm_client),
            PromptingStrategy.CHAIN_OF_THOUGHT: ChainOfThoughtStrategy(llm_client),
            PromptingStrategy.TREE_OF_THOUGHTS: TreeOfThoughtsStrategy(llm_client),
            PromptingStrategy.DIFFUSION_PROMPTING: DiffusionPromptingStrategy(llm_client),
            PromptingStrategy.QUANTUM_SUPERPOSITION: QuantumSuperpositionStrategy(llm_client),
            PromptingStrategy.NEUROMORPHIC_PROMPTING: NeuromorphicPromptingStrategy(llm_client),
            PromptingStrategy.ADAPTIVE_TEMPERATURE: AdaptiveTemperatureStrategy(llm_client)
        }
        self.execution_history = []
    
    async def execute_strategy(
        self,
        strategy: PromptingStrategy,
        prompt: str,
        **kwargs
    ) -> PromptResult:
        """Execute a specific prompting strategy"""
        if strategy not in self.strategies:
            raise ValueError(f"Strategy {strategy} not implemented")
        
        strategy_instance = self.strategies[strategy]
        result = await strategy_instance.execute(prompt, **kwargs)
        
        self.execution_history.append({
            "timestamp": datetime.now(),
            "strategy": strategy.value,
            "prompt": prompt[:100] + "..." if len(prompt) > 100 else prompt,
            "confidence": result.confidence,
            "execution_time": result.execution_time
        })
        
        return result
    
    async def ensemble_execution(
        self,
        strategies: List[PromptingStrategy],
        prompt: str,
        **kwargs
    ) -> PromptResult:
        """Execute multiple strategies and ensemble the results"""
        start_time = datetime.now()
        
        # Execute all strategies in parallel
        tasks = [
            self.execute_strategy(strategy, prompt, **kwargs)
            for strategy in strategies
        ]
        results = await asyncio.gather(*tasks)
        
        # Ensemble the results
        ensemble_content = await self._ensemble_results(results, kwargs)
        
        execution_time = (datetime.now() - start_time).total_seconds()
        
        return PromptResult(
            content=ensemble_content,
            strategy="ensemble",
            confidence=np.mean([r.confidence for r in results]),
            reasoning_steps=[f"{r.strategy}: {r.content[:100]}..." for r in results],
            execution_time=execution_time,
            metadata={
                "strategies_used": [r.strategy for r in results],
                "individual_confidences": [r.confidence for r in results]
            }
        )
    
    async def _ensemble_results(self, results: List[PromptResult], kwargs: Dict) -> str:
        """Ensemble multiple strategy results into a final answer"""
        ensemble_prompt = f"""
        Multiple AI reasoning strategies have been applied to the same problem:
        
        {chr(10).join([f"{r.strategy.upper()}: {r.content}" for r in results])}
        
        Strategy Confidence Scores:
        {chr(10).join([f"- {r.strategy}: {r.confidence:.2f}" for r in results])}
        
        Synthesize these different approaches into a single, optimal response that:
        1. Incorporates the best insights from each strategy
        2. Resolves any contradictions between approaches
        3. Provides a coherent, comprehensive answer
        4. Weighs each strategy's contribution based on its confidence score
        """
        
        if self.llm_client:
            response = await self.llm_client.chat_completion(
                messages=[{"role": "user", "content": ensemble_prompt}],
                model=kwargs.get("model", "o4"),
                temperature=0.1
            )
            return response.content
        else:
            return f"Ensemble of {len(results)} strategies"
    
    def get_strategy_recommendations(self, prompt: str) -> List[PromptingStrategy]:
        """Recommend strategies based on prompt characteristics"""
        recommendations = []
        
        prompt_lower = prompt.lower()
        
        # Always include these as baseline
        recommendations.extend([
            PromptingStrategy.CHAIN_OF_THOUGHT,
            PromptingStrategy.ADAPTIVE_TEMPERATURE
        ])
        
        # Complex reasoning tasks
        if any(word in prompt_lower for word in ['analyze', 'complex', 'reasoning', 'logic']):
            recommendations.append(PromptingStrategy.TREE_OF_THOUGHTS)
        
        # Creative or open-ended tasks
        if any(word in prompt_lower for word in ['creative', 'innovative', 'brainstorm']):
            recommendations.extend([
                PromptingStrategy.QUANTUM_SUPERPOSITION,
                PromptingStrategy.DIFFUSION_PROMPTING
            ])
        
        # Technical or systematic tasks
        if any(word in prompt_lower for word in ['technical', 'systematic', 'process']):
            recommendations.append(PromptingStrategy.NEUROMORPHIC_PROMPTING)
        
        return list(set(recommendations))  # Remove duplicates
    
    def get_execution_stats(self) -> Dict[str, Any]:
        """Get statistics about strategy execution"""
        if not self.execution_history:
            return {}
        
        strategies_used = [entry["strategy"] for entry in self.execution_history]
        confidences = [entry["confidence"] for entry in self.execution_history]
        execution_times = [entry["execution_time"] for entry in self.execution_history]
        
        return {
            "total_executions": len(self.execution_history),
            "strategies_used": list(set(strategies_used)),
            "avg_confidence": np.mean(confidences),
            "avg_execution_time": np.mean(execution_times),
            "strategy_usage_count": {
                strategy: strategies_used.count(strategy)
                for strategy in set(strategies_used)
            }
        } 