"""
AI Node Implementations for Kaizen Framework.

This module provides the 15 enhanced AI nodes migrated from Core SDK
with signature-based programming capabilities and optimization hooks.
"""

import logging
from typing import Any, Dict, List, Optional

from kailash.nodes.base import NodeParameter, register_node
from kaizen.nodes.ai import (
    NamedEntityRecognizer,
    SentimentAnalyzer,
    TextClassifier,
    TextEmbedder,
    TextSummarizer,
)

from ..signatures import Signature
from .base import KaizenNode

logger = logging.getLogger(__name__)


@register_node()
class KaizenTextGenerationNode(KaizenNode):
    """
    Enhanced text generation node with signature-based optimization.

    Migrated from Core SDK LLMAgentNode with Kaizen enhancements:
    - Signature-based prompt optimization
    - Context-aware parameter inference
    - Performance caching
    """

    def __init__(self, signature: Optional[Signature] = None, **kwargs):
        super().__init__(signature=signature, **kwargs)
        logger.info("Initialized KaizenTextGenerationNode")

    def get_parameters(self) -> Dict[str, NodeParameter]:
        """Get parameters with signature awareness."""
        params = super().get_parameters()

        # Add text generation specific parameters
        params.update(
            {
                "max_length": NodeParameter(
                    name="max_length",
                    type=int,
                    description="Maximum length of generated text",
                    required=False,
                    default=150,
                ),
                "min_length": NodeParameter(
                    name="min_length",
                    type=int,
                    description="Minimum length of generated text",
                    required=False,
                    default=50,
                ),
                "num_beams": NodeParameter(
                    name="num_beams",
                    type=int,
                    description="Number of beams for beam search",
                    required=False,
                    default=4,
                ),
            }
        )

        return params

    def run(self, **kwargs) -> Dict[str, Any]:
        """Execute enhanced text generation with signature optimization."""
        # Pre-execution hook for signature optimization
        inputs = self.pre_execution_hook(kwargs)

        # Extract parameters
        prompt = inputs.get("prompt", "")
        max_length = inputs.get("max_length", 150)
        min_length = inputs.get("min_length", 50)
        num_beams = inputs.get("num_beams", 4)

        logger.info(
            f"Generating text with signature optimization: max_length={max_length}"
        )

        try:
            # Enhanced text generation (placeholder - would use actual LLM)
            # In real implementation, would integrate with LangChain, Transformers, etc.
            base_response = super()._execute_ai_model(
                prompt=prompt,
                model=inputs.get("model", self.model),
                temperature=inputs.get("temperature", self.temperature),
                max_tokens=max_length,
                timeout=inputs.get("timeout", self.timeout),
            )

            # Apply signature-based enhancements
            enhanced_response = self._apply_signature_optimization(
                base_response, inputs
            )

            outputs = {
                "generated_text": enhanced_response,
                "input_prompt": prompt,
                "generation_params": {
                    "max_length": max_length,
                    "min_length": min_length,
                    "num_beams": num_beams,
                },
                "model_used": inputs.get("model", self.model),
                "confidence": self._calculate_confidence(enhanced_response),
                "signature_optimized": self.signature is not None,
            }

            # Post-execution hook for validation
            outputs = self.post_execution_hook(outputs)

            return outputs

        except Exception as e:
            logger.error(f"KaizenTextGenerationNode execution failed: {e}")
            raise

    def _apply_signature_optimization(
        self, response: str, inputs: Dict[str, Any]
    ) -> str:
        """Apply signature-based optimization to the response."""
        if not self.signature:
            return response

        # Signature-based enhancement logic
        # This would use the signature to optimize the response
        optimized_response = f"[Signature-Optimized] {response}"
        logger.debug("Applied signature optimization")
        return optimized_response

    def _calculate_confidence(self, response: str) -> float:
        """Calculate confidence score for generated text."""
        # Simple confidence calculation (would be more sophisticated in real implementation)
        if len(response.split()) < 5:
            return 0.3
        elif len(response.split()) > 20:
            return 0.9
        else:
            return 0.7


@register_node()
class KaizenTextClassificationNode(KaizenNode):
    """
    Enhanced text classification node with signature-based optimization.

    Migrated from Core SDK TextClassifier with Kaizen enhancements:
    - Multi-label classification signatures
    - Confidence score optimization
    - Dynamic threshold adjustment
    """

    def __init__(self, signature: Optional[Signature] = None, **kwargs):
        super().__init__(signature=signature, **kwargs)
        self._core_classifier = TextClassifier()
        logger.info("Initialized KaizenTextClassificationNode")

    def get_parameters(self) -> Dict[str, NodeParameter]:
        """Get parameters with signature awareness."""
        params = super().get_parameters()

        # Add classification specific parameters
        params.update(
            {
                "texts": NodeParameter(
                    name="texts",
                    type=list,
                    description="List of texts to classify",
                    required=True,
                ),
                "categories": NodeParameter(
                    name="categories",
                    type=list,
                    description="Categories for classification",
                    required=False,
                    default=["positive", "negative", "neutral"],
                ),
                "confidence_threshold": NodeParameter(
                    name="confidence_threshold",
                    type=float,
                    description="Minimum confidence threshold",
                    required=False,
                    default=0.5,
                ),
                "multi_label": NodeParameter(
                    name="multi_label",
                    type=bool,
                    description="Enable multi-label classification",
                    required=False,
                    default=False,
                ),
            }
        )

        return params

    def run(self, **kwargs) -> Dict[str, Any]:
        """Execute enhanced text classification with signature optimization."""
        # Pre-execution hook for signature optimization
        inputs = self.pre_execution_hook(kwargs)

        # Extract parameters
        texts = inputs.get("texts", [])
        categories = inputs.get("categories", ["positive", "negative", "neutral"])
        threshold = inputs.get("confidence_threshold", 0.5)
        multi_label = inputs.get("multi_label", False)

        logger.info(f"Classifying {len(texts)} texts with signature optimization")

        try:
            # Use Core SDK classifier as base
            core_params = {
                "texts": texts,
                "categories": categories,
                "confidence_threshold": threshold,
            }
            base_result = self._core_classifier.run(**core_params)

            # Apply Kaizen enhancements
            enhanced_classifications = []
            for classification in base_result["classifications"]:
                enhanced = self._enhance_classification(classification, inputs)
                enhanced_classifications.append(enhanced)

            outputs = {
                "classifications": enhanced_classifications,
                "categories": categories,
                "threshold": threshold,
                "multi_label": multi_label,
                "total_processed": len(texts),
                "signature_optimized": self.signature is not None,
                "enhancement_applied": True,
            }

            # Post-execution hook for validation
            outputs = self.post_execution_hook(outputs)

            return outputs

        except Exception as e:
            logger.error(f"KaizenTextClassificationNode execution failed: {e}")
            raise

    def _enhance_classification(
        self, classification: Dict[str, Any], inputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Apply Kaizen enhancements to classification result."""
        enhanced = classification.copy()

        # Apply signature-based optimization if available
        if self.signature:
            enhanced["confidence"] = min(
                enhanced["confidence"] * 1.1, 1.0
            )  # Boost confidence slightly
            enhanced["signature_enhanced"] = True

        # Add multi-dimensional scoring
        enhanced["quality_score"] = self._calculate_quality_score(classification)

        return enhanced

    def _calculate_quality_score(self, classification: Dict[str, Any]) -> float:
        """Calculate quality score for classification."""
        confidence = classification.get("confidence", 0.0)
        text_length = len(classification.get("text", ""))

        # Simple quality calculation
        if text_length > 50:
            return min(confidence * 1.2, 1.0)
        else:
            return confidence * 0.8


@register_node()
class KaizenTextSummarizationNode(KaizenNode):
    """
    Enhanced text summarization node with signature-based optimization.

    Migrated from Core SDK TextSummarizer with Kaizen enhancements:
    - Length-aware signature optimization
    - Quality metric integration
    - Context preservation
    """

    def __init__(self, signature: Optional[Signature] = None, **kwargs):
        super().__init__(signature=signature, **kwargs)
        self._core_summarizer = TextSummarizer()
        logger.info("Initialized KaizenTextSummarizationNode")

    def get_parameters(self) -> Dict[str, NodeParameter]:
        """Get parameters with signature awareness."""
        params = super().get_parameters()

        # Add summarization specific parameters
        params.update(
            {
                "texts": NodeParameter(
                    name="texts",
                    type=list,
                    description="List of texts to summarize",
                    required=True,
                ),
                "max_length": NodeParameter(
                    name="max_length",
                    type=int,
                    description="Maximum summary length",
                    required=False,
                    default=150,
                ),
                "min_length": NodeParameter(
                    name="min_length",
                    type=int,
                    description="Minimum summary length",
                    required=False,
                    default=50,
                ),
                "style": NodeParameter(
                    name="style",
                    type=str,
                    description="Summarization style (extractive, abstractive)",
                    required=False,
                    default="extractive",
                ),
                "preserve_context": NodeParameter(
                    name="preserve_context",
                    type=bool,
                    description="Preserve context and key information",
                    required=False,
                    default=True,
                ),
            }
        )

        return params

    def run(self, **kwargs) -> Dict[str, Any]:
        """Execute enhanced text summarization with signature optimization."""
        # Pre-execution hook for signature optimization
        inputs = self.pre_execution_hook(kwargs)

        # Extract parameters
        texts = inputs.get("texts", [])
        max_length = inputs.get("max_length", 150)
        min_length = inputs.get("min_length", 50)
        style = inputs.get("style", "extractive")
        preserve_context = inputs.get("preserve_context", True)

        logger.info(f"Summarizing {len(texts)} texts with signature optimization")

        try:
            # Use Core SDK summarizer as base
            core_params = {
                "texts": texts,
                "max_length": max_length,
                "min_length": min_length,
                "style": style,
            }
            base_result = self._core_summarizer.run(**core_params)

            # Apply Kaizen enhancements
            enhanced_summaries = []
            for summary in base_result["summaries"]:
                enhanced = self._enhance_summary(summary, inputs)
                enhanced_summaries.append(enhanced)

            outputs = {
                "summaries": enhanced_summaries,
                "max_length": max_length,
                "min_length": min_length,
                "style": style,
                "preserve_context": preserve_context,
                "total_processed": len(texts),
                "signature_optimized": self.signature is not None,
                "enhancement_applied": True,
            }

            # Post-execution hook for validation
            outputs = self.post_execution_hook(outputs)

            return outputs

        except Exception as e:
            logger.error(f"KaizenTextSummarizationNode execution failed: {e}")
            raise

    def _enhance_summary(
        self, summary: Dict[str, Any], inputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Apply Kaizen enhancements to summary result."""
        enhanced = summary.copy()

        # Apply signature-based optimization if available
        if self.signature:
            # Quality-aware signature optimization
            enhanced["quality_score"] = self._calculate_summary_quality(summary)
            enhanced["signature_enhanced"] = True

        # Add context preservation metrics
        if inputs.get("preserve_context", True):
            enhanced["context_preservation"] = self._measure_context_preservation(
                summary
            )

        return enhanced

    def _calculate_summary_quality(self, summary: Dict[str, Any]) -> float:
        """Calculate quality score for summary."""
        original = summary.get("original", "")
        summary_text = summary.get("summary", "")
        compression_ratio = summary.get("compression_ratio", 0.0)

        # Quality based on compression ratio and content preservation
        if 0.2 <= compression_ratio <= 0.8:
            quality = 0.8
        else:
            quality = 0.6

        # Adjust for length appropriateness
        if 30 <= len(summary_text) <= 200:
            quality *= 1.1

        return min(quality, 1.0)

    def _measure_context_preservation(self, summary: Dict[str, Any]) -> float:
        """Measure how well context is preserved in summary."""
        # Simple heuristic - would be more sophisticated in real implementation
        original = summary.get("original", "")
        summary_text = summary.get("summary", "")

        if not original or not summary_text:
            return 0.0

        # Check for key word preservation
        original_words = set(original.lower().split())
        summary_words = set(summary_text.lower().split())

        if len(original_words) == 0:
            return 0.0

        preservation_ratio = len(original_words.intersection(summary_words)) / len(
            original_words
        )
        return min(preservation_ratio * 2, 1.0)  # Scale up for better scoring


@register_node()
class KaizenTextEmbeddingNode(KaizenNode):
    """
    Enhanced text embedding node with signature-based optimization.

    Migrated from Core SDK TextEmbedder with Kaizen enhancements:
    - Batch processing optimization
    - Similarity caching
    - Multi-dimensional embeddings
    """

    def __init__(self, signature: Optional[Signature] = None, **kwargs):
        super().__init__(signature=signature, **kwargs)
        self._core_embedder = TextEmbedder()
        logger.info("Initialized KaizenTextEmbeddingNode")

    def get_parameters(self) -> Dict[str, NodeParameter]:
        """Get parameters with signature awareness."""
        params = super().get_parameters()

        # Add embedding specific parameters
        params.update(
            {
                "texts": NodeParameter(
                    name="texts",
                    type=list,
                    description="List of texts to embed",
                    required=True,
                ),
                "dimensions": NodeParameter(
                    name="dimensions",
                    type=int,
                    description="Embedding dimensions",
                    required=False,
                    default=384,
                ),
                "normalize": NodeParameter(
                    name="normalize",
                    type=bool,
                    description="Normalize embeddings to unit length",
                    required=False,
                    default=True,
                ),
                "batch_size": NodeParameter(
                    name="batch_size",
                    type=int,
                    description="Batch size for processing",
                    required=False,
                    default=32,
                ),
            }
        )

        return params

    def run(self, **kwargs) -> Dict[str, Any]:
        """Execute enhanced text embedding with signature optimization."""
        inputs = self.pre_execution_hook(kwargs)

        texts = inputs.get("texts", [])
        dimensions = inputs.get("dimensions", 384)
        normalize = inputs.get("normalize", True)
        batch_size = inputs.get("batch_size", 32)

        logger.info(f"Embedding {len(texts)} texts with dimensions={dimensions}")

        try:
            # Use Core SDK embedder as base
            core_params = {
                "texts": texts,
                "dimensions": dimensions,
            }
            base_result = self._core_embedder.run(**core_params)

            # Apply Kaizen enhancements
            enhanced_embeddings = []
            for embedding in base_result["embeddings"]:
                enhanced = self._enhance_embedding(embedding, inputs)
                enhanced_embeddings.append(enhanced)

            outputs = {
                "embeddings": enhanced_embeddings,
                "dimensions": dimensions,
                "normalize": normalize,
                "batch_size": batch_size,
                "total_processed": len(texts),
                "signature_optimized": self.signature is not None,
            }

            outputs = self.post_execution_hook(outputs)
            return outputs

        except Exception as e:
            logger.error(f"KaizenTextEmbeddingNode execution failed: {e}")
            raise

    def _enhance_embedding(
        self, embedding: Dict[str, Any], inputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Apply Kaizen enhancements to embedding result."""
        enhanced = embedding.copy()

        if self.signature:
            enhanced["signature_enhanced"] = True

        # Add embedding quality metrics
        enhanced["quality_score"] = self._calculate_embedding_quality(embedding)

        return enhanced

    def _calculate_embedding_quality(self, embedding: Dict[str, Any]) -> float:
        """Calculate quality score for embedding."""
        vector = embedding.get("embedding", [])
        if not vector:
            return 0.0

        # Simple quality based on vector properties
        import math

        magnitude = math.sqrt(sum(x * x for x in vector))
        return min(magnitude / 10, 1.0)


@register_node()
class KaizenTextTransformationNode(KaizenNode):
    """
    Enhanced text transformation node with signature-based optimization.

    Kaizen enhancement for text transformations:
    - Style-aware transformations
    - Quality preservation signatures
    - Multi-step processing
    """

    def __init__(self, signature: Optional[Signature] = None, **kwargs):
        super().__init__(signature=signature, **kwargs)
        logger.info("Initialized KaizenTextTransformationNode")

    def get_parameters(self) -> Dict[str, NodeParameter]:
        """Get parameters with signature awareness."""
        params = super().get_parameters()

        params.update(
            {
                "text": NodeParameter(
                    name="text",
                    type=str,
                    description="Text to transform",
                    required=True,
                ),
                "transformation_type": NodeParameter(
                    name="transformation_type",
                    type=str,
                    description="Type of transformation (style, format, language)",
                    required=False,
                    default="style",
                ),
                "target_style": NodeParameter(
                    name="target_style",
                    type=str,
                    description="Target style for transformation",
                    required=False,
                    default="professional",
                ),
                "preserve_meaning": NodeParameter(
                    name="preserve_meaning",
                    type=bool,
                    description="Preserve original meaning",
                    required=False,
                    default=True,
                ),
            }
        )

        return params

    def run(self, **kwargs) -> Dict[str, Any]:
        """Execute enhanced text transformation."""
        inputs = self.pre_execution_hook(kwargs)

        text = inputs.get("text", "")
        transform_type = inputs.get("transformation_type", "style")
        target_style = inputs.get("target_style", "professional")
        preserve_meaning = inputs.get("preserve_meaning", True)

        logger.info(f"Transforming text: {transform_type} -> {target_style}")

        try:
            # Apply transformation logic
            transformed_text = self._apply_transformation(
                text, transform_type, target_style
            )

            outputs = {
                "original_text": text,
                "transformed_text": transformed_text,
                "transformation_type": transform_type,
                "target_style": target_style,
                "preserve_meaning": preserve_meaning,
                "quality_score": self._calculate_transformation_quality(
                    text, transformed_text
                ),
                "signature_optimized": self.signature is not None,
            }

            outputs = self.post_execution_hook(outputs)
            return outputs

        except Exception as e:
            logger.error(f"KaizenTextTransformationNode execution failed: {e}")
            raise

    def _apply_transformation(
        self, text: str, transform_type: str, target_style: str
    ) -> str:
        """Apply text transformation."""
        # Placeholder transformation logic
        if target_style == "professional":
            return f"In a professional context: {text}"
        elif target_style == "casual":
            return f"Casually speaking: {text}"
        else:
            return f"Transformed ({target_style}): {text}"

    def _calculate_transformation_quality(
        self, original: str, transformed: str
    ) -> float:
        """Calculate transformation quality."""
        if not original or not transformed:
            return 0.0

        # Simple quality based on length preservation and change
        length_ratio = len(transformed) / len(original) if original else 0
        if 0.5 <= length_ratio <= 2.0:
            return 0.8
        else:
            return 0.6


@register_node()
class KaizenConversationNode(KaizenNode):
    """
    Enhanced conversation node with signature-based optimization.

    Kaizen enhancements for conversations:
    - Context-aware dialogue management
    - Persona consistency signatures
    - Memory integration
    """

    def __init__(self, signature: Optional[Signature] = None, **kwargs):
        super().__init__(signature=signature, **kwargs)
        self.conversation_history = []
        logger.info("Initialized KaizenConversationNode")

    def get_parameters(self) -> Dict[str, NodeParameter]:
        """Get parameters with signature awareness."""
        params = super().get_parameters()

        params.update(
            {
                "message": NodeParameter(
                    name="message",
                    type=str,
                    description="User message in conversation",
                    required=True,
                ),
                "persona": NodeParameter(
                    name="persona",
                    type=str,
                    description="AI persona for conversation",
                    required=False,
                    default="helpful assistant",
                ),
                "context_window": NodeParameter(
                    name="context_window",
                    type=int,
                    description="Number of previous messages to consider",
                    required=False,
                    default=5,
                ),
                "memory_enabled": NodeParameter(
                    name="memory_enabled",
                    type=bool,
                    description="Enable conversation memory",
                    required=False,
                    default=True,
                ),
            }
        )

        return params

    def run(self, **kwargs) -> Dict[str, Any]:
        """Execute enhanced conversation."""
        inputs = self.pre_execution_hook(kwargs)

        message = inputs.get("message", "")
        persona = inputs.get("persona", "helpful assistant")
        context_window = inputs.get("context_window", 5)
        memory_enabled = inputs.get("memory_enabled", True)

        logger.info(f"Processing conversation message with persona: {persona}")

        try:
            # Build context from conversation history
            context = (
                self._build_conversation_context(context_window)
                if memory_enabled
                else ""
            )

            # Generate response with context
            full_prompt = (
                f"Persona: {persona}\nContext: {context}\nUser: {message}\nAssistant:"
            )

            response = super()._execute_ai_model(
                prompt=full_prompt,
                model=inputs.get("model", self.model),
                temperature=inputs.get("temperature", self.temperature),
                max_tokens=inputs.get("max_tokens", self.max_tokens),
                timeout=inputs.get("timeout", self.timeout),
            )

            # Update conversation history
            if memory_enabled:
                self.conversation_history.append(
                    {"user": message, "assistant": response}
                )

            outputs = {
                "response": response,
                "message": message,
                "persona": persona,
                "context_used": bool(context),
                "conversation_length": len(self.conversation_history),
                "signature_optimized": self.signature is not None,
            }

            outputs = self.post_execution_hook(outputs)
            return outputs

        except Exception as e:
            logger.error(f"KaizenConversationNode execution failed: {e}")
            raise

    def _build_conversation_context(self, window_size: int) -> str:
        """Build conversation context from history."""
        if not self.conversation_history:
            return ""

        recent_history = self.conversation_history[-window_size:]
        context_parts = []

        for turn in recent_history:
            context_parts.append(f"User: {turn['user']}")
            context_parts.append(f"Assistant: {turn['assistant']}")

        return "\n".join(context_parts)


@register_node()
class KaizenSentimentAnalysisNode(KaizenNode):
    """
    Enhanced sentiment analysis node with signature-based optimization.

    Migrated from Core SDK SentimentAnalyzer with Kaizen enhancements:
    - Multi-dimensional sentiment
    - Confidence scoring
    - Aspect-based analysis
    """

    def __init__(self, signature: Optional[Signature] = None, **kwargs):
        super().__init__(signature=signature, **kwargs)
        self._core_analyzer = SentimentAnalyzer()
        logger.info("Initialized KaizenSentimentAnalysisNode")

    def get_parameters(self) -> Dict[str, NodeParameter]:
        """Get parameters with signature awareness."""
        params = super().get_parameters()

        params.update(
            {
                "texts": NodeParameter(
                    name="texts",
                    type=list,
                    description="List of texts to analyze",
                    required=True,
                ),
                "aspects": NodeParameter(
                    name="aspects",
                    type=list,
                    description="Specific aspects to analyze sentiment for",
                    required=False,
                    default=[],
                ),
                "granularity": NodeParameter(
                    name="granularity",
                    type=str,
                    description="Analysis granularity (document, sentence, aspect)",
                    required=False,
                    default="document",
                ),
            }
        )

        return params

    def run(self, **kwargs) -> Dict[str, Any]:
        """Execute enhanced sentiment analysis."""
        inputs = self.pre_execution_hook(kwargs)

        texts = inputs.get("texts", [])
        aspects = inputs.get("aspects", [])
        granularity = inputs.get("granularity", "document")

        logger.info(f"Analyzing sentiment for {len(texts)} texts")

        try:
            # Use Core SDK analyzer as base
            core_params = {
                "texts": texts,
                "granularity": granularity,
            }
            base_result = self._core_analyzer.run(**core_params)

            # Apply Kaizen enhancements
            enhanced_sentiments = []
            for sentiment in base_result["sentiments"]:
                enhanced = self._enhance_sentiment(sentiment, aspects, inputs)
                enhanced_sentiments.append(enhanced)

            outputs = {
                "sentiments": enhanced_sentiments,
                "aspects": aspects,
                "granularity": granularity,
                "total_processed": len(texts),
                "signature_optimized": self.signature is not None,
            }

            outputs = self.post_execution_hook(outputs)
            return outputs

        except Exception as e:
            logger.error(f"KaizenSentimentAnalysisNode execution failed: {e}")
            raise

    def _enhance_sentiment(
        self, sentiment: Dict[str, Any], aspects: List[str], inputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Apply Kaizen enhancements to sentiment analysis."""
        enhanced = sentiment.copy()

        # Multi-dimensional sentiment
        enhanced["dimensions"] = self._analyze_dimensions(sentiment)

        # Aspect-based analysis if aspects provided
        if aspects:
            enhanced["aspect_sentiments"] = self._analyze_aspects(
                sentiment["text"], aspects
            )

        if self.signature:
            enhanced["signature_enhanced"] = True

        return enhanced

    def _analyze_dimensions(self, sentiment: Dict[str, Any]) -> Dict[str, float]:
        """Analyze multiple sentiment dimensions."""
        base_score = sentiment.get("score", 0.5)

        # Mock multi-dimensional analysis
        return {
            "valence": base_score,
            "arousal": min(abs(base_score - 0.5) * 2, 1.0),
            "dominance": base_score * 0.8,
        }

    def _analyze_aspects(
        self, text: str, aspects: List[str]
    ) -> Dict[str, Dict[str, Any]]:
        """Perform aspect-based sentiment analysis."""
        aspect_sentiments = {}

        for aspect in aspects:
            # Simple aspect-based analysis
            if aspect.lower() in text.lower():
                aspect_sentiments[aspect] = {
                    "sentiment": "positive" if "good" in text.lower() else "neutral",
                    "confidence": 0.7,
                    "mentions": text.lower().count(aspect.lower()),
                }
            else:
                aspect_sentiments[aspect] = {
                    "sentiment": "neutral",
                    "confidence": 0.5,
                    "mentions": 0,
                }

        return aspect_sentiments


@register_node()
class KaizenEntityExtractionNode(KaizenNode):
    """
    Enhanced entity extraction node with signature-based optimization.

    Migrated from Core SDK NamedEntityRecognizer with Kaizen enhancements:
    - Schema-aware extraction
    - Confidence scoring
    - Relationship detection
    """

    def __init__(self, signature: Optional[Signature] = None, **kwargs):
        super().__init__(signature=signature, **kwargs)
        self._core_extractor = NamedEntityRecognizer()
        logger.info("Initialized KaizenEntityExtractionNode")

    def get_parameters(self) -> Dict[str, NodeParameter]:
        """Get parameters with signature awareness."""
        params = super().get_parameters()

        params.update(
            {
                "texts": NodeParameter(
                    name="texts",
                    type=list,
                    description="List of texts to process",
                    required=True,
                ),
                "entity_types": NodeParameter(
                    name="entity_types",
                    type=list,
                    description="Types of entities to extract",
                    required=False,
                    default=["PERSON", "ORGANIZATION", "LOCATION"],
                ),
                "extract_relationships": NodeParameter(
                    name="extract_relationships",
                    type=bool,
                    description="Extract relationships between entities",
                    required=False,
                    default=False,
                ),
            }
        )

        return params

    def run(self, **kwargs) -> Dict[str, Any]:
        """Execute enhanced entity extraction."""
        inputs = self.pre_execution_hook(kwargs)

        texts = inputs.get("texts", [])
        entity_types = inputs.get(
            "entity_types", ["PERSON", "ORGANIZATION", "LOCATION"]
        )
        extract_relationships = inputs.get("extract_relationships", False)

        logger.info(f"Extracting entities from {len(texts)} texts")

        try:
            # Use Core SDK extractor as base
            core_params = {
                "texts": texts,
                "entity_types": entity_types,
            }
            base_result = self._core_extractor.run(**core_params)

            # Apply Kaizen enhancements
            enhanced_entities = []
            for entity_result in base_result["entities"]:
                enhanced = self._enhance_entity_extraction(entity_result, inputs)
                enhanced_entities.append(enhanced)

            outputs = {
                "entities": enhanced_entities,
                "entity_types": entity_types,
                "extract_relationships": extract_relationships,
                "total_processed": len(texts),
                "signature_optimized": self.signature is not None,
            }

            outputs = self.post_execution_hook(outputs)
            return outputs

        except Exception as e:
            logger.error(f"KaizenEntityExtractionNode execution failed: {e}")
            raise

    def _enhance_entity_extraction(
        self, entity_result: Dict[str, Any], inputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Apply Kaizen enhancements to entity extraction."""
        enhanced = entity_result.copy()

        # Add confidence scores to entities
        enhanced_entities = []
        for entity in entity_result.get("entities", []):
            enhanced_entity = entity.copy()
            enhanced_entity["confidence"] = self._calculate_entity_confidence(entity)
            enhanced_entities.append(enhanced_entity)

        enhanced["entities"] = enhanced_entities

        # Extract relationships if requested
        if inputs.get("extract_relationships", False):
            enhanced["relationships"] = self._extract_relationships(enhanced_entities)

        if self.signature:
            enhanced["signature_enhanced"] = True

        return enhanced

    def _calculate_entity_confidence(self, entity: Dict[str, Any]) -> float:
        """Calculate confidence score for entity."""
        # Simple confidence based on entity properties
        text_length = len(entity.get("text", ""))
        if text_length > 1:
            return min(0.7 + (text_length / 20), 1.0)
        else:
            return 0.5

    def _extract_relationships(
        self, entities: List[Dict[str, Any]]
    ) -> List[Dict[str, Any]]:
        """Extract relationships between entities."""
        relationships = []

        # Simple relationship extraction (mock implementation)
        for i, entity1 in enumerate(entities):
            for entity2 in entities[i + 1 :]:
                if entity1["type"] == "PERSON" and entity2["type"] == "ORGANIZATION":
                    relationships.append(
                        {
                            "entity1": entity1["text"],
                            "entity2": entity2["text"],
                            "relationship": "works_for",
                            "confidence": 0.6,
                        }
                    )

        return relationships


@register_node()
class KaizenQuestionAnsweringNode(KaizenNode):
    """
    Enhanced question answering node with signature-based optimization.

    Kaizen enhancements for Q&A:
    - Context-aware reasoning
    - Source attribution
    - Confidence scoring
    """

    def __init__(self, signature: Optional[Signature] = None, **kwargs):
        super().__init__(signature=signature, **kwargs)
        logger.info("Initialized KaizenQuestionAnsweringNode")

    def get_parameters(self) -> Dict[str, NodeParameter]:
        """Get parameters with signature awareness."""
        params = super().get_parameters()

        params.update(
            {
                "question": NodeParameter(
                    name="question",
                    type=str,
                    description="Question to answer",
                    required=True,
                ),
                "context": NodeParameter(
                    name="context",
                    type=str,
                    description="Context or document for answering",
                    required=False,
                    default="",
                ),
                "max_answer_length": NodeParameter(
                    name="max_answer_length",
                    type=int,
                    description="Maximum length of answer",
                    required=False,
                    default=200,
                ),
                "require_source": NodeParameter(
                    name="require_source",
                    type=bool,
                    description="Require source attribution",
                    required=False,
                    default=True,
                ),
            }
        )

        return params

    def run(self, **kwargs) -> Dict[str, Any]:
        """Execute enhanced question answering."""
        inputs = self.pre_execution_hook(kwargs)

        question = inputs.get("question", "")
        context = inputs.get("context", "")
        max_length = inputs.get("max_answer_length", 200)
        require_source = inputs.get("require_source", True)

        logger.info(f"Answering question with context length: {len(context)}")

        try:
            # Build enhanced prompt
            if context:
                full_prompt = f"Context: {context}\n\nQuestion: {question}\n\nAnswer based on the context:"
            else:
                full_prompt = f"Question: {question}\n\nAnswer:"

            answer = super()._execute_ai_model(
                prompt=full_prompt,
                model=inputs.get("model", self.model),
                temperature=inputs.get(
                    "temperature", 0.3
                ),  # Lower temperature for factual answers
                max_tokens=max_length,
                timeout=inputs.get("timeout", self.timeout),
            )

            # Calculate confidence and source attribution
            confidence = self._calculate_answer_confidence(question, answer, context)
            source_attribution = (
                self._extract_source_attribution(answer, context)
                if require_source
                else None
            )

            outputs = {
                "question": question,
                "answer": answer,
                "context_used": bool(context),
                "confidence": confidence,
                "source_attribution": source_attribution,
                "max_answer_length": max_length,
                "signature_optimized": self.signature is not None,
            }

            outputs = self.post_execution_hook(outputs)
            return outputs

        except Exception as e:
            logger.error(f"KaizenQuestionAnsweringNode execution failed: {e}")
            raise

    def _calculate_answer_confidence(
        self, question: str, answer: str, context: str
    ) -> float:
        """Calculate confidence score for the answer."""
        # Simple confidence calculation
        if context and len(context) > 50:
            base_confidence = 0.8
        else:
            base_confidence = 0.6

        # Adjust based on answer length and specificity
        if 20 <= len(answer) <= 150:
            base_confidence *= 1.1

        return min(base_confidence, 1.0)

    def _extract_source_attribution(
        self, answer: str, context: str
    ) -> Optional[Dict[str, Any]]:
        """Extract source attribution for the answer."""
        if not context:
            return None

        # Simple source attribution (mock implementation)
        return {
            "has_attribution": len(context) > 0,
            "context_length": len(context),
            "attribution_confidence": 0.7,
        }


# Additional nodes (CodeGeneration, DataAnalysis, Reasoning, AIModel, PromptTemplate, AIWorkflow)
# would continue here following the same pattern...


@register_node()
class KaizenCodeGenerationNode(KaizenNode):
    """Enhanced code generation node with signature-based optimization."""

    def __init__(self, signature: Optional[Signature] = None, **kwargs):
        super().__init__(signature=signature, **kwargs)
        logger.info("Initialized KaizenCodeGenerationNode")

    def get_parameters(self) -> Dict[str, NodeParameter]:
        params = super().get_parameters()
        params.update(
            {
                "requirements": NodeParameter(
                    name="requirements",
                    type=str,
                    description="Code requirements or specification",
                    required=True,
                ),
                "language": NodeParameter(
                    name="language",
                    type=str,
                    description="Programming language",
                    required=False,
                    default="python",
                ),
                "include_tests": NodeParameter(
                    name="include_tests",
                    type=bool,
                    description="Include unit tests",
                    required=False,
                    default=False,
                ),
            }
        )
        return params

    def run(self, **kwargs) -> Dict[str, Any]:
        inputs = self.pre_execution_hook(kwargs)
        requirements = inputs.get("requirements", "")
        language = inputs.get("language", "python")
        include_tests = inputs.get("include_tests", False)

        prompt = f"Generate {language} code for: {requirements}"
        if include_tests:
            prompt += "\nInclude unit tests."

        code = super()._execute_ai_model(
            prompt=prompt,
            model=inputs.get("model", self.model),
            temperature=0.2,  # Lower for deterministic code
            max_tokens=inputs.get("max_tokens", self.max_tokens),
            timeout=inputs.get("timeout", self.timeout),
        )

        outputs = {
            "generated_code": code,
            "requirements": requirements,
            "language": language,
            "include_tests": include_tests,
            "code_quality_score": self._assess_code_quality(code),
            "signature_optimized": self.signature is not None,
        }

        return self.post_execution_hook(outputs)

    def _assess_code_quality(self, code: str) -> float:
        """Simple code quality assessment."""
        if not code:
            return 0.0

        score = 0.5
        if "def " in code:
            score += 0.2
        if "class " in code:
            score += 0.2
        if "import " in code:
            score += 0.1

        return min(score, 1.0)


@register_node()
class KaizenDataAnalysisNode(KaizenNode):
    """Enhanced data analysis node with signature-based optimization."""

    def __init__(self, signature: Optional[Signature] = None, **kwargs):
        super().__init__(signature=signature, **kwargs)
        logger.info("Initialized KaizenDataAnalysisNode")

    def get_parameters(self) -> Dict[str, NodeParameter]:
        params = super().get_parameters()
        params.update(
            {
                "data": NodeParameter(
                    name="data",
                    type=list,
                    description="Data to analyze",
                    required=True,
                ),
                "analysis_type": NodeParameter(
                    name="analysis_type",
                    type=str,
                    description="Type of analysis (descriptive, predictive, diagnostic)",
                    required=False,
                    default="descriptive",
                ),
            }
        )
        return params

    def run(self, **kwargs) -> Dict[str, Any]:
        inputs = self.pre_execution_hook(kwargs)
        data = inputs.get("data", [])
        analysis_type = inputs.get("analysis_type", "descriptive")

        # Perform analysis
        analysis_results = self._analyze_data(data, analysis_type)

        outputs = {
            "data_size": len(data),
            "analysis_type": analysis_type,
            "results": analysis_results,
            "statistical_significance": self._calculate_significance(analysis_results),
            "signature_optimized": self.signature is not None,
        }

        return self.post_execution_hook(outputs)

    def _analyze_data(self, data: List[Any], analysis_type: str) -> Dict[str, Any]:
        """Perform data analysis."""
        if not data:
            return {"error": "No data provided"}

        results = {
            "count": len(data),
            "type": analysis_type,
        }

        # Simple numeric analysis if possible
        try:
            numeric_data = [
                float(x)
                for x in data
                if isinstance(x, (int, float, str))
                and str(x).replace(".", "").isdigit()
            ]
            if numeric_data:
                results.update(
                    {
                        "mean": sum(numeric_data) / len(numeric_data),
                        "min": min(numeric_data),
                        "max": max(numeric_data),
                    }
                )
        except (TypeError, ValueError, ZeroDivisionError) as e:
            logger.debug(f"Statistical calculation failed for data: {e}")

        return results

    def _calculate_significance(self, results: Dict[str, Any]) -> float:
        """Calculate statistical significance."""
        count = results.get("count", 0)
        if count > 30:
            return 0.95
        elif count > 10:
            return 0.80
        else:
            return 0.60


@register_node()
class KaizenReasoningNode(KaizenNode):
    """Enhanced reasoning node with multi-step reasoning chains."""

    def __init__(self, signature: Optional[Signature] = None, **kwargs):
        super().__init__(signature=signature, **kwargs)
        logger.info("Initialized KaizenReasoningNode")

    def get_parameters(self) -> Dict[str, NodeParameter]:
        params = super().get_parameters()
        params.update(
            {
                "problem": NodeParameter(
                    name="problem",
                    type=str,
                    description="Problem to reason about",
                    required=True,
                ),
                "reasoning_type": NodeParameter(
                    name="reasoning_type",
                    type=str,
                    description="Type of reasoning (deductive, inductive, abductive)",
                    required=False,
                    default="deductive",
                ),
                "max_steps": NodeParameter(
                    name="max_steps",
                    type=int,
                    description="Maximum reasoning steps",
                    required=False,
                    default=5,
                ),
            }
        )
        return params

    def run(self, **kwargs) -> Dict[str, Any]:
        inputs = self.pre_execution_hook(kwargs)
        problem = inputs.get("problem", "")
        reasoning_type = inputs.get("reasoning_type", "deductive")
        max_steps = inputs.get("max_steps", 5)

        # Perform multi-step reasoning
        reasoning_chain = self._perform_reasoning(problem, reasoning_type, max_steps)

        outputs = {
            "problem": problem,
            "reasoning_type": reasoning_type,
            "reasoning_chain": reasoning_chain,
            "conclusion": reasoning_chain[-1] if reasoning_chain else "No conclusion",
            "logic_validation": self._validate_logic(reasoning_chain),
            "signature_optimized": self.signature is not None,
        }

        return self.post_execution_hook(outputs)

    def _perform_reasoning(
        self, problem: str, reasoning_type: str, max_steps: int
    ) -> List[str]:
        """Perform multi-step reasoning."""
        chain = []

        # Mock reasoning chain
        chain.append(f"Step 1: Analyze the problem: {problem}")
        chain.append(f"Step 2: Apply {reasoning_type} reasoning")

        if max_steps > 2:
            chain.append("Step 3: Consider evidence and constraints")

        if max_steps > 3:
            chain.append("Step 4: Evaluate alternatives")

        chain.append(f"Conclusion: Based on {reasoning_type} reasoning")

        return chain[:max_steps]

    def _validate_logic(self, reasoning_chain: List[str]) -> Dict[str, Any]:
        """Validate the logic of reasoning chain."""
        return {
            "is_valid": len(reasoning_chain) > 0,
            "chain_length": len(reasoning_chain),
            "consistency_score": 0.8,  # Mock score
        }


@register_node()
class KaizenAIModelNode(KaizenNode):
    """Enhanced AI model node with provider-agnostic interface."""

    def __init__(self, signature: Optional[Signature] = None, **kwargs):
        super().__init__(signature=signature, **kwargs)
        logger.info("Initialized KaizenAIModelNode")

    def get_parameters(self) -> Dict[str, NodeParameter]:
        params = super().get_parameters()
        params.update(
            {
                "provider": NodeParameter(
                    name="provider",
                    type=str,
                    description="AI provider (openai, anthropic, ollama)",
                    required=False,
                    default="openai",
                ),
                "fallback_provider": NodeParameter(
                    name="fallback_provider",
                    type=str,
                    description="Fallback AI provider",
                    required=False,
                    default="ollama",
                ),
                "use_fallback": NodeParameter(
                    name="use_fallback",
                    type=bool,
                    description="Enable fallback on provider failure",
                    required=False,
                    default=True,
                ),
            }
        )
        return params

    def run(self, **kwargs) -> Dict[str, Any]:
        inputs = self.pre_execution_hook(kwargs)
        provider = inputs.get("provider", "openai")
        fallback_provider = inputs.get("fallback_provider", "ollama")
        use_fallback = inputs.get("use_fallback", True)

        # Try primary provider
        try:
            result = self._execute_with_provider(provider, inputs)
            result["provider_used"] = provider
            result["fallback_used"] = False
        except Exception as e:
            if use_fallback:
                logger.warning(
                    f"Primary provider {provider} failed: {e}, trying fallback"
                )
                try:
                    result = self._execute_with_provider(fallback_provider, inputs)
                    result["provider_used"] = fallback_provider
                    result["fallback_used"] = True
                    result["primary_error"] = str(e)
                except Exception as fallback_error:
                    result = {
                        "error": f"Both providers failed. Primary: {e}, Fallback: {fallback_error}",
                        "provider_used": None,
                        "fallback_used": True,
                    }
            else:
                result = {
                    "error": f"Provider {provider} failed: {e}",
                    "provider_used": provider,
                    "fallback_used": False,
                }

        result["signature_optimized"] = self.signature is not None
        return self.post_execution_hook(result)

    def _execute_with_provider(
        self, provider: str, inputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Execute with specific provider."""
        # Provider-specific optimization logic would go here
        prompt = inputs.get("prompt", "")

        response = super()._execute_ai_model(
            prompt=f"[{provider.upper()}] {prompt}",
            model=inputs.get("model", self.model),
            temperature=inputs.get("temperature", self.temperature),
            max_tokens=inputs.get("max_tokens", self.max_tokens),
            timeout=inputs.get("timeout", self.timeout),
        )

        return {
            "response": response,
            "prompt": prompt,
            "performance_metrics": self._get_performance_metrics(provider),
        }

    def _get_performance_metrics(self, provider: str) -> Dict[str, Any]:
        """Get performance metrics for provider."""
        # Mock performance metrics
        performance_map = {
            "openai": {"latency": 1.2, "cost": 0.002, "quality": 0.95},
            "anthropic": {"latency": 1.5, "cost": 0.003, "quality": 0.93},
            "ollama": {"latency": 3.0, "cost": 0.0, "quality": 0.85},
        }
        return performance_map.get(
            provider, {"latency": 2.0, "cost": 0.001, "quality": 0.80}
        )


@register_node()
class KaizenPromptTemplateNode(KaizenNode):
    """Enhanced prompt template node with dynamic generation."""

    def __init__(self, signature: Optional[Signature] = None, **kwargs):
        super().__init__(signature=signature, **kwargs)
        logger.info("Initialized KaizenPromptTemplateNode")

    def get_parameters(self) -> Dict[str, NodeParameter]:
        params = super().get_parameters()
        params.update(
            {
                "template": NodeParameter(
                    name="template",
                    type=str,
                    description="Prompt template with placeholders",
                    required=True,
                ),
                "variables": NodeParameter(
                    name="variables",
                    type=dict,
                    description="Variables to fill in template",
                    required=True,
                ),
                "optimize_template": NodeParameter(
                    name="optimize_template",
                    type=bool,
                    description="Optimize template for better results",
                    required=False,
                    default=True,
                ),
                "quality_check": NodeParameter(
                    name="quality_check",
                    type=bool,
                    description="Check template quality",
                    required=False,
                    default=True,
                ),
            }
        )
        return params

    def run(self, **kwargs) -> Dict[str, Any]:
        inputs = self.pre_execution_hook(kwargs)
        template = inputs.get("template", "")
        variables = inputs.get("variables", {})
        optimize_template = inputs.get("optimize_template", True)
        quality_check = inputs.get("quality_check", True)

        # Fill template with variables
        filled_prompt = self._fill_template(template, variables)

        # Optimize template if requested
        if optimize_template:
            filled_prompt = self._optimize_prompt(filled_prompt)

        # Quality scoring
        quality_score = (
            self._score_prompt_quality(filled_prompt) if quality_check else None
        )

        outputs = {
            "original_template": template,
            "filled_prompt": filled_prompt,
            "variables": variables,
            "optimized": optimize_template,
            "quality_score": quality_score,
            "variable_count": len(variables),
            "template_length": len(template),
            "signature_optimized": self.signature is not None,
        }

        return self.post_execution_hook(outputs)

    def _fill_template(self, template: str, variables: Dict[str, Any]) -> str:
        """Fill template with variables."""
        filled = template
        for key, value in variables.items():
            placeholder = "{" + key + "}"
            filled = filled.replace(placeholder, str(value))
        return filled

    def _optimize_prompt(self, prompt: str) -> str:
        """Optimize prompt for better AI responses."""
        # Simple optimization - add clarity instructions
        if not prompt.strip().endswith(("?", ":", ".")):
            prompt += "."

        if len(prompt) < 50:
            prompt = f"Please provide a detailed response to: {prompt}"

        return prompt

    def _score_prompt_quality(self, prompt: str) -> float:
        """Score prompt quality."""
        score = 0.5

        # Length check
        if 20 <= len(prompt) <= 500:
            score += 0.2

        # Question presence
        if "?" in prompt:
            score += 0.1

        # Clear instructions
        if any(
            word in prompt.lower()
            for word in ["please", "explain", "describe", "analyze"]
        ):
            score += 0.1

        # Variable usage
        if "{" not in prompt:  # All variables filled
            score += 0.1

        return min(score, 1.0)


@register_node()
class KaizenAIWorkflowNode(KaizenNode):
    """Enhanced AI workflow node with nested workflow support."""

    def __init__(self, signature: Optional[Signature] = None, **kwargs):
        super().__init__(signature=signature, **kwargs)
        self.nested_workflows = []
        logger.info("Initialized KaizenAIWorkflowNode")

    def get_parameters(self) -> Dict[str, NodeParameter]:
        params = super().get_parameters()
        params.update(
            {
                "workflow_steps": NodeParameter(
                    name="workflow_steps",
                    type=list,
                    description="List of workflow steps to execute",
                    required=True,
                ),
                "parallel_execution": NodeParameter(
                    name="parallel_execution",
                    type=bool,
                    description="Execute steps in parallel where possible",
                    required=False,
                    default=False,
                ),
                "error_handling": NodeParameter(
                    name="error_handling",
                    type=str,
                    description="Error handling strategy (stop, continue, retry)",
                    required=False,
                    default="stop",
                ),
                "max_retries": NodeParameter(
                    name="max_retries",
                    type=int,
                    description="Maximum retries for failed steps",
                    required=False,
                    default=2,
                ),
            }
        )
        return params

    def run(self, **kwargs) -> Dict[str, Any]:
        inputs = self.pre_execution_hook(kwargs)
        workflow_steps = inputs.get("workflow_steps", [])
        parallel_execution = inputs.get("parallel_execution", False)
        error_handling = inputs.get("error_handling", "stop")
        max_retries = inputs.get("max_retries", 2)

        # Execute workflow steps
        execution_results = []
        total_execution_time = 0
        errors = []

        for i, step in enumerate(workflow_steps):
            step_result = self._execute_workflow_step(
                step, i, max_retries, error_handling
            )
            execution_results.append(step_result)

            if step_result.get("error") and error_handling == "stop":
                errors.append(step_result["error"])
                break
            elif step_result.get("error"):
                errors.append(step_result["error"])

            total_execution_time += step_result.get("execution_time", 0)

        outputs = {
            "workflow_steps": workflow_steps,
            "execution_results": execution_results,
            "total_steps": len(workflow_steps),
            "completed_steps": len(
                [r for r in execution_results if not r.get("error")]
            ),
            "errors": errors,
            "total_execution_time": total_execution_time,
            "parallel_execution": parallel_execution,
            "error_handling": error_handling,
            "success_rate": (
                len([r for r in execution_results if not r.get("error")])
                / len(workflow_steps)
                if workflow_steps
                else 0
            ),
            "signature_optimized": self.signature is not None,
        }

        return self.post_execution_hook(outputs)

    def _execute_workflow_step(
        self,
        step: Dict[str, Any],
        step_index: int,
        max_retries: int,
        error_handling: str,
    ) -> Dict[str, Any]:
        """Execute a single workflow step."""
        step_type = step.get("type", "prompt")
        step_data = step.get("data", {})

        retries = 0
        while retries <= max_retries:
            try:
                start_time = __import__("time").time()

                if step_type == "prompt":
                    result = self._execute_prompt_step(step_data)
                elif step_type == "analysis":
                    result = self._execute_analysis_step(step_data)
                else:
                    result = {"output": f"Unknown step type: {step_type}"}

                execution_time = __import__("time").time() - start_time

                return {
                    "step_index": step_index,
                    "step_type": step_type,
                    "result": result,
                    "execution_time": execution_time,
                    "retries": retries,
                    "success": True,
                }

            except Exception as e:
                retries += 1
                if retries > max_retries:
                    return {
                        "step_index": step_index,
                        "step_type": step_type,
                        "error": str(e),
                        "retries": retries - 1,
                        "success": False,
                    }

    def _execute_prompt_step(self, step_data: Dict[str, Any]) -> Dict[str, Any]:
        """Execute a prompt-based workflow step."""
        prompt = step_data.get("prompt", "")

        response = super()._execute_ai_model(
            prompt=prompt,
            model=step_data.get("model", self.model),
            temperature=step_data.get("temperature", self.temperature),
            max_tokens=step_data.get("max_tokens", self.max_tokens),
            timeout=step_data.get("timeout", self.timeout),
        )

        return {
            "prompt": prompt,
            "response": response,
            "step_type": "prompt",
        }

    def _execute_analysis_step(self, step_data: Dict[str, Any]) -> Dict[str, Any]:
        """Execute an analysis workflow step."""
        data = step_data.get("data", [])
        analysis_type = step_data.get("analysis_type", "summary")

        # Simple analysis
        if analysis_type == "summary":
            result = (
                f"Analysis of {len(data)} items: {data[:3] if len(data) > 3 else data}"
            )
        elif analysis_type == "count":
            result = f"Total items: {len(data)}"
        else:
            result = f"Unknown analysis type: {analysis_type}"

        return {
            "analysis_type": analysis_type,
            "data_size": len(data),
            "result": result,
            "step_type": "analysis",
        }
