"""
LV-Enhanced Action Templates for NeoCoder
=========================================

These templates integrate Lotka-Volterra dynamics into existing NeoCoder workflows
to maintain diversity while preserving quality in AI outputs.
"""

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

from .lv_ecosystem import LVEcosystem, LVCandidate, EntropyEstimator
from .incarnations.base_incarnation import BaseIncarnation

logger = logging.getLogger(__name__)


class LVKnowledgeExtractTemplate:
    """
    LV-Enhanced Knowledge Extraction Template

    Extends KNOWLEDGE_EXTRACT with Lotka-Volterra diversity preservation
    to maintain balanced extraction strategies across entropy levels.
    """

    keyword = "KNOWLEDGE_EXTRACT_LV"
    name = "LV-Enhanced Knowledge Extraction"
    version = "1.0"
    description = "Extract knowledge using ecological dynamics for strategy diversity"

    def __init__(self, neo4j_session: Any, qdrant_client: Any):
        """
        Initialize the LV-Enhanced Knowledge Extraction Template.

        Args:
            neo4j_session: An active Neo4j database session.
            qdrant_client: An active Qdrant client instance.
        """
        self.neo4j = neo4j_session
        self.qdrant = qdrant_client
        self.lv_ecosystem = LVEcosystem(neo4j_session, qdrant_client)
        self.entropy_estimator = EntropyEstimator()

    async def execute(self, context: Dict[str, Any]) -> Dict[str, Any]:
        """
        Execute the LV-enhanced knowledge extraction process.

        This method orchestrates the entire workflow, from analyzing the input
        document to generating and selecting extraction strategies, executing them,
        and finally storing the extracted knowledge.

        Args:
            context: A dictionary containing the execution parameters, including:
                - 'document_path': Path to the document to be processed.
                - 'prompt': The user\'s prompt for knowledge extraction.
                - 'extraction_mode': The mode of extraction (e.g., 'balanced').
                - 'history': A list of previous interactions for context.

        Returns:
            A dictionary containing a report of the extraction process,
            including metrics, strategies used, and storage results.
            In case of an error, it returns an error message and any
            fallback results.
        """
        try:
            # Extract parameters
            document_path: Optional[str] = context.get('document_path')
            if not document_path:
                raise ValueError("document_path is required in the context.")
            prompt = context.get('prompt', 'Extract key knowledge from document')
            extraction_mode = context.get('extraction_mode', 'balanced')

            logger.info(f"Starting LV knowledge extraction: {document_path}")
            logger.info(f"Extraction mode: {extraction_mode}")

            # Step 1: Document analysis and entropy estimation
            document_content = await self._load_document(document_path)
            extraction_entropy: float = self.entropy_estimator.estimate_prompt_entropy(
                prompt, context.get('history', [])
            )

            # Step 2: Generate extraction strategy candidates
            extraction_strategies: List[Dict[str, Any]] = await self._generate_extraction_strategies(
                document_content, prompt, extraction_entropy
            )

            # Step 3: Apply LV dynamics for strategy selection
            lv_results: Dict[str, Any] = await self.lv_ecosystem.select_diverse_outputs(
                candidates=[s['description'] for s in extraction_strategies],
                prompt=f"Extract knowledge: {prompt}",
                context={'task_type': 'knowledge_extraction', 'entropy': extraction_entropy}
            )

            # Step 4: Execute selected strategies
            selected_strategies: List[Dict[str, Any]] = self._map_selected_strategies(
                extraction_strategies, lv_results['selected_outputs']
            )

            extracted_knowledge: Dict[str, Any] = await self._execute_extraction_strategies(
                document_content, selected_strategies
            )

            # Step 5: Store in Neo4j and Qdrant
            storage_results: Dict[str, Any] = await self._store_extracted_knowledge(
                extracted_knowledge, document_path, prompt
            )

            # Step 6: Generate report
            extraction_report = {
                'document_path': document_path,
                'prompt': prompt,
                'extraction_entropy': extraction_entropy,
                'strategies_used': [s['name'] for s in selected_strategies],
                'entities_extracted': storage_results['entities_count'],
                'relationships_created': storage_results['relationships_count'],
                'chunks_stored': storage_results['chunks_count'],
                'diversity_metrics': lv_results['diversity_metrics'],
                'lv_analysis': {
                    'growth_rates': lv_results['growth_rates'],
                    'final_populations': lv_results['final_populations'],
                    'convergence_iterations': lv_results['convergence_iterations']
                }
            }

            logger.info(f"LV knowledge extraction completed: {extraction_report['entities_extracted']} entities")
            return extraction_report

        except Exception as e:
            logger.error(f"LV knowledge extraction failed: {e}")
            return {'error': str(e), 'fallback_executed': await self._fallback_extraction(context)}

    async def _generate_extraction_strategies(self,
                                            document_content: str,
                                            prompt: str,
                                            entropy: float) -> List[Dict[str, Any]]:
        """
        Generate a diverse set of extraction strategies for LV selection.

        These strategies represent different approaches to knowledge extraction,
        ranging from conservative to creative. The selection of strategies
        is guided by the estimated entropy of the prompt.

        Args:
            document_content: The content of the document to be analyzed.
            prompt: The user\'s prompt for extraction.
            entropy: The estimated entropy of the prompt.

        Returns:
            A list of dictionaries, where each dictionary defines an
            extraction strategy with its properties.
        """

        strategies = [
            {
                'name': 'conservative',
                'description': 'Extract only high-confidence entities and explicit relationships',
                'confidence_threshold': 0.9,
                'extraction_depth': 'shallow',
                'risk_tolerance': 'low'
            },
            {
                'name': 'aggressive',
                'description': 'Extract implicit relationships and inferred entities',
                'confidence_threshold': 0.6,
                'extraction_depth': 'deep',
                'risk_tolerance': 'high'
            },
            {
                'name': 'domain_specific',
                'description': 'Focus on domain-specific entities and technical relationships',
                'confidence_threshold': 0.8,
                'extraction_depth': 'medium',
                'specialization': self._detect_domain(document_content)
            },
            {
                'name': 'creative',
                'description': 'Discover novel connections and emergent patterns',
                'confidence_threshold': 0.7,
                'extraction_depth': 'exploratory',
                'novelty_bias': True
            },
            {
                'name': 'structured',
                'description': 'Maintain strict ontological consistency and formal relationships',
                'confidence_threshold': 0.85,
                'extraction_depth': 'systematic',
                'ontology_strict': True
            }
        ]

        return strategies

    def _detect_domain(self, content: str) -> str:
        """
        Detect the domain of the document content for specialized extraction.

        This method performs a simple keyword-based domain detection to tailor
        the extraction process.

        Args:
            content: The text content of the document.

        Returns:
            A string representing the detected domain (e.g., 'machine_learning',
            'medical', 'software', 'biology', or 'general').
        """
        content_lower = content.lower()

        if any(term in content_lower for term in ['neural', 'model', 'algorithm', 'training']):
            return 'machine_learning'
        elif any(term in content_lower for term in ['patient', 'treatment', 'medical', 'clinical']):
            return 'medical'
        elif any(term in content_lower for term in ['function', 'class', 'import', 'def']):
            return 'software'
        elif any(term in content_lower for term in ['species', 'ecosystem', 'ecological', 'biology']):
            return 'biology'
        else:
            return 'general'

    async def _execute_extraction_strategies(self,
                                           document_content: str,
                                           selected_strategies: List[Dict]) -> Dict[str, Any]:
        """
        Execute the selected extraction strategies and merge the results.

        This method runs each of the LV-selected strategies in parallel,
        collects their outputs, and then merges the results to create a
        consolidated set of extracted knowledge.

        Args:
            document_content: The content of the document to be analyzed.
            selected_strategies: The strategies selected by LV dynamics.

        Returns:
            A dictionary containing the merged extracted knowledge including
            entities, relationships, and text chunks.
        """
        try:
            # Initialize results structure
            consolidated_knowledge = {
                'entities': [],
                'relationships': [],
                'text_chunks': [],
                'strategy_results': {}
            }

            # Execute each strategy
            for strategy in selected_strategies:
                strategy_name = strategy['name']
                logger.info(f"Executing extraction strategy: {strategy_name}")

                strategy_result = await self._execute_single_strategy(
                    document_content, strategy
                )

                consolidated_knowledge['strategy_results'][strategy_name] = strategy_result

                # Merge entities (avoiding duplicates)
                for entity in strategy_result.get('entities', []):
                    if not self._entity_exists(entity, consolidated_knowledge['entities']):
                        consolidated_knowledge['entities'].append(entity)

                # Merge relationships (avoiding duplicates)
                for rel in strategy_result.get('relationships', []):
                    if not self._relationship_exists(rel, consolidated_knowledge['relationships']):
                        consolidated_knowledge['relationships'].append(rel)

                # Add text chunks with strategy attribution
                for chunk in strategy_result.get('text_chunks', []):
                    chunk['extraction_strategy'] = strategy_name
                    consolidated_knowledge['text_chunks'].append(chunk)

            logger.info(f"Knowledge extraction completed: {len(consolidated_knowledge['entities'])} entities, "
                       f"{len(consolidated_knowledge['relationships'])} relationships")

            return consolidated_knowledge

        except Exception as e:
            logger.error(f"Strategy execution failed: {e}")
            return {'entities': [], 'relationships': [], 'text_chunks': [], 'error': str(e)}

    async def _execute_single_strategy(self, content: str, strategy: Dict[str, Any]) -> Dict[str, Any]:
        """Execute a single extraction strategy"""
        strategy_name = strategy['name']

        # Simple rule-based extraction based on strategy type
        if strategy_name == 'conservative':
            return await self._conservative_extraction(content, strategy)
        elif strategy_name == 'aggressive':
            return await self._aggressive_extraction(content, strategy)
        elif strategy_name == 'domain_specific':
            return await self._domain_specific_extraction(content, strategy)
        elif strategy_name == 'creative':
            return await self._creative_extraction(content, strategy)
        elif strategy_name == 'structured':
            return await self._structured_extraction(content, strategy)
        else:
            return await self._default_extraction(content, strategy)

    async def _conservative_extraction(self, content: str, strategy: Dict[str, Any]) -> Dict[str, Any]:
        """Conservative extraction strategy - high confidence only"""
        entities = []
        relationships = []
        text_chunks = []

        # Simple sentence-based chunking
        sentences = [s.strip() for s in content.split('.') if s.strip()]

        # Extract clear, definitive statements
        for i, sentence in enumerate(sentences):
            if len(sentence) > 20:  # Substantial sentences only
                # Look for clear entity patterns
                if any(word in sentence.lower() for word in ['is', 'are', 'was', 'were']):
                    # Extract potential entities from definitive statements
                    words = sentence.split()
                    if len(words) >= 3:
                        entities.append({
                            'name': ' '.join(words[:2]).title(),
                            'entityType': 'Concept',
                            'observations': [sentence],
                            'confidence': 0.9
                        })

                # Create text chunk
                text_chunks.append({
                    'content': sentence,
                    'chunk_id': f"conservative_{i}",
                    'metadata': {'extraction_method': 'conservative', 'confidence': 0.9}
                })

        return {'entities': entities, 'relationships': relationships, 'text_chunks': text_chunks}

    async def _aggressive_extraction(self, content: str, strategy: Dict[str, Any]) -> Dict[str, Any]:
        """Aggressive extraction strategy - inferred relationships"""
        entities = []
        relationships = []
        text_chunks = []

        # More liberal chunking and extraction
        paragraphs = [p.strip() for p in content.split('\n\n') if p.strip()]

        for i, paragraph in enumerate(paragraphs):
            sentences = [s.strip() for s in paragraph.split('.') if s.strip()]

            for j, sentence in enumerate(sentences):
                if len(sentence) > 10:
                    # Extract multiple entities per sentence
                    words = sentence.split()
                    potential_entities = []

                    for k in range(0, len(words)-1, 2):
                        if k+1 < len(words):
                            entity_name = ' '.join(words[k:k+2]).title()
                            if len(entity_name) > 3:
                                potential_entities.append(entity_name)

                    # Create entities with lower confidence
                    for entity_name in potential_entities:
                        entities.append({
                            'name': entity_name,
                            'entityType': 'Inferred',
                            'observations': [sentence],
                            'confidence': 0.6
                        })

                    # Create relationships between adjacent entities
                    for k in range(len(potential_entities)-1):
                        relationships.append({
                            'from': potential_entities[k],
                            'to': potential_entities[k+1],
                            'relationType': 'RELATES_TO',
                            'confidence': 0.6
                        })

                    text_chunks.append({
                        'content': sentence,
                        'chunk_id': f"aggressive_{i}_{j}",
                        'metadata': {'extraction_method': 'aggressive', 'confidence': 0.6}
                    })

        return {'entities': entities, 'relationships': relationships, 'text_chunks': text_chunks}

    async def _domain_specific_extraction(self, content: str, strategy: Dict[str, Any]) -> Dict[str, Any]:
        """Domain-specific extraction based on detected domain"""
        domain = strategy.get('specialization', 'general')
        entities = []
        relationships = []
        text_chunks = []

        # Domain-specific patterns
        domain_patterns = {
            'machine_learning': ['model', 'algorithm', 'training', 'neural', 'learning'],
            'medical': ['patient', 'treatment', 'diagnosis', 'clinical', 'medical'],
            'software': ['function', 'class', 'method', 'code', 'programming'],
            'biology': ['species', 'ecosystem', 'organism', 'biological', 'evolution']
        }

        patterns = domain_patterns.get(domain, ['concept', 'idea', 'element', 'component'])

        sentences = [s.strip() for s in content.split('.') if s.strip()]
        for i, sentence in enumerate(sentences):
            # Look for domain-specific terms
            sentence_lower = sentence.lower()
            if any(pattern in sentence_lower for pattern in patterns):
                # Extract domain-specific entities
                for pattern in patterns:
                    if pattern in sentence_lower:
                        entities.append({
                            'name': pattern.title(),
                            'entityType': f'{domain.title()}Concept',
                            'observations': [sentence],
                            'confidence': 0.8
                        })

                text_chunks.append({
                    'content': sentence,
                    'chunk_id': f"domain_{domain}_{i}",
                    'metadata': {'extraction_method': 'domain_specific', 'domain': domain, 'confidence': 0.8}
                })

        return {'entities': entities, 'relationships': relationships, 'text_chunks': text_chunks}

    async def _creative_extraction(self, content: str, strategy: Dict[str, Any]) -> Dict[str, Any]:
        """Creative extraction strategy - novel connections"""
        entities = []
        relationships = []
        text_chunks = []

        # Look for creative patterns and metaphors
        sentences = [s.strip() for s in content.split('.') if s.strip()]
        creative_indicators = ['like', 'similar', 'metaphor', 'analogy', 'imagine', 'creative']

        for i, sentence in enumerate(sentences):
            sentence_lower = sentence.lower()
            if any(indicator in sentence_lower for indicator in creative_indicators):
                # Extract creative concepts
                words = sentence.split()
                if len(words) >= 4:
                    entities.append({
                        'name': ' '.join(words[:3]).title(),
                        'entityType': 'CreativeConcept',
                        'observations': [sentence],
                        'confidence': 0.7
                    })

                text_chunks.append({
                    'content': sentence,
                    'chunk_id': f"creative_{i}",
                    'metadata': {'extraction_method': 'creative', 'confidence': 0.7}
                })

        return {'entities': entities, 'relationships': relationships, 'text_chunks': text_chunks}

    async def _structured_extraction(self, content: str, strategy: Dict[str, Any]) -> Dict[str, Any]:
        """Structured extraction strategy - formal ontology"""
        entities = []
        relationships = []
        text_chunks = []

        # Look for structured patterns (lists, definitions, formal statements)
        lines = content.split('\n')
        for i, line in enumerate(lines):
            line = line.strip()
            if line and (line.startswith('-') or line.startswith('*') or ':' in line):
                # Structured content found
                if ':' in line:
                    parts = line.split(':')
                    if len(parts) >= 2:
                        entities.append({
                            'name': parts[0].strip(),
                            'entityType': 'StructuredConcept',
                            'observations': [parts[1].strip()],
                            'confidence': 0.85
                        })

                text_chunks.append({
                    'content': line,
                    'chunk_id': f"structured_{i}",
                    'metadata': {'extraction_method': 'structured', 'confidence': 0.85}
                })

        return {'entities': entities, 'relationships': relationships, 'text_chunks': text_chunks}

    async def _default_extraction(self, content: str, strategy: Dict[str, Any]) -> Dict[str, Any]:
        """Default extraction strategy"""
        entities = []
        relationships = []
        text_chunks = []

        # Simple balanced extraction
        sentences = [s.strip() for s in content.split('.') if s.strip()]
        for i, sentence in enumerate(sentences):
            if len(sentence) > 15:
                words = sentence.split()
                if len(words) >= 3:
                    entities.append({
                        'name': ' '.join(words[:2]).title(),
                        'entityType': 'General',
                        'observations': [sentence],
                        'confidence': 0.75
                    })

                text_chunks.append({
                    'content': sentence,
                    'chunk_id': f"default_{i}",
                    'metadata': {'extraction_method': 'default', 'confidence': 0.75}
                })

        return {'entities': entities, 'relationships': relationships, 'text_chunks': text_chunks}

    def _entity_exists(self, entity: Dict[str, Any], existing_entities: List[Dict[str, Any]]) -> bool:
        """Check if entity already exists in the list"""
        return any(e['name'] == entity['name'] for e in existing_entities)

    def _relationship_exists(self, rel: Dict[str, Any], existing_rels: List[Dict[str, Any]]) -> bool:
        """Check if relationship already exists in the list"""
        return any(
            r['from'] == rel['from'] and r['to'] == rel['to'] and r['relationType'] == rel['relationType']
            for r in existing_rels
        )

    async def _store_extracted_knowledge(self,
                                       knowledge: Dict[str, Any],
                                       document_path: str,
                                       prompt: str) -> Dict[str, Any]:
        """
        Store extracted knowledge in Neo4j and Qdrant using existing tools

        Args:
            knowledge: Extracted knowledge from strategies
            document_path: Source document path
            prompt: Original extraction prompt

        Returns:
            Storage results summary
        """
        try:
            from .lv_neo4j_storage import LVNeo4jStorage
            storage = LVNeo4jStorage(self.neo4j)

            # Store in Neo4j using the storage manager
            session_id = f"extract_{hash(prompt)}_{int(datetime.now().timestamp())}"

            storage_results = await storage.store_lv_extracted_knowledge(
                entities=knowledge.get('entities', []),
                relations=knowledge.get('relationships', []),
                session_id=session_id,
                source_document=document_path
            )

            # Store text chunks in Qdrant using real integration
            chunks = knowledge.get('text_chunks', [])
            if chunks and self.qdrant:
                try:
                    # Prepare chunks for Qdrant storage
                    qdrant_entries = []
                    for chunk in chunks:
                        entry = {
                            'content': chunk['content'],
                            'metadata': {
                                **chunk.get('metadata', {}),
                                'source_document': document_path,
                                'extraction_session': session_id,
                                'chunk_id': chunk.get('chunk_id', f"chunk_{hash(chunk['content'])}")
                            }
                        }
                        qdrant_entries.append(entry)

                    # Store in Qdrant using existing store_batch method
                    if hasattr(self.qdrant, 'store_batch') and callable(self.qdrant.store_batch):
                        self.qdrant.store_batch(
                            entries=qdrant_entries,
                            collection_name="knowledge_base"
                        )
                        storage_results['chunks_count'] = len(chunks)
                        logger.info(f"Stored {len(chunks)} chunks in Qdrant collection 'knowledge_base'")
                    elif hasattr(self.qdrant, 'upsert') and callable(self.qdrant.upsert):
                        # Alternative: use upsert method
                        for entry in qdrant_entries:
                            self.qdrant.upsert(
                                collection_name="knowledge_base",
                                **entry
                            )
                        storage_results['chunks_count'] = len(chunks)
                        logger.info(f"Upserted {len(chunks)} chunks in Qdrant collection 'knowledge_base'")
                    else:
                        logger.error("Qdrant client missing expected storage methods (store_batch or upsert)")
                        storage_results['chunks_count'] = 0

                except Exception as e:
                    logger.error(f"Failed to store chunks in Qdrant: {e}")
                    storage_results['chunks_count'] = 0
            else:
                storage_results['chunks_count'] = 0

            return storage_results

        except Exception as e:
            logger.error(f"Failed to store extracted knowledge: {e}")
            return {'entities_count': 0, 'relationships_count': 0, 'chunks_count': 0, 'error': str(e)}

    async def _fallback_extraction(self, context: Dict[str, Any]) -> str:
        """
        A fallback mechanism for knowledge extraction if the main process fails.
        Placeholder implementation.
        """
        logger.warning("Executing fallback extraction.")
        return "Could not complete the extraction due to an internal error. Please try again."

    def _map_selected_strategies(self,
                                 extraction_strategies: List[Dict[str, Any]],
                                 selected_outputs: List[str]) -> List[Dict[str, Any]]:
        """
        Maps selected strategy descriptions back to their full dictionary representations.
        """
        selected_map = {s['description']: s for s in extraction_strategies}
        return [selected_map[desc] for desc in selected_outputs if desc in selected_map]

    async def _load_document(self, document_path: str) -> str:
        """
        Load document content for extraction.

        Reads the file in chunks to avoid high memory usage with large files.
        """
        try:
            content = []
            chunk_size = 1024 * 1024  # 1MB
            with open(document_path, 'r', encoding='utf-8') as f:
                while True:
                    chunk = f.read(chunk_size)
                    if not chunk:
                        break
                    content.append(chunk)
            return ''.join(content)
        except Exception as e:
            logger.error(f"Failed to load document {document_path}: {e}")
            return ""


class LVKnowledgeQueryTemplate:
    """
    LV-Enhanced Knowledge Query Template

    Extends KNOWLEDGE_QUERY with diverse query strategy selection
    to provide multi-perspective answers with full citations.
    """

    keyword = "KNOWLEDGE_QUERY_LV"
    name = "LV-Enhanced Knowledge Query"
    version = "1.0"
    description = "Query knowledge using ecological dynamics for perspective diversity"

    def __init__(self, neo4j_session: Any, qdrant_client: Any):
        """
        Initialize the LV-Enhanced Knowledge Query Template.

        Args:
            neo4j_session: An active Neo4j database session.
            qdrant_client: An active Qdrant client instance.
        """
        self.neo4j = neo4j_session
        self.qdrant = qdrant_client
        self.lv_ecosystem = LVEcosystem(neo4j_session, qdrant_client)
        self.entropy_estimator = EntropyEstimator()

    async def execute(self, context: Dict[str, Any]) -> Dict[str, Any]:
        """
        Execute the LV-enhanced knowledge query with diverse perspectives.

        This method orchestrates the process of analyzing a query, generating
        diverse strategies to answer it, executing those strategies, and
        synthesizing a comprehensive, multi-perspective answer.

        Args:
            context: A dictionary containing the execution parameters, including:
                - 'query': The user\'s query.
                - 'query_type': The type of query (e.g., 'hybrid').
                - 'history': A list of previous interactions for context.

        Returns:
            A dictionary containing the synthesized answer, citations,
            and other metadata. In case of an error, it returns an
            error message and a fallback answer.
        """
        try:
            # Extract parameters
            query: Optional[str] = context.get('query')
            if not query:
                raise ValueError("query is required in the context.")
            query_type: str = context.get('query_type', 'hybrid')

            logger.info(f"Starting LV knowledge query: {query}")
            logger.info(f"Query type: {query_type}")

            # Step 1: Query analysis
            query_entropy: float = self.entropy_estimator.estimate_prompt_entropy(
                query, context.get('history', [])
            )

            # Step 2: Generate query strategy candidates
            query_strategies: List[Dict[str, Any]] = await self._generate_query_strategies(query, query_entropy)

            # Step 3: Apply LV dynamics for strategy selection
            lv_results: Dict[str, Any] = await self.lv_ecosystem.select_diverse_outputs(
                candidates=[s['description'] for s in query_strategies],
                prompt=f"Answer query: {query}",
                context={'task_type': 'knowledge_query', 'entropy': query_entropy}
            )

            # Step 4: Execute selected strategies in parallel
            selected_strategies: List[Dict[str, Any]] = self._map_selected_strategies(
                query_strategies, lv_results['selected_outputs']
            )

            query_results: Dict[str, Any] = await self._execute_query_strategies(
                query, selected_strategies
            )

            # Step 5: Synthesize with conflict detection
            synthesized_answer: Dict[str, Any] = await self._synthesize_multi_perspective_answer(
                query, query_results, selected_strategies
            )

            # Step 6: Generate comprehensive response
            response = {
                'query': query,
                'query_entropy': query_entropy,
                'strategies_used': [s['name'] for s in selected_strategies],
                'answer': synthesized_answer['content'],
                'citations': synthesized_answer['citations'],
                'conflicts_detected': synthesized_answer['conflicts'],
                'confidence_scores': synthesized_answer['confidence'],
                'diversity_metrics': lv_results['diversity_metrics'],
                'lv_analysis': {
                    'growth_rates': lv_results['growth_rates'],
                    'final_populations': lv_results['final_populations']
                }
            }

            logger.info(f"LV knowledge query completed with {len(response['citations'])} citations")
            return response

        except Exception as e:
            logger.error(f"LV knowledge query failed: {e}")
            return {'error': str(e), 'fallback_answer': await self._fallback_query(context)}

    async def _generate_query_strategies(self,
                                       query: str,
                                       entropy: float) -> List[Dict[str, Any]]:
        """
        Generate a diverse set of query strategies for LV selection.

        These strategies represent different ways of approaching the query,
        such as focusing on structured data, semantic context, or conflict
        detection.

        Args:
            query: The user\'s query.
            entropy: The estimated entropy of the query.

        Returns:
            A list of dictionaries, where each dictionary defines a query
            strategy with its properties.
        """

        strategies = [
            {
                'name': 'graph_centric',
                'description': 'Focus on structured facts and explicit relationships from Neo4j',
                'primary_source': 'neo4j',
                'strategy_type': 'authoritative'
            },
            {
                'name': 'vector_semantic',
                'description': 'Emphasize semantic context and implicit knowledge from Qdrant',
                'primary_source': 'qdrant',
                'strategy_type': 'contextual'
            },
            {
                'name': 'hybrid_balanced',
                'description': 'Equal weight to structured facts and semantic context',
                'primary_source': 'both',
                'strategy_type': 'balanced'
            },
            {
                'name': 'conflict_aware',
                'description': 'Actively seek and highlight contradictions between sources',
                'primary_source': 'both',
                'strategy_type': 'analytical'
            },
            {
                'name': 'narrative',
                'description': 'Synthesize information into coherent narrative structure',
                'primary_source': 'both',
                'strategy_type': 'synthetic'
            }
        ]

        return strategies

    async def _execute_query_strategies(self,
                                      query: str,
                                      selected_strategies: List[Dict]) -> Dict[str, Any]:
        """
        Execute the selected query strategies in parallel.

        This method runs each of the LV-selected query strategies and
        collects their results for synthesis.

        Args:
            query: The user\'s query.
            selected_strategies: A list of strategy dictionaries to execute.

        Returns:
            A dictionary where keys are strategy names and values are the
            results of executing that strategy.
        """

        results = {}

        # Execute strategies in parallel
        tasks = []
        for strategy in selected_strategies:
            task = self._execute_single_query_strategy(query, strategy)
            tasks.append(task)

        strategy_results = await asyncio.gather(*tasks, return_exceptions=True)

        # Combine results
        for i, result in enumerate(strategy_results):
            if not isinstance(result, Exception):
                strategy_name = selected_strategies[i]['name']
                results[strategy_name] = result

        return results

    async def _execute_single_query_strategy(self,
                                           query: str,
                                           strategy: Dict) -> Dict[str, Any]:
        """
        Execute a single query strategy based on its defined properties.

        This method determines whether to query Neo4j, Qdrant, or both,
        based on the strategy\'s 'primary_source'.

        Args:
            query: The user\'s query.
            strategy: The dictionary defining the strategy to execute.

        Returns:
            A dictionary containing the results of the query execution.
        """

        if strategy['primary_source'] == 'neo4j':
            return await self._query_neo4j_focused(query)
        elif strategy['primary_source'] == 'qdrant':
            return await self._query_qdrant_focused(query)
        else:  # both
            neo4j_results = await self._query_neo4j_focused(query)
            qdrant_results = await self._query_qdrant_focused(query)
            return self._combine_query_results(neo4j_results, qdrant_results, strategy)

    async def _synthesize_multi_perspective_answer(self,
                                                 query: str,
                                                 results: Dict[str, Any],
                                                 strategies: List[Dict]) -> Dict[str, Any]:
        """
        Synthesize a coherent answer from multiple perspectives with conflict detection.

        This method aggregates facts and citations from all the executed
        query strategies, detects conflicts, and then generates a
        synthesized answer.

        Args:
            query: The user\'s query.
            results: The results from executing the query strategies.
            strategies: The list of strategies that were executed.

        Returns:
            A dictionary containing the synthesized content, citations,
            detected conflicts, and confidence scores.
        """

        all_facts = []
        all_citations = []
        confidence_scores = {}

        # Extract facts and citations from each strategy result
        for strategy_name, result in results.items():
            strategy_facts = result.get('facts', [])
            strategy_citations = result.get('citations', [])

            all_facts.extend(strategy_facts)
            all_citations.extend(strategy_citations)
            confidence_scores[strategy_name] = result.get('confidence', 0.5)

        # Detect conflicts between facts
        conflicts = await self._detect_fact_conflicts(all_facts)

        # Synthesize coherent answer
        synthesized_content = await self._create_synthesized_content(
            query, all_facts, strategies, conflicts
        )

        return {
            'content': synthesized_content,
            'citations': list(set(all_citations)),  # Deduplicate
            'conflicts': conflicts,
            'confidence': confidence_scores
        }

    async def _detect_fact_conflicts(self, facts: List[Dict]) -> List[Dict]:
        """
        Detect conflicts between facts from different sources.

        This method compares facts to identify potential contradictions.
        The current implementation is a simple placeholder and can be
        enhanced with more sophisticated NLP models.

        Args:
            facts: A list of fact dictionaries to compare.

        Returns:
            A list of dictionaries, where each dictionary describes a
            detected conflict between two facts.
        """

        conflicts = []

        # Simple conflict detection (enhance with NLP models)
        for i, fact1 in enumerate(facts):
            for j, fact2 in enumerate(facts[i+1:], i+1):
                if self._are_facts_conflicting(fact1, fact2):
                    conflicts.append({
                        'fact1': fact1,
                        'fact2': fact2,
                        'conflict_type': 'contradiction',
                        'sources': [fact1.get('source'), fact2.get('source')]
                    })

        return conflicts

    def _are_facts_conflicting(self, fact1: Dict, fact2: Dict) -> bool:
        """
        Determine if two facts are conflicting.

        This is a placeholder for more advanced conflict detection logic.
        In a production system, this would involve semantic similarity,
        negation detection, and other NLP techniques.

        Args:
            fact1: The first fact dictionary.
            fact2: The second fact dictionary.
        """
        # Simple placeholder logic
        return fact1.get('name') == fact2.get('name') and fact1.get('value') != fact2.get('value')

    def _map_selected_strategies(self,
                                 query_strategies: List[Dict[str, Any]],
                                 selected_outputs: List[str]) -> List[Dict[str, Any]]:
        """
        Maps selected strategy descriptions back to their full dictionary representations.
        """
        selected_map = {s['description']: s for s in query_strategies}
        return [selected_map[desc] for desc in selected_outputs if desc in selected_map]

    async def _fallback_query(self, context: Dict[str, Any]) -> str:
        """
        A fallback mechanism for knowledge query if the main process fails.
        Placeholder implementation.
        """
        logger.warning("Executing fallback query.")
        return "Could not complete the query due to an internal error. Please try again."

    async def _query_neo4j_focused(self, query: str) -> Dict[str, Any]:
        """
        Executes a query focused on Neo4j.
        Placeholder implementation.
        """
        logger.info(f"Querying Neo4j for: {query}")
        return {'facts': [], 'citations': [], 'confidence': 0.8}

    async def _query_qdrant_focused(self, query: str) -> Dict[str, Any]:
        """
        Executes a query focused on Qdrant.
        Placeholder implementation.
        """
        logger.info(f"Querying Qdrant for: {query}")
        return {'facts': [], 'citations': [], 'confidence': 0.7}

    def _combine_query_results(self,
                               neo4j_results: Dict,
                               qdrant_results: Dict,
                               strategy: Dict) -> Dict[str, Any]:
        """
        Combines results from Neo4j and Qdrant based on a strategy.
        Placeholder implementation.
        """
        logger.info(f"Combining results for strategy: {strategy['name']}")
        # Simple combination logic.
        combined_facts = neo4j_results.get('facts', []) + qdrant_results.get('facts', [])
        combined_citations = neo4j_results.get('citations', []) + qdrant_results.get('citations', [])
        combined_confidence = (neo4j_results.get('confidence', 0) + qdrant_results.get('confidence', 0)) / 2
        return {'facts': combined_facts, 'citations': list(set(combined_citations)), 'confidence': combined_confidence}

    async def _create_synthesized_content(self,
                                        query: str,
                                        all_facts: List[Dict],
                                        strategies: List[Dict],
                                        conflicts: List[Dict]) -> str:
        """
        Creates a synthesized, human-readable answer from facts and conflicts.
        Placeholder implementation.
        """
        logger.info("Synthesizing final answer.")
        if conflicts:
            return f"Based on the query '{query}', conflicting information was found. Please review the citations."
        if all_facts:
            return f"Based on the query '{query}', the following information was found: {len(all_facts)} facts from {len(strategies)} perspectives."
        return f"No definitive answer could be synthesized for the query: '{query}'."

# LV_TEMPLATES registry for integration with action template system
LV_TEMPLATES = {
    LVKnowledgeExtractTemplate.keyword: LVKnowledgeExtractTemplate,
    LVKnowledgeQueryTemplate.keyword: LVKnowledgeQueryTemplate,
}
