"""
Multi-Modal Processing Example

This example demonstrates how to process multi-modal data (text, images, audio) using Semantica.

Key Features Demonstrated:
    - Multi-modal data ingestion
    - Cross-modal embedding generation
    - Multi-modal entity extraction
    - Cross-modal relationship detection
    - Multi-modal knowledge graph construction
    - Cross-modal similarity search

Use Cases:
    - Social media content analysis
    - Multimedia document processing
    - Video content analysis
    - Audio transcription and analysis
    - Cross-modal information retrieval
"""


class MultiModalProcessingExample:
    """
    Multi-modal processing example implementation.
    
    This example shows how to:
    • Process data from multiple modalities
    • Generate cross-modal embeddings
    • Extract entities from different modalities
    • Detect relationships across modalities
    • Build multi-modal knowledge graphs
    • Perform cross-modal similarity search
    """
    
    def __init__(self):
        """
        Initialize multi-modal processing example.
        
        • Setup Semantica framework
        • Configure multi-modal processors
        • Initialize cross-modal embedding generation
        • Setup multi-modal entity extraction
        • Configure cross-modal relationship detection
        • Setup multi-modal knowledge graph building
        """
        # TODO: Initialize Semantica framework
        # TODO: Setup multi-modal data processors
        # TODO: Configure cross-modal embedding generation
        # TODO: Initialize multi-modal entity extraction
        # TODO: Setup cross-modal relationship detection
        # TODO: Configure multi-modal knowledge graph building
        pass
    
    def process_multimodal_data(self, data_sources, **options):
        """
        Process multi-modal data from various sources.
        
        • Ingest data from multiple modalities
        • Process each modality appropriately
        • Generate cross-modal embeddings
        • Extract entities from all modalities
        • Detect relationships across modalities
        • Build multi-modal knowledge graph
        
        Args:
            data_sources: Dictionary of data sources by modality
            **options: Multi-modal processing options
            
        Returns:
            dict: Multi-modal processing results
        """
        # TODO: Ingest data from multiple modalities
        # TODO: Process each modality using appropriate processors
        # TODO: Generate cross-modal embeddings using MultiModalEmbedder
        # TODO: Extract entities from all modalities
        # TODO: Detect relationships across modalities
        # TODO: Build multi-modal knowledge graph
        # TODO: Return multi-modal processing results
        pass
    
    def process_text_and_images(self, text_data, image_data, **options):
        """
        Process text and image data together.
        
        • Process text content
        • Process image content
        • Generate cross-modal embeddings
        • Extract entities from both modalities
        • Detect relationships between text and images
        • Build cross-modal knowledge graph
        
        Args:
            text_data: Text data to process
            image_data: Image data to process
            **options: Text-image processing options
            
        Returns:
            dict: Text-image processing results
        """
        # TODO: Process text content using TextEmbedder
        # TODO: Process image content using ImageEmbedder
        # TODO: Generate cross-modal embeddings
        # TODO: Extract entities from text and images
        # TODO: Detect relationships between text and images
        # TODO: Build cross-modal knowledge graph
        # TODO: Return text-image processing results
        pass
    
    def process_audio_and_text(self, audio_data, text_data, **options):
        """
        Process audio and text data together.
        
        • Process audio content
        • Process text content
        • Generate cross-modal embeddings
        • Extract entities from both modalities
        • Detect relationships between audio and text
        • Build cross-modal knowledge graph
        
        Args:
            audio_data: Audio data to process
            text_data: Text data to process
            **options: Audio-text processing options
            
        Returns:
            dict: Audio-text processing results
        """
        # TODO: Process audio content using AudioEmbedder
        # TODO: Process text content using TextEmbedder
        # TODO: Generate cross-modal embeddings
        # TODO: Extract entities from audio and text
        # TODO: Detect relationships between audio and text
        # TODO: Build cross-modal knowledge graph
        # TODO: Return audio-text processing results
        pass
    
    def generate_cross_modal_embeddings(self, multimodal_data, **options):
        """
        Generate cross-modal embeddings.
        
        • Process data from multiple modalities
        • Generate embeddings for each modality
        • Align embeddings across modalities
        • Fuse embeddings from different modalities
        • Return cross-modal embeddings
        
        Args:
            multimodal_data: Multi-modal data to process
            **options: Cross-modal embedding options
            
        Returns:
            dict: Cross-modal embedding results
        """
        # TODO: Use MultiModalEmbedder to generate embeddings
        # TODO: Align embeddings across modalities
        # TODO: Fuse embeddings from different modalities
        # TODO: Return cross-modal embeddings
        pass
    
    def extract_cross_modal_entities(self, multimodal_data, **options):
        """
        Extract entities from multiple modalities.
        
        • Process each modality for entity extraction
        • Align entities across modalities
        • Resolve cross-modal entity conflicts
        • Return cross-modal entity extraction results
        
        Args:
            multimodal_data: Multi-modal data to process
            **options: Cross-modal entity extraction options
            
        Returns:
            dict: Cross-modal entity extraction results
        """
        # TODO: Extract entities from each modality
        # TODO: Align entities across modalities
        # TODO: Resolve cross-modal entity conflicts
        # TODO: Return cross-modal entity extraction results
        pass
    
    def detect_cross_modal_relationships(self, multimodal_data, entities, **options):
        """
        Detect relationships across modalities.
        
        • Process each modality for relationship extraction
        • Detect relationships within modalities
        • Detect relationships across modalities
        • Validate cross-modal relationships
        • Return cross-modal relationship detection results
        
        Args:
            multimodal_data: Multi-modal data to process
            entities: Extracted entities
            **options: Cross-modal relationship detection options
            
        Returns:
            dict: Cross-modal relationship detection results
        """
        # TODO: Extract relationships within each modality
        # TODO: Detect relationships across modalities
        # TODO: Validate cross-modal relationships
        # TODO: Return cross-modal relationship detection results
        pass
    
    def build_multimodal_knowledge_graph(self, multimodal_data, entities, relationships, **options):
        """
        Build multi-modal knowledge graph.
        
        • Integrate entities from all modalities
        • Integrate relationships from all modalities
        • Resolve cross-modal conflicts
        • Build unified knowledge graph
        • Return multi-modal knowledge graph
        
        Args:
            multimodal_data: Multi-modal data
            entities: Extracted entities
            relationships: Extracted relationships
            **options: Multi-modal knowledge graph building options
            
        Returns:
            dict: Multi-modal knowledge graph building results
        """
        # TODO: Integrate entities from all modalities
        # TODO: Integrate relationships from all modalities
        # TODO: Resolve cross-modal conflicts
        # TODO: Build unified knowledge graph
        # TODO: Return multi-modal knowledge graph building results
        pass
    
    def perform_cross_modal_similarity_search(self, query, multimodal_data, **options):
        """
        Perform cross-modal similarity search.
        
        • Process query across modalities
        • Generate cross-modal embeddings
        • Perform similarity search
        • Return cross-modal search results
        
        Args:
            query: Search query
            multimodal_data: Multi-modal data to search
            **options: Cross-modal search options
            
        Returns:
            dict: Cross-modal search results
        """
        # TODO: Process query across modalities
        # TODO: Generate cross-modal embeddings
        # TODO: Perform similarity search
        # TODO: Return cross-modal search results
        pass


def run_multimodal_processing_example():
    """
    Run the multi-modal processing example.
    
    This function demonstrates the complete multi-modal processing workflow.
    """
    # TODO: Create MultiModalProcessingExample instance
    # TODO: Define multi-modal data sources
    # TODO: Process multi-modal data
    # TODO: Extract cross-modal entities and relationships
    # TODO: Build multi-modal knowledge graph
    # TODO: Export results
    # TODO: Display results
    pass


def run_text_image_processing_example():
    """
    Run text-image processing example.
    
    This function demonstrates text-image cross-modal processing.
    """
    # TODO: Create MultiModalProcessingExample instance
    # TODO: Define text and image data
    # TODO: Process text and image data
    # TODO: Extract cross-modal entities and relationships
    # TODO: Build cross-modal knowledge graph
    # TODO: Export results
    # TODO: Display results
    pass


def run_audio_text_processing_example():
    """
    Run audio-text processing example.
    
    This function demonstrates audio-text cross-modal processing.
    """
    # TODO: Create MultiModalProcessingExample instance
    # TODO: Define audio and text data
    # TODO: Process audio and text data
    # TODO: Extract cross-modal entities and relationships
    # TODO: Build cross-modal knowledge graph
    # TODO: Export results
    # TODO: Display results
    pass