"""
Document Processing Example

This example demonstrates how to process documents and extract knowledge using Semantica.

Key Features Demonstrated:
    - Document ingestion from various sources
    - Document parsing and content extraction
    - Text normalization and cleaning
    - Entity extraction and relationship detection
    - Knowledge graph construction
    - Export to various formats

Use Cases:
    - Academic paper processing
    - Legal document analysis
    - Technical documentation processing
    - Research paper knowledge extraction
"""


class DocumentProcessingExample:
    """
    Document processing example implementation.
    
    This example shows how to:
    • Ingest documents from various sources
    • Parse and extract content from different formats
    • Normalize and clean text content
    • Extract entities and relationships
    • Build knowledge graphs
    • Export results to various formats
    """
    
    def __init__(self):
        """
        Initialize document processing example.
        
        • Setup Semantica framework
        • Configure document processing pipeline
        • Initialize knowledge extraction tools
        • Setup export handlers
        """
        # TODO: Initialize Semantica framework
        # TODO: Setup document processing pipeline
        # TODO: Configure entity extraction
        # TODO: Setup relationship detection
        # TODO: Initialize knowledge graph builder
        pass
    
    def process_documents(self, document_paths, **options):
        """
        Process documents and extract knowledge.
        
        • Ingest documents from specified paths
        • Parse documents and extract content
        • Normalize and clean text content
        • Extract entities and relationships
        • Build knowledge graph
        • Export results to various formats
        
        Args:
            document_paths: List of document file paths
            **options: Additional processing options
            
        Returns:
            dict: Processing results including knowledge graph and metrics
        """
        # TODO: Ingest documents using FileIngestor
        # TODO: Parse documents using DocumentParser
        # TODO: Normalize text using TextNormalizer
        # TODO: Extract entities using NamedEntityRecognizer
        # TODO: Extract relationships using RelationExtractor
        # TODO: Build knowledge graph using KnowledgeGraphBuilder
        # TODO: Export results using various exporters
        # TODO: Return processing results
        pass
    
    def process_pdf_documents(self, pdf_paths, **options):
        """
        Process PDF documents specifically.
        
        • Handle PDF-specific parsing
        • Extract text and metadata
        • Process embedded content
        • Handle multi-page documents
        
        Args:
            pdf_paths: List of PDF file paths
            **options: PDF processing options
            
        Returns:
            dict: PDF processing results
        """
        # TODO: Use PDFParser for PDF-specific processing
        # TODO: Extract text content and metadata
        # TODO: Handle embedded images and tables
        # TODO: Process multi-page documents
        # TODO: Return PDF processing results
        pass
    
    def process_word_documents(self, docx_paths, **options):
        """
        Process Word documents specifically.
        
        • Handle DOCX-specific parsing
        • Extract text and formatting
        • Process embedded objects
        • Handle document structure
        
        Args:
            docx_paths: List of DOCX file paths
            **options: DOCX processing options
            
        Returns:
            dict: DOCX processing results
        """
        # TODO: Use DOCXParser for DOCX-specific processing
        # TODO: Extract text content and formatting
        # TODO: Handle embedded objects and tables
        # TODO: Process document structure
        # TODO: Return DOCX processing results
        pass
    
    def extract_knowledge(self, processed_documents, **options):
        """
        Extract knowledge from processed documents.
        
        • Extract entities and relationships
        • Build knowledge graph
        • Identify knowledge patterns
        • Generate knowledge insights
        
        Args:
            processed_documents: Processed document data
            **options: Knowledge extraction options
            
        Returns:
            dict: Knowledge extraction results
        """
        # TODO: Extract entities using NamedEntityRecognizer
        # TODO: Extract relationships using RelationExtractor
        # TODO: Build knowledge graph using KnowledgeGraphBuilder
        # TODO: Identify knowledge patterns
        # TODO: Generate knowledge insights
        # TODO: Return knowledge extraction results
        pass
    
    def export_results(self, knowledge_graph, export_formats, **options):
        """
        Export knowledge graph to various formats.
        
        • Export to RDF formats
        • Export to JSON formats
        • Export to CSV formats
        • Generate reports
        
        Args:
            knowledge_graph: Knowledge graph to export
            export_formats: List of export formats
            **options: Export options
            
        Returns:
            dict: Export results
        """
        # TODO: Export to RDF using RDFExporter
        # TODO: Export to JSON using JSONExporter
        # TODO: Export to CSV using CSVExporter
        # TODO: Generate reports using ReportGenerator
        # TODO: Return export results
        pass


def run_document_processing_example():
    """
    Run the document processing example.
    
    This function demonstrates the complete document processing workflow.
    """
    # TODO: Create DocumentProcessingExample instance
    # TODO: Define document paths
    # TODO: Process documents
    # TODO: Extract knowledge
    # TODO: Export results
    # TODO: Display results
    pass


def run_pdf_processing_example():
    """
    Run PDF document processing example.
    
    This function demonstrates PDF-specific processing.
    """
    # TODO: Create DocumentProcessingExample instance
    # TODO: Define PDF document paths
    # TODO: Process PDF documents
    # TODO: Extract knowledge from PDFs
    # TODO: Export PDF processing results
    # TODO: Display results
    pass


def run_word_processing_example():
    """
    Run Word document processing example.
    
    This function demonstrates DOCX-specific processing.
    """
    # TODO: Create DocumentProcessingExample instance
    # TODO: Define DOCX document paths
    # TODO: Process DOCX documents
    # TODO: Extract knowledge from DOCX files
    # TODO: Export DOCX processing results
    # TODO: Display results
    pass